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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
December 2020 | Vol. 98 No.24

Title:	PLANNING AND INVENTORY CONTROL BASED ON IDENTFICATION SYSTEM AND PID/ LQR CONTROLLER
Author:	HICHAM SARIR
Abstract:	This article deals with development of an intelligent production and inventory control of an aerospace assembly line. The proposed architecture is based on control laws in automatic control systems field. The first step is to identify the transfer function linked input raw material flow and output flow of assembly line by using MATLAB System Identification Toolbox. The second step is to design the appropriate planning and control model. In fact two models are proposed and compared: The first model is based on PID controller and the second one is based on LQR controller. A real data of Moroccan aerospace line assembly is used to illustrate the feasibility of the proposed methodology. Simulation results confirm that the model based on LQR controller gives better performance than PID controller.
Keywords:	Transfer function, PID controller, LQR controller, MATLAB System Identification Toolbox.
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2020 -- Vol. 98. No. 24 -- 2020
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Title:	RESEARCH OF APPROACHES AND METHODS FOR ORGANIZATION OF COMPUTATIONAL PROCESSES IN THE CLOUD ENVIRONMENT
Author:	RAISSA USKENBAYEVA, ZHULDYZ KALPEYEVA, AIZHAN KASSYMOVA
Abstract:	Today, in the context of a constant increase in the number of solved scientific and applied problems and a significant increase in the load on computing systems, cluster systems, grid systems and cloud systems are widely used. Providers of network, information and computing services, relying on large consolidated data centers, began to pay special attention to improving methods of organizing the computing process, which includes planning and allocating suitable resources to meet the resource needs of users, as well as the discipline of servicing tasks with computing resources. An overview of modern approaches and methods of organizing the computational process in distributed systems is given. Based on the results of the analysis of the current situation in the studied area, the formulation of the research problem. The essence and mathematical models of the organization of the computational process are proposed.
Keywords:	Cloud Computing, Grid, Distributed Computing Systems (DCS), Iaas (Infrastructure As A Service), Task Scheduling
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2020 -- Vol. 98. No. 24 -- 2020
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Title:	ELECTRONIC LOYALTY IN THE RELATIONSHIP BETWEEN CONSUMER HABITS, GROUPON WEBSITE REPUTATION, AND ONLINE TRUST: A CASE OF THE GROUPON TRANSACTION
Author:	BUI THANH KHOA
Abstract:	Online group buying (Groupon) has become a business phenomenon; it is very popular in recent years. Like other online business forms, customer loyalty, which is one of the most important weapons of businesses, is also a challenge for online Groupon businesses to achieve in the competitive market. This study examined the factors that affect customer loyalty in online group buying based on the Theory of Reasonable Action. The mixed research method was done to achieve the research objectives, of which a survey through self-governing questionnaires with 633 participants. Research results pointed out that consumer habits and Groupon website reputation positively impact customers’ online trust. Ultimately, electronic loyalty was a consequence of the online trust in the Groupon site. Some managerial implications were proposed for businesses in the group buying model to build electronic customer loyalty.
Keywords:	Groupon, Consumer Habits, Groupon Website Reputation, Online Trust, Electronic Loyalty.
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2020 -- Vol. 98. No. 24 -- 2020
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Title:	A SIMPLE AND PREDICTIVE MODEL FOR COVID-19 EVOLUTION IN LARGE SCALE INFECTED COUNTRIES
Author:	YASIR HAMID, QAISER FAROOQ DAR, JAMAL N. AL-KARAKI, IME ROBSON NSEOBOT, ANIETIE IMO EFFIONG, VINESH DINNOO, AKPAN UDEMEOBONG EDET
Abstract:	This paper analyzes the reported COVID-19 cases in some largely affected countries around the world and accurately predicts the future values of new, death, recovery, and active COVID-19 cases for effective decision making. The objective is to provide scientific insights for decision makers in these countries to avoid higher levels of severity and large waves of infections. The data for this study were obtained from COVID-19 stylized facts, extracted from the well-known worlddometer website and verified against the WHO’s COVID-19 Dashboard, Johns Hopkins University’s COVID-19 Dashboard, and CDC from mid of February 2020 – Early April 2020. The data covered the highest five affected countries, namely, Brazil, India, Russia, South Africa, and the USA. The data were analyzed using time series forecasting model and presented pictorially in graphs bar charts and pie charts. Based on the outcome of the analyzed data, it was concluded that the predicted COVID-19 cases will reach the peak at the end of September 2020 and if the outbreak is not controlled, the studied countries may face inflated numbers and severe shortage of medical facilities that may worsen the outbreak. The paper concludes by few important recommendations about comprehensive and necessary actions that the government and other policymakers of these countries should take in order to control spread of the virus.
Keywords:	Covid-19, Machine Learning, Data Science, Forecasting, Computational Intelligence
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2020 -- Vol. 98. No. 24 -- 2020
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Title:	A QUALITATIVE STUDY ON SECURITY OPERATIONS CENTERS IN SAUDI ARABIA: CHALLENGES AND RESEARCH DIRECTIONS
Author:	SOLTAN ABED ALHARBI
Abstract:	The worldwide digital transformation of organizations in all sectors makes them depend increasingly on technology services which indirectly increases the risk of threats and cyber-attacks. Hence, organizations utilize Security Operation Centers (SOCs) to monitor their digital infrastructure for potential cyber incidents. SOC receives and collects information and consequently makes decisions and issues orders or commands. The increment utilization of SOC as a part of cyber security strategy has led to several studies in improving SOC operations. However, few studies have focused on challenges faced by the management and technical staffs working in SOCs. This paper aims to identify these challenges by conducting a qualitative study on SOCs in organizations from different industry sectors in Saudi Arabia. Analyzing the interview data determines the technical and non-technical issues that exist in SOC. The main challenges of SOCs are high false positive rate, low quality of threat intelligence, slow response speed, low visibility on devices and network, and insufficient automation level. Moreover, there are disagreements between managers and SOCs’ employee which could affect SOC efficiency and effectiveness if not addressed. The future research directions are presented highlighting the real-world needs of SOCs.
Keywords:	Security Operations Center, Qualitive Study, Data Security, Cyber-attacks, and Security Challenges
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2020 -- Vol. 98. No. 24 -- 2020
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Title:	IMPLEMENTATION STRATEGIES FOR ERP ADAPTATION IN MALAYSIAN SME AGRICULTURAL SECTOR
Author:	SYAIMAK ABDUL SHUKOR, AIDA SHEIKHI
Abstract:	Small and Medium-sized Enterprises (SMEs) are swiftly emerging. For SMEs to be competitive and survive in this challenging time, it is innovative and creative to contract out simple tasks and focus on complex activities. SMEs in Malaysia play a dynamic and active role in inspiring entrepreneurship, nurturing economic activities, and creating job opportunities. The government of Malaysia introduced the Small and Medium Industries Development Corporation (SMIDEC) to help SMEs develop and enhance their services by providing training and support. Enterprise Resource Planning (ERP) is well-known business process management software which allows businesses to integrate various automated mode processes related to technology, financial, services, and human resources. In Malaysia, ERP adaptation in SMEs shows quite a slow-moving pace, as there are issues to be addressed, and usually, ERP is required to be customized to fit with the SMEs' operation. Unfortunately for Agricultural SMEs in Malaysia, this sector seems to be unheeded by business IT firms since various specifications were not well-matched with the standard operating procedure (SOP), making the deliberation of the ERP system even perilous. This research aimed to identify the contributing factors to ERP adaptation failure in SMEs of the Malaysia Agriculture sector using mixed models. Firstly, a list of questions was sent to Agro companies' employees to get feedback on their existing ERP systems. Commonly, these questions asked in the initial stage of system evaluation for ERPs. Results were gathered and translated to rating tables and comments. The ratings and suggestions were then established and presented to three experts who are top-level managers of three different top agro companies in Malaysia, which practice various types of ERP systems. Based on the discussion with the experts, six implementation strategies were proposed and concluded for the purpose to reduce the risk in the ERP adaptation for the Malaysian SME Agriculture Industry. These implementation strategies will assist the SMEs in the Agriculture Industry in ensuring the ERP system adaptation process's efficiency and effectiveness.
Keywords:	ERP, ERP Adaptation, Failure ERP Adaptation Factors, Agricultural SME, Malaysia SME
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2020 -- Vol. 98. No. 24 -- 2020
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Title:	A QUICK AND ACCURATE TOMATO LEAF DISEASE DISCOVERY AT EARLIER STAGE OF HARVESTING BY UTILIZING THRESHOLD SEGMENTATION AND RFO CLASSIFIER
Author:	Mr. G. BALRAM, Dr. K.KIRAN KUMAR
Abstract:	In Agriculture, the prior period of harvesting feature gives splendid effectiveness. It lessens the collect disease rate with the ultimate objective that the economy has been balanced. The import and toll of agro things are extends because of the previous period of yield gathering technique. The various conventional methods can deal with a past development of harvesting, anyway improvement is required. Hence random forest optimization and Th segmentation schemes have used to find the diseases in the harvests, moreover predicts the sort of plant-infirmity. In this investigation, the continuous prior period of tomato leaf illness and its contrasting compost is proposed. For this tomato crop leaf pictures has been assembled from various data bases like reap science, yes-modes, nelson. wisc datasets. Dependent upon singular thought, plants give the Rise in progress, yet by using the manual methods for cultivating, more creation rate is past the domain of creative mind. So IoT with front line Machine Learning methodologies and huge making sure about pre-processing techniques can uphold the sound cultivation. Until various procedures are delivered for agribusiness at a previous period of social occasion, anyway they have more imperatives. Current development doesn't work with past procedures; thusly, improvement is required for future sound agribusiness systems. In this investigation, threshold-based segmentation is used for pre-processing, and Random Forest optimization for classification of tomato leaf disease detection at prior stage. Proposed threshold segmentation RFO (TS-RFO) gives the 97.6% detection accuracy and 99% True certain rate 59.82PSNR, 0.9989SSIM, 0.0081MSE has been gotten; this is a splendid achievement stood out from existed systems.
Keywords:	Crop Harvesting, Detection Of Plant Disease RFO, Classification, Threshold Segmentation.
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2020 -- Vol. 98. No. 24 -- 2020
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Title:	MULTI-OBJECTIVES ANT COLONY SYSTEM FOR SOLVING MULTI-OBJECTIVES CAPACITATED VEHICLE ROUTING PROBLEM
Author:	MODHI LAFTA MUTAR, M.A. BURHANUDDIN, ASAAD SHAKIR HAMEED, NORZIHANI YUSOF, MOHAMMED F. ALRIFAIE, ALI A. MOHAMMED
Abstract:	As a combinatorial optimization problem, the capacitated vehicle routing problem (CVRP) is a vital one in the domains of distribution, ransportation and logistics. Despite the fact that many researchers have solved the problem using a single objective, only little attention has been given to multi-objective optimization. As compared to multi-objectives, the comparison of solutions is easier with single-objective optimization fitness function. In this paper, the following objectives were achieved: (i) in view of the domain of the multi-objective CVRP, the total distance traveled by the vehicles and the total number of vehicles used are reduced, and (ii) in the view of the technique, a multi-objective Ant Colony System is proposed to solve the multi-objective of CVRP based on the experience of sub-paths. The proposed algorithm was evaluated using some standard benchmark problems of CVRP. The results show that the algorithm which has been proposed in this study is highly competitive and quite effective for multi-objective optimization of CVRP.
Keywords:	Combinatorial Optimization Problem, Ant Colony System Algorithm, Capacitated Vehicle Routing Problem, Multi-Objectives Capacitated Vehicle Routing Problem)
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2020 -- Vol. 98. No. 24 -- 2020
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Title:	EEG BASED BCI FOR ALS USING COMPLEX WAVELETS AND MULTI-LAYERED NEURAL NETWORKS
Author:	TEJASWINI. C, SREERAMA REDDY G. M, CYRIL PRASANNA RAJ. P
Abstract:	Amyotrophic Lateral Sclerosis (ALS) is a neuro regenerative disorder causing motor neuron loss, muscle paralysis and respiratory failure progressively. Use of Brain Computer Interface (BCI) technology to record the brain functions in ALS patients and generate responses accurately to either actuate gadgets or information to care takers is a challenging task that needs to be accurate and reliable. Over the last few years several methods or techniques have been developed considering signal processing methods for EEG signal analysis and neural networks for classification of EEG features. Wavelet based methods for EEG signal analysis has demonstrated limitations as they are shift variant. In analysis of ALS EEG signals there is a need for shift invariant signal processing algorithms. In this paper, Dual Tree Complex Wavelet Transform (DTCWT) is used for extraction of features from the complex sub bands considering Power Spectral Density (PSD), Cross Power Spectral Density Estimation (CPSD) and Shannon Entropy. These features are classified into normal and abnormal EEG data. The multi-level DTCWT algorithm and multi-layered neural network structure presented in this work is modelled in MATLAB and is evaluated for its performances for feature extraction and classification. Results of classifier considering energy features from all five EEG bands have demonstrated 100% classification accuracy as compared with other methods. The classified EEG data from ALS patients can be further considered for interpretation of information and is used for all BCI applications.
Keywords:	Brain Computer Interface, Amytrophic Lateral Sclerosis, Dual Tree Complex Wavelet Transform, Discrete Wavelet Transform, Neural network.
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2020 -- Vol. 98. No. 24 -- 2020
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Title:	DEVELOPMENT OF FUZZY DECISION SUPPORT SYSTEM FOR ACCIDENT PREVENTION BASED ON WORKER CONDITIONS AND PROJECT ENVIRONMENTS
Author:	LILA AYU RATNA WINANDA, ACHMAD ARIFIN, TRIJOKO WAHYU ADI, FAUZAN ARROFIQI, NADJADJI ANWAR
Abstract:	Complexity in construction implementation has been proven to cause fatal accidents as a result of human errors due to fatigue. Construction accidents could happen at any time and the occurrence is difficult to predict in dynamic project characteristics. There are relationships between internal (workers) and external factors (construction environments) as leading factors in the accident since the project conditions also cause fatigue. Therefore, this paper presents an integrated system through real-time monitoring, with appropriate tools to decide workers safety conditions. The purpose of the developed system is to prevent construction accidents by monitoring workers’ conditions during working hours. Fuzzy-based decision-making was proposed as a real-time monitoring system based on fatigue prediction, hazard level, environment effect, and safety analysis, according to internal conditions (heart rate, body temperature, and muscle activity) and external factors (hazard zone, safety protection, working space temperature, noise level, and illumination), then added with historical safety data. Fuzzy approach was used to develop the system according to the knowledge-based from experience, database, and expertise, since high accuracy of this method in analyzing qualitative issues such as safety assessment. Software development for fuzzy decision support system application has been realized to solve the problem in the form of safety decision-making. The performance evaluation showed that the overall fuzzy system was running well. Experimental tests on the system delivered the input data and proceed to expected output, as workers’ safety decisions. The results show that the varied output of workers’ safety conditions could be represented in the data model with different physiological conditions. Worker performance is assessed in preparation work to observe the readiness of workers, followed by real-time monitoring on working hours, where the data is updated hourly or minute. Therefore, fuzzy decision support system is appropriate in accident prevention and part of efforts in achieving construction safety. The integration with sensor networks is needed to realize the final goal in modern safety management in construction.
Keywords:	Construction Safety Monitoring, Decision Support System, Fuzzy Decision Making.
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2020 -- Vol. 98. No. 24 -- 2020
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Title:	EVOLVING SPIKING NEURAL NETWORK: A COMPREHENSIVE SURVEY OF ITS VARIANTS AND THEIR RESULTS
Author:	TASBIHA IBAD, SAID JADID ABDUL KADIR, NORSHAKIRAH BINTI AB AZIZ
Abstract:	This study presents the deep insight and comprehensive analysis of evolving spiking Neural network (eSNN) development in recent years (last eight years). eSNN has been used to a vast number of optimization problems. It has several advantages: computationally inexpensive, knowledge-based, on-line learning method, and we have analyzed the improvements of eSNN in different application zones. This review paper discusses eSNN optimization done by researchers using distinct optimization techniques to achieve the possible best accuracy. In this inclusive study, few publications using eSNN have been gathered and summarized. First, we introduce eSNN. Then, we characterized the current versions of eSNN into 5 variants mainly Hybridization, Modifications, Multi-objective, Dynamic, and Integration. Afterwards, the results of the studied eSNN models being evaluated. The review paper is summed up by giving a conclusion of the optimized eSNN model's fundamentals and providing thinkable future directions that can be explored in the current works on the Hyper-parameter optimization of eSNN.
Keywords:	eSNN, Variants, Hyper-Parameters, Optimization, Comprehensive Analysis
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2020 -- Vol. 98. No. 24 -- 2020
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Title:	UNDERSTANDING STUDENTS PERCEPTIONS AND MOTIVATIONS TOWARDS THE LEARNING OF PROGRAMMING IN MALAYSIAN HIGH SCHOOLS
Author:	RODZIAH LATIH, AHGILAN PEREMOL, NORLEYZA JAILANI
Abstract:	Computer programming as a taught subject was added to the Malaysian National School Curriculum in stages starting from 2017. This is seen as a positive step in light of the nation's future challenges, especially with the advent of the Fourth Industrial Revolution. However, a wealth of research has indicated that programming's teaching and learning are wrought with challenges and pedagogical issues. It is thus imperative that studies are done to investigate issues regarding the acceptance and motivation in learning programming among local school children. With this objective in mind, a study was done in several schools in Selangor, Malaysia. A total of 166 form-four students were surveyed to uncover their perceptions of the materials and approaches used in the teaching of programming and their motivations in learning programming. According to the results, the materials and teaching approach were important factors to motivate students to like the subject and make them perform better.
Keywords:	Coding, Computational Thinking, Algorithmic Thinking, Computer Science Curriculum, Turtle Graphic
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2020 -- Vol. 98. No. 24 -- 2020
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Title:	HARDWARE ACCELERATOR FOR COMPUTING DTCWT SUB BANDS USING SPLIT LIFTING ALGORITHM
Author:	SUNITHA . P.H , SREERAMA REDDY G. M, CYRIL PRASANNA RAJ. P
Abstract:	DTCWT based image processing is gaining popularity for its advantages such as shift invariance and additional directional features compared with DWT. Computation complexity of DTCWT have limited its use for real time image processing applications. Hardware accelerators for image processing algorithms implemented on FPGA platform have demonstrated improvement in computation speed. In this work, hardware accelerators for computing DTCWT based on split lifting scheme algorithm is designed and implemented on FPGA. The number of arithmetic operations and complexity in performing multiplication is reduced by multiplierless operations and reuse logic. The proposed design is implemented on FPGA and is demonstrated to operate at maximum frequency of 333 MHz and power dissipation is limited to 56 W. The DTCWT computation is reconfigurable to perform both forward and inverse transforms.
Keywords:	Hardware Accelerator, DTCWT, Lifting Scheme, Multiplierless Logic, Reuse Logic
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2020 -- Vol. 98. No. 24 -- 2020
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Title:	USING SAAS TO ENHANCE PRODUCTIVITY FOR SOFTWARE DEVELOPERS: A SYSTEMATIC LITERATURE REVIEW
Author:	AZRYNA AZLEN MOHD NORDIN, RODZIAH LATIH, NOORAZEAN MOHD ALI
Abstract:	Cloud computing has gain popularity as it provides on-demand software as a service (SaaS) over the internet. Nevertheless, the environment is intensely competitive and posed challenges for software developers in the development process. SaaS development is a very complex process, and the success depends highly on its productivity. The objectives of this review are twofold; to identify the key factors that influenced software developers' productivity and how cloud computing, especially SaaS, can increase productivity. This paper identifies essential factors from the literature and provides software developers' methods to support productivity. We perform a systematic literature review with total of 746 papers/documents and discover 61 papers that fit the inclusion and exclusion criteria and at the same time identify four major productivity factors i.e. cost, time, resources, and quality of the product or services.
Keywords:	Cloud Computing; SaaS; Influencing Factor; Software Developer; Productivity
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2020 -- Vol. 98. No. 24 -- 2020
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Title:	COMPUTATIONAL ANALYSIS OF MICROELECTRODE RECORDING OF SUB THALAMIC NUCLEUS NEURAL SIGNALS WITH DEEP BRAIN STIMULATION IN PARKINSON`S DISEASE USING MULTIVARIATE TECHNIQUES: MACHINE LEARNING APPROACH (A PRELIMINARY RESEARCH REPORT)
Author:	D ANJI REDDY, G NARASIMHA, V RAMA RAJU
Abstract:	Parkinsons disease (PD) is one of the most commonest neurodegenerative chronic movement disorders, is caused by damage to the central nervous system (CNS). The manifestations or symptoms analogous to cardinal motoric features of PD have been mentioned as `Kampavata` in ancient Sanskrit Vedic Hindi documents. Parkinson`s disease was termed “Shaking Palsy” by the Galen, a famed Roman physician. Irrespective of all the studies on PD, the formation mechanism of its symptoms remained unknown. It is still not obvious why damage only to the substantia nigra pars compacta, a small part of the brain, causes a wide range of symptoms. Moreover, the causes of brain damages remain to be fully elucidated. Exact understanding of the brain function seems to be impossible. Equally, various engineering and technological software tools are challenging to understand the behavior and performance of complex convoluted systems. Computational models are the most significant tools in this connection. Developing computational models and analysis for the PD has begun in recent decades which are effective not just in understanding the disease but contributing new therapies, and its prediction and control, and also in its early diagnosis. Modeling studies include two main groups: black-box models and gray-box models. Generally, in the black-box modeling, regardless of the system information, the symptom is only considered as the output. Such models, besides the computational analysis studies, increase our knowledge of the disorders behavior and the disease symptoms. The gray-box models consider the involved structures in the symptoms appearance as well as the final disease symptoms. These models can effectively save time and be cost-effective for the researchers and help them select appropriate treatment mechanisms among all possible options. In this study (survey/review paper), primary efforts are made to investigate some studies on Parkinson`s disease and computational analysis. Then, computational analysis of microelectrode recordings (MER) of subthalamic nucleus (STN) neural signal acquisition of Parkinson`s deep brain stimulation (DBS), i.e., MER with STN-DBS with a machine learning (ML) approach using clustering and principal component based targeting method followed by novel algorithms will be evaluated. Finally, the results of using such methods are presented significantly as a preliminary report. With the advent of high-speed powerful computing machines and artificial intelligence based machine learning techniques, the researchers are fully utilized these analyses for predicting and detecting early symptoms and signs of PD and for extracting its feature manifestations (tremor, Bradykinesia, postural, and postural instability).
Keywords:	Behavior Simulation, Brain Disorders, Cluster Analysis, Data Reduction, Decomposition (PCA), Deep Brain Stimulator (DBS), Feature Extraction/Selection, KL-Transform, Machine Learning (ML), Mathematical Analysis, Microrecording (MER), Parkinson`S Disease (PD), Principal Components Analysis (PCA), Subthalamic-Nucleus (STN), System Identification,
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2020 -- Vol. 98. No. 24 -- 2020
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Title:	CHARACTER PROPERTY METHOD FOR ARABIC TEXT STEGANOGRAPHY WITH BIOMETRIC MULTIFACTOR AUTHENTICATION USING LIVENESS DETECTION
Author:	NUUR ALIFAH ROSLAN, NUR IZURA UDZIR, RAMLAN MAHMOD, ZURIATI AHMAD ZUKARNAIN, MOHD IZUAN HAFEZ NINGGAL, REEMA THABIT
Abstract:	Arabic text steganography (ATS) offers a potential opportunity in hiding secret information in characters and features. The combination with any other security sub discipline such as cryptography usually will enhance its level of security. However, it is limited in its ability to optimize embedded data capacity with a high perceptual transparency level that will also not raise suspicion when written. Besides that, other concerns are active attacks by intruders which are a crucial security issue in the transmission of the shared secret key that enables the receiver to extract the secret information. Also, such attacks can be affected through a fake identity that allows the receiver to modify the secret information thus degrading its integrity. To overcome these drawbacks, we propose a hybrid ATS with biometric multi factor authentication (BMA), which uses liveness detection using fingerprints and heartbeat sensors as the authentication factors. We propose a new ATS method, the Character Property method (CPM) which uses the basic properties of the Arabic Text such as dots, calligraphy typographical proportions, and sharp-edges to hide the secret message using a table index mapping technique to optimize data capacity with high perceptual transparency to avert suspicion. The results for the biometric authentication showed that the proposed method correctly authenticates users, having a false rejection rate of only 4%, and a 0% false acceptance rate. As for liveness detection, the results were significant where the proposed method correctly detected live subjects compared to a fingerprint only biometric authentication approach, which had a high acceptance of fake inputs. BMA was implemented through a custom Arduino smartwatch with a fingerprint and heartbeat sensor as a ‘proof-of-concept’ device which increased the capacity in hiding the secret message up to 23.5% compared to the previous methods. Given our Arabic Character Properties method (CPM) did not affect the stego-text appearance, its 1.0 Jaro Similarity score was compared to the other methods proving high transparency of the stego-text, in addition to higher security regarding user authentication using BMA with liveness detection.
Keywords:	Arabic Text Steganography, Biometric Multifactor Authentication, Information Hiding
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2020 -- Vol. 98. No. 24 -- 2020
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Title:	SATELLITE-BASED MONITORING OF TERRITORY USING VEGETATION INDICES AND THEIR CORRELATION WITH GROUND DATA
Author:	SAGYNBAY KALDYBAYEV, NURLAN BEKMUKHAMEDOV, KAZBEK SMAILOV, KENZHE ЕRZHANOVA, NIET ABDIRAHYMOV
Abstract:	This article estimates pastures of Kazakhstan in terms of degradation processes of semidesert and dry steppe zones in various seasons using GIS technologies. Ground surveys and space data of medium and low resolution have been interrelated. Variations of vegetation index (NDVI) have been analyzed at all considered test sites. Space monitoring of semidesert and dry steppe zones of Kazakhstan by means of vegetation indices and their correlation with ground data make it possible to evaluate current status of pasture degradation, to detect sources of highly degraded pastures, and to perform their monitoring. An interval of vegetation period selected for the analysis allows to correlate maximum and minimum NDVI with maximum and minimum values of green biomass, that is, with pasture degradation.
Keywords:	GIS technology, Earth remote sensing (ERS), Vegetation index, Satellite estimate, Monitoring, Pasture degradation.
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2020 -- Vol. 98. No. 24 -- 2020
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Title:	ON THE CONSTRUCTIVE KNOWLEDGE-BASED EVENT INTELLIGENT IDENTIFICATION MECHANISM
Author:	ASHRAF ALDABBAS, ZOLTAN GAL
Abstract:	The tenor of Complex Event Processing (CEP) is overly exploited to notice endurable information from the latent knowledge flow. Within this research, we provide associate degree creative methodology of CEP so as to achieve a fully developed level of process patterns or events counting on the constructive feature computing. We presented constructive event detection technique to find situations when the event's statute has transferred over a special event to a complex event by generating an emphasis centerpiece to components of complex interconnection by utilizing stacked bidirectional Long Short-Term Memory (LSTM) networks. The Constructive Knowledge-based Event (CKE) detection method tested on the data set provides over 90% of the special events hit rate of the Saturn/Cassini-Huygens interplanetary project. This approach empowers analyses of vast volumes of data within a small-time interval.
Keywords:	Artificial Intelligence, Complex Event, Data Analytics, Constructive Knowledge-Based Event
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2020 -- Vol. 98. No. 24 -- 2020
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Title:	LOW POWER DESIGN OF ADAPTIVE TURBO DECODER WITH FEEDFORWARD MESSAGE DECODING AND LOOK-UP-TABLE APPROACH
Author:	GIRISH N, VEENA M B
Abstract:	Underwater acoustic communication networks (UWAN) have recently attracted much attention in the research community. Error control coding one of the primary building blocks in underwater MODEMs plays a vital role in achieving better Bit Error Rate (BER) performances. In this paper, Adaptive Turbo Decoder (ATD) algorithm is designed by considering Adaptive Viterbi Decoder (AVD) customized for underwater acoustic MODEM applications. The ATD module is designed with four parallel processing architectures to perform message decoding, multipath computing, branch metric unit (BMU), and Add-Compare-Select (ACS) operations. The designed ATD is modelled in MATLAB and evaluated for its BER performance considering Rayleigh and Rician channels demonstrating improvement in BER over 10-4. The ATD module is further coded in Verilog HDL and verified for its functionality considering different test-vectors and implemented on Virtex FPGA optimizing Area, Power, and Timing requirements.
Keywords:	Turbo Decoder, Underwater Communication, Viterbi Decoder, Adaptive Algorithm, FPGA Implementation
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2020 -- Vol. 98. No. 24 -- 2020
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Title:	AUTOMOBILE ACCIDENT ALERT SYSTEM USING INTERNET OF THINGS AND GLOBAL SYSTEM FOR MOBILE COMMUNICATION
Author:	CELESTINE I. UGWU, FRANCIS S. BAKPO, GEORGE E. OKEREKE, MODESTA E. EZEMA, S. ECHEZONA, M. C. OKORONKWO, Collins N. Udanor, UCHENNA K. OME
Abstract:	In a country like Nigeria that is highly populated and a place where the transport system is predominantly road-driven, accident cannot be completely avoided and as such, prompt rescue measures are of paramount to reduce the number of deaths during accident. Immediate attention to accident victims will go a very long way to ensure their survival. Heavy dependence on the road for transportation, coupled with other factors arising from environment, human beings and machines are responsible for most road accidents, which often result to huge loss of lives and properties. Hence, our paper proposes an automobile accident alert system that uses impact sensors which collaborates with proximity sensors to automatically detect an accident. Such accident is reported to the concerned agency with the help of Internet of things (IoTs) and Global System for Mobile communication (GSM). With IoTs, various objects across the globe can be connected to the internet for effective communication. Through GSM, automobile crash message containing the latitude and longitude location information obtained from Global Position System (GPS) can be sent to emergency services. RF module was also incorporated to the proposed system to report accident to other close vehicles for safety measures to be taken. The proposed system was simulated and implemented. It is capable of timely detection and reporting of accident to rescue agency. Both simulation and experimental methodologies were used in our proposed work. The proposed system will be beneficiary to individuals, governments and any organizations that make use of vehicles.
Keywords:	Automobile, Accident alert system, Sensors, IoTs/GSM and GPS.
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2020 -- Vol. 98. No. 24 -- 2020
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Title:	CAPACITATED MULTI DEPOT MULTI VEHICLE ROUTING PROBLEM USING GENETIC ALGORITHM (CASE STUDY: WATERING THE MEDAN CITY PARK)
Author:	DIAN RACHMAWATI, POLTAK SIHOMBING, VELLYNA ANGELICHA SITORUS
Abstract:	Watering a city park is one of the steps taken to treat city parks in the city of Medan so that plants remain alive and fresh so they can reduce air pollution. The water capacity needed to water the plants in each location is adjusted to the area of the site to be watered. Therefore, the city park watering vehicle must be able to adjust the route to be passed with the capacity of water that can be carried. To determine the route that must be traversed in quite complex problems like this, Genetic Algorithms can be used to obtain an approach solution to the optimization problem in this case. The genetic algorithm will generate chromosomes that represent the route to be followed, then the chromosomes will go through the process of evaluation, selection, crossover, and mutation so that new chromosomes are produced by many generations. After several trials in the case of the Capacitated Vehicle Routing Problem using the Genetic Algorithm for determining the park watering route in Medan, the route was found to be the closest to optimal for depot A in the 173rd generation with a fitness value of 0.00292227 and the route for depot B at 148th generation with a fitness value of 0.00261028. From several trials, it can be concluded that the chance of finding the best route is influenced by the size of the population and the maximum number of generations used. The greater the population size and the maximum number of generations used, the more optimal the best route found.
Keywords:	Capacitated Vehicle Routing Problem, Heuristic, Genetic, Route.
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2020 -- Vol. 98. No. 24 -- 2020
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Title:	DATA-DRIVEN NEURAL NETWORK MODEL FOR EARLY SELF-DIAGNOSIS OF DENGUE SYMPTOMS
Author:	NOR AZURA HUSIN, NURUL IMAN SAIFUL BAHARI, HAZLINA HAMDAN, NOOR AFIZA MOHD ARIFFIN, TEH NORANIS MOHD ARIS
Abstract:	Dengue fever is one of the main public health concerns and endemic diseases in many countries, especially in tropical and subtropical regions. In severe cases, people infected with dengue may experience severe bleeding which may lead to death if the infection is not properly treated. It is a standard procedure when a person is referred to the hospital with a high fever for more than two days to be required to undergo a dengue fever screening at the triage before further clinical tests are done to confirm the patient’s medical condition. Thus, an individual need to conduct an early self-diagnosis to identify the probability that he/she have been infected with dengue fever and further seek professional help from medical practitioners. There are many dengue fever symptoms outlined by the World Health Organization (WHO) such as sudden high fever, headache, abdominal pain, persistent vomiting, rapid breathing, bleeding gums, fatigue, restlessness, and vomiting blood, but the identification of the highly significant symptoms among the less significant symptoms are still scarce. Identification of significant symptoms from the dengue dataset may help patients and medical practitioners to acknowledge the alarming symptoms and endeavour for immediate action to prevent dengue outbreak and fatality. Hence, the identification of significant dengue symptoms may assist in system development to determine the weightage of each attribute in the system. This may result in better prediction of dengue. Therefore, the objective of this study is to develop an early self-diagnosis system using an artificial neural network with the ability to produce a reliable result based on the identification of significant symptoms. The model accuracy of 100% indicates the high reliability of the developed early self-diagnosis system. A mobile application was developed based on the prediction model for patients and medical practitioners as the target users. This study contributes to the field of public health by providing early detection for people who are at risk of being infected by dengue disease. The advancement in early detection technology brings such a huge positive impact in healthcare. Early identification of significant symptoms ensures that better focus can be given to the identified symptoms for a more reliable dengue fever assessment.
Keywords:	Dengue Fever, Diagnose, Early Self-Diagnosis, Machine Learning, Significant Symptoms.
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2020 -- Vol. 98. No. 24 -- 2020
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