IJACSA Volume 8 Issue 8 - thesai.org

IJACSA Volume 8 Issue 8

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.

View Full Issue

Paper 1: HappyMeter: An Automated System for Real-Time Twitter Sentiment Analysis

Abstract: The paper presents HappyMeter, an automated system for real-time Twitter sentiment analysis. More than 380 million tweets consisting of nearly 30,000 words, almost 6,000 hashtags and over 5,000 user mentioned have been studied. A sentiment model is used to measure the sentiment level of each term in the contiguous United States. The system automatically mines real-time Twitter data and reveals the changing patterns of the public sentiment over an extended period of time. It is possible to compare the public opinions regarding a subject, hashtag or a Twitter user between different states in the U.S. Users may choose to see the overall sentiment level of a term, as well as its sentiment value on a specific day. Real-time results are delivered continuously and visualized through a web-based graphical user interface.

Author 1: Joaquim Perotti Canela

Author 2: Tina Tian

Keywords: Twitter; social networks; data mining; sentiment analysis

Paper 2: A Comparison between Chemical Reaction Optimization and Genetic Algorithms for Max Flow Problem

Abstract: This paper presents a comparison between the performance of Chemical Reaction Optimization algorithm and Genetic algorithm in solving maximum flow problem with the performance of Ford-Fulkerson algorithm in that. The algorithms have been implemented sequentially using JAVA programming language, and executed to find maximum flow problem using different network size. Ford-Fulkerson algorithm which is based on the idea of finding augmenting path is the most popular algorithm used to find maximum flow value but its time complexity is high. The main aim of this study is to determine which algorithm will give results closer to the Ford-Fulkerson results in less time and with the same degree of accuracy. The results showed that both algorithms can solve Max Flow problem with accuracy results close to Ford Fulkerson results, with a better performance achieved when using the genetic algorithm in term of time and accuracy.

Author 1: Mohammad Y. Khanafseh

Author 2: Ola M. Surakhi

Author 3: Ahmad Sharieh

Author 4: Azzam Sleit

Keywords: Chemical reaction optimization; Ford-Fulkerson algorithm; genetic algorithm; maximum flow problem

Paper 3: Meteonowcasting using Deep Learning Architecture

Abstract: The area of deep learning has enjoyed a resurgence on its peak, in almost every field of interest. Weather forecasting is a complicated and one of the most challenging tasks that includes observing and processing huge amount of data. The present paper proposes an effort to apply deep learning approach for the prediction of weather parameters such as temperature, pressure and humidity of a particular site. The implemented predictive models are based on Deep Belief Network (DBN) and Restricted Boltzmann Machine (RBM). Initially, each model is trained layer by layer in an unsupervised manner to learn the non-linear hierarchical features from the input distribution of dataset. Subsequently, each model is re-trained globally in supervised manner with an output layer to predict the appropriate output. The obtained results are encouraging. It is found that the feature based forecasting model can make predictions with high degree of accuracy. This implies that the model can be suitably adapted for making longer forecasts over larger geographical areas.

Author 1: Sanam Narejo

Author 2: Eros Pasero

Keywords: Deep learning architectures; deep belief network; time series prediction; weather nowcasting

Paper 4: A Review of Towered Big-Data Service Model for Biomedical Text-Mining Databases

Abstract: The rapid growth of biomedical informatics has drawn increasing popularity and attention. The reason behind this are the advances in genomic, new molecular, biomedical approaches and various applications like protein identification, patient medical records, genome sequencing, medical imaging and a huge set of biomedical research data are being generated day to day. The increase of biomedical data consists of both structured and unstructured data. Subsequently, in a traditional database system (structured data), managing and extracting useful information from unstructured-biomedical data is a tedious job. Hence, mechanisms, tools, processes, and methods are necessary to apply on unstructured biomedical data (text) to get the useful business data. The fast development of these accumulations makes it progressively troublesome for people to get to the required information in an advantageous and viable way. Text mining can help us mine information and knowledge from a mountain of text, and is now widely applied in biomedical research. Text mining is not a new technology, but it has recently received spotlight attention due to the emergence of Big Data. The applications of text mining are diverse and span to multiple disciplines, ranging from biomedicine to legal, business intelligence and security. In this survey paper, the researcher identifies and discusses biomedical data (text) mining issues, and recommends a possible technique to cope with possible future growth.

Author 1: Alshreef Abed

Author 2: Jingling Yuan

Author 3: Lin Li

Keywords: Big data; biomedical data; text mining; information retrieval; feature extraction

Paper 5: A Non-Linear Regression Modeling is used for Asymmetry Co-Integration and Managerial Economics in Iraqi Firms

Abstract: This paper analyzes the cost asymmetry through managerial expectations in a nonlinear regression function. Two development determinants, asymmetry co-integration and managerial expectations are also considered. The results revealed that managerial expectation had an impact on the wholesale cost asymmetry response. The managerial optimism is pronounced that show cost asymmetry response for sales, and inventory assets increased higher than decreased with the changing of the expectation basic coefficient and the values of contract parameters. Finally, the impacts of the managerial expectations, cost basic coefficient, and values of the contract parameters are analyzed for illustrating the results of the proposed nonlinear models with the help of numerical experiments. The research examined the short-run and the long-run effects of asymmetry co-integration and managerial expectation changes on the cost behavior in Iraq using the nonlinear regression function.

Author 1: Karrar Abdulellah Azeez

Author 2: Han DongPing

Author 3: Marwah Abdulkareem Mahmood

Keywords: Cost asymmetry; managerial expectations; co-integration; nonlinear regression function

Paper 6: DDoS Attacks Classification using Numeric Attribute-based Gaussian Naive Bayes

Abstract: Cyber attacks by sending large data packets that deplete computer network service resources by using multiple computers when attacking are called Distributed Denial of Service (DDoS) attacks. Total Data Packet and important information in the form of log files sent by the attacker can be observed and captured through the port mirroring of the computer network service. The classification system is required to distinguish network traffic into two conditions, first normal condition, and second attack condition. The Gaussian Naive Bayes classification is one of the methods that can be used to process numeric attribute as input and determine two decisions of access that occur on the computer network service that is “normal” access or access under “attack” by DDoS as output. This research was conducted in Ahmad Dahlan University Networking Laboratory (ADUNL) for 60 minutes with the result of classification of 8 IP Address with normal access and 6 IP Address with DDoS attack access.

Author 1: Abdul Fadlil

Author 2: Imam Riadi

Author 3: Sukma Aji

Keywords: Distributed Denial of Service (DdoS); Gaussian Naive Bayes; Numeric

Paper 7: A Features-based Comparative Study of the State-of-the-art Cloud Computing Simulators and Future Directions

Abstract: Cloud computing has emerged during the last decade and turned out to be an essential component for today’s business. Therefore, many solutions are being proposed to optimize and secure the cloud computing environment. To test and validate the proposed solutions before deploying in real cloud infrastructure, a cloud computing simulator is the key requirement. There are several cloud computing simulators that have been used by research community for this purpose. In this paper, we have discussed modern cloud simulators and presented comprehensive comparison based on their features.

Author 1: Ahmad Waqas

Author 2: M. Abdul Rehman

Author 3: Abdul Rehman Gilal

Author 4: Mohammad Asif Khan

Author 5: Javed Ahmed

Author 6: Zulkefli Muhammed Yusof

Keywords: Cloud computing; simulation; cloud simulator; cloud performance analysis; simulator features

Paper 8: An Innovative Cognitive Architecture for Humanoid Robot

Abstract: Humanoid robot is appearing as most popular research tool and emerging research field. The greatest challenge in the development of robot is cognition, advancement and the understanding in the human like cognition. Humanoid robot requires a self-learning behavior like the humans that is able to get the experience from environment. Based on experience, it can modify their actions, or having conscious intellectual capability to reduce empirical factual knowledge. In this regard, we propose a novel framework called an Innovative Cognitive Architecture for Humanoid Robot (ICAHR) that is capable to develop cognitive through social interaction and autonomous exploration. It combines the modules of active memory, decision processor, and sensor listener that has capability to perform self-learning behavior like human, to make decisions in dynamic environment, and perform more valid and intelligent actions with better precision. The proposed architecture may result in safe, robust, flexible, and reliable machines that can be substitute of human beings in different tasks. The feasibility of new proposed ICAHR design has been examined through real-world case studies.

Author 1: Muhammad Faheem Mushtaq

Author 2: Urooj Akram

Author 3: Adeel Tariq

Author 4: Irfan Khan

Author 5: Muhammad Zulqarnain

Author 6: Umer Iqbal

Keywords: Humanoid robots; cognition; cognitive architecture; self-learning behavior; dynamic environment

Paper 9: Shadow Identification in Food Images using Extreme Learning Machine

Abstract: Shadow identification is important for food images. Different applications require an accurate shadow identification or removal. A shadow varies from one image to another based on different factors such as lighting, colors, shape of objects, and their arrangement. This makes shadow identification complex problem and lacking systematic approach. Machine learning has high potential to be used for shadow recognition if it is used to train algorithms on wide number of scenarios. In this article, Extreme Learning Machine (ELM) has been used to identify shadow in shadow mask area. This shadow mask area was determined in the image based on edge detection, and morphological operations. ELM has been compared with Support Vector Machine (SVM) for shadow identification and shown better performance.

Author 1: SALWA KHALID ABDULATEEF

Author 2: MASSUDI MAHMUDDIN

Author 3: NOR HAZLYNA HARUN

Keywords: Extreme machine learning; shadow identification; food images; support vector machine, edge detection; color spaces

Paper 10: PCA based Optimization using Conjugate Gradient Descent Algorithm

Abstract: The energy dissipation in Wireless Body Area Network (WBAN) systems is the biggest concern as it proportionally affects the system longevity. This energy dissipation in the WBAN system mainly takes place due to the signal interference from other networks causing reduction on the dimensionality. The data prediction in WBAN is also a considerable concern corresponding to misinterpretations and faults in the signals. In this paper a novel combination of Principle Component Analysis (PCA) pre-processing along with optimization using the conjugate gradient descent algorithm is proposed. Experimental observations show an improvement in the mean square error and the regression based correlation coefficient when compared to other standard techniques.

Author 1: Subhas A. Meti

Author 2: V.G. Sangam

Keywords: Associative neural network (AANN); conjugate gradient descent; Non-Linear Principle Component Analysis (NLPCA); Principle Component Analysis (PCA); Wireless Body Area Network (WBAN)

Paper 11: Improvement of Radial basis Function Interpolation Performance on Cranial Implant Design

Abstract: Cranioplasty is a neurosurgical operation for repairing cranial defects that have occurred in a previous operation or trauma. Various methods have been presented for cranioplasty from past to present. In computer-aided design based methods, quality of an implant depends on operator’s talent. In mathematical model based methods, such as curve-fitting and various interpolations, healthy parts of a skull are used to generate implant model. Researchers have studied to improve performance of mathematical models which are independent from operators’ talent. In this study, improvement of radial basis function (RBF) interpolation performance using symmetrical data is presented. Since we focused on the improvement of RBF interpolation performance on cranial implant design, results were compared with previous studies involving the same technique. In comparison with previously presented results, difference between the computed implant model and the original skull was reduced from 7 mm to 2 mm using newly proposed approach.

Author 1: Ferhat Atasoy

Author 2: Baha Sen

Author 3: Fatih Nar

Author 4: Ismail Bozkurt

Keywords: Cranioplasty; interpolation on medical images; radial basis function interpolation; symmetrical data

Paper 12: Performance Evaluation of Transmission Line Protection Characteristics with DSTATCOM Implementation

Abstract: To meet with the ever-enhancing load demands, new transmission lines should be bolted-on in the existing power system but the economic and environmental concerns are major constraints to this addition. Hence utilities have to rely on the existent power system infrastructure with some modifications. To enhance controllability and boost power transfer potential of the existing power system the use of Flexible Alternating Current Transmission System (FACTS) device is the most viable modification. FACTS devices include Static VAR Compensator (SVC), thyristor controlled series capacitor (TCSC), Thyristor Controlled Reactor (TCR), Thyristor Switched Capacitor (TSC) and Self Commutated VAR compensators i.e. Static Synchronous Compensator (DSTATCOM). Among the FACTS devices, DSTATCOM is the most feasible choice because of its capability to furnish both leading and lagging reactive power, faster response time in comparison with others, smaller harmonic content, inrush current generation is minimum and the dynamic performance with variations of voltage is quite good. DSTATCOM has the ability to have effective control over various issues concerning AC power transmission. However, the Parameters of the protection devices in the present power system are set without taking into account the reaction of these FACTS devices. So in order to ascertain stability and reliability of power system, reaction of FACTS devices with the existent protection schemes must be thoroughly investigated. This paper aims to explore the deviations in the performance characteristics of transmission line protection due to installation of DSTATCOM on a 220KV EHV transmission line using theoretical as well as MATLAB/SIMULINK simulation models. The dynamic performance of a DSTATCOM connected to an existing transmission line system is evaluated when large industrial induction motor is started and voltage sags are introduced.

Author 1: Yasar Khan

Author 2: Khalid Mahmood

Author 3: Sanaullah Ahmad

Keywords: Power system analysis; DSTATCOM; transmission line loss minimization; distribution dynamic compensation; transmission losses and efficiency

Paper 13: Synchronous Authentication Key Management Scheme for Inter-eNB Handover over LTE Networks

Abstract: Handover process execution without active session termination is considered one of the most important attribute in the Long Term Evolution (LTE) networks. Unfortunately, this service always is suffered from the growing of security threats. In the Inter-eNB handover, an attacker may exploit these threats to violate the user privacy and desynchronize the handover entities. Therefore, the authentication is the main challenge in such issue. This paper proposes a synchronous authentication scheme to enhance the security level of key management during Inter-eNB handover process in LTE networks. The security analysis proves that the proposed scheme is secure against the current security drawbacks with perfect backward/forward secrecy. Furthermore, the performance analysis in terms of operations cost of authentication and bandwidth overhead demonstrates that the proposed scheme achieves high level of security with desirable efficiency.

Author 1: Shadi Nashwan

Keywords: LTE network; X2 handover; horizontal and vertical key derivations; desynchronizing attack

Paper 14: A New Cryptosystem using Vigenere and Metaheuristics for RGB Pixel Shuffling

Abstract: In this article we present a new approach using Vigenere and metaheuristics to resolve a problem of pixel shuffling to cipher an image. First the image is adapted to match the resolution system by transforming it to a list of intensities and coordinates. The idea is to use Vigenere encryption to maximize the confusion by widening the domain of intensities. Then, metaheuristics play the major role of encryption, generating an appropriate Meta key in order to shuffle the lists. Thus, both Vigenere key and Meta-key are used for encryption and later in decryption by the recipient. Finally, a comparison of different metaheuristics is proposed to find the most suitable one for this cryptosystemt.

Author 1: Zakaria KADDOURI

Author 2: Mohamed Amine Hyaya

Author 3: Mohamed KADDOURI

Keywords: Cryptography; cryptosystem; Vigenere; metaheuristics; image; pixel shuffling

Paper 15: Improved Hybrid Model in Vehicular Clouds based on Data Types (IHVCDT)

Abstract: In Vehicular Cloud (VC), vehicles collect data from the surrounding environment and exchange this data among the vehicles and the cloud centers. To do that in an efficient way first we need to organize the vehicles into clusters, each one works as a VC, and every cluster is managed by the cluster head (broker). The vehicles are grouped in clusters with adaptive size based on their mobility and capabilities. This model is an approach that forms the clusters based on the vehicles capabilities and handles with different types of data according to its importance to select the best route. A hybrid model is proposed to deal with these differences; Long-Term Evolution (LTE) is used with IEEE 802.11P which forms the traditional wireless access for Vehicular Ad hoc Networks (VANETs). This merge gives the high data delivery, wide-range transmission, and low latency. However, using only LTE based VANET is not practical due to its high cost and the large number of occurrences in the base stations. In this paper, a new Vehicular Cloud (VC) model is proposed which provides data as a service based on Vehicular Cloud Computing (VCC). A new method is proposed for high data dissemination based on the data types. The model is classified into three modes: the urgent mode, the bulk mode, and the normal mode. In the urgent mode, Long-Term Evolution (LTE) is used to achieve a high delivery with minimum delay. In the bulk mode, the vehicle uses IEEE 802.11p and chooses two clusters to divide this huge data. In the normal mode, the model works as D-hops cluster based algorithm.

Author 1: Saleh A. Khawatreh

Author 2: Enas N. Al-Zubi

Keywords: Vehicular Cloud (VC); Vehicular Cloud Computing (VCC); Vehicular Ad hoc Networks (VANETs); cloud algorithms; hybrid transmissions; IEEE 802.11p; Long-Term Evolution (LTE); transmission cost

Paper 16: FPGA Implementation of SVM for Nonlinear Systems Regression

Abstract: This work resumes the previous implementations of Support Vector Machine for Classification and Regression and explicates the different methods and approaches adopted. Ever since the rarity of works in the field of nonlinear systems regression, an implementation of testing phase of SVM was proposed exploiting the parallelism and reconfigurability of Field-Programmable Gate Arrays (FPGA) platform. The nonlinear system chosen for application was a real challenging model: a fluid level control system existing in our laboratory. The implemented design with fixed point precision demonstrates good enough results comparing with the software performances based on the Normalized Mean Squared Error. Whereas, in term of computation time, a speed-up factor of 60 orders of time comparing to MATLAB results was achieved. Due to the flexibility of Xilinx System Generator, the design is capable to be reused for any other system with different data sets sizes and various kernel functions.

Author 1: Intissar SAYEHI

Author 2: Mohsen MACHHOUT

Author 3: Rached TOURKI

Keywords: Machine learning; nonlinear system; SVM regression; Reproducing Kernel Hilbert Space (RKHS); MATLAB; Field-Programmable Gate Arrays (FPGA); Xilinx System Generator

Paper 17: A Synthesis on SWOT Analysis of Public Sector Healthcare Knowledge Management Information Systems in Pakistan

Abstract: Healthcare is a community service sector and has been delivering its services for the betterment of civic health since its establishment at communal level. For working efficiently and effectively, this sector profoundly relies on correct and complete health information of people and a proficient integrated healthcare knowledge management information system (HKMIS) to manage this information. The performance of Healthcare organizations has significantly augmented by inception of Information and Communications Technology (ICT) in HKMIS in developed countries, but is yet to exhibit its full potential in developing countries specifically those with huge populations like Pakistan. An exploratory qualitative research methodology was adopted to conduct this study. The purpose and objective of this study was to determine and investigate the internal and external factors that influence the performance of HKMIS by performing SWOT analysis on two of the largest public-sector healthcare organizations of Pakistan. The findings of this study will certainly help authorities to devise methods of improvement in Pakistani HKMIS eventually paving ways towards a better and improved healthcare in the future.

Author 1: Arfan Arshad

Author 2: Mohamad Fauzan Noordin

Author 3: Roslina Bint Othman

Keywords: Healthcare; knowledge management; healthcare knowledge management information system; information and communications technology; SWOT analysis; internal and external factors; healthcare organizations

Paper 18: Multi-Agent based Functional Testing in the Distributed Environment

Abstract: Verification and testing are two formal techniques of defect reduction applied on designing and development phases of SDLC to rationalize quality assurance activities. The process of testing applications in the distributed environment becomes too complex. This study discusses a distributed testing framework that consists of many parallel tester components. The idea is based on utilizing client server environment to conduct software testing efficiently and in a short span of time. It is pertinent to mention that this study is restricted to testing of functional aspects of the software while testing of performance and other quality-of-service aspects are outside the scope of the study. An important factor influencing the use of agent technology in software testing is the dynamic nature of events. Since agents are characterized by intelligence and autonomy, their ability to interact with the environment offers added functionality to make decisions based on the needs of the scenarios that are dynamic in nature. This study shows that the use of agents to build a dynamic model for software testing in the distributed environment results in a more robust and efficient design. The proposed framework is based on distribution of test cases among multiple agents deployed across a distributed system which collaborate with each other to perform testing in an efficient manner. The proposed framework also provides an in-depth visibility into the software quality by providing the defect statistics on-the-fly. The experiments have been conducted using Selenium test automation tool. The test cases along with their test scripts and the test run results are described herein.

Author 1: Muhammad Fraz Malik

Author 2: M. N. A. Khan

Author 3: Uzma Bibi

Author 4: Muhammad Ayaz Malik

Keywords: Software quality assurance; software testing; distributed environment; input variation testing; test vectors; multi-agents

Paper 19: Modeling and Implementing Ontology for Managing Learners’ Profiles

Abstract: This paper presents an issue that is important to consider when developing a learning environment whose field is constantly evolving mainly in terms of the use of training platforms. Research in this field has enabled the successful use of information technologies for the benefit of human learning, while placing the learner at the heart of pedagogic situations. It is also an environment that integrates human agents (tutors, learners) and artificial (computers) and allows them to interact locally or through computer networks, as well as conditions for accessing local or distributed training resources. Moreover, several computing environments for human learning (CEHL) platforms are available on the web for free access. These platforms are environments that offer a learner a multitude of courses in various formats in order to satisfy the learner’s desire to learn. Several CEHL platforms are available on the web for free access. But learning itself is not enough and that is why a new generation of advanced learning systems that integrate new pedagogical approaches giving the learner an active role to learn and acquire knowledge has emerged by offering more Interactivity and incorporating a more learner-centered vision. These new generations of advanced learning systems adapt to learners and their profiles by taking into account their cognitive, intellectual and motivational characteristics. An adaptation that cannot be achieved without the complicity of ontological engineering, which plays a very important role in the sharing of knowledge between humans and computers, and between computers and sharing, and reuse of concepts through computational semantics. By the same way, this paper aims at creating a process of modeling and managing profiles of learners based on ontology whatever the learning situation may be. This management process is implemented in computer’s environment based on the learner’s ontology that supports the learner by detecting the gaps in several factors in order to improve them and adapt the pedagogical content to the learner’s profile.

Author 1: Korchi Adil

Author 2: El Amrani El Idrissi Najiba

Author 3: Oughdir Lahcen

Keywords: Ontology; computing environments for human learning (CEHL)-Learner – Learner’s Profile – XML/RDF – JENA API – OWL – PERFECT-LEARN – inference; Learner modeling – SPARQL - semantic links - concepts – sub-concepts

Paper 20: Suitable Personality Traits for Learning Programming Subjects: A Rough-Fuzzy Model

Abstract: Programming is a cognitive activity which requires logical reasoning to code for abstract presentation. This study aims to find out the personality traits of students who maintain the effective grades in learning programming courses such as structured programming (SP) and object oriented programming (OOP) by gender classification. Data were collected from three universities to develop, validate, and generalize the Rough-Fuzzy model. Genetic and Johnson algorithms were applied under Rough set theory’s (RST) principles to extract the decision rules. In addition, Standard Voting, Naïve Bayesian, and Object Tracking procedures were applied on the generated decision rules to find the prediction accuracy of each algorithm. Mamdani’s Fuzzy Inference System (FIS) was used for mapping the decision rules’ condition (input) to decision (output) based on fuzzy set theory (FST) to develop the model. The results highlighted that certain personality compositions can be suitable for scoring good grades in programming subjects. For instance, a female student is capable enough to improve the programming skills if she is composed of introvert and sensing personality traits. Therefore, it is important to investigate an appropriate personality composition for programming learners.

Author 1: Abdul Rehman Gilal

Author 2: Jafreezal Jaafar

Author 3: Mazni Omar

Author 4: Shuib Basri

Author 5: Izzatdin Abdul Aziz

Author 6: Qamar Uddin Khand

Author 7: Mohd Hilmi Hasan

Keywords: Software development; personality; programming; rough sets; fuzzy sets

Paper 21: Usability of Government Websites

Abstract: Usability of Government websites plays pivotal role in order to provide benefits and services to the citizens. This study presents a usability evaluation for investigating the Nielsen’s usability attributes in Government websites. Based on the previous studies, a proposed website template is used in this study. This template is compared with a selected Government website. Thirty (30) participants performed three (3) representative tasks for each website. The results show that the user responses for the parameters of efficiency, memorability and pleasantness are improved for the proposed template. This effort is a part of the study that may lead to the principles for improving the usability of Government websites of Pakistan.

Author 1: Mahmood Ashraf

Author 2: Faiza Shabbir Cheema

Author 3: Tanzila Saba

Author 4: Abdul Mateen

Keywords: Usability; statutory bodies websites; government websites

Paper 22: InstDroid: A Light Weight Instant Malware Detector for Android Operating Systems

Abstract: With the increasing popularity of Android operating system, its security concerns have also been raised to a new horizon in past few years. Different researchers have introduced different approaches in order to mitigate the malware attacks on Android devices and they succeed to provide security up to some extent but these antimalware techniques are still resource inefficient and takes longer time to detect the malicious behavior of applications. In this paper, basic security mechanisms, provided by Google Android, and their limitations are discussed. Also, the existing antimalware techniques which lie under the basic detection approaches are discussed and their limitations are also highlighted. This research proposes a light weight instant malware detector, named as InstDroid, for Android devices that can identify the malicious applications immediately. Through experiments, it is shown that InstDroid is an instant malware detector that provides instant security at low resource consumption, power and memory, in comparison to other well-known commercial antimalware applications.

Author 1: Saba Arshad

Author 2: Rabia Chaudhary

Author 3: Munam Ali Shah

Author 4: Neshmia Hafeez

Author 5: Muhammad Kamran Abbasi

Keywords: Android; static; resource efficient; power consumption; memory; detection rate; accuracy

Paper 23: A 7-Layered E-Government Framework Consolidating Technical, Social and Managerial Aspects

Abstract: E-Government has been hype for the last 2 decades and still several implementations do not reach the intended success. Different definitions and consequently different models of operations and assessment were developed. This required the formulation of various frameworks describing the different perceptions and understandings of e-Government. The different frameworks proposed tend to agree on a set of elements, but each framework seems to have one or few different elements, depending on the perception of the framework founder. Also, entire categories (or dimensions) of elements seem to be left out. Through a literature review and field survey, the authors identified challenges of an e-Government initiative, categorized in five dimensions: technical, adoption, organizational, strategy and cultural. Not all categories were covered in any of the existing government frameworks. This would prove to be awkward in the formulation of new government initiatives or in the assessment of existing ones and evolution plan. In an effort to represent the majority of the factors and elements involved in most e-Government initiatives, the authors present a proposed seven-layer-framework for e-government. The layers included are: 1) end user access layer, 2) e-government layer, 3) organization layer, 4) national infrastructure layer, 5) strategic layer, 6) social cultural layer, and 7) national execution layer. The proposed model is compared with existing models and demonstrates that it covers all the aforementioned dimensions.

Author 1: Mohammed Hitham M.H

Author 2: Dr. Hatem Elkadi H.K

Author 3: Dr. Sherine Ghoneim S.G

Keywords: E-government; framework; e-government; challenges, decision support system (DSS)

Paper 24: Validating a Novel Conflict Resolution Strategy Selection Method (ConfRSSM) Via Multi-Agent Simulation

Abstract: Selecting a suitable conflict resolution strategy when conflicts appear in multi-agent environments is a hard problem. There is a need to develop a method that can select a suitable strategy which guaranties low cost in terms of the number of messages and time ticks. This paper focuses on conflicts over agents’ individual opinion and decision making by taking into account an agent’s features such as collaborative, autonomous, and local communication. The significance of this research is two-fold. Firstly, this research attempts to prove the significance of giving agents the ability to select an appropriate strategy in different conflict states depending on conflict specifications such as conflict strengths and confidence levels of the conflicting agents. Secondly, the study developed a new method named as ConfRSSM for reducing the communication cost and time taken for selecting a conflict resolution strategy. The approach ignores some conflict states, and replaces complex strategies by a simpler one, in some conflicting cases. Results show ConfRSSM reduces the number of messages and time ticks and thus improving the entire conflict resolution process.

Author 1: Alicia Y.C. Tang

Author 2: Ghusoon Salim Basheer

Keywords: Multi-agent, conflict resolution strategy; conflict states; confidence level; simulation

Paper 25: Modeling and Verification of Payment System in E-Banking

Abstract: Formal modeling and verification techniques have been used to ensure the reliability and accuracy of multiple systems to be verified. In contrast to ordinary testing techniques which exhibit the presence of flaws and errors in a system, formal methods prove their absence. Electronic banking (e-banking) services have become very popular with the escalating development in the information and communication technology. Due to the presence of complexity, an e-banking system requires an efficient security model. One important approach to ensure the reliability and security of the e-banking system is through the use of formal methodologies. This study explores the opportunity of modeling interbank payment system through a case study of 1-link Automated Teller Machine (ATM). A generic verification system SPIN (Simple Promela Interpreter) is, therefore, employed to model and then to verify the integrity and security of payment system in e-banking. Linear temporal logic formulas are further summarized to assure the security of the e-banking system. The principal conclusion of the work includes a complete procedure of verification and modeling of the payment system in 1-link ATMs.

Author 1: Iqra Obaid

Author 2: Syed Asad Raza Kazmi

Author 3: Awais Qasim

Keywords: E-banking; model checking; Simple Promela Interpreter (SPIN); formal methods; Linear Temporal Logic (LTL) formula; Promela introduction

Paper 26: ReCSDN: Resilient Controller for Software Defined Networks

Abstract: Software Defined Networking (SDN) is an emerging network paradigm that provides central control over the network. Although, this simplifies the network management and makes efficient use of network resources, it introduces new threats to network reliability and scalability. In fact, a single centralized controller is a single point of failure. Moreover, a single controller may become a performance bottleneck as processing overhead increases. Distributed SDN controller platforms improve the reliability and scalability to some extent, however they remain vulnerable to Distributed Denial of Service (DDoS) attacks, specifically on control plane. We believe that there is a need for a distributed controller framework that is capable of providing service continuity without performance degradation in case of excessive network traffic or DDoS attacks on controller. In this paper, we aim to address the vulnerabilities of SDN control plane. We propose and implement an efficient and Resilient Controller for Software Defined Network (ReCSDN). This framework is capable of detecting and mitigating DDoS attacks timely and ensures the continuity of services without performance degradation. We created an experimental test bed using Mininet to conduct extensive experiments. We deployed ReCSDN on top of Open Network Operating System (ONOS) cluster to confirm the viability of our approach. The experiment results show that with ReCSDN, control plane is not only able to withstand excessive network load but will also continue to provide services in case of any controller failure.

Author 1: Soomaiya Hamid

Author 2: Narmeen Zakaria Bawany

Author 3: Jawwad Ahmed Shamsi

Keywords: Software Defined Networking (SDN); SDN Controller security; Distributed Denial of Service (DDoS) attack; load balancing; SDN controller cluster; Open Network Operating System (ONOS)

Paper 27: Detection and Prevention of SQL Injection Attack by Dynamic Analyzer and Testing Model

Abstract: With the emergence and popularity of web application, threats related to web applications has increased to large extent. Among many other web applications threats Structured Query Language Injection Attack (SQLIA) is the dominant in its use due to its ability to access the data. Many solutions are proposed in this regard that has success in specific conditions. The proposed model is based on the dynamic analyzer model. The proposed model also has certain advantages like wide applicability, fast response time, coverage to large number of techniques of SQL Injections (SQLI) and efficient in term of resource usage.

Author 1: Rana Muhammad Nadeem

Author 2: Rana Muhammad Saleem

Author 3: Rabnawaz Bashir

Author 4: Sidra Habib

Keywords: Structured Query Language (SQL); injection attack; request receiver; analyzer and tester

Paper 28: Normalisation of Technology Use in a Developing Country Higher Education Institution

Abstract: The purpose of this study is to understand how the use of an online course and lecturer evaluation becomes a normalised way of evaluating courses and lecturers in a developing country higher education institution. Extant literature on course and lecturer evaluations has concentrated on the approaches to evaluating courses, lecturers, and its effectiveness and benefits. However, less attention has been paid to how online evaluations become the medium for lecturer and course evaluation. To address this gap, this study used an interpretive case study approach to collect data through semi-structured interviews, documents and participant observation. Data analysis was conducted using hermeneutics and using Normalisation Process Theory as the theoretical lens. The results show that the online evaluation of courses and lecturers is now a normal practice because of participant’s investment in the meaning of the online evaluation process, their enrolment in the process and the crucial investment of their actions, feedback during implementation, and use of which ensured the normalization.

Author 1: Ibrahim Osman Adam

Author 2: Osman Issah

Keywords: Course and lecturer evaluation; Higher Education Institution (HEI); Normalisation; Normalisation Process Theory (NPT)

Paper 29: Design and Simulation of a Novel Dual Band Microstrip Antenna for LTE-3 and LTE-7 Bands

Abstract: Long Term Evolution (LTE) is currently being used in many developed countries and hopefully will be implemented in more countries. An antenna operating in LTE-3 band can support global roaming in ITU Regions 1 and 3, Costa Rica, Brazil and partially in some Caribbean countries and antenna operating in LTE-7 band are appropriate for global roaming in ITU regions 1, 2 and 3. An antenna operating at both the bands will make the place taken by the antenna in a device into half and allow roaming in all the regions mentioned above. The geometry of the current available antenna operating in LTE-3 and LTE-7 bands has a considerably large size. A dual band microstrip antenna operating in LTE-3 and LTE-7 bands is proposed in this work with notable size reduction. The proposed antenna simulation shows resonant frequencies at 1.88GHz and 2.55GHz with return loss below -10dB that covers both LTE-3 and LTE-7 bands. Design and simulation of the proposed antenna is done by IE3D Zeland software. This proposed antenna is suitable for global roaming in ITU regions 1, 2 and 3, which cover most of the world telecom network.

Author 1: Abdullah Al Hasan

Author 2: Mohammad Shahriar Siraj

Author 3: Muhammad Mostafa Amir Faisal

Keywords: Long Term Evolution (LTE); microstrip; dual band; u-slot

Paper 30: Mobile Learning Application Development for Improvement of English Listening Comprehension

Abstract: Trend towards English language learning has been increased because it is considered as Lingua franca i.e. language of communication for all. However students of Pakistan are behind in this pace, especially rural elementary students. In rural areas there is crucial need to get assistance in their own curriculum after school because mostly they do not find anyone to help at home. M-Learning (Mobile Learning) assists learning anywhere and anytime. This ubiquitous power of M-Learning helps in after school programs and education in rural areas. The aim of this study is to develop M-Learning application for improvement of English listening comprehension in rural primary school students. This study developed English learning application based on Listening Comprehension, which embeds English curriculum of Sindh Textbook board for grade 1, 2 and 3. This study took the form of an after-school program in a village in Pakistan. There were 45 students of grade 3 from rural primary school of Pakistan selected as participants. Since developed application is based on recognition and memorization of information, so that knowledge and comprehension level of cognitive domain from Bloom’s taxonomy were selected for choosing the type of evaluation questions. On the basis of those question types, EGRA (Early Grade Reading Assessment) test is used for evaluation. This test was conducted on two experimental groups and one control group and the results of the groups were compared to one another. The results confirm that English M-learning applications can become helpful tool for students who live in rural areas where they face problems in learning of their English curriculum, since their relatives are not capable to teach them as accordingly.

Author 1: Zahida Parveen Laghari

Author 2: Hameedullah Kazi

Author 3: Muhammad Ali Nizamani

Keywords: Mobile Learning (M-Learning); Early Grade Reading Assessment (EGRA); English as Secondary Language (ESL); Automatic Speech Recognition (ASR); Personal Digital Assistance (PDA)

Paper 31: Toward a New Massively Distributed Virtual Machine based Cloud Micro-Services Team Model for HPC: SPMD Applications

Abstract: This paper aims to propose a new massively distributed virtual machine with scalable and efficient parallel computing models for High Performance Computing (HPC). The message passing paradigm of the Processing Units has a significant impact on HPC with high communication cost that penalizes the performance of these models. Accordingly, the proposed micro-services model allows the HPC applications to enhance the processing power with low communication cost. Thus, the model based Micro-services Virtual Processing Units (MsVPUs) cooperate using asynchronous communication mechanism through the Advanced Message Queuing Protocol (AMQP) protocol in order to maintain the scalability of the Single Program Multiple Data (SPMD) applications. Additionally, this mechanism enhances also the efficiency of the model based load balancing service with time optimized load balancing strategy. The proposed virtual machine is tested and validated through an application of fine grained parallel programs for big data classification. Experimental results present reduced execution time compared to the virtual machine based mobile agent’s model.

Author 1: Fatéma Zahra Benchara

Author 2: Mohamed Youssfi

Author 3: Omar Bouattane

Author 4: Ouafae Serrar

Author 5: Hassan Ouajji

Keywords: Parallel and distributed computing; micro-services; cloud computing; distributed virtual machine; high performance computing

Paper 32: Energy Management Strategy of a PV/Fuel Cell/Supercapacitor Hybrid Source Feeding an off-Grid Pumping Station

Abstract: This work aims to develop an accurate energy management strategy for a hybrid renewable energy system feeding a pumping station. A developed model under Simulink environment is used to compare the performance of the pumping system when it is only fed by a photovoltaic generator, by a hybrid photovoltaic and fuel cell system and finally by a hybrid photovoltaic, fuel cell and a supercapacitor system. The developed control strategy is based on Fuzzy Logic control technique. Several simulations in different dramatic scenarios of working conditions show that the developed control strategy brought major enhancements in system performance and that the use of the supercapacitor makes economic profits by reducing the fuel cell production during critical solar irradiation periods.

Author 1: Houssem CHAOUALI

Author 2: Hichem OTHMANI

Author 3: Mohamed Selméne BEN YAHIA

Author 4: Dhafer MEZGHANI

Author 5: Abdelkader MAMI

Keywords: Energy management strategy; simulink; pumping station; photovoltaic generator; fuel cell generator; supercapacitor; fuzzy logic control technique

Paper 33: Object’s Shape Recognition using Local Binary Patterns

Abstract: This paper discusses the concept of object’s shape identification using local binary pattern technique (LBP). Since LBP is computationally simple it has been utilized successfully for recognition of various objects. LBP which has the potential to be used in various identification related fields was applied on a number of different shaped objects, the process converted the given image in to 3x3 binary matrices and several rounds of computation yields the final decision parameter, which is known as merit function. This parameter was then exploited to uniquely identify the shape of different objects.

Author 1: Muhammad Wasim

Author 2: Adnan Ahmed Siddiqui

Author 3: Abdul Aziz

Author 4: Lubaid Ahmed

Author 5: Syed Faisal Ali

Author 6: Fauzan Saeed

Keywords: Local binary patterns; object shape recognition; security technologies; content based recognition

Paper 34: Feature Extraction and Classification Methods for a Motor Task Brain Computer Interface: A Comparative Evaluation for Two Databases

Abstract: A comparative evaluation is performed on two databases using three feature extraction techniques and five classification methods for a motor imagery paradigm based on Mu rhythm. In order to extract the features from electroencephalographic signals, three methods are proposed: independent component analysis, Itakura distance and phase synchronization. The last one consists of: phase locking value, phase lag index and weighted phase lag index. The classification of the extracted features is performed using linear discriminant analysis, quadratic discriminant analysis, Mahalanobis distance based on classifier, the k-nearest neighbors and support vector machine. The aim of this comparison is to evaluate which feature extraction method and which classifier is more appropriate in a motor brain computer interface paradigm. The results suggest that the effectiveness of the feature extraction method depends on the classification method used.

Author 1: Oana Diana Eva

Author 2: Anca Mihaela Lazar

Keywords: Brain computer interface; independent component analysis; Itakura distance; phase synchronization; classifiers

Paper 35: Creating and Protecting Password: A User Intention

Abstract: Students Academic Information System (SAIS) is an application that provides academic information for the students. The security policy applied by our university requires the students to renew their SAIS password based on the university’s policy. This study aims to analyze SAIS users’ behavior by using six variables adapted from Protection Motivation Theory (PMT), which are Perceived Severity, Perceived Vulnerability, Fear, Response Efficacy, Response Cost and Intentions. The data was collected from 288 SAIS users as respondents. The data analysis method used is Structural Equation Modeling (SEM) analysis. The study result shows that the factors affecting the intention of changing the passwords are perceived severity, fear, response efficacy, and response cost.

Author 1: Ari Kusyanti

Author 2: Yustiyana April Lia Sari

Keywords: Students Academic Information Systems (SAIS); SEM; intention; PMT

Paper 36: Analyzing the Social Awareness in Autistic Children Trained through Multimedia Intervention Tool using Data Mining

Abstract: This study focuses on creating a guideline for the ASD children by simulating the situation and analyzing the understanding of ASD (Asperger Syndrome) children over social skills by using a multimedia intervention tool designed for this purpose. 84 ASD individuals belonging to NGOs and clinics were selected for studying their social and cultural awareness. Autistic kids were taught social skills using specially designed multimedia intervention tool, in a controlled environment under the supervision of special educators or parents. Data mining technique was used to extract knowledge from the data collected after intervention. The results were analyzed to understand the impact of the designed multimedia intervention tool and share with special educators and parents of autistic children. The proposed multimedia intervention tool is inexpensive and user friendly. Integration of this tool has been observed to improve the quality of training an individual with autism traits. The overall growth in social communication of the ASD children under observation was observed to be 26.19%. There were substantial variances between age groups, training set and behavior parameters on any of the measures at follow-up. It was considered that an intervention starts at early age and proves beneficiary to ASD children. The study is establishing the remarkable benefits of designed multimedia intervention tool to train the ASD children.

Author 1: Richa Mishra

Author 2: Divya Bhatnagar

Keywords: Asperger Syndrome (ASD); multimedia intervention tool; social skills; autism; computer aided training; autistic children

Paper 37: Context Aware Fuel Monitoring System for Cellular Sites

Abstract: The past decade has been very productive for cellular operators of Pakistan, as their subscribers have grown exponentially with increase in revenue. After this wave of rising, the operators have now reached to saturation level, with the highest teledensity of all time. These Cellular Networks consist of Cell sites, which need electrical power to run. Because of electrical power shortage in Pakistan, the power needs of cell site are fulfilled by the use of electrical power generators which are installed on each site. These generators run on fossil fuel, a large amount of which is being theft from sites. This has very negative impact on Network availability and Operator’s operational expenditure. To cope with this major issue of fuel theft, an embedded system is being designed and tested. This paper highlights this issue of the telecom sector and discusses the design and results of the proposed system. This system would reduce the cell site operational cost and will increase its availability in the service area.

Author 1: Mohammad Asif Khan

Author 2: Ahmad Waqas

Author 3: Qamar Uddin Khand

Author 4: Sajid Khan

Keywords: Fuel theft; fuel sensor; fuel management; remote monitoring

Paper 38: Text Steganography using Extensions Kashida based on the Moon and Sun Letters Concept

Abstract: Existing steganography methods are still lacking in terms of capacity. Hence, a new steganography method for Arabic text is proposed. The method hides secret information bits within Arabic letters using two features, which are the moon and sun letters and the redundant Arabic extension character “-” known as Kashida. The Arabic alphabet contains 28 letters, which are classified into 14 sun letters and 14 moon letters. This classification is based on the way these letters affect the pronunciation of the definite article (ال) at the beginning of words. This method uses the sun letters with one extension to hold the secret bits ‘01’, the sun letters with two extensions to hold the secret bits ‘10’, the moon letters with one extension to hold the secret bits ‘00’ and the moon letters with two extensions to hold the secret bits ‘11’. The capacity performance of the proposed method is then compared to three popular text steganographic methods. Capacity is measured based on two factors which are Embedding Ratio (ER) and The Efficiency Ratio (TER). The results show that the Letter Points and Extensions Method produces 24.91% and 21.56% as the average embedding ratio and the average efficiency ratio correspondingly. For the Two Extensions ‘Kashida’ Character Method, the results for the average embedding ratio and the efficiency ratio are 56.76% and 41.81%. For the Text Using Kashida Variation Algorithm method, the average embedding ratio and the average efficiency ratio are 31.61% and 27.82% respectively. Meanwhile, the average embedding ratio and the efficiency ratio for the Proposed Method are 61.16% and 55.70%. Hence, it is concluded that the Proposed Method outweighs the other three methods in terms of their embedding ratio and efficiency ratio which leads to the conclusion that the Proposed Method could provide higher capacity than the other methods.

Author 1: Anes. A. Shaker

Author 2: Farida Ridzuan

Author 3: Sakinah Ali Pitchay

Keywords: Text steganography; Arabic text; extension Kashida; capacity

Paper 39: Studying the Influence of Static Converters’ Current Harmonics on a PEM Fuel Cell using Bond Graph Modeling Technique

Abstract: This paper shows the results of adding static converters (Boost, Buck and Buck-Boost converters) as an adaptation solution between a PEM Fuel Cell generator and a resistive load in order to study different effects of the converter on the generator performances in terms of voltage and current behavior. The presented results are obtained by simulating the Bond Graph developed model under 20-Sim software and show current and voltage behaviors with each converter under different scenarios of working conditions.

Author 1: Wafa BEN SALEM

Author 2: Houssem CHAOUALI

Author 3: Dhia MZOUGHI

Author 4: Abdelkader MAMI

Keywords: Static converters; PEM Fuel Cell; boost, buck and buck-boost converters; bond graph; 20-Sim software

Paper 40: The Effect of Religious Beliefs, Participation and Values on Corruption: Survey Evidence from Iraq

Abstract: This research tests the role that religious beliefs, rituals and values plays on the corruption in Iraq. Furthermore, the research assesses ethical and moral ideals pertinent to religion, in the Iraqi educational sector. Correlation analysis and linear regression help assess the relations among the study’s constructs and variables. The hypotheses tested by multiple regression technique with the help of SPSS software. Grounded in the data collected from 600 employees, the results affirm that religious beliefs have negative association with levels of corruption. Prayers in religious institution are influenced by the clergy, which serves as a set of life instructions to avoid corrupt practices. The generalizability of our results might be limited because we surveyed workers from a single sector; this calls for future studies to verify the stability of our findings across another sectors and firms.

Author 1: Marwah Abdulkareem Mahmood Zuhaira

Author 2: Tian Ye-zhuang

Keywords: Beliefs; participation; values; corruption

Paper 41: Detection of Distributed Denial of Service Attacks Using Artificial Neural Networks

Abstract: Distributed Denial of Services (DDoS) is a ruthless attack that targets a node or a medium with its false packets to decline the network performance and its resources. Neural networks is a powerful tool to defend a network from this attack as in our proposed solution a mitigation process is invoked when an attack is detected by the detection system using the known patters which separate the legitimate traffic from malicious traffic that were given to artificial neural networks during its training process. In this research article, we have proposed a DDoS detection system using artificial neural networks that will flag (mark) malicious and genuine data traffic and will save network from losing performance. We have compared and evaluated our proposed system on the basis of precision, sensitivity and accuracy with the existing models of the related work.

Author 1: Abdullah Aljumah

Keywords: Distributed Denial of Services (DDoS); ANN; IDS

Paper 42: Artificial Intelligence in Bio-Medical Domain

Abstract: In this era and in the future, artificially intelligent machines are replacing and playing a key role to enhance human capabilities in many areas. It is also making life style better by providing convenience to all including normal human beings and professionals as well. That is why AI is gaining huge attention and popularity in the field of computer science by which it has revolutionized the rapidly growing technology known as expert system. The applications of AI are working in many areas with huge impact and being used widely as well. AI provides quality and efficiency in almost every area, we are evolving it in. The main purpose of this paper is to explore the area of medical and health-care with respect to AI along with ‘Machine Learning’, and ‘Neural Networks’. This work explores the current use of AI in innovations, in the particular field of Bio-Medical and evaluated that how it has improved hospital inpatient care and other sectors related to it i.e. smart medical home, virtual presence of doctors and patients, automation in diagnostic, etc. that has changed the infrastructure of medical domain. Finally, an investigation of some expert systems and applications is made. These systems and applications are widely used throughout the world and a ranking mechanism of their performance has proposed accordingly in an organized manner. We hope, this work will be helpful for the researchers coming to this particular area and to provide a syntactic information that how computer science (i.e. AI, ANN, ML) is revolutionizing the field of bio-medical and healthcare.

Author 1: Muhammad Salman

Author 2: Abdul Wahab Ahmed

Author 3: Omair Ahmad Khan

Author 4: Basit Raza

Author 5: Khalid Latif

Keywords: Artificial intelligence; expert systems; bio-medical; healthcare; innovations

Paper 43: A Hybrid Curvelet Transform and Genetic Algorithm for Image Steganography

Abstract: In this paper, we present a new hybrid image steganography algorithm by combining two famous techniques which are curvelet transform and genetic algorithm GA. The proposed algorithm is called Hybrid Curvelet Transform and Genetic Algorithm for image steganography (HCTGA). Curvelet transform is a multiscale geometric analysis tool, its main advantage is that it can solve the important problems efficiently and other transforms are not that ideal. Genetic algorithm is a famous optimization algorithm with the aim of finding the best solutions to a given computational problem that maximizes or minimizes a particular function. In the proposed algorithm the cover and secret images are passed through a preprocessing process by applying four different filters to them in order to remove the noise and achieve a better quality to both images before the hiding process. Then the curvelet transform is applied to the cover image to find the curvelet frequencies of the image, and the secret image is hided at the Least Significant Bits (LSB) of the curvelet frequencies of the cover image to reconstruct the stego image. Finally genetic algorithm operations are employed to find different scenarios for the hiding process by rearranging the hiding bits and finally choose the best scenario that can reach a better image quality and a higher Peak Signal to Noise Ratio (PSNR) results.

Author 1: Heba Mostafa Mohamed

Author 2: Ahmed Fouad Ali

Author 3: Ghada Sami Altaweel

Keywords: Image steganography; curvelet transform; least significant bits; genetic algorithm; Peak Signal to Noise Ratio (PSNR)

Paper 44: Automatic Music Genres Classification using Machine Learning

Abstract: Classification of music genre has been an inspiring job in the area of music information retrieval (MIR). Classification of genre can be valuable to explain some actual interesting problems such as creating song references, finding related songs, finding societies who will like that specific song. The purpose of our research is to find best machine learning algorithm that predict the genre of songs using k-nearest neighbor (k-NN) and Support Vector Machine (SVM). This paper also presents comparative analysis between k-nearest neighbor (k-NN) and Support Vector Machine (SVM) with dimensionality return and then without dimensionality reduction via principal component analysis (PCA). The Mel Frequency Cepstral Coefficients (MFCC) is used to extract information for the data set. In addition, the MFCC features are used for individual tracks. From results we found that without the dimensionality reduction both k-nearest neighbor and Support Vector Machine (SVM) gave more accurate results compare to the results with dimensionality reduction. Overall the Support Vector Machine (SVM) is much more effective classifier for classification of music genre. It gave an overall accuracy of 77%.

Author 1: Muhammad Asim Ali

Author 2: Zain Ahmed Siddiqui

Keywords: K-nearest neighbor (k-NN); Support Vector Machine (SVM); music; genre; classification; features; Mel Frequency Cepstral Coefficients (MFCC); principal component analysis (PCA)

Paper 45: The Identification of Randles Impedance Model Parameters of a PEM Fuel Cell by the Least Square Method

Abstract: One of the problems of industrial development of fuel cells is the reliability of their performances with time. The solution of this problem is through by the development of improved diagnostic methods such as the identification of parameters. This work focuses on the modeling and the identification of the impedance model parameters of a Proton Exchange Membrane (PEM) fuel cell. It is based on the Randles model represented by specific complex impedance at each cell. We have used the “Least square” method to determine the parameters model using measured reference values. The proposed authentication method is valid for Randles model, but it can be generalized to be applied to others.

Author 1: Selméne Ben Yahia

Author 2: Hatem Allagui

Author 3: Abdelkader Mami

Keywords: Randles model; impedance; Proton Exchange Membrane (PEM) fuel cell; modeling; parameters identification; least square

Paper 46: Using the Facebook Iframe as an Effective Tool for Collaborative Learning in Higher Education

Abstract: Facebook is increasingly becoming a popular senvironment for online learning. Despite the popularity of using Facebook as an e-learning tool, there is a limitation when it comes to presenting content: another platform is required to run the files. Presented in this paper is a case study of how the Facebook iframe code can be used as a hosting environment tool to support collaborative activities in higher education at Qassim University. The study was conducted on a sample of (N=45) university students who were enrolled in Selected Topics in Information Systems (INFO491) at the Faculty of Art & Science at Qassim University. We used Facebook markup language (FBML) to design and implement the course. An online questionnaire was used to investigate the students’ perceptions about using Facebook iframe for the course. Descriptive statistical analysis and chi-square test were used to analyze the data. According to our results, the participants reported that using the Facebook iframe page increased their understanding and improved their learning performance. In addition, for the majority of students, it enabled them to learn more quickly. Our findings also revealed that a Facebook iframe page is a distinctive hosting environment for presenting content.

Author 1: Mohamed A. Amasha

Author 2: Salem Alkhalaf

Keywords: Facebook iframe; collaborative learning; Facebook markup language (FMBL); hosting environment

Paper 47: AES-Route Server Model for Location based Services in Road Networks

Abstract: The now ubiquitous use of Location based services (LBS), within the mobile computing domain, has enabled users to receive accurate points of interest (POI) to their geo-tagged queries. While location-based services provide rich content, they are not without risks; specifically, the use of LBS poses many serious challenges with respect to privacy protection. Additionally, the efficiency of spatial query processing, and the accuracy of said results, can be problematic when applied to road networks. Existing approaches provide different online route APIs to deliver the precise POI, but mobile user demand not only Accurate, Efficient and Secure (AES) results, but results that do not threaten their privacy. In this paper, we have addressed these challenges by proposing an AES-Route Server (RS) approach for LBS, which supports common spatial queries, including Range Queries and k-Nearest Neighbor Queries. We can secure the user location through the proposed AES-RS model because it provides the query results accurate and efficiently. The proposed model satisfy the primary goals including accuracy, efficiency and privacy for a Location Base System.

Author 1: Mohamad Shady Alrahhal

Author 2: Muhammad Usman Ashraf

Author 3: Adnan Abesen

Author 4: Sabah Arif

Keywords: Mobile computing; location based services; LBS privacy; LBS accuracy; LBS efficiency; ubiquitous computing

Paper 48: Visualizing Computer Programming in a Computer-based Simulated Environment

Abstract: This paper investigated the challenges presented by computer programming (sequential/traditional, concurrent and parallel) for novice programmers and developers. The researcher involved Higher Education in Computer Science students learning programming at multiple levels, as they could well represent beginning programmers, who would struggle in successfully achieving a running program due to the complexity of this theoretical process, which has no similar real-life representation. The paper explored the difficulties faced by students in understanding this challenging, yet fundamental, subject of all Computer Science/Computing degree programmes, and focused on the advantages of visualization techniques to facilitate the learning of computer programming, with recommendations on effective computer-based simulated platforms to achieve this visualization. The paper recommended the application of virtual world technologies, such as ‘Second Life’, to achieve the visualization required to facilitate the understanding and learning of computer programming. The paper demonstrated extensive evidence on the advantages of these technologies to achieve program visualization, and how they facilitated enhanced learning of the programming process. The paper also addressed the benefits of collaboration and experimentation, which are ideal for learning computer programming, and how these aspects are strongly supported in virtual worlds.

Author 1: Dr. Belsam Attallah

Keywords: Computer programming; programming; object-oriented programming; programming language; parallelism; multi-threading; multithreading; concurrency;; visual; visualization; visual environment; virtual worlds; second life; virtualization.

Paper 49: Hybrid Technique for Java Code Complexity Analysis

Abstract: Software complexity can be defined as the degree of difficulty in analysis, testing, design and implementation of software. Typically, reducing model complexity has a significant impact on maintenance activities. A lot of metrics have been used to measure the complexity of source code such as Halstead, McCabe Cyclomatic, Lines of Code, and Maintainability Index, etc. This paper proposed a hybrid module which consists of two theories which are Halstead and McCabe, both theories will be used to analyze a code written in Java. The module provides a mechanism to better evaluate the proficiency level of programmers, and also provides a tool which enables the managers to evaluate the programming levels and their enhancements over time. This will be known by discovering the various differences between levels of complexity in the code. If the program complexity level is low, then of the programmer professionalism level is high, on the other hand, if the program complexity level is high, then the programmer professionalism level is almost low. The results of the conducted experiments show that the proposed approach give very high and accurate evaluation for the undertaken systems.

Author 1: Nouh Alhindawi

Author 2: Mohammad Subhi Al-Batah

Author 3: Rami Malkawi

Author 4: Ahmad Al-Zuraiqi

Keywords: Complexity; java code; McCabe; Halstead; hybrid technique

Paper 50: An Unsupervised Local Outlier Detection Method for Wireless Sensor Networks

Abstract: Recently, wireless sensor networks (WSNs) have provided many applications, which need precise sensing data analysis, in many areas. However, sensing datasets contain outliers sometimes. Although outliers rarely occur, they seriously reduce the precision of the sensing data analysis. In the past few years, many researches focused on outlier detection. However, many of them ignored one factor that WSNs are usually deployed in a dynamic environment that changes with time. Thus, we propose a new method, which is an unsupervised learning method based on mean-shift algorithm, for outlier detection that can be used in a dynamic environment for WSNs. To make our method adapt to a dynamic environment, we define two new distances for outlier detection. Moreover, the simulation shows that our method performed on real sensing dataset has ideal results; it finds outliers with a low false positive rate and has a high recall. For generality, we also test our method on different synthetic datasets.

Author 1: Tianyu Zhang

Author 2: Qian Zhao

Author 3: Yoshihiro Shin

Author 4: Yukikazu Nakamoto

Keywords: Wireless sensor networks; outliers detection; unsupervised learning; mean-shift algorithm

Paper 51: Adaptive e-learning using Genetic Algorithm and Sentiments Analysis in a Big Data System

Abstract: In this article we describe our adaptive e-learning system, which allows the learner to take courses adapted to his profile and to the pedagogical objectives set by the teacher, we use for adaptation the genetic algorithms to give the learner the concepts that must learn in an optimal way by seeking the objectives most adapted to his profile. And after a second level of adaptation using one of the social networks of the learner (twitter, facebook, Google + ...), based on his post on one of these social networks we propose two levels of analysis. The first one is to look for the period of activity which gives us an idea about the period when the learner is active and the second consists of making an analysis of the feelings on the publications that are published during the period of activity and related to education. Our work therefore is to adapt the profile of the learner with the pedagogical objectives by using the genetic algorithm and the notions of the research of information by doing this work in a Big Data system, that is to say we parallelize the search problem using Hadoop with Hadoop distributed file system (HDFS) and the MapReduce programming model,and after using information from a social network of the learner, we look for the period of activity of the learner and the feeling (sentiment analysis) related to the publications of the period of activity.

Author 1: Youness MADANI

Author 2: Jamaa BENGOURRAM

Author 3: Mohammed ERRITALI

Author 4: Badr HSSINA

Author 5: Marouane Birjali

Keywords: Adaptive E-learning; genetic algorithms; research of information; social network; period of activity; sentiment analysis; parallelize the search problem; big data; Hadoop; MapReduce; Hadoop distributed file system (HDFS)

Paper 52: Solving the Free Clustered TSP Using a Memetic Algorithm

Abstract: The free clustered travelling salesman problem (FCTSP) is an extension of the classical travelling salesman problem where the set of vertices is partitioned into clusters, and the task is to find a minimum cost Hamiltonian tour such that the vertices in any cluster are visited contiguously. This paper proposes the use of a memetic algorithm (MA) that combines the global search ability of Genetic Algorithm with local search to refine solutions to the FCTSP. The effectiveness of the proposed algorithm is examined on a set of TSPLIB instances with up to 318 vertices and clusters varying between 2 and 50 clusters. Moreover, the performance of the MA is compared with a Genetic Algorithm and a GRASP with path relinking. The computational results confirm the effectiveness of the MA in terms of both solution quality and computational time.

Author 1: Abdullah Alsheddy

Keywords: Combinatorial optimization; clustered travelling salesman problem; memetic algorithm; guided local search; genetic algorithm

Paper 53: Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN)

Abstract: This paper demonstrates a computer-aided diagnosis (CAD) system for lung cancer classification of CT scans with unmarked nodules, a dataset from the Kaggle Data Science Bowl, 2017. Thresholding was used as an initial segmentation approach to segment out lung tissue from the rest of the CT scan. Thresholding produced the next best lung segmentation. The initial approach was to directly feed the segmented CT scans into 3D CNNs for classification, but this proved to be inadequate. Instead, a modified U-Net trained on LUNA16 data (CT scans with labeled nodules) was used to first detect nodule candidates in the Kaggle CT scans. The U-Net nodule detection produced many false positives, so regions of CTs with segmented lungs where the most likely nodule candidates were located as determined by the U-Net output were fed into 3D Convolutional Neural Networks (CNNs) to ultimately classify the CT scan as positive or negative for lung cancer. The 3D CNNs produced a test set Accuracy of 86.6%. The performance of our CAD system outperforms the current CAD systems in literature which have several training and testing phases that each requires a lot of labeled data, while our CAD system has only three major phases (segmentation, nodule candidate detection, and malignancy classification), allowing more efficient training and detection and more generalizability to other cancers.

Author 1: Wafaa Alakwaa

Author 2: Mohammad Nassef

Author 3: Amr Badr

Keywords: Lung cancer; computed tomography; deep learning; convolutional neural networks; segmentation

Paper 54: Multiple Vehicles Semi-Self-driving System Using GNSS Coordinate Tracking under Relative Position with Correction Algorithm

Abstract: This paper describes a simple and low-cost semiself- driving system which is constructed without cameras or image processing. In addition, a position correction method is presented by using a vehicle dynamics. Conventionally, selfdriving vehicle is operated by various expensive environmental recognition sensors. It results in rise in prices of the vehicle, and also the complicated system with various sensors tends to be a high possibility of malfunction. Therefore, we propose the semi-self-driving system with a single type of global navigation satellite system (GNSS) receiver and a digital compass, which is based on a concept of a preceding vehicle controlled by a human manually and following vehicles which track to the preceding vehicle automatically. Each vehicle corrects coordinate using current velocity and heading angle from sensors. Several experimental and simulation results using our developed smallscale vehicles demonstrate the validity of the proposed system and correction method.

Author 1: Heejin Lee

Author 2: Hiroshi Suzuki

Author 3: Takahiro Kitajima

Author 4: Akinobu Kuwahara

Author 5: Takashi Yasuno

Keywords: Self-driving, positioning; global navigation satellite system (GNSS); Global Positioning System (GPS); GLONASS

Paper 55: An Efficient Scheme for Real-time Information Storage and Retrieval Systems: A Hybrid Approach

Abstract: Information storage and retrieval is the fundamental requirement for many real-time applications. These systems demand that data should be sorted all the time, real-time insertion, deletion and searching should be supported and system must support dynamic entries. These systems require search operations to be performed from massive databases implemented by various data structures. The common data structures used by these systems are stack, queue or linked list all having their own limitations. The biggest advantage of using stack is that binary search can be performed on it easily while on the other hand insertion and deletion of nodes involves more processing overhead. In linked list, insertion and deletion of nodes is easier but searching operation involves more processing overhead as binary search cannot be performed efficiently on it. In this paper, a hybrid solution is presented for such systems, which provides efficient insertion, deletion and searching operations. Results show the effectiveness of the proposed approach as it outperforms the existing techniques used by these systems.

Author 1: Syed Ali Hassan

Author 2: Imran Ul Haq

Author 3: Muhammad Asif

Author 4: Maaz Bin Ahmad

Author 5: Moeen Tayyab

Keywords: Insertion; deletion; array; linked list; binary search; linear search

Paper 56: Exploiting Temporal Information in Documents and Query to Improve the Information Retrieval Process: Application to Medical Articles

Abstract: In the medical field, scientific articles represent a very important source of knowledge for researchers of this domain. But due to the large volume of scientific articles published on the web, an efficient detection and use of this knowledge is quite a difficult task. In this paper, we propose a novel method for semantic indexing of medical articles by using the semantic resource MeSH (Medical Subject Headings) and the temporal information provided in the documents. The proposed indexing approach was evaluated by intensive experiments. These experiments were conducted on document test collections of real world clinical extracted from scientific collections, namely, CISMEF and CLEF. The results generated by these experiments demonstrate the effectiveness of our indexing approach.

Author 1: Jihen MAJDOUBI

Author 2: Ahlam Nabli

Keywords: Biomedical information retrieval; semantic indexing; temporal criteria; Medical Subject Headings (MeSH) thesaurus

Paper 57: A Comparison of Predictive Parameter Estimation using Kalman Filter and Analysis of Variance

Abstract: The design of a controller significantly improves if internal states of a dynamic control system are predicted. This paper compares the prediction of system states using Kalman filter and a novel approach analysis of variance (ANOVA). Kalman filter has been successfully applied in several applications. A significant advantage of Kalman filter is its ability to use system output to predict the future states. It has been observed that Kalman filter based predictive controller design outperforms many other approaches. An important drawback of such controllers is however that their performances deteriorate in situations where the system states have no correlation with the output. This paper takes a hypothetical model of a helicopter and builds system model using the state-space diagram. The design is implemented using SIMULINK. It has been observed that in situations where the states are dependent on system output, the ANOVA based state prediction gives comparable results with that of Kalman filter based parameter estimation. The ANOVA based parameter prediction, however outperforms Kalman filter based parameter prediction in situations where the output does not directly contribute in a particular state. The research was based on empirical results. Rigorous testing was performed on four internal states to prove that ANOVA based predictive parameter estimation technique outperforms Kalman based parameter estimation in situations where the system internal states is not directly linked with the output.

Author 1: Asim ur Rehman Khan

Author 2: Haider Mehdi

Author 3: Syed Muhammad Atif Saleem

Author 4: Muhammad Junaid Rabbani

Keywords: Analysis of variance (ANOVA); Kalman controllers; predictive controller

Paper 58: Fine-grained Accelerometer-based Smartphone Carrying States Recognition during Walking

Abstract: Due to the dependency of our daily lives on smartphones, the states of the device have impact on the quality of services offered through a smartphone. In this article, we focus on the carrying states of the device while the user is walking, in which 17 states, e.g., in the front-left trouser pocket, calling phone in the right hand, in a backpack are subjects to recognition based on supervised learning with accelerometer-derived features. A large-scale data collection from 70 persons with three walking speeds allows reliable evaluation regarding suitable features and classifiers model, the feature selection method, robustness of localization against unknown person, and effect of walking speed in training a classifier. Person-independent evaluation shows that average F-measures of 17 class classification and merged 9 class classification were 0.823 and 0.913, respectively.

Author 1: Kaori Fujinami

Author 2: Tsubasa Saeki

Author 3: Yinghuan Li

Author 4: Tsuyoshi Ishikawa

Author 5: Takuya Jimbo

Author 6: Daigo Nagase

Author 7: Koji Sato

Keywords: Smartphone; on-body localization; accelerometer; machine learning; feature selection; wearable computing

Paper 59: Automated Player Selection for a Sports Team using Competitive Neural Networks

Abstract: The use of data analytics to constitute a winning team for the least cost has become the standard modus operandi in club leagues, beginning from Sabermetrics for the game of basketball. Our motivation is to implement this enomenon in other sports as well, and for the purpose of this work we present a model for football, for which to the best of our knowledge, previous work does not exist. The main objective is to pick the best possible squad from an available pool of players. This will help decide which team of 11 football players is best to play against a particular opponent, perform prediction of future matches and helps team management in preparing the team for the future. We argue in favour of a semi-supervised learning approach in order to quantify and predict player performance from team data with mutual influence among players, and report win accuracies of around 60%.

Author 1: Rabah Al-Shboul

Author 2: Tahir Syed

Author 3: Jamshed Memon

Author 4: Furqan Khan

Keywords: Team selection; match outcome prediction; neural networks