IJACSA Volume 11 Issue 12 - thesai.org

IJACSA Volume 11 Issue 12

Copyright Statement: This is an open access publication licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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Paper 1: Performance based Comparison between Several Link Prediction Methods on Various Social Networking Datasets (Including Two New Methods)

Abstract: This work extends my previous work on link prediction in Social Networks. In this research, I used two additional datasets, Twitter dataset and Facebook Social Circles Dataset and I ran link prediction methods on these datasets. In my previous work, I performed experiment on the Facebook dataset and proposed two new link prediction methods: Neighbors Connectivity and Common Neighbors of Neighbors (CNN). As in my previous work, in this work, I ran the link prediction methods for several training and testing sizes. Results showed that For Facebook dataset, random had the highest precision, followed by Neighbors Connectivity, then Preferential Attachment, followed by Jaccard/CC, Adamic-Adar, finally CNN. For Twitter dataset, random achieved the highest precision. Preferential Attachment achieved the next highest precision, and Adamic-Adar achieved the least precision. For Facebook Social Circles dataset, Preferential-Attachment achieved the highest precision of 1.08891 followed by random for a training and testing sizes of (1535, 2504) respectively. That is said with slight variation on the orders depending on the training and testing size. The low precision values achieved with Facebook and Twitter datasets are due to the graph types which are sparse as indicated in the datasets websites which confirms Kleinberg finding.

Author 1: Ahmad Rawashdeh

Keywords: Social networks; link prediction; comparison; experiment

Paper 2: The Impact of Teaching Operating Systems using Two Different Teaching Modalities

Abstract: This paper presents a preliminary look at the performance of two cohorts enrolled in an Operating System course which was taught using two different teaching delivery methods. Operating systems is a technical, senior-level, undergraduate course that includes abstract concepts, mechanisms, and their implementations. This course exposes students to a UNIX-based operating system and includes concurrent programming (threads and synchronization), inter-process communication, CPU scheduling main memory, and virtual memory management. Technical courses present an additional dimension of difficulty when compared to non-technical courses which are more focused on soft skills because they require strong technical skills such as programming and problem-solving. This paper discusses other research studies and statistical data which underscore some of the challenges and differences encountered when teaching a traditional face-to-face versus an online course and the impact on student success. In this work, the 2019 cohort was taught operating systems in the traditional face-to-face modality, while the 2020 cohort was taught the course using the synchronous online modality. The synchronous online modality is very similar to the face-to-face traditional class, in that, lectures are delivered in real-time; this allows students to ask the instructor questions in real-time. Each cohort was tested on the same course objectives (topics) over one semester in 2019 and 2020. The instructor presents the students’ performance on three(3) course exams and discusses the differences and similarities in their overall performance between the two groups.

Author 1: Ingrid A. Buckley

Keywords: Operating systems; synchronous online course; traditional course; face-to-face course; online course

Paper 3: Reducing Energy Consumption in Microcontroller-based Systems with Multipipeline Architecture

Abstract: Current mobile battery powered systems require low power consumption as possible without affecting the overall performance of the system. The purpose of this article is to present a multi-pipeline architecture implemented on a RISC V processor with 4 levels pipeline. Each thread has an assigned CLKSCALE registry that allows to use a clock with a lower or higher frequency, depending on the value written in the CLKSCALE registry. Depending on the importance and the need to be executed at a lower or higher speed each thread will enter into execution with its frequency given by CLKSCALE. It is known that each system has its own “real time”. The notion of real time is very relative depending on the environment in which the system operates. Thus, if the system responds to external stimulus for a time that does not affect the operation of the whole, then we say the system is in real time. The system response can be quick or slow. It is important that this response does not lead to malfunction in operation. Therefore, certain threads can work at lower frequencies (those responding to slower external stimulus) and others must operate at high frequencies to allow quick response to fast external stimulus. It is known that the consumed power is directly proportional to the frequency of computing. Thus, the threads that do not require to run at maximum frequency, will consume less energy when they run. The entire system will consume less energy without affecting its performance. This architecture was implemented on a Xilinx FPGA ARTY A7 kit using the Vivado 2018.3 development tools.

Author 1: Cristian Andy Tanase

Keywords: Multi-pipeline register; RISC V (Reduced Instruc-tion Set Computer); power consumption; multi-threading; FPGA (Field Programmable Gate Array); variable frequency

Paper 4: Cryptanalysis and Countermeasure of “An Efficient NTRU-based Authentication Protocol in IoT Environment”

Abstract: A quantum computer is a paradigm of information processing that can show remarkable possibilities of exponentially improved information processing. However, this paradigm could disrupt the current cryptosystem, which is called quantum computing attacks, by calculating factoring problem and discrete logarithm problem. Recently, NTRU is applied to various security systems, because it provides security against to provide secu-rity against quantum computing attacks. Furthermore, NTRU provides similar security level and efficient computation time of encryption/decryption compared to traditional PKC. In 2018, Jeong et al. proposed an user authentication and key distribution scheme using NTRU. They claimed that their scheme provides various security properties and secure against quantum comput-ing attacks. In this paper, we demonstrate that their scheme has security pitfalls and incorrectness in login and authentication phase. We also suggest countermeasures to fix the incorrectness and provide security against various attacks.

Author 1: YoHan Park

Author 2: Woojin Seok

Author 3: Wonhyuk Lee

Author 4: Hong Taek Ju

Keywords: Post-quantum; NTRU; biometrics; user authentica-tion; key agreement

Paper 5: Developing a Mining Robot for Mars Exploitation: NASA Robotics Mining Competition (RMC)

Abstract: This paper focuses on demonstrating the design and build stages, and effort done by Systems Engineering students team (DustyTRON NASA Robotics) to develop a mining robot that was used in the 2016 National Aeronautics & Space Ad-ministration (NASA) Robotics Mining Competition (RMC). The objective of the NASA RMC challenge is to encourage engineering students to design and build a robot that will excavate, collect, and deposit a simulated Martian regolith. Mining water/ice, and regolith is very essential task for space missions and resource utilization, they contain many elements such as metals, minerals, and other compounds. The Mining will allow extracting pro-pellants from the regolith such as Oxygen and Hydrogen that can be used as an energy source for in-space transportation. In addition, the space mining system can be used in tasks that are important for human and robotics scientific investigations. The DustyTRON team consists of Systems Engineering students, who are divided into 1) hardware design, 2) electrical circuitry and 3) software development sub-teams. Each team works in harmony to overcome the challenges had previously experienced, such as heavy weight, circuitry layout design, autonomous and user control modes, and better software interface. They designed and built a remote controlled excavator robot, that can collect and deposit a minimum of ten (10) kilograms of regolith simulant within 15 minutes. The developed robot with its innovative mining mechanisms and control system and software will assist NASA in enhancing the current methodologies used for space/planet exploration and resources’ mining especially the Moon and Mars. NASA’s going-on project aims to send exploration robots that collect resources for analysis before sending astronauts. In 2016, only 56 United State (US) teams were invited to participate, and DustyTRON was one of three university teams from the state of Texas, the team placed the 16th in overall performance. This paper will address the full engineering life-cycle process including research, concept design and development, constructing the robot and system closeout by delivering the team’s robot for the competition in Kennedy Space Center in Florida.

Author 1: Tariq Tashtoush

Author 2: Agustin Velazquez

Author 3: Andres Aranguren

Author 4: Cristian Cavazos

Author 5: David Reyes

Author 6: Edgar Hernandez

Author 7: Emily Bueno

Author 8: Esteban Otero

Author 9: Gerardo Zamudio

Author 10: Hector Casarez

Author 11: Jorge Rullan

Author 12: Jose Rodriguez

Author 13: Juan Carlos Villarreal

Author 14: Michael Gutierrez

Author 15: Patricio Rodriguez

Author 16: Roberto Torres

Author 17: Rosaura Martinez

Author 18: Sanjuana Partida

Keywords: NASA Robotics Mining competition; mining robot; ice regolith; autonomous; NASA space exploration; systems life-cycle; mechanical structure design; control system; systems engineering

Paper 6: Context Classification based on Mixing Ratio Estimation by Means of Inversion Theory

Abstract: A contextual image classification method with a proportion estimation of the pixels composed of several classes, Mixed pixels (Mixels), is proposed. The method allows us to check the connectivity of separated road segments, which are observed frequently as discontinuity of roads in satellite remote sensing imagery. Under the assumption of almost same proportions for the Mixels in the discontinuous portion of road segments, a proportion estimation method utilizing Inverse Problem Solving is proposed. The experimental results with the simulation data including observation noise show 73.5~98.8(%) of improvements in terms of proportion estimation accuracy (Root Mean Square: RMS error), compared to the results from the previously proposed method with generalized inverse matrix. Also, usefulness of contextual classification based on the proposed proportion estimation was confirmed for the investigation of connectivity of roads in remotely sensed images from space.

Author 1: Kohei Arai

Keywords: Search engine; fuzzy expression; knowledge base system; membership function; mixed pixel: Mixel; context information; inverse problem solving

Paper 7: Concurrent Detection of Linear and Angular Motion using a Single-Mass 6-axis Piezoelectric IMU

Abstract: This paper exhibits operating system and performances of a novel single-mass 6-axis Inertial Measurement Unit (IMU) using piezoelectric detection. The electronic processing circuitry for the concurrent detection of linear and angular motion is proposed. The IMU structure is based on the use of 2 rings, connected with eight electrodes, implemented on the top of a piezoelectric membrane used for both sense and drive modes. The four inner electrodes are used for components detection due to the direct piezoelectric effect, while the outer electrodes are used to generate the drive mode due to the reverse piezoelectric effect. Through finite element analysis, we show that linear accelerations generate an offset voltage on the sensing electrodes, while angular rates lead to a change in the amplitude of the initial AC signal caused by the drive mode. The present work represents an innovative design able to separate 6 motion data from signals using only 4 electrodes. The specific electronic circuitry for acceleration and angular rate data dissociation shows a very efficient method for signal separation since no leakage readout occurs in all six axes. Besides, other particular interest is that under no circumstances, angular outputs disturb or affect acceleration ones and vice versa. The evaluated sensitivities are 364 mV/g and 65.5 mV/g for in-plane and out-of-plane linear accelerations, respectively. Similarly, angular rates sensitivities are 2.59 mV/rad/s and 522 mV/rad/s.

Author 1: Hela Almabrouk

Author 2: Mohamed Hadj Said

Author 3: Fares Tounsi

Author 4: Brahim Mezghani

Author 5: Guillaume Agnus

Author 6: Yves Bernard

Keywords: Inertial measurement unit; piezoelectric detection; angular rate; linear acceleration; electronic circuitry

Paper 8: Can Model Checking Assure, Distributed Autonomous Systems Agree? An Urban Air Mobility Case Study

Abstract: Advancement in artificial intelligence, internet of things and information technology have enabled the delegation of execution of autonomous services to autonomous systems for civil applications. It is envisioned, that with an increase in the demand for autonomous systems, the decision making associated in the execution of the autonomous services will be distributed, with some of the responsibility in decision making, shifted to the autonomous systems. Thus, it is of utmost importance that we assure the correctness of distributed protocols, that multiple autonomous systems will follow, as they interact with each other in providing the service. Towards this end, we discuss our pro-posed framework to model, analyze and assure the correctness of distributed protocols executed by autonomous systems to provide a service. We demonstrate our approach by formally modeling the behavior of autonomous systems that will be involved in providing services in the Urban Air Mobility framework that enables air taxis to transport passengers.

Author 1: Anubhav Gupta

Author 2: Siddhartha Bhattacharyya

Author 3: S. Vadivel

Keywords: Formal methods; autonomous systems; distributed algorithms; assurance for distributed protocols; distributed protocol modeling and verification; distributed autonomous systems

Paper 9: Prospects and Challenges of Learning Management Systems in Higher Education

Abstract: Many higher education institutions nowadays are equipped with Learning Management Systems (LMS) to provide rich online learning solutions and utilize its functions and capabilities to improve the learning practices. The current study aims to gain instructors’ perspective of LMS, investigate the use of its functions, and identify the barriers that may influence LMS utilization at the Gulf University for Science and Technology (GUST). This research aims to examine current practices, opinions, and challenges that help academicians and system developers contribute to better learning practices and academic achievement. The study used a quantitative method that included a sample of 58 faculty members. Findings obtained from the questionnaire indicated that instructors were generally comfortable and had positive perceptions about LMS Moodle. The results revealed that LMS's administrative functions, such as files and announcements, are widely used compared to the advanced interactive learning activities. Moreover, LMS's use on mobile devices is infrequent, and more emphasis must be placed on using LMS friendly user interfaces that can enable all tools and functions to use LMS.

Author 1: Ahmed Al-Hunaiyyan

Author 2: Salah Al-Sharhan

Author 3: Rana AlHajri

Keywords: Learning Management Systems (LMS); e-learning; Information Communication Technology (ICT); Higher Education (HE)

Paper 10: Deep Learning based Approach for Bone Diagnosis Classification in Ultrasonic Computed Tomographic Images

Abstract: Artificial intelligence (AI) in the area of medical imaging has shown a developed technology to have automatically the true diagnosis especially in ultrasonic imaging area. At this light, two types of neural networks algorithms have been developed to automatically classify the Ultrasonic Computed Tomographic (USCT) images into three categories, such as healthy, fractured and osteoporosis bone USCT images. In this work, at first step, a Convolutional Neural Network including two types of CNN models such (Inception-V3 and MobileNet) are proposed as a classifier system. At second step, an evolutionary neural network is proposed with the AmeobaNet model for USCT image classification. Results achieve 100% for train accuracy and 96%, 91.7% and 87.5% using Amoebanet, Inception-V3 and MobileNet respectively for the test accuracy. Results outperforms the state of the art and prove the robustness of the proposed classifier system with a short time process by its implementation on GPU.

Author 1: Marwa Fradi

Author 2: Mouna Afif

Author 3: Mohsen Machhout

Keywords: USCT; Inception-V3; MobileNet; Ameobanet-V2; classification; accuracy; transfer deep learning

Paper 11: Ice Concentration Estimation Method with Satellite based Microwave Radiometer by Means of Inversion Theory

Abstract: Ice concentration estimation method with satellite-based microwave radiometer by means of inversion theory is proposed. Through experiments, it is found that the proposed methods are superior to the existing methods, the NASA Team algorithm and the Comiso's Bootstrap algorithm with up to 45% of improvement on ice concentration estimation accuracy based on the simulation study. Also 1.5 to 2.1% of improvement was achieved for the proposed method compared to the NASA Team and Comiso's Bootstrap algorithms for the actual The Special Sensor Microwave Imager (SSM/I) data of Okhotsk using Japanese Earth Resources Satellite: JERS-1/Synthetic Aperture Radar: SAR data as a truth data for estimating ice concentration.

Author 1: Kohei Arai

Keywords: Ice concentration; Microwave radiometer; Inversion Theory; Comiso’s Bootstrap algorithm; The Special Sensor Microwave Imager (SSM/I); Japanese Earth Resources Satellite: JERS-1; Synthetic Aperture Radar: SAR

Paper 12: The Effectiveness of Adopting e-Learning during COVID-19 at Hashemite University

Abstract: e-Learning is the utilization of the electronic technologies and the media to deliver the educational content to the learners, enabling them to interact actively with the content, the teachers, and their peers. Students’ interaction can be either synchronous or asynchronous or a combination of both. One advantage of the e-learning is that learners can access the educational content at any place and time saving them effort, time, and cost. To deal with the unprecedented crisis of COVID-19 and the risk of virus transmission in the public, the vast majority of higher learning institutions globally were locked out and the delivery of the educational content moved from the traditional classroom teaching to the internet. The purpose of this study was to assess students’ perceptions of the effectiveness of the e-learning during COVID-19 pandemic at the Hashemite University, Jordan. A total of 399 students completed the online survey of the study. Study results showed that students’ overall evaluation of their e-learning experiences were generally positive. However, students reported that they faced problems in the e-learning experiences of which most were related to technical issues (e.g., lack of a viable internet network, lack of laptops, etc.). Microsoft Teams was the platform most preferred by students for e-learning and the majority of students accessed the educational content using smart phones. Only gender and student’s academic specialty had significant associations with their perceptions of the effectiveness of the e-learning.

Author 1: Alaa Obeidat

Author 2: Rana Obeidat

Author 3: Mohammed Al-Shalabi

Keywords: e-Learning; COVID19; classroom; ICT; Hashemite University; educational platform

Paper 13: Recovering UML2 Sequence Diagrams from Execution Traces

Abstract: Reverse engineering is a proven and efficient technique for automatically generating UML2 models from object-oriented legacy systems with missing or obsolete documentation. To perform reverse engineering, two techniques are used: dynamic and static analysis. Dynamic analysis refers to collecting information when the system is running while static analysis corresponds to inspecting the source code. Dynamic analysis is preferred than static one in order to extract dynamic models that represents the behavior of a systems because of polymorphism and dynamic binding. In this paper, we present new different methodology that use Colored Petri Nets (CPNs) to recover UML2 Sequence Diagram (SD). First, it generates execution traces corresponding to the different scenarios representing the system behavior. Then, CPNs are used to model and analyze these execution traces to extract UML2 sequence diagram. Our case study illustrates the process of our approach and show that sequence diagram can be extracted with a good accuracy.

Author 1: EL Mahi BOUZIANE

Author 2: Chafik BAIDADA

Author 3: Abdeslam JAKIMI

Keywords: Execution traces; Reverse engineering; UML2; Sequence Diagram; Colored Petri Nets

Paper 14: The Architecture of Intelligent Career Prediction System based on the Cognitive Technology for Producing Graduates to the Digital Manpower

Abstract: This research is a documentary research aimed at designing the architecture of the intelligent career prediction system based on the cognitive technology for producing graduates to the digital manpower. The research methods were divided into three phases: Phase 1, Composition Synthesis of Intelligent Career Prediction System. Phase 2, Intelligent Career Prediction System Architecture Designing based on the Cognitive Technology for producing graduates to the digital manpower. Phase 3, an assessment of the suitability of the architecture of the intelligent career prediction system based on the cognitive technology for producing graduates to the digital manpower. The architecture of the intelligent career prediction system by using the cognitive technology can be divided into three parts: 1) People involved in the architecture of the intelligent career prediction system consisting of five groups of related persons: students, staff, teachers, digital Enterprises, system administrator. 2) The architecture of the intelligent career prediction system consisting of four components: 1) User management, 2. Prediction Data Management, 3) Prediction Management system, 4) Prediction Display system, and cloud computing, an assessment of the suitability the architecture of the intelligent career prediction system based on the cognitive technology for producing graduates to the digital manpower by nine experts in the intelligent career prediction system and cognitive technology. The statistics used in the research are Mean and standard deviation. The evaluation results showed that the developed architecture was the most suitable, with the combined mean of 4.54, and the standard deviation was 0.49.

Author 1: Pongsaton Palee

Author 2: Panita Wannapiroon

Author 3: Prachyanun Nilsook

Keywords: Architecture of intelligent career prediction system; cognitive technology; producing graduates; digital manpower

Paper 15: Analyzing the Barriers and Possibilities with p-values towards Starting a New Postgraduate Computer and Engineering Programs at Najran University: A Cross-Sectional Study

Abstract: A cross-sectional study was conducted to find out the barriers and their possible solutions to start a new postgraduate computer and engineering programs at Najran University (NU), Kingdom of Saudi Arabia. This study includes interviews and surveys consist of 35 questions. The total number of the participant was 363; most of them were employees at the government and private sectors. In this study to analysis the result IBM’s Statistical Package for the Social Sciences (SPSS) version 22 is used to analyze the result with the respective p-values calculated using the Pearson Chi-square test. Study reveals that 95.6% of participants want to pursue a graduate degree. However, only (46.9%) can communicate in English academically. Among the respondents, about (42.1%) started a graduate program before, but only (11%) has completed the program. Others could not continue their graduate programs because they were not able to attend the classes coming from a far distance and uncomfortable classes schedule and time. Hence, questions were distributed among the participants to get their opinions to find the solution. Study reveals that among the participants' both government and private sectors employees shown the importance to have online classes with (69.56%) and to have a comfortable schedule and time of courses with (95.65%). This study outlined the main barriers and possible solutions and recommendations that may be helpful for higher education institutions and organizations to start new graduate programs.

Author 1: Abdullah Alghamdi

Keywords: Postgraduate program; p-values; barriers; Najran university; English language; schedule

Paper 16: Enhancing Convolutional Neural Network using Hu’s Moments

Abstract: Convolutional Neural Networks (CNN) is a powerful deep learning method which is mostly used in image classification and image recognition applications. It has achieved acceptable accuracy in these fields but it still suffers some limitations. One of these limitations of CNN is the lack of ability to be invariant to the input data due to some transformations such as rotation, scaling, skewness, etc. In this paper we present an approach to optimize CNN in order to enhance its performance regarding the invariant limitation by using Hu’s moments. The Hu’s moments of an image are weighted averages of the image’s intensities of the pixels, which produce statistics about the image, and these moments are invariant to image transformations. This means that, even if some changes were made to the image, it will always produce almost the same moments values. The main idea behind the proposed approach is extracting Hu’s moments of the image and concatenating them with the flatten vector then feeding the new vector to the fully connected layer. The experimental results show that an acceptable loss, accuracy, precision, recall and F1 score have been achieved on three benchmark datasets which are MNIST hand written digits dataset, MNIST fashion dataset and the CIFAR10 dataset.

Author 1: Sanad AbuRass

Author 2: Ammar Huneiti

Author 3: Mohammad Belal Al-Zoubi

Keywords: CNN; image transformations; invariant; Hu’s moments

Paper 17: Predicting Undergraduate Admission: A Case Study in Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Bangladesh

Abstract: The university admission tests find the applicant's ability to admit to the desired university. Nowadays, there is a huge competition in the university admission tests. The failure in the admission tests makes an examinee depressed. This paper proposes a method that predicts undergraduate admission in universities. It can help students to improve their preparation to get a chance at their desired university. Many factors are responsible for the failure or success in an admission test. Educational data mining helps us to analyze and extract information from these factors. Here, the authors apply three machine learning algorithms XGBoost, LightGBM, and GBM on a collected dataset to estimate the probability of getting admission to the university after attending or before attending the admission test. They also evaluate and compare the performance levels of these three algorithms based on two different evaluation metrics – accuracy and F1 score. Furthermore, the authors explore the important factors which influence predicting undergraduate admission.

Author 1: Md. Protikuzzaman

Author 2: Mrinal Kanti Baowaly

Author 3: Maloy Kumar Devnath

Author 4: Bikash Chandra Singh

Keywords: Undergraduate admission; educational data mining; XGBoost; Light GBM; GBM; evaluation metrics

Paper 18: Applications of Clustering Techniques in Data Mining: A Comparative Study

Abstract: In modern scientific research, data analyses are often used as a popular tool across computer science, communication science, and biological science. Clustering plays a significant role in the reference composition of data analysis. Clustering, recognized as an essential issue of unsupervised learning, deals with the segmentation of the data structure in an unknown region and is the basis for further understanding. Among many clustering algorithms, “more than 100 clustering algorithms known” because of its simplicity and rapid convergence, the K-means clustering algorithm is commonly used. This paper explains the different applications, literature, challenges, methodologies, considerations of clustering methods, and related key objectives to implement clustering with big data. Also, presents one of the most common clustering technique for identification of data patterns by performing an analysis of sample data.

Author 1: Muhammad Faizan

Author 2: Megat F. Zuhairi

Author 3: Shahrinaz Ismail

Author 4: Sara Sultan

Keywords: Clustering; data analysis; data mining; unsupervised learning; k-mean; algorithms

Paper 19: A Genetic Algorithm Approach for Inter and Intra Homogeneous Grouping Considering Multi-student Characteristics

Abstract: This paper addresses the problem of group formation in collaborative learning by considering the students’ characteristics. The proposed solution is based on a Genetic Algorithm (GA), which minimizes an objective function that has two main aims. Indeed, the proposed GA’s fitness function helps to achieve two objectives: Fairness in the formation of different groups, resulting in intergroup homogeneity, and a low gap in the levels of students within a group, which corresponds to intragroup homogeneity. Exhaustive experiments were conducted using three different sizes of randomly generated data sets and several crossover operators. Indeed, the order crossover and the crossovers based on random keys representation are experimented. The reported results show that the proposed approach guarantees the efficient grouping of students. In addition, comparisons with existing approaches based on GA confirm the ability of the proposed approach to provide greater intergroup and intragroup homogeneity. In addition, the uniform crossover based on random keys representation ensures better grouping quality than do the other experimented crossover operators.

Author 1: A. M. Aseere

Keywords: Genetic algorithm; group formation; intragroup homogeneity; intergroup homogeneity; fitness; permutation; random keys representation

Paper 20: The Effects of Privacy Preserving Data Publishing based on Overlapped Slicing on Feature Selection Stability and Accuracy

Abstract: Feature selection is vital for data mining as each organization gathers a colossal measure of high dimensional microdata. Among significant standards of the algorithms for feature selection, the primary one which is currently considered as significant is feature selection stability along with accuracy. Privacy preserving data publishing methods with various delicate traits are analyzed to lessen the likelihood of adversaries to figure the touchy values. By and large, protecting the delicate values is typically accomplished by anonymizing data by utilizing generalization and suppression methods which may bring about information loss. Strategies other than generalization and suppression are investigated to diminish information loss. Privacy preserving data publishing with the overlapped slicing technique with various delicate ascribes tackles the issues in microdata with numerous touchy attributes. Feature selection stability is a vital criterion of data mining technique because of the accumulation of ever increasing dimensionality of microdata due to everyday activities on the World Wide Web. Feature selection stability is directly correlated with data utility. Feature selection stability is data centric and hence modifications of a dataset for privacy preservation affects feature selection stability along with data utility. As feature selection stability is data-driven, the impacts of privacy preserving data publishing based on overlapped slicing on feature selection stability and accuracy is investigated in this paper.

Author 1: Mohana Chelvan P

Author 2: Perumal K

Keywords: Overlapped slicing; privacy preserving data publishing; feature selection; Jaccard Index; selection stability

Paper 21: Exploring UX Maturity in Software Development Environments in Saudi Arabia

Abstract: User experience (UX) design is becoming increasingly crucial for developing successful software today. It can determine whether or not users stay engaged with a product or service. It is, therefore, important that organizations have their users in mind when developing software and that there is a maturity for UX work. However, there are still organizations which do not value UX highly and where UX maturity is low. This paper reported the results of a survey of 75 practitioners working in software-development environments in Saudi Arabia. The survey was conducted in July 2020 and aimed to explore practitioners' perceptions of UX maturity, UX significance, and the challenges that face UX process in software development environments. The results show a higher than expected perception of organizational UX maturity amongst the practitioners surveyed, with the majority considering their organizations to be at an "Integrated phase". The degree of awareness of UX value was also found higher than anticipated. Furthermore, the study reveals important information about the most used UX methods as task analysis, prototyping, and heuristic evaluation. It also shows that UX assessment and user involvement being considered during different stages of product development, particularly in the prototyping phase. The major challenges that face UX process were found to be the need to improve UX consistency and the ability of teams and departments to collaborate.

Author 1: Obead Alhadreti

Keywords: User experience; UX maturity; Saudi Arabia

Paper 22: Noise and Restoration of UAV Remote Sensing Images

Abstract: Remotely sensed images captured from a camera mounted on a UAV (unmanned aerial vehicle) are exposed to noise caused by internal factors, such as the UAV system itself or external factors such as atmospheric conditions. Such images need to be restored before they can undergo further processing stages. This study aims to analyse the effects of salt and pepper noise on a UAV image and restore the image by removing the noise effects. In doing so, a UAV image, with red, green and blue channel and containing regions of different spectral properties, is experimented with salt and pepper noise of different densities. Image restoration procedure is formulated using median filtering of variable sizes. Peak-signal to noise ratio (PSNR) and mean square error (MSE) analysis are performed to measure image quality before and after restoration. An optimal filter size is chosen based on the highest PSNR of the restored image. The results show that the effects of noise on UAV images are dependent on the spectral properties of the image channels and the regions of interest. The proposed restoration works best for images with low- compared to high-density noises. Blue channel is found having the largest variation of optimal filter size, 18.5, compared to other channels because of the high response to noise within its short spectral wavelength region. Landscape’s vegetation has the largest variation of optimal filter size, 22, compared to other regions due to the sensitivity of its dark spectral properties.

Author 1: Asmala Ahmad

Author 2: Khadijah Amira Mohd Fauzey

Author 3: Mohd Mawardy Abdullah

Author 4: Suliadi Firdaus Sufahani

Author 5: Mohd Yazid Abu Sari

Author 6: Abd Rahman Mat Amin

Keywords: Noise; restoration; remote sensing; UAV; PSNR

Paper 23: Prevention of Attacks in Mobile Ad Hoc Network using African Buffalo Monitoring Zone Protocol

Abstract: Mobile ad hoc networks (MANET) can be utilized for communicating wirelessly. However, MANET is affected by many attacks and malicious activities. In MANET, the prevention approach is necessary to secure communication. MANET is easily affected by numerous attacks such as wormhole (WH) attack, Grey-hole (GH) attack, and black-hole (BH) attack in which the sender hubs can’t able to transmits the message to the target node due to the malicious behavior. To prevent the attacks in MANET, this research introduces a novel routing protocol as African Buffalo Monitoring Zone Protocol (ABMZP). This approach is utilized for preventing wormhole attack and other malicious activities in MANET. This mechanism monitors the communication channel continuously and identifies the attack detection. Sequentially, the ABMZP approach prevents the harmful nodes and finds the alternate path for communication. The simulation of this research is done with the use of Network Simulator 2 (NS-2) and finally, the efficiency of the projected ABMZP work outcomes are compared with the latest existing techniques and provides superior results.

Author 1: R. Srilakshmi

Author 2: M.Jaya Bhaskar

Keywords: Mobile ad hoc network; malicious nodes; routing protocol; wormhole attack; security

Paper 24: Analysis of Indonesian Motorcycle Gang with Social Network Approach

Abstract: Analysis of motorcycle gang networks in Indonesia was conducted to determine the dynamics of the motor gang network. This analysis is needed by the government in making appropriate and effective policies in overcoming social problems caused by the existence of this group. The purpose of this study is to detect and determine the community structure of motorcycle gang networks in Indonesia through the use of big data available on the internet, especially social media. This research also utilizes several approaches such as social and behavioral sciences, as well as the computer technology in understanding and finding solutions to problems that arise in society. This study uses a social network analysis method as an instrument that will reveal the social structure of motorcycle gangs with a network centrality approach and community detection. This research succeeded in finding the network structure pattern and network insight of motorcycle gangs by finding the most influential actors. The study also found 25 motorcycle gang groups with high-value network interactions and these groups had more than 2000 active members on social media. In the biker gang social network analysis, the most influential actor has 531 degrees with a weighted degree of 1557.

Author 1: Edi Surya Negara

Author 2: Ria Andryani

Author 3: Deni Erlansyah

Author 4: Rezki Syaputra

Keywords: Social network; data mining; data analytics; community detection; motorcycle gang

Paper 25: Predictive System of Semiconductor Failures based on Machine Learning Approach

Abstract: Maintenance in manufacturing has been developed and researched in the last few decades at a very rapid rate. It’s a major step in process control to build a decision tool that detects defects in equipment or processes as quickly as possible to maintain high process efficiencies. However, the high complexity of machines, and the increase in data available in almost all areas, makes research on improving the accuracy of fault detection via data-mining more and more challenging issue in this field. In our paper we present a new predictive model of semiconductor failures, based on machine learning approach, for predictive maintenance in industry 4.0. The framework of our model includes: Dataset and data acquisition, data preprocessing in three phases (over-sampling, data cleaning, and attribute reduction with principal component analysis (PCA) technique and CfsSubsetEval technique), data modeling, evaluation model and implementation model. We used SECOM dataset to develop four different models based on four algorithms (Naive Bayesian, C4.5 Decision tree, Multilayer perceptron (MLP), Support vector machine), according to the five metrics (True Positive rate, False Positive rate, Precision, F-Mesure and Accuracy). We implemented our new predictive model with 91, 95% of accuracy, as a new efficient predictive model of semiconductor failures.

Author 1: Yousef El Mourabit

Author 2: Youssef El Habouz

Author 3: Hicham Zougagh

Author 4: Younes Wadiai

Keywords: Machine learning; semiconductor; predictive maintenance; industry 4.0

Paper 26: Industrial Energy Load Profile Forecasting under Enhanced Time of Use Tariff (ETOU) using Artificial Neural Network

Abstract: The demand response program involves consumers to mitigate peak demand and reducing global CO2 emission. In sustaining this effort, energy provider such as Tenaga Nasional Berhad (TNB) in Peninsular Malaysia has introduced Enhance Time of Use (ETOU) tariff. However, since 2015, small numbers join the ETOU program due to less confidence in managing their energy consumption profile. Thus, this study provides an optimum forecasting load profile model for TOU and ETOU tariffs using Artificial Neural Network (ANN). An industry's average energy profile has been used as a case study, while the forecasting technique has been conducted to find the optimum energy load profile congruently. The load shifting technique has been adopted under ETOU tariff price while integrating to the ANN procedure. A significant comparison in terms of cost reduction between TOU and ETOU electricity tariffs has been made. In contrast, ANN performance results in searching for the best-shifted load profile have been analyzed accordingly. From the proposed method, the total electricity cost saving has been founded to be saved for about 7.9% monthly. It is hoped that this work will benefit the energy authority and consumers in future action, respectively.

Author 1: Mohamad Fani Sulaima

Author 2: Siti Aishah Abu Hanipah

Author 3: Nur Rafiqah Abdul Razif

Author 4: Intan Azmira Wan Abdul Razak

Author 5: Aida Fazliana Abdul Kadir

Author 6: Zul Hasrizal Bohari

Keywords: Time of use; artificial neural network; energy forecasting; load profile

Paper 27: A Fast Military Object Recognition using Extreme Learning Approach on CNN

Abstract: Convolutional Neural Network (CNN) is an algorithm that can classify image data with very high accuracy but requires a long training time so that the required resources are quite large. One of the causes of the long training time is the existence of a backpropagation-based classification layer, which uses a slow gradient-based algorithm to perform learning, and all parameters on the network are determined iteratively. This paper proposes a combination of CNN and Extreme Learning Machine (ELM) to overcome these problems. Combination process is carried out using a convolution extraction layer on CNN, which then combines it with the classification layer using the ELM method. ELM method is Single Hidden Layer Feedforward Neural Networks (SLFNs) which was created to overcome traditional CNN’s weaknesses, especially in terms of training speed of feedforward neural networks. The combination of CNN and ELM is expected to produce a model that has a faster training time, so that its resource usage can be smaller, but maintaining the accuracy as much as standard CNN. In the experiment, the military object classification problem was implemented, and it achieves smaller resources as much as 400 MB on GPU comparing to standard CNN.

Author 1: Hari Surrisyad

Author 2: Wahyono

Keywords: Training-speed; resource; backpropagationm; CNN; ELM

Paper 28: A New Traffic Distribution Routing Algorithm for Low Level VPNs

Abstract: Virtual Private Networks (VPN) constitute a particular class of shared networks. In such networks, the resources are shared among several customers. The management of these resources requires a high level of automation to obtain the dynamics necessary for the well-functioning of a VPN. In this paper, we consider the problem of a network operator who owns the physical infrastructure and who wishes to deliver VPN service to his customers. These customers may be Internet Service providers, large corporations and enterprises. We propose a new routing approach referred to as Traffic Split Routing (TSR) which splits the traffic as fairly as possible between the network links. We show that TSR outperforms Shortest Path Routing (SPR) in terms of the number of admitted VPN and in terms of Quality of Service.

Author 1: Abdelwahed Berguiga

Author 2: Ahlem Harchay

Author 3: Ayman Massaoudi

Author 4: Radhia Khdhir

Keywords: Virtual Private Networks (VPN); Quality of Service (QoS); NS-2; Simulations; Shortest Path Routing (SPR); Traffic Split Routing (TSR); Routing algorithm

Paper 29: A Platform for Extracting Driver Behavior from Vehicle Sensor Big Data

Abstract: Traffic analysis of vehicles in densely populated areas and places of public gathering can provide interesting insights into crowd behavior. Hajj is a spatio-temporally bound religious activity that is held annually and attended by more than 2 million people. More than 17,000 buses are used to transport pilgrims on fixed days to fixed locations. This poses great challenges in terms of crowd management. Using Global Positioning System (GPS) and Automatic Vehicle Location (AVL) sensors attached to buses, a large amount of spatio-temporal vehicle data can be collected for traffic analysis. In this paper, we present a study whereby driver behavior was extracted from an analysis of vehicle big data. We have explained in detail how we collected data, cleaned it, moved it to a big data repository, processed it and extracted information that helped us characterize driver behavior according to our definition of aggressiveness. We have used data from 17,000 buses that has been collected during Hajj 2018.

Author 1: Sultan Ibrahim bin Ibrahim

Author 2: Emad Felemban

Author 3: Faizan Ur Rehman

Author 4: Ahmad Muaz Qamar

Author 5: Akhlaq Ahmad

Author 6: Abdulrahman A. Majrashi

Keywords: GPS Data; AVL sensors; hajj; big data; traffic analysis

Paper 30: Simulation and Analysis of Variable Antenna Designs for Effective Stroke Detection

Abstract: The variety of applications of patch antenna for portable applications has opened the avenues for the possibilities of having compact, cost-efficient, and life-saving devices. Considering the challenges of portability and cost in making it feasible for detecting strokes in the masses of developing countries where the demand is quite high, this study builds the groundwork for such device fabrication. In total five antenna designs were investigated for their assessment in identifying the stroke. Two main studies of electromagnetic wave interaction and bio-heating of the human head phantom had been accomplished and the results are compared. The main comparison and identification of the stroke location with the human head phantom are presented by the specific absorption rate (SAR), both visualized as volumetric plot and stacked contour slices for clarifying the shape and positioning of the stroke in vertical and horizontal dimensions. The results show that the SAR values for Antenna A & D are the lowest with the values of 1.44 x 10-5 W/kg and 1.96 x 10-5 W/kg, respectively. But the induced electric field and isothermal temperature achieved were highest by Antenna D, with values of 0.25 emw and 133.92 x 10-8 K, respectively; and, the 2-D far-field radiation patterns confirmed better performance by it amongst all others. Hence, making the Antenna D as the most preferred choice for the prototyping stage. The overall trade-off of key parameters is studied herein in this simulation study and based on that the most suitable antenna design is proposed for the experimental prototype testing. The results suggest that the simulation results give a clear insight into the feasibility of stroke detection with the proposed setup and presents high viability for portable, low-cost, and rapid stroke detection applications.

Author 1: Amor Smida

Keywords: Stroke detection; Specific Absorption Rate (SAR); Patch Antenna; S-parameter (S11); electromagnetic wave; bio-heat transfer

Paper 31: Verification of Himawari-8 Observation Data using Cloud Optical Thickness (COT) and Cloud Image Energy

Abstract: Himawari-8 satellite cloud observation data covers all areas of Indonesia. The cloud observation data can be used for observations of current weather conditions and short-term predictions. This paper reports the verification method of Himawari-8 Observation Data using Cloud Optical Thickness (COT) and compared to Cloud Image Energy. The verification test was carried out to determine the accuracy of Himawari-8's observations. COT data were verified using energy data from the observation image of the time-lapse camera. First, the time-lapse camera captures and classifies the cloud image. Subsequently, the energy of each image frame was calculated and re-grouped the result based on the energy to determine the type of the cloud. The results show that there is a positive correlation between COT and low energy values with cumulonimbus cloud detection, on the contrary for Cirrus-cloud type. However, the data requires a more accurate observation method to obtain data from cloud images on the Himawari-8 satellite, specifically for regions with a small spatial size of 4 km and thin clouds in the lower layer.

Author 1: Umar Ali Ahmad

Author 2: Wendi Harjupa

Author 3: Dody Qory Utama

Author 4: Risyanto

Author 5: Alex Lukmanto Suherman

Author 6: Wahyu Pamungkas

Author 7: Prayitno Abadi

Author 8: Agus Virgono

Author 9: Burhanuddin Dirgantoro

Author 10: Reza Rendian Septiawan

Author 11: Mas’ud Adhi Saputra

Keywords: Himawari-8; COT; image classification; cloud energy

Paper 32: A Smart Approximation Algorithm for Minimum Vertex Cover Problem based on Min-to-Min (MtM) Strategy

Abstract: In this paper, we have proposed an algorithm based on min-to-min approach. In the proposed algorithm first the degree of each vertex of the graph is calculated. Next the vertex with minimum degree is selected, after which all the neighbors of the minimum degree are located. In the neighbors of the minimum degree vertex, again the vertex with the minimum degree is found and put into the set minimum vertex cover and deleted from the graph. Again, the degree of each vertex of the updated graph is calculated and again the same process is repeated until the graph becomes empty. In case of tie, all the neighbors of the minimum degree vertices are computed and then the minimum degree vertex in all of them is added to minimum vertex degree set. The same process is repeated until the graph becomes empty. The proposed algorithm is a very simple, efficient, and easy to understand and implement. The proposed min-to-min algorithm is evaluated on small as well as on large benchmark instances and the results indicate that the performance of the min-to-min algorithm is far better as compared to the other state-of-art algorithms in term of accuracy and computation complexity. We have also used the proposed method to solve the maximum independent set problem.

Author 1: Jawad Haider

Author 2: Muhammad Fayaz

Keywords: Minimum vertex cover; approximation algorithms; maximum independent set; benchmark instances; graph theory

Paper 33: Developing an Intelligent Framework for Improving the Quality of Service in the Government Organizations in the Kingdom of Saudi Arabia

Abstract: The Kingdom of Saudi Arabia is enhancing the services and applications in government organizations through the number of systems that generate a massive amount of data through Big Data technology. Recently, the Global Artificial Intelligent Summit 2020, Saudi Data and Artificial Intelligence Authority (SDAIA), NEOM have launched an Artificial Intelligence (AI) strategy that aligns with the Kingdom Vision 2030. AI opens a wide door for opportunities and new strategies that will narrow the gap in the skillset of individuals and promote research and innovation in the IT industry. Organizations lack advanced techniques to evaluate the performance of individuals and departments that supports improving the quality of service. The introduction of AI-based applications in the government and private sectors will facilitate decision-makers in tracking and optimizing the efficiency of departments and individuals. This research aims to develop an intelligent framework for government organizations to improve the quality of services rendered to customers and businesses. In addition, it highlights the importance of AI policies in archiving metadata. This paper presents a framework for an organization that contains Chatbot, Sentiment Analysis, and Key Performance Indicators to improve the services. A synthetic dataset is employed as a testbed to evaluate the performance of the framework. The outcome of this study shows that the proposed framework able to improve the performance of organizations. Using this proposed framework, organizations can build a mechanism for their workforce to retrieve meaningful information. Moreover, it provides significant features include efficient data extraction, data management, and AI-based security for effective document management.

Author 1: Abdulelah Abdallah AlGosaibi

Author 2: Abdul Rahaman Wahab Sait

Author 3: Abdulaziz Fahad AlOthman

Author 4: Shadan AlHamed

Keywords: Key performance indicators; big data; hierarchical analysis; artificial intelligence; privacy policies; metadata; pattern generation

Paper 34: iDietScoreᵀᴹ: Meal Recommender System for Athletes and Active Individuals

Abstract: Individualized meal planning is a nutrition counseling strategy that focuses on improving food behavior changes. In the sports setting, the number of experts who are sports dietitians or nutritionists (SD/SN) is small in number, and yet the demand for creating meal planning for a vast number of athletes often cannot be met. Although some food recommender system had been proposed to provide healthy menu planning for the general population, no similar solution focused on the athlete's needs. In this study, the iDietScoreTM architecture was proposed to give athletes and active individuals virtual individualized meal planning based on their profile, includes energy and macronutrients requirement, sports category, age group, training cycles, training time and individual food preferences. Knowledge acquisition on the expert domain (the SN) was conducted prior to the system design through a semi-structured interview to understand meal planning activities' workflow. The architecture comprises: (1) iDietScoreTM web for SN/SD, (2) mobile application for athletes and active individuals and (3) expert system. SN/SD used the iDietScoreTM web to develop a meal plan and initiate the compilation meal plan database for further use in the expert system. The user used iDietScoreTM mobile app to receive the virtual individualized meal plan. An inference-based expert system was applied in the current study to generate the meal plan recommendation and meal reconstruction for the user. Further research is necessary to evaluate the prototype's usability by the target user (athletes and active individuals).

Author 1: Norashikin Mustafa

Author 2: Abdul Hadi Abd Rahman

Author 3: Nor Samsiah Sani

Author 4: Mohd Izham Mohamad

Author 5: Ahmad Zawawi Zakaria

Author 6: Azimah Ahmad

Author 7: Noor Hafizah Yatiman

Author 8: Ruzita Abd Talib

Author 9: Poh Bee Koon

Author 10: Nik Shanita Safii

Keywords: Expert system; meal planning; sports nutrition; inference engine; design and development

Paper 35: Learner Behavior in e-Learning as a Multicriteria Attribute based on Perspective of Flow Experience

Abstract: Flow experience describe psychological condition in the form of optimal experience of an activity. Flow shows an interconnection, interest, and pleasure toward an activity, thus enable user to fully participate in the activity. In e-learning activity, flow provides positive experience of a learning process. This condition is essential in user’s ability to achieve high performance. Therefore, it is important to identify user’s flow experience during his interaction with e-learning. This information can be used as reference of how e-learning model provide response that in accordance with user’s psychological condition. Assessment of psychological experience based on flow theory have been conducted in many studies, particulary based on experience sampling method. However, these survey methods require high effort thus they are inefficient. The previous studies in this topic only covers conventional learning, with face-to-face interaction. In e-learning, particulary those that use adaptive context aware e-learning approach, flow experience can be assessed by conducting inference based on learning behavior parameters of learners during interaction with e-learning. However, there is no study that provide relation among learner’s learning behavior in e-learning with parameters of flow experience. Therefore, this study tested hypotheses aimed to obtain relation between learning behavior and flow experience. Hypotheses model constructed by involving technology acceptance model (TAM), expectation confirmation model, and flow experience as learning psychological condition. Learning behavior as a multicriteria attribute was represented by actual usage in form of intensity of using e-learning. Meanwhile, perceived balance of skill and challenge as representation of flow experience was selected as main variable in the proposed hypotheses. The result showed that these variables had positive relation with each other.

Author 1: Dadang Syarif Sihabudin Sahid

Keywords: Flow experience; learning behavior; multicriteria attribute; TAM; e-learning

Paper 36: Determinants of Privacy Protection Behavior in Social Networking Sites

Abstract: Social Networking Sites (SNSs) are an attractive online platform for social interaction and communication. Since SNSs are easily accessed by a large number of people, a large quantity of data is also stored in the SNSs. Consequently, concern regarding the exposure to privacy risk will emerge. In this case, users need privacy protection behavior to protect their privacy in SNSs. This paper aims to determine the motivational determinants of privacy protection behavior among high school students in protecting their data or personal information when using SNSs. To identify the determinants of privacy protection behavior, a questionnaire survey was administered on 200 high school students. This study proposed a conceptual model that offers an understanding of motivational determinants of privacy protection behavior in social networking sites. Results indicate that perceived anonymity is the most significant determinant in motivating privacy behavior followed by perceived intrusiveness, perceived severity, self-efficacy, perceived vulnerability, and response efficacy. The results of this study will shed some light on understanding the levels of privacy protection behavior in SNSs, and identify suitable interventions in motivating privacy protection behavior among high school students. Finally, with the combined theory of Protection Motivation Theory (PMT) and Hyperpersonal Communication Theory (HCT), this model provides the basis to direct future studies in the related field.

Author 1: Siti Norlyana Suhaimi

Author 2: Nur Fadzilah Othman

Author 3: Raihana Syahirah

Author 4: Syarulnaziah Anawar

Author 5: Zakiah Ayop

Author 6: Cik Feresa Mohd Foozy

Keywords: Privacy; social networking sites; privacy; protection motivation theory; hyperpersonal communication theory

Paper 37: A Hybird Framework based on Autoencoder and Deep Neural Networks for Fashion Image Classification

Abstract: Deep learning has played a huge role in computer vision fields due to its ability to extract underlying and complex features of input images. Deep learning is applied to complex vision tasks to perform image recognition and classification. Recently, Apparel classification, is an application of computer vision, has been intensively explored and investigated. This paper proposes an effective framework, called DeepAutoDNN, based on deep learning algorithms for apparel classification. DeepAutoDNN framework combines a deep autoencoder with deep neural networks to extract the complex patterns and high-level features of fashion images in supervised manner. These features are utilized via categorical classifier to predict the given image to the right label. To evaluate the performance and investigate the efficiency of the proposed framework, several experiments have been conducted on the Fashion-MNIST dataset, which consists of 70000 images: 60000 and 10000 images for training and test, respectively. The results have shown that the proposed framework can achieve accuracy of 93.4%. In the future, this framework performance can be improved by utilizing generative adversarial networks and its variant.

Author 1: Aziz Alotaibi

Keywords: Fashion detection; fashion classification; convolutional autoencoder; deep learning; insert

Paper 38: Adaptive e-Learning AI-Powered Chatbot based on Multimedia Indexing

Abstract: With the rapid evolution of e-learning technology, the multiple sources of information become more and more accessible. However, the availability of a wide range of e-learning offers makes it difficult for learners to find the right content for their training needs. In this context, our paper aims to design an e-learning AI-powered Chatbot allowing interaction with learners and suggesting the e-learning content adapted to their needs. In order to achieve these objectives, we first analysed the e-learning multimedia content to extract the maximum amount of information. Then, using Natural Language Processing (NLP) techniques, we introduced a new approach to extract keywords. After that, we suggest a new approach for multimedia indexing based on extracted keywords. Finally, the Chatbot architecture is realized based on the multimedia indexing and deployed on online messaging platforms. The suggested approach aims to have an efficient way to represent the multimedia content based on keywords. We compare our approach with approaches in literature and we deduce that the use of keywords on our approach result on a better representation and reduce time to construct multimedia indexing. The core of our Chatbot is based on this indexed multimedia content which enables it to look for the information quickly. Then our designed Chatbot reduce response time and meet the learner’s need.

Author 1: Salma El Janati

Author 2: Abdelilah Maach

Author 3: Driss El Ghanami

Keywords: e-Learning; Chatbot; Speech-To-Text; NLP; Keywords Extraction; Text Clustering; Multimedia Indexing

Paper 39: An Assessment of Organizational Capabilities for ERP Implementation in SMEs: A Governance Model for IT Success using a Resource-based Approach

Abstract: One of the most coveted technological innovations is the increasing use of Integrated Management Software (ERP) since the early 1990s. ERP is considered a powerful reengineering tool that profoundly transforms a company's business processes and changes the way to conduct reengineering projects and implement new software. The significant number of failures as reported in the literature on ERP emphasizes the fact that some companies may not realize the expected benefits. This result becomes particularly significant in the case of SMEs which have their own contingencies in addition to the scarcity of resources which may, in turn, lead to the failure of ERP implementation. This leads initially to ask, in one hand, about the variables of success of this innovation i.e. the determinants of the techno-organizational innovation; and on the other hand, about the existence of a model of dependences analysis between these determinants and their success as perceived by the management. The current empirical research is carried out in 92 companies having adopted a whole or a part of their IS with an ERP system. Having analyzed the data collected via a questionnaire, and applying the method of structural equation (MSE), results prove the existence of one "general fit" between the data and the supposed relations of causality.

Author 1: Houcine Chatti

Author 2: Evan Asfoura

Author 3: Gamal Kassem

Keywords: ERP success; systemic approach; quantitative study; structural equation modelling

Paper 40: On Developing High-Speed Heterogeneous and Composite ES Network through Multi-Master Interface

Abstract: These days, many heterogeneous and composite embedded systems contain many subnets developed using different bus-based protocols, such as I2C, CAN, USB, and RS485. There is always a requirement to Interface and interconnect the heterogeneous ES networks to achieve and establish a composite network. The ES networks developed using different protocols differ in many ways, considering the speed of communication, Arbitration, Synchronization, and Timing. Many solutions are being offered using heterogeneous embedded systems, especially in implementing automation systems, without addressing integration and proper interfacing. In this paper, a Multi-Master based interfacing of a CAN and I2C networking through Ethernet-based interfacing has been presented especially to find the optimum speeds at which the networks must be operated for different data packet sizes. It has been shown in the paper that it is quite efficient and effective when a data packet of size 40 bytes is driven using an I2C speed of 5120 bits, Ethernet speed of 20480 bits, and CAN speed of 500 bits.

Author 1: J Rajasekhar

Author 2: JKR Sastry

Keywords: Embedded systems; embedded networks; hybridization of embedded networks; hybridizations through multi-master communication

Paper 41: An Analysis of Human Activities Recognition using Smartwatches Dataset

Abstract: Today, the era of smart devices evolving the human behavior interaction to a changing environment where the learning of activities is monitored to predict the next step of human behavior. The smart devices have these sensors built-in (accelerometer and gyroscope), which are continuously generating a large amount of data. The data used to identify the novel patterns of human behavior, together with machine learning and data mining techniques. Classification of human motions with motion sensor data is among the current topics of study. The classification is an important part of data mining techniques and used in this work to find the accuracy of instances in the given dataset. Thus, it is possible to follow the activities of a user carrying only a smartwatch. The smartwatches consisting of four different models from two manufacturers are used. Furthermore, the experiment contains nine users and seven activities performed by them. After the classification was determined, the data set to which the principal component analysis has been applied was classified by decision stump, j48, Bayes net, naive Bayes, naive Bayes multinomial text, random forest, and logit boost methods, and their performances were compared. The most successful result was obtained from the random forest method. The accuracy of the Random Forest classification algorithm on nominal datasets is 99.99% on both accelerometer and gyroscope sensors.

Author 1: Saadia Karim

Author 2: SM Aqil Burney

Author 3: Nadeem Mahmood

Keywords: Human activity recognition; smartwatches; big data; machine learning; random forest

Paper 42: Performance Analysis of Fermat Factorization Algorithms

Abstract: The Rivest-Shamir-Adleman (RSA) cryptosystem is one of the strong encryption approaches currently being used for secure data transmission over an insecure channel. The difficulty encountered in breaking RSA derives from the difficulty in finding a polynomial time for integer factorization. In integer factorization for RSA, given an odd composite number n, the goal is to find two prime numbers p and q such that n = p q. In this paper, we study several integer factorization algorithms that are based on Fermat’s strategy, and do the following: First, we classify these algorithms into three groups: Fermat, Fermat with sieving, and Fermat without perfect square. Second, we conduct extensive experimental studies on nine different integer factorization algorithms and measure the performance of each algorithm based on two parameters: the number of bits for the odd composite number n, and the number of bits for the difference between two prime factors, p and q. The results obtained by the algorithms when applied to five different data sets for each factor reveal that the algorithm that showed the best performance is the algorithms based on (1) the sieving of odd and even numbers strategy, and (2) Euler’s theorem with percentage of improvement of 44% and 36%, respectively compared to the original Fermat factorization algorithm. Finally, the future directions of research and development are presented.

Author 1: Hazem M. Bahig

Author 2: Mohammed A. Mahdi

Author 3: Khaled A. Alutaibi

Author 4: Amer AlGhadhban

Author 5: Hatem M. Bahig

Keywords: Integer factorization; Fermat’s algorithm; RSA; factorization with sieving; perfect square

Paper 43: Agent-based Model for Simulating Urban System

Abstract: In this paper, we address the issue related to the modelling and simulation of urban systems. We propose a new approach for simulating urban system based on multi-agent paradigm. Our proposed model is based on the use of a coupling between cellular agents and vector agents. These agents made it possible to take into account the spatial dimension of urban systems as well as the modelling of all the rules that govern them. To allow reusability of our model, we apply the VOYELLE approach by defining an environment model, an organization model, an agent model and an interaction model. We test our proposed model with a case study on Casablanca city. We discuss the problem of urbanization of Casablanca by following an approach that reduces the problem into two sub-problems similar but are treated differently: first, predict the city’s need of housing (individual housing zone, multifamily housing zone, ...) and then anticipate the city’s need of public services and ensure better spatial distribution of these equipment to best serve the people needs. Then we did experimentation with two simulation scenarios by changing in each scenario the hypotheses concerning urban planning especially in terms of demographic growth rate and residential sprawl.

Author 1: Fatimazahra BARRAMOU

Author 2: Malika ADDOU

Keywords: Multi agent systems; simulation; modelling; urban system; cellular agent; vector agent

Paper 44: Applying Digital Image Processing Technology in Discovering Green Patches in the Desert of Saudi Arabia

Abstract: In recent years, the Kingdom of Saudi Arabia has witnessed a noticeable growth of grass and small trees in the desert, forming green patches. Those green patches may have the potential to spread and cover a wider area in the desert in the coming years, thus, making areas of the desert potential agricultural land. This research aims to detect the change of green patches in the desert of Saudi Arabia to solve the challenge that is mainly due to the lack of an organized dataset. Using a series of satellite images of the desert landscape, a change detection algorithm is used to identify the changes in green spaces. This algorithm includes the presentation of multi-temporal datasets to evaluate the chronological special effects. This paper presents an optical flow analysis among images captured at different time sequences. The algorithm shows promising results of change detection in green patches in the desert of Saudi Arabia detected by color segmentation. The algorithm has been validated over a set of satellite images demonstrating an effective performance.

Author 1: Ali Mehdi

Author 2: Md Alamin Bhuiyan

Keywords: Image processing; change detection; optical flow analysis; green patches; color segmentation

Paper 45: A Decision Support System for Detecting Age and Gender from Twitter Feeds based on a Comparative Experiments

Abstract: Author profiling aims to correlate writing style with author demographics. This paper presents an approach used to build a Decision Support System (DSS) for detecting age and gender from Twitter feeds. The system is implemented based on Deep Learning (DL) algorithms and Machine Learning (ML) algorithms to distinguish between classes of age and gender. The results show that every algorithm has different results of age and gender based on the model architecture and power points of each algorithm. Our decision support system is more accurate in predicting the age and the gender of author profiling from his\her written tweets. It adopts the deep learning model using CNN and LSTM methods. Our results outperform those obtained in the competitive conference s CLEF 2019.

Author 1: Roobaea Alroobaea

Author 2: Sali Alafif

Author 3: Shomookh Alhomidi

Author 4: Ahad Aldahass

Author 5: Reem Hamed

Author 6: Rehab Mulla

Author 7: Bedour Alotaibi

Keywords: Decision support system; age detection; gender detection; author profiling; deep learning; machine learning

Paper 46: Admission Exam Web Application Prototype for Blind People at the University of Sciences and Humanities

Abstract: Currently, there is a large sector of Peru's population that has some type of disability. Every year, the government creates norms for their integration into society. However, to date, total integration is not achieved. One of the points that can be seen is at the moment of taking an entrance exam, where to this day, they do not have the tools to perform in an autonomous and optimal way. Taking this into account, the objective of this article is the development of a prototype admission test for blind people at the University of Sciences and Humanities. A hybrid methodology is used with between Soft Systems and Scrum. The results were obtained from the analysis of both methodologies and a final product prototyped with Balsamiq, demonstrating an optimal union between these two methodologies. Therefore, the prototype will facilitate the performance of blind people during their entrance exam.

Author 1: Alexis Carrion-Silva

Author 2: Carlos Diaz-Nunez

Author 3: Laberiano Andrade-Arenas

Keywords: Admission; Balsamiq; blind; scrum; soft systems

Paper 47: Credit Card Business in Malaysia: A Data Analytics Approach

Abstract: The revolution of big data has made resonance in the banking sector especially in dealing with the massive amount of data. The banks have the opportunity to know about the customer's opinions and satisfaction regarding their products by analyzing the data gathered every day. So, the banks can transform these data into high-quality information that allow banks to improve their business especially in credit cards which is becoming a short-term business for the banks nowadays. Further, the sentiment analysis has become immense in the field of data analytics especially the customers’ opinion makes a huge impact in making profitable business decisions. The outcome of the sentiment analysis does assist the banks to know the deficiencies of their product and allow them to improve their products to satisfy the customers. From the sentiment analysis, 45% of the customers were negative, 30% were positive and 25% were neutral towards the credit card facility offered by the commercial banks. Also, the prediction of credit card customer satisfaction will contribute in a significant way to create new opportunities for the banks to enhance their promotion aspects as well as the credit card business in future. Random Forest algorithm was applied with three various experiments utilizing the normal data, balanced data and the optimized model with the normal data. The optimized model with the normal data obtained the highest accuracy of 87.38% followed by the normal dataset by 85.82% and the least accuracy was for the balanced dataset by 82.83%.

Author 1: Mohamed Khaled Yaseen

Author 2: Mafas Raheem

Author 3: V. Sivakumar

Keywords: Credit card; predictive analytics; random forest; sentiment analysis; banking

Paper 48: Automatic Detection of Elbow Abnormalities in X-ray Imagery

Abstract: Abnormality or deformity in any of the bone disrupts overall function of human skeleton. Hence, orthopedic abnormalities are common reasons for emergency department visits and elbow deformation is one of the common issue seen among emergency patients. Despite high frequency of elbow-related casualties, there is no standardized method for interpretation of digital X-rays. Accordingly, we have proposed a model for automatic deformation detection in elbow and connected forearm bones using Image Processing techniques. The X-ray images were preprocessed and the region of interest is segmented using Multi Class Probabilistic Segmentation in first step. Subsequently, multi-phase canny edge detector was used to highlight the edges and descriptive features were extracted to differentiate among normal and abnormal X-rays. On the basis of those features, three tests were performed to automatically trace deformities in different bones associated with elbow. The publically available Musculoskeletal Radiographs (MURA) dataset has been used in this research. Hence, 250 elbow X-rays from the dataset were investigated for geometrical shape distortions, crack, damage and extra-ordinary distance between the bones. Accordingly, the proposed method shows promising results in terms of 86.20% accuracy.

Author 1: Mashal Afzal

Author 2: M. Moazzam Jawaid

Author 3: Rizwan Badar Baloch

Author 4: Sanam Narejo

Keywords: Deformation detection; multi-class probabilistic segmentation; edge detection and geometrical shape detection

Paper 49: Compact Scrutiny of Current Video Tracking System and its Associated Standard Approaches

Abstract: With an increasing demands of video tracking systems with object detection over wide ranges of computer vision applications, it is necessary to understand the strengths and weaknesses of the present situation of approaches. However, there are various publications on different techniques in the visual tracking system associated with video surveillance application. It has been seen that there are prime classes of approaches that are only three, viz. point-based tracking, kernel-based tracking, and silhouette-based tracking. Therefore, this paper contributes to studying the literature published in the last decade to highlight the techniques obtained and brief the tracking performance yields. The paper also highlights the present research trend towards these three core approaches and significantly highlights the open-end research issues in these regards. The prime aim of this paper is to study all the prominent approaches of video tracking system which has been evolved till date in various literatures. The idea is to understand the strength and weakness associated with the standard approach so that new approaches could be effectively act as a guideline for constructing a new upcoming model. The prominent challenge in reviewing the existing approaches are that all the approaches are targeted towards achieving accuracy, whereas there are various other connected problems with internal process which has not been considered for e.g. feature extraction, processing time, dimensional problems, non-inclusion of contextual factor, which has been an outcome of the proposed review findings. The paper concluded by highlighting this as research gap acting as contribution of this review work and further states that there are some good possibilities of new work evolution if these issues are considered prior to developing any video tracking system. Overall, this paper offers an unbiased picture of the current state of video tracking approaches to be considered for developing any upcoming model.

Author 1: Karanam Sunil Kumar

Author 2: N P Kavya

Keywords: Video tracking; object tracking; visual tracking; video surveillance; object detection

Paper 50: Algorithm Design to Determine Possibility of Student Graduate Time in Student Grade Recapitulation Application

Abstract: This study aims to create an algorithm model to determine the potential time for student graduation to be applied to the grade recapitulation information system at XYZ University Information System Study Program. The XYZ University Information System Study Program already has a grade recapitulation information system, but the grade recapitulation information system has not been able to provide potential information about when a student can graduate from college. Information about probability graduating from college is very important as evaluation material in providing direction to students as an effort to achieve graduate on time. The more students who graduate on time, it can help increase the value of accreditation. The existing grade recapitulation information system can only display a history of grades and courses that have been taken by a student, so that the guardian lecturer has difficulty checking the courses that have not been taken by the student guidance and difficulty obtaining information when the student's guidance can graduate from college. In this study, an algorithm model was used to calculate the student's graduate time based on the calculation and mapping of subjects that had not been taken and had not yet passed. Based on the test results, the average time needed to determine student graduation time is 0.165 seconds.

Author 1: Marliana Budhiningtias Winanti

Author 2: Umi Narimawati

Author 3: Suwinarno Nadjamudin

Author 4: Hendar Rubedo

Author 5: Syahrul Mauluddin

Keywords: Algorithm; graduate; subject; grade

Paper 51: Hybrid Invasive Weed Optimization with Tabu Search Algorithm for an Energy and Deadline Aware Scheduling in Cloud Computing

Abstract: The current existing high flexibility, profitability, and potential have made cloud computing extremely popular among the companies. This is used for improving and applying resources in an efficient manner and optimize makespan of the tasks. Scheduling is easy while there are only a few tasks to complete with few resources. Contrastingly, at the time the users forward several demands to the environment of the cloud, there may be a need for optimally selecting and allocating resources for achieving the desired quality of service that makes scheduling challenging. In this work, using intelligent metaheuristic algorithms for processing the requests and tasks of users in energy-aware scheduling made for a deadline is proposed. Genetic Algorithm (GA) the evolutionary algorithm that is inspired by the natural process of selection and the evolution theory. The Invasive Weed Optimization (IWO) was yet another novel stochastic based on the population that was a derivative-free technique of optimization inspired by the growth of the weed plants. The TABU Search (TS) was a generalization technique of local search where the TABU list was used for preventing cycling and further generating the candidates of the neighborhood. A hybrid GA with the TS (GA-TS) with a hybrid IWO with TS (IWO-TS) has been proposed for the energy and deadline aware scheduling. The framework further offers optimization of energy and performance. The primary purpose of this algorithm has been to improve deadline and scheduling in cloud computing along with local as well as global search algorithms. This framework will offer optimization of performance and energy. The reason behind presenting this algorithm was improving both scheduling and deadline in cloud computing using both local and global algorithms and results proved the algorithm to have better results.

Author 1: Pradeep Venuthurumilli

Author 2: Sridhar Mandapati

Keywords: Cloud computing; scheduling; Genetic Algorithm (GA); Invasive Weed Optimization (IWO) and Tabu Search (TS)

Paper 52: Adaptive Retrieval Time-Related Data Model for Tracking Factors Affecting Diabetes

Abstract: In the last four decades several dozens of representing time-oriented data/knowledge bases have been presented. Some of these representations violate First Normal Form (1NF) by using Non-First Normal Form (N1NF) prototypes and temporal nested representations, while others simulated the concepts of temporal data with relational data representation without violating 1NF. In this article, a new interval-based knowledge representational data model with an optimized retrieval techniques are employed for modeling and optimality retrieve a biomedical time-varying data (factors/observations that affect the diabetes). The used time-related data model is more compact to represent time-varying data with less memory (capacity) storage with respect to the main representations in the literature, but which is as expressive as those representations (a transformation algorithms show that data represented in this model can be transferred to/from the representations in the literature with zero percent loss of information). A new data structure is defined with the optimal retrieval techniques to prove some basic properties of the used time-model and to ensure that the time-model is an extension and reduction of the main representations in the literature, namely TQuel and BCDM. The expressive power, reducibility, and easy implementation of the proposed model, especially for the legacy systems, are considered as advantages of the proposed model.

Author 1: Ibrahim AlBidewi

Author 2: Nashwan Alromema

Author 3: Fahad Alotaibi

Keywords: Diabetes database; time-data model; diabetes observations; valid-time data; knowledge-based data

Paper 53: Artificial Neural Network based Emotion Classification and Recognition from Speech

Abstract: Emotion recognition from speech signals is still a challenging task. Hence, proposing an efficient and accurate technique for speech-based emotion recognition is also an important task. This study is focused on four basic human emotions (sad, angry, happy, and normal) recognition using an artificial neural network that can be detected through vocal expressions resulting in more efficient and productive machine behaviors. An effective model based on a Bayesian regularized artificial neural network (BRANN) is proposed in this study for speech-based emotion recognition. The experiments are conducted on a well-known Berlin database having 1470 speech samples carrying basic emotions with 500 samples of angry emotions, 300 samples of happy emotions, 350 samples of a neutral state, and 320 samples of sad emotions. The four features Frequency, Pitch, Amplitude, and formant of speech is used to recognize four basic emotions from speech. The performance of the proposed methodology is compared with the performance of state-of-the-art methodologies used for emotion recognition from speech. The proposed methodology achieved 95% accuracy of emotion recognition which is highest as compared to other states of the art techniques in the relevant domain.

Author 1: Mudasser Iqbal

Author 2: Syed Ali Raza

Author 3: Muhammad Abid

Author 4: Furqan Majeed

Author 5: Ans Ali Hussain

Keywords: Emotion States; ANN; BR; BRANN; emotion classifier; speech emotion recognition

Paper 54: Problematic Use of Mobile Phones during the COVID-19 Pandemic in Peruvian University Students, 2020

Abstract: The problematic use of mobile phones during the COVID-19 pandemic has been predicted as a mental alteration in university students due to confinement due to the health crisis that exists in our country and in the world, therefore, the objective of the study is to determine the problematic use of mobile phones during the COVID-19 pandemic. COVID-19 in Peruvian university students, 2020. This is a quantitative, non-experimental, descriptive, and cross-sectional study, with a population of 163 Peruvian university students, who answered a questionnaire of sociodemographic data and the Mobile Phone Problem Use Scale. In the results where it can observe regarding the problematic use of mobile phones that 103 (63.2%) of the university students have a high problematic use, 59 (36.2%) medium problematic use and 1 (0.6%) use low problematic use. In conclusion, programs on mental health should be carried out during the COVID-19 pandemic in university students.

Author 1: Rosa Perez-Siguas

Author 2: Randall Seminario-Unzueta

Author 3: Hernan Matta-Solis

Author 4: Melissa Yauri-Machaca

Author 5: Eduardo Matta-Solis

Keywords: Mental health; pandemic; coronavirus; mobile phones

Paper 55: Examining Users’ Willingness to Post Sensitive Personal Data on Social Media

Abstract: Reaping the vast benefits of ubiquitous social media requires users to share their information, preferences, and interests on these websites. This research draws on communications privacy management theory and the online privacy literature to develop and validate an empirical research model testing users’ willingness to share sensitive data on Facebook. The data were collected using an online survey from 569 respondents, however; 515 responses were valid for the statistical analysis. The valid data were analyzed using SMART-PLS2. The findings showed the need for attention as a significant predictor of Facebook users’ willingness. Neither individual perceptions of privacy control nor privacy risks had an impact on the variable of interest. Furthermore, the evidence supported the positive impact of each of deposition to value privacy and the perceived effectiveness of Facebook’s privacy policy on mitigating Facebook users’ perceptions of the risks of posting their private data on the website. The paper discusses the study’s theoretical and managerial implications along with its limitations.

Author 1: O’la Hmoud Al-laymoun

Author 2: Ali Aljaafreh

Keywords: Self-disclosure; sensitive data; Facebook policy; government regulations; privacy control; privacy risk

Paper 56: Investigating Epidemic Growth of COVID-19 in Saudi Arabia based on Time Series Models

Abstract: Predictive mathematical models for simulating the spread of the COVID-19 pandemic are an interesting and fundamental approach to understand the infection growth curve of the epidemic and to plan effective control strategies. Time series predictive models are one of the most important mathematical models that can be utilized for studying the pandemic growth curve. In this study, three-time series models (Susceptible-Infected-Recovered-Death (SIRD) model, Susceptible-Exposed-Infected-Recovered-Death (SEIRD) model, and Susceptible-Exposed-Infected-Quarantine-Recovered-Death-Insusceptible, (SEIQRDP) model) have been investigated and simulated on a real dataset for investigating Covid-19 outbreak spread in Saudi Arabia. The simulation results and evaluation metrics proved that SIRD and SEIQRDP models provided a minimum difference error between reported data and fitted data. So using SIRD, and SEIQRDP models are used for predicting the pandemic end in Saudi Arabia. The prediction results showed that the Covid-19 growth curve will be stable with detected zero active cases on 2 February 2021 according to the prediction computations of the SEIQRDP model. Also, the prediction results based on the SIRD model showed that the outbreak will be stable with active cases after July 2021.

Author 1: Mohamed Torky

Author 2: M. Sh Torky

Author 3: Azza Ahmed

Author 4: Aboul Ella Hassanein

Author 5: Wael Said

Keywords: COVID-19 outbreak; time series models; SIRD; SEIRD; SEIQRDP

Paper 57: Learners’ Activity Indicators Prediction in e-Learning using Fuzzy Logic

Abstract: With the idea of introducing computer supports in education, Online Learning (named also e-learning) associated on one hand, the concept of network, therefore that of distance and concepts of communicating interaction, whether between the learner and the teacher (or tutor), or between the learners themselves and on the other hand exchanges and collaboration. Any activity in e-learning leaves recorded traces stored in a database system. Until now, data on student activity is stored as low-level information; however, the volume of this information is too large to be processed and interpreted by tutors, requiring data collection and preparation to give it meaning. In addition, according to the studies carried out in this direction, the tracking of learners must be guaranteed in all stages of e-learning process, to assist and help them when they encounter problems that they cannot solve. The lack of direct contact between the tutor and the learners can cause a lack of feedback of the learning activity; all these problems can lead to a high rate of abundance in e-learning. Our work aims to develop a model for predicting learner activity indicators using fuzzy logic without going through rigid calculations but based on consultation traces and skill assessment scores. Based on the traces collected from the Learning Management System (LMS) Moodle, it could give the tutor high level processing of the learning activity.

Author 1: Sanae CHEHBI

Author 2: Rachid ELOUAHBI

Author 3: Chakir FRI

Keywords: e-Learning; tracking; Moodle; traces; activity indicators; fuzzy logic

Paper 58: An Evolutionary Algorithm for Short Addition Chains

Abstract: The encryption efficiency of the Rivest-Shamir-Adleman cryptosystem is based on decreasing the number of multiplications in the modular exponentiation (ME) operation. An addition chain (AC) is one of the strategies used to reduce the time consumed by ME through generating a shortest/short chain. Due to the non-polynomial time required for generating a shortest AC, several algorithms have been proposed to find a short AC in a faster time. In this paper, we use the evolutionary algorithm (EA) to find a short AC for a natural number. We discuss and present the role of every component of the EA, including the population, mutation operator, and survivor selection. Then we study, practically, the effectiveness of the proposed method in terms of the length of chain it generates by comparing it with three kinds of algorithms: (1) exact, (2) non-exact deterministic, and (3) non-exact non-deterministic. The experiment is conducted on all natural numbers that have 10, 11, 12, 13, and 14 bits. The results demonstrate that the proposed algorithm has good performance compared to the other three types of algorithms.

Author 1: Hazem M. Bahig

Author 2: Khaled A. Alutaibi

Author 3: Mohammed A. Mahdi

Author 4: Amer AlGhadhban

Author 5: Hatem M. Bahig

Keywords: Addition chain; short chain; evolutionary algorithm; modular exponentiation; RSA

Paper 59: A User-centered Design Approach to Near Field Communication-based Applications for Children

Abstract: There is an abundance of technology targeting children in terms of education, entertainment, and health; however, little research has been conducted on the usability of Near Field Communication (NFC) to create an interactive, digital environment for children easily accessible on a mobile device. NFC technology is a component of Radio Frequency Identification (RFID) technology and is affordable, intuitive, and accessible. The following research evaluates existing NFC applications for children in terms of their ease of use, appropriateness, and areas in need of improvement. Recommendations are provided in visual design, audio enhancements, reward system, and privacy and security concerns. It is concluded that adopting NFC technology in all facets of life will positively benefit the most vulnerable population, children, but first progress toward a user-centered design for this group is required.

Author 1: Mrim Alnfiai

Keywords: Near Field Communication (NFC); mobile device applications; NFC tags; RFID; early learning; K-12 students; preschool children; educational software; usability; user-centered design

Paper 60: Enhancement of Natural Language to SQL Query Conversion using Machine Learning Techniques

Abstract: In the age of information explosion, there is a huge data that is stored in the form of database and accessed using various querying languages. The major challenges faced by a user accessing this data is to learn the querying language and understand the various syntax associated with it. Query given in the form of Natural Language helps any naïve user to access database without learning the query languages. The current process of conversion of Natural Language to SQL Query using a rule-based algorithm is riddled with challenges -- identification of partial or implied data values and identification of descriptive values being the predominant ones. This paper discusses the use of a synchronous combination of a hybrid Machine Learning model, Elastic Search and WordNet to overcome the above-mentioned challenges. An embedding layer followed by a Long Short-Term Memory model is used to identify partial or implied data values, while Elastic Search has been used to identify descriptive data values (values which have lengthy data values and may contain descriptions). This architecture enables conversion systems to achieve robustness and high accuracies, by extracting meta data from the natural language query. The system gives an accuracy of 91.7% when tested on the IMDb database and 94.0% accuracy when tested on Company Sales database.

Author 1: Akshar Prasad

Author 2: Sourabh S Badhya

Author 3: Yashwanth YS

Author 4: Shetty Rohan

Author 5: Shobha G

Author 6: Deepamala N

Keywords: Machine learning; natural language to SQL query; long short-term memory; embedding layer; elastic search; hybrid architecture

Paper 61: Digital Transformation in Higher Education: A Framework for Maturity Assessment

Abstract: Literature in digital transformation maturity is scarce. Digital transformation in higher education, especially after COVID-19 is seen as inevitable. This research explores digital transformation maturity and challenges within Higher Education. The significance of this study stems from the role digital transformation plays in today’s knowledge economy. This study proposes a new framework based on Deloitte’s 2019 digital transformation assessment framework with Petkovic 2014 mega and major higher education process mapping. The study triangulates the findings of multiple research instruments, including survey, interviews, case study, and direct observation. The research findings show a significant variance between the respondents’ perception of digital transformations maturity levels, and the core requirements of digital transformation maturity. The findings also show the lack of holistic vision, digital transformation competency, and data structure and processing as the leading challenges of digital transformation.

Author 1: Adam Marks

Author 2: Maytha AL-Ali

Author 3: Reem Atassi

Author 4: Abedallah Zaid Abualkishik

Author 5: Yacine Rezgu

Keywords: Digital transformation; higher education; COVID-19

Paper 62: Temporal-based Optimization to Solve Data Sparsity in Collaborative Filtering

Abstract: Collaborative Filtering (CF) is a widely used technique in recommendation systems. It provides personal recommendations for users based on their preferences. However, this technique suffers from the sparsity issue which occurs due to a high proportion of missing rating scores in a rating matrix. Several factorization approaches have been used to address the sparsity issue. Such techniques have also been considered to tackle other challenges such as the overfitted predicted scores. Nevertheless, they suffer from setbacks such as drift in user preferences and items’ popularity decay. These challenges can be solved by prediction approaches that accurately learn the long-term and short-term preferences integrated with factorization features. Nonetheless, the current temporal-based factorization approaches do not accurately learn the convergence of the assigned k clusters due to a lower number of short-term periods. Additionally, the use of optimization algorithms in the learning process to reduce prediction errors is time-consuming which necessitates a faster optimization algorithm. To address these issues, a new temporal-based approach named TWOCF is proposed in this paper. TWOCF utilizes the elbow clustering method to define the optimal number of clusters for the temporal activities of both users and items. This approach deploys the whale optimization algorithm to accurately learn short-term preferences within other factorization and temporal features. Experimental results indicate that TWOCF exhibits a superior CF prediction accuracy achieved within a shorter execution time when compared to the benchmark approaches.

Author 1: Ismail Ahmed Al-Qasem Al-Hadi

Author 2: Mohammad Ahmed Alomari

Author 3: Eissa M. Alshari

Author 4: Waheed Ali H. M. Ghanem

Author 5: Safwan M Ghaleb

Keywords: Collaborative filtering; matrix factorization; temporal-based approaches; whale optimization

Paper 63: Enhanced Algorithm for Reconstruction of Three-Dimensional Mesh from Medical Images using Tessellation of Recent Graphics Cards

Abstract: The reconstruction of a 3D mesh using displacement vectors for medical images is a recent method that allows the exploitation of modern GPUs. This method demonstrated its efficiency by accelerating 3D visualization calculations and optimizing the storage process. In fact, it is divided into two main stages. The first step is the construction of a basic mesh by applying the Marching Cubes algorithm, and the second step is the extraction of the displacement vectors, which represent the details lost in the basic mesh. In fact, the Marching Cubes algorithm used to build the basic mesh suffers from some problems that we will try to overcome in this article. These problems are summarized in the ambiguity encountered during the construction of the basic mesh in some cases. Also, the resulting basic mesh must undergo modifications, in order not to have errors of form, which requires time and memory, and which gives the end a final mesh which is not optimal and even erroneous in certain situations. Our method is based on extracting the contours of the anatomy to be reconstructed from a sequence of 2D images. Each contour will be represented by a triangle. The shape of the basic mesh will then be the result of the connection of these triangles. This strategy avoids the use of the marching cubes algorithm in the reconstruction of the basic mesh in order to overcome the problems mentioned above.

Author 1: Lamyae Miara

Author 2: Said Benomar El Mdeghri

Author 3: Mohammed Oucamah Cherkaoui Malki

Keywords: 3D reconstruction; medical imaging; marching cubes; displacement vectors; contour extraction; contour matching

Paper 64: Determinants towards a Better Acceptance Model of IoT in KSA and Eradication of Distrust in Omnipresent Environments

Abstract: This paper highlights several of the key determinants that play a vital role in the acceptance of Internet of Things (IoT) technologies in the Kingdom of Saudi Arabia (KSA). Based on the governmental focus towards technology and the response of the citizens towards embracing new technologies, several determining factors are presented. Certain essential application areas of IoT are analyzed including the local industry, agriculture and livestock, health, education, smart metropolitans and smart government. In addition, we also explore acceptance at the personal level, such as home and privacy of individuals, security, and personal management with IoT wearables. Towards the end of this paper, some challenges of the IoT acceptance are presented along with the analysis of key enablers. All the rationalizations lead to the conclusion that IoT acceptance is inevitable based on the number of associated benefits which will enhance once the posed challenges are addressed.

Author 1: Abdulaziz A. Albesher

Author 2: Adeeb Alhomoud

Keywords: Internet of Things (IoT); security; health; IoT acceptance; smart cities

Paper 65: Fraud Detection in Credit Cards using Logistic Regression

Abstract: Due to the increasing number of customers as well as the increasing number of companies that use credit cards for ending financial transactions, the number of fraud cases has increased dramatically. Dealing with noisy and imbalanced data, as well as with outliers, has accentuated this problem. In this work, fraud detection using artificial intelligence is proposed. The proposed system uses logistic regression to build the classifier to prevent frauds in credit card transactions. To handle dirty data and to ensure a high degree of detection accuracy, a pre-processing step is used. The pre-processing step uses two novel main methods to clean the data: the mean-based method and the clustering-based method. Compared to two well-known classifiers, the support vector machine classifier and voting classifier, the proposed classifier shows better results in terms of accuracy, sensitivity, and error rate.

Author 1: Hala Z Alenzi

Author 2: Nojood O Aljehane

Keywords: Classifier; logistic regression; accuracy; smoothing; artificial intelligence; cross validation

Paper 66: New Sector Scan Geometry for High Frame Rate 2D-Echocardiography using Phased Arrays

Abstract: 2D echocardiography high frame rate techniques do have some drawbacks such as crosstalk artifacts caused by the interactions between the parallel transmitted and received beams. In this paper, we suggest a new cardiac imaging technique based on MLT (Multi-Line Transmission). The main idea of our approach is to benefit from the scan geometry to reduce the interference between the simultaneously transmitted beams. We propose to do the scan at different depths and in parallel to the diagonal scan sector. Therefore, compared to existing MLT techniques, the new scan sector strategy will result in artifacts' reduction in the ultrasound imaging systems. We entitled our approach the Synthetic Sum of Multi-Line Transmission (SS-MLT). Simulations of Point Spread Function (PSF), multiple Point Spread Functions (PSFs) and Cyst Phantom (CP) provided in this paper are compared in our approach to the main classical ultrasound imaging approaches. Therefore, the SS-MLT exhibits a very similar lateral profile as the Single Line Transmission (SLT) algorithm. Hence, the simulation results indicate a potential value of a future hardware implementation of SS-MLT technique.

Author 1: Wided Hechkel

Author 2: Brahim Maaref

Author 3: Nejib Hassen

Keywords: 2D Echocardiography; high frame rate; multi-line transmit beamforming; new scan geometry; reduced crosstalk

Paper 67: Secure Energy Efficient Attack Resilient Routing Technique for Zone based Wireless Sensor Network

Abstract: Security and Energy efficiency are two key factors to be contemplated in the design of applications based on wireless sensor networks (WSNs). Optimization of energy consumption is obligatory for an increased life time of the network. Without security, attackers can disrupt the entire operation of sensor network by instigating diverse attacks like message tampering, message dropping either in partial or whole and message flooding etc. This work proposes a secure energy efficient attack resilient routing technique for zone based wireless sensor network with proactive detection of malicious zones and mitigation from the attacks. Different from earlier works on detecting each malicious node, this work cleaves the network to zones and allots a probabilistic fuzzy score to model the success ratio of packet propagation through the zone. The routing is adaptive to ongoing residual energy and security risks. A Firm decision cannot be made on the frameworks influencing the life time of the network considering it may influence the operations of network. Experimentation of the proposed solution is done in NS2 and contrasted with the existing solutions to prove the effectiveness of the approach.

Author 1: Venkateswara Rao M

Author 2: Srinivas Malladi

Keywords: Energy efficiency; malicious zones; preference score probabilistic fuzzy score; residual energy

Paper 68: An Effective Solution to Count-to-Infinity Problem for both Complex and Linear Sub-Networks

Abstract: Distance vector routing protocol determines the best route for forwarding information from one node to another node based on distance. For calculating the best route, Distance-vector routing protocols use the Bellman-ford algorithm and the Ford-Fulkerson algorithm. The Bellman-Ford distributed algorithm calculates the shortest path. On the other hand, Routing Information Protocol is commonly used for managing router information management protocol within a Local Area Network or an interconnected Local Area Network group. The main problem with Distance Vector Routing protocols is routing loops. Because the Bellman-Ford Algorithm cannot prevent loops. Moreover, the routing loop triggers a problem with Count to Infinity. This research paper gives an effective solution to the Count to Infinity problem for link down situation and also for the router down situation in both complex and linear sub-network. For the router down situation, when any router goes down, then other nodes will recalculate their routing table with the dependency column. Moreover, the costs are calculated by the shortest path algorithm. If any link is down and all routers are up, then all routers will recalculate their routing table using Dijkstra instead of the Bellman-Ford algorithm. To determine the loops and prevent the loops are the main objectives. This method is mainly based on a routing table algorithm where the Dijkstra algorithm will be used after each iteration and will modify the routing table for each node. Preventing the routing loops will not converge into Count to Infinity Problem.

Author 1: Sabrina Hossain

Author 2: Kazi Mushfiquer Rahman

Author 3: Ahmed Omar

Author 4: Anisur Rahman

Keywords: Distance vector routing; local area network; routing information protocol

Paper 69: An Efficient Smart Weighted and Neighborhood-enabled Load Balancing Scheme for Constraint Oriented Networks

Abstract: In wireless sensor networks (WNSs), uniform load or traffic distribution strategy is one of the main challenging issues, which is tightly coupled with the resource-limited networks. To address this problem, various mechanisms have been developed and presented in the literature. However, these approaches were either application specific that is designed for a specific application area such as smart building or overlay complex. Therefore, a simplified and energy efficient load-balancing scheme is always needed for the resource-limited networks. In this paper, an efficient and neighborhood-enabled load-balancing scheme is presented to resolve the aforementioned issues specifically with available resources. For this purpose, the proposed scheme bounds every member node to collect various information about neighboring nodes i.e., those nodes resides in its communication range. Moreover, if residual energy Er of sensor node is less than the defined threshold value then it shares this information with neighboring nodes. In the proposed neighborhood-enabled load balancing scheme, every sensor node Ci prefers to route packets through the optimal paths particularly those paths where probability of critical nodes is negligible i.e., path where critical node(s) are not deployed. Simulation results showed that the proposed neighborhood-enabled load-balancing scheme is better than existing approaches in terms of network lifetime (both individual node and whole WSNs), throughput, and average packet delivery ratio and end-to-end delay performance metrics.

Author 1: Mohammed Amin Almaiah

Keywords: Wireless Sensor Networks (WNSs); load balancing; PSO; routing protocol; low power devices

Paper 70: Evaluation of Blockchain-based Data Sharing Acceptance among Intelligence Community

Abstract: Intelligence data are among the critical elements used as a reference for risk-assessment and decision-making regarding national security. The intelligence data are shared among intelligence agencies in the intelligence community in improving the efficiency of their services. Centralised data with central authority is highly exposed to being an easy target of attackers. Leaked or unauthorised access of the intelligence data to a non-intelligence community will bring severe effect to a state. Blockchain as immutable and high-security technology is capable of providing cryptographic data in a decentralised environment and potentially can be applied for data sharing among the intelligence community. However, the acceptance and readiness of users on blockchain usage in the intelligence community are yet to be systematically studied. Considering the statement, this paper proposed an evaluation method to study the acceptance of blockchain technology by integrating a reliable acceptance model for blockchain technology implementation in the intelligence community. The acceptance model consisted of constructs from the Technology Acceptance Model 3 (TAM 3) and Technology Readiness Index 2 (TRI 2) and was analysed using partial least squares structural equation modelling (PLS-SEM). In this study, the result indicates that TAM 3 and TRI 2.0 integration model could contribute to determining the acceptance level in developing blockchain-based intelligence data sharing for the intelligence community.

Author 1: Wan Nurhidayat Wan Muhamad

Author 2: Noor Afiza Mat Razali

Author 3: Muslihah Wook

Author 4: Khairul Khalil Ishak

Author 5: Norulzahrah Mohd Zainudin

Author 6: Nor Asiakin Hasbullah

Author 7: Suzaimah Ramli

Keywords: Technology acceptance model; technology readiness index; blockchain acceptance; PLS-SEM; data sharing

Paper 71: Predicting Mental Illness using Social Media Posts and Comments

Abstract: From the last decade, a significant increase of social media implications could be observed in the context of e-health. The medical experts are using the patient’s post and their feedbacks on social media platforms to diagnose their infectious diseases. However, there are only few studies who have leveraged the capabilities of machine learning (ML) algorithms to classify the patient’s mental disorders such as Schizophrenia, Autism, and Obsessive-compulsive disorder (OCD) and Post-traumatic stress disorder (PTSD). Moreover, these studies are limited to large number of posts and relevant comments which could be considered as a threat for their effectiveness of their proposed methods. In contrast, this issue is addressed by proposing a novel ML methodology to classify the patient’s mental illness on the basis of their posts (along with their relevant comments) shared on the well-known social media platform “Reddit”. The proposed methodology is exploit by leveraging the capabilities of widely-used classifier namely “XGBoost” for accurate classification of data into four mental disorder classes (Schizophrenia, Autism, OCD and PTSD). Subsequently, the performance of the proposed methodology is compared with the existing state of the art classifiers such as Naïve Bayes and Support vector machine whose performance have been reported by the research community in the target domain. The experimental result indicates the effectiveness of the proposed methodology to classify the patient data more effectively as compared to the state of the art classifiers. 68% accuracy was achieved, indicating the efficacy of the proposed model.

Author 1: Mohsin kamal

Author 2: Saif Ur Rehman khan

Author 3: Shahid Hussain

Author 4: Anam Nasir

Author 5: Khurram Aslam

Author 6: Subhan Tariq

Author 7: Mian Farhan Ullah

Keywords: Machine learning; mental disorders; Reddit; Schizophrenia; Autism; OCD; PTSD

Paper 72: You Aren’t Alone: Building Arabic Online Supporting Communities using Recommender System

Abstract: People are now digitally connected, making the world a single large community. This remarkable benefit has solved many communication issues. For instance, people who go through difficult times and lack the emotional support required to overcome these crises can now join an online support group. For many years, such people had to travel to a predetermined location in a predetermined time to join a support group. Today, with the increasing availability of digital services, these groups can now meet online. For these reasons, this paper presents ‘You aren’t alone’ mobile application, an interactive mobile-based application designed for Arab people who need psychological support. This application will help in enriching the Arabic content in the field of social support and will help in building supporting communities by peering users to the appropriate support group, anonymously without the fear of judgment. The application will enhance the peering process through a recommender system that reads the user’s Twitter timeline and classifies the tweets as belonging to one of the available support groups.

Author 1: Monirah Alajlan

Author 2: Nouf Alsuhaymi

Author 3: Sara Alnasser

Author 4: Abeer Almohaidib

Author 5: Nouf Bin Slimah

Author 6: Madawi Alruwaished

Author 7: Najla Alosaimi

Keywords: Emotional support; recommender system; classification; support group

Paper 73: Facebook Profile Credibility Detection using Machine and Deep Learning Techniques based on User’s Sentiment Response on Status Message

Abstract: Recently, the impact of online Social Networks sites (SNS) has dramatically changed, and fake accounts became a vital issue that has rapidly evolved. This issue gives rise to how to assess and measure the credibility of User-Generated Content (UGC). This content is used in finding trusted sources of information on SNS like Facebook, Twitter, etc. Consequently, classifying users’ profiles and analyzing each user’s behavior response based on the content generated became a challenge that must be solved. One of the most significant approaches is Sentiment Analysis (SA) which plays a major role in assessing and detecting the credibility degree of each user account behavior. In this paper, the aim of the study is to measure and predict the user’s profile credibility by declaring the correlation degree among the UGC features that affect users’ responses to status messages. The proposed models were implemented using six Supervised Machine Learning classifiers, an Unsupervised Machine Learning cluster model, and a Deep Learning Neural Network (NN) model. The research paper presents two experiments to evaluate Facebook profile credibility. At first, we applied a binary classification model to classify profiles into fake or genuine users. Then, we conducted a classification model on genuine users based on the credibility theory by using the Analytical Hierarchical Process (AHP) approach and computed the credibility score for each. Secondly, we selected and analyzed a public Facebook page (CNN public page) and obtained data from it for users’ sentiment reactions and responses on statuses Messages relating to different topics on the period (2016/2017). Then, we performed LDA on the status corpus (Topic Modeling algorithm, Latent Dirichlet Allocation) to generate topic vectors. In addition, we performed Principal Component Analysis (PCA) method to visualize and classify each status topic distribution. Afterthought, we produced a status corpus cluster to classify users’ behaviors through statuses posted and users’ comments. As a conclusion of this study, the first experimental results achieved 95% and 99% accuracy to classify fake/genuine users and incredible/credible accounts, respectively. The second experiment outcome identified the clusters for the status corpus in 10 topic-features distribution and classified users’ contents into credible or not according to the final calculated credibility score.

Author 1: Esraa A. Afify

Author 2: Ahmed Sharaf Eldin

Author 3: Ayman E. Khedr

Keywords: Fake profiles detection; credible profiles detection; sentiment analysis; supervised machine learning classifiers; unsupervised machine learning; binary classification; deep learning neural network; evaluation metrics

Paper 74: Agent Mining Framework for Analyzing Moroccan Olive Oil Datasets

Abstract: Data mining and intelligent agents have become two promising research areas. Each intelligent agent functions independently while cooperating with other agents, to perform effectively assigned tasks. The main goal of this research, is to provide a mining implementation that can help biological researchers for discovering parameters that affect the cost of olive oil in Morocco. To solve this problem, we used a method involving two data mining techniques, clustering of variables, quantitative association rules and multi-agent system to fuse these two techniques. Therefore, we have developed a multi-agent framework that has been validated by using concrete data from the Provincial Direction of Agriculture of Berkane, Morocco. To prove the performance of our framework, we tested the proposed multi-agent tool using three datasets from different fields. Conforming to biological researchers, our method generates a clear knowledge because the framework proposes high-confidence rules that can correctly identify olive oil factors.

Author 1: Belabed Imane

Author 2: Jaara El Miloud

Author 3: Belabed Abdelmajid

Author 4: Talibi Alaoui Mohammed

Keywords: Quantitative association rules; clustering of variables; multi-agent system

Paper 75: Development of Smart Healthcare System for Visually Impaired using Speech Recognition

Abstract: This paper presents a solution for the Visually Impaired (VI) based on wearable devices. VI people need support or a guide to support them to locomote from one place to another. Using Wearables help users to achieve a great understanding of the surrounding environment. The proposed system is based on wearable smart glasses to support VI to locomote. It provides a solution integrated with speech recognition to get the destination name and look for the routes. The proposed system is based on Google maps with speech recognition to work as user assistance. The results of the research results proved that the system works with high accuracy of 99% and can help the person as an effective tool for localization guidance. The system can assist VI people to move and have a better life quality.

Author 1: Khloud Almutairi

Author 2: Ahmed Ismail Ebada

Author 3: Samir Abdlerazek

Author 4: Hazem Elbakry

Keywords: Personal assistance; speech recognition; visually impaired person assistant; smart wearable device; smart sensory system

Paper 76: Medical Data Classification using Fuzzy Min Max Neural Network Preceded by Feature Selection through Moth Flame Optimization

Abstract: Prediction of the diseases are possible using medical diagnosis system. This type of health care model can be developed using soft computing techniques. Hybrid approaches of data classification and optimization algorithm increases data classification accuracy. This research proposed applications of Moth Flame optimization (MFO) and Fuzzy Min Max Neural Network (FMMNN) for the development of medical data classification system. Here MFO algorithm considers bulk of features from the disease dataset and produces optimized set of features based on fitness function. MFO is able to avoid local minima problem and this is the main cause behind production of optimal set of features. Optimized features are then passed to FMMNN for classification of malignant and benign cases. As classification is concerned, model experiment achieved 97.74% accuracy for Liver Disorders and 86.95 % accuracy for Pima Indian Diabetes dataset. Improving the medical data classification accuracy is directly related to attain good human health.

Author 1: Ashish Kumar Dehariya

Author 2: Pragya Shukla

Keywords: Moth flame optimization; nature inspired optimization; feature selection; fitness function; fuzzy min-max neural network

Paper 77: A Preliminary Intergenerational Photo Conversation Support System based on Fine-tuning VGG16 Model

Abstract: China has the largest number of elderly people in the world, young volunteers have become the main force in caring for the elderly. It is urgent to establish a photo conversation support system to build a bridge of communication between young volunteers and the elderly. Previous research generally used perceptual analysis or machine learning methods to find photos suitable for intergenerational conversation, this paper uses deep learning models to further learn the potential features of two datasets suitable for and not suitable for intergenerational conversations. However, the original datasets are too small, it was first proposed to use TF-IDF in conversation recording and data augmentation technology in images to expand the datasets. Then on the basis of the VGG16 model combined with transfer learning and fine-tuning technologies, five models were designed. The accuracy of the best model on the validation set and test set reached 96% and 94.5%. In particular, the recall rate of the not suitable dataset reached 100%, all not suitable photos were identified. At the same time, the recall rate of other datasets reached 71% for not suitable photos. It shows that the system is also applicable to other datasets and can effectively eliminate photos that are not suitable for intergenerational conversations.

Author 1: Lei JIANG

Author 2: Panote Siriaraya

Author 3: Dongeun Choi

Author 4: Noriaki Kuwahara

Keywords: Intergenerational photo conversation support system; TF-IDF; VGG16; transfer learning; fine-tune

Paper 78: Performance Analysis of Advanced IoT Encryption on Serialization Concept: Application Optimization Approach

Abstract: This study investigates the effect of serialization concepts with cipher algorithms and block mode on structured data on execution time in low-level computing IoT devices. The research was conducted based on IoT devices, which are currently widely used in online transactions. The result of overheating on the CPU is fatal if the encryption load is not reduced. One of the consequences is an increase in the maintenance obligations. So that from this influence, the user experience level will have bad influence. This study uses experimental methods by exploring serialization, ciphers, and block mode using benchmarks to get better data combination algorithms. The four test data groups used in benchmarking will produce an experimental benchmark dataset on the selected AES, Serpent, Rijndael, BlowFish, and block mode ciphers. This study indicates that YAML minify provides an optimal encryption time of 21% and decryption of 27% than JSON Pretty if an average of the whole test is taken. On the other hand, the AES cipher has a significant effect on the encryption and decryption process, which is 51% more optimal for the YAML minify serialization.

Author 1: Johan Setiawan

Author 2: Muhammad Taufiq Nuruzzaman

Keywords: Internet of Things; benchmark; cipher; block mode; serialization

Paper 79: An Iterative, Self-Assessing Entity Resolution System: First Steps toward a Data Washing Machine

Abstract: Data curation is the process of acquiring multiple sources of data, assessing and improving data quality, standardizing, and integrating the data into a usable information product, and eventually disposing of the data. The research describes the building of a proof-of-concept for an unsupervised data curation process addressing a basic form of data cleansing in the form of identifying redundant records through entity resolution and spelling corrections. The novelty of the approach is to use ER as the first step using an unsupervised blocking and stop word scheme based on token frequency. A scoring matrix is used for linking unstandardized references, and an unsupervised process for evaluating linking results based on cluster entropy. The ER process is iterative, and in each iteration, the match threshold is increased. The prototype was tested on 18 fully-annotated test samples of primarily synthetic person data varied in two different ways, good data quality versus poor data quality, and a single record layout versus two different record layouts. In samples with good data quality and using both single and mixed layouts, the final clusters had an average F-measure of 0.91, precision of 0.96, and recall of 0.87 outcomes comparable to results from a supervised ER process. In samples with poor data quality whether mixed or single layout, the average F-measure was 0.78, precision 0.74, and recall 0.83 showing that data quality assessment and improvement is still a critical component of successful data curation. The results demonstrate the feasibility of building an unsupervised ER engine to support data integration for good quality references while avoiding the time and effort to standardize reference sources to a common layout, design, and test matching rules, design blocking keys, or test blocking alignment. Also, the paper proposes how unsupervised data quality improvement processes could also be incorporated into the design allowing the model to address an even broader range of data curation applications.

Author 1: John R. Talburt

Author 2: Awaad K. Al Sarkhi

Author 3: Daniel Pullen

Author 4: Leon Claassens

Author 5: Richard Wang

Keywords: Unsupervised entity resolution; data curation; frequency blocking; entropy regulated; data washing machine

Paper 80: A Model for Traffic Management based on Text Mining Techniques

Abstract: It is very important for traffic management to be able to correctly recognize traffic trends from large historical traffic data, particularly the congestion pattern and road collisions. This can be used to reduce congestion, improve protection, and increase the accuracy of traffic forecasting. Choosing the correct and effective text mining methodology helps speed up and reduces the time and effort needed to retrieve valuable knowledge and information for future prediction and decision-making processes. Modeling collisions or accident risk has also been an important aspect of traffic management and road safety, as it helps recognize problems and causes that contribute to a higher risk of accidents, promotes treatment delivery, and reduces crashes to save more lives and avoid road congestion. Therefore, this work-study proposed a model that relies on the different text mining methodology to determine clearly what circumstances affect and who is involved more in an accident. Using different classification and machine learning techniques applied to get the optimum classifiers used in this model. The experimental results on real-world datasets demonstrate that the proposed models outperform Prayag Tiwari’s Research Work related to the Leeds UK Dataset.

Author 1: Ahmed Ibrahim Naguib

Author 2: Hala Abdel-Galil

Author 3: Sayed AbdelGaber

Keywords: Classification; machine learning; text mining; traffic management

Paper 81: Detection of nCoV-19 from Hybrid Dataset of CXR Images using Deep Convolutional Neural Network

Abstract: The Corona-virus spreads too quickly among humans and reaches more than 72 million people around the world until now. To avoid spread, it is very important to recognize the individuals infected. The deep learning (DL) technique for the detection of patients with Corona-virus infection using Chest X-rays (CXR) images is proposed in this article. Besides, we show how to implement an advanced model for deep learning, using chest X-rays (CXR) images, to identify COVID-19 (nCoV-19). The goal is to provide an intellectual image recognition model for over-stressed medical professionals with a second pair of eyes. In using the current publicly available COVID-19 data-sets we emphasize the challenges (including image data-set size and image quality) in developing a valuable deep learning model. We suggest a pre-trained model of a semi-automated image, create a robust image data-set for designing and evaluating a deep learning algorithm. This will provide the researchers and practitioners with a solid path to the future development of an improved model.

Author 1: Muhammad Ahmed Zaki

Author 2: Sanam Narejo

Author 3: Sammer Zai

Author 4: Urooba Zaki

Author 5: Zarqa Altaf

Author 6: Naseer u Din

Keywords: COVID-19; artificial intelligence; deep learning; chest x-ray image analysis; convolutional neural network; InceptionV3

Paper 82: Secure Software Engineering: A Knowledge Modeling based Approach for Inferring Association between Source Code and Design Artifacts

Abstract: Secure software engineering has emerged in recent decades by encouraging the idea of software security has to be an integral part of all the phases of the software development lifecycle. As a result, each phase of the lifecycle is associated with security-specific best practices such as threat modeling and static code analysis. It was observed that various artifacts (i.e., security requirements, architectural flaws, bug reports, security test cases) generated as a result of security best practices tend to be segregated. This creates a significant barrier to resolve the security issues at the implementation phase since most of them are originated in the design phase. In order to address this issue, this paper presents a knowledge-modeling based approach to semantically infer the associations between architectural level security flaws and code-level security bugs, which is manually tedious. Threat modeling and static analysis are used to identify security flaws and security bugs, respectively. The case study based experimental results revealed that the architectural level security flaws have a significant impact on originating security bugs in the code level. Besides, the evaluation results confirmed the scalability of the proposed approach to large-scale industrial software products.

Author 1: Chaman Wijesiriwardana

Author 2: Ashanthi Abeyratne

Author 3: Chamal Samarage

Author 4: Buddika Dahanayake

Author 5: Prasad Wimalaratne

Keywords: Software security; threat modeling; knowledge mod-eling; security flaws

Paper 83: SDN based Intrusion Detection and Prevention Systems using Manufacturer Usage Description: A Survey

Abstract: Internet of things (IoT) is an emerging paradigm that integrates several technologies. IoT network constitutes of many interconnected devices that include various sensors, actu-ators, services and other communicable objects. The increasing demand for IoT and its services have created several security vulnerabilities. Conventional security approaches like intrusion detection systems are not up to the expectation to fulfil the security challenges of IoT networks, due to the conventional technologies used in them. This article presents a survey of intrusion detection and prevention system (IDPS), using state of art technologies, in the context of IoT security. IDPS constitutes of two parts: intrusion detection system and intrusion prevention system. An intrusion detection system (IDS) is used to detect and analyze both inbound and outbound network traffic for malicious activities. An intrusion prevention system (IPS) can be aligned with IDS by proactively inspecting a system’s incoming traffic to mitigate harmful requests. The alignment of IDS and IPS is known as intrusion detection and prevention systems (IDPS). The amalgamation of new technologies, like software-defined network (SDN), machine learning (ML), and manufacturer usage description (MUD), in IDPS is putting the security on the next level. In this study IDPS and its performance benefits are analyzed in the context of IoT security. This survey describes all these prominent technologies in detail and their integrated applications to complement IDPS in the IoT network. Future research directions and challenges of IoT security have been elaborated in the end.

Author 1: Noman Mazhar

Author 2: Rosli Salleh

Author 3: Mohammad Asif Hossain

Author 4: Muhammad Zeeshan

Keywords: Intrusion Detection and Prevention Systems (IDPS); Internet of Things (IoT); Software Defined Network (SDN); Machine Learning (ML); Deep learning (DL); Manufacturer Usage Description (MUD)

Paper 84: Multi Modal RGB D Action Recognition with CNN LSTM Ensemble Deep Network

Abstract: Human action recognition has transformed from a video processing problem into multi modal machine learning problem. The objective of this work is to perform multi modal human action recognition on an ensemble hybrid network of CNN and LSTM layers. The proposed CNN - LSTM ensemble network is a 2 - stream framework with one ensemble stream learning RGB sequences and the other depth. This proposed framework can learn both temporal and spatial dynamics in both RGB and depth modal action data. The hybrid network is found to be receptive towards both spatial and temporal fields because of the hierarchical structure of CNNs and LSTMs. Finally, to test our proposed model, we used our own BVCAction3D and three RGB D benchmark action datasets. The experiments were conducted on all the datasets using the proposed framework and was found to be effective when compared to similar deep learning architectures.

Author 1: D. Srihari

Author 2: P. V. V. Kishore

Keywords: Human action recogniiton; RGB D video data; convolutional neural networks; long short-term memory

Paper 85: A Rule-based Approach toward Automating the Assessments of Academic Curriculum Mapping

Abstract: Curriculum mapping is the blueprint of a successful academic program. It is progressively utilised in higher education as a monitoring tool in the current age of standard-based regu-lations and empowers program leaders and course instructors to align their curricula for the offered courses and the corresponding learning outcomes. It is often depicted by a two-dimensional matrix expressing the relationship between the students learning outcomes (i.e., SOs) and the courses. However, its mapping remains a challenging exercise, even for experienced program leaders. The complexity stems from the fact that mistakes are prone to happen during the mapping, and program leaders need to be aware of the rules and the acceptable practices of curriculum-effective mapping. Besides, it is not straightforward to spot contradictions in the SO-course mappings. Consequently, this paper aims to tackle these challenges by investigating effective-mapping rules from existing curriculum mappings, which allows one to inspect the SO-course mappings, discover inefficiencies, and provide suggestions for improving the curriculum mapping. We identify the main mapping criteria and propose a rule-based algorithm for curriculum matrix assessments. This algorithm is implemented in an online application and evaluated using a user-based experiment, relying on curriculum mapping experts. The findings have shedded light on the promise of our approach.

Author 1: Abdullah Alshanqiti

Author 2: Tanweer Alam

Author 3: Mohamed Benaida

Author 4: Abdallah Namoun

Author 5: Ahmad Taleb

Keywords: Rule based algorithm; curriculum mapping; student learning outcomes; program outcomes; assessment; curriculum matrix

Paper 86: Factors Influencing Computing Students’ Readiness to Online Learning for Understanding Software Engineering Foundations in Saudi Arabia

Abstract: The spread of Coronavirus disease (COVID-19) has enforced most universities/institutions over the world to transform their educational models (face-to-face and blended) bearing in mind the online educational environments as a temporary substitute. Consequently, all universities/institutions in Saudi Arabia have requested their students to continue the learning process using online environments. This transition has provided an opportunity to deeply investigate possible challenges as well as factors that influence the adoption of online learning as a future educational model for undergraduate students. This research measures the current undergraduate students’ readiness for online learning and investigates factors that influence their level of readiness. Firstly, the research proposes the adoption of a validated multidimensional instrument to measure under-graduate students’ readiness for online learning in different universities. Secondly, the research elaborates the findings by an in-depth study that highlights the main obstacles that hinder computing students’ readiness to learn Software Engineering (SE) foundations using online learning. The research adopts survey research to measure students’ readiness and analyzes the data to extract the readiness levels of different dimensions of the adopted instrument. Furthermore, interviews were conducted to specify the influential factors on computing students’ readiness levels regarding learning SE foundations. Results show that students’ readiness level for online learning is within the acceptable range while some improvements are needed. Furthermore, the study found that students’ cognition, willingness, ignorance, and the amount of assistant and help they receive play a significant role in the success/failure of the adoption of learning SE foundations through online environment.

Author 1: Abdulaziz Alhubaishy

Keywords: Readiness to online learning; software-engineering education; improving online learning; university students

Paper 87: Road Traffic Accidents Injury Data Analytics

Abstract: Road safety researchers working on road accident data have witnessed success in road traffic accidents analysis through the application data analytic techniques, though, little progress was made into the prediction of road injury. This paper applies advanced data analytics methods to predict injury severity levels and evaluates their performance. The study uses predictive modelling techniques to identify risk and key factors that contributes to accident severity. The study uses publicly available data from UK department of transport that covers the period from 2005 to 2019. The paper presents an approach which is general enough so that can be applied to different data sets from other countries. The results identified that tree based techniques such as XGBoost outperform regression based ones, such as ANN. In addition to the paper, identifies interesting relationships and acknowledged issues related to quality of data.

Author 1: Mohamed K Nour

Author 2: Atif Naseer

Author 3: Basem Alkazemi

Author 4: Muhammad Abid Jamil

Keywords: Traffic Accidents Analytics (RTA); data mining; machine learning; XGBOOST

Paper 88: Analysis of National Cybersecurity Strategies

Abstract: Nowadays the use of information and communica-tion technology has been incorporated in a general way in the daily life of a nation allowing the optimization in its processes. However, with it comes serious risks and threats that can affect cyber security because of the vulnerability they show. In addition, there are several factors that contribute to the proliferation of criminal actions in cyber security, the profitability offered by its exploitation in economic, political or other terms, the ease and low cost of the tools used to carry out attacks and the ease of hiding the attacker, make it possible for these activities to be carried out anonymously, from anywhere in the world and with impunity.The main objective of the research is to analyze and design National Cybersecurity Strategies to counter attacks. The methodology of this research was conducted in an exploratory and descriptive manner. As a result of the research work, a design of National Cybersecurity Strategies was obtained after an in-depth analysis of the appropriate strategies and thus minimizing the different attacks that can be carried out.

Author 1: Alexandra Santisteban Santisteban

Author 2: Lilian Ocares Cunyarachi

Author 3: Laberiano Andrade-Arenas

Keywords: Cybersecurity; national strategies; risks; threats; vulnerability

Paper 89: A Big Data Framework for Satellite Images Processing using Apache Hadoop and RasterFrames: A Case Study of Surface Water Extraction in Phu Tho, Viet Nam

Abstract: Earth data, collected from many sources such as remote sensing imagery, social media, and sensors, are growing tremendously. Among them, satellite imagery which play an important roles for monitoring environment and natural changes are increased exponentially in term of both volume and speed. This paper introduces an approach to managing and analyzing such data sources based on Apache Hadoop and RasterFrames. First, it presents the architecture and the general flow of the proposed distributed framework. Based on this, we can imple-ment and perform efficient computations on a big data in parallel without moving data to the center computer which might lead to network congestion. Finally, the paper presents a case study that analyzes the water surface of a Vietnam region using the proposed platform.

Author 1: Dung Nguyen

Author 2: Hong Anh Le

Keywords: Satellite imagery; big data; water surface; Apache Hadoop; RasterFrames

Paper 90: The Influence of Loss Function Usage at SIAMESE Network in Measuring Text Similarity

Abstract: In a text matching similarity task, a model takes two sequence of text as an input and predicts a category or scale value to show their relationship. A developed model is to measure the similarity - one of relationship between those two text. The model is SIAMESE network that implement two copies of same network of CNN, it takes text_1 and text_2 as the inputs respectively for two CNN networks. The output of each CNN network is features vector of the corresponding text input, both outputs are then fed by a loss function to calculate the value of loss (i.e. similarity). This research implemented two types of loss functions, i.e. Triplet loss and Contrastive loss. The usage purpose of these two types of loss functions was to see the influence toward the measurement results of similarity between two text being compared. The metrices used for this comparison are precision, recall, and F1-score. Based on the experimental results done on 1500 pairs of sentences, and varied on the epoch value starting from 10 until 200 with an increment of 10, showed the best result was for epoch value of 180 with precision 0.8004, recall 0.6780, and F1-score 0.6713 for Triplet loss function; and epoch value of 160 with precision 0.6463, recall 0.6440, and F1-score 0.6451 for Contrastive loss function gave the best performance. So that, the Triplet loss function gave better influence than Contrastive loss function in measuring similarity between two given sentences.

Author 1: Suprapto

Author 2: Joseph A. Polela

Keywords: Sentence; similarity; triplet; contrastive; CNN; Siamese; dataframe

Paper 91: Comparative Evaluation of CNN Architectures for Image Caption Generation

Abstract: Aided by recent advances in Deep Learning, Image Caption Generation has seen tremendous progress over the last few years. Most methods use transfer learning to extract visual information, in the form of image features, with the help of pre-trained Convolutional Neural Network models followed by transformation of the visual information using a Caption Generator module to generate the output sentences. Different methods have used different Convolutional Neural Network Architectures and, to the best of our knowledge, there is no systematic study which compares the relative efficacy of different Convolutional Neural Network architectures for extracting the visual information. In this work, we have evaluated 17 different Convolutional Neural Networks on two popular Image Caption Generation frameworks: the first based on Neural Image Caption (NIC) generation model and the second based on Soft-Attention framework. We observe that model complexity of Convolutional Neural Network, as measured by number of parameters, and the accuracy of the model on Object Recognition task does not necessarily co-relate with its efficacy on feature extraction for Image Caption Generation task. We release the code at https://github.com/iamsulabh/cnn variants.

Author 1: Sulabh Katiyar

Author 2: Samir Kumar Borgohain

Keywords: Convolutional Neural Network (CNN); image caption generation; feature extraction; comparison of different CNNs

Paper 92: A Perceptual Matching based Deduplication Scheme using Gabor-ORB Filters for Medical Images

Abstract: In the ever widening field of telemedicine, there is a greater need for intelligent methods to selectively choose data that are relevant enough to be transmitted over a network and checked remotely. By the very nature of medical imaging, a large amount of data is generated per imaging or scanning session. For instance, a Magnetic Resonance Images (MRI) scan consist of hundreds to thousands of images related to slices of the organ being scanned. But at often times all of these slices are not of interest during the process of medical diagnosis by the medical practitioner. Not only does this result in the access of unwanted data remotely, but it can also put greater strain on the bandwidth available over the network. If the relevant images can be selected automatically without human intervention, ensuring great sensitivity, the above-mentioned issues can also be alleviated. This paper proposes a novel method of perceptual matching and selection of relevant MRI images by using a deduplicating technique of combining Gabor filter with Oriented FAST and Rotated BRIEF (ORB) feature extraction technique on a vast set of MRI scan images. The outcome of this method are relevant deduplicated MRI scan images which can save the bandwidth and will be easy for the medical practitioner to verify remotely.

Author 1: Sonal Ayyappan

Author 2: C Lakshmi

Keywords: Perceptual matching; ORB feature extraction; Gabor filters; MRI scan; deduplication

Paper 93: Post Classification in the Social Networks using the Map-reduce Algorithm

Abstract: Wrongdoing is increasing through social media. Detecting them requires highlighting the most interesting topics in the posts. This essential part in the characterization of social network users could be done by a classification of posts. For this, we use a tuple of keywords and the Map-reduce algorithm for data collection and extraction. The main purpose is to achieve success on software realization which will establish a network between social networks to extract data and to speed up the classification of posts. The proposed method consists of verifying a sequence of keywords in the posts, following a grammar in order to determine classes. It allows the categorization of posts and monitoring of social networks. The categorization facilitates research of a particular post containing specific words. Thus, we contribute to increase capacity for wrongdoing prevention and strengthening cyber-security.

Author 1: Abdoulaye SERE

Author 2: Jose Arthur OUEDRAOGO

Author 3: Boureima ZERBO

Author 4: Oumarou SIE

Keywords: Big Data; map-reduce; social network; cyber-security; classification

Paper 94: Predicting Hospitals Hygiene Rate during COVID-19 Pandemic

Abstract: COVID-19 pandemic has reached global attention with the increasing cases in the whole world. Increasing awareness for the hygiene procedures between the hospital’s staff, and the society became the main concern of the World Health Organization (WHO). However, the situation of COVID-19 Pan-demic has encouraged many researchers in different fields to investigate to support the efforts offered by the hospitals and their health practitioners. The main aim of this research is to predict the hospital’s hygiene rate during COVID-19 using COVID-19 Nursing Home Dataset. We have proposed a feature extraction, and comparing the results estimating from K-means clustering algorithm, and three classification algorithms: random forest, decision tree, and Naive Bayes, for predicting the hospital’s hygiene rate during COVID-19. However, the results show that classification algorithms have addressed better performance than K-means clustering, in which Naive Bayes considered the best algorithm for achieving the research goal with accuracy value equal to 98.1%. AS a result the research has discovered that the hospitals that offered weekly amounts of personal protective equipment (PPE) have passed the personal quality test, which lead to a decrease in the number of COVID-19 cases between the hospital’s staff.

Author 1: Abdulrahman M. Qahtani

Author 2: Bader M. Alouffi

Author 3: Hosam Alhakami

Author 4: Samah Abuayeid

Author 5: Abdullah Baz

Keywords: COVID-19; machine learning; hospitals hygiene; World Health Organization (WHO); personal protective equipment; K-means clustering; Naive Bayes; random forest

Paper 95: A Comparison of EDM Tools and Techniques

Abstract: Several higher educational institutions are adapting the strategy of predicting the student’s performance throughout the academic years. Such a practice ensures not only better academic outcomes but also helps the institutions to reorient their curriculums and teaching pedagogies so as to add to the students’ learning curve. Educational Data Mining (EDM) has risen as a useful technology in this league. EDM techniques are now being used for predicting the enrolment of students in a specific course, detection of any irregular grades, prediction about students’ performance, analyzing and visualizing of data, and providing feedback for overall improvement in the academic spheres. This paper reviews the studies related to EDM, including the approaches, data sets, tools, and techniques that have been used in those studies, and points out the most efficient techniques. This review paper uses true prediction accuracy as a standard for the comparison of different techniques for each EDM applications of the surveyed literature. The results show that the J48 and K-means are the most effective techniques for predicting the students’ performance. Furthermore, the results also cite that Bayesian and Decision Tree Classifiers are the most widely used techniques for predicting the students’ performance. In addition, this paper highlights that the most widely used tool was WEKA, with approximately 75% frequency. The present study’s empirical assessments would be a significant contribution in the domain of EDM. The comparison of different tools and techniques presented in this study are corroborative and conclusive, thus the results will prove to be an effective reference point for the practitioners in this field. As a much needed technological asset in the present day educational context, the study also suggests that additional surveys are recommended to be driven for each of the EDM applications by taking into account more standards to set the best techniques more accurately.

Author 1: Eman Alshehri

Author 2: Hosam Alhakami

Author 3: Abdullah Baz

Author 4: Tahani Alsubait

Keywords: Educational Data Mining (EDM); students’ performance; prediction; higher education; WEKA