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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 2022 | Vol. 100 No.24

Title:	ANALYSIS OF THE EFFECT OF ORGANIZATIONAL CULTURE, SECURITY COUNTERMEASURES, AND NATIONAL CULTURE ON USER SECURITY BEHAVIOUR IN PT NUSA NETWORK PRAKARSA
Author:	JAROT S. SUROSO, HAFIZH FIISABILILLAH, MUHAMMAD FADHIL A.S.
Abstract:	Internal threats have been a hot topic in information security for several years, according to a 2018 Insider Threat Reports survey, 51% of users are more concerned about internal carelessness and negligence than 47% of external attacks. Currently, the PT Nusa Network Prakarsa organization which is engaged in IT solutions found that there were several malicious anomalies based on daily, weekly, and monthly firewall reports. This study aims to examine the influence of Organizational Culture, Security Countermeasures, and National Culture on User Security Behavior at PT Nusa Network Prakarsa. The sample of this research is employees who work at PT Nusa Network Prakarsa. The sample was carried out using the Likert Scale method, data collection was carried out by questionnaires distributed directly to employees as many as 160 respondents. Statistical method using Linear Regression Analysis, with statistical test hypothesis testing
Keywords:	Internal threat, Organisational Culture, Security Countermeasures, National Culture, dan User Security Behaviour
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	INTEROPERABILITY IN SMART EDUCATION: A SYSTEMIC REVIEW BASED ON BIBLIOMETRIC AND CONTENT ANALYSIS METHODS
Author:	AMINE DEHBI1, RACHID DEHBI, ABDELLAH BAKHOUYI, MOHAMED TALEA
Abstract:	The current educational system radically changed during the last years and has become the main theme of education development in the era where technology is changing education. The introduction of these technologies in education has been associated with a rise in the evolution of people's quality of life by improving teaching and learning. In the age of information technologies, developing with modern technology means growing and taking advantage of these new technologies: Mobile technology, Cloud Computing, Big Data, Internet of Things (IoT), Smart campus, Artificial Intelligence, Virtual reality (VR), Blockchain, Social Education, are some of the recent technologies which have been introduced recently in the solutions and infrastructures of the e-learning platforms. Based on a Bibliometric study, this article gives an overview of the research and application of Interoperability in Smart Education, from a bibliometric and content analysis on the use of interoperability in Smart Education over the years.
Keywords:	Smart Education, Interoperability, Bibliometric, E-Learning, Virtual reality (VR), Internet of Things (IoT)
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	THE IMPACT OF QUALITY MOBILE E-GOVERNEMENT SERVICIS ON SERVICE USAGE : THE MEDIATING ROLE CITIZEN’S SATISFACTION
Author:	KHALED MOHAMMAD ALOMARI, HISHAM O.MBAIDIN ,RADWAN SALEH AL JBOUR, SATTAM RAKAN ALLAHAWIAH
Abstract:	In order to determine how pleased residents are with M-government services and how frequently they use them in the United Arab Emirates, this study will look at many aspects of service quality. 520 people who were both UAE citizens and residents contributed the data. The study used the statistical package for social sciences (SPSS 26) and appropriate statistical procedures to evaluate the hypotheses. This study revealed that the relationship is partially mediated by citizens' satisfaction, and the results of the analysis can be used to motivate UAE society to increase the availability of mobile government services and improve the ones that are already provided in light of a better understanding of its citizens' needs. The acceptance of these mobile services would increase as a result. The study's conclusions allow for the formulation of a number of recommendations. Future studies are required, for example, to examine how to enhance government services for the benefit of users, developers, and decision-makers.
Keywords:	Citizen Satisfaction, Service Use, And Quality Of Service In Mobile Government, E-Government.
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	IMPLEMENTATION OF VARIOUS SOFTWARE TESTING TECHNIQUES ON MERIT BASED MANAGEMENT SYSTEM FOR BEHAVIORAL AUTISM SPECTRUM DISORDER (MBMSB-ASD)
Author:	ABD RASID MAMAT, ROHANA ISMAIL, MOHAMAD AFENDEE MOHAMED, MOHD FADZIL ABD KADIR, ANIS NURSYAFIQAH AB WAHAB, BASHIR MUZAKKARI
Abstract:	Software testing is an important process in software development. If proper testing is not done on the developed software, this can cause major problems for the software. Among the purposes of system testing is to test compliance with software requirements, detect bugs, errors and so on. In this paper various testing techniques are discussed and then implemented into a web-based software that has been developed known as Merit Based Management System for Behavioral of Autism Spectrum Disorder (MBMSB-ASD). The advantages and disadvantages of each technique are discussed while testing the software above. As a conclusion, which technique or combination of techniques is more appropriate to use depends on attributes such as cost, time and people allocated.
Keywords:	Testing, Error, Bug, Requirements, Software
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	WHAT MODELING APPROACHES USED FOR A SUSTAINABLE RESILIENT SUPPLY CHAIN
Author:	DOUNIA SAIDI, KHALIL JHARNI, JAMILA EL ALAMI, MUSTAPHA HLYAL
Abstract:	Covid-19 crisis has emphasized weaknesses of supply chains and pushed them to reinvent themselves and rethink the configurations adopted. The consideration of resilience in sustainable supply chain has lately played a vital role, to cope with disruptions for the business continuity. The main challenge of supply chains is to balance between achieving competitive advantage and acting sustainably. Through this paper, we review research contributions related to sustainability and resilience of supply chain. The aim to provide a holistic overview about the modeling approaches based on mathematical programming, used in the field of Sustainable Resilient Supply Chain and its applications. A primary search is set and a total of 66 papers has been analyzed to focus only the ones that include mixed programming models. Thus, 19 papers are selected, screened and studied meticulously, then categorized by modeling approach, sustainability and resilience aspects, supply chain structures and flow complexity. The resulted findings are particularly interesting for both practitioners and researchers to highlight gaps and areas for enhancement. Finally, some future research directions are suggested with issues emphasized.
Keywords:	Sustainability, Resilience, Supply Chain, Mixed Programming, Modeling.
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	DEVELOPMENT OF STRATEGIC MANAGEMENT MECHANISM OF OMNICHANNEL MARKETING IN RETAIL CHAINS
Author:	SERGEY NOVIKOV, ANDREY SAZONOV, DIEGO FELIPE ARBELÁEZ CAMPILLO
Abstract:	The introduction of omnichannel marketing for each individual organization has its own unique specifics, but at the same time it is based on the universal formed theoretical principles and approaches to organizing the process, which are currently insufficiently represented and are often superficial and fragmentary. In this regard, the topic of using omnichannel marketing requires a more detailed study, conducting relevant research aimed at developing a universal mechanism for strategic management of omnichannel marketing in retail chains. It has been established that under the influence of modern digital marketing technologies, it is necessary to redefine the disparate points of contact with customers that affect the process of engaging consumers and making a profit through the competent and effective use of various omnichannels. A comparative analysis of multichannel and omnichannel marketing strategies is presented. A schematic diagram has been developed based on combining four directions of integration within the framework of omnichannel marketing: marketing channels, product, price and marketing logistics. The theoretical significance of the research is in the generalization of scientific concepts, theories of Russian and foreign scientists in terms of the content of the definition of omnichannel marketing; development of theoretical provisions for the transformation of the digital consumer behavior model and omnichannel marketing management in retail organizations. The practical significance of the dissertation research is in the possibility of using its results in the implementation of omnichannel marketing in retail organizations.
Keywords:	Omnichannel Marketing, Strategic Management, Digital Marketing, Brand Value, IT Technologies, Omnichannel Marketing Performance, Omnichannel Marketing Management Algorithm.
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	PREDICTING THE PERFORMANCE IN SEMESTER AND TO IMPROVE THE STUDY SKILLS OF HEARING-IMPAIRED STUDENTS IN SPECIAL EDUCATION USING RNN-HFP
Author:	MS. MARINA. B, DR. SENTHILRAJAN. A
Abstract:	Education was one of the fundamental need and rights for all people across the world. Every government formulates different schemes to ensure education for all as it results in the countries growth on various aspects. The people who are physically impaired (PI) are also included in these aspects. The performance of those students requires continuous monitoring to acknowledge their attention towards studies and to guide them towards better academic achievements. In this paper, the Recurrent Neural network (RNN) and Hybrid firefly – particle (HFP) algorithm based novel predictor is proposed to predict semester performance of the hearing-impaired students. The RNN algorithm predict the performance of the student and HFP is involved to optimize the prediction performance that may suffer from convergence error. The proposed model was evaluated for its accuracy at both the testing and training phase. The model was initially trained with 80% of data and tested with 20% of it. The proposed model was evaluated for its accuracy at both the testing and training phase. The outcome showed that the MSE loss in training is 0.05 with testing RMSE value of 0.24. The proposed model can be enhanced to predict the drop out probability for the PI students in future.
Keywords:	Accuracy, Hearing Impaired Students, RNN-HFP, Prediction Model.
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	A NOVEL ENERGY EFFICIENT APPROACH FOR IMPROVING NETWORK LIFETIME USING MULTI-HOP ROUTING PROTOCOL WITH MOBILE WIRELESS SENSOR NETWORKS
Author:	BATTINA SRINUVASU KUMAR DR. S.G. SANTHI DR. S. NARAYANA
Abstract:	Sensors are devices that monitor and control the physical environment. They can either be self-propelled through springs, wheels or attached to transporters, such as a truck, in mobile sensor networks Optimizing the sensors' energy usage is crucial for a sensor network that will be in operation for an extended period of time. Because of their proximity to sinks, sensors in static sensor networks are more likely to die than those elsewhere in the network. Sensors near the sink not only provide their own information, but they also assist in forwarding data for those further away. Network partitioning and the network's lifespan are both limited by this uneven energy usage. Wireless sensor networks (WSN) are becoming increasingly used in many New Generation Networks (NWGN) applications. Energy efficiency and data aggregation are two major concerns for these networks. Data aggregation has the dual effect of reducing the amount of data sent and improving the life span of the energy used. Routing protocols are in charge of maximizing network efficiency while minimizing their impact on the environment. Using a multi-hop mobile data collector (MDC), this work presents a novel routing scheme for efficient data aggregation. As a result, the suggested method is superior to current state-of the-art Wireless Sensor Network Hybrid Multi-Hop Routing Protocols (hybrid multi-hop routing protocol).
Keywords:	Clustering Protocol, Routing Protocol, Energy Efficiency, Wireless Sensor Networks, Mobile Sensors
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	PREPUTATION BASED TRUST MANAGEMENT SYSTEM FOR MALICIOUS FOG NODE DETECTION
Author:	R PRIYADARSHINI, N MALARVIZHI
Abstract:	Fog computing is a geo-distributed computing network; trust must be established between fog nodes for secured data communication. Decision-making can be aided by each nodes' ability to foresee the behaviours of other nodes according to trust value calculated, either directly or indirectly. Especially in indirect trust value calculation, decision to accept the recommending nodes value is critic due to malicious fog nodes which cause internal attacks like self-promotion attack, bad-mouthing attack, and on-off attack. This paper proposes a reputation based trust management system (RTMS) for malicious fog node detection among the recommending fog nodes using geometric probability distribution on multi-dimensional attributes. This RTMS keeps track of the past records of the fog node to calculate the reputation value of each recommender using weighted geometric mean in the fog environment. The proposed technique successfully detects the malicious fog nodes, that are possible among the recommending fog nodes, and allows only the trustworthy node’s recommendation for indirect trust calculation, thereby eliminating malicious nodes. The simulation shows that RTMS outperforms the previous work in terms of suitability and security.
Keywords:	Fog Computing, Trust Management, Reputation, Weighted Geometric, Internal Attacks
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	SYNCHRONIZATION ERROR MINIMIZATION USING SPECTRAL CHARACTERISTICS AND DWT DENOISING FOR COOPERATIVE COMMUNICATION NETWORKS
Author:	ANAND RANJAN, O.P. SINGH, HIMANSHU KATIYAR
Abstract:	The application of a cooperative communication system with a source, relay, and destination is discussed, along with a sensor data-aided DWT-based symbol timing synchronization. For low value of the signal-to-noise ratio (SNR) cases, a power spectrum estimation error correction is carried out. Cross spectral density based modal shape application uses timing synchronization using DFT property, and the results are compared. It is demonstrated that the cross power spectrum modal shape error is significantly reduced or insignificant when time synchronization is performed using DWT denoising. The benefit of diversity in cooperative communication may be lost over long periods of time due to a little time lag, it has been noted. It has been found that a large correction can be achieved by combining the DFT time lag correction properties with the DWT denoising method. The sensor response in the cooperative communication system faces high level of anomaly in spectral estimates due to error in sampling time of sensor nodes. Since the sensor nodes are of low hardware complexity hence simple but accurate methods are very challenging to find in wireless communication applications.This paper aims to: i) minimize the mismatch in sampling delay between sensor nodes to mitigate their impact on the global synchronization error of the cooperative communication network; ii) simulate and validate spectral characteristics based sampled sequences delay correction that enable the high accuracy in estimate of sample time mismatch on every node and calculation of modal value of sensor data frequency response; and iii) evaluate the conditions for nodes employing DWT based denoising prior to correction of time delay shift.
Keywords:	Symbol Timing Error , Cooperative Communications , DWT Denoising , Timing Synchronization
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	MARKETING INFORMATION AS A BASIS FOR DEVELOPING ANTI-CRISIS INVESTMENT STRATEGIES FOR RENEWING THE FLEET OF RUSSIAN HELICOPTERS
Author:	YURIY KRIVOLUTSKY
Abstract:	The article shows that for a helicopter manufacturing enterprise, adaptation to the requirements of the external environment is one of the main conditions for achieving success and reducing the risk of not selling the created helicopters. For the best use of its scientific potential with limited financial and material resources and to reduce the risks of production and financial activities, the implementation of anti-crisis investment programs with the maximum possible effect, comprehensive marketing research is needed. Conceptual directions of marketing researches of predictive parameters of operations performed by helicopters are proposed, on the basis of which a helicopter manufacturing enterprise can develop a forecast of changes in the structure and number of the available helicopter fleet in the medium term and propose structural and parametric optimization of the fleet, taking into account new models of helicopters that meet the economic interests of operators to a greater extent and consumers of helicopter services.
Keywords:	Airlines, Anti-Crisis Strategies, Customers Of Work, Marketing Research, Structure And Number Of Helicopter Fleets.
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	ARTIFICIAL INTELLIGENCE AND ITS APPLICATIONS IN E- COMMERCE – A REVIEW ANALYSIS AND RESEARCH AGENDA
Author:	DR. ANIL KUMAR KASHYAP, ITY SAHU, DR. AJAY KUMAR
Abstract:	The exponential evolution of computing technology is responsible for the massive change in practices and behavior. Artificial Intelligence (AI) is one of the fastest growing computing technologies which have remarkable contribution in transforming business practices and customer experience. However, it is at infancy stage and therefore its application in various industries is in early stage. Research on AI and its application is growing across the World but the existing literature is largely fragmented. To reach on a conclusion or to understand AI application a systematic review is essential. This study synthesizes research on artificial intelligence in electronic commerce. The systematic analysis approach along with extensive review of literature is utilized. A total of 170 literatures were analyzed and out of it 106 are reviewed. The study describes the status quo of AI technology and its role in modernizing e-commerce. The present study begins with introduction of study consisting need of the study, gap in the literature, Artificial intelligence and its prospects, aims of conducting the systematic literature review followed by methodology adopted in the current study. The next part of the study highlights AI and its subsets, e-commerce and application of AI in different e-commerce operations. The study describes how the element of personalization and human touch can be restored in e- commerce using AI. At last discussion, conclusion, future research and limitations of the study are presented. The study identified prominent themes that could be the area of interest for many researchers and academicians. The outcome of the study will bring new dimensions of AI application in e-commerce.
Keywords:	Literature review, Artificial intelligence, AI- subsets, E-commerce, Systematic review.
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	TRUST IN TECHNOLOGY VS. TRUST IN TECHNOLOGY SUPPLIER: SUCCESS FACTORS FOR CAMEROON CUSTOMS INFORMATION SYSTEM ADOPTION
Author:	CHANGHYUN LEE, HEEJIN LEE, KYUNGJIN CHA
Abstract:	This study investigates the main factors of e-Customs system adoption in Cameroon, focusing on suppliers’ role in providing trust. Although previous technology adoption studies in developing countries have dealt with trust in technology, most are biased against developing countries than technology suppliers. In contrast, this study extended the unified theory of acceptance and use of technology (UTAUT)-based research model to consider trust in technology suppliers separately from trust in technology. After collecting data from external and internal users of the e-Customs system in Cameroon, the model was tested using structural equation modeling. Results showed that trust in technology and technology suppliers should be considered independently, apart from the significant effects of other prime factors on behavioral intention. This study suggests that technology suppliers have a strategic business reason for trust from users for successful technology adoption, suggesting that future researchers should not attribute trust issues to developing countries solely.
Keywords:	Trust In Technology Supplier, Cameroon Customs Information System, E-Customs, UTAUT Model, Technology Trust
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	A NOVEL APPROACH BASED ON VOTING ENSEMBLE AND PCA DIMENSIONALITY REDUCTION METHOD FOR THE PREDICTION OF HEART DISEASE
Author:	SADIYAMOLE P A, Dr.S MANJU PRIYA
Abstract:	It is assumed that 32% of all deaths around worldwide are due to different types of CVDs.Advanced noticing and realizing heart diseases can be a boom to patients as they get a chance for switching their habits and life styles to a more healthy way and thus they can save their lives.Scientific researchers all over the world have been working on creating a much more intelligent decision support model for the early prediction of CVD.Healthcare people around the globe collects heart disease datasets .With the help of already available data and application of machine learning algorithms can be a cathartic feeling to the medical area.One of the main reasons of failure of intelligent prediction system is the inaccurate feature set and suffering of high overfitting and low variance.In order to predict the heart health of a patient in a more effective way,some machine learning models can be combined for better performance.Principal Component Analysis(PCA) is used to control the dimensionality of attributes.This paper mainly focuses on voting ensemble method for improving the performance of individual classifiers.
Keywords:	Machine Learning, Ensemble, PCA, Voting, LR, DT
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	APPLICATION OF GAME THEORY, FUZZY LOGIC AND NEURAL NETWORKS FOR ASSESSING RISKS AND FORECASTING RATES OF DIGITAL CURRENCY
Author:	BEBESHKO B., MALYUKOV V., LAKHNO M., SKLADANNYI P., SOKOLOV V., SHEVCHENKO S., ZHUMADILOVA M.
Abstract:	In this scientific work is aimed to obtain mathematical tools for solving the problem of finding optimal investment strategies in digital cryptocurrencies (hereinafter referred to as the CX) or a CX set from the side of investor/investors were proposed. The solution was found on the basis of the application of the games theory, the theory of fuzzy sets and artificial neural networks (ANN). The developed model, which allows one to obtain an algorithm for the success forecast assessment of the investment procedure in the CX by the investor, which can be then implemented in one of the modules of the intellectual information system for the CX rates forecasting. The scientific novelty of the results is that for the first time to solve the problem of the CX market evaluation in the context of the CX investment problem, gaming approaches based on solving a bilinear quality game in fuzzy production, as well as ANN were used.
Keywords:	Digital Cryptocurrency, Forecasting, Games Theory, Fuzzy Logic, Neural Networks
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	DEEP LEARNIG MODEL IMPLEMENTING PIPELINED AUTO ENCODERS AND ONE CLASS LEARNING FOR ANOMALY IDENTIFICATION AND LOCALIZATION FROM SURVEILLANCE STREAM VIDEO
Author:	KALLEPALLI ROHIT KUMAR, DR.NISARG GANDHEWAR
Abstract:	The demand for greater security measures for monitoring and protecting operations has made video anomaly detection one of the most significant study areas in the field of computer vision. This is due to the fact that the detection of anomalies in video surveillance systems has increased, making it one of the leading focus areas in the current field of research. When looking for suspicious behaviours like aggression, robbery, and wrong U-turns, assigning real people to frequently analyse the surveillance footage is a process that is both time-consuming and prone to error. As a direct consequence of this, there is an urgent requirement for the development of advanced, fully automated video surveillance systems for use in public areas. The research uses pipelined deep encoders to find a solution to the problem of locating and identifying anomalies in surveillance videos. The authors especially combined the LSTM autoencoder with the convolutional autoencoder. This strategy helps gather spatial and temporal information from the input video stream. This allowed us to achieve optimal results. The authors used the one-class classification principle during the training phase of the model. The training is done on normal data, and when they were verifying or testing it, they used anomalous testing data. The analysis of the study is based on standard benchmark practises for error rate and the duration of time required for identifying anomalies in the sequence of the video stream. This study satisfies the requirements for operating in near real time.
Keywords:	Deep Learning, Auto Encoders, LSTM, Machine Learning
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	AUTOMATIC IDENTIFICATION OF TUMOR BASED ON IMPROVED CLUSTER ENSEMBLE SEGMENTATION FROM MRI BRAIN MEDICAL IMAGES
Author:	M. KRUTHIKA REDDY, DR. K. NAVEEN KUMAR
Abstract:	Classification of Magnetic Resonance Imaging (MRI) is an aggressive concept to handle mathematical relations like Fuzzy, Rough Soft sets evaluation from bio-medical brain images. Processing of Soft, Rough, and Fuzzy mathematical theories are of scientific importance in the field of Bio-medical applications. Some of the authors have introduced Rough, Soft, and Fuzzy sets with connected and mathematical relations associated with each other. In this paper, we propose an Improved Intuitionistic Cluster Ensemble Segmentation Approach (IICESA) for the identification of brain tumor based on weight of image pixel with associative noise reduction in image selection. This technique also reduces Magnetic Resonance (MR) of brain from selected region based on threshold, which is explored from pixel weight to produce segmentation of brain tumor tissue from bio-medical images. All selected regions created intutionistic fuzzy, rough sets to enable and predict brain tumor tissue from medical brain images. This results of the simulated proposed approach gives efficient accuracy when compared to traditional approaches like Fuzzy C-Means (FCM), Generalized FCM (GFCM), and Soft Rough based FCM (SRFCM), Intutionistic Rough FCM (IRFCM) based on analysis of results.
Keywords:	Brain Image Segmentation, Magnetic Resonance Imaging, Bio-Medical Images, Mathematical Relations, Cluster Ensemble Process, Fuzzy C-Means Clustering.
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	APPLICATION OF A CONVOLUTIONAL NEURAL NETWORK WITH A MODULE OF ELEMENTARY GRAPHIC PRIMITIVE CLASSIFIERS IN THE PROBLEMS OF RECOGNITION OF DRAWING DOCUMENTATION AND TRANSFORMATION OF 2D TO 3D MODELS
Author:	KHOROLSKA K., SKLADANNYI P., SOKOLOV V., KORSHUN N., BEBESHKO B.5, LAKHNO V., ZHUMADILOVA M.
Abstract:	This paper presents the results of the research related to the design of a convolutional neural network with a module of graphic primitives elementary classifiers (EC) in the tasks of drawing documentation recognition and transformation of the 2D into 3D models. An architecture of a convolutional neural network with an elementary classifiers module of graphic primitives was proposed for solving the drawing recognition and 2D→3D transformation problem. A graphic image classifier model based on covered classes and elementary primitive classifiers has been developed to increase the effectiveness of CNN training.
Keywords:	Information Protection, Information Security, Organizational and Economic Support, Infrastructure Management, Decision Support System, Risk Minimization.
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	SECURE INTEGRATION OF WIRELESS SENSOR NETWORK WITTH CLOUD USING CODED PROBABLE BLUEFISH CRYPTOSYSTEM
Author:	NV RAVINDHAR, S SASIKUMAR, N BHARATHIRAJA, M VINOTH KUMAR
Abstract:	Wireless sensor networks (WSNs) connect hundreds or thousands of physically separate sensor nodes (or "motes") to gather data from all over a given area. The medical monitoring sector, the weather service, government and military applications, and many more all use WSNs for data collecting. The limitations of WSNs in terms of processing speed, data storage capacity, and power availability all present challenges. Due to the rise of Cloud computing and all the advantages it brings, the idea of combining the two systems has been proposed. The proposed system provides a fresh approach to how WSNs might link up with the Cloud. Using Cloud computing, this research provides a unified WSN for environmental surveillance. The primary goal is to provide a robust and astute infrastructure for the constant flow of sensor data. To do this, the WSN will collect data from its immediate vicinity, compress it, and then relay it via a gateway, which will then expand it and store it in the Cloud server. Both the opportunities and limitations of the WSN, including security concerns and Cloud computing services, will be discussed in this article. Therefore, a hierarchical structure of coded probable blue fish cryptosystem has been constructed in the design of data aggregation in WSN and the Distributed optimized LEACH (DO LEACH) protocol can deal with the restricted resources of the nodes and the route selection necessary for Cloud integration. The bio-inspired compressed cuckoo optimization technique was utilized to fine-tune the protocols. It should be evident by the completion of the article how well the proposed security protocols may maintain the network's total energy by decreasing the energy consumption of individual nodes, all while enabling reliable secure transmission between Wireless Sensor Networks and Cloud servers.
Keywords:	Wireless Sensor Network; Security; Transmission; Energy Consumption; Coded Probable Blue Fish; Cloud.
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	ENHANCED VEHICLE DETECTION USING POOLING BASED DENSE-YOLO MODEL
Author:	SRIHARSHA VIKRUTHI, DR. MARUTHAVANAN. ARCHANA, DR. RAMA CHAITHANYA TANGUTURI
Abstract:	The field of transportation management is seeing a rise in the importance of intelligent transportation detection and counting. Yet, detecting them remains difficult because of the wide range in vehicle sizes, which has a direct impact on the precision of vehicle counts. In this research, we present a vehicle counting and detection system that uses computer vision to solve this problem. The Intelligent Transportation System (ITS) has been developed through the enhanced optimization tool which belongs to the conventional time prediction framework, normally used in industry. The non-parametric approach has been developed with the help of machine learning approach and enhances the computational power. Huge historical information is used to generate the accuracy through traffic pattern with non-parametric approach. Additionally, this approach needs strong dataset, system for training and testing. The main problem with the current concept is that it takes too long to detect vehicles using machine vision. In order to alleviate traffic and safety concerns, the proposed model centres on precise vehicle recognition for usage in the intelligent transport system. The core components of the technique, which include convolution layers, pooling layers, and fully-connection layers, provide it exceptional robustness and allow it to efficiently complete the task of vehicle detection. The proposed pooling based dense- You Only Look Once (YOLO) model is constructed to enhance the accuracy in vehicle detection and eliminating the vanishing gradient issue. The pooling strategy is used to pool and update the multi-scale region features for enhanced detection. The loss function is established with MSE and the performance analysis shows that the proposed technique is performed well on UA-DETRAC dataset.
Keywords:	Intelligent Transportation System, YOLO, accuracy, vanishing gradient, UA-DETRAC dataset.
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	PARALLEL APPROACH IN R-DIFFSET ALGORITHM FOR INFREQUENT ITEMSET MINING
Author:	JULAILY AIDA JUSOH, MUSTAFA MAN, WAN AEZWANI ABU BAKAR, MOHD NORDIN ABDUL RAHMAN, SHARIFAH ZULAIKHA TENGKU HASSAN
Abstract:	Data Mining is an established methodology for obtaining critical information from databases utilizing the Association Rule Mining (ARM) technique. This vital information can lead to the association rule, which may reveal a good trend. Association rules' beneficial pattern is frequently stated as frequent and infrequent. To perform Itemset mining, the data formats that are necessary are horizontal format and vertical format. The vertical data format is focused on current research trends in infrequent mining techniques. An example of a vertical data mining technique for an infrequent pattern is called Rare Incremental Equivalence Class Transformation or shorten as R-Eclat. Out of the four variations of the R-Eclat approach, this study will only focus on the R-Diffset form. Prior studies have shown that the R-Diffset algorithm's data processing execution time is time-consuming. The R-Diffset technique is a solution to load imbalance problems that employ numerous nodes and cluster power to provide a novel parallel approach. In response to the positive findings of mining in terms of faster processing time and less memory consumption, R-Diffset will be supplemented with a parallel technique. Finally, a novel parallel strategy is provided to overcome the restrictions of sequential processing in terms of speed.
Keywords:	Data mining, Association rule mining (ARM), Infrequent mining, Rare Incremental Equivalence Class Transformation (R-Eclat), R-Diffset
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	CORONARY ARTERY DISEASE PREDICTION BASED ON OPTIMAL FEATURE SELECTION USING IMPROVED ARTIFICIAL NEURAL NETWORK WITH META-HEURISTIC ALGORITHM
Author:	D.VETRITHANGAM, V. SENTHILKUMAR, NEHA, A. RAMESH KUMAR, P.NARESH KUMAR, MRADULA SHARMA
Abstract:	Scientific breakthroughs in understanding the etiology of coronary artery disease (CAD) will allow for more accurate coronary artery disease (CAD) diagnosis and treatment techniques. Coronary Artery Disease (CAD) is a type of cardiovascular disease in which atherosclerotic plaques in the coronary arteries cause myocardial infarction or sudden cardiac death. In medicine, disease prediction based on Artificial Neural Networks (ANN) plays a significant role in enhancing the reliability of general population health care. So, our main goal is to propose an improved artificial neural network model in conjunction with a Meta-heuristic algorithm that works with distinct types of CAD datasets with good accuracy. The system selects the most relevant or similar features from the raw dataset; this feature selection is achieved by the Meta-heuristic algorithm. This model uses 18 input nodes, 18 hidden nodes, and 1 output node in an 18-18-1 multilayered feed-forward network architecture, which is the best network for the prediction of CAD with the selected dataset. When using different methodologies on datasets dealing with coronary artery disease (CAD), the results may vary. Efficient medical diagnosis and analysis are important in selecting the important features. The Cleveland Heart Disease dataset, obtained from the UCI repository, was used in this paper; it contains 37079 person data records with 50 attributes. The proposed Improved Artificial Neural Network model with Meta-heuristic Algorithm results in 97.63% Sensitivity, 97.5% Accuracy, and 97.35 % Specificity.
Keywords:	Coronary Artery Disease, Deep Learning, Risk Factor Meta-Heuristic Algorithm And Artificial Neural Networks.
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	KNOWLEDGE MANAGEMENT SYSTEM IMPLEMENTATION AND THE PERFORMANCE OF HIGHER EDUCATION INSTITUTIONS IN THE DEVELOPING COUNTRIES: A CONCEPTUAL FRAMEWORK
Author:	MOHAMMED R. ABDULLAH, KAMSURIAH AHMAD, NUR FAZIDAH ELIAS
Abstract:	The major challenge that, the Higher Education Institutions (HEI)s are facing, at present time, is the unproductive management of information, therefor the implementation of Knowledge Management System (KMS) becomes a must to provide many profits to students and lecturers with updated information as well as documented records as an evidence that reflects the process of improving of the whole performance sooner or later. Instead of manually controlling the information process, the dynamic development of ICT has made it possible for HEIs to adapt their processes to be electronic-based. One system that gives this issue a lot of thought is KMS, but because users have refused to use it, it hasn't been widely embraced. This paper will shade lights on the effective factors of the decision adaptation-rejection of KMS. This study is a collective one, provides a serious review of the related lecture regarding this issue. Based on interviews that had been conducted previously and hosted KMS experts from many prestigious and important institutions. They focused on eleven factors that play a great rule in making the decision of the implementation of KMS, as suggested by the literature review and technological adoption theories. Experts have validated these factors and put them in ranks. From these results, a comprehensive integrated implementation conceptual model of KMS that developed to serve the Iraqi HEIs and lead the performance to the next level by the adoption of technology
Keywords:	KMS, Performance, Adoption, Higher Education Institutions, Iraq, Experts Validation, Factor Extraction
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	HOW REMOTE WORKING CAN AFFECT EMPLOYEE PERFORMANCE USING SCRUM IN SOFTWARE DEVELOPMENT COMPANIES
Author:	MUHAMMAD ARDI RIZMALDI, RIYANTO JAYADI
Abstract:	Context. Lately, the rise of remote work has increasingly become an alternative to being present in the office, especially for IT employees in software development companies. However, remote working is not easy. It requires personal skills and a supporting workspace, one of which is the internet connection. Objective. This paper seeks to understand the impact of remote working in Scrum software development. Method. The questionnaire survey was made mainly from existing and validated scales. The questionnaire received 316 responses from scrum practitioners. The data were analyzed using SmartPLS version 3.0 and applying PLS-SEM analysis, including inner and outer model testing. Results. The structural model achieved a good fit (SRMR = 0.073, NFI = 0.714). The research findings show that team influence has a more significant effect than individual influence on employee performance with the most significant effect is communication and teamwork in scrum teams. By knowing the factors that affect employee performance, company stakeholders can help facilitate employee and provide a platform for scrum activities to improve the project's development. We cover this topic, along with the limitations of future research and work.
Keywords:	Remote Working, Scrum, Employee Performance, Software Development, Information Systems
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	CART METHOD APPROACH AND HIGH DIMENSION SIMULATION DATA SELECTION AND RANDOM UNDER-SAMPLING METHOD IN STUNTING CASE
Author:	WIDIARNI GINTA SASMITA, WAEGO HADI NUGROHO, AND ANI BUDI ASTUTI
Abstract:	Stunting has become the main problem in every area for repair and investigation. Data modeling with To do classification could help for however many variables big is problematic in modeling classification and can complicate the interpretation process. Data modeling with an amount of enough significant variables could handle the selection process stepwise method. This study will create a classification model using tree decision Classification and Regression Tree (CART) with challenge amount variable predictor. A total of 26 variables and 650 observations were applied using the simulation data taken from real stunting data in Java east and the data used on the variable predictor which is stunting and normal categories. The result of the study is a method used that could propose selected variables in the stunting process with high accuracy. The acquired model has decisive information related to influencing factors stunting incidents by grouping family internal factors namely the health of parents who divides the knot root model decision as factor main in stunting incident.
Keywords:	CART, Stepwise, Stunting, Height Dimension
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	SEAWATER FOR THE NOVELTY ERA OF COMMUNICATIONS-OPPORTUNITIES AND CHALLENGES
Author:	IMADELDIN ELSAYED ELMUTASIM, IZZELDIN I. MOHD, KHALID HAMID BILAL
Abstract:	Seawater is an abundant resource that could play an indispensable role in many applications involving electrical conductivity, monitoring security, improved battery power efficiency, and as an antenna transmitter and receiver. Thus in certain circumstances seawater could serve a useful purpose for service providers when other materials are unavailable. The study assesses the comparative strengths and weaknesses of different underwater techniques for creating a large-scale practical utilization while the contribution is to offer a computation method when considering seawater connectivity. . The objective of the comprehensive investigation is to examine the signal behaviour for underwater communication to give a remarkable way for developing the new upcoming technology era, particularly in terms of water communication technology. A significant result shows the parameters of the permittivity, salinity, and frequency could notably affect the underwater communication techniques in terms of signal magnitude, phase, and delay rate. The higher frequency leads to more fluctuation in magnitude, while degradation begins after 10 KHz and shows more slope gradient after 100 KHz in the signal phase, added to the notable delay rate that would start after 100 Hz severely.
Keywords:	Seawater Communication; Sonar; Electromagnetic Wave; Optical; Frequency.
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	GLOBAL CLIMATE PREDICTION USING DEEP LEARNING
Author:	BASEL Y. EL-HABIL, SAMY S. ABU-NASER
Abstract:	Climate scientists are gaining an understanding and data of the past and are projecting what the future climate might be like through applying the climate models. A climate model is like a Virtual Earth, it's designed to mimic the real world, so that scientists can forecast future scenarios of climate changes. Climate models are composed of computerized representations of components that represent the atmosphere, ocean, sea, ice, surfaces and other processes. Climate models do not rely on speculation, they describe the climate system with mathematical equations based on the physics and solved with high advanced computers. This research presents a significantly Climate forecasting model using a deep convolutional Long Short-Term Memory (LSTM) to forecast temperatures world widely. New developments in this model include the next-days prediction with Convolutional LSTMs mapping of past climate change to project future climate change since the observed changes. In addition, the model of unsupervised Deep Learning networks is for tackling climate patterns detection problems and the improvements Recurrent Neural Network-RNN architecture by minimization the loss function over multiple sequence steps. Our model is considered one of the best models comparing with others, due to its high testing accuracy (100%).
Keywords:	Artificial Intelligence, Neural Network, Deep Learning, Global Climate Prediction
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	VOLTAGE SAG MITIGATION WITH PRE-SAG BASED DYNAMIC VOLTAGE RESTORER METHOD
Author:	MUHAMAD HADDIN, ARIEF MARWANTO, YANU SHALAHUDDIN
Abstract:	Quality control issues that can cause complex stack hardware to fail operation resulting in voltage drop occurring. A short circuit to the ground in the conveyance control frame is the cause of the failure. To overcome this failure, the elective arrangement of the stack safeguard against the voltage registers with electronic control based on a device that provides a three-phase voltage source can be controlled, namely a dynamic voltage restorer (DVR). This research focuses on pre-sag based of DVR execution with a discovery framework placed on the input side of the control supply. The DVR incorporates the voltage difference between the annoying droop and pre-fault state. The effect of a two-phase short circuit to ground fault with four different transformer winding arrangements was carried out using Matlab/Simulink. The simulation results show that various distribution transformer arrangements will produce various voltage drop effects. Phase shift and maintain stack voltage between 0.9-1.1 pu of real voltage to compensate for voltage-sag.
Keywords:	Voltage Sag, DVR, Injection Transformer, Pre-Sag, Two-phase fault
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	AN INTRUSION DETECTION SYSTEM ON IOT NETWORKS USING HYBRID PCA-GWSO MODEL
Author:	JAYABRABU RAMAKRISHNAN
Abstract:	In any distributed system, network security is a critical concern. For this reason, intrusion detection system (IDS) has been offered to defend the networks against the hostile activity. This analysis aims to create and present an anomaly detection system that can be used to detect intrusions and abnormal activity in the Internet of Things (IoT) networks. The intrusion detection system is critical in identifying diverse attack types on the Internet of Things and improving the IoT’s overall functionality. The anomaly identification in the Internet of Things network with glowworm swarm optimization (GWSO) in conjunction with principal components analysis (PCA) was implemented in this work. The proposed framework is a metaheuristic approach-based anomaly detection system that could be used for identifying attacks from the NSL-KDD data set. The anomaly identification process is carried out using the GWSO method based on PCA. The PCA algorithm is employed for feature extraction, and the GWSO technique is employed for classification. Various factors such as accuracy, recall, precision, FAR, and detection rate are assessed in order to conduct a performance analysis. The proposed model achieved 94.14 percent accuracy in the normal class, 95.52 percent accuracy in the DoS class, 93.15 percent accuracy in the R2L class, 93.50 percent accuracy in the probe class, and 88.62 percent accuracy in the U2R class. The detection rate was 94.08 percent, while the false alarm rate (FAR) was 3.41 percent.
Keywords:	IoT, IDS, Anomaly Detection, GWSO, PCA, NSL-KDD Dataset
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	A STUDY OF NON-BINARY PRODUCT CODES AND THEIR TURBO DECODING
Author:	ZAKARIA M RABET, FOUAD AYOUB, MOSTAFA BELKASMI
Abstract:	In this paper we study a family of non-binary product codes (nb-PC) constructed from the family of low density parity check (LDPC) codes known as Euclidean geometry (EG) codes. A coding/decoding scheme of these non-binary product codes (nb-PC) is presented where the turbo decoder is based on a soft input soft output (SISO) decoder suitable for non- binary one-step majority-logic decodable (OSMLD) codes, which is described in details in this work. We study the effect of iterations on the performance of our (PC) decoder, and exhibit the symbol and block error rates of some constructed codes transmitted over Additive Gaussian Noise (AWGN) channel. The obtained results are satisfying.
Keywords:	Non-binary, Product Codes, Turbo Decoding, One-Step Majority-Logic Decodable (OSMLD) codes, Low Density Parity Check (LDPC) codes
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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Title:	ANN-ABC META-HEURISTIC HYPER PARAMETER TUNING FOR MAMMOGRAM CLASSIFICATION
Author:	AJAY KUMAR MAMINDLA, DR. Y. RAMADEVI
Abstract:	In recent past, artificial neural networks (ANN) have reaped improvements in the domain of medical image processing by addressing many unmanageable problems. The initialized hyperparameters control ANN performance and selecting sensible hyperparameters by hand is time-consuming and tiresome. This study suggests a metaheuristic optimization of the fine-tuning hyperparameters approach to remedy this flaw. The method is then evaluated on mammography images to assess whether the mammogram contains cancer. In the proposed ANN model, a modified Artificial bee colony (ABC) optimization method is used to fine tune the hyperparameters, and it categorizes the tumors in the breast as benign or malignant in two-class case and normal, benign, and malignant in three-class case with an accuracy of 97.52% and 96.58% respectively. Hyperparameters to the neural network framework were assigned instantly with the help of ABC method with wrapped ANN as objective function. Manual search, Grid Search, Random Grid search, Bayes search are all cutting edge ANN hyperparameters methods. In addition to the mentioned, nature-inspired optimization methods such as PSO and GA have adopted for fine tuning parameters. Additionally, the suggested model's performance in classifying breast pictures was compared to that of the published hyperparameter technique using sizable datasets on breast cancer that were made accessible to the public.
Keywords:	Artificial Neural Networks, Hyperparameters, Artificial bee colony, Mammogram images, Grid Search
Source:	Journal of Theoretical and Applied Information Technology 31^st December 2022 -- Vol. 100. No. 24-- 2022
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