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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
April 2024 | Vol. 102 No.8

Title:	ETHEREUM BLOCKCHAIN MODEL TO ENHANCE DATA INTEGRITY & COST REDUCTION IN OIL & GAS
Author:	MOHAMED RAMADAN A. ABDELHAMED, SHEREEN A. TAIE, MOHAMED H. FARRAG
Abstract:	Data in the oil and gas supply chain faces numerous challenges due to big data, contracts, inaccuracy, data accessibility, and the loss of some of this data, leading to significant problems. Critical data is at a greater risk of cyberattacks because the oil and gas industry is a very important sector that involves multiple-party transactions, ultimately resulting in data inconsistency and insecurity. This research aims to overcome these issues by providing an Ethereum blockchain model to enhance data integrity, cost reduction using blockchain technology in the oil and gas industry. The first step involves installing IoT sensors on oil and gas valve ports to monitor the quantities sent and received, utilizing the Ethereum blockchain platform to secure this data. Three smart contracts have been created and specific conditions have been added to these contracts, where data for oil and gas is aggregated and only the total value for each transaction is stored on the blockchain network. This leads to a reduction in transaction costs on the blockchain network since only the important transactions are recorded. This also leads to the protection of data in the supply chain from malicious attacks and data loss, real-time data recording, and the absence of a trusted third party as the system performs these tasks while also reducing traditional system costs. The proposed model offers promising results in enhancing data consistency and security, cost reduction using blockchain technology in the oil and gas industry.
Keywords:	Blockchain Technology, Oil and Gas Industry, Ethereum Smart Contract, Data Integrity
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	IT’S CONTRIBUTION TO THE NEW ECONOMETRIC MODDELLING OF THE TAX COMMITMENT: CASE OF AGRICULTURAL BUSINESSES IN MOROCCO
Author:	ROUAINE ZAKARIA, ABDALLAH-OU-MOUSSA SOUKAINA
Abstract:	Delving into the heart of the dilemmas between taxation and agriculture, this article explores the intriguing perspective of value-added tax (VAT) taxation of farmers in Morocco. Within this complex debate, attention focuses on farmers' perceptions, offering a unique window on how they apprehend tax reforms, asking how do farmers perceive and engage with the introduction of VAT in Morocco's agricultural sector? This article sets out to unveil the subtleties surrounding the vision of agricultural units in the face of VAT. Using an innovative methodology based on information technology to collect and process data, ranging from Multiple Correspondence Analysis to binary logistic regression, our research aims to predict the act of engaging farmers in this new fiscal reality. In this vein, a painless tax promoting the economic viability of farms emerges as a powerful determinant stimulating the commitment of agricultural units to VAT, underlining the importance of designing mechanisms that ease the financial burden on farmers while contributing to public revenues. Based in the Rabat-Salé-Kénitra region, this work explores the intricacies of agricultural perceptions, revealing crucial insights for the future of Moroccan agriculture, where each perception becomes a key piece in understanding the complex dynamics between taxation, competitiveness, and sustainability.
Keywords:	Tax reform, value added tax, tax commitment, agricultural sector, binary logistic regression
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	UNLOCKING USER PRIVACY: A PRIVACY-FOCUSED CRYPTOCURRENCIES FRAMEWORK FOR CONCEALING TRANSACTIONS USING ZERO-KNOWLEDGE PROOFS (ZKPS)
Author:	MOHAMMED AMIN ALMAIAH1, AITIZAZ ALI, TING TIN TIN4, TAYSEER ALKHDOUR, ABDALWALI LUTFI, MAHMAOD ALRAWAD
Abstract:	In the era of digital transactions and decentralized cryptocurrencies, ensuring user privacy has become a paramount concern. This abstract presents a groundbreaking framework designed to enhance user privacy by concealing transactions within privacy-focused cryptocurrencies. The proposed framework leverages the power of Zero-Knowledge Proofs (ZKPs) to enable users to conduct transactions while preserving their privacy. By concealing the transaction details and participant identities, this framework eliminates the potential for transaction information leakage. The utilization of ZKPs ensures that the integrity of transactions is maintained while simultaneously safeguarding user privacy. This abstract explores the underlying principles of the framework and highlights its potential impact on enhancing user privacy in the realm of cryptocurrencies. The novel framework holds great promise for revolutionizing the way privacy is preserved in digital transactions, setting a new standard for privacy-focused cryptocurrencies.
Keywords:	Privacy; Cryptocurrencies; Zero-Knowledge Proofs (ZKPs); Transaction Concealment; User Privacy; Blockchain; Privacy-Focused Framework
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	EMPOWERING WOMEN'S COOPERATIVES: HOW NEW TECHNOLOGY MEDIATES ORGANIZATIONAL LEARNING, PARTICIPATION, AND NORMS ON COMPETITIVENESS
Author:	RAMON SYAHRIAL, AMIARTUTI KUSMANINGTYAS, RIYADI NUGROHO
Abstract:	This research developed Wang et al.'s (2021) research model by adding “organizational participation” as independent variables and “availing new technology” as a mediating variable utilising RBV theory as grand theory. The purpose of this study is to develop a conceptual framework model from the replication of research modifications conducted by Wang et al.'s (2021). This quantitative study analysed data using SEM-AMOS. The population in women's cooperatives in the Greater Surabaya region. 193 sample respondents were the administrators of the women's cooperative. The results showed Organizational Learning and Organizational Norms has a significant effect on Availing New Technology, while Organizational Participation has no significant effect on Availing New Technology; Availing New Technology acts as a mediator between Organizational Learning, Organizational Participation, and Organizational Norm on Competitiveness. This research recommends that women's cooperatives need to establish cooperative regulations that encourage innovation and adaptation to new technologies in order to improve competitiveness.
Keywords:	Organizational Learning, Organizational Participation, Organizational Norms, Availing New Technology, Competitiveness
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	ANALYSIS OF INDONESIAN PEOPLE'S SENTIMENT TOWARDS ELECTRIC CARS ON SOCIAL MEDIA
Author:	MUHAMMAD DAFFA ATTARIQ, RIYANTO JAYADI
Abstract:	One of the steps that can be taken to reduce climate change is to reduce greenhouse gas emissions, which are often caused by the fulfillment of fossil energy. Because fossil energy is energy that will run out in the future, it is necessary to reduce its use. Electric cars are one form of transportation that can reduce the use of fossil energy. However, the presence of electric cars has caused pros and cons that are widely discussed, one of which is on social media Twitter. Based on the many responses, sentiment analysis can be carried out to find out the public's views regarding the presence of electric cars based on data taken from Twitter totaling 22783 tweets from January 2019 to December 2023. Sentiment analysis is carried out to analyze the text of opinions so as to produce information that is positive, neutral, or negative. Therefore, this research aims to analyze public sentiment using LSTM and lexicon based. Based on the results of the study, the highest accuracy was obtained by the LSTM algorithm with an accuracy of 96% with a precision in the negative class of 95%, neutral class 95%, positive class 98%. Recall for negative class is 93%, neutral class is 96%, positive class is 98%. And the f1-score of the negative class is 94%, neutral class 96%, positive class 98%. Meanwhile, the lexicon-based algorithm obtained an accuracy of 37% with precision in negative classes, namely 29%, neutral classes 46%, positive classes 43%. Recall negative class is 75%, neutral class 7%, positive class 54%. And the f1-score of the negative class is 41%, neutral class 13%, positive class 48%. So that the tendency of public sentiment towards electric cars on Twitter social media produces a positive trend.
Keywords:	Sentiment Analysis, Machine Learning, Electric Car, LSTM, Lexicon Based
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	ARTIFICIAL INTELLECTUALIZATION IN THE ASSESSMENT SYSTEM OF THE SAFE DEVELOPMENT OF ECONOMIC ENTITIES
Author:	SVITLANA TULCHYNSKA, OLHA POPELO, OLEKSANDR SOLOSICH, NATALIIA KASIANOVA, OLENA KOSTIUNIK, TETIANA SHCHEPINA
Abstract:	The processes of artificial intellectualized development of economic systems form an applied toolkit for ensuring effective security development, which determines the need for a well-founded methodological basis for assessing the security development of business entities, which properly takes into account the specific influence of factors of the intellectualized development in the conditions of the dynamic development of artificial intelligence technologies. The purpose of the article is to justify the methodical approach of assessing the security development of economic entities in the conditions of artificial intellectualization by the method of stochastic factor analysis of the main components. The legality and expediency of applying the methodical approach to assessing the security development of economic entities in the conditions of artificial intellectualization due to stochastic factor analysis, the tools of which are the method of principal components, rotation of Varimax components, and Bartlett's method, have been proven. The verification of the proposed methodical approach to assessing the safety development of economic entities in the conditions of artificial intellectualization by the method of stochastic factor analysis of the main components was carried out on the example of PJSC "Southern Mining and Processing Plant". A qualitative analysis of the distribution of analytical parameters as a result of a stochastic factor analysis of the safe development of economic entities was carried out using the example of PJSC "Southern Mining and Processing Plant" in the conditions of artificial intellectualization. The practical significance of the proposed methodical approach lies in the possibility of evaluating the processes of artificial intellectualization in the system of the safe development of an economic entity, to single out the components of artificial intellectualization of the safe development with the corresponding indicators of importance, to more comprehensively assess current security challenges and the objective need for the implementation of artificial intellectualization as a factor of accelerated growth on the basis of which to develop the basic requirements of strategic development.
Keywords:	Artificial Intellectualization, Artificial Intelligence, Security Development, Security, Intellectualization Of Economic Development, Intellectual Resource, Enterprise, Economic Entities, Socio-Economic System.
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	PYTHON BASED CONVOLUTION NEURAL NETWORK TO DETECT LEAF DISEASE -AN IMPLEMENTATION
Author:	MR.A.ASHOK BABU, R.CHANDRAMOHAN M.V.GANESWARA RAO, G PRASANNA KUMAR, G.N.SOWJANYA KURRA UPENDRA CHOWDARY
Abstract:	In the area of image categorization, the most recent convolution neural networks (CNNs) have shown excellent results. In this research, deep convolution networks will be used to develop a novel method of classifying leaf images in order to recognise plant diseases. Innovative training methods and the methodology employed make it simple and quick to apply the system in real-world settings. With the capacity to differentiate between plant leaves and their surroundings, the created model can identify nine distinct forms of plant illnesses from healthy leaves. To our knowledge, this approach to identifying plant diseases is the first one to be put out. All essential steps required for implementing this disease recognition model are fully described throughout the project, starting from gathering images in order to create a database, assessed by agricultural experts. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%.
Keywords:	CNN, Python, Leaf, Disease, Implementatoin
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	VOLTAGE STABILITY OF A PHOTOVOLTAIC DC MICROGRID WITH GaN-BASED BIDIRECTIONAL CONVERTER FOR EV CHARGING APPLICATION
Author:	KALANGIRI MANOHAR, KOTTALA PADMA
Abstract:	The integration of electric vehicles (EVs) into modern energy systems presents a unique set of challenges, particularly in DC microgrid environments where voltage stability is paramount. This research investigates the application of Gallium Nitride (GaN) based bidirectional converters to enhance voltage stability within DC microgrids tailored for electric vehicle charging stations. Traditional power electronic converters, often based on silicon technology, are limited in their ability to efficiently handle the dynamic power flow demands of EVs. GaN power devices, known for their superior switching capabilities, offer a promising alternative. This study proposes a novel GaN-based bidirectional converter design optimized for EV charging applications, focusing on achieving high efficiency, reduced switching losses, and enhanced voltage regulation. Through PLECS simulation, the effectiveness of the GaN-based bidirectional converter in improving voltage stability is demonstrated. The converter's high-frequency switching capabilities enable rapid response to load fluctuations, ensuring a stable voltage profile even under challenging operating conditions. The findings of this research contribute to the advancement of DC microgrid technology, offering a scalable solution for the growing demand in EV charging infrastructure. The adoption of GaN-based converters not only enhances voltage stability but also paves the way for more efficient and reliable integration of electric vehicles into the grid, ultimately promoting sustainable transportation solutions
Keywords:	PV Array; DC Microgrid; Gallium Nitride Bi-directional DC-DC Converters; PI Controller; Electrical Vehicles; Voltage Stability, PLECS Software
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	A LOW SAR VALUE CIRCULAR-BASED FEEDING OF CPW WITH A MONOPOLE STRUCTURE WEARABLE ANTENNA FOR WIRELESS BODY AREA NETWORK (WBAN) APPLICATIONS
Author:	HARITHA THOTAKURA, ROOPA KRISHNA CHANDRA G, K SRINIVASA RAO, P.SRINIVAS, MERUGUMALLI RAMA KRISHNA, G. VIJAYA KUMAR
Abstract:	In this paper, The wideband, low-profile, semi-flexible antenna for wearable technology is presented and covers an industrial, scientific, and medical (ISM) frequency bands. The investigation and analysis focuses on the free space and on-body antenna performance parameters for the proposed antenna with the operating range of 2.45 to 5.8 GHz. The circular construction with triangular slots attains dual-band applications. The antenna's dimensions are 39 x 29 x 0.2 and it is made of flexible polyimide with a loss tangent of 0.008. It has a dielectric constant of 3.5. The antenna's bending analysis is done to determine how flexible it is. The suggested antenna has fractional bandwidths of 41% and 28%, covering the frequency ranges 2.3-3.5 and 5.1-6.8 GHz. The patterns in which the antenna is provided under omnidirectional and stable dipole arrangements. The final antenna model has the frequencies are 2.7GHz and 6.1GHz with gain peak values approximately 2.4 dBi, 4.8 dBi with frequency variation of 2.7 Hz and 6.1 GHz associated to efficiency for reflected region sampled at 2.7 GHz, 6.1 GHz reaching 79%, and 85%. The simulation findings demonstrate that bending the antenna has no influence at all on the bandwidth, efficiency, gain, or reflection coefficient by mounting on human structure. The outcome gained through the performance analysis has been certainly validated through the comparative study under the findings of simulation and measurement.
Keywords:	SAR; On-body applications; Circular Ring; ISM (industrial, scientific, and medical).
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	IMPROVING THE CROSS-LAYER FUNCTIONALITY TO OVERCOME THE COLLISION IN MOBILE ADHOC NETWORK
Author:	S.HEMALATHA, J.NAGARAJ, R.V.V. KRISHNA, S. MANIKANDAN, E.UMA MAHESWARI, RAMU KUCHIPUDI, LAKSHMANA PHANEENDRA MAGULURI, S.KAYALVILI
Abstract:	Collision avoidance in widest self organizing and easily formed network for making instant communication of Mobile Adhoc Network was a tedious task due to the mobility of the communication nodes. Many research work address to overcome the issues with the support of the physical layer as well as MAC layers protocols. But none of the methods has given solution to overcome the problem. Incorporating the Directional antenna in to the MAC layer functionality can support for hidden and exposed nodes problem to avoid collision .This research article discuss cross layer functionality with beam sector directional antenna technique in to Physical and hidden and exposed nodes table in MAC layer. This proposed work combines the physical and MAC layers to resolves the collision among the nodes; improve the signal power and routing efficiency in MANET. This antenna find out the receiver direction based on the hidden and exposed nodes location of the next hope received and focus the packet floating, this technique to support the Hidden and Exposed node problem in MANET also improve the routing efficiency and power optimization also this cross layer functionality does not require and Handshaking signals.. This work is simulated using Network simulation and result gained 35 % to 60% of improvement in overall MANET Network performance and metric value 30 % to 60 %, overall antenna gain in Beam sector Antenna 21.5 dBi.
Keywords:	MANET, Antenna, Physical Layer, MAC layer, Hidden and Exposed node, Cross Layer
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	EXPLORING SOCIAL INFORMATION RETRIEVAL: A CRITICAL ASSESSMENT AND COMPREHENSIVE REVIEW
Author:	HAMID KHALIFI, SARA RIAHI, AZIZ BOUJEDDAINE, YOUSSEF GHANOU, HICHAME KABIRI
Abstract:	Nowadays, social networks are used daily by thousands of people who utilize the internet worldwide. This activity, which has become one of the main ways that people use the Internet, provides several social functions, including content sharing among people who share interests and conversational exchanges. Scholars have known for almost two decades that social networks are valuable resources for comprehending how different facets of information retrieval (IR) have developed. Large volumes of important information are produced by social networks. The analysis of this valuable data is missed by standard information retrieval systems that treat documents basing on their content rather than their social surroundings. The solution to this problem is Social Information Retrieval (SIR). Its models take into account social media platforms and use social material as a secondary source of information within the IR system to progress the quality and relevance of search results. For this purpose, we propose a novel approach involving query expansion that integrates social information from documents into the similarity calculation between the query and the document. Our method involves refining the original query by adding additional details, thereby broadening the search scope to produce more satisfactory results.
Keywords:	IR, Sir, Social Networking Platforms, Netizens-Created Content, Social Surroundings, Social Behaviors.
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	INTERNET OF THINGS (IOT) APPLICATIONS IN THE AUTOMOTIVE SECTOR
Author:	N. MAHDAR, N. RAFALIA, F. GHANIMI, AL KARKOURI, A, J. ABOUCHABAKA, S.BOUREKKADI
Abstract:	This article examines the many uses of the Internet of Things (IoT) within the automobile industry, with specific attention given to the Moroccan setting. This study provides a comprehensive analysis of many critical domains, including road safety, predictive maintenance, intelligent traffic management, and tailored user experience. In the realm of road safety, the Internet of Things (IoT) is seen as a vital entity, since it enables the identification of impending collisions and fosters inter-vehicle communication to avert accidents. The use of Internet of Things (IoT) sensors for the purpose of predictive maintenance is regarded as a strategy to enhance the longevity of vehicles, hence diminishing expenses associated with maintenance and augmenting overall dependability. The article further emphasizes the use of the Internet of Things (IoT) in the realm of intelligent traffic management, with the objective of mitigating traffic congestion and enhancing the efficacy of urban transportation. This study ultimately examines the role of the Internet of Things (IoT) in enhancing the customization of the vehicle user experience in Morocco. It specifically focuses on the integration of intelligent infotainment systems and the implementation of automated changes that are tailored to individual preferences. In its whole, the article presents a thorough examination of the improvements in Internet of Things (IoT) technology, emphasizing its notable influence on the transformation of the automobile industry within the particular context of Morocco.
Keywords:	Internet of Things (IoT), Automotive Connectivity, Smart Road Safety, Predictive Maintenance, Connected Traffic Management
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	FACTORS THAT INFLUENCE CONSUMER BEHAVIOR IN USING ONLINE FOOD DELIVERY SERVICES
Author:	RUDY, NATHANIEL
Abstract:	Current developments in food delivery systems are greatly influenced by technology, where almost everything can be done by mobile phone or website. A solution for food delivery was created by creating an online food delivery order where we can order food through an online platform. Today the development of online food delivery is also growing. However, because the market is so large, many platforms are interested in creating online food delivery services with the characteristics of their respective platforms. The problem that arises from the development of online food delivery is that competition between merchants and competition between platforms is becoming increasingly tight. This research aims to summarize the basic things that a platform must have to attract consumers' interest in buying food through the online food delivery services they offer and provide data on how big an impact these factors have regarding customer interests. Such as the importance of user interface quality, product information quality, variety of choices, security perception, and discounts to save customers money. So, we argue that this research can be used to develop online food delivery services to be more developed than they are presently and develop small industries (merchant) that are starting their businesses, as well as large companies to further develop. The results show that the existence of a variety of choices, data security, and discounts offered that can save customers money will be able to influence customer attitudes, which will then increase someone's interest in making a purchase. Meanwhile, the user interface and product information have a less significant impact on changes in customer attitudes.
Keywords:	Online Food Delivery, E-Commerce, Consumer Buying Behavior
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	OPTIMAL SIZING OF A PV-WT HYBRID SYSTEM FOR AN ELECTRIC VEHICLE RECHARGE STATION APPLICATION BASED ON MIXED INTEGER LINEAR PROGRAMMING
Author:	NADA SOURI, DERRHI MOSTAFA
Abstract:	As global efforts advance toward a more environmentally sustainable future, there is a significant surge in the availability of electric vehicles for purchase. A growing number of electric vehicles does, nevertheless, give rise to novel challenges, including the requirement for environmentally sustainable and efficient charging alternatives. At this juncture, the microgrids initiate operation. Decentralized energy systems, such as microgrids, have the capability to function autonomously or in tandem with traditional centralized energy infrastructures. It possesses the ability to operate autonomously or in contrast to the conventional system. There are several benefits associated with microgrids, such as increased productivity, reliability, and resiliency. This article aims to evaluate the feasibility of integrating microgrids into electric vehicle charging stations. Case studies of successful microgrid deployments will be analyzed, the challenges and limitations of using microgrids in charging stations will be assessed, and the benefits of employing microgrids in this context will be discussed. Upon perusing this article's conclusions, you will possess an enhanced comprehension of the potential for microgrids to furnish environmentally sustainable and efficient charging solutions for electric vehicles. In Tangier, the scarcity of electric vehicle charging stations can be attributed to the substantial expenditure required for infrastructure development, such as electrical grids. On one side, the current infrastructure lacks sufficient development, thus impeding the widespread adoption of charging stations. On the other hand, Morocco has made significant progress in the field of renewable energy, particularly through investments in solar and wind power. Consequently, this study holds great potential for yielding valuable insights.
Keywords:	Microgrids, Renewable Energy, Energy Management Plan, Electric Vehicles, Charging Stations
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	THE RELATIONSHIP BETWEEN CROSS-CUTTING FACTORS AND KNOWLEDGE, LEARNING OUTCOMES, AND SKILLS IN DUAL DEGREE PROGRAMS
Author:	MOHAMMAD HASAN ALTARAWNEH, WAEL ALZYADAT, BELAL MAHMOUD ALWADI, ALA AL-SHAIKH, AMEEN SHAHEEN, AYSH ALHROOB
Abstract:	This study delves into the pivotal role played by cross-cutting factors within the curriculum life cycle, with a particular emphasis on their impact on the development, implementation, and evaluation of a knowledge-based curriculum. We highlight the National Qualifications Framework (NQF) as a crucial component in ensuring that students acquire subject-specific knowledge and skills. In our research, we underscore the primary goal of the knowledge base integrated into the NQF. A knowledge-based curriculum, as our findings reveal, places a strong emphasis on the acquisition and retention of subject-specific knowledge, skills, and understanding. Regarding learning outcomes, our analysis highlights the focal point of a knowledge-based approach, concentrating on what students should know and understand upon completing a course or program. Our findings illustrate the stress placed on acquiring and retaining specific knowledge and understanding relevant to a particular subject or discipline. Our study suggests that a knowledge-based approach to learning outcomes is invaluable in ensuring that students possess a robust foundation in subject-specific knowledge, which, in turn, contributes to their academic and professional success. This study holds significance for individuals interested in dual study, apprenticeship, and NQF implementation, as it directly addresses the challenge of ensuring that students acquire both the knowledge and skills necessary for their chosen field of study or profession. The role of cross-cutting factors within the curriculum life cycle is a central theme, shedding light on the intricate interplay between knowledge-based education, learning outcomes, and skills development.
Keywords:	E-Learning, Dual study, apprenticeship, National Qualifications Framework, Knowledge.
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	ENHANCING LARGE UNDERGROUND EXCAVATION RISK ASSESSMENT: OPTIMIZATION THROUGH INFORMATION SYSTEMS
Author:	MALABIKA ADAK, ALTAF USMANI, ANIRBAN MANDAL
Abstract:	This article endeavours to introduce a thorough risk management framework tailored for the assessment of underground facilities situated within rock formations. Drawing insights from the data analysis gathered from two notable underground cavern projects in India, the framework undergoes development and refinement. It revisits the setbacks encountered in these projects alongside the corresponding strategies for mitigation. By leveraging empirical data, the article explores risk assessment using a Decision Tree model grounded on entropy. This risk assessment model integrates various factors concerning geological conditions such as rock mass classification, Q value, joint set orientation, and shear zones, which are correlated with diverse sources of failure. The outcomes of this model underscore the significance of prioritizing resource allocation and costs based on the importance of parent attributes and the associated levels of child attributes. Thus present method is expected to optimize the construction of underground structures.
Keywords:	Decision Tree, Risk Management, Geological Uncertainties, Tunnelling.
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	IMPACT OF PAYMENT METHODS ON BUYING BEHAVIOUR IN E-COMMERCE
Author:	MICHELLE ANGELINA SANTOSO, SONY CHANDRA, VICKY, ZEEVA NATHANIA NUGROHO, TANTY OKTAVIA
Abstract:	E-commerce encompasses business transactions including the exchange of goods and services over online platforms, which involve the transferring of funds and data. Commonly, conventional electronic commerce systems encompass a diverse array of payment alternatives. Although each payment method has its own advantages and disadvantages, these factors might impact a user's purchasing decision within the realm of E-Commerce. Therefore, a research endeavour is now being conducted to examine the decision-making process of customers in the domain of electronic commerce. The primary objective of this study is to examine the possible influence of different payment methods on individuals' lifestyles and consumer expenditure patterns. The aim of this study is to examine the factors that influence the acceptance of payment methods by users on an E-Commerce platform, using the UTAUT 2 (Unified Theory of Acceptance and Use of Technology) framework. An electronic survey was created and disseminated to gather data from a wide range of participants across Indonesia, including individuals from various age cohorts, genders, professions, and geographical locations. The use of SmartPLS software enables the analysis of the study model and the collection of meaningful findings through the implementation of the Partial Least Squares - Structural Equation Modelling (PLS-SEM) approach. The study findings, based on a sample size of 134 individuals, demonstrate that the enabling condition, price value, habit, and habit on use behaviour significantly influenced behavioural intention. The data provided allows for the acceptance of all hypotheses. The findings indicate that the payment method has no substantial influence on customers' purchase decisions in the e-commerce setting.
Keywords:	E-Commerce, Payment Methods, purchasing behavior UTAUT 2. purchasing decisions
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	EXAMINING THE EFFICACY OF THE INTERNET OF BEHAVIOUR (IOB) IN THE IDENTIFICATION OF CLIENT NEEDS ON ELECTRONIC COMMERCE PLATFORMS
Author:	ERIC WIJAYA, JONATHAN CHRISTIAN, CARLA VENNA, JENNIE ROWAN, IVAN SURACHMAN, SELLYNA, TANTY OKTAVIA
Abstract:	The Internet of Behaviour (IoB) is a strategic approach to integrating technology into company operations. Analyzing consumers' behavior through internet use to process the data. E-commerce platforms presently utilize the Internet of Behaviour to handle client behavior data, thanks to technological advancements. Through the analysis of client behavior data, the e-commerce platform will effectively cater to the requirements of customers. This research employed a theoretical framework to ascertain the determinants that impact online behavior and client requirements inside e-commerce platforms. This study employs a quantitative approach, using a questionnaire as the primary data collection tool, followed by a literature review. The study administered an online questionnaire to gather data from users of the e-commerce platform inside Jabodetabek, as well as from customers of diverse ages, genders, domiciles, and occupations. The findings from the survey participants indicate four distinct pathways: the impact of E-commerce platforms on customer needs, the influence of the Internet of Behaviour on customer needs, the influence of purchase intention on E-commerce platforms, and the impact of purchase satisfaction on E-commerce platforms.
Keywords:	Internet Of Behavior, Customer Needs, E-Commerce, Purchase Intention, Purchase Satisfaction
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	UTILISATION OF DIGITAL FINANCIAL SERVICES BY CUSTOMERS AND ITS EFFECTS
Author:	FRANSISKUS FRANKLIN, ALLYCIA METTA LIYANTO, DANINDRA RASYAD RABBANI HANARTYO, GEOVANNI WICAKSONO, DARREN RAFAEL, TANTY OKTAVIA
Abstract:	This study is driven by the advancement of information technology, which has the potential to facilitate many daily chores for a significant number of individuals in Indonesia. Indonesian individuals may conveniently make payments using digital financial services. PT. XYZ developed a smartphone-based application to cater to the requirements of the Indonesian population. The objective of this study is to assess the demand for the MyXYZ application in Indonesian society, to enhance consumer trust and loyalty. This will enable the firm to broaden its prospects and gather customer satisfaction statistics. The study employs both quantitative and qualitative methodologies. Data is obtained by use of surveys and interview methodologies. The sample for this study consisted of 100 individuals who satisfied the established criteria. Incorporating pre-existing theories can facilitate the research process. The program SmartPLS will be used to analyze the results using the selected approach. Out of a total of 119 respondents, this study required a sample size of 100 to analyze the variables. Approximately 50% of the factors examined in this study were found to be statistically insignificant in our research. The questionnaire results indicate that the majority of respondents are MyXYZ users, and the findings demonstrate that they have successfully addressed several concerns. Nevertheless, they advocate for MyXYZ to their colleagues. Merely 10 out of the respondents do not endorse it. As a consequence, users of MyXYZ saw a beneficial influence from their usage of MyXYZ. These data suggest that there is a favorable relationship between User Experience and Functional Values in the MyXYZ application.
Keywords:	Digital Financial Services, Payment, Smartphone, User Experience, Functional Value
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	IMPACT OF MACHINE LEARNING AND E-LEARNING ON THE OPTIMIZATION OF TERRITORIAL INTELLIGENCE
Author:	H. ELMORTAJI, J. ABOUCHABAKA, S. JAOUHAR, N. RAFALIA, S. BOUREKKADI
Abstract:	The article explores the enhancement of territorial management via the integration of Machine Learning (ML) and Territorial Intelligence (IT) to get optimal results. In an ever-changing digital world, this combination provides intelligent solutions for proactive, predictive, and customized administration of territories. The rapid advancement of technology is revolutionizing urban planning, resource allocation, and the well-being of individuals. The paper examines the prospects, difficulties, and possible advantages of the novel collaboration between machine learning (ML) and information technology (IT) to tackle the intricate issues faced by local authorities. ML facilitates the mechanization of monotonous jobs, anticipatory analysis for long-term planning, and the enhancement of administrative procedures. Simultaneously, IT facilitates a comprehensive comprehension of geographical data to enable more knowledgeable decision-making. The paper emphasizes the significance of customizing municipal services using AI, specifically in the Moroccan setting, to enhance public happiness. To summarize, the essay provides a thorough analysis of how the combination of machine learning (ML) and information technology (IT) might revolutionize territorial administration, resulting in the development of intelligent and resilient communities.
Keywords:	Territorial Intelligence, Machine Learning, Territorial Management, Smart Cities, Energy Transition
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	OPTIMIZED IRDN-IBDA: AN OPTIMAL FEATURE SELECTION AND RECOGNITION FOR NETWORK INTRUSION DETECTION SYSTEM
Author:	APPALARAJU GRANDHI, SUNIL KUMAR SINGH
Abstract:	The demand for an efficient intrusion detection system has grown as attackers continue to create new attacks and network sizes expand. Recently, many techniques have been released for network intrusion detection systems (NIDSs). However, new threats are constantly developing and outside existing systems reach. The intrusion detection algorithms high error rate, significant dimensionality, false alarm rate, redundancy, meaningless data, and false negative rate now in use are only a few of the many issues with them. Given its exceptional performance in various detection and recognition tasks, we present a novel and efficient deep learning-based NIDS in this research. Initially in preprocessing data encoding and normalization are performed using raw input data. After preprocessing, the pre-processed data are fed into the feature extraction phase. The features are extracted by utilizing the SE-ResNeXt-101 approach. Then, the essential features are selected with the help of an Improved Binary Dandelion Algorithm (IBDA). The presented novel Improved Residual Dense Network (IRDN) is employed to identify attacks which enhance security and privacy inside the network framework. The lyrebird optimization technique is used to tune further the hyperparameters derived from the IRDN approach to increase performance. The Modified Generator GAN (MG-GAN) algorithm also solves the data imbalance issue. The research shows that the suggested technique outperforms current NIDS methods regarding assessment metrics. Additionally, this method is more suitable for complicated detection of network intrusion requirements.
Keywords:	Network Intrusion Detection Systems (Nidss), SE-Resnext-101, Improved Binary Dandelion Algorithm (IBDA), Modified Generator GAN (MG-GAN), Improved Residual Dense Network (IRDN).
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	INTELLIGENT IMPUTATION OF MISSING DATA USING BIDIRECTIONAL NEIGHBOR GRAPH MODELING FOR DIABETIC RISK PREDICTION
Author:	BASHAR HAMAD AUBAIDAN, RABIAH ABDUL KADIR, MOHAMAD TAHA LJAB, BAKR AHMED TAHA
Abstract:	Diabetes is a global health issue, affecting countless individuals. This study dives into the critical task of filling gaps in data crucial for diabetes forecasts. These gaps can weaken the reliability of medical datasets, leading to less effective diagnostics and predictions. We put forward a cutting-edge approach employing the bidirectional neighbour graph (BNG) algorithm. This graph-based, semi-supervised learning technique is adept at managing the intricacies of incomplete data. Compared to traditional machine learning methods, the BNG algorithm shines by forming a network where nodes represent patients. Each node links to its closest neighbors in both directions, ensuring thorough data assimilation. Our method stands out, showcasing an Area Under the Curve (AUC) score of 0.86. This score reflects a strong model with the ability to extract extensive and distinct features from the data, resulting in enhanced classification accuracy. The study highlights the necessity of precisely tackling missing values to boost model trustworthiness. Moreover, it proposes extending the BNG technique to various sectors where maintaining data accuracy is crucial. Thanks to its computational efficiency and adaptability, the BNG algorithm is championed as a flexible instrument for medical researchers. It sets the stage for more precise and dependable diabetes risk assessment and management.
Keywords:	Graph-Based Methods, Bidirectional Neighbour Graph, Classification, Feature Extraction, Machine Learning.
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	EFFICIENT WAY TO MEASURE THE OXYGEN IN WATER USING IOT DEVICES
Author:	PRAVEENA BAI DESAVATHU, KADIYALA SUDHAKAR, BALAJI TATA, BALA PRASANTHI PAVULURI, G.N.SOWJANYA, G PRASANNA KUMAR, KURRA UPENDRA CHOWDARY
Abstract:	Quality water is one the prime requirements of any living organisms and dissolved oxygen is one the essential parameter for ensuring the basic needs of aquatic life. Manually collecting samples for dissolved oxygen measurement in a given area is complex ,time consuming and the “number of samples required” are critical for accuracy. Sensor devices for dissolved oxygen level measurement forming cluster of wireless sensor networks (WSN) are providing solutions for such problems. These sensor devices connected to internet evolving Internet of Things network provides solutions for dissolved oxygen measurement in water. These features are exploited for this research and enhancements are made for collection of data from the gateway nodes of WSN transported through Long Term Evolution (LTE) mobile communication networks on to Cloud platform for computational purposes .Real time SMS trigger to stake holders and web based information flow from the cloud are the unique proposition. The innovative ideas facilitate in the transportation of data to cloud and on line computational analysis coupled with real time reporting will save time and energy in dissolved oxygen level measurements and also aims to prove information with accuracy on real time from any part of the world.
Keywords:	Water, Oxygen, WSN, LTE.
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	APPLYING ARTIFICIAL INTELLIGENCE TECHNIQUES IN CARDIOMEGALY DETECTION USING CHEST X-RAYS
Author:	BELAL GHANEM MOHAMMAD AL-ATHAMNEH ,MOHAMMAD IBRAHIM AHMED AL-OMAR
Abstract:	The evolution of artificial intelligence in several areas has allowed machines or techniques to accomplish any task with high accuracy, like detecting and classifying chest X-rays as cardiomegaly or healthy. The goal of this paper is to develop a deep learning technique to identify and classify chest X-rays, whether the images are health-related or cardiomegaly. Firstly, the chest X-ray dataset is used that called ChestX-ray8, which contains medical images about many diseases, including cardiomegaly. After that, we apply the preprocessing steps to the dataset, like making all images the same size and normalizing them. Before applying the deep learning techniques, it should use data augmentation methods, such as random rotation, random zoom, and random brightness. The deep learning technique used is the VGG16, which is a convolutional neural network model. The results show that the VGG16 model gives a high accuracy of 91% compared with the previous works.
Keywords:	Artificial Intelligence, Cardiomegaly, Images, Chestx-Ray8, VGG16
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	ENHANCING BEAMFORMING AND INTELLIGENT BEAM SELECTION FOR MILLIMETER-WAVE COMMUNICATION USING ADAPTIVE BEAMFORMING TECHNIQUES
Author:	PRIYANKA A, CHANDRASEKAR C, ASHOK KUMAR M
Abstract:	In the rapidly evolving landscape of mobile telecommunication, the optimization of beamforming strategies stands as a critical element for ensuring the efficiency and robustness of wireless communication networks. This research endeavors to contribute to the advancement of beamforming in mobile networks by delving into the analysis of beamforming feedback datasets. The primary objective is to employ machine learning techniques, with a focus on Support Vector Machines (SVM), to categorize beams into distinct strength categories. Subsequently, an adaptive beamforming algorithm is applied to identify and select the optimal line-of-sight (LOS) beam, thereby enhancing the overall performance of 5G beamforming. The classification of beams using SVM with a beamforming feedback dataset serves as a pivotal technique in optimizing the 5G beamforming process. The ultimate goal is to dynamically adjust the direction of beams based on the strength of received signals, thereby augmenting communication quality and efficiency. Parameters such as interference level, bit error rate, and latency are crucial in evaluating the performance and reliability of communication systems. This research not only investigates the intricacies of beamforming feedback datasets but also proposes a novel approach to adaptively optimize beam directionality in 5G networks. By integrating SVM-based beam classification and adaptive algorithms, this work aims to contribute to the evolution of mobile telecommunication, enhancing the communication quality and reliability of 5G networks.
Keywords:	Beamforming, Mobile Networks, Machine Learning, Support Vector Machines (SVM), Beamforming Feedback, Line-of-Sight.
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	EFFICIENT DETECTION OF TOMATO LEAF DISEASES USING GPU-ACCELERATED DEEP LEARNING FRAMEWORKS
Author:	LAKSHMI NAGA JAYAPRADA GAVARRAJU, PULLURI SRINIVAS RAO, NEELIMA GURRAPU, DR.V.N.V. SATYA PRAKASH, G. SIVA SANKAR, DR. JAYAVARAPU KARTHIK, SIVA KUMAR PATHURI
Abstract:	Crop yield and efficiency in farming are significantly dependent on the early identification and prediction of plant leaf diseases. In recent times, machine learning algorithms have surfaced as potent instruments for mechanizing this procedure, offering farmers a precise and effective way to recognize and handle leaf illnesses. With a focus on early disease prediction before the formation of observable symptoms. In the field of agriculture, sustaining high yields and guaranteeing food security depends on the early diagnosis of diseases in crops like tomato plants. This problem may be solved by machine learning approaches, which have demonstrated promise in automating disease diagnosis procedures. However, these techniques might have high computing requirements, especially when working with complicated models and huge datasets. This paper aims to construct a predictive model for plant leaf disease detection by machine learning approaches with GPU computing. In this work, we suggest a novel method CUDA-ResNet50 Classifier (CUDA-ResNet50 Leaf Disease Detection Classifier) for forecasting disease of tomato leaves that makes use of GPU (Graphics Processing Unit) computing to speed up the computational procedures. We use GPUs' parallel processing features to speed deep learning model training and conclusion, allowing for faster and more effective disease detection. In this article, some base classifiers like Support Vector Machine and Decision Tree were used and compared with the proposed algorithm which the proposed algorithm gave 94% accuracy.
Keywords:	Plant leaf diseases, GPU computing, CUDA-ResNet50 Classifier, Support Vector Machine, Decision Tree
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	DISASTER RECOVERY PLANNING FOR IT/IS OF HOSPITALITY INDUSTRY USING NIST SP 800-34 REV.1 METHOD
Author:	JOHANES FERNANDES ANDRY, NADIA KAREPOWAN, HENDY TANNADY
Abstract:	The object of this study is a hotel that is in great demand by visitors and continues to develop its business processes. This hotel has made use of the information system to support activities in the hotel which can make it easier for employees. It is the responsibility of the hotel to have a strategy so that the assets and information in the hotel can be safe from accidents, whether intentionally or unintentionally. However, in practice, this hotel still does not have a strategy related to recovery steps in the event of a disaster that can damage assets that can hinder business processes at the hotel. Therefore, as a preventive solution to minimize disaster losses in hotels, disaster recovery is necessary. Diasaster Recovery Planning (DRP) is a structured, documented strategic approach with an approach to dealing with disasters or incidents that can occur at any time. This research was conducted using NIST SP 800-34 Rev. 1 which is one of the methods often used in DRP. The results of the research contain service recovery recovery with disaster actions. Furthermore, there are three information systems that have medium level impact, namely the booking information system and the reports. And what has a low level of impact is the check in and check out information system. Preparation of a Disaster Recovery Plan adapted to the existing situation and conditions, so that planning and handling can be carried out appropriately.
Keywords:	Disaster Recovery Planning, NISP 800-34 Rev.1, Hotel, Information System
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	ANSWER THE USE OF INDUSTRIAL INTERNET OF THINGS FOR INDUSTRY 4.0 INTEGRATION IN THE MANUFACTURING COMPANY
Author:	WAHYU SARDJONO, WIDHILAGA GIA PERDANA, and GUNTUR SALIM
Abstract:	Industry 4.0 production deployment relies heavily on the industrial Internet of Things or IIoT. Manufacturing must overcome the problems and obstacles related to the adoption of industrial technology 4.0 to gain a competitive edge. An overview of the issues that manufacturing must deal with is provided by the research, and it covers finance, human resources, technological integration, management and organization, and security. in addition to approaches that may be used to get beyond these obstacles and incorporate IIoT into the current system, enabling businesses to adopt Industry 4.0.
Keywords:	Integration, Industry 4.0, Industrial Internet Of Things, Manufacturing, Company
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	STRUCTURED CLASSIFICATION OF CRUCIAL RESOURCES AND ECONOMIC IMPACTS IN INDUSTRY 4.0 CYBERSECURITY
Author:	WAHYU SARDJONO, GUNTUR SALIM
Abstract:	Gained popularity throughout the Industrial Revolution, where manufacturing processes are deeply interconnected through smart devices, the threat landscape of cyber-attacks has expanded significantly. This research addresses the complex repercussions of cybersecurity incidents within the context of Industry 4.0 and introduces a systematic four-step methodology for evaluating the magnitude of business impacts resulting from such breaches. The study begins by examining the increasing range of types and sizes of the operational aspects of enterprises that can be significantly impacted by cyberattacks in the context of Industry 4.0. This environment can result in financial losses, harm to reputation, instances of safety breaches and conservation laws, and even pose life-threatening situations to workers. The resultant four-step methodology empowers companies to assess the financial and operational consequences of cybersecurity breaches effectively. Practical insights and guidance are offered to companies to facilitate the identification of critical manufacturing data, prioritize cybersecurity initiatives, and estimate the costs and scale of business impacts resulting from cybersecurity breaches. In conclusion, as Industry 4.0 continues to transform manufacturing, the effective management of cybersecurity becomes paramount. This study equips organizations with a structured approach to assess and mitigate the business impacts of cybersecurity breaches, emphasizing the necessity for proactive cybersecurity strategies and securing top management support to make cybersecurity both a strategic and operational priority.
Keywords:	Industrial Revolution 4.0, Industry 4.0, Cybersecurity, Cyberattacks, Business Impacts
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	EMPIRICAL INVESTIGATIONS TO DISEASE PREDICTION USING MACHINELEARNING AND WEB INTEGRATION
Author:	MR ANIL KUMAR PALLIKONDA, R ABHINAYA, PVRS PADMARAJU, DR CHANDANAPALLI SURESH, DR DNVSLS INDIRA, SUDHAKAR ATCHALA
Abstract:	Health has become the utmost priority of people's lives after the whole pandemic situation. Many new emerging technologies are striving for better human livelihood. In technology, particularly Machine Learning has a great potential in making Human lifestyle better and much simpler. Machine learning has extended its arms in Marketing Banking, Smart life, Production and Manufacturing and widely in the health care sector.ML has a great impact on the healthcare industry ML is widely used for prediction of Cancer, prediction and identifying the damage in kidneys, heart disease predictions etc. By integrating the complex algorithms with real-time medical training datasets can produce high accurate results. Hospitals and doctors have been extremely busy during the COVID period. There was a time to avoid visiting hospitals during these times unless it was absolutely necessary. This application takes the common symptoms of the patient and tries to give possible predictions as a primary disease. The application is built by using the most famous algorithms in the ML domain like Decision Tree classification algorithm, Random Forest classification algorithm, Naïve bayes classifier algorithm. This application shows the mechanism of three algorithms for disease prediction
Keywords:	Random Forest, Naïve Bayes Classifier, Disease Prediction
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	A HEURISTIC-BASED METHOD FOR DEMAND-BASED REUSABLE COMPONENT DESIGN CONFIGURE: AN EMPIRICAL ANALYSIS
Author:	N MD JUBAIR BASHA,Dr GOPINATH GANAPATHY, Dr MOHAMMED MOULANA
Abstract:	During software development, the developer must select components from the repository that meet the needs of the customer. If the repository has legacy components that fit the client's needs, they may easily be removed and delivered to the customer. If the developer is unable to locate the specific components required, the developer must configure the equivalent components from legacy components before delivering the solution. In other circumstances, when the developer is unable to locate components in the repository that do not satisfy the customer's needs, the components must be developed from scratch. The linked work now addresses the situation in which the developer discovers components that only partially satisfy the customer's needs. The identification of components, their reusability, and their ability to be grouped with other components are all investigated in this proposed work. The use of a heuristic method in the creation of configurable reusable components is discussed in this article. The major focus of this work is on the situations in the components, with the characteristics from the scenarios being identified. Furthermore, in a facade, these functions are organized as configurable reusable components. An empirical analysis was also conducted. This makes it possible for developers to locate the configured reusable components.
Keywords:	Software Components, Configured Reusable Components, Façade, Feature Point, Lack Of Cohesion In Methods, Heuristic Function
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	STRENGTHENING SECURITY IN HEALTHCARE MOBILE WIRELESS SENSOR NETWORKS USING RESILIENT BELUGA WHALE OPTIMIZATION-BASED ENHANCED TEMPORALLY ORDERED ROUTING ALGORITHM (RBWO-TORA)
Author:	S. KAWSALYA, D. VIMAL KUMAR
Abstract:	The integration of Enhanced Temporally Ordered Routing Algorithm (E-TORA) and Resilient Beluga Whale Optimization (RBWO) presents a groundbreaking approach to network optimization. E-TORA establishes a foundation with secure multi-path routing, incorporating cryptographic measures for heightened data security. Concurrently, RBWO introduces innovative optimization strategies inspired by beluga whale behaviors, fostering adaptability and cooperative exploration. The fusion of these algorithms synergistically enhances network performance, ensuring efficient data delivery, minimal delay, and optimized energy consumption. The cooperative exploration patterns inspired by RBWO complement E-TORA's multi-path routing, striking an effective balance. Results demonstrate consistently high Packet Delivery Ratios, decreased Delay values, and efficient Throughput, affirming the algorithm's success. This research contributes to the advancement of network protocols, offering valuable insights for refining and optimizing dynamic network environments.
Keywords:	Network Optimization - Beluga Whale Optimization - TORA - Routing - Security.
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	IOT BASED SMART AND ECONOMIC GREENHOUSE MONITORING AND AUTO-TUNED CONTROL SYSTEM FOR RURAL FARMING
Author:	M. THILLAI RANI, RAHUL S G, S. D. GOVARDHAN, DR. S.VELMURUGAN, J. REJINA PARVIN, P. ROHINI, RAJKUMAR.R
Abstract:	Greenhouse cultivation plays a significant role in the agricultural sector, particularly in Asia, where it supports a substantial population. However, the challenges of water scarcity, food shortages, and the need for precise environmental parameter control necessitate innovative solutions. In this study, an IoT-based Smart Greenhouse Monitoring System is proposed to optimize greenhouse conditions and improve agricultural practices. This system utilizes sensors to monitor key environmental parameters within the greenhouse such as temperature, light intensity, and soil moisture. These sensors continuously collect data, which is then transmitted to a microcontroller board. The board performs data analysis and sends the information to an online web server through a Wi-Fi connection, allowing real-time monitoring and control. By leveraging the Internet of Things (IoT) technology with an auto tuned PID Control algorithm. The developed system also enables efficient water usage during crop irrigation by providing accurate information on soil moisture levels. Also, the IoT-based Smart Greenhouse Monitoring System leads to labour savings and enhanced time management. Through automated monitoring and control, farmers can optimize their workflow and reduce manual interventions, resulting in increased efficiency and productivity. Overall, this study aims to integrate IoT technology into greenhouse operations, contributing to the sustainability, productivity, and economic viability of greenhouse agriculture. A comparative analysis is also carried out between ATmega-based microcontroller Vs PID algorithm implemented Arduino microcontroller. By providing real-time monitoring capabilities, the IoT-based Smart Greenhouse Monitoring System offers a promising solution to optimize resource usage, enhance crop yield, and foster economic growth in the agricultural sector.
Keywords:	Soil moisture, PID controller, IoT Technology, Agriculture, Greenhouse
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	KNOWLEDGE MANAGEMENT IS A CRUCIAL COMPONENT IN PREPARING THE WORKFORCE FOR THE ADOPTION OF INDUSTRY 4.0
Author:	WAHYU SARDJONO, NILO LEGOWO, DITDIT NUGERAHA UTAMA
Abstract:	Industrial Revolution 4.0 is marked by the radical adoption of technology whose goal is to increase productivity and efficiency and overcome various limitations through innovation. On the other hand, this new era of the industry can lead to substantial workforce challenges, including job losses from unskilled employees, workers must compete not only with a new, more skilled generation, but vulnerabilities are replaced by automated machines, which tend to be favored by top capital owners, in the name of efficiency. Major skill gaps are a new threat for labor workers and companies as well. Companies need to fulfill their duty to prepare their labor workforces by bridging those skill gaps, one of the solutions is knowledge management. Knowledge management has been known as the key to utilizing and capitalizing on knowledge throughout the centuries, as a key to enhancing performance, efficiency, productivity, and gaining sustainable competitive advantage.
Keywords:	Industrial Revolution 4.0, Workforce, Challenges, Knowledge Management, Adoption
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	NIR SPECTROSCOPY FOR RAPID FRESHNESS ASSESSMENT AND QUALITY CLASSIFICATION OF CHICKEN EGGS
Author:	PRITI PRAKASH PATIL, V.N.PATIL, N.A.DOSHI
Abstract:	Eggs are the affordable and easy to available still mislabelling and wrong practice while selling the eggs is normal in the India or in the world. Egg freshness can be predicted accurately by using the proven methodology called Near Infrared (NIR) spectroscopy. Now a days affordable, and easy to handle technology is taking rise in food quality detection field which is basically in situ process. The main aim of this research was to provide low cost, fastest and on-site estimation of the freshness of the chicken eggs. The spectral analysis is based on the two parameters Haugh Unit (HU) and storage days which determines the freshness and quality respectively. To correlate the HU and storage days the partial least squares prediction model was developed and tested while its accuracy was the prime moto to evaluate by comparing predicted HU values with manually measured values. The developed model has a high correlation coefficient (R) of up to 0.98, showing a strong relationship between measured and predicted HU values. The model also shows the low root mean square error values (RMSEV) up to 1.119. This research concludes the scope of NIR spectroscopy as an important tool to detect the freshness of chicken eggs during storage period. The low mean square error value and high correlation assures the productivity of this non-invasive method to detect the egg freshness. The outcome ensures the effectivity of small, low cost and portable NIR spectrometer and project it as reliable device to measure the chicken egg quality. This research provides the vast scope for quality supply of eggs to the consumer. The results of the study can be compared with the other methodologies and are found better in predicting the egg freshness.
Keywords:	NIR, Freshness Detection, Eggs, Classification, Spectroscopy, Non-Destructive, Prediction Model.
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	NOVEL TECHNIQUE FOR PREDICTION OF WEATHER FORECASTING USING MACHINE LEARNING
Author:	KATTA TRINADHA RAVI KUMAR, DR P SURESH VARMA ,DR M V RAMA SUNDARI
Abstract:	Many primary sector operations, including farming, rely on the weather to be productive. Weather forecasting has a significant impact on both life and productivity. The need of accurately predicting the serious repercussions of climate change has increased. Weather forecasts are produced by analyzing vast amounts of data that are sent from satellites for certain uses. Analysis of such a large amount of data takes time. The forecast of meteorological conditions, such as rain, wind, heat, humidity, etc., is possible with this innovative approach. It is helpful in agriculture as well. Therefore, with this new method (KNN+ RF), the occurrence, forecast time, and accuracy of sandstorms are compared with Decision Trees. As a consequence, our model outperforms the current approach in terms of results.
Keywords:	Atmospheric Condition, Decision Tree (DT), K-Nearest Neighbours (KNN), Random Forest (RF)
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	DEEP HUMAN FACIAL EMOTION RECOGNITION: A TRANSFER LEARNING APPROACH USING EFFICIENTNETB0 MODEL
Author:	R. ANGELINE, A. ALICE NITHYA
Abstract:	Because of its potential usefulness, Facial Emotion Recognition (FER) has become one of the computer vision field's fastest-growing applications. One of the main ways to communicate appropriately with people is through facial expressions. Communication success largely depends on one's capacity to read others' facial expressions. Finding the emotional states connected to varied facial expressions is the main objective of FER. In this research, different emotions are analysed and classified into eight categories: anger, contempt, disgust, fear, happiness, neutrality, sorrow, and surprise, using a CNN-based transfer learning approach. By employing 33,000+ accurately re-annotated images from popular datasets like FER2013, KDEF, and CK+ to train our model using a transfer learning strategy. EfficientNetB0, pre-trained on the ImageNet dataset, is used as the base model in this paper. The above model is fine-tuned and validated after performing Test Time Augmentation (TTA), achieving a training accuracy of 87%.
Keywords:	Facial Emotion Recognition, Anger, Contempt, Disgust, Fear, Happiness, Neutrality, Sorrow, Srprise, CK+, Test Time Augmentation.
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	ANALYSING IOT SMART SYSTEM APPLICATION AND ENVIRONMENT BY USING SIMULATED ANNEALING TECHNIQUES
Author:	P.SENTHIL PANDIAN, P.SUNDARAVADIVEL, R.AUGUSTIAN ISAAC, P.HEMAVATHY, E.S.VINOTHKUMAR, P.JANAKI RAMAL
Abstract:	A collection of online-connected devices, tools, and networks is known as the Internet of Things (IoT). It interacts with the environment both outwardly and internally. IoT detects its surroundings and responds to them. By supplying the environment with state-of-the-art techniques, it elevates people's living standards. IoT allows the devices to communicate with each other both electronically and physically. The environment can become intelligent and connect to any device at any time thanks to the Internet of Things (IoT). Through the use of IoT, data is collected and processed from a variety of actuators and sensors and wirelessly sent to computers or smartphones. To improve professionalism, IoT is used in supply chain, logistics, automation, and remote monitoring. By foreseeing the market's early growth, IoT greatly raises people's quality of life, is widely embraced by the device network, and establishes a new environment for application development. In today's fast-paced world, IoT can keep up with people's needs and demands. An overview of IoT ecosystem usages and smart system applications is given in this article.
Keywords:	Internet of Things, Smart Energy, Sensors, Wireless Networks, Simulated Annealing Techniques, Cluster Algorithms.
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	BENCHMARKING AUSTRALIA’S QUALITATIVE INTERNET OF THINGS (IOT) EXTENSIVENESS
Author:	OKTA NURIKA, CHE ZALINA ZULKIFLI, LOW TANG JUNG
Abstract:	Australia as one of the most referred industrial countries in the world is currently going through national scale Industrial Revolution 4.0 (IR4.0) that is driven by Internet of Things (IoT). The technical deployment and business model have been devised in a roadmap, which mainly covers historically successful use cases (industrial solutions) in Australia – hence giving it the globally renown sophisticated reputation with international technology companies making up the IoT business along with local enterprises and start-ups. However, this roadmap has never been assessed and given the importance of Australia as a point of worldwide technological reference, it is crucial to qualitatively benchmark it against a tested standard. In this paper, the roadmap would be measured against the proven Key Performance Indicators (KPIs) specified in enhanced CREATE-IoT standard. The original CREATE-IoT successfully assessed smart cities in European countries, while its enhanced version has plausibly evaluated Malaysia’s national IoT deployment roadmap. The assessment outcome finally discovers that 41 out of 50 (82%) of Australia’s IoT KPIs are of advanced quality. This score reflects the maturity of current Australia’s IoT ecosystem, which is deemed fit for purposes.
Keywords:	Australia IoT, Australia economy, Australia assessment, Australia KPI, Australia CREATE-IoT
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	EXTRACTION OF CAUSAL RELATIONSHIP USING THE BASE LINE LIGHTWEIGHTING MODEL AND CROSS ATTENTION
Author:	CHAEBYEOL LEE, SEUNGWOO WOO, JIHOON SEO
Abstract:	Although research on causal inference has been actively conducted in recent years in the field of natural language processing, research on this has been insufficient in the field of image processing. Therefore, in this paper, we propose a new methodology to solve the problem of visual causal inference based on input images by utilizing the Vision Transformer (ViT) structure. After lightening the existing baseline model provided with the causal relationship-based inference dataset, the causal relationship between images is extracted using cross-attention. This method reduces the complexity of the model, improves the efficiency, and can effectively understand the complex relationships embedded in visual data.
Keywords:	Vision Transformer, Visual Causal Inference, Data Analysis
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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Title:	EXACT QUANTUM STATE COMPUTATION IN QUANTUM PHASE ESTIMATION
Author:	EUN-JIN IM
Abstract:	Quantum phase estimation is a fundamental algorithm in quantum computing for estimating the phase of an eigenvalue for a unitary operator. While the output state of quantum phase estimation circuit exactly matches to the phase represented as rational number with denominator as powers of 2, the measurement could be read in many different states for phases outside this form. In this paper, we address this gap by formulating and precisely computing the quantum state of QPE when the phase is not exactly represented as a rational number with a denominator in a power of 2. Leveraging mathematical techniques from number theory and trigonometry, we derive explicit expressions for the quantum state of QPE in these cases. To validate our results, we compare our theoretical predictions with simulations using the Qiskit framework, confirming the accuracy of our formulations. Our findings provide insights into the behavior of QPE for phases, expanding the understanding and applicability of this fundamental quantum algorithm.
Keywords:	Quantum Phase Estimation, Quantum Fourier Transform, Shor’s algorithm, Probability Distribution, Quantum State
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2024 -- Vol. 102. No. 8-- 2024
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