Joseph Bamidele AWOTUNDE
@unilorin.edu.ng
Lecturer, Faculty of Communication and Information Sciences
Lecturer, Faculty of Communication and Information Sciences
Department of Computer Science, University of Ilorin, Ilorin, Nigeria
J. B. Awotunde was born in Ayetoro-Ile Town, Ilorin, Kwara State, Nigeria in 1982. He received the B.Sc. degree in Mathematics/Computer Science from Federal University of Technology, Minna, Nigeria, in 2007. M.Sc. and Ph.D. degrees in Computer Science from the University of Ilorin, Ilorin, Nigeria, in 2014 and 2019 respectively. From 2012 to 2015, and 2018, he was a Computer Science Instructor with the University School, University of Ilorin, Ilorin, Nigeria. From 2017 to 2018, he was a Lecturer II with the McPherson University, Ijebo, Seriki-Sotayo, Nigeria. Since 2019, he has been a Lecturer II with the Computer Science Department, University, of Ilorin, Ilorin, Nigeria. He is the author of more than 40 articles, and more than 15 Conference Proceedings. His research interests include Information Security, Cybersecurity, Bioinformatics Artificial Intelligence, Internet of Medical Things, Wireless Body Sensor Networks, Wireless Networks, Telemedicine, m-Health/e-health, and Medical Ima
EDUCATION
• University of Ilorin, Ilorin, Kwara State 2015 – 2019
• University of Ilorin, Ilorin, Kwara State 2012 – 2014
• Federal University of Technology, Minna, Niger State 2003 – 2007
• The Federal Polytechnic, Bida, Niger State 1999 – 2000
• Government Secondary School, Share 2001
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Computer Networks and Communications, Information Systems and Management, Artificial Intelligence
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Sunday Adeola Ajagbe, Joseph Bamidele Awotunde, and Hector Florez
Springer Science and Business Media LLC
AbstractThe worldwide process of converting most activities of both corporate and non-corporate entities into digital formats is now firmly established. Machine learning models are necessary to serve as a tool for preventing illegal intrusion onto different networks. The machine learning (ML) model's strengths and drawbacks pertain to intrusion detection (IDS) tasks. This study used an experimental methodology to assess the efficacy of various ML models, including linear SVC, LR, random forest (RF), decision tree (DT), and XGBoost, in detecting intrusion on the UNSW NB15 datasets. The objective is to compare the strengths and shortcomings of these models. Data exploration, Feature engineering, selection and a test set of 15%, a validation set of 15%, and a training set of 70% respectively were used for data splitting. Performance evaluation was carried out using accuracy, recall, precision F1-score and confusion matrix plotted. The outcome of the experiment shows a percentage of 92.71% (1, normal) and 7.29% (0, attack) for normal traffic and attack traffic respectively. Performance evaluation results showed that RF and XGBoost outperformed the other ML models. Hence, ML models can effectively be used to detect system attacks. We intend to expand this research in the future and use the paradigm in a real-world setting with further conclusions and justifications.
Tehseen Mazhar, Syed Faisal Abbas Shah, Syed Azeem Inam, Joseph Bamidele Awotunde, Mamoon M. Saeed, and Habib Hamam
Springer Science and Business Media LLC
Samuel-Soma M. Ajibade, Gloria Nnadwa Alhassan, Abdelhamid Zaidi, Olukayode Ayodele Oki, Joseph Bamidele Awotunde, Emeka Ogbuju, and Kayode A. Akintoye
Elsevier BV
Roseline Oluwaseun Ogundokun, Joseph Bamidele Awotunde, Emmanuel Abidemi Adeniyi, and Femi Emmanuel Ayo
Springer Science and Business Media LLC
Abidemi Emmanuel Adeniyi, Joseph Bamidele Awotunde, Peace Busola Falola, and Halleluyah Oluwatobi Aworinde
IGI Global
Blockchain and the Internet of Things (IoT) are becoming pivotal in the IT industry, finding applications in sectors like supply chain, logistics, and the automotive industry. IoT devices often have limited processing and storage capabilities, leading to the centralization of user medical data in third-party repositories or cloud environments. Blockchain offers decentralized processing and storage for IoT data, making it an attractive option for creating decentralized IoT-enabled e-healthcare systems. This chapter begins with an overview of blockchain technology, followed by a discussion of prominent consensus algorithms within the context of e-health. It also evaluates various blockchain platforms for their suitability in IoT-based e-healthcare systems. Several case studies are presented methodically to demonstrate the utilization of IoT and blockchain core features in enhancing healthcare services and ecosystems.
Peace Busola Falola, Joseph B. Awotunde, Abidemi Emmanuel Adeniyi, and Temitope Ololade Idowu
IGI Global
Blockchain and cloud computing offer promising avenues to address these needs by enhancing data security, improving access to personal health records, and facilitating real-time health monitoring and telehealth services. Therefore, this chapter explores the use of blockchain and cloud computing in developing intelligent healthcare systems for elderly citizens. It highlights the potential for secure data exchange, improved access to personal health records, and real-time health monitoring. It also discusses challenges like technical barriers and privacy concerns and proposes strategies for overcoming them. The chapter emphasizes the significant impact of these technologies on improving health outcomes and the quality of elderly citizens.
Abidemi Emmanuel Adeniyi, Rasheed Gbenga Jimoh, and Joseph Bamidele Awotunde
Elsevier BV
Olayinka Olufunmilayo Olusanya, Rasheed Gbenga Jimoh, Sanjay Misra, and Joseph Bamidele Awotunde
Elsevier BV
Femi Emmanuel Ayo, Joseph Bamidele Awotunde, Lukman Adebayo Ogundele, Olakunle Olugbenga Solanke, Biswajit Brahma, Ranjit Panigrahi, and Akash Kumar Bhoi
Springer Science and Business Media LLC
Esau Taiwo Oladipupo, Oluwakemi Christiana Abikoye, and Joseph Bamidele Awotunde
MDPI AG
Cloud computing and the increasing popularity of 5G have greatly increased the application of images on Internet of Things (IoT) devices. The storage of images on an untrusted cloud has high security and privacy risks. Several lightweight cryptosystems have been proposed in the literature as appropriate for resource-constrained IoT devices. These existing lightweight cryptosystems are, however, not only at the risk of compromising the integrity and security of the data but also, due to the use of substitution boxes (S-boxes), require more memory space for their implementation. In this paper, a secure lightweight cryptography algorithm, that eliminates the use of an S-box, has been proposed. An algorithm termed Enc, that accepts a block of size n divides the block into L n R bits of equal length and outputs the encrypted block as follows: E=L⨂R⨁R, where ⨂ and ⨁ are exclusive-or and concatenation operators, respectively, was created. A hash result, hasR=SHA256P⨁K, was obtained, where SHA256, P, and K are the Secure Hash Algorithm (SHA−256), the encryption key, and plain image, respectively. A seed, S, generated from enchash=Enchashenc,K, where hashenc is the first n bits of hasR, was used to generate a random image, Rim. An intermediate image, intimage=Rim⨂P, and cipher image, C=Encintimage,K, were obtained. The proposed scheme was evaluated for encryption quality, decryption quality, system sensitivity, and statistical analyses using various security metrics. The results of the evaluation showed that the proposed scheme has excellent encryption and decryption qualities that are very sensitive to changes in both key and plain images, and resistance to various statistical attacks alongside other security attacks. Based on the result of the security evaluation of the proposed cryptosystem termed Hash XOR Permutation (HXP), the study concluded that the security of the cryptography algorithm can still be maintained without the use of a substitution box.
Sakinat Oluwabukonla Folorunso, Joseph Bamidele Awotunde, Y. Pandu Rangaiah, and Roseline Oluwaseun Ogundokun
World Scientific Pub Co Pte Ltd
Breast cancer (BC) is one of the major principal sources of high mortality among women worldwide. Consequently, early detection is essential to save lives. BC can be diagnosed with different modes of medical images such as mammography, ultrasound, computerized tomography, biopsy, and magnetic resonance imaging. A histopathology study (biopsy) that results in images is often performed to help diagnose and analyze BC. Transfer learning (TL) is a machine-learning (ML) technique that reuses a learning method that is initially built for a task to be applied to a model for a new task. TL aims to enhance the assessment of desired learners by moving the knowledge contained in another but similar source domain. Consequently, the challenge of the small dataset in the desired domain is reduced to build the desired learners. TL plays a major role in medical image analysis because of this immense property. This paper focuses on the use of TL methods for the investigation of BC image classification and detection, preprocessing, pretrained models, and ML models. Through empirical experiments, the EfficientNets pretrained neural network architectures and ML classification models were built. The support vector machine and eXtreme Gradient Boosting (XGBoost) were learned on the BC dataset. The result showed a comparative but good performance of EfficientNetB4 and XGBoost. An outcome based on accuracy, recall, precision, and F1_Score for XGBoost is 84%, 0.80, 0.83, and 0.81, respectively.
Femi Emmanuel Ayo, Joseph Bamidele Awotunde, Sakinat Oluwabukonla Folorunso, Ranjit Panigrahi, Amik Garg, and Akash Kumar Bhoi
Springer Science and Business Media LLC
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
Femi Emmanuel Ayo, Lukman Adebayo Ogundele, Solanke Olakunle, Joseph Bamidele Awotunde, and Funmilayo A. Kasali
Elsevier BV
Muyideen AbdulRaheem, Idowu Dauda Oladipo, Agbotiname Lucky Imoize, Joseph Bamidele Awotunde, Cheng-Chi Lee, Ghaniyyat Bolanle Balogun, and Joshua Oluwatobi Adeoti
Springer Science and Business Media LLC
Joseph Bamidele Awotunde, Ranjit Panigrahi, Shubham Shukla, Baidyanath Panda, and Akash Kumar Bhoi
Springer Science and Business Media LLC
Abidemi Emmanuel Adeniyi, Biswajit Brahma, Joseph Bamidele Awotunde, Halleluyah Oluwatobi Aworinde, and Hemanta Kumar Bhuyan
Springer Nature Switzerland
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
Odunayo Dauda Olanloye, Abidemi Emmanuel Adeniyi, Halleluyah Oluwatobi Aworinde, Joseph Bamidele Awotunde, Agbotiname Lucky Imoize, and Youssef Mejdoub
Springer Nature Switzerland
Joseph Bamidele Awotunde, Biswajit Brahma, Abidemi Emmanuel Adeniyi, Edogbo Lauretta Nkonyeasua, Agbotiname Lucky Imoize, and Youssef Mejdoub
Springer Nature Switzerland
Joseph Bamidele Awotunde, Abidemi Emmanuel Adeniyi, Agbotiname Lucky Imoize, Youssef Mejdoub, and Zakariyya Abdualazizu
Springer Nature Switzerland