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Journal receives papers in continuous flow and we will consider articles from a wide range of Information Technology disciplines encompassing the most basic research to the most innovative technologies. Please submit your papers electronically to our submission system at http://jatit.org/submit_paper.php in an MSWord, Pdf or compatible format so that they may be evaluated for publication in the upcoming issue. This journal uses a blinded review process; please remember to include all your personal identifiable information in the manuscript before submitting it for review, we will edit the necessary information at our side. Submissions to JATIT should be full research / review papers (properly indicated below main title).

Journal of Theoretical and Applied Information Technology
November 2019 | Vol. 97 No.21

Title:	ARABIC TEXT CLUSTERING BASED ON K-MEANS ALGORITHM WITH SEMANTIC WORD EMBEDDING
Author:	HASNAA R. H. SOLIMAN, MOHAMED GRIDA, MOHAMED HASSAN
Abstract:	With the massive growth of Arabic content on the web, clustering of the Arabic textual data into a small number of meaningful groups becomes an essential component in various information retrieval applications, such as recommender systems, sentiment analysis, question answering systems, and search engines. Clustering methods, which are traditionally based on bag of words (BOW) model for text representation, do not consider the order relationships between terms and may result in unsatisfactory clusters especially with complex languages as Arabic. This study introduces a model for enhancing the accuracy of Arabic document clusters by integrating the K-means clustering algorithm with embedding approaches, including Word to Vector (Word2Vec) as a representational basis instead of BOW to capture the semantic information between individual terms. The model performance in the clustering news dataset utilized in previous similar studies was investigated. Accordingly, it was concluded that combing embedding techniques with the k-means algorithm improves the various evaluation measures of clustering as purity, F-measure, and accuracy.
Keywords:	Arabic Text Clustering, Document Embeddings, Word Embeddings, Doc2vec, Word2Vec.
Source:	Journal of Theoretical and Applied Information Technology 15^th November 2019 -- Vol. 97. No. 21 -- 2019
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Title:	THE IMPACT OF TECHNOLOGY, ORGANIZATIONAL, AND TRUST FACTORS ON SOCIAL COMMERCE ADOPTION
Author:	WALID ABDULLA ALI , MURIATI MUKHTAR , IBRAHIM MOHAMED
Abstract:	The utilization of Web 2.0 applications in supporting peoples’ online interactions where the users’ contributions enable firms to lower cost, extend market reach and improve efficiency is termed social commerce. Businesses are able to set up an online community and provide a safe haven for consumers to share experiences, knowledge and data about their services and products. Yet, small and medium sized enterprises of developing countries are unable to quickly adopt social commerce. Thus, it is imperative to analyze the scenario and define the reasons that influence the implementation of social commerce. As such, via a thorough literature review, this study’s objective is to define the organizational, technological and trust factors that influence the adoption of social commerce (SC), and the way in which implementation of social commerce influences organization’s performance. There is also a suggestion of a conceptual framework following the strengths of three models: Unified Theory for Acceptance and Use Technology (UTAUT), Information Systems Success Model and Technology, Organization and Environment (TOE). Hopefully, the suggested framework may assist in deciding policies and actions that encourage adoption of social commerce.
Keywords:	Electronic Commerce; Social Commerce (SC); Small And Medium Sized Enterprises (Smes); Conceptual Framework.
Source:	Journal of Theoretical and Applied Information Technology 15^th November 2019 -- Vol. 97. No. 21 -- 2019
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Title:	THE CIPP-SAW EVALUATION MODEL DESIGN IN MEASUREMENT THE EFFECTIVENESS OF E-LEARNING AT HEALTH UNIVERSITIES IN BALI
Author:	I PUTU WISNA ARIAWAN, MADE KURNIA WIDIASTUTI GIRI, DEWA GEDE HENDRA DIVAYANA
Abstract:	This research aims to provide information about the design of an evaluation model that is suitable to be used to evaluate the level of effectiveness of the application of e-learning in health colleges in Bali. The evaluation model intended is called CIPP-SAW, where this model is a combination of the CIPP (Context-Input-Process-Product) evaluation model with SAW (Simple Additive Weighting) method. The CIPP-SAW model is capable of presenting accurate calculation results in determining evaluation aspects which are classified based on preference values ranging from the lowest to the highest value. This research was carried out with a development approach used the Borg and Gall method which focused on three stages, including: 1) design development; 2) initial trial; and 3) initial trial revision. Subjects that were involved in the stage of design development and stage of initial trial revision were three people (all researchers). Subjects that were involved in the stage of initial trial were 34 respondents (4 knowledge experts and 30 lecturers). The instrument that was used in data collection was a questionnaire consisting of 10 question items. The analysis technique was used was the quantitative descriptive, with the results of the analysis showed the evaluation model design that was classified as good with an effectiveness percentage was 88.00%.
Keywords:	CIPP, SAW, Evaluation Model, E-Learning, Health Universities
Source:	Journal of Theoretical and Applied Information Technology 15^th November 2019 -- Vol. 97. No. 21 -- 2019
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Title:	A REVIEW OF ACADEMIC PERFORMANCE FACTORS IN THE CONTEXT OF E-LEARNING: THEORIES AND EMPIRICAL STUDIES
Author:	MBANGATA L., EYONO OBONO S.D.
Abstract:	There are currently more than 500 commercial e-Learning software packages and 300 educational e-Learning software packages, and their benefits to their users are well documented. For instance, with e-Learning, the rigidity of teaching and learning timetables can be overcome. Shockingly, high education still witnesses intolerable levels of academic failure even in this e-Learning era. This study will therefore attempt to examine the impact of e-Learning on academic performance based on the perceptions of academics. It is a literature review of thirty-four (34) studies. Its findings reveal that according to the perceptions of academics, the impact of e-Learning on academic performance depends on: the demographics of the learners; their intensity of use of e-Learning; their self-efficacy and learning approach; their sense of community and interactivity; their perceptions on the suitability of e-Learning; and their motivation and pride. This study also recommends more research on the validation of its proposed theoretical framework, and its identified factors, because the reviewed literature is not unanimous on their perceived effect on the impact of e-Learning on academic performance. The main contribution of this study is to broaden the scope of academic performance factors in the context of e-Learning compared to similar past reviews with a limited pre-defined scope of academic performance factors.
Keywords:	e-Learning, Academic Performance, Learning Theories, Theoretical Model, Literature Review
Source:	Journal of Theoretical and Applied Information Technology 15^th November 2019 -- Vol. 97. No. 21 -- 2019
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Title:	AN EFFICIENT DEEP LEARNING FRAMEWORK FOR PEDESTRIAN DETECTION
Author:	HOANH NGUYEN
Abstract:	Vision-based pedestrian detection has achieved a large successful with the fast development of deep convolutional neural network (CNN). However, due to difficult environments such as large-scale variation, heavy occlusion and small scale of pedestrian, recent deep CNN-based approaches for pedestrian detection still do not achieve very good accuracy over public benchmark dataset. In this paper, an efficient framework based on deep CNN for pedestrian detection is proposed, especially for small-scale pedestrians. The proposed method first uses the reduced ResNet-34 architecture for generating convolution feature maps. Then, deconvolutional modules is used after the base convolution layers to bring additional context information which is more effective to detect small-scale pedestrians. In the region of interest pooling process, different feature maps at different scales are used to produce high quality region proposals. Furthermore, a modified loss function based on cross entropy loss is used to increase the loss contribution from hard-to-detect small-sized pedestrians. Experiment results demonstrate the effectiveness of the proposed approach with good detection performance over the Caltech pedestrian dataset.
Keywords:	Pedestrian Detection, Convolutional Neural Network, Intelligent Transportation Systems, Object Detection, Deep Learning
Source:	Journal of Theoretical and Applied Information Technology 15^th November 2019 -- Vol. 97. No. 21 -- 2019
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Title:	QUALITY OF SERVICE-BASED RESOURCE ALLOCATION FOR WEB CONTENT DELIVERY ON CLOUD COMPUTING INFRASTRUCTURE
Author:	OMOTUNDE, AYOKUNLE A , IDOWU, SUNDAY KUYORO, ‘SHADE , AYANKOYA, FOLASADE Y , JOSHUA, JONAH V , ADEGBENJO, ADERONKE , ABEL, SAMUEL B.
Abstract:	Demand for web content continues to increase at exponential rates and this has intensified the challenges of satisfying customer’s Quality of Service. Several techniques for Web content delivery vis-à-vis resource allocation have been proposed, one of which is the use of Content Distribution Networks. However, in recent times, cloud computing has become a driving force in Information Technology where Service Providers’ limited resources are shared among numerous users with different QoS requirements. In this work, focus is on developing a model for allocation of resources on cloud computing Infrastructure in order to improve delivery of Web content and optimize service cost. An analytical approach was adopted and expressed as an optimization problem subject to QoS metrics: delay, throughput, and bandwidth. The optimization problem was formulated as an Integer Linear Programming problem in which the decision variable takes the value of 0 or 1. A single Infrastructure-as-a-Service cloud with Virtual Machine (VM) instances running in Physical Machines (PM) was assumed. The model was considered for different values of delay, throughput, and bandwidth for each VM to obtain minimum cost of delivering Web content to users. An algorithm was developed and sample data were collected from Amazon Elastic Cloud Compute/storage pricing model to obtain optimal results. The implementation of the algorithm was done using ‘A Mathematical Programming Language/Modular In-core Nonlinear Optimization Systems’ (AMPL/MINOS).
Keywords:	Quality of Service, Resource allocation, Web content, Web content delivery, cloud computing
Source:	Journal of Theoretical and Applied Information Technology 15^th November 2019 -- Vol. 97. No. 21 -- 2019
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Title:	EFFECT OF CLUSTERING DATA IN IMPROVING MACHINE LEARNING MODEL ACCURACY
Author:	SAMIH M. MOSTAFA , HIROFUMI AMANO
Abstract:	Supervised machine learning algorithms consider the relationship between dependent and independent variables rather than the relationship between the instances. Machine learning algorithms try to learn the relationship between the input and output from the historical data in order to attain precise predictions about unseen future. Conventional foretelling algorithms are usually based on a model learned and trained from historical data. The instances in the historical data may vary in its characteristics. The variation may be a result of difference in case's pertinence degree to some cases compared to others. However, the problem with such machine learning algorithms is their dealing with the whole data without considering this variation. This paper presents a novel technique to the trained model to improve the prediction accuracy. The proposed method clusters the data using K-means clustering algorithm, and then applies the prediction algorithm to every cluster. The value of K which gives the highest accuracy is selected. The authors performed comparative study of the proposed technique and popular prediction methods namely Linear Regression, Ridge, Lasso, and Elastic. On analysing on five datasets with different sizes and different number of clusters, it was observed that the accuracy of the proposed technique is better from the point of view of Root Mean Square Error (RMSE), and coefficient of determination 〖(R〗^2).
Keywords:	Prediction accuracy, K-means, clustering, regression, machine learning algorithms.
Source:	Journal of Theoretical and Applied Information Technology 15^th November 2019 -- Vol. 97. No. 21 -- 2019
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Title:	UNCERTAIN INPUT SELECTION MODEL FOR NEURON
Author:	ZULFIAN AZMI, MUHAMMAD ZARLIS, HERMAN MAWENGKANG, SYAHRIL EFENDI
Abstract:	The application of Artificial Neural Network model does not provide optimal results in learning with the large quantity of inputs and real time. The input stated in matrix with plenty of quantity makes the process in pattern recognition getting slow. A model is required to minimize input for training and to recognize patterns faster. Input recognition is required to know the special characteristics of inputs which may represent all inputs using a model. In addition, input recognition is necessary to know the inputs with the binary value not only 1 and 0, but it may be with the value in between. To know uncertain inputs, it is conducted by determining the degree of membership of each variable. And, for each selection of input, it can be done by declaring it as a row vector and by calculating the euclidean distance between each row vector. Furthermore, the selected input may represent input for training. Training is carried out with input variables consisting of dissolved oxygen, water pH, salinity and water temperature to determine the quality of water. With the model algorithm which is called as Uncertain Input Selection Model for neurons, it helps to accelerate training in the system to determine the water wheel can rotate.
Keywords:	Selection, Input, Uncertain, Euclidean
Source:	Journal of Theoretical and Applied Information Technology 15^th November 2019 -- Vol. 97. No. 21 -- 2019
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Title:	AN EVOLUTIONARY APPROACH FOR SOFTWARE DEFECT PREDICTION ON HIGH DIMENSIONAL DATA USING SUBSPACE CLUSTERING AND MACHINE LEARNING
Author:	SUMANGALA PATIL, A.NAGARAJA RAO, C. SHOBA BINDU
Abstract:	Since last decade, due to increasing demand, huge amount of software is being developed, whereas the data intensive applications have also increased the complexity in these types of systems. Also, during the development process, software bugs may severely impact the growth of industries. Hence, the development of bug free software application is highly recommended in the real-time systems. Several approaches have been developed recently that are based on the manual inspection but those techniques are not recommended for huge software development scenario due to maximum chances of error during manual inspection. Thus, machine learning based data mining techniques has gained huge attraction from researchers due to their analyzing and efficiently detect the defect by learning the different attributes. In this work, we present machine learning based approach for software defect prediction. However, software defect datasets suffer from the high dimensionality issues, thus we present a novel subspace clustering approach using evolutionary computation based optimal solution identification for dimension reduction. Later, Support Vector Machine Classification scheme is implemented to obtain the defect prediction performance. Proposed approach is implemented using MATLAB simulation tool by considering NASA software defect dataset. A comparative study is presented which shows that proposed approach achieves better performance when compared with the existing techniques.
Keywords:	Software Defect Prediction, Evolutionary Computation, Subspace Clustering, High Dimensional Data, Machine Learning
Source:	Journal of Theoretical and Applied Information Technology 15^th November 2019 -- Vol. 97. No. 21 -- 2019
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Title:	A REVIEW OF CONSTRUCTIVE INTERFERENCE FLOODING IN WIRELESS SENSOR NETWORKS
Author:	HUDA A. H. ALHALABI , TAT-CHEE WAN
Abstract:	Constructive interference is a promising concurrent transmission technique for multiple senders concurrently transmitting the same packet in wireless sensor networks. It enables reliable and fast network flooding in order to decrease the scheduling overhead of MAC protocols, improve link quality of lossy links, achieve accurate time synchronization, and to realize efficient data collection. This paper discusses the concept of constructive interference, its importance, pre-conditions and open issues and challenges that should be considered by the researchers. This paper delivers the knowledge about constructive interference in WSN as a literature review, to get more knowledge about this emerging technique.
Keywords:	Constructive Interference, Wireless Sensor Networks, Flooding, Concurrent Transmissions, Synchronization
Source:	Journal of Theoretical and Applied Information Technology 15^th November 2019 -- Vol. 97. No. 21 -- 2019
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Title:	MODEL FOR ESTIMATING BUS ARRIVAL TIMES BY COMPARING VARIOUS CLASSIFICATIONS
Author:	HAEKAL MOZZIA PUTRA, MOHAMMAD FAIDZUL NASRUDIN
Abstract:	The availability of reliable and precise bus information such as bus arrival times renders public transportation more attractive. It helps passengers plan by reducing their waiting times. This paper aims to develop an estimated time of arrival (ETA) model that is based on a comparison of various classifications and groupings of real-time bus tracking data. The empirical analysis results demonstrate that the prediction accuracy differs across methods, even using the same dataset. The methodology consists of three stages: literature review and identification of existing problems; development of an ETA model; and testing and comparison of models. Data are obtained from a mobile bus tracking application, namely, BasKita, in Universiti Kebangsaan Malaysia (UKM). Data such as the route ID, bus stop ID, distance, day, time interval and log time are used as features. Groupings of data are suggested, such as daily data, data by the path and the complete data set. In this paper, linear regression (LR), artificial neural network (ANN) and sequential minimum optimization regression (SMOreg) are used to develop the model. The performances of ETA models are compared via the correlation coefficient (CC) method and in terms of the root mean square error (RMSE) and the mean absolute error (MAE). This work uses moving average (MA) technical analysis on the data to reduce the estimation error. The results that are obtained using the ANN method with daily grouping and using MA as a feature are the most accurate. The results of this study contribute to the development of an ETA model that can achieve satisfactory accuracy to increase the quality of the bus service.
Keywords:	Estimated Time of Arrival, Machine Learning, UKM
Source:	Journal of Theoretical and Applied Information Technology 15^th November 2019 -- Vol. 97. No. 21 -- 2019
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Title:	VICTIM LOCALIZATION USING DARWINIAN IMPERIALIST COMPETITIVE ALGORITHM
Author:	AHMAD AL_QEREM
Abstract:	Grid search for the victim in unknown environments using a swarm of robots tend to be not practical. Many research has been done using evolutionary algorithms for victim localization. The search problem is modelled as an optimization problem. The convergence to the optimal solution is one of the key factors in such a problem. Due to the harsh and unpredictable nature of the environment, cooperative navigation and victim identification in these environments are incredibly challenging to achieve. The obstacle avoidance, collision of robots and intra communication between them are still needed to assist the victim localization process in unpredictable situations. In this study, the Robotics Darwinian Imperialist Competitive Algorithm (RDICA) algorithm has been used for victim localization, to avoid a local optima problem, the exploration facility using do-revolution feature has been used. According to multiple experiments that conducted under different scenarios, distinctive results have been revealed. The average of crashed robots significantly reduced. Moreover, a 7.3 and 6.93% achieved improvement over the well-known state of the arts DPSO and RDPSO algorithms respectively while preserving the comparable number of iterations adequate to gain these results.
Keywords:	Imperialist Competitive Algorithm Victim Localization Swarm Robotics Do-Revolution
Source:	Journal of Theoretical and Applied Information Technology 15^th November 2019 -- Vol. 97. No. 21 -- 2019
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Title:	SYSTEMATIC REVIEW OF DATA QUALITY RESEARCH
Author:	M.IZHAM JAYA, FATIMAH SIDI, LILLY SURIANI AFFENDEY, MARZANAH A. JABAR, ISKANDAR ISHAK
Abstract:	Data quality drawn a major concern when dealing with data especially in the event that insightful outputs is needed. Research in data quality emerged in various topics and diversification in known knowledge and used approach is inevitable. In this paper, we apply systematic review study to explain the landscape of data quality and to identify available research gap by using categorization and mapping. Our search scope is limited to research articles from journals, conference proceedings and magazine published between 2010 until 2016. We defined three types of main categorization to map the selected research articles and to answer our research questions. These categorization focus on research topics, research type and contribution type. On average, fifty-four research articles related to data quality were published every year. This number shows the importance of data quality research in various research topics such as online users, database, web information, sensors and big data. This study also indicates that almost half of the selected articles proposed a novel solution or an essential extension of an existing data quality technique. Moreover, most of the selected research articles belongs to the model type in the contribution category. Our mapping also suggests that obvious contribution disparity happen between contribution in metric type and model type category.
Keywords:	Data Quality, Information Quality, Systematic Review
Source:	Journal of Theoretical and Applied Information Technology 15^th November 2019 -- Vol. 97. No. 21 -- 2019
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Title:	MALWARE VISUALIZATION TECHNIQUE: A SYSTEMATIC REVIEW
Author:	ABDALRAHMAN ALFAGI , AZIZAH ABD MANAF, AZIDA ZAINOL, ALAA ABDULSALAM ALAROOD
Abstract:	Recently, there has been a massive increase in number of malware types which poses a severe threat to smart devices and to internet security. Thus, different techniques have been applied to detect, classify and identify malware. Among those techniques, visualization becomes the most attractive and popular. Visualization techniques have been applied to view static data, monitor network traffic or managing networks to detect and visualize the behavior of the malware. Addressing malware visualization techniques are of prime importance for protecting smart devices, monitoring network traffic or securing internet and digital resources. Although there are some literature review papers on malware detection techniques, none of them are addressed in a Systematic Literature Reviews (SLR) which details a range of related work, provides a systematic and rigorous approach to illustrate the current trend of malware detection techniques. In contrast, this paper followed general guidelines for conducting SLR to illustrate the malware visualization technique and its applications, statistically showing the most common malware types and extracted features that used to identify the malware. In this paper, an advanced search has been performed in most relevant digital libraries to obtain potentially relevant articles published until the end of 2016. About 80 primary studies (PSs) have been identified based on inclusion and exclusion criteria. The analytical study is mainly based on the PSs to achieve the papers’ objectives. The results illustrate the importance of visualization techniques and which are the most common malware as well as the most useful features.
Keywords:	Malware Detection, Malware Visualization, Malware Visualization Technique, Systematic Literature Review, Malware Classification
Source:	Journal of Theoretical and Applied Information Technology 15^th November 2019 -- Vol. 97. No. 21 -- 2019
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Title:	THE ADVANCES OF INFORMATION TECHNOLOGY GOVERNANCE IN UNIVERSITIES: A SYSTEMATIC REVIEW
Author:	ALEJANDRA OÑATE-ANDINO1 , DAVID MAURICIO
Abstract:	This study analyzes the advances of information technology governance (ITG) in the university context, through a systematic review of the literature. The following research questions are addressed: What progress has been made? What implementation studies have been developed? What factors influence success? What models have been created? And lastly, what are the relevant case studies? In the period from 2006-2017, 17 studies reported advances in the five areas of IT governance, 9 studies referred to aspects related to implementation, zero studies were identified that analyzed critical success factors, 6 studies developed ITG models and 34 studies analyzed implementation initiatives. Our analysis of the relevant studies shows that the advances of ITG in the university context are still incipient.
Keywords:	Systematic Literature Review (SLR), IT Governance, Information Technology Government; Universities; Information Technologies Government Systems; Higher Education.
Source:	Journal of Theoretical and Applied Information Technology 15^th November 2019 -- Vol. 97. No. 21 -- 2019
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Title:	A REVIEW OF DIGITAL BREAST TOMOSYNTHESIS HARD-WARE TECHNIQUES
Author:	ABUAJELA ARHUMA GHUMEDH , S.MASHOHOR , W.A.W ADNAN , ROZI MAHMUD
Abstract:	Digital Mammography (DM) technique is a well-rooted mode of imaging for early breast cancer detection and diagnosis. After the introduction of digital imaging in the field of radiology, several progressive meth-odologies have been developed, specifically tomographic imaging methodologies that are capable of cap-turing intricate details. The three dimensional (3D) Digital Breast Tomosynthesis (DBT) is one of such methodologies which has witnessed extensive penetration in clinics and possesses the capabilities to replace DM for the screening of breast cancer in the future. A lot of pre-acquisition processes of DBT influence its performance clinically. Therefore, this research focuses on the comprehensive review of the DBT system hardware design, the X-ray supply geometry optimization, radiation dose for breast glandular tissue, X-ray image scatter and breast compression minimization.
Keywords:	Digital Breast Tomosynthesis, Acquisition Geometry, Image Acquisition, Radiation Dose, X-Ray Scatter.
Source:	Journal of Theoretical and Applied Information Technology 15^th November 2019 -- Vol. 97. No. 21 -- 2019
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Title:	SDCM: SECURE DYNAMIC END-TO-END CONGESTION AVOIDANCE PROTOCOL FOR MANETS
Author:	RUSHDI A. HAMAMREH
Abstract:	Ad-hoc Mobile Networks (MANETs) are vulnerable to various attacks. In addition, congestion can occur due to limitation in resources and lead to high packet loss, long delay and waste of resource utilization time. The major objective of congestion control is to best utilize the available network resources by keeping the load below the capacity. The great demand for capacity, place particular emphasis on congestion management approaches. Recently, researchers have developed many effective and well-studied algorithms for congestion control within Transmission Control Protocol (TCP) to improve its performance over MANET environment. TCP is designed to be reliable and ensure end to end delivery in wired network. However, each existing TCP variant over MANET has its weaknesses and strengths when changing MANET factors like: node mobility, traffic loads, network size and wireless channel conditions. In this paper a new approach to decrease packet loss using secure and dynamic path congestion estimation. The simulation results show an improvement in TCP performance and security over MANET in different scenarios.
Keywords:	TCP-VEGAS; TCP-WESTWOOD; TCP-WELCOME; TCP-DCM; MANET; Congestion; Link Failure; Signal Loss, RTO.
Source:	Journal of Theoretical and Applied Information Technology 15^th November 2019 -- Vol. 97. No. 21 -- 2019
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Title:	MODIFIED FAST GRAY LEVEL GROUPING APPROACH FOR ENHANCING IMAGE CONTRAST
Author:	OSAMA HOSAM , HANI M. IBRAHIM ; MAHMOUD ELMEZAIN1; SAHAR SHOMAN2 & ALY MELIGY
Abstract:	Images with low contrast may have intensity properties that make it difficult to use the traditional algorithms and approaches to enhance its contrast. Image’s histogram may contain high frequency values in specific area in the image and low frequency in the remaining area. That leads to inconsistency in the histogram and results in image with unacceptable contrast. Two algorithms are proposed to solve this problem, namely MFGLG and ACEIF/MFGLG approaches. The first approach is Modified Fast Gray Level Grouping (MFGLG). It is a modification of Fast Gray Level Grouping (FGLG). MFGLG uses two sets of gray level bins and uses them as a startup bins assigned to the histogram. The proposed approach results in more efficient contrast enhancement compared to FGLG. The algorithm has no user intervention. Moreover, the proposed algorithm can be applied to vast range of contrast perturbed images. It also can be applied to images with the highest frequency in the histogram concentrated in any image location. The second approach is Automatic Contrast Enhancement Image Fusion based on Modified Fast Gray Level Grouping (ACEIF/MFGLG). With ACEIF/MFGLG, the output of MFGLG is fused with the original image to get more detailed image. The original image is used to provide more accurate contrast and intensity. The proposed two approaches are applied on low-contrast images and provides high quality images. The algorithm can be applied without human intervention with acceptable speed compared to the other methods in literature.Keywords: Image Fusion, Histogram, Contrast Enhancement, Image Enhancement
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Source:	Journal of Theoretical and Applied Information Technology 15^th November 2019 -- Vol. 97. No. 21 -- 2019
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Title:	A METAHEURISTIC APPROACH FOR STATIC SCHEDULING BASED ON CHEMICAL REACTION OPTIMIZER
Author:	Over the past several decades, scheduling has emerged as an area of critical research, thereby constituting a requisite process for myriad applications in real life. In this regard, many researchers have experimented and utilized various optimization algorithms to obtain optimized schedules. It is also noteworthy that the concepts of some optimization algorithms are essentially derived from nature. This paper aims to augment a compiler using a chemical reaction optimizer in order to identify an optimized instructions static schedule capable of being used within both single and multicore computer systems. This scheduling algorithm, which is denoted as SS-CRO (static scheduling using chemical reaction optimizer), is unique in that it provides alternative schedules involving different costs. Subsequently, SS-CRO tests the schedules in accordance with different types of instructions dependencies before making an appropriate selection. SS-CRO demonstrates that it can not only provide different schedule orders, but also make a competent selection of accepted solutions, whilst dismissing the inappropriate ones in a reasonable span of time. So, this paper presents SS-CRO algorithm that is used to obtain an optimized static scheduling, where SS-CRO has been implemented and evaluated analytically and experimentally. As analytical results, the number of steps for the SS-CRO approximately is O(Num_iteration×CROFun), where CROFun is the number of steps of the selected function. In the experiments results, SS-CRO achieved better execution time and higher accepted solutions in comparison with other optimization algorithms such as; SS-DA (static scheduling using duelist algorithm) and SS-GA (static scheduling using genetic algorithm). Furthermore, SS-CRO achieved the maximum percentage of number of solutions with respect to the execution time of all experiments for all proposed input cases, which is ranged as (10%-30%).
Abstract:	OMAYYA MURAD, RIAD JABRI, BASEL A. MAHAFZAH
Keywords:	Chemical Reaction Optimizer, Compiler, Instruction Set, Metaheuristic Approach, Static Scheduling
Source:	Journal of Theoretical and Applied Information Technology 15^th November 2019 -- Vol. 97. No. 21 -- 2019
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