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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 2023 | Vol. 101 No.24

Title:	A LIGHT WEIGHT OF PARALLEL ENCRYPTION WITH DIGIT ARITHMETIC OF COVERTEXT ENCRYPTION MODEL
Author:	WIDIYANTO TRI HANDOKO, EKA ARDHIANTO , HARI MURTI , RARA SRIARTATI REDJEKI
Abstract:	The speed of sending documents over the internet network is influenced by the file size. Confidential documents require fast processing, reducing the risk of hacking while on the network. So, secret documents must have a light ciphertext size. The Parallel Encryption with Digit Arithmetic of Covertext (PDAC) encryption model produces covertext with an average size of 124.88% larger than plaintext. Thus, the process in the network will take longer, and give intruders a lot of time to translate it. This research aims to design a proposed new PDAC method to produce lighter ciphertext. This research was conducted using experimental methods. Proposed PDAC model that has a different covertext generator and encryption key generator design. The results obtained are a plaintext and ciphertext ratio of 100%. This means that the size of the ciphertext is the same as the size of the plaintext. Thus, the proposed new PDAC method can reduce the size of the ciphertext, thereby speeding up the transfer process over the network, saving storage space, and making it difficult for intruders to retrieve.
Keywords:	Encryption, PDAC, Ciphertext, Cryptography.
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	DESIGN OF A NOVEL PI FUNCTIONAL OBSERVER BASED LOAD FREQUENCY CONTROLLER FOR INTERCONNECTED POWER SYSTEM
Author:	DUNDI JESRAJ TATAJI, ANAND GONDESI, K R SUDHA, A. CHANDRASEKHAR
Abstract:	The increase in population has led to the increase in the demand of the power in terms the size and. To meet this demand a decentralized power structure has emerged. The main advantage of decentralized power system is to overcome the delay in decision making unlike the centralized power system. In the present paper, load frequency control (LFC) in the decentralized scenario is analyzed using state space. The stability of the power system can be observed through a state-feedback controller rather than being directly monitored by the system Instead of guessing the system states, a Functional Observer (FO) is made to evaluate the control input. The stability can also be guaranteed by using the suggested controller, as the observer gains can be calculated theoretically. An industry-standard IEEE test system is used to evaluate the effectiveness of the suggested methods. It has been found that functional observers perform more effectively than both functional and traditional state observers.
Keywords:	Inter Connected Power System, Load Frequency Control (LFC), PI Functional Observer, Leunberger Observer
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	ANONYMOUS AND ONLINE HATE SPEECH WHEN GOVERNMENT RAISED SUBSIDIZED FUEL PRICES: AN ANALYSIS ON INSTAGRAM ACCOUNT OF INDONESIAN STATED OWN COMPANY
Author:	RYNALDI SETIABUDI, LA MANI1, DAVID WILLIEM, WILIATER SITUMORANG
Abstract:	The study aims to investigate the impact of anonymity on the frequency of online hate speech in the comments section of the instagram account @pertamina due to government's decision to increase the prices of subsidized fuel (Pertalite and Solar) in Indonesia. Hate speech can be delivered online, known as cyberhate, and anonymity in online communication can lead to aggressive behavior and the exploitation of anonymity for hate speech. This study employed a positivist research paradigm with a quantitative approach to examine social phenomena and their relationships. Data were collected by documenting the top 10 comments on @pertamina instagram posts using a coding sheet for intercoder analysis on August 31 2022 to September 7 2022. The variables of interest are anonymity and online hate speech, operationalized through some indicators. Our study found a significant relationship between anonymity and the frequency of online hate speech, with anonymous users showing a 2.42 times higher prevalence of engaging in such behavior compared to non-anonymous accounts. This underscores the role of anonymity as a potential risk factor contributing to the initiation of hate speech. Despite these findings, it is important to acknowledge that the research has limitations, such as not considering other factors that may influence online hate speech, such as educational level and sociodemographic factors. Nonetheless, the study provides valuable insights into the impact of anonymity on the prevalence of online hate speech on social media platforms like instagram.
Keywords:	Anonymous, Online Hate Speech, Fuel Prices, Subsidized, Instagram, Pertamina
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	FUZZY SIMPLE ADDITIVE WEIGHTING FOR OPTIMIZATION TOURIST DESTINATION RECOMMENDATIONS
Author:	ADIE WAHYUDI OKTAVIA GAMA, GEDE HUMASWARA PRATHAMA, DESAK MADE FEBRI PURNAMA SARI, KADEK JANUARSA ADI SUDHARMA, DEWA AYU PUTU ADHIYA GARINI PUTRI, IDA BAGUS PUNIARDHI ISABHA PIDADA
Abstract:	The difficulty of choosing destinations according to tourist preferences requires the development of an appropriate Decision Support System to optimize tourist destination recommendations. This research aims to develop a Decision Support System using the Fuzzy SAW algorithm to support the growth of the tourism sector. This research develops a decision support system. The objects used are 13 tourist destinations in Karangasem Regency, Bali. The algorithm used is Fuzzy Simple Additive Weighting, where the algorithm considers costs and benefits. Determine the criteria, then determine alternative costs and benefits and give fuzzy weight to the criteria for use. The result of this study is the ranking of tourist destination recommendations according to Fuzzy SAW calculations. This research contributes to implementing the appropriate SAW algorithm in the tourism sector. In addition, the results of this study make it easier for tourists to choose tourist destinations that suit their preferences so that they can support the growth of the tourism sector.
Keywords:	Fuzzy Simple Additive Weighting, Decision Support System, Tourist Destination, Tourism Growth, Karangasem Regency
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	CLT-CRP : A CROSS LAYER TECHNIQUE FOR CLUSTER BASED ROUTING PROTOCOL IN MOBILE WIRELESS SENSOR NETWORK
Author:	D. SWAPNA, M. NAGARATNA
Abstract:	The Wireless Sensor Network(WSN)s attains tremendous changes in the field of communication networks. Due to the nature of ubiquitous capability of WSN, used in different applications such as monitoring of environment, surveillance in military and health care applications. The innovations in WSN are widely used in data transmission applications. Basically the WSN consists of sensor nodes and clusters. The sensor nodes can move from one cluster to another cluster uniformly and randomly. The phenomenon leads to high energy consumption and data loss in the network. The field of WSN attracts many researchers to work with different routing protocols. However, the previous routing protocols were unable to reduce energy consumption and data loss. Many issues in data transmission with the sensor nodes and nature of resource constrained. To address the limitations of WSN, in this research paper proposed a Cross Layer Technique for Cluster Routing Protocol (CLT-CRP) for Mobile Wireless Sensor Network(MWSN). The proposed protocol is designed with a combination of MAC layer and Network layer configurations. The implementation of the proposed CLT-CRP is made using the NS2 simulator. The proposed protocol outperforms with the comparison of LEACH, CRPD protocols in terms of residual energy, delay, throughput and packet delivery ratio. The results revealed that the proposed protocol improves the performance of WSN.
Keywords:	Wireless Sensor Network, Cluster Head, Energy Consumption, Data Transmission
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	PREDICTION OF LUNG DISEASE SEVERITY BY APPLYING MACHINE AND DEEP LEARNING TECHNIQUES
Author:	SURYA PRAKASH GUTTULA, SANJEEV KUMAR GUPTA, LAXMI SINGH, K VIDYA SAGAR
Abstract:	Virus infected diseases are increasing rapidly. SARS covid -19 is one emerged into human body to extinct the human life. Prediction of the rapid changes and meticulous interpretation of the type of decease is challenging. Various stages of the risk severity prediction and interpretation is challenging. This paper discussed various machine learning algorithms applied on X-radiation chest images to predict the severity of the decease. Significant features are extracted using Principal component analysis (PCA). Bagging, Ada boost, XG boost, KNN machine learning methodologies applied to achieve the reliable performance. Bagging methodology shows dominant performance over other machine learning methodologies with 98.82% precision value and 98.67% accuracy. The F1 score and recall value is significantly good with 98.755 and 98.69 respectively. Bagging methodology is much reliable to interpret X-radiation images for Sars Covid-19 infections.
Keywords:	SARs Covid-19, X-radiation images, Bagging, Ada boost, KNN, XG boost, PCA.
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	METHODS OF MODELING, CALCULATION AND ANALYSIS OF ROBOTIC SYSTEMS FROM COMPOSITE MATERIALS UNDER DYNAMIC INFLUENCES
Author:	KAMIL KHAYRNASOV
Abstract:	The methods of modeling, calculation and analysis of a robotic system are considered: a semi-natural simulation stand designed to imitate flight characteristics in ground conditions. The originality of this study is in the uniqueness of the design of the stands and the modeling of the stand from a composite material. A method has been developed for approximating bearings, gearboxes and gear rims by rod systems identical to them in terms of rigidity. A method for creating a stand of maximum rigidity by locating the base of composite material along the lines of maximum stresses has been developed. A study of composite of materials and magnesium alloy, traditionally used in the manufacture of stands for dynamic operational loads, was made. A method for creating a three-layer stand structure that provides maximum strength and rigidity has been developed. The problem by the finite element method is solved. The convergence of the results of the study was determined by thickening the mesh of finite elements and comparing the results obtained. The stress-strain state of stands made of composite material and magnesium alloy has been obtained. Comparison of the results shows that the stand made of composite material has advantages over the stand made of magnesium alloy. A layer-by-layer stress state of a five-layer composite material has been obtained. The results of the failure of the composite were determined based on the proven criteria for the failure of layered materials. The considered research methods are applicable to a wide class of robotic systems under dynamic influences, containing bearings, gear rims, gearboxes and motors. The study revealed the structure of the arrangement of the base layers of the composite material, in which the construction has the greatest strength and rigidity. The production of dynamic stands from a composite material exceeds the strength characteristics of the magnesium alloy traditionally used in the manufacture of stands.
Keywords:	Robotic Systems; Stands; Composite Materials; Calculation; Analysis; Finite Element Method; Dynamic Effects
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	TRANSFORMER-BASED MODEL FOR HANDWRITTEN RECOGNITION ARABIC WORDS AL-SOUDANI MAGHREBI SCRIPT
Author:	SIDI AHMED MAOULOUD, M. H OULD MOHAMED DYLA, CHEIKH BA
Abstract:	Automatic handwriting recognition is a crucial element for various applications across different domains. It’s a complex problem that has garnered significant attention over the past three decades. For the Arabic language, research has primarily focused on recognizing historical Eastern Arabic scripts and manuscripts. However, fewer studies have been conducted on Maghrebi Arabic scripts and their variants. In this article, we introduce a novel dataset of Arabic words written in the widely used Maghrebi Al-Soudani script, primarily found in West Africa, extracted from three different manuscripts. Our dataset comprises 30,430 words. We also propose a Handwritten Text Recognition (HTR) model based on an Encoder-Decoder architecture, utilizing a Convolutional Neural Network (CNN) for image encoding and a Transformer for image-to-text decoding. We train our model, as well as a recent reference model (FPHR), on this dataset. The results demonstrate promising performance of our model, outperforming the FPHR model with accuracy rates of 10% for Character Error Rate (CER) and 9.9% for Word Error Rate (WER).
Keywords:	Dataset Transformer Encoder-Decoder Handwritten recognition Al-Soudani Arabic script Manuscript Image-to-Sequence
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	USING DOMAIN ONTOLOGY UNCERTAINTY OR ONTOLOGY MAPPING UNCERTAINTY TO IMPROVE LEARNING
Author:	TATYANA IVANOVA
Abstract:	A grand amount of research, related to the application of ontologies in education has been done in recent years. Hot topics are personalization of tutoring and learning and knowledge modeling. Most of ontologies are mainly implemented in software systems (i.e. personalized e-learning environments) and are rarely used directly by learners or teachers. This is because of the needs of specific knowledge and skills. We think that in our days some teachers and learners mainly in higher education have all the knowledge and skills for using ontologically represented knowledge directly in learning and tutoring. Knowledge uncertainty exists in all real domains, but it is rarely discussed in e-learning courses. Most of semantic technologies are based on crisp logics and cannot deal with uncertain knowledge. In this research we analyze the specifics and sources of uncertain knowledge and technologies for its semantic modeling. We also discuss strategies for direct use of uncertainty both in domain knowledge and ontological models to improve learning. In such a way we search possibilities for direct usage of methods of one of the most important artificial intelligence fields, as knowledge modeling for improving learning and tutoring. We propose a model of interactive ontology and ontology alignment evaluation environment, aimed at involving users in solving uncertainty problems during learning. We discuss strategies for application of domain and ontological models uncertainty for e-learning purposes and situations of its practical usage.
Keywords:	Ontology, Uncertainty, E-Learning, Ontology Evaluation, Ontology Mapping
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	MODELING, CALCULATION AND ANALYSIS OF ROBOTIC STRUCTURE MADE OF COMPOSITE MATERIAL UNDER DYNAMIC IMPACT
Author:	KAMIL KHAYRNASOV
Abstract:	The paper develops methods for calculating and analyzing robotic systems: multi-step dynamic systems under dynamic loading. At present, stands are made of magnesium alloys, which have good specific strength characteristics, and composite materials with higher specific strength characteristics are not used in the manufacture of stands. Therefore, the issues of researching stands made of composite material are important and relevant. The paper gives dependencies for determining the given characteristics of a multilayer composite material. Modeling and approximation of the stand by finite elements is given. The stand model had a three-layer structure: the model received the characteristics of the filler, and the surface of the model had the characteristics of a five-layer composite material. A technique for approximating the bench elements: bearings, gear rims and gearboxes by a system of rod elements of the same stiffness is proposed. An algorithm and a program for determining the stiffness of these elements have been developed. A technique has been developed for arranging a base made of a composite material along the lines of maximum stresses to ensure maximum rigidity of the stand. A comparative analysis of a stress-strain stand made of a composite and magnesium material has been carried out. Research methods can be applied to a wide class of robotic systems.
Keywords:	Robotic Systems; Benches; Modelling; Finite Element Method; Composite Materials; Stress-Strain State; Dynamic Loading
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	MANTA RAY FORAGING OPTIMIZATION ALGORITHM WITH DEEP LEARNING ASSISTED AUTOMATED PHISHING URL DETECTION MODEL
Author:	K SUBASHINI, DR. V NARMATHA
Abstract:	In current scenario, phishing attacks are vital threats to cyberspace security. Phishing is one of the common types of scams that attract individuals to access mischievous URLs (Uniform Resource Locators) as well as their personal data like IDs, passwords, and others. Many intelligent attacks have been launched to cheat users by retrieving a trustworthy website or any online platform in order to get data. Phishing URL classification is one of the crucial cybersecurity tasks intended to classify and moderate malevolent web addresses considered to cheat consumers by revealing sensitive data. Numerous researchers in cyberspace are interested in generating intelligent techniques as well as offering security services on a phishing website that grows more clever and malicious daily. Therefore, this study introduces a manta ray foraging optimization with deep learning-based phishing website detection (MRFODL-PWD) technique. The major intention of the MRFODL-PWD technique is to recognize and classify the presence of legitimate or phishing URLs. In the presented MRFODL-PWD technique, several stages of pre-processing to transfer data into a useful setup, and BERT is applied for feature extraction. Moreover, deep belief network (DBN) model can be used for automated phishing URL detection. Furthermore, the MRFO algorithm selects the hyperparameter values of the DBN model. An extensive comparison study stated that the MRFODL-PWD technique accomplishes enhanced phishing URL detection results over other models.
Keywords:	Phishing Attacks; Cybersecurity; Deep Learning; Parameter Tuning; Manta Ray Foraging Optimization
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	A NEW SMART COMMUNICATION PROTOCOL AND INTERNET OF THINGS (IOT) FOR WASTE MANAGEMENT SYSTEM
Author:	Dr.B. RAGHURAM, Dr.C.SRINIVAS, Dr.S.VENKATRAMULU, Dr.V.CHANDRASHEKAR RAO4, Dr.K.VINAYKUMAR, UMA N DULHARE
Abstract:	Rapid population growth throughout the world has resulted in the improper handling of garbage in many nations, leading to several health problems and environmental contamination. Every week, the garbage vehicles collect garbage only once or twice. Due to improper wastage collection procedures, the contents of the trash can are scattered throughout the streets. This paper proposes a solution for wise and effective waste management based on deep learning (DL) and the Internet of Things (IoT) to combat this problem. The proposed method combines the ResNet-50 and Inception network models, which receive input and provide the correct solution for identifying and classifying Waste and organizing it into the appropriate waste receptacle (recyclable, organic, and hazardous wastes) without human intervention. An ultrasonic sensor is inserted in each wastage container to measure the amount of garbage filling. The integrated GPS module monitors the bin's location in real-time. Utilizing the LoRa communication protocol, the receptacle's location, real-time, and filling level are transmitted. RFID module is embedded for personnel identification in waste management. Recycled and organic residues that have been categorized can be used for greater purposes in the future. This procedure will assist the environment is becoming more valuable and ecologically secure, as well as assist us in creating a thriving verdant ecosystem and a brighter future. This method will help us establish a lush, verdant ecology and a brighter, hopeful future while making the environment more valued and environmentally secure. The proposed Res50+IncV3 network obtains 0.98 accuracy,0.97 precision, and 23% loss.
Keywords:	Waste management, Internet of Things, neural networks, ultrasonic sensor, environmental pollution
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	NABGAMES: NASH BARGAINING GAME FOR IMPROVING COVERAGE IN UNMANNED AERIAL VEHICLES (UAV)
Author:	UMA N DULHARE, Dr.B.RAGHURAM, Dr.C.SRINIVAS, S. VENKATRAMULU, Dr.V.CHANDRASHEKAR RAO, Dr.K.VINAYKUMAR
Abstract:	The deployment and positioning of unmanned aerial vehicles (UAVs) are heavily reliant on the amount of power accessible, a workable situation, and a protected connectivity. Appropriate height is required for the practical deployment of UAV modules to reach the mobile networks. Resources wastage or operation inefficiencies result from the under or overestimation of nodes' air time. The UAV coverage issue is taken into account in this research, and then a Nash bargaining game model (NaBGames) is suggested to enhance the range by utilizing UAV nodes which are controlled and managed for re - allocation and can shift positions as needed. Cellular users are permitted to flexibly alter their service cost during every round of negotiation in order to maximize their earnings. Afterwards, every user adopts "bargaining" and "warning" to create their finest reaction. Such method could function even in hostile circumstances because it is dependent on the connections between UAVs and its nearby neighbors in a distributed system. By employing the least number of UAVs required to encompass the potential specific region, the altitudes of the UAVs are modeled based on the transmitters and other installation needs. The proposed NaBGames is compared with three base line methods, whereas it achieves 18.3 ms delay, 587Mbps throughput, 16.4% of power consumption and 97.8% of Trade success probability.
Keywords:	Game theory, Nash bargain, Unmanned Aerial Vehicles, Coverage, Power consumption.
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	GRØSTL STOCHASTIC GRADIENT DEEP MULTILAYER PERCEPTIVE BLOCKCHAIN BASED SIDE CHANNEL ATTACK DETECTION FOR ENHANCED SECURITY IN CLOUD COMPUTING
Author:	RAMAKRISHNA SUBBAREDDY, Dr.P. TAMIL SELVAN
Abstract:	Data security has a vital role in Cloud Computing. Cache side channel attacks are a type of cryptanalysis in the cloud for acutely threatens the security of the cryptosystem. Therefore, a novel cryptography model named Grøstl stochastic gradient deep multilayer perceptive Blockchain (GSGDMPB) model is designed for detecting side channel attacks in the cloud computing environment. At first, the Grøstl cryptography function generates hash value for every user data. Then Borda positional voting consensus algorithm is also applied for identifying the active blocks based on the majority votes. Secondly, Lai-Massey stochastic gradient deep multilayer perceptive learning is employed to perform encryption and decryption. After that, the generated cloud user block validation is performed based on the simplex matching coefficient. Then the max-out activation function is employed to provide final attack classification outcomes through enhanced accuracy. Experimental assessment of proposed model is performed by dissimilar metrics by a different number of traces. The results of GSGDMPB model improves data communication security through enhanced channel attack detection accuracy, throughput, and minimum overhead and time than the conventional methods.
Keywords:	Cloud Computing, Security, Side Channel Attacks, Grøstl Cryptography, Borda Positional Voting Consensus Algorithm, Lai-Massey Stochastic Gradient Deep Multilayer Perceptive Learning, Simplex Matching Coefficient, Maxout Activation Function.
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	A SURVEY ON COMPONENTS THAT GOVERN THE EXECUTION OF CRYPTOSYSTEMS
Author:	YAHAYA GARBA SHAWAI, MOHAMAD AFENDEE MOHAMED, USMAN HARUNA, MOHAMMED AMIN ALMAIAH ,ABDALWALI LUFTI, SULAIMAN IBRAHIM MUHAMMAD
Abstract:	The aim of the review was to carry out a survey on the components that govern the experimental simulation of a cryptosystems, which includes input images, hardware and software facilities, algorithm formulations. The components were analysed with regards to the performance of the encryption algorithms, based on specific finding of individual execution time. The review paper discovered the existing literature within the fields of image encryption based chaotic system, with aid of adapting new proposed systematic review framework known on as YAFSU. The search strategy process based on search and selection were considered as to be the pre-requisite of the framework, with regards to the extracted data and synthesis implementation. It is believed that the review may put impact on the researcher’s with interest on chaotic cryptosystems based on images, to discover the input images, hardware and software facilities that should be considered for the experimental simulations. In addition, performances of existing encryption algorithms were analysed, to highlights ways to be followed in developing robust image encryption algorithm that may outperform the existing ones. The limitations of the reviewed literature were discovered, that some image encryption algorithm simulations suffered from fewer input images utilizations, which in many cases resolved to poor performance or in-efficiency of the encryption algorithm. Moreover, it was observed that no research exist to prove the claim “The effectiveness of the execution time depends on the Hardware capacity of the computer systems”. Considering reviewed literatures, it was discovered that both Saleh et al and Mohamed et al. Used high capacity computers than that of Nadeem et al. But, various execution time obtained by the researcher’s disproving the earlier claim.. It was recommended that more finding should be discovered based on utilization of many input images during the experimental simulation, which will assist in monitoring both the security and performance of the proposed encryption schemes. Both the gray/coloured images need a discovery that may clear the claim which said “The effectiveness of the execution time does not depend on the Hardware capacity of the computer systems”.. in addition, it was observe that recent articles used older version of software tool being utilized during the experimental simulation.
Keywords:	Encryption Scheme, Experimental simulations, Images, Inputs, Hardware and Software, Cryptography
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	ANALYSIS OF TECHNICAL INDICATORS OF EFFICIENCY AND QUALITY OF INTELLIGENT SYSTEMS
Author:	KRYVORUCHKO O., KOSTIUK Y., DESIATKO A., STEPASHKINA K., TYSHCHENKO D., FRANCHUK T., HNATCHENKO D., ZAKHAROV R., BRZHANOV R.
Abstract:	The efficiency of intelligent systems refers to their ability to achieve set goals and perform tasks with high quality and efficiency. It is determined by the effectiveness of the system in the context of its functionality, productivity, accuracy, and speed of solving tasks. The efficiency of intelligent systems can be evaluated according to several criteria. For example, efficiency can be related to the time it takes to complete tasks, the amount of resources used, the quality of problem-solving, or the system's ability to adapt to changing conditions. Various indicators and metrics can be used to measure the performance of intelligent systems, such as response time, classification accuracy, data processing speed, use of resources (for example, memory or computing power), etc. The performance of intelligent systems is an important aspect of their development and application, as it determines their ability to achieve positive results and perform tasks with maximum productivity and quality.
Keywords:	Intelligent Systems, Multi-Agent Systems, Adaptive Systems, Efficiency Indicators
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	PROPOSED ENHANCED FEATURE EXTRACTION FOR MULTI-FOOD DETECTION METHOD
Author:	SUHAILA ABUOWAIDA, ESRAA ELSOUD, ADAI AL-MOMANI, MOHAMMAD ARABIAT, HAMZA ABU OWIDA, NAWAF ALSHDAIFAT, HUAH YONG CHAN
Abstract:	This research presents a comprehensive system that utilizes computer vision and deep learning techniques to develop the detection of multiple food methods. Despite the incorporation of deep learning techniques, the effectiveness of the existing detection method for different food products is unsatisfactory due to the utilization of ResNet-101 for feature extraction. The features maps of the ResNet-101 exhibit a size reduction or may vanish entirely following the down-sampling process. The ResNet-101 blocks may have been subject to varying degrees of repetition, with certain blocks receiving a restricted number of repeats and others being excessively repeated. There is an ongoing need to develop the rate of detection in the field of food recognition. The procedure under consideration consists of a series of primary steps. The optimization of the ResNet-101 block entails the careful selection of an appropriate number of repetitions. A supplementary convolutional layer is suggested. The results produced from this approach were later compared to the outcomes reached by the latest algorithms in object detection, specifically Mask R-CNN and CASCADE R-CNN. The evaluated algorithm exhibits exceptional performance in accuracy AP over multiple thresholds. The numbers regularly exceed the relevant criteria of three commonly utilized techniques. The thresholds demonstrate higher magnitudes than the thresholds of three frequently utilized methods.
Keywords:	Deep Learning, Object Detection, Resnet-101, Features Maps, Down-Sampling Process.
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	OLGV3 NET: OPTIMIZED LIGHTGBM WITH INCEPTIONV3 FOR ACCURATE MULTI-CLASS BREAST CANCER IMAGE CLASSIFICATION
Author:	D. VETRITHANGAM, B. ARUNADEVI, ANGATI KALYAN KUMAR, S. NALINI, NEHA , NIRMAL MAHESH
Abstract:	In todays world, computer models, especially those using deep learning, are helpful in diagnosing breast cancer by analyzing special images called histopathological images. Understanding and classifying these images for breast cancer diagnosis is crucial in the field of medical information technology. The existing deep learning models for breast cancer image classification include a lack of diversity in the training dataset, leading to reduced model robustness and an inability to accommodate variations in different imaging conditions. Furthermore, there exists a deficiency in the model's sensitivity and generalization capabilities, accompanied by suboptimal hyperparameter configurations. This inadequacy has the potential to hinder the model's efficiency in breast cancer classification. Additionally, the absence of regularization options heightens the susceptibility to overfitting. These identified gaps directly impact the effectiveness of current technologies in addressing crucial issues encountered in clinical practice and biomedical research concerning breast cancer diagnosis and prognosis. This research aims to overcome these challenges by focusing on important factors like making the model work well with different types of images, avoiding unnecessary information, ensuring efficient performance, and handling difficulties when there are only a few cancer cells present. The proposed solution is a new model called OLGV3 Net Classifier, which combines enhanced Inception V3 for understanding images and LightGBM for making accurate classifications. By using Sequential Model-Based Optimization (SMBO) to fine-tune the model's settings, this research achieved a remarkable accuracy of 99.80%, surpassing other models and making a significant improvement in breast cancer image classification.
Keywords:	Multi-classification, Breast cancer, Inception V3, LightGBM, Optimization
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	INDUSTRY 5.0: AN OVERALL ASSESSMENT OF USING ARTIFICIAL INTELLIGENCE IN INDUSTRIES
Author:	T S KARTHIK, BEATRIZ LUCIA SALVADOR BIZOTTO, MITHILEYSH SATHIYANARAYANAN
Abstract:	In recent years, the increasing complexity of digital technologies and the emergence of Artificial Intelligence (AI)-based solutions have posed challenges to maintaining a competitive edge. Industry 4.0, known as the fourth industrial revolution, entails an increased degree of automation with the goal of improving operational productivity and efficiency in both the digital and physical domains of an organization. The importance of AI computing is seeing significant growth in tandem with the advancements made during the Fourth Industrial Revolution. Notwithstanding the persistent challenges, several enterprises continue to face obstacles in their endeavor to digitalize their operations via the utilization of technologies such as the Internet of Things (IoT), AI, and other analogous improvements. To address this challenge, the introduction of the fifth industrial revolution, sometimes referred to as "Industry 5.0," aimed to address individualized production processes and enhance people across different industries. The advent of Industry 5.0 signifies the emergence of a new era characterized by personalized approaches, which enable digital production systems to achieve improved outcomes. In recent times, there has been a notable surge in the prominence of AI-based wireless applications, owing to the rapid advancements in remote devices and the emergence of AI-driven technologies. Creative AI-based advancements are emerging in industrial applications to address demands that cannot be fulfilled by traditional remote handling and communication technologies. These demands include high throughput, high mobility, low latency, heterogeneity, and flexibility. The primary aim of this research paper is to explore the effect of AI in the Industries. In order to accomplish this, initially, it provide a thorough examination of AI in the Industries, including its technological, economic, and regulatory dimensions. This analysis will include all facets of the technical assessment and impact assessment of AI in industries.
Keywords:	Industry 5.0, Artificial Intelligence, Technological Assessment, Impact Assessment
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	DETECTION OF EPILEPTIC SEIZURES USING EEG SIGNALS
Author:	DR. SHARMISHTA DESAI, PUJA A. CHAVAN
Abstract:	According to the World Health Organization (WHO), epilepsy is a chronic neurological circumstance illustrated by an excessive and uncontrolled electrical explosion. It is a rare occurrence that affects people of various ages. An electroencephalogram (EEG) of brain activity is a well-known tool for studying epileptic convulsions and recording changes in electrical activity in the brain. Consequently, epilepsy prediction and early diagnosis are required to give timely preventative measures to free patients from the detrimental repercussions of epileptic seizures. Despite decades of research, accurately projecting these seizures remains an unsolved challenge. This paper proposed epilepsy seizure detection and classification using a deep learning model called CNN and LSTM. In the convolutional layer, numerous features are extracted from EEG signal files, while in the optimization layer, non-essential elements are eliminated. In an extensive experimental analysis, we validated the system with a real-time EEG dataset, where we obtained 82.5% accuracy for epilepsy detection for the entire testing dataset.
Keywords:	EEG Signals, Epilepsy Seizure Detection, Brain Computer Interaction, Feature Extraction, Classification, Deep Learning
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	EXPLORING PRIORITY OF UCD IN AN AGILE DEVELOPMENT ENVIRONMENT: A CASE STUDY IN IRAQ
Author:	FOUAD ABDULAMEER SALMAN, BAKHTAWAR BALUCH, ZURIANA ABU BAKAR
Abstract:	User-Centred Design (UCD) and agile software development share common values, such as customer satisfaction, continuous improvement, and flexibility. However, integrating UCD into agile software development poses several challenges that must be addressed to achieve successful outcomes. This paper investigates the existing practices of UCD, which are carried out in an actual development environment. It also explores the obstacles that software development team members may face while applying UCD practices alongside agile development activities. The qualitative and quantitative approaches are used to collect the primary data from the practitioners in different job positions. Two methods are used to gather primary data: semi-structured interviews and closed-ended surveys. The results reveal a growing realization of the usability concept in software development among Iraqi agile practitioners. Further, the results provide insight into how well they can incorporate UCD activities within agile development circumstances.
Keywords:	User-Centred Design (Ucd), Usability, Usability Engineering, Agile Development Process
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	EXPLORING THE CAPABILITIES OF DEEP LEARNING FOR ADVANCING CREDIT CARD FRAUD DETECTION: A REVOLUTIONARY APPROACH
Author:	ABDERRAHMANE DAIF, SOUMAYA OUNACER, SOUFIANE ARDCHIR, MOHAMED GHAZOUANI, MOHAMED AZZOUAZI
Abstract:	The use of credit cards for both online and in-person purchases has become increasingly prevalent in our daily lives. However, this convenience also exposes users to the risks associated with credit card fraud. Credit card fraud presents a significant challenge for banks, merchants, and consumers, emphasizing the crucial need for the swift and accurate detection of such fraudulent activities. In response to this challenge, recent research has delved into the application of deep learning techniques for credit card fraud detection. This article presents a study that combines a Bi-LSTM (Bidirectional Long Short-Term Memory) with an attention layer to identify fraudulent transaction patterns and achieve a balanced classification of data. The results of this study demonstrate the method's high accuracy, surpassing the performance of other fraud detection approaches. Notably, this innovative approach efficiently identifies critical transactions within input sequences, significantly improving the prediction accuracy for fraudulent transactions. This research provides a unique perspective on the use of deep learning to enhance security in credit card transactions.
Keywords:	Deep Learning, Machine Learning, Credit Card Fraud, Bi-LSTM, Attention Layer, SMOTE.
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	OPTIMIZED FUZZY C-MEANS (FCM) CLUSTERING FOR HIGH-PRECISION BRAIN IMAGE SEGMENTATION AND DIAGNOSIS USING DENSENET FEATURES
Author:	PRIYA KANTAMANENI, D.VETRITHANGAM, M MAITHILI SAISREE3, S.SHARGUNAM, S.SRINIVAS KUMAR, ASHOK BEKKANTI
Abstract:	Brain imaging techniques play a crucial role in identifying the causes of brain cell injury. Consequently, early diagnosis of such conditions can yield significant benefits, improving treatment prospects and minimizing potential patient complications. Among the most formidable challenges in medical image analysis is brain tumor segmentation. Challenges include limited spatial context, increased occurrences of false positives and negatives, the inability to distinguish tumor components, and a lack of preprocessing. To address these issues, we propose an approach that combines Optimized Fuzzy C-Means (FCM) Clustering with DenseNet Features and employs efficient preprocessing techniques. Our improved DenseNet architecture meticulously extracts relevant features from preprocessed images. FCM assigns each feature vector to one or more clusters based on their degrees of membership and its output encompasses cluster centers and membership values, indicating the degree of association for each data point with each cluster. Hence, FCM improves interpretability by distinctly delineating tissue regions through the utilization of these features. Markov Random Field (MRF) Optimization is probabilistic graphical model that capture spatial dependencies among neighboring pixels or regions in an image. As each MRI modality possesses the unique ability to emphasize distinct tissue characteristics. All the MRI Modalities (Flair, T1, T1c, T2) can be combined to get valuable and complementary wealth of information regarding the tissues and structures undergoing examination. Our optimized FCM model is experimented on the Original FLAIR -MR images of patients and Combined MRI Modalities (Flair, T1, T1c, T2).The Optimized Fuzzy C-Means (FCM) Clustering achieved train Dice Coefficient score of 99.18% and test Dice Coefficient score 98.64%, and the Optimized Fuzzy C-Means (FCM) Clustering with Combined MRI modality feature achieved train Dice Coefficient score 100% and test Dice Coefficient score of 99.413%.The results shows that the proposed model out performs the existing models.
Keywords:	Brain Image, Segmentation, DenseNet, Fuzzy C-Means, Optimization, Diagnosis
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	BILAE-GAN FRAMEWORK FOR ANOMALY DETECTION IN VIDEO SURVEILLANCE
Author:	SWAPNA.C, DR.B.PADMAJA RANI
Abstract:	In recent years, increasing the use of surveillance cameras with less manpower makes automatic video surveillance systems to become more important. Recent advances in video anomaly identification have mostly focused on improving performance with available datasets. We propose a Bidirectional Long-Short term memory-based Convolutional Autoencoder Generative Adversarial Network (BiLAE-GAN) method for video surveillance. During training the model learns the normal data distribution of data in the Generator and the detection of anomalies in the discriminator. Bidirectional Long-Short term network in Convolutional Autoencoder in Generator for reconstruction, Encoder features of Generated image and real image to discriminator to identify Anomaly. At the anomaly detection phase, anomalies are identified based on reconstruction error and discrimination results. Our proposed method validation benchmark datasets such as UCSD Ped1, UCSD Ped2, and CHUCK Avenue dataset with performance metrics AUC, EER.
Keywords:	BiLAE-GAN, Encoder-Decoder, BILSTM, Svdloss, Advloss
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	MAHALANOBIS-TAGUCHI SYSTEM AND TIME-DRIVEN ACTIVITY-BASED COSTING INTEGRATION OF PRINTED CIRCUIT BOARD
Author:	NURUL HAZIYANI ARIS, MOHD YAZID ABU, SRI NUR AREENA MOHD ZAINI, MUHAMMAD ARIEFFUDIN MOHD JAMIL, NUR SYAFIKAH PINUEH, WAN ZUKI AZMAN WAN MUHAMMAD, FAIZIR RAMLIE, NOLIA HARUDIN, EMELIA SARI
Abstract:	System integration involves creating or implementing a customized architecture, as well as integrating hardware, packaged and customized software, and communications. The electronic component demand is rising nowadays. However, a weekly rejection report for rejected parts is necessary in reflect the severity level as it can influence the criteria used to identify relevant parameters. Meanwhile, activity-based costing (ABC) is outdated since it employs interviews or surveys to get numerical data, which can lead to erroneous corporate data. This research aim is to integrate Mahalanobis-Taguchi system (MTS) with time-driven activity-based costing (TDABC) at the electronic component workstation. MTS optimizes 22 printed circuit board (PCB) assembly top side workstation environments. After generating time equation and capacity cost rate, TDABC considers critical elements to identify underutilized capacity. As a result, October and December have the most significant parameters (16) and August the fewest (10). Next, the TDABC confirmed that the PCB input into auto loader machine sub-activity has a large unused capacity of time (239,550 minutes) and cost (MYR18,120.67), which allows this workstation to minimize unused capacity by shifting employees to another load workstation. In spite of to minimize capacity utilization in sub-activity solder paste preparation, extra labor should be given since it has an overutilized capacity of time (-790,100 minutes) and cost (MYR-1,000,535.13). Lastly, the reduction of waste and better forecasting is obtained after MTS and TDABC integration is implemented.
Keywords:	MTS, TDABC, Integration, Classification, Optimization.
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	SKIN LESION SEGMENTATION AND CLASSIFICATION USING FCN-ALEXNET FRAMEWORK
Author:	V. AUXILIA OSVIN NANCY, V.RAJASEKAR, MEENAKSHI S ARYA
Abstract:	Malignant melanoma tumors diagnosis relies as essential tasks for segmentation and classification of skin lesion. However, previous deep learning methods still face challenges in effectively handling aspects like boundary identification, artifact presence, and limited dataset availability. The automatic application poses significant difficulties, especially considering the intricate nature of melanoma. Its inconspicuous contrast with surrounding skin makes it a formidable task for clinicians to detect. This research governs the approach that leverages medical image segmentation to aid dermatologists in swift melanoma identification. The AlexNet framework is used in this study to develop a skin lesion segmentation and classification system. It begins with an encoder-decodifier fully convolutional network (FCN) to recognise the complex features of lesions and to learn about their boundaries using the decoder. Using a succession of skip paths, FCN subnetworks are connected to each other. The implementation involves the modification of two CNN architectures into fully convolutional network models. Four distinct FCN architectures, namely modified FCN-AlexNet, FCN-8s, FCN-16s, and FCN-32s, are employed to autonomously dissect skin lesions based on their semantic characteristics. The evaluation on the HAM 10000 dataset, a pivotal aspect of this study, marks the pioneering assessment of their capabilities on this dataset. Through these advancements, this research seeks to enhance the diagnostic accuracy and efficiency of melanoma identification in dermatology.
Keywords:	Skin Lesion, Classification, FCN, Alexnet, Melanoma, Encoder-Decoder, Deep Learning
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	DETECTION OF CASING LACERATION USING DENSE CNN
Author:	MR.K.PHANI RAMA KRISHNA, DUVVURI ESWARA CHAITANYA DR.M.PREMA KUMAR, VENKATA BHUJANGA RAO MADAMANCHI, BALAJI. TATA, KURRA UPENDRA CHOWDARY
Abstract:	The conveyance of dermatological administrations could be totally changed by the utilization of teledermatology. Using broadcast interchanges progresses, teledermatology is utilized to pass clinical information on to experts to investigate disease. The goal of our investigation is to perceive skin wounds by gathering the image trial of skin bruises that were gained from various patients. In this work, input information is taken from the Kaggle. The next step involves scaling the input images using the PC vision library in order to focus in more on the irritated area. The dataset is then divided into preparation dataset and testing dataset. Eighty percent of the dataset is utilised for planning and twenty percent is used for testing in the accompanying stage. Through the use of planning datasets, our suggested DenseNet Model—which has five convolutional layers—is suitable for use at 100 years old. The testing dataset is attempted by the pre-configured DenseNet model, and the accuracy is measured and reviewed. Our experimental analyses highlight how information pictures can identify skin injuries.
Keywords:	Casing, Laceration, Uncovering, Dense CNN
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	DYNAFLOW DEVICE OPTIMAL PLACEMENT USING ARTIFICIAL INTELLIGENCE
Author:	RAGALEELA DALAPATI RAO, PADMANABHA RAJU CHINDA, KUMAR CHERUKUPALLI
Abstract:	Numerous studies on steady-state control problems in power systems have made heavy use of flexible AC transmission systems (FACTS). For example, the Combined controllers, also known as the Dynaflow Controller, are just one of the several available FACTS controllers. This coordinated controller combines a TCPST and a TSSC, making it a new member of the FACTS family. It also belongs to the FACTS group of standards. Power flow can be controlled in either the way the Dynaflow Controller is positioned, or in parallel, using the combined skills of TSSC and TCPST. To address this issue, the particle movement optimization-based bee colony algorithm (PMBCA) has been proposed. In order to address the OPF problem under a wider range of conditions, including normal operation, network contingency, and overload, the idea of using Decision Making to determine the optimal location of a Dynaflow device has been developed. The regular case, the network emergency case, and the network overflow case are all examples. The outcomes of the IEEE 30-bus system are used to demonstrate the proposed method. The results indicate that the dynaflow device may be placed most effectively using MADM techniques.
Keywords:	Dynaflow Controller; MADM methods; OPF problem; Particle Movement Bee Colony.
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	PREDICTING THE DIFFICULTY OF WEAVING A NEW FABRIC USING ARTIFICIAL INTELLIGENCE (FUZZY LOGIC)
Author:	M. EL BAKKALI, R. MESSNAOUI, O. CHERKAOUI, AZIZ SOULHI
Abstract:	This research work presented in this paper is the modelling of the prediction of the weavability of a new fabric at the time of its creation. Using artificial intelligence (fuzzy logic), we show the feasibility of a decision support model for designers and production experts. This model is based on fuzzy set theory and uses knowledge and expertise to help designers predict the degree of difficulty in making new fabric and to avoid material waste, time loss, and material damage. With this model, designers have more opportunity to choose the right decisions. Below we find an application of this model based on fuzzy logic. The simulations produced convincing results, and demonstrated the extent to which the knowledge and expertise of weaving experts can be exploited to anticipate production problems.
Keywords:	Weaving, Weavability Limits, Fuzzy Logic, Modelling, Saturation Index.
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	AN ENSEMBLE APPROACH FOR HARVEST VINTAGE FORECAST WITH MACHINE LEARNING TECHNIQUES: AN EXPERIMENTAL STUDY
Author:	DR.SURESH KUMAR PITTALA, DR. KONGARA SRINIVASA RAO, BALAJI.TATA, P.RAVI KUMAR, N.JAYA, DR Y ANURADHA, KURRA UPENDRA CHOWDARY
Abstract:	Since crop yield forecasting directly affects the production and security of food, it is an essential part of agricultural research and development. The traditional methods of calculating harvest vintages centered on agriculturalists' explanations and expertise have become increasingly challenging as a result of the rapid ups and downs in soil and climatic state of affairs. Machine-learning techniques have been applied in recent years to find a solution to this issue. This study centers around the utilization of a few AI models, for example, nearest neighbor relapse, closest polynomial relapse, irregular woodland relapse, slope helped tree relapse, and backing vector relapse, for the expectation of farming efficiency. The raw data was transformed into a format that is conducive to machine learning using efficient feature selection techniques. The study's findings showed that, in comparison to other tactics, when choosing features, using an ensemble technique can lead to more accurate predictions. By combining several data sources and machine learning algorithms, the ensemble approach generates a detailed and precise forecast. This work highlights both the benefits of using machine learning techniques to forecast agricultural amount produced and the recompenses of using a collaborative line of attack to feature selection
Keywords:	Harvest, Vintage, Ensemble, Forecast, Agriculture.
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	AN EFFICIENT CYBER SECURITY AND DATA SCIENCE FOR ANALYZING BIG MEDICAL DATA
Author:	MOHAMMAD AL-OMAR, SALEH ALOMARI, TAMER BANI AMER
Abstract:	This study aims to the impact of the use of cyber security and data science in analyzing big medical data. The study sample comprised 120 participants, including hospital chief information officers, chief information security officers, and healthcare cyber security professionals, who were selected from all 33 government hospitals in Jordan connected to the Ministry of Health as the research sample. The primary independent variable, cyber security, was evaluated using information security, network security, operational security, and end-user education. massive amounts of medical data were used as the dependent variable. The study used SPSS to determine the impact of cybersecurity on the analysis of big medical data in Jordanian hospitals. The results found that 75% of participants confirmed that analyzing big data in the medical field will have a high impact on the evaluation of medical diagnoses and 63.3% of the participants agreed that analyzing big data in the medical field, it will have a high impact in predicting the incidence of diseases, the results also found the role of cybersecurity in protecting the storage of a large amount of data in hospital information systems (HIS), ranking first with an arithmetic mean of (3.73), The study recommends that future research should explore the benefits to medical organizations of analyzing structured and unstructured data in clinical and administrative fields, such as the limitations they face in these areas. Additionally, it is suggested that further research should also include medical institutions from outside Jordan borders to enable international comparative analyses.
Keywords:	Cyber Security; Big Medical Data; Data Analyzing; Jordan Hospitals
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	AN IOT FRAMEWORK FOR ACCURATE DIAGNOSIS OF COVID-19 TO ELIMINATE FALSE POSITIVES USING RESNEXT
Author:	A. PHANI SRIDHAR, DR.P.V.LAKSHMI
Abstract:	Background: Novel Corona Virus is increasing day by day; it is needed to identify the formal techniques and innovative trendy approaches. The responsive effort in short times is needed. Objective: The aim of this study was to consequently, display things that recognize COVID-19 pneumonia accurately. This examine work is pointing to particularly offer assistance with the conclusion of COVID-19. Methods: The proposed work is centred on the headway of an AI-based examination of CT pictures of the cutting edge crown infection. The proposed system classifies CT pictures utilizing balanced Resnet appear name ResNext. The Convolutional layers of particular sizes were utilized in each of the confined ways of Resnet illustrated. In Resnext, 32 channels are bound together at the same bottleneck and convolve them. This made it conceivable to perform indistinguishable changes to gather convolution in 32 bunches, which compares to the initial 32 courses. The proposed show effectively separated between viral pneumonia and COVID-19 influenced lung CT pictures. The accuracies of DenseNet, mobileNet and VggNet are 90.91,75.24 and 35.75 respectively for testing as shown in table 4. Results: False positives are identified among normal images, Covid effected images and viral pneumonia images. Also the validation loss and validation accuracy of the training process is to be processed and observed. The training loss is also calculated and observed. The training accuracy and validation accuracy both reached 100%. The final testing accuracy of the proposed model is 100%. The performance comparison of existing methods with the proposed method is also observed. The proposed method obtained better classification accuracy when compared to the existing deep learning models DenseNet, mobileNet and VggNet. The accuracies of DenseNet, mobileNet and VggNet are 89.3, 72.72 and 30.30 respectively for training. Conclusion: The clinical execution of the PCR test for COVID-19 pneumonia is not accurate. This paper points to assist make stride the AI examination stage for COVID-19 pneumonia investigate and make strides the precision of AI choice and judgment. The proposed demonstrate gotten an exactness of 100% in dispensing with wrong positives delivering accurate COVID-19 detection.
Keywords:	COVID-19, lung CT, Resnet, ResNext, viral pneumonia, Reverse Transcriptase Polymerase Chain Reaction (RT-PCR)
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	ENGENDERING FINEST TEST CASES BY USING G-GENETIC ALGORITHM VARIANT SSGA
Author:	T J PRASANNA KUMAR, JASTI SURENDRA, PONNURU ANUSHA, DR M. BABU PRASAD, MANASA BANDLAMUDI, K.PRAVEEN KUMAR, ANIL KUMAR PALLIKONDA
Abstract:	Programming Testing is the most difficult occupation among every one of the companions of the business. Thorough programming Testing is never conceivable just streamlined programming testing is conceivable. Thus Programming Testing can be seen as streamlining issue as it fall under NP Hard. Because of the enormous number of experiments that are expected to perform adequate testing of the ideal programming application; the assorted strategies to decrease the test suite is required. One of the normal concentrated on techniques is eliminating the repetitive experiments; the explanation is negligible number of experiments and most extreme number of mistakes disengagement or uncovering. In this examination work review is led to address the use and viability of Consistent satisfy hereditary calculation to reduce the quantity of experiments that don't added unmistakable worth in that frame of mind of test inclusion or where the experiments can't disconnect blunders. Hereditary calculation is used in this work to help in limiting the experiments or upgrading the experiments , where the hereditary calculation produces the fundamental populace arbitrarily, ascertains the wellness esteem utilizing inclusion measurements, and afterward particular the posterity in sequential ages utilizing hereditary tasks determination, get over and change. The hereditary displaying activities are explicit and in light of the activity might shift to typical hereditary calculation. This course of age is rehashed until there is no adjustment of the wellness values for two successive ages, when there is no adjustment of the information age for two emphases so intermingling achieved or a limited experiment is accomplished. The aftereffects of review show the way that, hereditary calculations can essentially lessen the size of the experiments
Keywords:	SSGA, NP Hard, Test Case, Error, Testing.
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	THE REPRESENTATION OF THE CULTURAL CRISIS IN SOCIAL MEDIA AS A NEW ECONOMIC REALITY IN THE CULTURE INDUSTRY
Author:	AHMAD MULYANA, DEWI SAD TANTI, AMINAH SWAENAWATI, IRMULANSATI TOMOHARDJO
Abstract:	In the dynamic landscape of the digital culture industry, messages on social media go beyond mere expression, realising economic value as commodities carefully packaged by content creators to garner followers. Operating within the sphere of mass culture and the culture of irreverence, content representations align themselves with market tastes, prompting an exploration of the complex interplay between cultural preferences and economic motives. Using netnographic methods, this research aims to uncover content creators' intentional design of messages and identify dominant orientations within the social media industry. Through a critical lens, this research reveals the ideological workings intricately woven into the dynamics of digital content, investigating the reflection and potential perpetuation of cultural norms and values. The results show that vulgar language on platforms such as Bunda Corla and Nikita Mirzani includes categories of profanity, racism, explicit content, harassment and cyberbullying, demonstrating the multifaceted nature of content in the digital cultural landscape. Beyond its direct impact, this type of content significantly affects society's economy and culture, evolving alongside technological advancements and shifting consumer trends. This research emphasises the understanding required to navigate the intersection between cultural representation, economic interests and societal evolution in the digital culture industry.
Keywords:	Representation, Youtube, Culture Industry, Cultural Crisis, Netnography
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	ASSESSING THE EFFECTIVENESS OF QUILLBOT-MEDIATED INSTRUCTION IN ENHANCING EFL STUDENTS’ PARAPHRASING SKILLS
Author:	TAJ MOHAMMAD, ALI ABBAS FALAH ALZUBI, MOHD NAZIM, SOADA IDRIS KHAN
Abstract:	Paraphrasing skills have reportedly been challenging for EFL learners due to limited linguistic exposure, which hinders their language development and academic growth. This challenge necessitates effective learning strategies, such as artificial intelligence (AI)-mediated tools, which have the potential to improve writing skills. Therefore, QuillBot, an online paraphrasing tool, was experimented with in this study to investigate its effect on developing students' paraphrasing skills at university. The quasi-experimental method research design was followed to achieve the study objectives. Thirty students enrolled in Technical Writing were recruited. A test and a semi-structured interview were employed for data collection. The QuillBot-mediated instructional program highly benefited students’ paraphrasing skills in Technical Writing. Also, students were satisfied with using QuillBot as it helped them improve vocabulary, sentence structure, the substitution of grammar units, and comprehensibility. In addition, the EFL students perceived QuillBot as user-friendly, simplified, and adaptable. The current findings contribute to the role of AI tools like QuillBot in improving students' paraphrasing skills; therefore, teachers should incorporate AI tools in teaching writing.
Keywords:	QuillBot, University Students, Paraphrasing Skills, EFL Writing.
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	A TECH SAVVY TOMATO LEAF AND FRUIT DISEASE CLASSIFICATION SYSTEM BASED ON ENSEMBLE DEEP LEARNING MODEL AND SHUFFLED SHEPHERD OPTIMIZATION ALGORITHM
Author:	MRS. M. SHARMILA, DR. M. NATARAJAN
Abstract:	Deep learning based artificial intelligent solutions are proving to be very successful in many different fields. It shows promising results in precision agriculture as well. Hence in this article, disease classification model based on deep learning has been proposed for identifying and classifying tomato leaf and fruit diseases. The proposed system acquires data set from Kaggle image repository and augments them using data warping techniques. The augmented data is preprocessed using median filter and contrast enhanced using dynamic histogram equalization. U2 net architecture is used for background removal and Segnet algorithm is used for disease region segmentation. Features extraction is achieved through Nasnet Large model and classified using ensemble model. The obtained results are optimized using shuffled shepherd optimization algorithm and the proposed system is tested against existing classifiers like and is proved to work efficiently and accurately than the previous models.
Keywords:	Tomato Diseases, U2Net, Dynamic Histogram Equalization, Segnet, Nasnetlarge, Ensemble Model.
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	MULTIMODAL SIGNAL ANALYSIS FOR DEPRESSION AND ANXIETY PREDICTION: A HYBRID CNN-RNN APPROACH
Author:	MAMTA KUMARI, DR. MURALI KRISHNA PUTTAGUNTA, ANISHA J, DR. TAVITI NAIDU GONGADA, DILFUZA GULYAMOVA
Abstract:	Anxiety is a serious global problem. The cornerstones of management are drug and psychological therapy. Because some individuals will respond to traditional medications, regardless of their efficacy, there is a demand for alternative strategies for preventing and treating depression. The physiological markers that can be employed in combination with a method to recognize depression are described in this study. This article contributes to the successful instructional approach by optimizing a convolutional neural network model. In this instance, the attributes are extracted using a Convolutional Neural Network (CNN), which increases the classification accuracy and predictability of sadness. RNN forecasts the existence or absence of depression. This unique approach aims to reduce training time and improve accuracy by combining the use of convolutional neural networks with bi-directional short-term and long-term memory. Using the suggested method, the CNN-RNN classifier uses ECG data to predict sadness and anxiety. The proposed method for identifying depressive illnesses effectively obtained 99.6% accuracy, 99.5% recall 99% precision, and a 99.7%F1 score using a combination of evaluation of heterogeneous emotional signals to anticipate melancholy and assess stress levels. The success of the proposed strategy is confirmed through the comparison of its outcome indicators with the validation data.
Keywords:	Anxiety; Psychological Treatments; CNN; RNN; Melancholy
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING-DRIVEN DECISION-MAKING IN SPINAL DISEASE TREATMENT
Author:	DR. SUNDARAMOORTHY K, DR. MOHAMMED SALEH AL ANSARI, DR. S. KOTESWARI, MAMTA KUMARI
Abstract:	Aside from people with significant neurological abnormalities, it's unclear whether conservative or surgical therapy of spinal disorders is better for each patient. This research looks into the use of Artificial Intelligence (AI) and Machine Learning (ML) in the therapy of spinal diseases decision-making. A supervised ML was trained and tested using the datasets of 70 patients with goal of accurately predicting the Oswestry Disability Index (ODI) 5 months following operation or the beginning of conservative therapy. In addition, using tenfold cross-validation, created an approach that forecasts the ODI of 5 arbitrarily chosen testing patients. The use of AI in this study also enabled for a comparison of the genuine patient data after 5 months with different treatment forecast, revealing variances of up to 19.6%. In the supervised version used here, ML can detect patients who'd receive from conservative treatment earlier on and, on the other hand, avoid unnecessary delays and painful for patients who'd gain from surgical treatment. Furthermore, despite tiny diagnostic categories, this strategy can be applied in various other fields of medical as an excellent instrument for decision-making when deciding between competing treatments alternatives.
Keywords:	Artificial Intelligence, Machine Learning, Spine, ODI, Pain, Therapy
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	ANALYTIC HIERARCHY PROCESS-BASED EVALUATION APPROACH FOR DIGITAL TECHNOLOGY SERVICE MANAGEMENT
Author:	KATHARI SANTOSH, MR.P.RAVI PRAKASH, L. B. ABHANG, ISKANDAR MUDA, DR. DYUTI BANERJEE, DR.S.SUMA CHRISTAL MARY, MANIKANDAN RENGARAJAN
Abstract:	Rapid digital technology advancement, automating of production procedures, and effective decision-making in company operations are all results of the industry 4.0 revolution. There is a lack of empirical study examining the desire to embrace industrial technology for managing services, considering the potential advantages of industrial technologies in operations administration that have been described in the existing literature. By discussing how disruptive innovations that includes the Cloud Computing (CC), Internet of Things (IoT), and Artificial Intelligence (AI) support service transformation in industrial firms. In a time of lightning-fast technological development, the contributions of research in information technology are important because they are essential in spurring innovation, improving system performance, and tackling difficult problems all of which help to shape the course of the digital revolution. Component-level issues like data are transformed into information and knowledge by the technologies using the Analytical Hierarchy Process (AHP) model, which enables the evolution of services. Even though it is primarily required for becoming an availability provider, the study identify that IoT is the cornerstone of any service change. Advancing to an effective provider profile requires AI. CC is specifically utilized to apply an industrializer approach, resulting in established, predictable, and component products, in addition to offering adaptability across every profile. The study examines the potential for operations managers to efficiently and effectively recognize technologies intended to increase productivity in operations according to the findings.
Keywords:	Industry 4.0; Digital Technology; Digital Transformation; Service Management; Analytical Hierarchy Process;
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	HATE SPEECH DETECTION USING LSTM AND NAÏVE BAYES ALGORITHM
Author:	ANANDA WONGWATANA NASUTION , ARIEL ZAHRAN, MUHAMMAD FAZAR AKBAR ,GHINAA ZAIN NABIILAH ,ROJALI
Abstract:	Hate speech detection requires effective strategies to ensure a safe and inclusive online environment. This research paper presents a comparative study of hate speech detection using Natural Language Processing (NLP) techniques, specifically Naïve Bayes and Long Short-Term Memory (LSTM) approaches. The objective is to develop models capable of automatically identifying and analyzing hate speech in written language. The prevalence and impact of hate speech are emphasized, as it can lead to psychological harm and incite criminal acts. NLP offers a valuable tool for automatically detecting potentially dangerous content and addressing this problem. The study utilizes a dynamically generated dataset containing diverse words and expressions to train and evaluate the Naïve Bayes and LSTM models. The results show that the LSTM and the Naïve Bayes model, achieving an accuracy of 74% and 64%.
Keywords:	Hate Speech, Naive Bayes, LSTM, Text Classification,NLP
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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Title:	ENERGY EXCHANGE PROCESS FOR SMART GRID BASED ON INTEGRATING BLOCKCHAIN WITH GCN-LSTM
Author:	DR. VUDA SREENIVASA RAO, AFSANA ANJUM, DR.S.SUMA CHRISTAL MARY, IBRAHIM AQEEL, DR. S. KOTESWARI, SHAMIM AHMAD KHAN, MANIKANDAN RENGARAJAN
Abstract:	The energy exchange process lies at the heart of the modern smart grid, a transformative energy infrastructure. This process involves the seamless transfer of electricity among various grid components, including consumers, producers, and storage units, to meet dynamic demand patterns efficiently. Traditional energy exchange systems often lack transparency, building it tough on behalf of clients to track the source of their energy and verify the fairness of prices. By addressing these issues and increasing effectiveness, security, also transparency of energy trading and supply. The modernization of energy systems has ushered in the era of the smart grid, promising enhanced efficiency and resilience. In the pursuit of these goals, this study explores a novel approach by integrating two cutting-edge technologies: Block chain and Graph Convolutional Networks with Long Short-Term Memory (GCN-LSTM). Block chain, renowned for its transparency and security features, is leveraged to enable transparent, tamper-proof energy transactions within the smart grid. Complementing this, GCN-LSTM, a fusion of graph neural networks and deep learning, enhances grid intelligence and decision-making, optimizing energy distribution and consumption patterns. This research delves into the intricacies of this integration, offering insights into its benefits, challenges, and potential applications. By combining the decentralized ledger capabilities of Block chain with the data-driven power of GCN-LSTM, This study hopes to open the door to a more resilient and adaptive smart grid, heralding a new era in energy exchange and management. Overall, the proposed methods are highly effective and have demonstrated their superiority by achieving an impressive classification accuracy of 99.30%, which outperforms several existing state-of-the-art methods in the same task.
Keywords:	Graph Convolutional Networks; Deep Learning; Long Short-Term Memory; Block Chain; Smart Grid;
Source:	Journal of Theoretical and Applied Information Technology 31^st Decmeber 2023 -- Vol. 101. No. 24-- 2023
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