HAMMED ADELEYE MOJEED

@unilorin.edu.ng

Lecturer, Faculty of Communication and Information Sciences
university of ilorin

HAMMED ADELEYE MOJEED


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https://researchid.co/hammedmojeed

RESEARCH, TEACHING, or OTHER INTERESTS

Software, Artificial Intelligence, Information Systems
26

Scopus Publications

1025

Scholar Citations

19

Scholar h-index

27

Scholar i10-index

Scopus Publications

  • Learning Software Overtime Estimation From Experts‘ Annotations: A Greedy Cross-Validation-Based Machine Learning Approach
    Hammed A. Mojeed, Rafal Szlapczynski
    IEEE Access, 2025
  • Sampling-based novel heterogeneous multi-layer stacking ensemble method for telecom customer churn prediction
    Fatima E. Usman-Hamza, Abdullateef O. Balogun, Ramoni T. Amosa, Luiz Fernando Capretz, Hammed A. Mojeed, et al.
    Scientific African, 2024
  • Empirical analysis of tree-based classification models for customer churn prediction
    Fatima E. Usman-Hamza, Abdullateef O. Balogun, Salahdeen K. Nasiru, Luiz Fernando Capretz, Hammed A. Mojeed, et al.
    Scientific African, 2024
  • Cascade Generalization-Based Classifiers for Software Defect Prediction
    Aminat T. Bashir, Abdullateef O. Balogun, Matthew O. Adigun, Sunday A. Ajagbe, Luiz Fernando Capretz, et al.
    Lecture Notes in Networks and Systems, 2024
  • A systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogram
    Nehemiah Musa, Abdulsalam Ya’u Gital, Nahla Aljojo, Haruna Chiroma, Kayode S. Adewole, et al.
    Journal of Ambient Intelligence and Humanized Computing, 2023
  • Machine Learning Assisted Interactive Multi-objectives Optimization Framework: A Proposed Formulation and Method for Overtime Planning in Software Development Projects
    Hammed A. Mojeed, Rafal Szlapczynski
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2023
  • Expert System and Decision Support System for Electrocardiogram Interpretation and Diagnosis: Review, Challenges and Research Directions
    Kayode S. Adewole, Hammed A. Mojeed, James A. Ogunmodede, Lubna A. Gabralla, Nasir Faruk, et al.
    Applied Sciences Switzerland, 2022
    Electrocardiography (ECG) is one of the most widely used recordings in clinical medicine. ECG deals with the recording of electrical activity that is generated by the heart through the surface of the body. The electrical activity generated by the heart is measured using electrodes that are attached to the body surface. The use of ECG in the diagnosis and management of cardiovascular disease (CVD) has been in existence for over a decade, and research in this domain has recently attracted large attention. Along this line, an expert system (ES) and decision support system (DSS) have been developed for ECG interpretation and diagnosis. However, despite the availability of a lot of literature, access to recent and more comprehensive review papers on this subject is still a challenge. This paper presents a comprehensive review of the application of ES and DSS for ECG interpretation and diagnosis. Researchers have proposed a number of features and methods for ES and DSS development that can be used to monitor a patient’s health condition through ECG recordings. In this paper, a taxonomy of the features and methods for ECG interpretation and diagnosis were presented. The significance of the features and methods, as well as their limitations, were analyzed. This review further presents interesting theoretical concepts in this domain, as well as identifies challenges and open research issues on ES and DSS development for ECG interpretation and diagnosis that require substantial research effort. In conclusion, this paper identifies important future research areas with the purpose of advancing the development of ES and DSS for ECG interpretation and diagnosis.
  • Intelligent Decision Forest Models for Customer Churn Prediction
    Fatima Enehezei Usman-Hamza, Abdullateef Oluwagbemiga Balogun, Luiz Fernando Capretz, Hammed Adeleye Mojeed, Saipunidzam Mahamad, et al.
    Applied Sciences Switzerland, 2022
    Customer churn is a critical issue impacting enterprises and organizations, particularly in the emerging and highly competitive telecommunications industry. It is important to researchers and industry analysts interested in projecting customer behavior to separate churn from non-churn consumers. The fundamental incentive is a firm’s intent desire to keep current consumers, along with the exorbitant expense of gaining new ones. Many solutions have been developed to address customer churn prediction (CCP), such as rule-based and machine learning (ML) solutions. However, the issue of scalability and robustness of rule-based customer churn solutions is a critical drawback, while the imbalanced nature of churn datasets has a detrimental impact on the prediction efficacy of conventional ML techniques in CCP. As a result, in this study, we developed intelligent decision forest (DF) models for CCP in telecommunication. Specifically, we investigated the prediction performances of the logistic model tree (LMT), random forest (RF), and Functional Trees (FT) as DF models and enhanced DF (LMT, RF, and FT) models based on weighted soft voting and weighted stacking methods. Extensive experimentation was performed to ascertain the efficacy of the suggested DF models utilizing publicly accessible benchmark telecom CCP datasets. The suggested DF models efficiently distinguish churn from non-churn consumers in the presence of the class imbalance problem. In addition, when compared to baseline and existing ML-based CCP methods, comparative findings showed that the proposed DF models provided superior prediction performances and optimal solutions for CCP in the telecom industry. Hence, the development and deployment of DF-based models for CCP and applicable ML tasks are recommended.
  • Empirical Analysis of Forest Penalizing Attribute and Its Enhanced Variations for Android Malware Detection
    Abimbola G. Akintola, Abdullateef O. Balogun, Luiz Fernando Capretz, Hammed A. Mojeed, Shuib Basri, et al.
    Applied Sciences Switzerland, 2022
    As a result of the rapid advancement of mobile and internet technology, a plethora of new mobile security risks has recently emerged. Many techniques have been developed to address the risks associated with Android malware. The most extensively used method for identifying Android malware is signature-based detection. The drawback of this method, however, is that it is unable to detect unknown malware. As a consequence of this problem, machine learning (ML) methods for detecting and classifying malware applications were developed. The goal of conventional ML approaches is to improve classification accuracy. However, owing to imbalanced real-world datasets, the traditional classification algorithms perform poorly in detecting malicious apps. As a result, in this study, we developed a meta-learning approach based on the forest penalizing attribute (FPA) classification algorithm for detecting malware applications. In other words, with this research, we investigated how to improve Android malware detection by applying empirical analysis of FPA and its enhanced variants (Cas_FPA and RoF_FPA). The proposed FPA and its enhanced variants were tested using the Malgenome and Drebin Android malware datasets, which contain features gathered from both static and dynamic Android malware analysis. Furthermore, the findings obtained using the proposed technique were compared with baseline classifiers and existing malware detection methods to validate their effectiveness in detecting malware application families. Based on the findings, FPA outperforms the baseline classifiers and existing ML-based Android malware detection models in dealing with the unbalanced family categorization of Android malware apps, with an accuracy of 98.94% and an area under curve (AUC) value of 0.999. Hence, further development and deployment of FPA-based meta-learners for Android malware detection and other cybersecurity threats is recommended.
  • INTELLIGENT TREE-BASED ENSEMBLE APPROACHES FOR PHISHING WEBSITE DETECTION
    Journal of Engineering Science and Technology, 2022
  • Performance Analysis of Machine Learning Methods with Class Imbalance Problem in Android Malware Detection
    Abimbola Ganiyat Akintola, Abdullateef Balogun, Hammed Adeleke Mojeed, Fatima Usman-Hamza, Shakirat Aderonke Salihu, et al.
    International Journal of Interactive Mobile Technologies, 2022
  • 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, et al.
    Lecture Notes in Networks and Systems, 2022
  • Ensemble models for predicting warts treatment methods
    Journal of Engineering Science and Technology, 2021
  • A comprehensive survey on low-cost ECG acquisition systems: Advances on design specifications, challenges and future direction
    Nasir Faruk, Abubakar Abdulkarim, Ifada Emmanuel, Yusuf Y. Folawiyo, Kayode S. Adewole, et al.
    Biocybernetics and Biomedical Engineering, 2021
  • Ensemble-Based Logistic Model Trees for Website Phishing Detection
    Victor E. Adeyemo, Abdullateef O. Balogun, Hammed A. Mojeed, Noah O. Akande, Kayode S. Adewole
    Communications in Computer and Information Science, 2021
  • 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, et al.
    International Journal of Interactive Mobile Technologies, 2021
  • Optimized Decision Forest for Website Phishing Detection
    Abdullateef O. Balogun, Hammed A. Mojeed, Kayode S. Adewole, Abimbola G. Akintola, Shakirat A. Salihu, et al.
    Lecture Notes in Networks and Systems, 2021
  • Application of Shuffled Frog-Leaping Algorithm for Optimal Software Project Scheduling and Staffing
    Ahmed O. Ameen, Hammed A. Mojeed, Abdulazeez T. Bolariwa, Abdullateef O. Balogun, Modinat A. Mabayoje, et al.
    Lecture Notes on Data Engineering and Communications Technologies, 2021
  • 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, et al.
    Lecture Notes in Networks and Systems, 2021
  • 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, et al.
    Advances in Science Technology and Innovation, 2021
  • Internet of Robotic Things: Its Domain, Methodologies, and Applications
    Amos Orenyi Bajeh, Hammed Adeleye Mojeed, Ahmed Oloduowo Ameen, Oluwakemi Christiana Abikoye, Shakirat Aderonke Salihu, et al.
    Advances in Science Technology and Innovation, 2021
  • 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, et al.
    Symmetry, 2020
  • 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, et al.
    Advances in Intelligent Systems and Computing, 2020
  • 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
  • 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, et al.
    Intelligent Iot Systems in Personalized Health Care, 2020
  • Memetic approach for multi-objective overtime planning in software engineering projects
    Journal of Engineering Science and Technology, 2019

RECENT SCHOLAR PUBLICATIONS

  • Cross-platform mobile application development using the low code technology and free and open-source technology
    F Usman-Hamza, OA Olutuase, AO Balogun, HA Mojeed, SA Salihu, ...
    Technoscience Journal for Community Development in Africa 4, 211-225 , 2025
    2025
  • Learning Software Overtime Estimation from Experts ‘Annotations: A Greedy Cross-Validation-Based Machine Learning Approach
    HA Mojeed, R Szlapczynski
    IEEE Access , 2025
    2025
  • Empirical Analysis of Data Sampling-Based Decision Forest Classifiers for Software Defect Prediction
    FE Usman-Hamza, AO Balogun, H Mamman, LF Capretz, S Basri, ...
    Software 4 (2), 7 , 2025
    2025
    Citations: 2
  • Sampling-based novel heterogeneous multi-layer stacking ensemble method for telecom customer churn prediction
    FE Usman-Hamza, AO Balogun, RT Amosa, LF Capretz, HA Mojeed, ...
    Scientific African 24, e02223 , 2024
    2024
    Citations: 13
  • Cascade generalization-based classifiers for software defect prediction
    AT Bashir, AO Balogun, MO Adigun, SA Ajagbe, LF Capretz, JB Awotunde, ...
    Computer Science On-line Conference, 22-42 , 2024
    2024
    Citations: 3
  • Empirical analysis of tree-based classification models for customer churn prediction
    FE Usman-Hamza, AO Balogun, SK Nasiru, LF Capretz, HA Mojeed, ...
    Scientific African 23, e02054 , 2024
    2024
    Citations: 33
  • A Machine Learning Approach for Estimating Overtime Allocation in Software Development Projects
    H Mojeed, R Szlapczynski
    2024
    Citations: 1
  • Finger Vein Presentation Attack Detection Method Using a Hybridized Gray-Level Co-Occurrence Matrix Feature with Light-Gradient Boosting Machine Model
    K Shaheed, P Szczuko, I Ullah, HA Mojeed, AO Balogun, LF Capretz
    2024
    Citations: 1
  • Detection and classification of corn diseases using convolutional neural networks
    SA Salihu, MA Ajeigbe, AO Balogun, FE Usman-Hamza, AG Akintola, ...
    Adeleke University Journal of Engineering and Technology 6 (2), 46-55 , 2023
    2023
    Citations: 3
  • A systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogram
    N Musa, AY Gital, N Aljojo, H Chiroma, KS Adewole, HA Mojeed, N Faruk, ...
    Journal of ambient intelligence and humanized computing 14 (7), 9677-9750 , 2023
    2023
    Citations: 78
  • Machine Learning Assisted Interactive Multi-objectives Optimization Framework: A Proposed Formulation and Method for Overtime Planning in Software Development Projects
    HA Mojeed, R Szlapczynski
    International Conference on Artificial Intelligence and Soft Computing, 415-426 , 2023
    2023
    Citations: 2
  • Classification of Music Genres Using Catboost Algorithm
    SA Salihu, IO Lawal, OC Abikoye, AO Balogun, HA Mojeed, ...
    2023
  • Automatic summarization of legal documents using sumy
    SA Salihu, A Musa, FE Usman-Hamza, AG Akintola, AO Balogun, ...
    Proceedings of the international joint conference on advances in … , 2023
    2023
    Citations: 3
  • Expert system and decision support system for electrocardiogram interpretation and diagnosis: review, challenges and research directions
    KS Adewole, HA Mojeed, JA Ogunmodede, LA Gabralla, N Faruk, ...
    Applied Sciences 12 (23), 12342 , 2022
    2022
    Citations: 27
  • Intelligent decision forest models for customer churn prediction
    FE Usman-Hamza, AO Balogun, LF Capretz, HA Mojeed, S Mahamad, ...
    Applied Sciences 12 (16), 8270 , 2022
    2022
    Citations: 65
  • Empirical analysis of forest penalizing attribute and its enhanced variations for android malware detection
    AG Akintola, AO Balogun, LF Capretz, HA Mojeed, S Basri, SA Salihu, ...
    Applied Sciences 12 (9), 4664 , 2022
    2022
    Citations: 15
  • An empirical study on data sampling methods in addressing class imbalance problem in software defect prediction
    BJ Odejide, AO Bajeh, AO Balogun, ZO Alanamu, KS Adewole, ...
    Computer science on-line conference, 594-610 , 2022
    2022
    Citations: 32
  • Performance analysis of machine learning methods with class imbalance problem in android malware detection
    AG Akintola, AO Balogun, HA Mojeed, F Usman-Hamza, SA Salihu, ...
    International journal of interactive mobile technologies 16, 140-162 , 2022
    2022
    Citations: 13
  • Intelligent tree-based ensemble approaches for phishing website detection
    YA Alsariera, AO Balogun, VE Adeyemo, OH Tarawneh, HA Mojeed
    J. Eng. Sci. Technol 17 (1), 563-582 , 2022
    2022
    Citations: 29
  • Heterogeneous Ensemble with Combined Dimensionality Reduction for Social Spam Detection.
    AG Oladepo, AO Bajeh, AO Balogun, HA Mojeed, AA Salman, AI Bako
    International Journal of Interactive Mobile Technologies 15 (17) , 2021
    2021
    Citations: 9

MOST CITED SCHOLAR PUBLICATIONS

  • A comprehensive survey on low-cost ECG acquisition systems: Advances on design specifications, challenges and future direction
    N Faruk, A Abdulkarim, I Emmanuel, YY Folawiyo, KS Adewole, ...
    biocybernetics and biomedical engineering 41 (2), 474-502 , 2021
    2021
    Citations: 97
  • Impact of feature selection methods on the predictive performance of software defect prediction models: an extensive empirical study
    AO Balogun, S Basri, S Mahamad, SJ Abdulkadir, MA Almomani, ...
    Symmetry 12 (7), 1147 , 2020
    2020
    Citations: 90
  • A systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogram
    N Musa, AY Gital, N Aljojo, H Chiroma, KS Adewole, HA Mojeed, N Faruk, ...
    Journal of ambient intelligence and humanized computing 14 (7), 9677-9750 , 2023
    2023
    Citations: 78
  • Application of internet of thing and cyber physical system in Industry 4.0 smart manufacturing
    OC Abikoye, AO Bajeh, JB Awotunde, AO Ameen, HA Mojeed, ...
    Emergence of cyber physical system and IoT in smart automation and robotics … , 2021
    2021
    Citations: 78
  • SMOTE-based homogeneous ensemble methods for software defect prediction
    AO Balogun, FB Lafenwa-Balogun, HA Mojeed, VE Adeyemo, ON Akande, ...
    International Conference on Computational Science and its Applications, 615-631 , 2020
    2020
    Citations: 72
  • Intelligent decision forest models for customer churn prediction
    FE Usman-Hamza, AO Balogun, LF Capretz, HA Mojeed, S Mahamad, ...
    Applied Sciences 12 (16), 8270 , 2022
    2022
    Citations: 65
  • Ensemble-based logistic model trees for website phishing detection
    VE Adeyemo, AO Balogun, HA Mojeed, NO Akande, KS Adewole
    International Conference on Advances in Cyber Security, 627-641 , 2020
    2020
    Citations: 49
  • Parameter tuning in KNN for software defect prediction: an empirical analysis
    MA Mabayoje, AO Balogun, HA Jibril, JO Atoyebi, HA Mojeed, ...
    Jurnal Teknologi dan Sistem Komputer 7 (4), 121-126 , 2019
    2019
    Citations: 41
  • Comparative analysis of selected heterogeneous classifiers for software defects prediction using filter-based feature selection methods
    AG Akintola, AO Balogun, FB Lafenwa-Balogun, HA Mojeed
    FUOYE Journal of Engineering and Technology 3 (1), 134-137 , 2018
    2018
    Citations: 38
  • Empirical analysis of tree-based classification models for customer churn prediction
    FE Usman-Hamza, AO Balogun, SK Nasiru, LF Capretz, HA Mojeed, ...
    Scientific African 23, e02054 , 2024
    2024
    Citations: 33
  • An empirical study on data sampling methods in addressing class imbalance problem in software defect prediction
    BJ Odejide, AO Bajeh, AO Balogun, ZO Alanamu, KS Adewole, ...
    Computer science on-line conference, 594-610 , 2022
    2022
    Citations: 32
  • Intelligent tree-based ensemble approaches for phishing website detection
    YA Alsariera, AO Balogun, VE Adeyemo, OH Tarawneh, HA Mojeed
    J. Eng. Sci. Technol 17 (1), 563-582 , 2022
    2022
    Citations: 29
  • Performance analysis of selected clustering techniques for software defects prediction
    A Balogun, R Oladele, H Mojeed, B Amin-Balogun, VE Adeyemo, TO Aro
    Afr. J. Comput. ICT 12 (2), 30-42 , 2019
    2019
    Citations: 28
  • Expert system and decision support system for electrocardiogram interpretation and diagnosis: review, challenges and research directions
    KS Adewole, HA Mojeed, JA Ogunmodede, LA Gabralla, N Faruk, ...
    Applied Sciences 12 (23), 12342 , 2022
    2022
    Citations: 27
  • Internet of robotic things: its domain, methodologies, and applications
    AO Bajeh, HA Mojeed, AO Ameen, OC Abikoye, SA Salihu, ...
    Emergence of Cyber Physical System and IoT in Smart Automation and Robotics … , 2021
    2021
    Citations: 21
  • Application of computational intelligence models in IoMT big data for heart disease diagnosis in personalized health care
    AO Bajeh, OC Abikoye, HA Mojeed, SA Salihu, ID Oladipo, ...
    Intelligent IoT systems in personalized health care, 177-206 , 2021
    2021
    Citations: 21
  • Optimized decision forest for website phishing detection
    AO Balogun, HA Mojeed, KS Adewole, AG Akintola, SA Salihu, AO Bajeh, ...
    Proceedings of the Computational Methods in Systems and Software, 568-582 , 2021
    2021
    Citations: 20
  • Software defect prediction: A multi-criteria decision-making approach
    AO Balogun, AO Bajeh, HA Mojeed, AG Akintola
    Nigerian Journal of Technological Research 15 (1), 35-42 , 2020
    2020
    Citations: 19
  • Wrapper feature selection based heterogeneous classifiers for software defect prediction
    MA Mabayoje, AO Balogun, MS Bello, JO Atoyebi, HA Mojeed, ...
    Adeleke University Journal of Engineering and Technology , 2019
    2019
    Citations: 19
  • Data sampling-based feature selection framework for software defect prediction
    AO Balogun, FB Lafenwa-Balogun, HA Mojeed, FE Usman-Hamza, ...
    The International Conference on Emerging Applications and Technologies for … , 2020
    2020
    Citations: 17