An adaptive neuro-fuzzy inference system for multinomial malware classification Amos Orenyi Bajeh, Mary Olayinka Olaoye, Fatima Enehezei Usman-Hamza, Ikeola Suhurat Olatinwo, Peter ogirima Sadiku, et al. Journal of the Nigerian Society of Physical Sciences, 2025 Malware detection and classification are important requirements for information security because malware poses a great threat to computer users. As the growth of technology increases, malware is getting more sophisticated and thereby more difficult to detect. Machine learning techniques have been extensively used for malware detection and classification. However, most of them are binomial classifications that only detect the presence of malware but do not classify them into types. This study sets out to develop a multinomial malware classifier using an adaptive neuro-fuzzy inference system (ANFIS) and investigate the effectiveness of ANFIS in the classification. A first-order Sugeno ANFIS model was developed. It has five layers and uses two if-then rules. The ANFIS model was trained and tested with two prominent malware datasets from the Canada Institute of Cyber Security. The experimental results showed that the performance of the ANFIS model degrades as the size of the datasets increases, and the accuracy, precision, recall, and root mean square error is 94%, 0.88, 0.87, and 0.19 respectively.
Affective e-learning approaches, technology and implementation model: A systematic review Marion Olubunmi Adebiyi, Abayomi Aduragba Adebiyi, Deborah Olaniyan, Bajeh Amos Orenyi International Journal of Electrical and Computer Engineering, 2024 A systematic literature study including articles from 2016 to 2022 was done to evaluate the various approaches, technologies, and implementation models involved in measuring student engagement during learning. The review’s objective was to compile and analyze all studies that investigated how instructors can gauge students’ mental states while teaching and assess the most effective teaching methods. Additionally, it aims to extract and assess expanded methodologies from chosen research publications to offer suggestions and answers to researchers and practitioners. Planning, carrying out the analysis, and publishing the results have all received significant attention in the research approach. The study’s findings indicate that more needs to be done to evaluate student participation objectively and follow their development for improved academic performance. Physiological approaches should be given more support among the alternatives. While deep learning implementation models and contactless technology should interest more researchers. And, the recommender system should be integrated into e-learning system. Other approaches, technologies, and methodology articles, on the other hand, lacked authenticity in conveying student feeling.
Comparative Analysis of Feature Selection Methods for Software Bug Classification Bajeh Amos Orenyi, Olowe Oluwambo Tolulope, Asani Emmanuel Tobi International Conference on Science Engineering and Business for Driving Sustainable Development Goals Seb4sdg 2024, 2024 Software bug classification is a critical task in software engineering aimed at identifying defects early to improve software quality and reliability. Despite its importance, effectively classifying software defects remains a challenge, necessitating the use of advanced techniques such as feature selection. This research presents a comprehensive study on feature selection methods for software defect classification, to evaluate their effectiveness in enhancing classification accuracy. The study investigates the impact of feature selection methods (filter-based, wrapper-based, and embedding) on model performance using classification algorithms such Naïve Bayes, Support Vector Machines, K-Nearest Neighbour, and Random Forest. The studies are carried out using publicly accessible software defect datasets, and conventional evaluation measures such as accuracy, precision, recall, and F1-score are used to evaluate the effectiveness of each feature selection strategy. The findings of the study confirm the effectiveness of ensemble methods in bug severity classification, with Random Forest achieving notable accuracy rates for different datasets. Additionally, the study highlights the superiority of wrapper feature selection techniques over filter methods, demonstrating their ability to select informative features for defect severity classification. The findings of this work give useful information for practitioners in selecting and implementing feature selection approaches when developing defect classification models, ultimately contributing to the enhancement of software quality and reliability.
Strengthening Bioinformatics and Genomics Analysis Skills in Africa for Attainment of the Sustainable Development Goals: Report of the 2nd Conference of the Nigerian Bioinformatics and Genomics Network Itunuoluwa Isewon, Chisom Soremekun, Marion Adebiyi, Charles Adetunji, Adewale Joseph Ogunleye, et al. American Journal of Tropical Medicine and Hygiene, 2022 The second conference of the Nigerian Bioinformatics and Genomics Network (NBGN21) was held from October 11 to October 13, 2021. The event was organized by the Nigerian Bioinformatics and Genomics Network. A 1-day genomic analysis workshop on genome-wide association study and polygenic risk score analysis was organized as part of the conference. It was organized primarily as a research capacity building initiative to empower Nigerian researchers to take a leading role in this cutting-edge field of genomic data science. The theme of the conference was “Leveraging Bioinformatics and Genomics for the attainments of the Sustainable Development Goals.” The conference used a hybrid approach—virtual and in-person. It served as a platform to bring together 235 registered participants mainly from Nigeria and virtually, from all over the world. NBGN21 had four keynote speakers and four leading Nigerian scientists received awards for their contributions to genomics and bioinformatics development in Nigeria. A total of 100 travel fellowships were awarded to delegates within Nigeria. A major topic of discussion was the application of bioinformatics and genomics in the achievement of the Sustainable Development Goals (SDG3—Good Health and Well-Being, SDG4—Quality Education, and SDG 15—Life on Land [Biodiversity]). In closing, most of the NBGN21 conference participants were interviewed and interestingly they agreed that bioinformatics and genomic analysis of African genomes are vital in identifying population-specific genetic variants that confer susceptibility to different diseases that are endemic in Africa. The knowledge of this can empower African healthcare systems and governments for timely intervention, thereby enhancing good health and well-being.
Software defect prediction using wrapper feature selection based on dynamic re-reranking strategy Abdullateef Oluwagbemiga Balogun, Shuib Basri, Luiz Fernando Capretz, Saipunidzam Mahamad, Abdullahi Abubakar Imam, et al. Symmetry, 2021 Finding defects early in a software system is a crucial task, as it creates adequate time for fixing such defects using available resources. Strategies such as symmetric testing have proven useful; however, its inability in differentiating incorrect implementations from correct ones is a drawback. Software defect prediction (SDP) is another feasible method that can be used for detecting defects early. Additionally, high dimensionality, a data quality problem, has a detrimental effect on the predictive capability of SDP models. Feature selection (FS) has been used as a feasible solution for solving the high dimensionality issue in SDP. According to current literature, the two basic forms of FS approaches are filter-based feature selection (FFS) and wrapper-based feature selection (WFS). Between the two, WFS approaches have been deemed to be superior. However, WFS methods have a high computational cost due to the unknown number of executions available for feature subset search, evaluation, and selection. This characteristic of WFS often leads to overfitting of classifier models due to its easy trapping in local maxima. The trapping of the WFS subset evaluator in local maxima can be overcome by using an effective search method in the evaluator process. Hence, this study proposes an enhanced WFS method that dynamically and iteratively selects features. The proposed enhanced WFS (EWFS) method is based on incrementally selecting features while considering previously selected features in its search space. The novelty of EWFS is based on the enhancement of the subset evaluation process of WFS methods by deploying a dynamic re-ranking strategy that iteratively selects germane features with a low subset evaluation cycle while not compromising the prediction performance of the ensuing model. For evaluation, EWFS was deployed with Decision Tree (DT) and Naïve Bayes classifiers on software defect datasets with varying granularities. The experimental findings revealed that EWFS outperformed existing metaheuristics and sequential search-based WFS approaches established in this work. Additionally, EWFS selected fewer features with less computational time as compared with existing metaheuristics and sequential search-based WFS methods.
Ensemble models for predicting warts treatment methods Journal of Engineering Science and Technology, 2021
SMOTE-Based Homogeneous Ensemble Methods for Software Defect Prediction Abdullateef O. Balogun, Fatimah B. Lafenwa-Balogun, Hammed A. Mojeed, Victor E. Adeyemo, Oluwatobi N. Akande, et al. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2020
Empirical validation of object-oriented inheritance hierarchy modifiability metrics Amos Orenyi Bajeh, Shuib Basri, Low Tan Jung, Malek Ahmad Almomani Conference Proceedings 6th International Conference on Information Technology and Multimedia at Uniten Cultivating Creativity and Enabling Technology Through the Internet of Things Icimu 2014, 2014
