International Journal of Software Engineering and Computer Systems
IJSECS is dedicated to address the challenges in the areas of Software Engineering and Computer Systems, thereby presenting a consolidated view to the interested researchers in the aforesaid fields. The journal looks for significant contributions to Software Engineering and Computer Systems in theoretical and practical aspects. IJSECS is indexed by MyCite, Directory of Research Journal Indexing, RsearchBib, Scientific Index Services, Google Scholar and Computer Science Directory.
ENHANCED CROSS-CROP PLANT DISEASE RECOGNITION MODEL VIA DOMAIN-SPECIFIC ALIGNMENT NETWORK AND ELASTIC-MIXUP
ABSTRACT - Reliable identification of crop diseases across different species is often limited by variations in imaging conditions and feature distribution differences between crops. This study specifically targets cross-crop and zero-shot recognition settings where target-domain labels are unavailable, introducing a domain-invariant plant-disease recognition framework that combines Elastic-Mixup and a Domain-Specific Alignment Network (DsAN). A pre-trained MobileNetV2 acts as the visual encoder, producing 1280-dimensional features projected into a 300-dimensional semantic space derived from Word2Vec representations of disease names. The Elastic-Mixup module jointly interpolates visual and semantic embeddings under a shared Beta (0.4, 0.4) distribution, promoting smoother transitions and richer feature diversity across domains. Meanwhile, the DsAN aligns class-conditional subdomains through a localized maximum-mean-discrepancy criterion and a weak gradient-reversal mechanism, enabling balanced adaptation without margin collapse. Comprehensive experiments on the PlantVillage dataset (tomato to potato transfer) demonstrate the effectiveness of the proposed approach, achieving 97.58 % accuracy, 96.60 % precision, 96.59 % recall, and 96.59 % F1-score, representing an 88.9 % reduction in classification error compared to existing zero-shot transfer methods. These results highlight the study’s principal contribution demonstrating that the cooperative integration of Elastic-Mixup and DsAN yields a scalable, semantically consistent, and domain-invariant solution for intelligent agricultural diagnostics.
Copyright (c) 2026 The Author(s) https://creativecommons.org/licenses/by-nc/4.0
DESIGN AND EVALUATION OF QKLINIK: A WEB-BASED QUEUE MANAGEMENT SYSTEM FOR OUTPATIENT CLINICS
In outpatient clinics, where extended patient waits times frequently result in discontent and inefficient workflow, effective queue management is still a recurring problem. The effectiveness of current digital queuing systems in cutting down on overall waiting time is sometimes limited by their lack of interaction with pharmacy services or mobile-based real-time updates. In order to improve patient flow and service transparency in outpatient settings, this study introduces QKlinik, a web-based queue management system. Through an integrated interface, the system enables patients to easily make pharmacy purchases, track real-time wait status, and reserve queue numbers online using QR codes. The system was developed iteratively via planning, design, development, testing, and evaluation phases using the Agile methodology. PHP, HTML, and PhpMyAdmin were used to implement the prototype and maintain the backend data. Despite not having been verified in a real-world clinical setting yet, QKlinik was put through simulated user assessments and black box testing with patients, physicians, and clinic employees. According to the results, the system successfully lowers perceived waiting time, maintains precise queue sequencing, and offers a smooth and user-friendly experience, with an average usability rating of 4.78 out of 5. All things considered, QKlinik shows how web-based queue management may enhance clinical operations and raise patient happiness in outpatient environments.
Copyright (c) 2025 The Author(s) https://creativecommons.org/licenses/by-nc/4.0
AN INTEGRATED HYBRID SOFT VOTING ENSEMBLE AI MODEL OF MACHINE LEARNING AND DEEP LEARNING MODELS FOR DIABETES PREDICTION
The goal of the study is to make a hybrid prediction model that uses both machine learning and deep learning methods to make diabetes predictions more accurate, generalizable, and strong. It combines ML and DL models, fixes class imbalance in medical datasets, and tests performance on several datasets, such as the Pima Indians Diabetes Dataset and the LMCH dataset, to see how well it works in real-life healthcare. The ML Ensemble, which included RF, LR, and XGBoost, and the DL Ensemble, which included CNN, FNN, and ENN, were the two stacked ensembles used in the study. Soft voting was used to aggregate the results in order to improve the accuracy of the predictions. In order to prepare the structured medical data, we employed feature preprocessing techniques and the Synthetic Minority Over-sampling Technique (SMOTE). Cross-validation was used to ensure that the results were good and to prevent them from being overly specific. The performance was compared to independent models and standard methods. The ensemble hybrid AI model performed better than the traditional ML and DL models. Its best performance metrics were Accuracy of 98.89%, Precision of 98.99%, Recall of 87.07%, F1-score of 92.05%, ROC-AUC of 92.48%, and Cohen's Kappa of 84.96%. This shows that it was better at making generalizations and working with datasets that weren't balanced. The stacking of ensembles with soft voting combines machine learning and deep learning models to improve diabetes prediction performance and fix problems with class imbalance in medical datasets. The model's ability to be used in the real world and its ability to be generalized show that it could be used to find diabetes early and accurately, which could help with preventive healthcare strategies.
TECHNOLOGY ACCEPTANCE AND HABITUAL SOCIAL MEDIA USE: A QUANTITATIVE STUDY OF MALAYSIAN KOREAN POP FANS
K-pop is a kind of fandom that has gone global, with Malaysia being an active hub. This study investigates the concept of habit-as-repeated, automatic behavior-across demographic factors such as age, gender, and years of experience among Malaysian K-pop fans. This quantitative survey inquired from 950 participants about their responses, and descriptive analyses were made by using frequency distribution and cross-tabulation to discern patterns of engagement. The sample included mostly females, 84.84% (n = 806), compared to males, 15.16% (n = 144). By age, 47.16% (n = 448) were 18–24 years old, and 52.84% (n = 502) were 25–34 years old. Regarding fandom experience, the 6–10-year range was most common: 33.33% of females and 32.14% of males in the younger group, and 28.07% of females and 60.87% of males in the older group. Among older females, 42.11% reported 11–15 years of fandom experience, showing long-term and sustained engagement. This research represents one of the first exploratory studies attempting to understand Malaysian K-pop fandom and habit across a set of demographics, while also extending previous research applications of UTAUT theory towards a hedonic and a communal online environment.
COMPARATIVE ANALYSIS OF BACK-TRANSLATION MODELS FOR NORMALIZATION MOBILE APP USER REVIEWS
The increase of mobile apps has led to an exponential growth of user-generated reviews, which are often noisy, informal, and linguistically diverse, thereby posing significant challenges for automated analysis in requirements engineering. This study evaluates whether back-translation (BT) can normalize informal reviews while preserving meaning, and which model (Google Translate vs Facebook M2M100_418M) offers better semantic preservation, grammatical quality, and lexical alignment. We collected 323 Google Play reviews (667 sentences) from three Malaysian government apps. Texts were cleaned, expanded for colloquial forms, and then BT was applied using Malay as an intermediate language. Evaluation used four metrics which are semantic similarity (Sentence-BERT), grammar error count (LanguageTool), BLEU (NLTK), and perplexity (GPT-2). Models differences were tested with paired t-tests and Wilcoxon signed- rank tests, while paired scatterplots showed distributional patterns. Google was significantly better on semantic similarity (t(322)=5.38, p<.001), grammar errors (t(322)=3.66, p<.001), and BLEU (t(322)=2.99, p=.003); effect sizes were small to moderate. Perplexity differences were not significant, indicating comparable sentence-level fluency. Visualizations confirmed Google’s steadier performance with fewer extreme outliers. BT is a practical normalization step for noisy reviews. For the English–Malay pipeline studied here, Google provides more reliable semantic preservation and grammatical quality, while both systems are similar in fluency. However, the generalizability of these results are constrained by the relatively modest sample size (323 reviews, 667 sentences), and future work should validate results on large datasets and explore hybrid strategies combining strengths of both models.
ENHANCED STACKING ENSEMBLE MODEL FOR CERVICAL CANCER PREDICTION
Early prediction of cervical cancer is crucial for positive outcome of treatments and reducing high mortality rate among women. This research aims at enhancing ensemble Machine Learning model to improve existing methods so as to have early diagnosis of the disease. Traditional methods have limitations in terms of sensitivity and specificity hence, there is need for more accurate and reliable predictive models. Existing machine learning models often face challenges such as overfitting, underfitting, and variability in prediction performance, this research which integrates different Machine Learning techniques in improving prediction, offers a potential solution to these challenges. In this study, an ensemble approach was used to develop an enhanced ensemble Machine Learning model using Support Vector Machine (SVM), Extreme Gradient Boosting (XGB) and Random Forest (RF). The three algorithms where chosen for their robustness against overfitting and improving accuracy of prediction. Furthermore the study Overcome issues of imbalanced datasets by applying Synthetic Minority Oversampling Technique (SMOTE) and Random Under Sampling (RUS) before training and testing the Machine Learning algorithms so as to obtain better result and improve accuracy of the model. This study achieved a higher accuracy of 95%, 97% precision, recall of 96% and 97% F1-score. The result indicates that the enhanced ensemble approach correctly identifies large majority of cervical cancer cases showing its reliability in prediction, assisting medical experts on early diagnosis. However, this study is limited to secondary datasets which involves collection of only secondary source datasets from the UCI repository for training and validating Machine Learning models. Future work should focus on advancing to Deep Learning to overcome limitation of utilising a feature extraction in Machine Learning algorithms technique and adding a lot more medical images to the dataset.
Copyright (c) 2025 Fatima Isa Jibrin*, Lawan Jibrin Muhammad https://creativecommons.org/licenses/by-nc/4.0/deed.en
THE DEVELOPMENT OF AN ISLAMIC-INSPIRED DIGITAL THERAPEUTICS APP: EXPLORING MENTAL HEALTH CHALLENGES AMONG UNIVERSITY STUDENTS
Mental health difficulties, including anxiety, depression and stress, are prevalent among Muslim university students and are often intensified by academic demands and societal stigma that discourage help-seeking. Existing mental health services frequently lack cultural and religious sensitivity, limiting their relevance and effectiveness for this population. Furthermore, although numerous Islamic-themed mobile applications exist globally, a comprehensive literature review reveals a notable absence of digital platforms specifically designed to address the distinct mental health challenges faced by Muslim students, particularly those offering live access to therapists and experts. This study seeks to address this critical gap by developing and evaluating a digital therapeutic application rooted in Islamic principles to enhance the psychological well-being of Muslim university students aged 18 to 30. The application combines evidence-based psychological interventions with Islamic spiritual practices such as Dhikr, Dua, and faith-based coping mechanisms. It delivers structured daily spiritual exercises, personalised support, and direct access to culturally competent clinicians, thereby providing a culturally congruent approach to mental healthcare. A total of 308 Muslim university students participated in a quantitative evaluation involving surveys, interviews and app usage data to assess the intervention’s impact on emotional resilience, stress reduction and engagement with spiritual practice. Statistical analysis revealed significant improvements in emotional regulation (p < 0.01) and coping strategies (p < 0.05) following app utilisation. While awareness of digital therapeutic apps (p = 0.497) and perceived helpfulness of Islamic-themed apps (p = 0.424) were not significant predictors, the model demonstrated strong explanatory capacity with adjusted R² values of 0.768 and 0.816 for mental health outcomes and age-related variance, respectively. These findings highlight the potential efficacy of integrating digital mental health interventions with Islamic values to overcome culturally specific barriers faced by Muslim students. The study emphasises the importance of culturally sensitive digital therapeutics and recommends further longitudinal research to evaluate their long-term impact within diverse religious populations.
Copyright (c) 2025 Nahreen Zannat*, Murni Mahmud https://creativecommons.org/licenses/by-nc/4.0/deed.en
RECONNAISSANCE OF THE INTERDEPENDENCY AMONG REQUIREMENT CLARITY, SPRINT EFFICIENCY, AND SOFTWARE QUALITY: A CONCEPTUAL FRAMEWORK APPROACH
This study has developed a theoretical model capable of evaluating the relationships among requirement clarity, sprint efficiency, and software quality in Agile software development. Although Agile adopts a highly flexible and continuous delivery approach, requirement ambiguity remains one of the major issues that negatively impacts both sprint performance and product quality. Grounded in information processing theory, cognitive load theory, and systems theory, this research reveals how unclear or insufficient requirements lead to increased defect rates, reduced sprint velocity, and diminished planning efficiency. Furthermore, the model establishes a set of metrics to support outcome measurement, including the Requirement Volatility Index (RVI), Sprint Velocity (SV), Defect Density (DD), and Quality Index (QI). From a practical perspective, this study affirms that requirement validation processes, stakeholder alignment, and continuous feedback are essential to Agile success. Overall, this research develops a theoretically and empirically grounded model that assists Agile practitioners, project managers, and quality engineers in anticipating and managing requirements-based risks, while also serving as a tool for real-time quality assurance during sprints.
APPLICATION OF AFRICAN BUFFALO OPTIMIZATION ALGORITHM TO CULTURAL HERITAGE
The cultural heritage of a people defines the people. Cultural heritage refers to the physical and intangible artefacts, traditions and values that are passed down from one generation to another within a community or society, nation or race. Over the years, it has been observed that cultural heritage studies have been bogged by optimization challenges in the areas of resource allocation, route planning and site selection. Providing solutions using the African Buffalo Optimization is the focus of this paper. The relationship between the African Buffalo Optimization which is a swarm optimization technique in computer science and cultural heritage is also examined using the qualitative research method. Using the analytical research method, not an experimental one, to track the relationship between humanities, computer science, Artificial Intelligence, cultural studies and the social sciences, this paper highlights the application of the African Buffalo Optimization to confronting the challenges of cultural heritage and concludes that the algorithm can be applied to optimize various processes, such as digital preservation and conservation, collection management and curation, exhibition and display design, community engagement and participation and cultural heritage tourism management. Such application benefits the global cultural heritage organizations and institutions in several ways. It is, therefore, recommended that the global research community in cultural studies, digital humanities and social sciences should explore the use of swarm optimization techniques, in general, and the African Buffalo Optimization, in particular, to cultural heritage research and scholarship.
Copyright (c) 2025 Julius Beneoluchi Odili https://creativecommons.org/licenses/by-nc/4.0/deed.en
MACHINE LEARNING APPROACH TO ETHEREUM FRAUD DETECTION: COMPARATIVE ANALYSIS OF LOGISTIC REGRESSION AND RANDOM FOREST
Ethereum's pseudonymous and decentralized nature has made it challenging to trace fraud since its inception. The fraud type identified is fraudulent reuse of the same funds from copied unique received addresses and a different time between the first and last user transaction. This paper is concerned with comparing and examining the performance of Logistic Regression and Random Forest models in detecting unusual Ethereum transactions and finding the most significant features influencing these outcomes. Traditional approaches have a tendency to overlook lightweight, interpretable models that may be utilized for real-time filtering, or they focus on Bitcoin rather than Ethereum. In order to fill this gap, this study utilizes machine learning to detect meaningful patterns of behaviour related to fraud. This study compares the performance of Random Forest and Logistic Regression models in classifying fraudulent Ethereum transactions. The dataset is extracted from a publicly available repository called Ethereum Fraud Detection. The dataset was normalized and cleaned using MinMaxScaler, and then split into 80% training and 20% testing subsets. Feature scaling, correlation analysis, and removal of duplicate variables (such as ERC20 tokens) were part of the preprocessing. With 90.40% accuracy and F1-score of 89.17%, the model suggests that Random Forest is a better performer than Logistic Regression. The ability of the model to identify non-fraud instances is clear from the visualization of the confusion matrix, which also identifies areas of improvement in identifying actual fraud cases. These results give business organizations actionable recommendations on how to deploy real-time detection, rank high-risk transaction signals, and utilize adaptive machine learning architectures. The paper promotes further research into anomaly detection based on deep learning and demonstrates the potential of feature-based machine learning in bolstering Ethereum security infrastructure.
MACHINE LEARNING FOR IOT SECURITY: DETECTING AND MITIGATING CYBER ANOMALIES
Rapid IoT device proliferation creates critical security vulnerabilities often missed by conventional methods. This vital research evaluates seven machine learning models (Random Forest, Gradient Boosting, Neural Networks, kNN, SVM, Decision Tree, and Naive Bayes) for robust IoT anomaly detection using comprehensive ToN-IoT and BoT-IoT datasets. Random Forest and Gradient Boosting significantly advanced IoT security, demonstrating superior, often perfect, and performance on key metrics like AUC, accuracy, and F1. Neural Networks also excelled. SVM and kNN achieved high accuracy but showed varied sensitivity to rare attacks. Naive Bayes struggled with data complexity, while Decision Tree's operational failure on one dataset stressed the need for careful validation. This study underscores machine learning's potential to enhance IoT resilience. Performance variations and challenges such as class imbalance necessitate tailored solutions. These findings establish a foundation for future work in ensemble methods, explainable AI (XAI), feature engineering, and strategies for managing large-scale imbalanced data to fortify IoT security.
Copyright (c) 2025 Hiba Ahmed, Razan Alharith, Ashraf Osman Ibrahim,* and Nada Adam https://creativecommons.org/licenses/by-nc/4.0/deed.en
TASK-TECHNOLOGY FIT: INTELLIGENT EVALUATION MODEL FOR USER SATISFACTION WITH HEALTH INFORMATION SYSTEMS
This study presents a predictive model aimed at investigating the level of satisfaction with healthcare information platforms in Gaza, focusing on medical professionals such as doctors, pharmacists, nurses, and laboratory technicians. Addressing the need for reliable assessment tools for HIS performance, the research adopts the Task-Technology Fit (TTF) framework and employs artificial neural networks to model user satisfaction. The dataset comprises 150 records, each including four input variables, technology characteristics, task characteristics, task-technology fit, and security; and one output variable representing user satisfaction. The model was developed and tested using RapidMiner, achieving a prediction accuracy of 86.7%. These findings suggest that the proposed approach offers a viable method for assessing HIS effectiveness. This study is among the first in the Gaza Strip to incorporate security as an integral component within the TTF model using ANN, thus contributing empirical insights into the design and evaluation of context specific HIS systems. The research recommends further validation of the model across different electronic or online management information systems to confirm its generalizability.
THE RELATIONSHIP BETWEEN THE AFRICAN BUFFALO OPTIMIZATION AND DIGITAL HUMANITIES
The African Buffalo Optimization (ABO) is a swarm optimization technique developed in 2015 by Odili and Kahar. It obtained its inspiration from the movement of cape buffalos across the wide African rainforests and savannah using basically two sounds: /waaa/ and /maaa/. Since its development, the algorithm has been applied to a wide range of applications ranging from numerical function optimization, tuning of the Peripheral, Integral, Derivative parameters of Automatic Voltage Regulators, Travelling salesman problem etc. Despite the enormous potential benefits of combining stochastic optimization algorithms and Digital Humanities (DH), there is a lack of research exploring this intersection. DH data, such as historical texts, images, and artifacts etc. often require complex analysis and visualization techniques. Traditional methods may not effectively capture the nuances and complexities of these data. In this study, the relationship between the African Buffalo Optimization and Digital Humanities is explored with the aim of attracting digital humanities researchers’ attention to the enormous potentials available to the research community in swarm optimization algorithms like the ABO algorithm. Deploying Java programming language, this study implements the ABO algorithm to the various aspects of DH such as text analysis, digital pedagogy, cultural analytics, digital preservation, digital scholarship, digital museums and curation as well as digital edition and publishing. It was discovered that the ABO is good in optimizing topic modelling for literary analysis, sentiment analysis for historical text analysis and entity recognition for cultural trend analysis. In addition, the algorithm is effective in digital cultural analytics, digital pedagogy and scholarship. The successful implementation in this study proved to be a good contribution as it unravels the close relationship between the two disciplines that seemed to have nothing in common. In view of our findings, we recommend that global researchers in DH should explore the diverse opportunities inherent in swarm optimization algorithms to further DH scholarship and research.
Copyright (c) 2025 The Author(s) https://creativecommons.org/licenses/by-nc/4.0/deed.en
HYBRID LEARNING FOR BOTTLENOSE DOLPHIN MOTION APPROXIMATION USING WEIGHTED POLLING KNN AND BAYES MECHANISM
Artificial intelligence has been extensively employed to examine social animal behaviours, such as feeding habits, reproduction, swarm structures, and hibernation. For marine biologists, grasping essential habitat characteristics like swimming depth and temperature preferences is vital for evaluating ecological boundaries, habitat deterioration, and conservation initiatives. Machine learning has been used to help identify bottlenose dolphins and their cohabitation patterns, as computer vision and deep learning are often used in studies of fish habitats involving predation, feeding habits, breeding, and other fish. Existing techniques do not provide sufficient variety in estimating classification characteristics for assigning unique animals to their corresponding subclasses, particularly for differentiating various marine species along with their respective groups. The objective for this proposed research framework is aimed at integrating supervised clustering and probabilistic classifiers such as K-Nearest Neighbors (KNN) or Bayes classifying with weighted voting to classify dolphin types, apart from consolidated behavioral patterns. The study further integrated the use of oxygen usage into the dataset for monitoring movement activity after pup pre-feeding intervals in addition to metabolic rates of other animals. Data was cross verified, encompassing trial laps, dolphin identifiers, timing, segment length, and carbon dioxide emission rates, for the purpose of classification. Findings showed an average classification accuracy of 97% for weighted KNN types and 91% for weighted Bayes classifiers, emphasizing their effectiveness in differentiating dolphin classes in swarming settings. These machine learning methods provide essential tools for enhancing marine research and aiding conservation and habitat preservation through improved identification of ecological traits.
Copyright (c) 2024 Farid Morsidi https://creativecommons.org/licenses/by-nc/4.0/deed.en
THREE LAYER MEDIAN FILTER METHOD FOR IDENTIFYING CONCRETE STRENGTH LEVELS BASED ON CONCRETE IMAGES
This study introduces a novel approach utilizing digital image processing techniques to analyze concrete surface images for categorizing concrete strength levels based on two-dimensional RGB digital photographs. The research addresses the limitations of traditional median filters, such as insufficient noise reduction and edge preservation, by proposing a three-layer median filter for enhanced image preprocessing. The methodology involves three main phases. First, RGB images are converted to the Lab color space, followed by segmentation using the K-Means clustering method and noise reduction through the proposed three-layer median filter. This approach improves noise suppression by 15% compared to traditional median filters, as verified through quantitative analysis. Second, shape and texture features are extracted from the processed images to capture distinctive characteristics of the concrete surface. Finally, the images are classified into strength levels ranging from K100 to K300 using these features. The proposed method achieved a 90% accuracy rate, correctly identifying 46 true positives (TP) and 44 true negatives (TN), with minimal errors from 6 false negatives (FN) and 4 false positives (FP). This represents a significant improvement over conventional methods. The findings validate the robustness and reliability of the proposed method in accurately classifying concrete strength levels. By addressing key challenges in traditional approaches and integrating advanced image processing and clustering techniques, this research provides a non-destructive and efficient alternative for evaluating concrete strength. The study establishes a foundation for future advancements in automated material characterization and quality control in construction and engineering domains.
Copyright (c) 2024 Agung Ramadhanu, Halifia Hendri, Mardison, Larissa Navia Rani, Sofika Enggari, Muhammad Reza Putra https://creativecommons.org/licenses/by-nc/4.0/deed.en
OPTIMIZING SUPPORT VECTOR MACHINE FOR IMBALANCED DATASETS BY COMBINING POSTERIOR PROBABILITY AND CORRELATION METHODS
The challenge of classifying imbalanced data persists in machine learning, particularly in critical applications such as medical diagnosis, fraud detection, and anomaly identification, where detecting the minority class is essential. Conventional classifiers like Support Vector Machine (SVM) tend to favor the majority class, leading to reduced sensitivity in identifying minority instances. This study introduces Posterior Probability and Correlation-Support Vector Machine (PC-SVM), a novel approach that integrates posterior probability estimation with correlation analysis to enhance SVM’s performance on imbalanced datasets. Unlike traditional SVM models, which struggle with class imbalance and require additional data balancing techniques, PC-SVM dynamically adjusts classification thresholds using posterior probability values and correlation-weighted features, simplifying the classification process while improving its effectiveness. The effectiveness of PC-SVM was evaluated using multiple imbalanced datasets from KEEL, UCI, and Kaggle repositories. Results demonstrate that PC-SVM achieves 100% recall for the minority class, significantly outperforming traditional SVM, which attained only 80% recall on average. This 20% improvement in recall underscores PC-SVM’s ability to mitigate the imbalance issue without relying on oversampling or cost-sensitive adjustments. Furthermore, PC-SVM exhibits consistent performance across various evaluation metrics, including accuracy, precision, recall, and F1-score, ensuring robust classification results. By improving the detection of minority classes, PC-SVM offers a transformative solution for real-world applications that demand high sensitivity in identifying rare but crucial instances. Its ability to maintain classification integrity without additional balancing techniques positions it as a valuable model for industries such as healthcare, finance, and cybersecurity, where accurate minority class recognition is critical.
Copyright (c) 2025 Canggih Ajika Pamungkas, Megat Farez Zuhairi https://creativecommons.org/licenses/by-nc/4.0/deed.en
A BLOCKCHAIN - ARTIFICIAL INTELLIGENCE CONVERGENCE FRAMEWORK FOR ENHANCED IOT SECURITY
The Internet of Things (IoT) enables seamless machine-to-machine communication and data sharing, transforming sectors such as smart transportation and urban cities. However, the expansion of industrial IoT generates vast volumes of sensor data, posing significant processing and analytical challenges. Current IoT systems have several limitations, including centralized structures, privacy concerns, limited resource availability, and insufficient training data for AI-powered analytics, which hinder efficient large-scale data processing. This paper introduces BlockIoTIntelligence, a novel architecture that integrates blockchain and artificial intelligence (AI) for decentralized and secure IoT networks. The proposed framework combines blockchain's distributed trust with AI's analytical capabilities to address scalability and security challenges. Experimental evaluation demonstrates BlockIoTIntelligence's outperforms existing frameworks across key metrics, including 15% higher accuracy, 25% lower latency, enhanced security, and privacy compared to existing frameworks. The architecture effectively resolves data processing challenges while maintaining energy efficiency.
Copyright (c) 2025 Ritesh Kumar Thakur, Mansaf Alam https://creativecommons.org/licenses/by-nc/4.0/deed.en
EMBEDDED RESIDUAL NEURAL NETWORKS FOR REAL-WORLD PLANT DISEASE IDENTIFICATION IN DIGITAL AGRICULTURE
This study addresses the challenge of real-time plant disease identification on resource-constrained embedded platforms, a critical need for improving agricultural productivity. Using the NVIDIA Jetson Orin Developer Kit and the PlantVillage dataset, the research evaluates Residual Neural Networks (ResNets), focusing on ResNet-50, ResNet-101, and ResNet-152. The study highlights the balance between model depth, batch size, accuracy, and computational efficiency. ResNet-101, optimized with a batch size of 64, achieved 90.62% accuracy and an average identification time of 17.6 milliseconds, emerging as the most effective configuration. These findings demonstrate the feasibility of deploying deep learning models on embedded devices and provide insights into optimizing architectures for real-time agricultural applications.
Copyright (c) 2024 Daniel Udekwe https://creativecommons.org/licenses/by-nc/4.0/deed.en
PERFORMANCE ANALYSIS OF SELECTED MACHINE LEARNING ALGORITHMS IN THE DETECTION OF PHISHING ATTACKS ON VULNERABLE WEBSITES
Phishing is a cyber-attack where attackers pretend to be trustworthy, to trick individuals into providing sensitive information. Phishing also involves directing web users to fake websites that closely resemble legitimate ones and asking their victims to enter their personal information. It is imperative to restrict all forms of phishing websites or URLs. This study analyzes the performance of several machine-learning algorithms in creating models capable of detecting phishing websites. To achieve this central goal, massive phishing website detector datasets were retrieved from an online open repository, Kaggle. Relevant libraries in Python were explored for pre-processing, uploading, and partitioning of the datasets for training and testing the model. Models were created based on each of the six machine-learning algorithms implemented in this study. Evaluation of each model reveals that; Random Forest has the highest accuracy of 96.7%. followed by Support Vector Machine which records 96.4%. However, evaluating the model created using the Naive Bayes Classifier shows the lowest accuracy of 60.5%. This study has revealed the strength of each algorithm for detecting phishing websites; it also unveiled the procedures that need to be taken to mitigate and curb the spread of phishing attacks.
Copyright (c) 2024 Fatima Enehezei Usman-Hamza, Adeleke Raheem Ajiboye https://creativecommons.org/licenses/by-nc/4.0/deed.en
AN EFFECTIVE APPROACH FOR ELICITING REQUIREMENTS AND ENSURING TRACEABILITY: A CASE STUDY OF AUTOMATED TELLER MACHINE
Requirement Elicitation plays a great role in the successful completion of a software project. Elicitation is a productive effort to extricate project-associated information from the stakeholders. To gather complete, concise, and clear requirements, the concept of requirement elicitation allows various analytics and techniques. Requirement elicitation is significant due to the lack of efficiency in accurately articulating their demands towards the intended system. Therefore, requirement engineers realize the need to perform elicitation to ensure that the requirements produced are easily accessible, useable, and applicable according to the client’s demands. A software project can be considered successful if it satisfies all the requirements of the client. In certain situations, changes in requirements from the client side may arise during the development of the intended software. This research focuses on the challenges of this context mentioned as follows-many stakeholders are unable to clearly express their needs toward the intended system and the client-side requirements may change often. In this case, traceability plays a vital role in making the necessary changes in the components of the corresponding requirements. Therefore, the impact of any change can be easily visible & the changes can be applied correspondingly. After all, a project’s success can be achieved by ensuring traceability among the components used in eliciting requirements. So, this research proposes an effective approach for eliciting functional and non-functional requirements and ensuring traceability among the corresponding components used in eliciting requirements. In addition, a tool has been designed to ensure traceability among functional requirements, non-functional requirements, and other corresponding components. If any change request arises to any of the requirements, all the corresponding components associated with that requirement can be traced with the help of this tool. In overall, this proposed approach has been applied successfully to a case study of an Automated Teller Machine using this tool.
PREDICTION OF CARBON DIOXIDE EMISSIONS FROM MOTOR VEHICLES USING GRU AND DBSCAN
Extreme climatic conditions resulting from climate change pose significant challenges to mankind, prompting urgent action across various fronts. With vehicle emissions contributing substantially to carbon dioxide emissions, there is a pressing need for efficient prediction methods for informed mitigation strategies. While some research has been carried out on predicting carbon dioxide using machine learning, there have been few studies that explore Recurrent Neural Networks particularly those that can be modelled with a smaller dataset to improve emissions prediction. The study aimed to improve the prediction of carbon dioxide emissions by employing a new approach. The study was conducted on a Canadian dataset with 7,385 samples featuring motor vehicle parameters. Therefore, this research employs a deep learning model known as Gated Recurrent Units (GRU) for predictive modelling coupled with another algorithm called the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for robust outlier detection and subsequent removal. Other algorithms that were used for preprocessing include label encoding and min-max normalisation. The study was evaluated using four metrics, namely root MSE (RMSE), mean square error (MSE), mean absolute error (MAE) and determination coefficient (R2). Compared with the highest-performing results from prior studies, our approach achieved a 4.59% increase in R² and a 2.14% increase in R, alongside reductions in MSE and RMSE by 0.0008766 and 0.0015804, respectively. Thus, showing superior handling of data irregularities and temporal patterns. These results highlight the potential for improved emissions predictions, which can be instrumental in refining carbon tax calculations and informing environmental policy. Future works will involve expanding the dataset to include the age of the motor vehicle and using hybrid deep learning models.
AN IMPROVED FLASHCARD-BASED ICS MOBILE APPLICATION FOR EFFECTIVE TEACHING AND LEARNING OF DFC10033 – INTRODUCTION TO COMPUTER SYSTEM
A mobile application (mobile app) is a software designed to operate on mobile devices such as smartphones and tablets, allowing users to access information or complete tasks quickly and easily without referring to traditional notes like books. In education, mobile apps function as interactive teaching tools that can be accessed easily and quickly by students. One of the initiatives under Continuous Quality Improvement in Polytechnics is the development of a Teaching Aid Tool to enhance students' understanding of the DFC10033-Introduction to Computer System (DFC10033) course. After a Continuous Quality Improvement meeting a held with the course lecturers and coordinators, a decision was taken on improving the teaching of DFC10033 Course. This made the need for additional teaching aid tools inevitable his led to the development of ICS Flashcard-Based Mobile App, which includes graphic elements and explanatory notes to improve students' understanding. This app is available for free on the Google Playstore.A study was then conducted to evaluate the usability and effectiveness of the app for students of the Diploma in Information Technology (Digital Technology) at Polytechnic Sultan Abdul Halim Mu'adzam Shah (Polimas), Jitra, Kedah, who are taking the DFC10033 course from the Department of Information Technology. The development of the app followed the ADDIE model, which consists of five phases: Analysis, Design, Development, Implementation, and Evaluation. This quantitative study involved 171 male and female students aged between 18 and 25 years. Questionnaires were distributed through Google Forms to collect data, which was then analyzed to assess the objectives and effectiveness of the developed application. The results of the study indicate that the ICS Flashcard Mobile App made a positive contribution, with a mean value of Section B=3.77 and Section C=4.65, which, according to the mean score interpretation, indicates acceptance. The app effectively helped students to understand the course concepts more efficiently, aligning with the Continuous Quality Improvement objectives of developing the DFC10033 Teaching Aid Tool application and testing its effectiveness.
Copyright (c) 2024 Mohamad Shaufi Bin Kambaruddin, Wan Ahmad Ramzi Bin Wan Yusuf, Mohd Aishamuddin Bin Yaakob https://creativecommons.org/licenses/by-nc/4.0/deed.en
AN AUTHOR-CENTRIC SCIENTIFIC PAPER RECOMMENDER SYSTEM TO IMPROVE CONTENT-BASED FILTERING APPROACH
Scholarly publications on the web are rapidly expanding, making it difficult for scholars to identify relevant study materials. Information overload makes it harder to find important material, especially for new researchers. Scholarly recommender systems solve this issue by employing recommendation techniques to assist researchers in locating appropriate literature based on their interests. Existing systems frequently rely on user profiles and public and non-public metadata, which leads to the persistent problem in scholarly recommendations called cold start. To deal with the challenges of cold start in scholarly-based recommender systems, this research suggests an improved Content-Based Filtering (CBF) approach that takes advantage of publicly available metadata, specifically the author(s) feature. The approach incorporated author(s) features into a scholarly recommender system to serve as a basis and key component for alleviating "A New Paper Cold Start Problem." The proposed approach implements the feature vectors of the metadata using the Count Vectorizer and similarity computation was performed using the Cosine Similarity formula." An experiment using a publicly available dataset shows that the suggested method surpasses the approaches previously proposed by other researchers regarding recommendation accuracy and relevancy, making it a dependable and efficient instrument for scholarly recommendation. The result also shows the effectiveness of the author(s) feature in tackling new papers in scholarly recommendation systems.
Copyright (c) 2024 Mukhtar Nura, Zaharaddeen Adamu Hamisu https://creativecommons.org/licenses/by-nc/4.0/deed.en
AR MOBILE APPLICATION FOR ENHANCING NATIONAL MUSEUM HERITAGE VISUALIZATION
This study explores the integration of mobile Augmented Reality (AR) applications within the context of the National Museum of Malaysia, focusing on enhancing the educational experience for visitors. The project aims to address the challenge of enhancing museum experiences through innovative technologies. The research problem centers on designing an AR application for the National Museum of Malaysia to enrich the visitor engagement and understanding, as stemmed from the decline in museum visitors, with the National Museum of Malaysia experiencing a staggering 70% drop in income in 2020 and 2021. Utilizing Unity Editor and other libraries, the project developed the National Museum AR App, featuring four distinct AR experiences corresponding to different exhibits within the museum. Methods involve AR content creation, user testing, and data analysis. Overall, findings suggest that the AR application effectively meets visitors' learning needs and enhances the educational value of museum exhibits by providing unique AR features. The significance of the project lies in its potential to transform cultural heritage tourism by bridging the gap between traditional museum experiences and digital engagement. The study also serves as a preliminary investigation to guide the future development and deployment of AR applications within the museum setting.
Copyright (c) 2024 Chin Yee Khor, Siti Azreena Mubin https://creativecommons.org/licenses/by-nc/4.0/deed.en
PARTIAL OCCLUSION OBJECT DETECTION BASED ON IMPROVED MASK-RCNN
In the grasping task of industrial robots, multi-target objects are often placed in disorder or even partially occlusion or stacked, which brings certain difficulties to visual detection such as accuracy and real-time. The traditional Mask-RCNN algorithm can achieve high detection accuracy in scene which the target objects are neatly placed, but in the complex scenarios such as disorderly placed or partially occlusion is still have space for improvement in accuracy and speed. Mask-RCNN introduces the mask head structure to achieve pixel level segmentation mask prediction, it achieves high detection accuracy but increases the amount of calculation, this cause the detection speed is limited. To deal the above problems, this paper proposes an indirect frame subtraction for loss function to improved Mask-RCNN, which uses adjacent frames as comparison templates to find differences for image, that is, after one recognition, the previous recognition result is used as the background, and the next change is used as the target. thereby improving the recognition accuracy, reducing the repetitive estimation of regions, and improving the detection accuracy and running speed. Through experiments on self-made datasets, it is proved that new method can improve the image recognition accuracy about 2.3%, another, image recognition time has been reduced by 9% and the FPS value is improved by 6 frames which indicate the speed was improved. The research has important reference significance for the realization of robot flexible grasping task in intelligent manufacturing environment.
Copyright (c) 2024 QINGCHUAN LIU, Azmi Ayub Muhammad , Fazlina Ahmat Ruslan, Mohd Nor Azmi Ab Patar, Shuzlina Abdul-Rahman https://creativecommons.org/licenses/by-nc/4.0/deed.en
AN IMPROVED 5G MOBILITY HANDOVER EFFICIENT BY CREATING A DIGITAL TWIN NETWORK: A REVIEW
In the age of 5G, seamless mobility handovers are vital, especially in densely populated areas like Malaysia, to prevent disruptions and resource inefficiencies. A proposed solution involves a Digital Twin Network mirroring Malaysia's 5G infrastructure, integrating real-time data and user behaviors to optimize energy consumption during handovers. Emphasis is placed on energy-efficient protocols and algorithms to enhance network performance. The research follows the format of Systematic Literature Review (SLR). The algorithms predict and manage handovers proactively, enabling adaptive resource allocation for improved efficiency. The Digital Twin Network aims to significantly enhance mobility handover efficiency through predictive handovers and adaptive resource allocation, supported by energy-efficient protocols and edge computing for sustainability. This research offers a tailored solution to Malaysia's 5G mobility handover challenges, promising seamless connectivity and sustainability. It introduces a customized Digital Twin Network focusing on energy efficiency, evaluated against practical applications in information retrieval. Evaluation standards gauge effectiveness, supplemented by in-depth analysis of methods and performance metrics, concluding with insights, limitations, and recommendations for future research.
Copyright (c) 2024 Umar Danjuma Maiwada, Kamaluddeen Usman Danyar, Aliza Bt Sarlan, Abdussalam Ahmad Alashhab https://creativecommons.org/licenses/by-nc/4.0/deed.en
A NOVEL APPROACH FOR ADDRESSING IOT NETWORKS VULNERABILITIES IN DETECTION AND CLASSIFICATION OF DOS/DDOS ATTACKS
The substantial growth of Internet-connected devices within the Internet of Things (IoT) has given rise to significant security challenges. Among the various threats confronting these interconnected devices, Denial of Service (DoS)/Distributed Denial of Service (DDoS) attacks emerge as significant concerns. The attacks, which seek to disrupt IoT services by flooding networks with unnecessary traffic, there is a critical need for robust security measures. Intrusion Detection Systems (IDS) are vital in identifying suspicious activities, yet many existing systems lack real-time capabilities to address evolving attack strategies. This study investigates the vulnerabilities of IoT networks and the pressing need to detect and classify DoS/DDoS attacks in real time. Traditional IDS, while effective in recognizing known attack patterns, fall short in identifying new attack types due to their reliance on historical data. To bridge this gap, this research focuses on developing an enhanced hybrid network intrusion detection and classification system. This study seeks to make contribution to the advancement of resilient security measures within IoT environments. The framework uses the Kth Nearest Neighbor (KNN) algorithm and dense neural networks to efficiently detect and categorize DoS/DDoS attacks in real time. To achieve this, a simulation model implementing the proposed hybrid algorithm will be developed. The framework utilized the MQTT-IoT-IDS2020 dataset, and a comparison was presented with recent approaches found in the literature. The results demonstrated enhanced performance in terms of true positive rate, accuracy, speed of detection.
Copyright (c) 2024 Aisha Ibrahim Gide, Abubakar Aminu Mu’azu https://creativecommons.org/licenses/by-nc/4.0/deed.en
INVESTIGATION OF CODE SMELLS IN ECLIPSE FRAMEWORK USING SONARQUBE: AN EMPIRICAL ANALYSIS
The Eclipse Framework provides both public and internal Application Programming Interfaces (APIs). Public APIs are widely supported and encouraged for use, while internal APIs are considered immature and subject to frequent changes. However, the quality of these APIs is not guaranteed, with many users reporting code smells, which could lead to application failures if unresolved. While some studies indicate that not all code smells can be easily fixed, users often face the challenge of either addressing these issues themselves or abandoning problematic APIs. To address this, we conducted an empirical investigation using the SonarQube static code analysis tool on 28 major Eclipse releases, aiming to identify code-smell-free APIs. Our study provides a dataset of 218K code-smell-free public APIs and 321K internal APIs. We found that 87.3% of public APIs and 91.5% of internal APIs in the analyzed releases are free from code smells, highlighting the importance of using these cleaner alternatives for application stability and long-term usability. Furthermore, we have discovered that the number of code smells proportionately increases as the Eclipse framework evolves. The average number of code smells and technical debt is 147K and 2,744 days respectively in all the studied Eclipse releases. Results from this study can be used by both interface providers and users as a starting point to recognize code smell-free interfaces and estimate efforts needed to fix code smells in each version of Eclipse.
HETEROGENEOUS ENSEMBLE FEATURE SELECTION: AN ENHANCEMENT APPROACH TO MACHINE LEARNING FOR PHISHING DETECTION
Presently, phishing attacks are recognized as a global pandemic, which is adversely affecting global security and causing setbacks to global economy. A successfully conducted phishing attack (cybercrime) results in devastating effects such as: bankruptcy for people and corporations, mostly leading to information and financial fatalities. In the pursuit of accurately providing solutions against phishing threats, machine learning techniques were found to be the right antidote in the detection processes. One of the most important sub-tasks in supervised ML models is feature selection as it helps to eliminate unnecessary features from the dataset without sacrificing data quality. Feature selection is a serious challenge in phishing detection and other classification tasks. The worth of the selected attributes/variables plays a key role in building powerful models and poor-quality data frustrates the process. This work explores the use of ensemble feature selection in data mining to select meaningful features. A novel feature selection technique for phishing detection is proposed, based on frequent, necessary, and correlated items. The innovative Heterogeneous Ensemble Feature Selection framework (HEFS) framework produced a new set of webpage features highly informative apart from the usual common features used for phishing detection. Two experiments were conducted in the process, and the results show that both the classical models and their ensemble versions performed amazingly well when evaluated on the baseline features compared to the component features. However, Boosted_NB recorded the highest accuracy of 0.974 (97.4%). The HEFS is highly recommended as an efficient feature selection method to detect correlated, frequent, and phishing-behaved features for machine learning-based detectors.
Copyright (c) 2024 Bamidele Musiliu Olukoya, Gabriel Opeyemi Ogunleye, Patrick Olaniyi Olabisi, and Adesoye Sikiru Adegoke https://creativecommons.org/licenses/by-nc/4.0/deed.en
PERFORMANCE ANALYSIS OF SELECTED CLASSIFICATION ALGORITHMS ON ANDROID MALWARE DETECTION
Android mobile devices are widely used across all platforms and the development of malicious apps can compromise a user’s mobile system. Considering the large amount of new malicious apps, there is a need for a detection system that can operate efficiently to identify these apps. The study analyzes and compares the performance of DREBIN and MALGENOME data sets with the dataset’s SMOTE version on selected machine learning algorithms using WEKA tools. The performance of bayesian, function, rule, and tree-based classification algorithms on the two datasets was explored in this work. WEKA tool was used in pre-processing and SMOTE class balancing of the datasets before the model training using different classification algorithms on the two datasets and the performance evaluation. In the performance evaluation, parameters such as accuracy, precision, f-measure, the area under cover, true positive, recall, and false positive rate were employed. According to the study, tree-based classifiers (Recursive Tree, Decision Tree and Classification and Regression Tree) algorithms have 97.24%, 98.21% and 98.21% accuracy on the Malgenome dataset and 97.30%, 97.33% & 97.28% of accuracy on Drebin dataset and functionbased classifiers (Support Vector Machine (SVM) and Logistic Regression) algorithms has 97.81% & 96.87% of accuracy on Malgenome dataset and 97.00% & 97.81% of accuracy on Drebin dataset which concludes that classifier algorithms in these groups proofed to be promising for the detection of android malware. The function-based classifier is the most outstanding method for the two datasets as it outperforms all other classifiers for both classes with 97.81% and 97.33%. SVM and Logistic Regression, are highly effective in detecting malicious Android apps, outperforming other classifier types with accuracy rates up to 97.81%. Tree-based classifiers also showed strong performance across DREBIN and MALGENOME datasets. This research underscores the potential of function-based algorithms as robust tools for enhancing mobile security against malware threats.
Copyright (c) 2023 The Author(s) https://creativecommons.org/licenses/by-nc/4.0/