HAMMED ADELEYE MOJEED
@unilorin.edu.ng
Lecturer, Faculty of Communication and Information Sciences
university of ilorin
RESEARCH, TEACHING, or OTHER INTERESTS
Software, Artificial Intelligence, Information Systems
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Fatima E. Usman-Hamza, Abdullateef O. Balogun, Ramoni T. Amosa, Luiz Fernando Capretz, Hammed A. Mojeed, Shakirat A. Salihu, Abimbola G. Akintola, and Modinat A. Mabayoje
Elsevier BV
Fatima E. Usman-Hamza, Abdullateef O. Balogun, Salahdeen K. Nasiru, Luiz Fernando Capretz, Hammed A. Mojeed, Shakirat A. Salihu, Abimbola G. Akintola, Modinat A. Mabayoje, and Joseph B. Awotunde
Elsevier BV
Aminat T. Bashir, Abdullateef O. Balogun, Matthew O. Adigun, Sunday A. Ajagbe, Luiz Fernando Capretz, Joseph B. Awotunde, and Hammed A. Mojeed
Springer Nature Switzerland
Nehemiah Musa, Abdulsalam Ya’u Gital, Nahla Aljojo, Haruna Chiroma, Kayode S. Adewole, Hammed A. Mojeed, Nasir Faruk, Abubakar Abdulkarim, Ifada Emmanuel, Yusuf Y. Folawiyo,et al.
Springer Science and Business Media LLC
Hammed A. Mojeed and Rafal Szlapczynski
Springer Nature Switzerland
Kayode S. Adewole, Hammed A. Mojeed, James A. Ogunmodede, Lubna A. Gabralla, Nasir Faruk, Abubakar Abdulkarim, Emmanuel Ifada, Yusuf Y. Folawiyo, Abdukareem A. Oloyede, Lukman A. Olawoyin,et al.
MDPI AG
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.
Fatima Enehezei Usman-Hamza, Abdullateef Oluwagbemiga Balogun, Luiz Fernando Capretz, Hammed Adeleye Mojeed, Saipunidzam Mahamad, Shakirat Aderonke Salihu, Abimbola Ganiyat Akintola, Shuib Basri, Ramoni Tirimisiyu Amosa, and Nasiru Kehinde Salahdeen
MDPI AG
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.
Abimbola G. Akintola, Abdullateef O. Balogun, Luiz Fernando Capretz, Hammed A. Mojeed, Shuib Basri, Shakirat A. Salihu, Fatima E. Usman-Hamza, Peter O. Sadiku, Ghaniyyat B. Balogun, and Zubair O. Alanamu
MDPI AG
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.
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
Abimbola Ganiyat Akintola, Abdullateef Balogun, Hammed Adeleke Mojeed, Fatima Usman-Hamza, Shakirat Aderonke Salihu, Kayode Sakariyau Adewole, Ghaniyyat Bolanale Balogun, and Peter Ogirima Sadiku
International Association of Online Engineering (IAOE)
Due to the exponential rise of mobile technology, a slew of new mobile security concerns has surfaced recently. To address the hazards connected with malware, many approaches have been developed. Signature-based detection is the most widely used approach for detecting Android malware. This approach has the disadvantage of being unable to identify unknown malware. As a result of this issue, machine learning (ML) for identifying and categorising malware apps was created. Conventional ML methods are concerned with increasing classification accuracy. However, the standard classification method performs poorly in recognising malware applications due to the unbalanced real-world datasets. In this study, an empirical analysis of the detection performance of ML methods in the presence of class imbalance is conducted. Specifically, eleven (11) ML methods with diverse computational complexities were investigated. Also, a synthetic minority oversampling technique (SMOTE) and random undersampling (RUS) are deployed to address the class imbalance in the Android malware datasets. The experimented ML methods are tested using the Malgenome and Drebin Android malware datasets that contain features gathered from both static and dynamic malware approaches. According to the experimental findings, the performance of each experimented ML method varies across the datasets. Moreover, the presence of class imbalance deteriorated the performance of the ML methods as their performances were amplified with the deployment of data sampling methods (SMOTE and RUS) used to alleviate the class imbalance problem. Besides, ML models with SMOTE technique are superior to other experimented methods. It is therefore recommended to address the inherent class imbalance problem in Android Malware detection.
Nasir Faruk, Abubakar Abdulkarim, Ifada Emmanuel, Yusuf Y. Folawiyo, Kayode S. Adewole, Hammed A. Mojeed, Abdukareem A. Oloyede, Lukman A. Olawoyin, Ismaeel A. Sikiru, Musa Nehemiah,et al.
Elsevier BV
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
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.
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
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
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
Ahmed O. Ameen, Hammed A. Mojeed, Abdulazeez T. Bolariwa, Abdullateef O. Balogun, Modinat A. Mabayoje, Fatima E. Usman-Hamzah, and Muyideen Abdulraheem
Springer International Publishing
Victor E. Adeyemo, Abdullateef O. Balogun, Hammed A. Mojeed, Noah O. Akande, and Kayode S. Adewole
Springer Singapore
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.
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
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
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