Amos Orenyi Bajeh

@unilorin.edu.ng

Computer Science
University of Ilorin, Ilorin, Nigeria



                          

https://researchid.co/bajehamos

RESEARCH, TEACHING, or OTHER INTERESTS

Software, Artificial Intelligence, Computer Science, Multidisciplinary

29

Scopus Publications

Scopus Publications

  • Affective e-learning approaches, technology and implementation model: A systematic review
    Marion Olubunmi Adebiyi, Abayomi Aduragba Adebiyi, Deborah Olaniyan, and Bajeh Amos Orenyi

    Institute of Advanced Engineering and Science
    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, and Asani Emmanuel Tobi

    IEEE
    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, Amos Orenyi Bajeh, Emmanuel Oluwatobi Asani, Babatunde Gbadamosi, Opeyemi Soremekun, Brenda Udosen,et al.

    American Society of Tropical Medicine and Hygiene
    ABSTRACT. 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.

  • Empirical Analysis of Data Sampling-Based Ensemble Methods in Software Defect Prediction
    Abdullateef O. Balogun, Babajide J. Odejide, Amos O. Bajeh, Zubair O. Alanamu, Fatima E. Usman-Hamza, Hammid O. Adeleke, Modinat A. Mabayoje, and Shakirat R. Yusuff

    Springer International Publishing

  • An Empirical Study on Data Sampling Methods in Addressing Class Imbalance Problem in Software Defect Prediction
    Babajide J. Odejide, Amos O. Bajeh, Abdullateef O. Balogun, Zubair O. Alanamu, Kayode S. Adewole, Abimbola G. Akintola, Shakirat A. Salihu, Fatima E. Usman-Hamza, and Hammed A. Mojeed

    Springer International Publishing

  • An Integrated IDS Using ICA-Based Feature Selection and SVM Classification Method
    Roseline Oluwaseun Ogundokun, Sanjay Misra, Amos O. Bajeh, Ufuoma Odomero Okoro, and Ravin Ahuja

    Springer International Publishing

  • 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, Malek A. Almomani, Victor Elijah Adeyemo, Ammar K. Alazzawi, Amos Orenyi Bajeh, and Ganesh Kumar

    MDPI AG
    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


  • Cascade Generalization Based Functional Tree for Website Phishing Detection
    Abdullateef O. Balogun, Kayode S. Adewole, Amos O. Bajeh, and Rasheed G. Jimoh

    Springer Singapore

  • Optimized Decision Forest for Website Phishing Detection
    Abdullateef O. Balogun, Hammed A. Mojeed, Kayode S. Adewole, Abimbola G. Akintola, Shakirat A. Salihu, Amos O. Bajeh, and Rasheed G. Jimoh

    Springer International Publishing

  • Heterogeneous Ensemble with Combined Dimensionality Reduction for Social Spam Detection
    Abdulfatai Ganiyu Oladepo, Amos Orenyi Bajeh, Abdullateef Oluwagbemiga Balogun, Hammed Adeleye Mojeed, Abdulsalam Abiodun Salman, and Abdullateef Iyanda Bako

    International Association of Online Engineering (IAOE)
    This study presents a novel framework based on a heterogeneous ensemble method and a hybrid dimensionality reduction technique for spam detection in micro-blogging social networks. A hybrid of Information Gain (IG) and Principal Component Analysis (PCA) (dimensionality reduction) was implemented for the selection of important features and a heterogeneous ensemble consisting of Naïve Bayes (NB), K Nearest Neighbor (KNN), Logistic Regression (LR) and Repeated Incremental Pruning to Produce Error Reduction (RIPPER) classifiers based on Average of Probabilities (AOP) was used for spam detection. The proposed framework was applied on MPI_SWS and SAC’13 Tip spam datasets and the developed models were evaluated based on accuracy, precision, recall, f-measure, and area under the curve (AUC). From the experimental results, the proposed framework (that is, Ensemble + IG + PCA) outperformed other experimented methods on studied spam datasets. Specifically, the proposed method had an average accuracy value of 87.5%, an average precision score of 0.877, an average recall value of 0.845, an average F-measure value of 0.872 and an average AUC value of 0.943. Also, the proposed method had better performance than some existing methods. Consequently, this study has shown that addressing high dimensionality in spam datasets, in this case, a hybrid of IG and PCA with a heterogeneous ensemble method can produce a more effective method for detecting spam contents.

  • Data Sampling-Based Feature Selection Framework for Software Defect Prediction
    Abdullateef O. Balogun, Fatimah B. Lafenwa-Balogun, Hammed A. Mojeed, Fatimah E. Usman-Hamza, Amos O. Bajeh, Victor E. Adeyemo, Kayode S. Adewole, and Rasheed G. Jimoh

    Springer International Publishing

  • Application of Internet of Thing and Cyber Physical System in Industry 4.0 Smart Manufacturing
    Oluwakemi Christiana Abikoye, Amos Orenyi Bajeh, Joseph Bamidele Awotunde, Ahmed Oloduowo Ameen, Hammed Adeleye Mojeed, Muyideen Abdulraheem, Idowu Dauda Oladipo, and Shakirat Aderonke Salihu

    Springer International Publishing

  • Internet of Robotic Things: Its Domain, Methodologies, and Applications
    Amos Orenyi Bajeh, Hammed Adeleye Mojeed, Ahmed Oloduowo Ameen, Oluwakemi Christiana Abikoye, Shakirat Aderonke Salihu, Muyideen Abdulraheem, Idowu Dauda Oladipo, and Joseph Bamidele Awotunde

    Springer International Publishing

  • Exploring the Constructive Alignment of Pedagogical Practices in Science and Engineering Education in Sub-Saharan African Universities: A Nigerian Case Study
    David Olubiyi Obada, Olayinka Adewumi, Chika Yinka-Banjo, Amos Bajeh, and Razak Alli-Oke

    International Association of Online Engineering (IAOE)
    Improved pedagogical approaches in teaching science and engineering are crucial to solving the most pressing technological challenges faced in most developing countries. Despite the avalanche of programs to train faculty members to fill this need, there is a need to conceptualize benchmarks for evaluating how teaching should be delivered to students in our institutions. The aim of this paper is to understand how science and engineering courses can be taught in a more effective manner in our universities, especially in developing countries, using the backward design approach. Several case studies of undergraduate science and engineering courses were outlined and the backward design approach was used to put them in context. Questions generated by the conceptual framework guided the analyses and these components constructively aligned with each other.

  • Impact of feature selection methods on the predictive performance of software defect prediction models: An extensive empirical study
    Abdullateef O. Balogun, Shuib Basri, Saipunidzam Mahamad, Said J. Abdulkadir, Malek A. Almomani, Victor E. Adeyemo, Qasem Al-Tashi, Hammed A. Mojeed, Abdullahi A. Imam, and Amos O. Bajeh

    MDPI AG
    Feature selection (FS) is a feasible solution for mitigating high dimensionality problem, and many FS methods have been proposed in the context of software defect prediction (SDP). Moreover, many empirical studies on the impact and effectiveness of FS methods on SDP models often lead to contradictory experimental results and inconsistent findings. These contradictions can be attributed to relative study limitations such as small datasets, limited FS search methods, and unsuitable prediction models in the respective scope of studies. It is hence critical to conduct an extensive empirical study to address these contradictions to guide researchers and buttress the scientific tenacity of experimental conclusions. In this study, we investigated the impact of 46 FS methods using Naïve Bayes and Decision Tree classifiers over 25 software defect datasets from 4 software repositories (NASA, PROMISE, ReLink, and AEEEM). The ensuing prediction models were evaluated based on accuracy and AUC values. Scott–KnottESD and the novel Double Scott–KnottESD rank statistical methods were used for statistical ranking of the studied FS methods. The experimental results showed that there is no one best FS method as their respective performances depends on the choice of classifiers, performance evaluation metrics, and dataset. However, we recommend the use of statistical-based, probability-based, and classifier-based filter feature ranking (FFR) methods, respectively, in SDP. For filter subset selection (FSS) methods, correlation-based feature selection (CFS) with metaheuristic search methods is recommended. For wrapper feature selection (WFS) methods, the IWSS-based WFS method is recommended as it outperforms the conventional SFS and LHS-based WFS methods.

  • Object-oriented measures as testability indicators: An empirical study


  • Application of computational intelligence models in IoMT big data for heart disease diagnosis in personalized health care
    Amos Orenyi Bajeh, Oluwakemi Christiana Abikoye, Hammed Adeleye Mojeed, Shakirat Aderonke Salihu, Idowu Dauda Oladipo, Muyideen Abdulraheem, Joseph Bamidele Awotunde, Arun Kumar Sangaiah, and Kayode S. Adewole

    Elsevier

  • An Approach for Journal Summarization Using Clustering Based Micro-Summary Generation
    Hammed A. Mojeed, Ummu Sanoh, Shakirat A. Salihu, Abdullateef O. Balogun, Amos O. Bajeh, Abimbola G. Akintola, Modinat A. Mabayoje, and Fatimah E. Usman-Hamzah

    Springer International Publishing

  • 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, Abimbola G. Akintola, Amos O. Bajeh, and Fatimah E. Usman-Hamza

    Springer International Publishing

  • Search-Based Wrapper Feature Selection Methods in Software Defect Prediction: An Empirical Analysis
    Abdullateef O. Balogun, Shuib Basri, Said A. Jadid, Saipunidzam Mahamad, Malek A. Al-momani, Amos O. Bajeh, and Ammar K. Alazzawi

    Springer International Publishing

  • Memetic approach for multi-objective overtime planning in software engineering projects


  • Software defect prediction: Analysis of class imbalance and performance stability


  • Software development practices and problems in Malaysian small and medium software enterprises: A pilot study
    Malek Ahmad Theeb Almomani, Shuib Basri, Ahmad Kamil B. Mahmood, and Amos Orenyi Bajeh

    IEEE
    This paper presents an empirical study that investigates Software Process Improvement (SPI) current practices amongst software development Small and Medium Enterprises (SMEs) in Malaysia. The empirical study determines the current practices of adoption of SPI and related problems. Six Malaysian software development SMEs were involved in the empirical study. The results of the study showed that the level of adoption of Software Process Improvement in Malaysian software development SMEs is still very much at the low level. Other organisational, software development and project issues were identified as contributing to the low level of SPI adoption in Malaysia.

  • An empirical validation of coupling and cohesion metrics as testability indicators
    Amos Orenyi Bajeh, Shuib Basri, and Low Tang Jung

    Springer Berlin Heidelberg

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