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
Samuel-Soma M. Ajibade, Gloria Nnadwa Alhassan, Abdelhamid Zaidi, Olukayode Ayodele Oki, Joseph Bamidele Awotunde, Emeka Ogbuju, and Kayode A. Akintoye
Elsevier BV
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
Halleluyah Oluwatobi Aworinde, Biswajit Brahma, Abidemi Emmanuel Adeniyi, Oduayo Dauda Olanloye, Joseph Bamidele Awotunde, Mrakpor Emuejevoke Osamede, and Hemanta Kumar Bhuyan
Springer Nature Switzerland
Femi Emmanuel Ayo, Joseph Bamidele Awotunde, Sanjay Misra, and Akshat Agrawal
Springer Nature Singapore
Joseph Bamidele Awotunde, Sanjay Misra, Thairu Olanrewaju Abdullahi, and Akshat Agrawal
Springer Nature Singapore
Abidemi Emmanuel Adeniyi, Olugbenga Ayomide Madamidola, Joseph Bamidele Awotunde, Sanjay Misra, and Akshat Agrawal
Springer Nature Singapore
Joseph Bamidele Awotunde, Moses Kazeem Abiodun, Abidemi Emmanuel Adeniyi, Bakare Hameed Abiodun, Jide Kehinde Adeniyi, Dayo Rueben Aremu, Ayodele A Adebiyi, and Oladayo G. Atanda
IEEE
There is an abundance of music content available to listeners across numerous platforms as a result of the music industry's digital transition. Users have the problem of navigating this huge musical landscape to find songs that resonate with their unique preferences when they have millions of songs at their fingertips. This paper presents a machine learning-based music recommendation system. The system takes into account various factors such as user preferences and music genre. The recommendation system is based on recent advancements in machine learning and uses collaborative filtering to provide personalized music recommendations for each user. The system is tested using publicly available datasets and achieves satisfactory results. The paper first provides an overview of existing music recommendation systems and their shortcomings. It then outlines the principles of machine learning to explain the process of personalized music recommendation. It describes the various stages in the recommendation process such as data collection, data preprocessing, feature selection, model selection, model training, model testing, and validation. The system is tested with publicly available datasets and the results are compared with existing recommendation systems. The results show that the proposed system achieves a satisfactory performance in comparison to the existing recommendation systems. Furthermore, it is observed that the system is able to recommend personalized music to users.
Moses Kazeem Abiodun, Jide Kehinde Adeniyi, Abidemi Emmanuel Adeniyi, Joseph Bamidele Awotunde, Dayo Rueben Aremu, Olabode Samuel, Ayodele A Adebiyi, and Oladayo G. Atanda
IEEE
Voting is an important activity, and it is an inevitable event in our everyday life. Voting has been conducted in various ways some of which are the use of ballot papers or raising of hands and further collating its statistics manually. The election process in Nigeria has a lot of vulnerabilities, ranging from when the users are casting their votes manually to when the votes are being counted by officials. In previous years Nigerian elections has had a lot of controversy concerning its accuracy and efficiency.The major reason for this research is to reduce the rate of corruption in voting and further make it easier for citizens of Nigeria to cast their votes. An electronic voting system is proposed that make use of two-level authentication system, the National Identification Number (NiN) and the facial recognition of voters. The system was implemented using technologies like HTML (Hypertext Markup Language), CSS (Cascade Styling Sheet), JS (JavaScript) language used for the client-side and node JS (JavaScript) used on the server-side. Mongo DB was used as the database.The results of evaluation of the system are very encouraging, 87.7% of the respondent attest to how the integration of the application and its functionality. There was a positive respond for determining the speed of page transition. 87.7% says the loading time of the web application is very good.Based on the evaluation of this system and its performance, the e-voting system is therefore recommended to be used in Nigeria for next election.
Aminat Adebayo, Moses Kazeem Abiodun, Ifeoluwa Temitayo Awoniran, Abidemi Emmanuel Adeniyi, Joseph Bamidele Awotunde, Jide Kehinde Adeniyi, and Dayo Rueben Aremu
IEEE
The fashion industry has long been interested in utilizing computer vision techniques to automate tasks such as recognizing different types of clothing items in images. This study proposed a novel convolutional neural network (CNN) architecture for the classification of fashion outfits into three categories: shoes, sunglasses, and trousers. The proposed CNN architecture is based on state-of-the-art deep learning techniques and is trained on a fairly large-scale dataset of fashion images. The effectiveness of the proposed CNN architecture is evaluated through extensive experiments and analysis. The result demonstrates that the proposed CNN architecture achieves a high accuracy rate of 0.99 on each diagonal value of the confusion matrix. This indicates that the proposed CNN is capable of accurately classifying each item type with high accuracy. Additionally, the study investigates the impact of various hyperparameters on the performance of the proposed CNN architecture and found that the model which uses a 7x7 filter size and 64 filter number yields higher accuracy compared to other filter combinations. The study demonstrates the potential of CNN in automating fashion item recognition, which can lead to improved efficiency and accuracy in the fashion industry. It can also form the basis for developing more advanced computer vision systems for the fashion industry.
Jide Kehinde Adeniyi, Tunde Taiwo Adeniyi, Emmanuel Abidemi Adeniyi, Moses Kazeem Abiodun, and Joseph Bamidele Awotunde
IEEE
Abidemi Emmanuel Adeniyi, Rasheed Gbenga Jimoh, Joseph Bamidele Awotunde, Mukaila Olagunju, Deborah Olufemi Ninan, Odunayo Dauda Olanloye, Halleluyah Oluwatobi Aworinde, and Abdulrauf Olarenwaju Babatunde
CRC Press
Joseph Bamidele Awotunde, Abidemi Emmanuel Adeniyi, Abdulrauf Olarenwaju Babatunde, Mukaila Olagunju, Agbotiname Lucky Imoize, and Odunayo Dauda Olanloye
CRC Press
Roseline Oluwaseun Ogundokun, Joseph Bamidele Awotunde, Hakeem Babalola Akande, Cheng-Chi Lee, and Agbotiname Lucky Imoize
Tech Science Press
Roseline Oluwaseun Ogundokun, Joseph Bamidele Awotunde, Emmanuel Abidemi Adeniyi, and Femi Emmanuel Ayo
Springer Science and Business Media LLC
Jide Kehinde Adeniyi, Ahmed Babajide Olanrewaju, Abidemi Emmanuel Adeniyi, Biswajit Brahma, Joseph Bamidele Awotunde, and Hemanta Kumar Bhuyan
IEEE
Ear recognition is an example of a biometric system that uses human biological traits for recognition. This kind of recognition has been recently examined due to its distinctive properties such as its invariant shape. When performing analysis on image processing or pattern recognition, one of the major problems encountered is the number of features involved. It is necessary to extract a well-defined feature to make the classification process more efficient. Hence, this paper aims to propose an ear recognition system that extracts two features from the human ear: textural and geometrical features. This is aimed at improving the accuracy of the biometric trait. The extracted features were saved as a template and used for matching. The proposed system was evaluated with two online ear image datasets (AMI Ear database and USTB Ear database) and it produced an accuracy of 98.15.
Sakinat Oluwabukonla Folorunso, Joseph Bamidele Awotunde, Abdullahi Abubakar Kawu, and Oluwatobi Banjo
Elsevier