Shakirat Aderonke Salihu

@unilorin.edu.ng

Lecturer, Faculty of Communication and Information Sciences
University of Ilorin, Ilorin

Shakirat Aderonke Salihu


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https://researchid.co/shaksoft
Shakirat A. Salihu is a lecturer at the Department of Computer Science, Faculty of Communication and Information Sciences, University of Ilorin, Ilorin, Nigeria. She received her B.Sc. in Computer Science in 2006 from the University of Ilorin, and subsequently obtained her M. Sc and Ph.D. degrees from the same University in 2011 and 2022 respectively. Her academic career began at Federal Polytechnic, Mubi, Adamawa State, Nigeria as an Assistant Lecturer in 2010 and rose to the level of Lecturer III in 2013. She later joined the service of the University of Ilorin as a lecturer in the Department of Computer Science in 2014. Her research interest includes Software Maintenance, Information Retrieval, Natural Language Processing and Machine Learning.

EDUCATION

B. Sc, M.Sc. and Ph.D Computer Science
Postgraduate Diploma in Education

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Artificial Intelligence, Information Systems, Software
16

Scopus Publications

499

Scholar Citations

13

Scholar h-index

17

Scholar i10-index

Scopus Publications

  • Performance Analysis of MIMO-OFDM Systems in 5G Wireless Networks
    Akande Hakeem Babalola, Oloyede Ayopo Abdulkarim, Shakirat Aderonke Salihu, Taibat O. Adebakin
    Communications in Computer and Information Science, 2025
  • Detection and Classification of Potato Leaves Diseases Using Convolutional Neural Network and Adam Optimizer
    Shakirat Aderonke Salihu, Sunday Olamilekan Adebayo, Oluwakemi Christiana Abikoye, Fatima Ehenezie Usman-Hamza, Modinat Abolore Mabayoje, et al.
    Procedia Computer Science, 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
  • Integration of Load Balancing in SDN Based 5G Core Network
    Hakeem Babalola Akande, Oluwatobi Noah Akande, Shakirat Aderonke Salihu
    International Conference on Science Engineering and Business for Driving Sustainable Development Goals Seb4sdg 2024, 2024
    The invention of the 5G network, which has facilitated novel innovations and applications was prompted by the escalating need for higher data rates and larger capacity. Nevertheless, this rising demand can lead to improper control of data and control traffic, which would make the 5G network more difficult to scale and flexible. These issues can be resolved with Software-Defined Networking (SDN), which separates the network's control and data planes for simpler management. As a result, this article suggested a 5G architecture built on SDN and loaded with load balancing. This paper's specific goal is to simulate and assess the suggested SDN architecture for the 5G core network. Simulation tools such as Mininet, Wireshark, and MATLAB were utilized. Experimental results showed that the proposed system performed better than the conventional system in terms of throughput, latency, arrival time, and resource allocation.
  • 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.
  • Detection of Phishing URLs Using Heuristics-Based Approach
    Shakirat Aderonke Salihu, Idowu Dauda Oladipo, Abdul Afeez Wojuade, Muyideen Abdulraheem, Abdulrauph Olanrewaju Babatunde, et al.
    Proceedings of the 5th International Conference on Information Technology for Education and Development Changing the Narratives Through Building A Secure Society with Disruptive Technologies Ited 2022, 2022
    Phishing is one of the types of cybercrime in which the attacker poses as a trustworthy entity with a view to obtaining sensitive information or data from the victim, this occurs usually through email. In the process, the victim may release information such as login credentials, credit card details, and other personally identifiable information that normally should not be revealed. The existing approaches used for phishing detection, therefore, need to be enhanced to effectively detect phishing. This study proposed a novel method for detecting phishing based on some heuristic features by extracting some relevant attributes, filtering these attributes, and classifying the same according to their impact on a website. The data explored for this study was retrieved from PhishTank and Alexa, which was later preprocessed for smooth model creation in python. The model created was evaluated and consistently gives a true positive rate of 85% based on the threshold set and an accuracy of 95.52%. The resulting output of this study has shown its reliability in the detection of phishing and could serve as a good benchmark for similar studies.
  • 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
    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.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Hybrid Rule-Based Model for Phishing URLs Detection
    Kayode S. Adewole, Abimbola G. Akintola, Shakirat A. Salihu, Nasir Faruk, Rasheed G. Jimoh
    Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering Lnicst, 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
  • Detection and classification of potato leaves diseases using convolutional neural network and Adam optimizer
    SA Salihu, SO Adebayo, OC Abikoye, FE Usman-Hamza, MA Mabayoje, ...
    Procedia Computer Science 258, 2-17 , 2025
    2025
    Citations: 9
  • Performance analysis of MIMO-OFDM systems in 5G wireless networks
    AH Babalola, OA Abdulkarim, SA Salihu, TO Adebakin
    International Conference on Applied Informatics, 278-291 , 2024
    2024
    Citations: 8
  • 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
  • Integration of Load Balancing in SDN Based 5G Core Network
    HB Akande, ON Akande, SA Salihu
    2024 International Conference on Science, Engineering and Business for … , 2024
    2024
    Citations: 1
  • 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 Systematic Literature Review of Machine Learning and AutoML in Software Effort Estimation
    SA SALIHU, KB SALIU, OA OWOYEMI
    Conference Organising Committee, 145 , 2024
    2024
    Citations: 1
  • Performance Analysis of Some Machine Learning Algorithms in Prediction of Heart Disease
    SA SALIHU, OA OWOYEMI, KB SALIU
    Conference Organising Committee, 169 , 2024
    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
  • INTRODUCTION TO SYSTEM PROGRAMMING
    SA Salihu, MHA Mojeed, M Abdulraheem
    2023
  • 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
  • Detection of phishing URLs using heuristics-based approach
    SA Salihu, ID Oladipo, AA Wojuade, M Abdulraheem, AO Babatunde, ...
    2022 5th Information Technology for Education and Development (ITED), 1-7 , 2022
    2022
    Citations: 10
  • An enhanced information retrieval-based bug localization system with code coverage, stack traces, and spectrum information.
    SA Salihu, OC Abikoye
    2022
  • 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
  • An Enhanced Information Retrieval-Based Bug Localization System with Code Coverage, Stack Traces, and Spectrum Information
    OCA Shakirat Aderonke Salihu
    Journal of Hunan University Natural Sciences 49 (4) , 2022
    2022
  • 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
  • Basic issues and challenges on Explainable Artificial Intelligence (XAI) in healthcare systems
    OI Dauda, JB Awotunde, M AbdulRaheem, SA Salihu
    Principles and methods of explainable artificial intelligence in healthcare … , 2022
    2022
    Citations: 17

MOST CITED SCHOLAR PUBLICATIONS

  • 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
  • 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
  • Hybrid rule-based model for phishing URLs detection
    KS Adewole, AG Akintola, SA Salihu, N Faruk, RG Jimoh
    International Conference for Emerging Technologies in Computing, 119-135 , 2019
    2019
    Citations: 39
  • 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
  • Advances in bioinformatics
    V Singh, A Kumar
    Springer , 2021
    2021
    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
  • Intelligent IOT systems in personalized health care
    AK Sangaiah, SC Mukhopadhyay
    Academic Press , 2020
    2020
    Citations: 20
  • Basic issues and challenges on Explainable Artificial Intelligence (XAI) in healthcare systems
    OI Dauda, JB Awotunde, M AbdulRaheem, SA Salihu
    Principles and methods of explainable artificial intelligence in healthcare … , 2022
    2022
    Citations: 17
  • 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
  • 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
  • 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
  • Performance evaluation of manhattan and euclidean distance measures for clustering based automatic text summarization
    SA Salihu, IP Onyekwere, MA Mabayoje, HA Mojeed
    Journal of Engineering and Technology 4 (1), 135-139 , 2019
    2019
    Citations: 11
  • Enhanced Classification via Clustering Techniques using Decision Tree for Feature Selection
    S Shakirat
    International Journal of Applied Information Systems 9 (6), 11-16 , 2015
    2015
    Citations: 11
  • Detection of phishing URLs using heuristics-based approach
    SA Salihu, ID Oladipo, AA Wojuade, M Abdulraheem, AO Babatunde, ...
    2022 5th Information Technology for Education and Development (ITED), 1-7 , 2022
    2022
    Citations: 10
  • Detection and classification of potato leaves diseases using convolutional neural network and Adam optimizer
    SA Salihu, SO Adebayo, OC Abikoye, FE Usman-Hamza, MA Mabayoje, ...
    Procedia Computer Science 258, 2-17 , 2025
    2025
    Citations: 9
  • Performance analysis of MIMO-OFDM systems in 5G wireless networks
    AH Babalola, OA Abdulkarim, SA Salihu, TO Adebakin
    International Conference on Applied Informatics, 278-291 , 2024
    2024
    Citations: 8
  • Tree-based homogeneous ensemble model with feature selection for diabetic retinopathy prediction
    TE Dagogo-George, HA Mojeed, AO Balogun, MA Mabayoje, SA Salihu
    Jurnal Teknologi dan Sistem Komputer 8 (4), 297-303 , 2020
    2020
    Citations: 5