Joseph Bamidele AWOTUNDE

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

Generative AI, IoT, and blockchain in healthcare: application, issues, and solutions
Tehseen Mazhar, Sunawar khan, Tariq Shahzad, Muhammad Amir khan, Mamoon M. Saeed, Joseph Bamidele Awotunde, and Habib Hamam
Springer Science and Business Media LLC



An Enhanced Product Recommendation System Using Decision Tree Algorithm
Joseph Bamidele Awotunde, Samarendra Nath Sur, Agbotiname Lucky Imoize, and Oluwatimilehin Moses Akinyoola
Springer Nature Singapore



An Enhanced Keylogger Detection Systems Using Recurrent Neural Networks Enabled with Feature Selection Model
Joseph Bamidele Awotunde, Samarendra Nath Sur, Agbotiname Lucky Imoize, Demóstenes Zegarra Rodríguez, and Boluwatife Akanji
Springer Nature Singapore



Artificial Intelligence in Industry 5.0: Transforming Manufacturing through Machine Learning and Robotics in Collaborative Age
Bhanu Priya, Vivek Sharma, Joseph Bamidele Awotunde, and Abidemi Emmanuel Adeniyi
Auerbach Publications



The Role of Augmented Reality, Virtual Reality, and Mixed Reality in Industries 4.0 and 5.0
Peace Busola Falola, Abidemi Emmanuel Adeniyi, Joseph Bamidele Awotunde, Solomon Olamilekan Akinola, Mukaila Olagunju, and Odunayo Dauda Olanloye
Auerbach Publications



Fine-Tuning for a Deep Learning Model in Optimizing Ransomware Attack Detection in the Industry 4.0 and 5.0 Era
Ahmad Sanmorino, Rendra Gustriansyah, Shinta Puspasari, Joseph Bamidele Awotunde, and Demóstenes Zegarra Rodríguez
Auerbach Publications



Computational Intelligence in Industry 4.0 and 5.0 Applications: Trends, Challenges and Applications
JOSEPH BAMIDELE AWOTUNDE, Kamalakanta Muduli, and BISWAJIT BRAHMA
Auerbach Publications



Security, Privacy, Trust, and Other Issues in Industries 4.0 and 5.0
Abidemi Emmanuel Adeniyi, Rasheed Gbenga Jimoh, Joseph Bamidele Awotunde, Deborah Olufemi Ninan, Hallelujah Oluwatobi Aworinde, Adedoyin Oyebade, Peace Busola Falola, and Abdulrauf Olarenwaju Babatunde
Auerbach Publications



Integration of Digital of Twins in Industries 4.0 and 5.0
Joseph Bamidele Awotunde, Abidemi Emmanuel Adeniyi, and Peace Busola Falola
Auerbach Publications



Novel Advance Image Caption Generation Utilizing Vision Transformer and Generative Adversarial Networks
Shourya Tyagi, Olukayode Ayodele Oki, Vineet Verma, Swati Gupta, Meenu Vijarania, Joseph Bamidele Awotunde, and Abdulrauph Olanrewaju Babatunde
MDPI AG
In this paper, we propose a novel method for producing image captions through the utilization of Generative Adversarial Networks (GANs) and Vision Transformers (ViTs) using our proposed Image Captioning Utilizing Transformer and GAN (ICTGAN) model. Here we use the efficient representation learning of the ViTs to improve the realistic image production of the GAN. Using textual features from the LSTM-based language model, our proposed model combines salient information extracted from images using ViTs. This merging of features is made possible using a self-attention mechanism, which enables the model to efficiently take in and process data from both textual and visual sources using the self-attention properties of the self-attention mechanism. We perform various tests on the MS COCO dataset as well as the Flickr30k dataset, which are popular benchmark datasets for image-captioning tasks, to verify the effectiveness of our proposed model. The outcomes represent that, on this dataset, our algorithm outperforms other approaches in terms of relevance, diversity, and caption quality. With this, our model is robust to changes in the content and style of the images, demonstrating its excellent generalization skills. We also explain the benefits of our method, which include better visual–textual alignment, better caption coherence, and better handling of complicated scenarios. All things considered, our work represents a significant step forward in the field of picture caption creation, offering a complete solution that leverages the complementary advantages of GANs and ViT-based self-attention models. This work pushes the limits of what is currently possible in image caption generation, creating a new standard in the industry.


Intrusion Detection: A Comparison Study of Machine Learning Models Using Unbalanced Dataset
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.


Analysis of integration of IoMT with blockchain: issues, challenges and solutions
Tehseen Mazhar, Syed Faisal Abbas Shah, Syed Azeem Inam, Joseph Bamidele Awotunde, Mamoon M. Saeed, and Habib Hamam
Springer Science and Business Media LLC



Evolution of machine learning applications in medical and healthcare analytics research: A bibliometric analysis
Samuel-Soma M. Ajibade, Gloria Nnadwa Alhassan, Abdelhamid Zaidi, Olukayode Ayodele Oki, Joseph Bamidele Awotunde, Emeka Ogbuju, and Kayode A. Akintoye
Elsevier BV



Retraction Note: Crypto-Stegno based model for securing medical information on IOMT platform (Multimedia Tools and Applications, (2021), 80, 21-23, (31705-31727), 10.1007/s11042-021-11125-2)
Roseline Oluwaseun Ogundokun, Joseph Bamidele Awotunde, Emmanuel Abidemi Adeniyi, and Femi Emmanuel Ayo
Springer Science and Business Media LLC



Application of blockchain-based internet of things in medical healthcare
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.


Integration of blockchain and cloud computing in intelligent healthcare systems for elderly citizens
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.


A systematic review on elliptic curve cryptography algorithm for internet of things: Categorization, application areas, and security
Abidemi Emmanuel Adeniyi, Rasheed Gbenga Jimoh, and Joseph Bamidele Awotunde
Elsevier BV



A neuro-fuzzy security risk assessment system for software development life cycle
Olayinka Olufunmilayo Olusanya, Rasheed Gbenga Jimoh, Sanjay Misra, and Joseph Bamidele Awotunde
Elsevier BV



Ontology-Based Layered Rule-Based Network Intrusion Detection System for Cybercrimes Detection
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



A Lightweight Image Cryptosystem for Cloud-Assisted Internet of Things
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.


EfficientNets transfer learning strategies for histopathological breast cancer image analysis
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.


Bot-FFX: A Robust and Efficient Framework for Fast Flux Botnet (FFB) Detection
Femi Emmanuel Ayo, Joseph Bamidele Awotunde, Sakinat Oluwabukonla Folorunso, Ranjit Panigrahi, Amik Garg, and Akash Kumar Bhoi
Springer Science and Business Media LLC



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, Shakirat A. Salihu, Abimbola G. Akintola, Modinat A. Mabayoje, and Joseph B. Awotunde
Elsevier BV



A hybrid correlation-based deep learning model for email spam classification using fuzzy inference system
Femi Emmanuel Ayo, Lukman Adebayo Ogundele, Solanke Olakunle, Joseph Bamidele Awotunde, and Funmilayo A. Kasali
Elsevier BV



Machine learning assisted snort and zeek in detecting DDoS attacks in software-defined networking
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

RECENT SCHOLAR PUBLICATIONS

5 Security, Privacy, Trust
AE Adeniyi, AO Babatunde
Computational Intelligence in Industry 4.0 and 5.0 Applications: Trends 2025



10 The Reality, Role Virtual of AugmentedReality, and Mixed Reality
PB Falola, AE Adeniyi, JB Awotunde, SO Akinola, M Olagunju, ...
Computational Intelligence in Industry 4.0 and 5.0 Applications: Trends 2025



8 IntegrationofTwinsin of Digital
JB Awotunde, AE Adeniyi, PB Falola
Computational Intelligence in Industry 4.0 and 5.0 Applications: Trends 2025



Efficient Hybrid Precoding for Millimeter‐Wave Massive MIMO‐NOMA Systems: A Low‐Complexity Approach
S Nath Sur, A Kumar Singh, A Lucky Imoize, J Bamidele Awotunde, ...
Massive MIMO for Future Wireless Communication Systems: Technology and 2025



Generative AI, IoT, and blockchain in healthcare: application, issues, and solutions
T Mazhar, S khan, T Shahzad, MA khan, MM Saeed, JB Awotunde, ...
Discover Internet of Things 5 (1), 5 2025



Health monitoring system for cancer care using IoT
PB Falola, JB Awotunde, AE Adeniyi, GO Ajiboye
Blockchain and Digital Twin for Smart Healthcare, 207-227 2025



Digital twin for smart wireless sensor networks
AE Adeniyi, HO Aworinde, OD Olanloye, JB Awotunde, BO Olorunfemi, ...
Blockchain and Digital Twin for Smart Healthcare, 271-302 2025



Introduction to smart hospital
JB Awotunde
Blockchain and Digital Twin for Smart Hospitals, 1-35 2025



Blockchain technology and neural networks for the Internet of Medical Things
PB Falola, AE Adeniyi, JB Awotunde
Blockchain and Digital Twin for Smart Hospitals, 89-108 2025



Word sense disambiguation in biomedical applications
JB Awotunde
Mining Biomedical Text, Images and Visual Features for Information Retrieval 2025



Enhancing Mobile Cloud Computing Security with SHA-256 and RSA for User Authentication and Data Sharing
GS Chauhan, K Srinivasan, R Jado, JB Awotunde
2024 International Conference on Emerging Research in Computational Science 2024



Analysis of integration of IoMT with blockchain: issues, challenges and solutions
T Mazhar, SFA Shah, SA Inam, JB Awotunde, MM Saeed, H Hamam
Discover Internet of Things 4 (1), 1-36 2024



Novel Advance Image Caption Generation Utilizing Vision Transformer and Generative Adversarial Networks
S Tyagi, OA Oki, V Verma, S Gupta, M Vijarania, JB Awotunde, ...
Computers 13 (12), 305 2024



Intrusion detection: a comparison study of machine learning models using unbalanced dataset
SA Ajagbe, JB Awotunde, H Florez
SN Computer Science 5 (8), 1028 2024



Evolution of machine learning applications in medical and healthcare analytics research: A bibliometric analysis
SSM Ajibade, GN Alhassan, A Zaidi, OA Oki, JB Awotunde, E Ogbuju, ...
Intelligent Systems with Applications, 200441 2024



A randomized encryption algorithm for the MPEG-DASH digital rights management
RG Jimoh, JB Awotunde, RO Ogundokun, DS Adeoti
Information Security Journal: A Global Perspective 33 (5), 473-485 2024



12 An Cryptographic Enhanced Lightweight Algorithm Towards Securing Wireless Networks and Big Data
JB Awotunde, AE Adeniyi, AO Babatunde, M Olagunju, AL Imoize, ...
Computational Modeling and Simulation of Advanced Wireless Communication 2024



Cybersecurity in Emerging Healthcare Systems
AL Imoize, C Meshram, JB Awotunde, Y Farhaoui, DT Do
IET 2024



Blockchain for secured cybersecurity in emerging healthcare systems
AE Adeniyi, RG Jimoh, JB Awotunde, HO Aworinde, PB Falola, DO Ninan
2024



Developing a Novel Cardiac Disease Prediction Framework Utilizing Advanced Machine Learning Algorithms
O Mukaila, EA Abidemi, DO Odunayo, BA Joseph
LAUTECH JOURNAL OF COMPUTING AND INFORMATICS 4 (2), 101-112 2024

MOST CITED SCHOLAR PUBLICATIONS

Intrusion detection in industrial internet of things network‐based on deep learning model with rule‐based feature selection
JB Awotunde, C Chakraborty, AE Adeniyi
Wireless communications and mobile computing 2021 (1), 7154587 2021
Citations: 180



Privacy and security concerns in IoT-based healthcare systems
JB Awotunde, RG Jimoh, SO Folorunso, EA Adeniyi, KM Abiodun, ...
The fusion of internet of things, artificial intelligence, and cloud 2021
Citations: 147



Predictive modelling of COVID-19 confirmed cases in Nigeria
RO Ogundokun, AF Lukman, GBM Kibria, JB Awotunde, BB Aladeitan
Infectious Disease Modelling 5, 543-548 2020
Citations: 137



IoMT-based wearable body sensors network healthcare monitoring system
EA Adeniyi, RO Ogundokun, JB Awotunde
IoT in healthcare and ambient assisted living, 103-121 2021
Citations: 123



Application of big data with fintech in financial services
JB Awotunde, EA Adeniyi, RO Ogundokun, FE Ayo
Fintech with artificial intelligence, big data, and blockchain, 107-132 2021
Citations: 94



Disease diagnosis system for IoT-based wearable body sensors with machine learning algorithm
JB Awotunde, SO Folorunso, AK Bhoi, PO Adebayo, MF Ijaz
Hybrid artificial intelligence and IoT in healthcare, 201-222 2021
Citations: 91



Network intrusion detection based on deep learning model optimized with rule-based hybrid feature selection
FE Ayo, SO Folorunso, AA Abayomi-Alli, AO Adekunle, JB Awotunde
Information Security Journal: A Global Perspective 29 (6), 267-283 2020
Citations: 84



An enhanced intrusion detection system using particle swarm optimization feature extraction technique
RO Ogundokun, JB Awotunde, P Sadiku, EA Adeniyi, M Abiodun, ...
Procedia Computer Science 193, 504-512 2021
Citations: 83



Medical diagnosis system using fuzzy logic
JB Awotunde, OE Matiluko, OW Fatai
African Journal of Computing & ICT 7 (2), 99-106 2014
Citations: 80



An ensemble tree-based model for intrusion detection in industrial internet of things networks
JB Awotunde, SO Folorunso, AL Imoize, JO Odunuga, CC Lee, CT Li, ...
Applied Sciences 13 (4), 2479 2023
Citations: 71



Machine learning algorithm for cryptocurrencies price prediction
JB Awotunde, RO Ogundokun, RG Jimoh, S Misra, TO Aro
Artificial intelligence for cyber security: methods, issues and possible 2021
Citations: 68



A safe and secured iris template using steganography and cryptography
OC Abikoye, UA Ojo, JB Awotunde, RO Ogundokun
Multimedia Tools and Applications 79 (31), 23483-23506 2020
Citations: 62



IoT-based wearable body sensor network for COVID-19 pandemic
JB Awotunde, RG Jimoh, M AbdulRaheem, ID Oladipo, SO Folorunso, ...
Advances in Data Science and Intelligent Data Communication Technologies for 2021
Citations: 60



Big data analytics of iot-based cloud system framework: Smart healthcare monitoring systems
JB Awotunde, RG Jimoh, RO Ogundokun, S Misra, OC Abikoye
Artificial intelligence for cloud and edge computing, 181-208 2022
Citations: 59



A deep learning-based intrusion detection technique for a secured IoMT system
JB Awotunde, KM Abiodun, EA Adeniyi, SO Folorunso, RG Jimoh
International Conference on Informatics and Intelligent Applications, 50-62 2021
Citations: 57



Feature extraction and artificial intelligence-based intrusion detection model for a secure internet of things networks
JB Awotunde, S Misra
Illumination of artificial intelligence in cybersecurity and forensics, 21-44 2022
Citations: 55



Cloud and IoMT-based big data analytics system during COVID-19 pandemic
JB Awotunde, RO Ogundokun, S Misra
Efficient data handling for massive internet of Medical things: Healthcare 2021
Citations: 54



Internet of medical things (IoMT): applications, challenges, and prospects in a data-driven technology
SA Ajagbe, JB Awotunde, AO Adesina, P Achimugu, TA Kumar
Intelligent Healthcare: Infrastructure, Algorithms and Management, 299-319 2022
Citations: 53



AiIoMT: IoMT-based system-enabled artificial intelligence for enhanced smart healthcare systems
JB Awotunde, SO Folorunso, SA Ajagbe, J Garg, GJ Ajamu
Machine learning for critical Internet of Medical Things: applications and 2022
Citations: 53



A decision support system for multi-target disease diagnosis: A bioinformatics approach
FE Ayo, JB Awotunde, RO Ogundokun, SO Folorunso, AO Adekunle
Heliyon 6 (3) 2020
Citations: 50