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| | | Submit Paper / Call for Papers | 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 in case of review papers). |
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| Journal of Theoretical and Applied Information Technology
July 2024 | Vol. 102 No.13 |
| Title: | SMOTE-2DCNN FOR ENHANCING SPEECH EMOTION RECOGNITION | | Author: | NURUL NADHRAH KAMARUZAMAN, NOR AZURA HUSIN, NORWATI MUSTAPHA, RAZALI YAAKOB, MUHAMMAD MUDASSIR EJAZ | | Abstract: | Speech emotion recognition (SER) is a specialized form of audio classification that aims to identify and classify emotional states expressed from spoken language or speech signals. In this study, the main objective is to propose an accurate audio classification model for the SER. This study primarily focuses on two key issues: the insufficient training data within each available dataset and the imbalanced distribution of data, both of which contribute to overfitting and negatively impact the accuracy of the audio classification model. Henceforth, we present the SMOTE-2DCNN, which is a combination of the Synthetic Minority Oversampling Technique (SMOTE) with a 2-Dimensional Convolutional Neural Network (2DCNN), designed to effectively address imbalanced data distributions and achieve accurate emotion classification. Our proposed SMOTE-2DCNN demonstrates outstanding performance with a UA rate of 81% and a WA rate of 80%. This represents a substantial enhancement, achieving approximately 15% higher accuracy compared to the leading state-of-the-art method. | | Keywords: | Speech Emotion Recognition, Audio Classification, Deep Learning, SMOTE, Imbalanced Data | | Source: | Journal of Theoretical and Applied Information Technology
15thJuly 2024 -- Vol. 102. No. 13-- 2024 | | Full Text | | |
| Title: | 5G APPLICATIONS VIA VIRTUAL REALITY TECHNOLOGY IN EDUCATION | | Author: | ROSILAH HASSAN, MUHAMAD IRSAN, MIRNA NACHOUKI, NURUL HALIMATUL ASMAK ISMAIL, SAMER ADNAN BANI AWWAD | | Abstract: | Fifth-generation (5G) technology has been widely adopted in all spheres of society, fostering excellent development across a range of sectors and domains. In education, 5G technology has greatly improved the interactive communication between teachers and students, and students and human-machine in the smart teaching mode. With its functions for teaching, research, management, and evaluation, the smart teaching mode has created a new paradigm for digital education. It provides smart teaching cloud services to external tutors as well as instructors and students at affiliated colleges and universities. However, educational institutions today are still unaware of the importance of 5G and VR (virtual reality) in education, because they do not apply their use in classroom teaching and learning activities. In fact, they are still faced with unstable network problems that interfere with the teaching and learning process. Therefore, this preliminary study is dedicated to discussing the awareness of 5G applications with VR technology in education. This is to see the extent of the knowledge of instructors and students regarding the use of 5G and VR in their educational activities. The study approach has been decided upon as an online survey based on an opinion poll (questionnaire) due to the rapid turnaround, prompt delivery, and simple return. The results showed that 90% of the respondents said they had heard of VR technology, and 89.13% had used the 5G application for teaching and learning. This shows that 5G technology has been widely used in education, and VR technology is gradually entering people's vision. In conclusion, this study will be able to give some awareness to educational institutions in particular, to apply the use of 5G and VR in future education. | | Keywords: | 5G, Education, Virtual Reality Technology | | Source: | Journal of Theoretical and Applied Information Technology
15thJuly 2024 -- Vol. 102. No. 13-- 2024 | | Full Text | | |
| Title: | COGNITIVE FEATURES FOR EARLY ALZHEIMER'S DISEASE DETECTION: A STACKING-BASED ENSEMBLE MACHINE LEARNING METHOD | | Author: | PULI SUKESH, RADHIKA RANI CHINTALA | | Abstract: | This work proposes a novel ensemble machine-learning approach for early AD detection, focusing on cognitive features. The method employs a stacking-based ensemble model, combining the strengths of multiple base learners to improve prediction performance. It utilizes a comprehensive dataset containing cognitive features, demographic information, and clinical scores from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The proposed method achieves high accuracy in distinguishing between AD patients and healthy controls, with several models, including Decision Tree, Decision Tree - NCA, Voting Classifier, Voting Classifier - NCA, Stacking Classifier, and Stacking Classifier - NCA, achieving 100% accuracy. This demonstrates the potential of the approach as a valuable tool for early AD detection. A crucial advancement in this domain is the adoption of ensemble machine learning models, which significantly enhance the robustness of predictive systems by amalgamating diverse machine learning algorithms. This novel approach incorporates a feature selection method referred to as Neighborhood Component Analysis and Correlation-based Filtration (NCA-F) to sift through a given dataset and pinpoint pivotal cognitive features. The proposed research contributes to the advancement of early Alzheimer's disease detection by leveraging machine learning techniques, specifically stacking-based ensemble methods, to identify cognitive features indicative of the disease in its early stages. | | Keywords: | Alzheimer's disease, early detection, cognitive features, ensemble machine learning, stacking, Alzheimer's Disease Neuroimaging Initiative (ADNI). | | Source: | Journal of Theoretical and Applied Information Technology
15thJuly 2024 -- Vol. 102. No. 13-- 2024 | | Full Text | | |
| Title: | AUTOMATIC ROAD ROUGHNESS DETECTION AND RANKING USING DEEP LEARNING AND COMPUTER VISION | | Author: | MUHAMMED SAFFARINI, AMJAD RATTROOT, YOUSEF-AWWAD DARAGHMI, MUATH SABHA | | Abstract: | Roads roughness is considered one of the most important problems that government institutions face because it requires many complex issues to find the roughness of the street. It also requires a lot of expensive tools which, in turn, measure the roughness of the roads. so, in this research paper we create a new model study road roughness and rank the roughness of this road automatically without the need for any cost or human intervention. Our proposed model checks the roughness by capturing the imaging using a drone, then it processes and analyzes the images coming from the drone, using several models that work together. our model shows the pattern of roads from the captured image using Gray level Size Zone Matrix(GLSZM) features Zone Percentage (ZP) and Size Zone Non-Uniformity (SZN) and then take the spikes of its distributions then take these spike to get optimal value K for Kmean to segment the image, the result of first model enter to second model that make sorting for this images depending on GLSZM features (ZP and SZV) to improve the result of our model, after that the image enter to CNN to get the outcomes by classifying it into which category this roughness belongs. The best accuracy we achieved in our model reached 91.94%, which is a very high accuracy, and therefore by a large percentage all correctly captured images from the drone has accurate results. | | Keywords: | Computer Vision, Deep Learning, GLSZM, CNN, Road Roughness | | Source: | Journal of Theoretical and Applied Information Technology
15thJuly 2024 -- Vol. 102. No. 13-- 2024 | | Full Text | | |
| Title: | AN INTEGRATED ACCEPTANCE MODEL FOR VIRTUAL REALITY ADOPTION IN DISTANCE LEARNING: INVESTIGATING MOROCCAN STUDENTS' PERSPECTIVES | | Author: | LOBNA EL AMRANI, MOHAMED MOUGHIT | | Abstract: | Virtual Reality (VR) is a technology with diverse applications across different sectors, such as education, healthcare, psychology, and gaming. In education, VR is being explored as a tool for distance learning. Its use can potentially motivate students to engage with online lessons. The purpose of this research paper was to investigate which variables would influence the use of VR in distance learning among students. Using the Technology Acceptance Model (TAM) as a framework within four factors, a series of hypotheses were formed. Data has been collected from 122 Moroccan students and analyzed using regression. The findings indicate that user support, perceived ease of use (PEOU), perceived usefulness (PU), and attitudes toward technology use (ATU) significantly influenced the behavioral intention (BI) to use VR systems for educational purposes. The study's results can guide decision-makers in developing sustainable distance learning and educational systems in Moroccan universities. This study presents an integrated acceptance model for understanding Moroccan students' perspectives on adopting VR in distance learning. By investigating factors influencing students' acceptance of VR technology, this research contributes to the development of sustainable distance learning systems in Moroccan universities | | Keywords: | Distance education, Virtual Reality, Technology Acceptance Model, e-learning, TAM | | Source: | Journal of Theoretical and Applied Information Technology
15thJuly 2024 -- Vol. 102. No. 13-- 2024 | | Full Text | | |
| Title: | E-LEARNING USING ARTIFICIAL INTELLIGENCE: AN INNOVATIVE APPROACH TO DISTANCE LEARNING FOR ENHANCED DATA GENERATION | | Author: | A. LECHHAB, M. EZZAKI, D. BENAMMI, M. BOUJARRA1, Y. FAKHRI, S. BOUREKKADI | | Abstract: | The article highlights the increasing development of intelligent systems in the modern world, aimed at simplifying human learning. It highlighted the emergence of e-learning, designed to disseminate knowledge using artificial intelligence as a solution for higher level education. The main goal of e-learning is to deliver high-quality education in an efficient manner, based on sound technological design. Creating e-learning courses is presented as a complex and expensive task, involving many people and skills. However, recent advances in artificial intelligence offer the possibility of automating this process. This article proposes an innovative approach to strengthen data security in the field of e-learning by integrating artificial intelligence (AI). Using advanced data analysis and statistical modeling techniques, we identify potential vulnerabilities and propose proactive measures to mitigate risks. Our method uses AI to monitor suspicious activities in real time and adapt security policies accordingly. By leveraging AI's versatility in anomaly detection and malicious behavior prediction, our approach provides dynamic defense against emerging threats. The results of this study demonstrate the effectiveness of AI-powered e-learning in ensuring data security while optimizing remote learning processes. The authors thus propose the automatic generation of e-learning courses using intelligent systems, claiming that this method would be more effective than traditional course development methods. The study focuses on automatic generation of e-learning courses, followed by evaluation using concept maps. The researchers claim that this approach is not only more effective than traditional methods, but also that the quality of the courses generated is higher. The article highlights the potential of artificial intelligence to transform the way e-learning courses are developed and delivered, providing a more efficient and higher quality solution for online education. | | Keywords: | E-learning, Artificial intelligence, Automatic course generation, Concept maps | | Source: | Journal of Theoretical and Applied Information Technology
15thJuly 2024 -- Vol. 102. No. 13-- 2024 | | Full Text | | |
| Title: | ENHANCING RETAIL PRODUCT RECOGNITION USING MODIFIED YOLOV8 AND SELF-SUPERVISED LEARNING | | Author: | FELIX CORPUTTY, SURYO ADHI, WIBOWO, UNANG SUNARYA3, RISSA RAHMANIA, SIDDIQ WAHYU HIDAYAT | | Abstract: | Artificial intelligence has several parts, one of them is computer vision. Computer vision is a technology that allows computers to recognize objects as humans do. Computer vision has been widely applied in various applications, one of an example is in retail product recognition. However, the current computer vision technology is still difficult to distinguish between one product and another in the same category known as intra-class variation. Therefore, this research developed an algorithm that uses the concept of computer vision to be able to distinguish one product and another in the same category. The research was conducted using two stages. In the first stage the dataset was trained using YOLOv8. There are four experiments conducted using YOLOv8, namely YOLOv8 original, YOLOv8 with 4 detection heads (YOLOv8-4DH), YOLOv8 with additional convolutional layer and C2f layer on the backbone (YOLOv8-Conv) and the last is YOLOv8 with 4 detection heads, additional convolutional layer and C2f layer on the backbone (YOLOv8-Conv-4DH). The best model is selected based on the highest mAP value. The model with the highest mAP value is YOLOv8-4DH at 91%. The best model is used to crop the image to be used as input in the second stage. In the second stage, the cropped image is trained using SimCLR. The training weights from SimCLR are stored and loaded back into the SimCLR model for training and evaluation. The results of the second stage showed that the best model YOLOv8-4DH combined with SimCLR algorithm got an accuracy of 97.76%. | | Keywords: | Artificial Intelligence, Computer Vision, Retail Product, SimCLR, YOLOv8 | | Source: | Journal of Theoretical and Applied Information Technology
15thJuly 2024 -- Vol. 102. No. 13-- 2024 | | Full Text | | |
| Title: | DETECTION OF GEOMAGNETIC STORM SUDDEN COMMENCEMENTS WITH THE USE OF NEURAL NETWORK ARCHITECTURES | | Author: | TARAS VOLOSHIN, KONSTANTIN ZAYTSEV | | Abstract: | The purpose of the study is to examine various options to address the task of detecting the starting stage of geomagnetic storms, storm sudden commencement (SSC or SC), based on measurements of the Earth's magnetic field collected by INTERMAGNET observatories. These observatories are located in different regions of the world, allowing the full range of geomagnetic observations to be processed. Through a comprehensive analysis involving time series and machine learning techniques, including both statistical and neural network models, we developed models that integrate scalar and vector data to enhance detection accuracy. Discontinuities on the time scale in the measurements of individual observatories have been registered. In addition to the time series of magnetic field measurements, sudden commencement was detected using such scalar values as the change of the level of induction components and change of rhythm. Various methods of modeling and analyzing time series have been proposed, including statistical and machine-learning methods. To use vector and scalar indicators at the same time, the model was built with two streams of information. Various models were built using the data of both single and multiple laboratories. In the latter case, data from different sources were combined by the methods of hard voting and soft voting. A quantitative assessment of the results delivered by the models was carried out using accuracy, recall, and precision metrics. | | Keywords: | Geomagnetic Storms, Sudden Commencement, Time Series, Machine Learning, Neural Networks. | | Source: | Journal of Theoretical and Applied Information Technology
15thJuly 2024 -- Vol. 102. No. 13-- 2024 | | Full Text | | |
| Title: | ELECTROCARDIOGRAM PLETHYSMOGRAPHIC ELECTROMYOGRAMS BASED BIOMETRIC AUTHENTICATION MODELS | | Author: | SUNEETHA MADDULURI, T. KISHORE KUMAR | | Abstract: | To verify humans identity, biometric authentication techniques examine observable characteristics. This may be based on a person's fingerprint, iris, retina, Electrocardiogram (ECG), Plethysmographic (PPG), Electromyograms (EMGs) or some other identifying features. There is flexibility in the usage of a single trait or a combination of traits. Because they are both discrete and distinctive, electrocardiograms (ECGs), photoplethysmograms (PPGs), and electromyograms (EMGs) have been investigated as possible biometric features in the last several decades. Research into biometric recognition technologies that are user-unobtrusive has been accelerated by the increased availability of wearable sensors and mobile devices. Due to their distinct characteristics, electrocardiogram (ECG) signals have recently been investigated as a potential biometric identification trait. An electrocardiogram (ECG) can only be used to collect data from individuals who are still alive, as it measures the electrical activity of the heart. The research community is interested in evaluating cardiac signals derived from PPG signals for a number of reasons, one of which is the capacity to perform continuous authentications with affordable devices that can gather signals without user intervention. With the declining quality and resolution of gathered images and security issues such spoofing and copying, this study intends to discuss and analyze biosignals based biometric authentication, which has been dominating former conventional methods. This research provides a brief analysis of ECG, PPG and PCG and their advantages and limitations and proposed an ECG based Biometric Authentication using CNN (ECG-BA-CNN). This analysis helps numerous researchers to design novel biometric innovations overcoming the limitations of traditional models. | | Keywords: | Electrocardiogram, Plethysmographic, Electromyograms, Biometric Authentication, User Identity, Security. | | Source: | Journal of Theoretical and Applied Information Technology
15thJuly 2024 -- Vol. 102. No. 13-- 2024 | | Full Text | | |
| Title: | THE IMPACT OF ARTIFICIAL INTELLIGENCE ON IMPROVING TEXT IN THE PROCESS OF CONCEPTUALIZATION IN BIOLOGY: CASE OF EDUCATION SECTOR | | Author: | GHIZLANE GHARIZ, HAKIMA SEGHIR, NAJAT BOUCETTA, SAID BOUBIH, RACHID JANATI-IDRISSI, MUSTAFA EL ALAOUI | | Abstract: | This article investigates the influence of artificial intelligence (AI) on enhancing the process of text production, emphasizing the advantages, difficulties, and consequences associated with its use. The author emphasizes the transformative impact of sophisticated language models on the development of textual material, since they provide high-quality output that is both natural and instructive. The use of artificial intelligence (AI) has shown a significant enhancement in productivity and a reduction in production time. However, it is crucial to acknowledge that the integration of AI also presents ethical dilemmas that need meticulous examination and contemplation. This article explores the impact of artificial intelligence (AI) on written communication, focusing on its influence on contextual comprehension, creative enhancement, and the transformation of human linguistic interactions and perspectives. Furthermore, the paper delves into contemporary implementations of artificial intelligence (AI), including automated writing, chatbot systems, and educational contexts. The study ultimately delves into the integration of artificial intelligence in the creative process, specifically focusing on co-creation, and also explores the reinterpretation of literary genres. Although AI offers several advantages, it also presents ethical dilemmas, including those related to data bias and editorial accountability. The promise of AI-assisted text production in the future seems great; yet, its successful implementation requires continuous ethical oversight and a comprehensive comprehension of the associated ramifications. This approach is crucial in order to optimize the advantages while effectively addressing any possible obstacles that may arise. | | Keywords: | Artificial Intelligence, Text Generation, Linguistic Models, Editorial Creativity | | Source: | Journal of Theoretical and Applied Information Technology
15thJuly 2024 -- Vol. 102. No. 13-- 2024 | | Full Text | | |
| Title: | CYBERSECURITY RISK MANAGEMENT IN IOT SYSTEMS: A SYSTEMATIC REVIEW | | Author: | TAYSEER ALKHDOUR, MOHAMMED AMIN ALMAIAH, MARIAM ALI ALAHMED, MOHMOOD A. AL-SHAREEDA, ABDALWALI LUTFI, MAHMAOD ALRAWAD | | Abstract: | With the revolution of IoT technologies, cybersecurity risks are considered one of the challenges of IoT. Therefore, this study aims to discuss the risk management process for IoT in order to identify the main vulnerabilities and threats in IoT. In addition, this paper discusses the best mitigation techniques and risk management frameworks and models in order to ensure that the IoT users protected from any cyber-attacks. The study indicates that the DDoS attacks is the highest percentage of risk in IoT technologies. The paper also finds that IoT risks can be divided into four types including privacy risks, security risks, technical risks and ethical risks. The study find that the ISO is the best framework for the risk management in IoT technologies. Finally, the paper presents for researchers important recommendations for determining the types of risks and attacks in IoT and identifying the most important risk management frameworks and models for IoT. | | Keywords: | IoT; Privacy; Cybersecurity Risks; Cybersecurity Management; User Privacy; Blockchain. | | Source: | Journal of Theoretical and Applied Information Technology
15thJuly 2024 -- Vol. 102. No. 13-- 2024 | | Full Text | | |
| Title: | VOCATIONAL EDUCATION SKILL ASSESSMENT AND INTELLIGENT ASSISTANCE: A STUDY ON THE APPLICATION OF MACHINE LEARNING ALGORITHMS IN THE ASSESSMENT OF VOCATIONAL INFORMATION LITERACY TEACHING ABILITY | | Author: | Dr. Vishal M. Tidake, Dr. Utpala Das, Dr. Kulbir Kaur Bhatti, R. Swathi Gudipati, Dr. S. Farhad, Prof. Ts. Dr. Yousef A.Baker El-Ebiary, Manikandan Rengarajan | | Abstract: | Vocational education skill assessment and intelligent assistance involve evaluating people' talent in precise vocational abilities and conveying personalized assist to enhance studying results. The need for such assessment and assistance arises from the significance of appropriately evaluating learners’ readiness and proficiency in vocational abilities, identifying areas for improvement in teaching practices, and presenting timely feedback and guidance to learners. However, present strategies often depend on conventional assessment techniques which can lack granularity and fail to provide personalised assistance. To address those demanding situations, this study introduces a novel method that integrates SMOTE data processing, Federated LSTM (Fed-LSTM) for skill word extraction and classification, and fuzzy rule-based vocational education talent evaluation. This approach targets to overcome class imbalances in datasets through SMOTE, permit collaborative learning across distributed data sources, and improve the accuracy and robustness of talent assessment models. The proposed study improves data representation, facilitating collaborative learning, enhancing skill extraction accuracy, and presenting robust skill assessment. The results of study are applied in a Python software, offering educators and stakeholders a realistic approach to enhance vocational education skill assessment and intelligent assistance. The proposed Fed-LSTM technique demonstrates a substantial growth in accuracy compared to the LSTM approach. With an accuracy of 99.4%, the proposed technique considerably outperforms the LSTM method, which achieved an accuracy of 76. 98%. This represents a substantial improvement of 22.42% in accuracy. | | Keywords: | Vocational Education, Skill Assessment, Intelligent Assistance, Teaching Practices, Synthetic Minority Oversampling Technique | | Source: | Journal of Theoretical and Applied Information Technology
15thJuly 2024 -- Vol. 102. No. 13-- 2024 | | Full Text | | |
| Title: | AN EFFICIENT AND ROBUST PROOF OF STAKE ALGORITHM BASED ON COIN-AGE SELECTION | | Author: | ANJANEYULU ENDURTHI, AKHIL KHARE | | Abstract: | A consensus protocol is used to achieve agreement among the nodes in a distributed system. Proof of stake is one such protocol. Proof of stake is based upon two different strategies. The first one is randomized block selection and the second is coin-age selection. Each of these strategies results in an unfair selection of validators and converges to a problem called wealth concentration among a few validators. This paper proposes a modified proof of stake protocol based on the coin-age strategy to mitigate the issue and improve the coin-age selection algorithm. The participants will generate new tokens to compete for the validator role to create the next block. | | Keywords: | Blockchain, Consensus, Proof of stake, Coin-age, Timestamp, Tokens | | Source: | Journal of Theoretical and Applied Information Technology
15thJuly 2024 -- Vol. 102. No. 13-- 2024 | | Full Text | | |
| Title: | DESIGN OF AN ITERATIVE METHOD FOR PLANT NUTRIENT DEFICIENCY DETECTION USING GRAPH CONVOLUTIONAL NETWORKS & ENSEMBLE LEARNING | | Author: | PARNAL P. PAWADE, DR. A. S. ALVI | | Abstract: | Identifying nutrient deficiencies in plants with enhanced precision is crucial for sustainable food production. Traditional methods often fail to capture the complex biological scenes in various use cases. This work introduces a novel, precision-aware, learning-based approach to significantly improve the detection and classification of nutrient deficiencies in plants. Unlike available methodologies that rely solely on image-based analysis, our method employs Graph Convolutional Networks (GCNs) to create graph-based representations of plant structures from high-resolution images. This technique captures intricate relationships between plant parts, such as leaves, stems, and roots, by treating them as interconnected nodes in a graph. GCNs extract hierarchical features, providing a comprehensive and discriminative representation for nutrient deficiency detection. We also propose an ensemble model combining Capsule Networks and Transformers. Capsule Networks understand hierarchical and spatial relationships within plant data, while Transformers capture long-range dependencies and complex patterns across various plant sections. This combination results in an ensemble with enhanced accuracy. To overcome the limitations of training data and biases in real samples, we introduce a novel data augmentation method using Generative Adversarial Networks (GANs). This method generates synthetic images reflecting real growth variations, lighting conditions, and nutrient deficiency symptoms, thus improving model generalization and robustness. Furthermore, we present an innovative interpretability technique to display attribution-based visualizations of graph-based features. This approach elucidates the model's reasoning by identifying influential regions and structures within the dataset, thereby increasing trust in the model's decisions and providing biologically relevant insights. Our method advances agricultural technology by enhancing nutrient deficiency detection accuracy and interpretability, aligning with biological agricultural knowledge. This comprehensive approach paves the way for more sustainable and informed agricultural practices, leading to improved crop health and productivity. | | Keywords: | Graph Convolutional Networks, Ensemble Learning, Plant Nutrient Deficiency, Data Augmentation, Interpretability Techniques | | Source: | Journal of Theoretical and Applied Information Technology
15thJuly 2024 -- Vol. 102. No. 13-- 2024 | | Full Text | | |
| Title: | A MACHINE LEARNING-BASED OPTIMIZED FRAMEWORK FOR DETECTION OF ARRHYTHMIA FROM ECG DATA | | Author: | MS.K.SHILPA, DR.T.ADILAKSHMI | | Abstract: | Heart diseases are causing health issues for people across the globe due to various reasons, including lifestyle changes. With the emergence of artificial intelligence (AI), it is possible to have learning-based approaches for the automatic detection of several types of heart diseases. Many existing approaches followed data-driven techniques for the diagnosis of heart diseases. Some methods focused on ECG data, which has the potential to support the detection of different kinds of diseases, particularly arrhythmia. The literature shows that machine learning models result in deteriorated performance unless specific optimizations support them. Motivated by this fact, we proposed a machine-learning framework that exploits many classification models for detecting arrhythmia and classification. The proposed framework is subjected to multiple optimizations in terms of preprocessing, feature engineering, and hyperparameter tuning. To develop an optimized machine learning approach, we proposed two algorithms known as Feature Selection and Hyperparameter Optimization (FSHO) and Learning-based Arrhythmia Detection and Classification (LbADC). We used our empirical study's benchmark dataset, known as the MIT-BIH Arrhythmia dataset. The experimental results reveal that the proposed optimizations and machine learning framework could improve arrhythmia diagnosis and classification performance. The proposed optimizations of our framework achieved 96.8% accuracy in multi-class classification. | | Keywords: | Healthcare, Machine Learning, Feature Engineering, Heart Disease Prediction, Arrhythmia Diagnosis | | Source: | Journal of Theoretical and Applied Information Technology
15thJuly 2024 -- Vol. 102. No. 13-- 2024 | | Full Text | | |
| Title: | EXPLORING EMERGING CYBERSECURITY RISKS FROM AI-BASED IOT CONNECTIONS | | Author: | HANAN KHALID ALSUWAELIM | | Abstract: | The Internet of Things connects things and networks, such as devices and infrastructure, in an at-tempt to make life easier. These networked areas, however, frequently have few resources and are thus the most susceptible to assaults. We must search for an all-encompassing security strategy for the Internet of Things that safeguards these nodes as well as the data they manage. In addition to the existing security protocols for networks, we might employ intelligent strategies deriving from artificial intelligence principles and basic and sophisticated machine learning approaches to pre-vent threats. The future may be brighter if artificial intelligence is connected to the Internet of Things. The aim of this paper is to review and analyze the cyber risks and threats associated with IoT devices and artificial intelligence published from 2020 to 2024. Then, the paper highlights privacy and ethical concerns, introduces security frameworks and tactics, classifies IoT security difficulties, explores the use of AI-based in IoT security, and offers insights from real-world case studies. A total of 25 articles were selected using the PRISMA framework. This thorough analysis of the status of IoT security today and how AI affects it advances our knowledge of how to create trustworthy and secure IoT systems. | | Keywords: | Intelligence, Security, Risk, Risk Analysis, and Internet of Things. | | Source: | Journal of Theoretical and Applied Information Technology
15thJuly 2024 -- Vol. 102. No. 13-- 2024 | | Full Text | | |
| Title: | ISSUES AND CHALLENGES IN ONLINE LEARNING: A CASE STUDY IN MALAYSIA | | Author: | FAAIZAH SHAHBODIN, ZULISMAN MAKSOM, CHE KU NURAINI CHE KU MOHD, HANIF AL-FATTA, ULKA CHANDINI, HELMI MOHD KASIM | | Abstract: | This paper focused on exploring the secondary school teachers’ perceptions toward online learning program which developed during COVID-19 pandemic in Malaysia. To evaluate teachers’ perceptions of teaching and learning engagement, a quantitative survey was conducted. Therefore, teachers’ understanding of teaching and the relation to their engagement in learning are explored in this survey. Hence, there are factors which determined the success of implementing online learning in Malaysia during COVID-19 pandemic such as the readiness of technology which in line with the national humanist curriculum, support and collaboration from all stakeholders including government, teachers, parents, schools, and community. The findings in this paper highlight the teachers’ good sense of teaching and strong correlations between teachers’ perceptions and students’ engagement are significantly influence the online teaching and learning process. Teachers are also suggested to apply more appropriate types of learning tools during classes and pay attention to the nature of the student. The results may assist in advocating for a paradigm shift in online education. The research underscores the need for innovative approaches that leverage the power of technology to inspire online learners and educators thereby contributing to the ongoing improvement of online education. | | Keywords: | Technology, Personalize Learning, Online Learning, Higher Education | | Source: | Journal of Theoretical and Applied Information Technology
15thJuly 2024 -- Vol. 102. No. 13-- 2024 | | Full Text | | |
| Title: | ENHANCING SECURE V2V AND V2I COMMUNICATION: DESIGNING AN EFFICIENT VEHICLE-AUTHORIZED SHORTEST ROUTE SELECTION ALGORITHM FOR MINIMIZING DATA LOSS | | Author: | SPANDANA MANDE MANDE, NANDHA KUMAR RAMACHANDRAN | | Abstract: | Ensuring the security and efficiency of information exchange between vehicles (V2V) and infrastructure (V2I) is of utmost importance in the realm of vehicular communication systems. The main objective of this study is to enhance the security and efficiency of these systems. To achieve this, we will create a vehicle-authorized algorithm for selecting the shortest route, to minimize data loss. This algorithm employs cryptographic authentication mechanisms to prioritize secure routes based on vehicle authorization, effectively mitigating potential security risks during information exchange. Implementing optimized routing protocols, such as the A* algorithm, allows the system to determine the most efficient routes for vehicles, taking into account factors like traffic conditions and network congestion. It specifically focuses on the critical issues of choosing the best route and ensuring data security in communications between vehicles. This algorithmic solution enhances both route selection and network security while also establishing a robust framework for secure and efficient vehicular communication systems. It guarantees the accurate and secure reception of information between vehicles or infrastructure. | | Keywords: | Vehicle to Vehicle, Vehicle to Infrastructure, A *, Shortest Route Selection, Vehicular Communications, Data Loss. | | Source: | Journal of Theoretical and Applied Information Technology
15thJuly 2024 -- Vol. 102. No. 13-- 2024 | | Full Text | | |
| Title: | ENHANCING GLAUCOMA DIAGNOSIS: DEEP LEARNING MODELS FOR AUTOMATED IDENTIFICATION AND EXPLAINABILITY USING FUNDUS IMAGES | | Author: | MANEESHA VADDURI, KUPPUSAMY. P | | Abstract: | Glaucoma is a serious eye condition that poses a significant threat to vision health, often resulting in permanent sight loss by damaging the optic nerves. Detecting glaucoma early is crucial for effective management, aiming to reduce intraocular pressure and inflammation. However, current detection methods are resource-intensive and prone to human error, failing to detect the disease in its early stages. Deep Learning (DL) offers promising avenues for automated diagnosis, yet concerns persist regarding model reliability. Addressing this, the Enhanced Deep Learning Approach for Glaucoma Diagnosis (EDAGD) is introduced. Leveraging SegNet and ResNet-50 architectures, EDAGD achieves exceptional segmentation accuracies of 98.58% for the Optic Disc (OD) and 96.52% for the Optic Cup (OC) on the RIM-ONE dataset, while also demonstrating robust performance on the ACRIMA and REFUGE datasets. Furthermore, EDAGD utilizes cutting-edge visualization techniques such as Gradient-weighted Class Activation Mapping (Grad-CAM) and Grad-CAM++ to generate interpretable heatmaps, aiding in pinpointing critical regions for diagnosis. By accurately classifying segmented images, EDAGD achieves impressive performance metrics of 97.97% accuracy, 98.41% sensitivity, and 96.58% specificity. The potential impact of automated glaucoma diagnosis on healthcare systems includes reducing the burden on ophthalmologists, increasing accessibility to diagnostic tools in remote areas, and potentially lowering healthcare costs. By integrating advanced Deep Learning techniques with explainable AI methods, our approach not only improves the accuracy of glaucoma diagnosis but also builds trust among clinicians. This fosters seamless integration into clinical practice, ultimately advancing patient care by enabling timely and accurate diagnosis of glaucoma. | | Keywords: | Glaucoma, segmentation, classification, Fundus images, Explainability | | Source: | Journal of Theoretical and Applied Information Technology
15thJuly 2024 -- Vol. 102. No. 13-- 2024 | | Full Text | | |
| Title: | EVALUATING TEXTBLOB, LEXICON, SUPPORT VECTOR MACHINE, NAIVE BAYES, AND CHATGPT APPROACHES FOR SENTIMENT ANALYSIS OF NASDAQ LISTED COMPANIES | | Author: | AZWARNI, NATHAR SHAH | | Abstract: | Sentiment analysis is a type of contextual text mining that finds and extracts subjective information from the source material in order to assist companies in understanding the social sentiment of their brand, product, or service while monitoring online conversations, especially Twitter has become a popular medium for individuals to express their opinions, share news, and discuss various topics, including stocks and companies. Stock market sentiment analysis is useful for understanding investor sentiments and forecasting market moves. Market players can use sentiment analysis tools to evaluate market sentiment and make educated investing decisions. The previous study examined data with fewer than ten thousand rows; however, this research will work with very huge data sets of more than one hundred thousand Nasdaq companies acquired from @Nasdaq and @AppleSupport Twitter accounts and @nasdaq and @apple from subreddit in Reddit social media. This study will compare the classification accuracy of Naive Bayes and SVM, as well as the time consumption of each strategy while classifying vast quantities of data. The TextBlob NLTK (Natural Language Toolkit) will be used in this study to label each phrase in the data using a lexicon-based method; also, this effort will employ ChatGPT, an OpenAI chatbot, to label each row of data received. As a consequence, it was discovered that SVM is the most superior approach in its classification, both in terms of Precision, Recall, and F1-Score metrics, as well as total accuracy, which reaches 93.5%, while Naive Bayes is at 61.5% and ChatGPT is at 42.2%. | | Keywords: | Big Data, TextBlob, SVM (Support Vector Machine), Naïve Bayes, ChatGPT, Sentiment Analysis, Nasdaq
| | Source: | Journal of Theoretical and Applied Information Technology
15thJuly 2024 -- Vol. 102. No. 13-- 2024 | | Full Text | | |
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