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Stephen Ojo
College of Engineering
College of Engineering
Assistant Professor, Electrical and Computer Engineering
sojo@andersonuniversity.edu
(864) 231-5526
Room 102 College of Engineering
Academic Background
Dr. Ojo is an Assistant Professor, Electrical and Computer Engineering at Anderson University, SC, and a senior member of IEEE. He is a research mentor in the NIH R25 research program and is the lead guest editor for AI for Network Management in MDPI. He holds a B.S. and M.S. in Electrical and Electronics Engineering and a Ph.D. in Information Systems. His research applies AI and machine learning to wireless networks and biomedical applications, with over 60 publications. He also reviews for top journals like IEEE, Elsevier, and Springer.
BS in Electrical and Electronics Engineering, Federal University of Technology, Nigeria
MS in Electrical and Electronics Engineering , Girne American University, Cyprus
Ph.D in Information Systems, Girne American University, Cyprus
Fast Facts
What I find most enjoyable about teaching at Anderson University is the opportunity to integrate faith with engineering education. AU’s Christ-centered environment allows me to not only teach technical skills but also instill ethical responsibility, integrity, and a sense of purpose in my students. I enjoy guiding them in understanding how engineering can be used to serve others and solve real-world problems while upholding Christian values. The supportive and faith-driven community makes teaching here truly fulfilling.
Electrical engineering and wireless communication systems are at the core of modern technology, driving advancements in AI, IoT, and smart networks. With the rise of 5G and the incoming 6G, the demand for engineers skilled in wireless systems, signal processing, and network optimization is growing. These fields offer exciting careers in telecommunications, autonomous systems, and AI-driven connectivity, where innovation shapes the future of global communication and intelligent networking.
1. Recent elevation to IEEE Senior Member grade
2. Currently a lead guest editor with the special issue on “Artificial Intelligence for Network Management” published by MDPI
Contributions to Science
1.Machine Learning and Biological Innovations in Cardiovascular Research. My work has made significant strides in advancing solutions for heart disease and related computational challenges. In my work, I investigated innovative microbial bio-systems to enhance biological pathways critical for cardiovascular health. Additionally, I utilized advanced neural modeling techniques to study heart-brain interactions, uncovering how neural dynamics influence cardiac function. My contributions also include the development of cutting-edge algorithms that optimize diagnostic and therapeutic systems for heart disease, enabling more efficient and accurate interventions. These studies provide actionable insights into cardiovascular mechanisms, integrate computational tools with biomedical applications, and offer innovative strategies to address heart disease through interdisciplinary approaches. The following publications represent my work in this area.
1. Abbas, S., Ojo, S., Al Hejaili, A. et al. Artificial intelligence framework for heart disease classification from audio signals. Sci Rep 14, 3123 https://doi.org/10.1038/s41598-024-53778-7 2024
2. Kim TH, Krichen M, Ojo S, Sampedro GA and Alamro MA (2024) SS-DRPL: self-supervised deep representation pattern learning for voice-based Parkinson’s disease detection. Front. Comput. Neurosci. 18:1414462. doi: 10.3389/fncom.2024.1414462
3. Alqahtani, A., Alqahtani, N., Alsulami, A. A., Ojo, S., Shukla, P. K., Pandit, S. V., Pareek, P. K., & Khalifa, H. S. (2023). Classifying electroencephalogram signals using an innovative and effective machine learning method based on chaotic elephant herding optimum. Expert Systems, 40(6), e13383. https://doi.org/10.1111/exsy.13383
2.AI-Driven Innovations in Medical Diagnostics. My research integrates machine learning and AI to enhance medical diagnostics across various conditions. One major contribution is LesionNet, an automated system that classifies skin lesions using SIFT features combined with a customized convolutional neural network (CNN), significantly improving diagnostic accuracy for skin cancer detection. I also developed a data-centric, automated approach for predicting autism spectrum disorder (ASD) using selective features and explainable AI, providing both accurate predictions and transparent results to aid clinicians in early ASD detection. Additionally, my work on multiple sclerosis (MS) prediction utilizes a hybrid deep learning model to improve diagnostic accuracy and monitor disease progression, enabling early intervention. Together, these studies showcase my commitment to applying advanced computational techniques to medical diagnostics, improving both the efficiency and reliability of healthcare systems.
1. Alzakari SA, Ojo S, Wanliss J, Umer M, Alsubai S, Alasiry A, Marzougui M and Innab N (2024) LesionNet: an automated approach for skin lesion classification using SIFT features with customized convolutional neural network. Front. Med. 11:1487270. doi: 10.3389/fmed.2024.1487270
2. Aldrees A, Ojo S, Wanliss J, Umer M, Khan MA, Alabdullah B, Alsubai S and Innab N (2024) Data-centric automated approach to predict autism spectrum disorder based on selective features and explainable artificial intelligence. Front. Comput. Neurosci. 18:1489463. doi: 10.3389/fncom.2024.1489463
3. Ojo, S., Krichen, M., Alamro, M. A., Mihoub, A., Sampedro, G. A., & Kniezova, J. (2024). Improving Multiple Sclerosis Disease Prediction Using Hybrid Deep Learning Model. Computers, Materials and Continua, 81(1), 643-661.
3.Use of deep learning models in healthcare predictive modeling. My research explores the application of cutting-edge AI techniques, particularly deep learning models, to transform medical diagnostics, with a primary focus on cervical cancer, tuberculosis, and myocardial infarction. A standout contribution is the HO-SsNF model, a heap optimizer-based self-systematized neural fuzzy approach designed for cervical cancer classification using Pap smear images. This deep learning model enhances diagnostic accuracy and provides an efficient, automated solution for early cancer detection, which is crucial for improving patient outcomes.
In parallel, I developed the TSSG-CNN model, which combines semantic segmentation with an adaptive convolutional neural network (CNN) for tuberculosis detection. This deep learning approach improves the detection of tuberculosis in complex medical images, ensuring higher accuracy in diagnosis and better management of the disease.
Furthermore, I introduced an innovative deep learning model for myocardial infarction detection, capable of multi-label classification. This model allows for the simultaneous identification of multiple cardiovascular conditions, significantly advancing early detection and risk assessment in heart disease, offering a comprehensive tool for clinicians.
Together, these deep learning-driven innovations illustrate my commitment to improving healthcare through accurate, efficient, and early diagnosis across a range of life-threatening conditions, particularly cervical cancer.
1. Shanmugam A, Kvn K, Radhabai PR, Natarajan S, Imoize AL, Ojo S, Nathaniel TI. HO-SsNF: heap optimizer-based self-systematized neural fuzzy approach for cervical cancer classification using pap smear images. Front Oncol. 2024 May 1;14:1264611. doi: 10.3389/fonc.2024.1264611. PMID: 38751808; PMCID: PMC11094217
2. Kim, T. H., Krichen, M., Ojo, S., Alamro, M. A., & Sampedro, G. A. (2024). TSSG-CNN: A Tuberculosis Semantic Segmentation-Guided Model for Detecting and Diagnosis Using the Adaptive Convolutional Neural Network. Diagnostics, 14(11), 1174. https://doi.org/10.3390/diagnostics14111174
3. S. Abbas, S. Ojo, M. Krichen, M. A. Alamro, A. Mihoub and L. Vilcekova, “A Novel Deep Learning Approach for Myocardial Infarction Detection and Multi-Label Classification,” in IEEE Access, vol. 12, pp. 76003-76021, 2024, doi: 10.1109/ACCESS.2024.3401744
4. Contributions to Advancing Machine Learning in Healthcare and Education. My research has significantly advanced the application of machine learning in healthcare and education. In the paper “Advancing healthcare and elderly activity recognition,” I contributed to the development of active machine and deep learning models that improve the recognition of fine-grained activities in elderly populations, enhancing healthcare monitoring. In “Multi-class adaptive active learning for predicting student anxiety,” I helped develop predictive models that use adaptive learning techniques to forecast student anxiety, providing a novel approach to mental health monitoring in educational settings. These contributions have the potential to positively impact both healthcare and educational environments by improving personalized care and mental well-being.
1. Abbas, S., Sampedro, G. A., Alsubai, S., Ojo, S., Almadhor, A. S., & Al Hejaili, A. (2024). Advancing healthcare and elderly activity recognition: Active machine and deep learning for fine-grained heterogeneity activity recognition. IEEE Access, 12, 44949–44959. https://doi.org/10.1109/ACCESS.2024.3380432
2. Almadhor, A., Abbas, S., Sampedro, G. A., Alsubai, S., Ojo, S., & Al Hejaili, A. (2024). Multi-class adaptive active learning for predicting student anxiety. IEEE Access, 12, 58097–58105. https://doi.org/10.1109/ACCESS.2024.3391418
I enjoy the engaging and collaborative learning environment at AU, where I can mentor and inspire students through hands-on learning and real-world applications. Seeing students develop problem-solving skills and apply concepts to practical challenges is especially rewarding. The supportive academic community and opportunities for innovation make teaching at AU a fulfilling experience.
Integrating artificial intelligence and machine learning into wireless signal propagation, 5G network modeling, path loss prediction, wireless sensor networks, UAVs, and predictive modeling for biomedical applications using machine learning.
1. Almuzaini, K. K., Stalin, S., Menon, S. P., Kumar, S., Ojo, S., Shukla, P. K., … & Shukla, P. K. (2025). Design and analysis of energy aware interior gateway routing algorithm with particle swarm optimization. International Journal of Communication Systems, 38(1), e5466.
2. Alzakari S. A, Ojo S, Wanliss J, Umer M, Alsubai S, Alasiry A, Marzougui M and Innab N. (2024). LesionNet: an automated approach for skin lesion classification using SIFT features with customized convolutional neural network. Front. Med. 11:1487270. doi: 10.3389/fmed.2024.1487270
3. Aldrees A, Ojo S, Wanliss J, Umer M, Khan MA, Alabdullah B, Alsubai S and Innab N (2024). Data-centric automated approach to predict autism spectrum disorder based on selective features and explainable artificial intelligence. Front. Comput. Neurosci. 18:1489463. doi: 10.3389/fncom.2024.1489463
4. Sampedro, G. A., Ojo, S., Krichen, M., Alamro, M. A., Mihoub, A., & Karovic, V. (2024). Defending AI Models Against Adversarial Attacks in Smart Grids Using Deep Learning. IEEE Access.
5. Ojo, S., Krichen, M., Alamro, M. A., Mihoub, A., Sampedro, G. A., & Kniezova, J. (2024). Improving Multiple Sclerosis Disease Prediction Using Hybrid Deep Learning Model. Computers, Materials and Continua, 81(1), 643-661.
6. Abbas, S., Ojo, S., Bouazzi, I., Avelino Sampedro, G., Al Hejaili, A., Almadhor, A. S., & Kulhánek, R. (2024). Securing Data From Side-Channel Attacks: A Graph Neural Network-Based Approach for Smartphone-Based Side Channel Attack Detection. IEEE Access, 12, 138904-138920.
7. Wanliss, J., & Ojo, S. (2024). Multifractal Domain and Machine Learning for the Analysis of Space Weather. IAU General Assembly, 380.
8. Almuzaini, K.K., Joshi, S., Ojo, S. et al. Survelliance monitoring based routing optimization for wireless sensor networks. Wireless Netw (2023). https://doi.org/10.1007/s11276-023-03381-x
9. Kim TH, Krichen M, Ojo S, Sampedro GA and Alamro MA (2024) SS-DRPL: self-supervised deep representation pattern learning for voice-based Parkinson’s disease detection. Front. Comput. Neurosci. 18:1414462. doi: 10.3389/fncom.2024.1414462
10. Abbas, S., Ojo, S., Krichen, M., Alamro, M. A., Mihoub, A., & Vilcekova, L. (2024). A Novel Deep Learning Approach for Myocardial Infarction Detection and Multi-Label Classification. IEEE Access, 12, 76003-76021.
11. Abbas, S., Ojo, S., Krichen, M., Alamro, M. A., Mihoub, A., & Vilcekova, L. (2024). A Novel Deep Learning Approach for Myocardial Infarction Detection and Multi-Label Classification. IEEE Access, 12, 76003-76021.
12. Ojo, Stephn, Moez Krichen, Meznah A. Alamro, and Alaeddine Mihoub. “TXAI-ADV: Trustworthy XAI for Defending AI Models against Adversarial Attacks in Realistic CIoT.” Electronics 13, no. 9 (2024): 1769.
13. Abbas, S., Alsubai, S., Ojo, S. et al. Active Learning for Detecting Hardware Sensors-Based Side-Channel Attack on Smartphone. Springer J Sci Eng (2024). https://doi.org/10.1007/s13369-024-09046-x 2024
14. A. Almadhor, S.Abbas, G.Sampedro, S.Alsubai, S.Ojo et al., “Multi-Class Adaptive Active Learning for Predicting Student Anxiety,” in IEEE Access, vol. 12, pp. 58097-58105, 2024, doi: 10.1109/ACCESS.2024.3391418. 2024
15. M. Blose, L. Akinyemi, S. Ojo, M. Faheem, A. L. Imoize and A. A. Khan, “Scalable Hybrid Switching-Driven Software Defined Networking Issue: From the Perspective of Reinforcement Learning,” in IEEE Access, doi: 10.1109/ACCESS.2024.3387273.
16. Abbas, S., Sampedro, G. A., Alsubai, S., Ojo, S., Almadhor, A. S., & Al Hejaili, A. (2024). Advancing healthcare and elderly activity recognition: Active machine and deep learning for fine-grained heterogeneity activity recognition. IEEE Access, 12, 44949–44959. https://doi.org/10.1109/ACCESS.2024.3380432
17. Abbas, S., Alsubai, S., Ojo, S. et al. An efficient deep recurrent neural network for detection of cyberattacks in realistic IoT environment. J Supercomput (2024). https://doi.org/10.1007/s11227-024-05993-2
18. Abbas, S., Ojo, S., Al Hejaili, A. et al. Artificial intelligence framework for heart disease classification from audio signals. Sci Rep 14, 3123 (2024). https://doi.org/10.1038/s41598-024-53778-7
19. Abbas S, Bouazzi I, Ojo S, Al Hejaili A, Sampedro GA, Almadhor A, Gregus M. 2024. Evaluating deep learning variants for cyber-attacks detection and multi-class classification in IoT networks. PeerJ Computer Science 10:e1793 https://doi.org/10.7717/peerj-cs.1793
20. Abbas, S., Ojo, S., Krichen, M., Alamro, M. A., Mihoub, A., & Vilcekova, L. (2024). Autism Spectrum disorder Detection in Toddlers and Adults Using Deep Learning. International Journal of Applied Mathematics and Computer Science, 34(4), 631-645.
21. Kim, T. H., Krichen, M., Ojo, S., Alamro, M. A., & Sampedro, G. A. (2024). TSSG-CNN: A Tuberculosis Semantic Segmentation-Guided Model for Detecting and Diagnosis Using the Adaptive Convolutional Neural Network. Diagnostics, 14(11), 1174. https://doi.org/10.3390/diagnostics14111174
22. Shanmugam, A., Kvn, K., Radhabai, P. R., Natarajan, S., Imoize, A. L., Ojo, S., & Nathaniel, T. I. (2024). HO-SsNF: heap optimizer-based self-systematized neural fuzzy approach for cervical cancer classification using pap smear images. Frontiers in oncology, 14, 1264611.
23. S. Abbas, S.Ojo; et al., “Improving Smart Grids Security: An Active Learning Approach for Smart Grid-based Energy Theft Detection,” in IEEE Access, doi: 10.1109/ACCESS.2023.3346327.
24. S. Ojo et al., “Graph Neural Network for Smartphone Recommendation System: A Sentiment Analysis Approach for Smartphone Rating,” in IEEE Access, doi: 10.1109/ACCESS.2023.3341222.
25. Joshi, S., Ojo, S., Yadav, S., Gulia, P., Gill, N. S., Alsberi, H., Rizwan, A., & Hassan, M. M. (2023). Object detection and classification from compressed video streams. Expert Systems, e13382. https://doi.org/10.1111/exsy.13382
26. Alqahtani, A., Alqahtani, N., Alsulami, A. A., Ojo, S., Shukla, P. K., Pandit, S. V., Pareek, P. K., & khalifa, H. S. (2023). Classifying electroencephalogram signals using an innovative and effective machine learning method based on chaotic elephant herding optimum. Expert Systems, e13383. https://doi.org/10.1111/exsy.13383
27. Khan, A. A., Almuzaini, K. K., Macedo, V. D. J., Ojo, S., Minchula, V. K., & Roy, V. (2023). MaReSPS for energy efficient spectral precoding technique in large scale MIMO-OFDM. Physical Communication, 58, 102057.
28. Ojo, S., Allabun, S., Shukla, P. K., Alqahtani, M. S., Pareek, P. K., Abbas, M., … & Soufiene, B. O. (2023). Manoeuvre a Cross-Spectral Algorithm and Machine Learning Approach to Categorize Seizures.
29. Almuzaini, K.K., Joshi, S., Ojo, S. et al. Optimization of the operational state’s routing for mobile wireless sensor networks. Wireless Netw (2023). https://doi.org/10.1007/s11276-023-03246-3
30. Joshi, S.; Allabun, S.; Ojo, S.; Alqahtani, M.S.; Shukla, P.K.; Abbas, M.; Wechtaisong, C.; Almohiy, H.M. Enhanced Drug Delivery System Using Mesenchymal Stem Cells and Membrane-CoatedNanoparticles. Molecules 2023, 28,2130. https://doi.org/10.3390/molecules28052130
31. Isabona, J.; Imoize, A.L.; Ojo, S.; Do, D.-T.; Lee, C.-C. Machine Learning-Based GPR with LBFGS Kernel Parameters Selection for Optimal Throughput Mining in 5G Wireless Networks. Sustainability 2023, 15, 1678. https://doi.org/10.3390/su15021678
32. Isabona, J., Imoize, A. L., & Ojo, S. (2023). Image Denoising based on Enhanced Wavelet Global Thresholding Using Intelligent Signal Processing Algorithm. International Journal of Image, Graphics and Signal Processing (IJIGSP), 15(5), 1-16.
33. Vinoth Babu Kumaravelu, Arthi Murugadass, C. Suganthi Evangeline, Mary X. Anitha, Agbotiname Lucky Imoize, R Nandakumar, Stephen Ojo, Joseph Isabona. Vehicular ad hoc networks employing intelligent reflective surfaces for physical layer security, IET (2023) https://doi.org/10.1049/PBSE021E_ch8
34. Baazeem, R., Maheshwary, P., Binjawhar, D. N., Gulati, K., Joshi, S., Ojo, S., … & Shukla, P. K. (2023). Digital image processing for evaluating the impact of designated nanoparticles in biomedical applications. Intelligent Data Analysis, 27(S1), 83-94.
35. Uppal, M., Gupta, D., Goyal, N., Imoize, A. L., Kumar, A., Ojo, S., … & Choi, J. (2023). A Real‐Time Data Monitoring Framework for Predictive Maintenance Based on the Internet of Things. Complexity, 2023(1), 9991029.
36. Isabona, J., Imoize, A. L., Ojo, S., Venkatareddy, P., Hinga, S. K., Sánchez-Chero, M., & Ancca, S. M. (2023). Accurate base station placement in 4G LTE networks using multiobjective genetic algorithm optimization. Wireless Communications and Mobile Computing, 2023(1), 7476736. https://doi.org/10.1155/2023/7476736
37. Tripathy, S.S.; Imoize, A.L.; Rath, M.; Tripathy, N.; Bebortta, S.; Lee, C.-C.; Chen, T.-Y.; Ojo, S.; Isabona, J.; Pani, S.K. A Novel Edge-Computing-Based Framework for an Intelligent Smart Healthcare System in Smart Cities. Sustainability 2023, 15, 735. 2023 https://doi.org/10.3390/su15010735.
38. Ojo, S. , Olugbade, S. and Ojo, T. (2022). A Review of Road Accidents Detection through Wireless Technology—5G, MIMO and Internet of Things. Open Journal of Applied Sciences, 12, 1968-1978. doi: 10.4236/ojapps.2022.1212137.
39. RG Jimoh, AL Imoize, JB Awotunde, S Ojo, MB Akanbi, JA Bamigbaye. “An Enhanced Deep Neural Network Enabled with Cuckoo Search Algorithm for Intrusion Detection in Wide Area Networks,” 2022 5th Information Technology for Education and Development (ITED), Abuja, Nigeria, 2022, pp. 1-5, doi: 10.1109/ITED56637.2022.10051526.
40. J. B. Awotunde, S.Ojo; et al., “An Enhanced DFFNN for Location-Based Services of Indoor Device-Free Submissive Localization,” 2022 5th Information Technology for Education and Development (ITED), Abuja, Nigeria, 2022, pp. 1-7, doi: 10.1109/ITED56637.2022.10051582.
41. A. L. Imoize, H. I. Obakhena, F. I. Anyasi, J. Isabona, S. Ojo and N. Faruk, “Reconfigurable Intelligent Surfaces Enabling 6G Wireless Communication Systems: Use Cases and Technical Considerations,” 2022 5th Information Technology for Education and Development (ITED), Abuja, Nigeria, 2022, pp. 1-7, doi: 10.1109/ITED56637.2022.10051543.
42. Olugbade, S.; Ojo, S.; Imoize, A.L.; Isabona, J.; Alaba, M.O. A Review of Artificial Intelligence and Machine Learning for Incident Detectors in Road Transport Systems. Math. Comput. Appl. 2022, 27, 77. https://doi.org/10.3390/ mca27050077
43. Akinyemi, L., Oladejo, S., Ekwe, S., Imoize, A. L., & Ojo, S. A. (2022). Effects of Damping Constant of Electron and Size on Quantum-Based Frequency-dependent Dielectric Function of Small Metallic Plasmonic Devices, Scientific African, Volume 16, 2022, e01242,ISSN 2468-2276, https://doi.org/10.1016/j.sciaf.2022.e01242.
44. Ojo, S., Ojo, T.P. and Etta, V.O. (2022) A Fuzzy-Logic Based Path Loss Model at 3.4 GHz for LTE Networks. Open Journal of Applied Sciences, 12, 1271-1283. https://doi.org/10.4236/ojapps.2022.127087
45. Ojo, S. , Sari, A. and Ojo, T. (2022) Path Loss Modeling: A Machine Learning Based Approach Using Support Vector Regression and Radial Basis Function Models. Open Journal of Applied Sciences, 12,990-1010. doi: 10.4236/ojapps.2022.126068
46. Isabona, J.; Imoize, A.L.; Ojo, S.; Karunwi, O.; Kim, Y.; Lee, C.-C.; Li, C.-T. Development of a Multilayer Perceptron Neural Network for Optimal Predictive Modeling in Urban Microcellular Radio Environments. Appl. Sci. 2022, 12, 5713. https://doi.org/10.3390/app12115713
47. Ojo S, Akkaya, M, Sopuru, JC. An ensemble machine learning approach for enhanced path loss predictions for 4G LTE wireless networks. Int J Commun Syst. 2022; 35( 7):e5101. doi:10.1002/dac.5101
48. J. Isabona, R. Kehinde, A. L. Imoize, S. Ojo and N. Faruk, “Large-scale Signal Attenuation and Shadow Fading Measurement and Modelling for Efficient Wireless Network Design and Management,” 2022 IEEE Nigeria 4th International Conference on Disruptive Technologies for Sustainable Development (NIGERCON), 2022, pp. 1-5, doi: 10.1109/NIGERCON54645.2022.9803167.
49. Isabona, J., Imoize, A. L., Rawat, P., Jamal, S. S., Pant, B., Ojo, S., & Hinga, S. K. (2022). Realistic prognostic modeling of specific attenuation due to rain at microwave frequency for tropical climate region. Wireless Communications and Mobile Computing, 2022(1), 8209256. https://doi.org/10.1155/2022/8209256
50. Olawumi, T. O., Chan, D. W., Ojo, S., & Yam, M. C. (2022). Automating the modular construction process: A review of digital technologies and future directions with blockchain technology. Journal of Building Engineering, 46, 103720. 10.1016/j.jobe.2021.103720.
51. Isabona J, Imoize AL, Ojo S, Lee C-C, Li C-T. Atmospheric Propagation Modelling for Terrestrial Radio Frequency Communication Links in a Tropical Wet and Dry Savanna Climate. Information. 2022; 13(3):141. https://doi.org/10.3390/info13030141
52. K.V. N. Kavitha, Sharmila Ashok, Agbotiname Lucky Imoize, Stephen Ojo, K. Senthamil Selvan, Tariq Ahamed Ahanger, Musah Alhassan, “On the Use of Wavelet Domain and Machine Learning for the Analysis of Epileptic Seizure Detection from EEG Signals”, Journal of Healthcare Engineering, vol. 2022, Article ID 8928021, 16 pages, 2022. https://doi.org/10.1155/2022/8928021
53. Joseph, I., Imoize, A. L., Ojo, S., & Risi, I. (2022). Optimal call failure rates modelling with joint support vector machine and discrete wavelet transform. Int. J. Image Graph. Signal Process.(IJIGSP), 14(4), 46-57.DOI:10.5815/ijigsp.2022.04.04
54. Ojo, S., Imoize, A., Alienyi, D. (2021). Radial basis function neural network path loss prediction model for LTE networks in multitransmitter signal propagation environments. Int J Commun Syst. 2020; e4680. https://doi.org/10.1002/dac.4680
55. Michael Adebola, Stephen Ojo, Gabriel Oluleye “An Embeded Systems-based Proximity Detector and Monitoring Device for Security Enhancement” International Journal of Scientific and Technology Research, volume 10, Issue 1, January 2021 pages 1-8. ISSN 2277-8616
56. Olawumi, T. O., Ojo, S., Chan, D. W. M., & Yam, M. C. H. (2021). Factors Influencing the Adoption of Blockchain Technology in the Construction Industry – A System Dynamics Approach. In Proceedings of the CRIOCM 2020 Conference – 25th International Symposium on Advancement of Construction Management and Real Estate (pp. 1235–1249). 28-29 November 2020, Central China Normal University, Wuhan, China. Print ISBN 978-981-16-3586-1, Online ISBN 978-981-16-3587-8 (in electronic format).: CRIOCM. https://doi.org/10.1007/978-981-16-3587-8_84
57. Ojo, S., Etta, V.O. (2020). A Fuzzy-Logic Based Signal Loss Model At 2.6 ghz For Wireless Networks. International Journal Of Scientific & Technology Research 9 (10), 69-73
58. Bilgehan, B., & Ojo, S. (2018). Multiplicative based path loss model. International Journal of Communication Systems, 31(17), e3794. https://doi.org/10.1002/dac.3794
59. Stephen Ojo, Arif Sari, Murat Akaya “An optimal Signal loss Propagation Model for Wireless Channels”, International Journal of Scientific and Technology Research, volume 9, Issue 6 June 2020 pages 742-750 ISSN 2277-8616
60. Almuzaini KK, Stalin S, Menon SP, S.Ojo, et al. Design and analysis of energy aware interior gateway routing algorithm with particle swarm optimization. Int J Commun Syst. 2023;e5466. doi:10.1002/dac. 5466
61. Ashok Shanmugam, KVN Kavitha, Stephen Ojo et al HO-SsNF: Heap Optimizer-Based Self-Systematized Neural Fuzzy Approach for Cervical Cancer Classification Using Pap Smear Images, Frontiers in Oncology, 2024 Volume 14 – 2024 | doi: 10.3389/fonc.2024.1264611
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