International Journal of Computer and Information Technology(2279-0764)
International Journal of Computer and Information Technology (IJCIT) is an international scholarly open access, peer reviewed bi-monthly journal. The journal aims at providing a platform and encourages emerging scholars and academicians globally to share their professional and academic knowledge in the fields of computer science, engineering, technology and related disciplines. IJCIT also aims to reach a large number of audiences worldwide with original and current research work completed on the vital issues of the above important disciplines. Other original works like, well written surveys, book reviews, review articles and high quality technical notes from experts in the field to promote intuitive understanding of the state-of-the-art are also welcome.
The articles published in International Journal of Computer and Information Technology (IJCIT) is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Topics, Trends, and Sentiments in Software Testing: An Analysis of Developers’ Engagement on Stack Overflow
This study investigated software testing discussions on Stack Overflow from 2020 to 2024 to uncover key trends, topics, and developer sentiments. 14 key topics, including unit testing, machine learning testing, mobile testing (especially Flutter), and Docker testing were identified. The study revealed a decline in developer engagement, as the number of posts answered and with accepted answers decreased, particularly after 2022. Sentiment analysis showed a predominance of negative sentiments across most topics, especially in mobile and machine learning testing. While some topics like machine learning testing initially saw positive sentiment, this shifted toward frustration as the years progressed. These findings suggest that the rise of AI-based tools, such as ChatGPT, has affected the way developers engage with traditional forums like Stack Overflow. The decline in engagement and the prevalence of negative sentiments highlight the challenges developers face in software testing. This research points to the need for further investigation into how AI tools influence developer behavior and their reliance on peer support platforms. Additionally, it suggests exploring how sentiment analysis can be integrated into software testing tools to better address developer frustrations and improve support for testing emerging technologies. The study provides insights that could guide the development of more effective tools and frameworks to enhance the software testing process.
Copyright (c) 2025 Anthony Wambua https://creativecommons.org/licenses/by-nc/4.0
Automated Techniques for Detecting Healthcare Associated Infections: A Review
Automated detection of Healthcare-Associated Infections (HAIs) faces major obstacles due to unclear medical documentation, scarcity of well-annotated data, and multiple symptoms that overlap between HAIs. This review investigates recent advances in using classical machine learning, deep learning, transformers, and natural language processing (NLP) methods in detecting healthcare-associated infections. It examines empirical studies from 2019 to 2025, focusing on models' performance based on various metrics, data issues, and ethical considerations. The study sought to assess and compare the performance of natural language processing (NLP) approaches of detecting Healthcare-Associated Infections (HAIs). Ethical and technical concerns such as data privacy and data imbalance, are critical barriers to implementation of NLP to detection of HAIs. The review underscores the promise of NLP to detection of HAIs while emphasizing the need for standardized metrics for evaluating HAI detection model and ethical frameworks of handling the datasets.
Copyright (c) 2025 Joseph Karuri Mwaura, Kennedy Malanga Ndenga, James Mwikya Reuben https://creativecommons.org/licenses/by-nc/4.0
A Lightweight 3D Convolutional Network for Hyperspectral–LiDAR Patch Classification
We propose a simple yet effective three-dimensional convolutional neural network (3D-CNN) for urban land-cover classification using co-registered hyperspectral imagery (HSI) and LiDAR data. The network treats the entire spectral-LiDAR stack as a three-dimensional volume and uses a series of 3×3×3 convolutions to capture both spectral and spatial context simultaneously. LiDAR elevation data is added as an extra channel in the input. During preprocessing, each hyperspectral band and LiDAR DSM are normalized to zero mean and unit variance. Training uses small local patches (P×P) centered on labeled pixels, with random flips and 90-degree rotations, called dihedral augmentation, applied across all channels. To address class imbalance, inverse-frequency class weighting and label smoothing are included in the cross-entropy loss. Evaluation on the Houston2013 dataset shows that the model achieves high accuracy, a single model reaches an Overall Accuracy (OA) of about 0.90 and an Average Accuracy (AA) of about 0.92 over five runs. An ensemble of five runs improves these results to OA ≈ 0.912, AA ≈ 0.928, and a kappa coefficient (κ) of approximately 0.904. Classes with distinctive spectral and spatial signatures, like water, synthetic grass, and tennis courts, reach nearly 100% recall. Meanwhile, classes with similar appearances, such as highway and road, show higher confusion, with highway recall around 46.9%. These results confirm that combining spectral and three-dimensional structural information significantly enhances accuracy in urban classification.
hyperspectral imagery, LiDAR, 3D-CNN;data fusion;patch classification;class imbalance; remote sensing
Copyright (c) 2025 Junhua Ku, Jie Zhao https://creativecommons.org/licenses/by-nc/4.0
A Comparative Analysis of Deep Learning Models for Detection of Lumpy Skin Disease with emphasis on Shifted Window Transformers
Lumpy Skin Disease (LSD) in cattle is an increasingly prevalent viral infection with significant economic impact. Traditional detection methods are often labor-intensive and delayed. In this study, five state-of-the-art deep learning (DL) architectures—ResNet50, EfficientNetB0, MobileNetV2, Vision Transformer (ViT-B16), and Swin Transformer Tiny (Swin-T)—were evaluated and compared for image-based LSD classification. Publicly available Kaggle datasets of infected and healthy cattle were used. All models were fine-tuned using transfer learning and tested for classification accuracy, F1-score, inference time, explainability (via Grad-CAM), and real-world deployability. Results show that Swin-T achieved the highest classification accuracy of 95.3%, while MobileNetV2 emerged as the most deployment-friendly model. Grad-CAM visualizations confirmed that transformer-based models captured relevant lesion features with greater spatial sensitivity than CNNs. The study highlights the promise of hybrid transformer-CNN models for practical livestock diagnostics, especially in resource-constrained environments.
Copyright (c) 2025 George Mwangi Muhindi, Geoffrey Mariga Wambugu, Aaron Mogeni Oirere https://creativecommons.org/licenses/by-nc/4.0
Comparison with Deep Learning Methods For Predicting Stock Prices
Recently, machine learning has been an essential tool for analysis in diverse fields, including science, sports management, and economics. In particular, the stock market comprises a complex network of buyers and sellers engaged in stock trading. So, predicting stock prices has been developed using machine-learning techniques to significantly enhance such forecasts' accuracy. Recent advancements have improved the performance of several algorithms, such as Linear Regression, Support Vector Machines (SVM), and K-nearest neighbors (KNN) to predict stock prices. Stock price datasets typically contain information such as opening and closing prices, high and low values, dates, trading volume, and adjusted closing prices provided by Yahoo Finance. Based on the data, this research evaluates the prediction accuracy of each machine-learning method and presents the results through data visualizations, including box plots and tables. The compiled results will assist in identifying the most effective model for stock price prediction.
Copyright (c) 2025 Seonguk Kim, Colton Nutter, Nayeong Kong https://creativecommons.org/licenses/by-nc/4.0
Development of an NFC-Based Tracking System for Production, Inventory, and Maintenance in Manufacturing
This paper presents the development, implementation, and evaluation of a computational system for integrating production control, inventory management, and maintenance operations in a manufacturing company using Near Field Communication (NFC) technology. The proposed solution leverages NFC tags as a core element to ensure real-time tracking of products and components throughout the manufacturing process. The system architecture consists of NFC tags for product identification, a custom mobile application for data acquisition, and a centralized database for storage and analysis. The system workflow enables operators to register, monitor, and update the status of products using mobile devices, enhancing traceability, efficiency, and data accuracy. The solution was validated in a real-world manufacturing environment through functional tests and a System Usability Scale (SUS) questionnaire, which yielded an average usability score of 72.08, indicating a good level of user satisfaction. These results demonstrate the feasibility, usability, and practical benefits of the proposed system, which contributes to the digital transformation of industrial operations and aligns with Industry 4.0 principles.
Copyright (c) 2025 Camile L. F. de Macedo, Flavio Cezar Amate https://creativecommons.org/licenses/by-nc/4.0
An Ensemble Predictive Model for Learner Attrition in Online Learning
To increase student retention and the success of online learning initiatives, it is critical to make very accurate predictions about learner attrition. In order to put early intervention strategies into place, universities must identify students who are likely to withdraw early. A number of variables, such as academic achievement, demographic traits, and engagement metrics, affect how accurately learner attrition is predicted. Effective prediction models will be developed by analysing these characteristics using machine learning techniques. This study's main goal is to create an ensemble-based machine learning model that predicts early learner attrition in Kenyan online learning environments by combining XGBoost, Neural Networks Decision Trees (DT), and Random Forests (RF). Learning Management Systems (LMS) secondary data collected from Kenya's five universities will be used in the study. In order to provide a strong framework for the early detection of learners who are at risk, this study describes the technique for data preprocessing, feature selection, model training, and integration. The research's conclusions will help institutions and policymakers enhance online learning platforms, maximise student retention strategies, and tackle e-learning issues. The research intends to aid in the creation of a more effective and inclusive online learning system in Kenya by early detection of students who are at risk.
learner attrition, ensemble learning, gradient boosting, neural networks, online education, predictive modeling
Copyright (c) 2025 Stanley Munga Ngigi, James Mwikya, Victor Mageto https://creativecommons.org/licenses/by-nc/4.0
A Systematic Review of Predictive Factors for Learner Attrition in Online Learning: Insights for Machine Learning Models
Over the past ten years, online education has expanded rapidly due to its accessibility, scalability, and flexibility. Despite its potential, high attrition rates in online education threaten both student progress and the legitimacy of the institution. A comprehensive analysis of empirical research on the factors influencing learner attrition in online learning settings is presented in this study. To identify the individual, course-level, institutional, and technical causes of attrition, it incorporates and categories the body of existing work. The results point to the complex aetiology of attrition and identify important domains for focused intervention and predictive modelling.
Learner Attrition, online learning, dropout, e-learning retention
Big Data Analytics in Healthcare: Predictive Modeling, Privacy Challenges, and Global Regulatory Compliance
Big Data Analytics (BDA) is increasingly central to modern healthcare, promising transformative improvements in patient care, operational efficiency, and predictive disease modeling. However, the sensitive nature of health data also introduces significant challenges, particularly regarding privacy, confidentiality, and global regulatory compliance. This article synthesizes key insights from contemporary research, highlighting methods, existing gaps, proposed solutions, and the critical need for stronger global data privacy harmonization.
Big Data, Healthcare, Data Privacy, Regulatory
Copyright (c) 2025 rachael ndung'u https://creativecommons.org/licenses/by-nc/4.0
Features of Lightweight Proof of Stake Models for Enhancing Data Privacy in Telemedicine Systems: A Systematic Literature Review
Proof of Stake (PoS) models are energy-efficient and require limited computational power. These features are critical in telemedicine environments, where resource-constrained devices must handle sensitive data securely. The growing need for auditable and privacy-preserving data storage in telemedicine underscores the importance of PoS models optimized for lightweight devices while complying with strict regulatory requirements, such as the Health Insurance Portability and Accountability Act (HIPAA).This study was guided by two research questions: (i) Which PoS models are lightweight and suitable for telemedicine? and (ii) What features make lightweight PoS models effective for privacy and efficiency in telemedicine? To address these questions, a systematic literature review (SLR) guided by the PICOC framework was conducted to investigate lightweight PoS models that can enhance privacy in telemedicine systems. Out of 2,394 papers studies screened, 55 were included in the analysis. The findings identified Algorand, Ouroboros Praos, Tendermint, Nxt, and Casper CBC as promising candidates. Key enabling features included lightweight voting mechanisms, such as Byzantine Agreement protocols and Verifiable Random Functions, as well as cryptographic techniques like symmetric encryption and multiparty computation. Performance metrics evaluated included latency, throughput, energy efficiency, and battery consumption, with Grey Relational Analysis ranking Algorand highest due to its low latency, high throughput, and minimal energy consumption.
Copyright (c) 2025 Denis Wapukha Walumbe, Gabriel Ndugu Kamau, Jane Wanjiru Njuki https://creativecommons.org/licenses/by-nc/4.0
The Trade-Off Between Anonymity and Accountability in Blockchain: A Framework for Secure and Compliant Systems
Blockchain technology has revolutionized digital transactions by offering decentralization, transparency, and immutability. However, its inherent transparency often conflicts with the need for user privacy and anonymity, raising significant concerns regarding accountability, especially in regulatory and legal contexts. This study explores the delicate balance between anonymity and accountability in blockchain systems, proposing a framework that ensures both privacy and compliance with regulatory requirements. The research addresses key challenges in balancing these two aspects, evaluates the effectiveness of existing privacy-preserving technologies such as zero-knowledge proofs and ring signatures, and introduces the Privacy-Accountability Balanced Blockchain (PABB) Framework. This framework integrates Selective De-Anonymization, Self-Sovereign Identity (SSI), and the Adaptive Privacy-Accountability Control (APAC) Algorithm to dynamically adjust privacy levels based on regulatory conditions. Through theoretical analysis, mathematical modeling, and empirical validation, preserving privacy for 92% of transactions while enabling selective de-anonymization in high-risk cases, the study demonstrates that the APAC Algorithm effectively balances privacy and compliance needs. The findings suggest that privacy-conscious blockchain systems can coexist with accountability mechanisms, paving the way for ethical and legally sound blockchain applications. The study concludes that the PABB Framework offers a practical and scalable approach to achieving this balance, fostering trust among users and regulators alike.
tive Privacy-Accountability Control (APAC)
Copyright (c) 2025 Idris Olanrewaju Ibraheem, Abdulrauf Uthman Tosho, Kamil Saka, Bolakale Lawal Aremu https://creativecommons.org/licenses/by-nc/4.0
A Novel Method for Secure Key-Sharing in 5G and Beyond
In today’s world, online communication is essential, and infrastructure for cutting-edge mobile technologies like 5G, is growing daily to meet the demand. So, information sharing security needs to be safeguarded as electronic communications spread. To implement this, cryptography is typically used, and most commonly symmetric key cryptography, due to its many advantages over other crypto-systems. However, one significant disadvantage of symmetric key system is that the single-key-sharing is exposed to all entities in a network communication system, which makes the subsequent communications vulnerable to unauthorized access. There are many approaches for securing the single-key-sharing transmission, but each has its own drawbacks. In this paper, we propose and present a novel approach to secure key-sharing over a communication network in symmetric key cryptography system which makes the single-key immune to unauthorized access. A total of four messages are exchanged between two devices for our secure key-sharing method. To implement the key sharing process, our method employs a few techniques, including asymmetric key cryptography, hash functions, machine learning-based pseudo random number generators, and timers. Our analysis shows that, besides providing similar level of confidentiality as the existing approaches, it also provides other significant improvements over the current ones, such as enhanced integrity maintenance, and authenticity verification of the two devices involved in the process. The short latency of modern 5G networks helps to balance the increased network demand caused by sending four independent messages. To determine timer duration and key validity, we propose applying AI algorithms and extending the security of our method; nevertheless, these applications fall within the purview of our upcoming study.
Copyright (c) 2025 Debjyoti Bagchi, Sanjib Sil, Soubhik Sen https://creativecommons.org/licenses/by-nc/4.0
Assessing IPv6 Readiness and Adoption in Palestine, A Case Study with Strategic Recommendations
This research examines the current state of IPv6 adoption in Palestine, comparing it to the global and regional adoption trends, particularly in Arab countries. While the global IPv6 adoption rate exceeds 45%, and Arab countries average 12.79%, Palestine remains at a negligible adoption rate. Through a mixed-method approach that includes experiments, technical analysis, and stakeholder interviews, the study highlights the readiness of end-user devices for IPv6, primarily due to international compliance standards. However, significant challenges exist within Internet Service Providers (ISPs) and large enterprises in Palestine, such as dual-stack resource demands, insufficient technical expertise, and lack of customer demand. This paper underscores the urgent need for a national strategy, stakeholder collaboration, and increased awareness to accelerate IPv6 transition in Palestine. Recommendations are made to address these obstacles and position Palestine for future compatibility in the global internet ecosystem.
Copyright (c) 2025 Ihab Atieh, Sami Sarhan https://creativecommons.org/licenses/by-nc/4.0
A Centrality Maximization Approach for Link Recommendation
In social networks, the goal of link recommendation is to recommend links for nodes and add them to the network, thereby satisfying the potential link interests of the nodes. The centrality of nodes in social networks typically quantifies the importance of nodes in the network. Some nodes may desire to increase their centrality by adding links. First, a multi-community centrality measurement method is proposed, and based on harmonic centrality, a hybrid centrality measurement method is introduced. Next, the link recommendation problem is regarded as a problem of maximizing node hybrid centrality, which can be formally modeled as a submodular function maximization problem. A greedy algorithm with performance guarantees can be directly applied to select the best links. Compared to existing link prediction and link recommendation algorithms, our algorithm recommends links that better improve the hybrid centrality of users.
Copyright (c) 2025 Qi Zhang, Hao Zhong https://creativecommons.org/licenses/by-nc/4.0
User Rating Prediction Method Based on Fine-tuning of Large Language Models
Online reviews in social networks reflect users' preferences for specific attributes of products. Accurate predictions of user ratings based on these reviews can help businesses better understand genuine user feedback. The purpose of this study is to fine-tune large language models using online reviews and corresponding user rating data, generating a large model for predicting user ratings based on reviews. An attention mechanism is introduced to calculate attention weights for fine-grained review texts, reflecting the contribution of different text features to user rating prediction. By visualizing these weights, the process of calculating the predicted rating values can be explained. Experimental results show that the proposed interpretable user rating prediction method can effectively visualize the attention weights of important text features in the decision-making process of the large rating prediction model. Compared to the baseline model, the mean absolute error is reduced by 1.96, and the root mean square error is reduced by 1.73.
The Evolution of Software Engineering: From Prehistoric Beginnings to the Age of Artificial Intelligence
This article presents a thorough overview of the historical background of software engineering, tracing its roots from antiquity to the contemporary time dominated by artificial intelligence (AI). Beginning with the basic programming languages of the mid-1900s, the article stresses major milestones, including the birth of object-oriented programming, the expansion of software engineering practices, and the transformational impact of structured programming. Additionally, the paper examines the limitations, possible hazards, ethical considerations, and environmental consequences of AI-powered software engineering, including machine learning (ML), the Agile revolution and its revolutionary impact on software development practices. Through the analysis of this developmental trajectory, the essay offers vital insights into the progressive nature of software engineering and the pivotal role of artificial intelligence in shaping its future.
Copyright (c) 2025 Jonah Vincent Joshua, Diosdado Asumu Ndong Andeme https://creativecommons.org/licenses/by-nc/4.0
The Assessment of Organizations Readiness to Comply with the Kenya Data Protection Act: A Case of a Humanitarian Arganization
Privacy is a crucial aspect of life as it impacts on how we behave, feel and make decisions. It recognizes the dignity and inherent worth of individuals. The right to privacy as a fundamental right is recognized in our 2010 constitution under article 31 sub article c & d. Kenya enacted the Kenya Data Protection Act in 2019 (KDPA, 2019) to safeguard personal information, in accordance with a set of statutory principles. The act requires organizations to register with data commissioner’s office (ODPC), demonstrate safeguards in place for personal data processing, carry out a data protection impact assessment (DPIA) for processes that pose a significant risk to the privileges and autonomies of its citizens and report any breach within 72 hours. In order to evaluate an organization's compliance with the act, it is imperative to perform readiness assessment to review organizations privacy practices across different domains and identify any gaps as well as the necessary steps for achieving and maintaining compliance. To streamline readiness evaluation therefore, this study reviewed the privacy maturity models currently in use for organizations to measure their readiness to comply with privacy laws and assessed readiness of a humanitarian organization to comply with the act. The AICPA / CICA privacy maturity model informed the readiness assessment of the humanitarian organization to KDPA compliance. The study adopted Quantitative research methodology. The research identified regulatory, culture and technology readiness as dimensions influencing organizations readiness to comply with KDPA and to improve the overall readiness score, organizations need to put emphasis on all the three domains (regulatory, culture and technology). Organizations can evaluate their compliance with the provisions of the act using the study’s findings, identify areas of non-compliance and prioritize remediation efforts.
Readiness assessment for Organizations to comply with Kenya Data Protection Act
Copyright (c) 2025 Francis Ndichu, Agnes Wausi https://creativecommons.org/licenses/by-nc/4.0
A Study on Wearable Tech Interfaces and Perception: Cognitive AI-Enabled Device
In our paper, we explore the possibility of utilizing state-of-the-art hardware architecture to develop an interactive Artificial Intelligence(AI) based virtual agent in an Internet of Things (IoT) enabled smart glass. In contrast to the traditional system that relies on Central Processing Unit(CPU) and Graphical Processing Unit(GPU), we examine the possibility of a proposed hardware system that employs neuromorphic computing for precise and energy-efficient processing in real-time, resolving the problems of latency and excessive power consumption. When combined with conventional Complementary Metal-Oxide-Semiconductor (CMOS) technology, Resistive Random Access Memory(ReRAM) offers non-volatile, fast memory that facilitates parallel computing and guarantees smooth data store and retrieval for wearables with limited resources. This study also emphasizes the application of intelligent virtual agents based on cognitive AI in wearable technology. In order to create an immersive human-computer interface, we attempted to create an intelligent interactive AI avatar with Cycle Generative Adversarial Network (CycleGAN) that mimics the user's traits and further pushes it to the large Language Model(LLM) to generate motion sequences that perform additional tasks, transforming LLM prompt reactions into movements.
Copyright (c) 2025 Debjyoti Bagchi, Samir Biswas, Uryaswi Bhowmick, Titash Das, Srijaa Chatterjee, Gouree Purkait, Tathagata Ghosh https://creativecommons.org/licenses/by-nc/4.0
An Android-Based Smart RSU Framework for Enhanced Urban Traffic Management
This paper addresses critical challenges in the deployment and effectiveness of traditional Roadside Units (RSUs) in traffic monitoring systems and proposes a novel, cost-effective approach using Android-based smart RSUs. Leveraging mobile phone architecture, YOLOv8 , the SAHI algorithm and chat gpt-4o, the system provides real-time traffic data collection, vehicle detection, and congestion analysis. This paper evaluates the performance of different cost tiers of mobile devices, discusses traditional traffic monitoring challenges, and identifies key gaps in current RSU technologies. The proposed system offers enhanced scalability, flexibility, and reduced cost, making it an ideal solution for urban traffic management.
Copyright (c) 2025 Mohammed Fadhil, Qutaiba Ibrahim Ali https://creativecommons.org/licenses/by-nc/4.0
Aspect-Based Sentiment Analysis for Turkish Reviews Using Token and Sequential Classification Methods
Aspect-Based Sentiment Analysis (ABSA) aims to identify sentiments expressed toward specific aspects or attributes of entities in text. This study addresses the under-explored area of ABSA in the Turkish language by extracting aspect terms (targets) and their categories from customer reviews and determining the sentiment polarity for each aspect. Turkish, being a morphologically rich and structurally complex language, poses unique challenges that often hinder the direct application of methods developed for other languages. Hence, developing sentiment analysis approaches tailored to Turkish is of significant importance. We propose a two-stage pipeline: a token-level classification to recognize aspect terms and assign them to one of 12 predefined aspect categories, followed by a sequence-level (sentence-level) classification to predict sentiment (positive, negative, or neutral) for each identified aspect. We fine-tuned five transformer-based language models (BERT, ConvBERT, ELECTRA, DeBERTa, and DistilBERT) for aspect term and category extraction, and four models (BERT, ConvBERT, ELECTRA, DistilBERT) for sentiment classification. Experimental results on the SemEval-2016 Turkish ABSA Restaurant dataset show that the BERT model achieved the highest accuracy (92.20%) for aspect term and category identification, closely followed by ConvBERT (91.68%). For sentiment analysis, ConvBERT performed best with an accuracy of 86.91%, outperforming ELECTRA (85.34%), BERT (82.75%), and DistilBERT (77.48%). These findings demonstrate that pre-trained transformer models can effectively handle fine-grained sentiment analysis in Turkish, substantially improving on previous approaches. The proposed pipeline and comparative results provide a novel benchmark for Turkish ABSA, with potential applications in analyzing Turkish customer feedback to glean actionable insights.
Token Classification, Sequential Model Classification, Aspect Extraction, Aspect-Based Sentiment Analysis, Deep Learning
Copyright (c) 2025 Metin Bilgin, Melek Turan https://creativecommons.org/licenses/by-nc/4.0
Deep Learning Model for Crop Diseases and Pest Classification
The deep learning model for crop diseases and pest classification research examined how deep learning might improve farming methods, particularly for the purpose of accurately classifying pests and illnesses that affect crops. The importance of crop diseases and pests to world food security was highlighted in the introduction, along with the need for new approaches, such as deep learning models, to improve the accuracy and effectiveness of pest and disease control in farming. In order to evaluate the classification accuracy, the secondary datasets obtained from kaggle website were used to train and test various deep learning models, one of which being DenseNet. The researcher used a thorough assessment methodology to compare DenseNet's performance to that of other models, including AlexNet, EfficientNet,Visual Geometry Group, and Convolution Neural Network.With an impressive accuracy score of 96.988% on the maize disease dataset and 96.9382% on the pest dataset, DenseNet proved to be the best model among the others. More accurate predictions were the result of DenseNet's capacity to effectively collect intricate characteristics and patterns within the visual data, which led to its improved performance. The researcher examined the implications of DenseNet's high accuracy in the discussion section, implying that its sophisticated design rendered it optimal for the categorization of agricultural diseases and pests. In addition, the researcher investigated the feasibility of incorporating DenseNet into practical agricultural systems, where its strong performance might greatly enhance methods of crop monitoring and disease control. The discussion came to a close with suggestions for future studies, such as looking at whether DenseNet can be used for other types of crops and if hybrid models or transfer learning may improve its performance.
Copyright (c) 2025 Vincent Mbandu Ochango, Geoffrey Mariga Wambugu, Aaron Mogeni Oirere https://creativecommons.org/licenses/by-nc/4.0
Multi Moving Objects Detection in Video Using Pre-trained Deep Convolutional Neural Networks
Abstract- Nowadays object tracking is a critical concern in the field of machine vision. With the advent of powerful computers, affordable cameras, and growing demand for automatic video analysis, researchers have shown significant interest in object tracking. Various methods have been proposed for tracking objects in machine vision, but a key challenge remains: ensuring the robustness of tracking algorithms across consecutive video frames. In recent years, deep neural networks have emerged as a promising approach for accurate position estimation. In this study, we propose an enhanced method that combines deep convolutional neural networks with established techniques like K-means clustering. Our approach addresses challenges such as object disappearances and severe displacements. The selection of deep neural networks is motivated by their compatibility with target identification in video sequences, and achieving a remarkably low error rate in tracking validates our claim.
Object Tracking; Video processing; Deep neural network; K-mean clustering.
Copyright (c) 2025 Abolfazl Ansaripour, Hosein Mahvash Mohamadi https://creativecommons.org/licenses/by-nc/4.0
Detailed Analyses and Efficient Identification of Malware Evidence in CLaMP Dataset based on Machine Learning Approaches
Malware is a malicious software that is used to launch attacks of different types in computer networks and cyber space. Several signature and machine learning-based approaches have been used for the identification of malware types in the past. However,signature-based detection approaches have been reported to have serious limitations which gave room for machine learning-based malware identification techniques to be more popular. Despite the promises of the ML methods in the identification of malware evidence, some of the ML approaches in literature have poor detection rates which can be as a result of the size and nature of the patterns in the datasets used. This study used a dataset named CLaMP for the training and testing of the malware classification models. Firstly, comprehensive exploratory analyses of the dataset were carried out with a view to understanding the data distributions in it better and make informative decisions on how to pre-process and apply it for malware identification. During the experimentations, two scenarios were established before feeding the data into the learning algorithms. Scenario 1 involves building malware identification model without data cleaning and feature selection while scenario 2 involves the cleaning of the data and selection of promising features for building the models.In scenario 2, Recursive Feature Elimination (RFE) technique was used for selecting the promising attributes which were used to build the two malware classification models. Naive Bayes (NB) and Logistic Regression (LR) algorithms were used for building the models. The hyper parameters of the two selected algorithms were varied and the models tested and validated severally before optimal performances were arrived at. The results of the models were compared based on the selected metrics, namely: accuracy, precision, recall, f1-score and Area Under the Curve (AUC). Experimental results showed that in the scenario 1, where the dataset was not pre-processed and all the attributes were used for the model building, poor results were obtained by both models in all metrics except in recall. However, NB-based malware identification model slightly performed better than LR in all the metrics. It was also discovered that both NB and LR-based malware identification models performed well in scenario 2 when the dataset was pre-processed and promising features were selected using RFE. This study concluded that the detailed exploratory analyses, data cleaning and feature subset selection methods helped in achieving promising results from the malware identification models.
Malware Identification, Machine Learning Algorithms, Feature Selection, Windows PE headers
Copyright (c) 2025 M. O. Ayinla, A. M. Oyelakin, U. A. Adeniyi, K. O. Tajudeen, O. J. Olaleye https://creativecommons.org/licenses/by-nc/4.0
Enhancing Image Processing Capabilities based on Optimized Neural Networks
Image processing is the ability of machines to interpret and understand visual data, has been significantly advanced by Convolutional Neural Networks (CNNs). This study investigates the enhancement of image procesing performance through the optimization of CNN architectures. By performing comparison between basic CNN models with optimized versions, incorporating advanced techniques such as deeper convolutional layers, batch normalization, dropout, and data augmentation, the aim of the study is to improve accuracy and robustness in image detection and classification tasks. The experiments are carried out on benchmark datasets and the results demonstrate that optimized CNNs substantially outperform their basic counterparts, achieving higher training and validation accuracies. These findings highlight the critical role of architectural refinements and regularization techniques in advancing visual intelligence capabilities. This research presents a novel approach that underscores the capability of optimized CNNs to drive future innovations in the area of visual intelligence, offering more accurate and reliable visual data interpretation for real life applications.
Deep Learning, CNN Optimization, Batch Normalization, Dropout, Regularization Techniques, Implementation Code.
Copyright (c) 2025 Kavita Mittal https://creativecommons.org/licenses/by-nc/4.0
Visualization of Prediction of The Spread of Covid-19 in Indonesia using Cellular Automata
The first case of COVID 19 was detected in Indonesia in early March 2020. One way to assist the government in making decisions to deal with COVID-19 is to create a map of the distribution of COVID-19 patients based on which can only be accessed by people who have an interest through the website. The data used in this study is the period, location, total cases. After getting the data, the data is then processed to get weekly rules. After getting the weekly rules, the data is entered into the calculation of the Moore scheme to get the prediction results for the next week. Then the prediction results are poured in the form of a map. The prediction process using CA neighbors is carried out using Moore's formula, a formula that applies the adjacent neighbors of 8 neighbors. The accuracy level of Cellular Automata with Moore's neighbors reaches 431.1466353% using MAPE. The error value in the Cellular Automata method is quite high due to several factors that make the prediction results with the original data results different, but this method can be used to research cases of covid 19
Copyright (c) 2024 Safira Aurellia Azzahra, Dian Pratiwi, Syandra Sari https://creativecommons.org/licenses/by-nc/4.0
Ensemble Feature Selection for Network Intrusion Detection: Combining Information Gain and Random Forest with Recursive Feature Elimination
Network intrusion detection systems (NIDS) are essential for protecting computer networks against cyberattacks. The selection of a nominal set of essential features that may adequately discriminate malicious traffic from normal traffic is indispensable while developing a NIDS. As such, a more reliable and accurate detection result may be realized when intrusion detection is carried out on a dataset based on an inclusive feature representation. This work presents the pre-processing and feature selection workflow as well as its results in the case of the CIC-IDS-2017 dataset, with a focus on two cyber-attacks, namely Denial-of-Service (DoS) and PortScan. The study applied an ensemble feature selection method based on information gain and Random Forest to filter out important features. Recursive Feature Elimination method was then applied to the reduced features to optimize the selected feature subset. The selected feature subset was experimented with using two classification algorithms, namely support vector machine and multi-layer perceptron. In the evaluation process, four widely used performance metrics were considered. The study results demonstrated the efficacy of the proposed ensemble approach to optimize the selected feature subset for detecting PortScan and DoS attacks in network traffic. Experimental results revealed that the support vector machine had a slight advantage in accuracy and could train more quickly. According to the study's evaluation, the NIDS may be able to shorten processing times without sacrificing the ability to detect PortScan and DoS attacks accurately by choosing a narrow subset of informative features. This suggests the approach might be applicable to real-world NIDS scenarios involving these attacks. The study also provides encouraging perspectives on how ensemble feature selection utilizing MLP and SVM can enhance the effectiveness of NIDS. Building on these findings, more research can create NIDS solutions that are even more reliable and efficient for the dynamic field of cybersecurity.
Classification; ensemble; feature selection; network intrusion detection system; pre-processing; recursive feature elimination
Copyright (c) 2025 Stephen Wanjau, Gabriel Kamau https://creativecommons.org/licenses/by-nc/4.0
Embedded Feature Selection Augmented Thyroid Disorder Prediction using MLP
Due to its considerable fatality rate and increasing frequency, thyroid disorders pose a severe hazard to people's health in the modern era. Thus, it has become a useful topic to predict thyroid disease early on using a few basic physical indications that are gathered from routine physical examinations. Being aware of these thyroid-related signs is crucial from a clinical standpoint in order to forecast outcomes and offer a solid foundation for additional diagnosis. However, manual analysis and prediction are difficult and tiring due to the vast volume of data. Our goal is to use a variety of bodily signs to swiftly and reliably predict thyroid disorders. This research presents a novel prediction model for thyroid disorders. We provide a deep neural network and embedded feature selection method-based algorithm for predicting thyroid disorders. Based on the LinearSVC algorithm, this embedded feature selection method selects a subset of characteristics that are strongly linked with thyroid condition by employing the L1 norm as a penalty item. We feed these features into the deep neural network that we constructed. To improve the performance of the predictor, gradient varnishing or explosion is avoided by initializing the network's weight using the He initializer. The predictor is evaluated using a number of indicators like accuracy, recall, precision and F1-score. The results indicate that our model achieves 98.3%, 98.1%, 98.0% and 0.982 respectively, and that its average AUC score is 0.98, indicating that the approach we proposed is effective and trustworthy for predicting thyroid disorders.
Thyroid Disorder, Feature Selection, Classification, Deep Neural Network, MLP
Copyright (c) 2025 Mir Saleem, Shabir Najar, Malik Akhtar Rasool https://creativecommons.org/licenses/by-nc/4.0
Optimized Q-Learning-Based Handover Decision Algorithm for Femtocells Using Load Balancing in LTE-A Networks
The rapid growth of mobile devices and demand for mobile data has made it challenging to maintain capacity, high coverage, and data speed. With the emergence of small cell networks, the Long-Term Evolution (LTE) system helped to address these issues, Femtocell technology is being deployed to provide improved indoor coverage. However, a major challenge is the frequent handover and unequal distribution of cell loads, which lead to a reduction in call and data rates. Small cells have changing and unplanned load distribution over time, resulting in certain cells suffering high user density and strong resource competition, while others are having low user density and wasteful resource due to low consumption. This imbalance in cell load distribution greatly influences overall network performance and prevents Femtocells from realizing their full potential. Despite several efforts by researchers to enhance network communication, handover is still a challenging issue, many related works have been done in the field but still it needs improvement. This research proposes an Optimized Q-learning-based Handover Decision Algorithm for Femtocells using Load Balancing in LTE-A Networks to improve overall network performance. The algorithm learns to prioritize and select cells with low load during target cell selection and not only provides good Quality of Service (QoS) but also has a low load, resulting in better traffic distribution across the cells. Several simulations were performed using LTE-Sim. Results proved the outperformance of the proposed algorithm over the existing algorithm in terms of QoS with a packet loss ratio for CBR packet transmission of 512 bytes with a rate of 8 packets/second intervals, 88.53%, and VoIP packet transmission of 32 bytes per 20 ms/time interval, 89.24% respectively.
LTE-A; Q-Learning; Load Balancing; Macrocell; Femtocell; CBR; VoIP.
Copyright (c) 2024 Babangida Abubakar Albaba, Aliyu Saidu, Muhammad Sani Iliyasu, Abdulhamid Usman Nuruddeen, Kamal Sabo Wada https://creativecommons.org/licenses/by-nc/4.0
File Carving: Analyzing Data Retrieval in Digital Forensics
In the current scenario, mostly the data are stored in digital media. Managing the storage and security of huge volume of data is emerging as a significant challenge for data science researchers and engineers. As data is considered as more costly and powerful than anything else, so during damage or loss of data thousands of dollars are being invested for data recovery. File caving is a technique used for data recovery from the file without the any contextual information when the storage media is formatted or file system got damaged. In this study, we have tried to describe the various types of file caving techniques and the tools used for file caving, along with their limitations and the categories of files which are supported along with the scenario for such recovery.
Digital Forensic; File Carving; Data Recovery;File Carving Tools and Techniques.
Copyright (c) 2024 Purna Chandra Sethi https://creativecommons.org/licenses/by-nc/4.0
Legal Frameworks for Digital Space Protection in Kenya
Strong cyber laws are necessary to protect digital spaces in Kenya, where the digital landscape is changing quickly. This need has grown. This article explores Kenya's legal framework for cyberspace, focusing on important topics such as privacy, cybercrime, data protection, and intellectual property rights. It investigates the nation's current state of cyber laws through a thorough assessment, determining whether it is sufficient to handle the problems brought about by emerging technologies and cyber threats. Furthermore, the article acknowledges the interdependence of national legislation with international trends and examines the critical role that international norms and standards have played in influencing Kenya's approach to cyber law. The paper highlights the importance of collaborative efforts between government agencies, private enterprises, and civil society in strengthening cyber resilience and promoting a secure digital environment. This article adds to the ongoing discussion on cyber law in Kenya by critically examining the current framework and suggesting possible improvements. Its goal is to promote the creation of a thorough and functional legal system that guarantees the nation's digital spaces are protected.
Copyright (c) 2024 Richard Mathenge, Josphat Karani https://creativecommons.org/licenses/by-nc/4.0