Information Technology

Permanent URI for this collectionhttps://dspace.summituniversity.edu.ng/handle/123456789/165

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    Bio-inspired computational intelligence (BioCOIN) for cyber security during COVID-19 pandemic: A bibliometric review
    (Innovative Research Publishing -International Journal of Innovative Research and Scientific Studies, 2025-10-02) Agbehadji Israel Edem, Abayomi Abdultaofeek, Aroba Oluwasegun Julius, Freeman Emmanuel, Nketsiah Richard Nana
    The COVID-19 pandemic profoundly disrupted the global economy, accelerating e-commerce and online activities while increasing the vulnerability of Information Technology (IT) systems. This paper aims to review the role of bio-inspired algorithms in enhancing cyber-security during the pandemic, addressing an evident research gap in this domain. A systematic review was conducted using data obtained from an online repository with broad coverage, focusing on the period 2020–2022. This timeframe captures the onset, peak, and aftermath of the pandemic. The study analyzed computational systems and applications that incorporated bio-inspired algorithms for security purposes. The review reveals that bio-inspired algorithms were already in use for cyber-security prior to the pandemic, with notable applications in Internet of Things Cyber-Physical Systems (IoT-CPS)-based Trojan detection circuits and network-layer optimization of security settings. However, the pandemic accelerated the need for resilient cyber-security frameworks and highlighted the potential of bio-inspired methods to adapt to rapidly evolving digital threats. Bio-inspired algorithms represent a valuable approach to strengthening cyber-security in times of crisis. Their adaptability and robustness make them suitable for addressing dynamic threats in increasingly interconnected ecosystems. The findings emphasize the need for continuous integration of bio-inspired approaches into cyber-security policies and infrastructures. Doing so can support more resilient digital ecosystems, enhance organizational processes, and promote better work practices in both crisis and post-crisis environments.
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    Development of a Conditional Generative Adversarial Network Model for Television Spectrum Radio Environment Mapping
    (2024) Dare, O. E.; Okopujie, K; Adetiba, E.; Idowu-Bismark O.; Abayomi, A; Kala, R. J.; Owolabi, E; Ukpong, U. C.
    To efficiently use the finite wireless communication resource (radio spectrum), a Radio Environment Map (REM) is needed to monitor, analyse and provide rich awareness of spectrum activities in a radio propagating environment. REM shows radio coverage metrics in a geographical region. A REM construction model with few constraints and optimal performance is needed to better support cognitive radio for dynamic spectrum sharing (DSS) and other benefits of REM. This study aims to estimate fine-resolution REM from sparse radio signal strength measurement. In this study, we utilised conditional generative adversarial network (CGAN) to create a television spectrum radio environment map in order to improve cognitive television white space (TVWS) radio performance in real-time propagation environments. Measurement campaign was carried out to acquire a TV-band (470-862MHz) radio frequency and geographical dataset at Covenant University, Ota, Nigeria. A preprocessing procedure which was implemented with Python script was employed to group the dataset using Nigerian Communications Commission TV spectrum channel spacing and to create incomplete spectrograms for 49 channels. Xgboost, SVM, and kriging variogram models were explored to generate ground truth datasets for the CGAN model training, and the best algorithm was employed. A CGAN REM model was developed using U-Net as a generator and PatchGan as a discriminator. The U-Net generator is a 3-channel input, 16-layer architecture while the PatchGan discriminator is a 6-channel input, 7-layer architecture. The model performance was evaluated using mean square error (MSE) and mean absolute error (MAE). 12 different experiments were carried out varying the training parameters of the CGAN architecture to obtain an optimal model. The achieved root mean square error (RMSE) is 0.1145dBm and MAE is 0.0820dBm, which shows the deviation between the ground truth and the generated REM. This low deviation means that the proposed CGAN REM model possesses an improved accuracy in predicting the spectrum activities within the television spectrum which is considered appropriate for DSS technology. This study also revealed that 41 channels within TV-band in Covenant University are totally unoccupied.
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    FedLoBA-1: A Load Balancing Architecture for Mitigating Resource Overloading in Federated Cloud Infrastructures
    (2025-02-28) Damola Gideon Akinola; Emmanuel Adetiba; Abdultaofeek Abayomi; Surendra Thakur; Uche Nnaji; Sibusiso Moyo
    In a subscription-based service such as cloud computing, clients have scheduled access to shared resources such as data, software, storage, and other assets as needed. Despite several benefits, cloud computing still faces significant difficulties. Load balancing, which is the capacity of the cloud infrastructure to equally distribute tasks among the resources in the cloud environment has significant issues. Cloud federation is a novel concept in cloud deployment that was developed to overcome load imbalance and other drawbacks that come with standalone clouds. However, in a federated cloud system, effective workload sharing among participating Cloud Service Providers (CSP) is also challenging. Therefore, this study presents a Federated Load Balancing Architecture version 1 (FedLoBA-1) for optimal distribution of inter-cloud and intra-cloud loads within federated cloud infrastructures. The inter-cloud load balancing was realized using Ant Colony Optimization (ACO) whereas the intra-cloud component was realized with the Throttled algorithm. The implementation of the FedLoBA-1 and simulation of the federated cloud were carried out using the Cloud Analyst simulation toolkit. Experimental results show that FedLoBA-1 gave an average response time of 92.33 ms as compared with 328.4ms and 176.55 ms for Closest Datacenter (CDC) and Optimize Response Time (ORT) algorithms respectively. The minimum average processing time obtained for FedLoBA-1, CDC, and ORT were 1.49, 17.00, and 6.68 ms respectively. FedLoBA-1 is a valuable solution for effective resource utilization in federated cloud environments. It significantly improves load balancing in cloud federation by offering an optimized two-tiered approach for intra-cloud and inter-cloud load distribution. This approach results in significantly better performance than existing algorithms.
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    The Transformative Influence of Generative AI on Teaching and Learning
    (Springer, Cham, 2025-07-25) Mavundla Khulekani, Abayomi Abdultaofeek, Adetiba Emmanuel, Olaitan Olutoyin and Thakur Surendra
    The emergence of Generative Artificial Intelligence (AI) has marked a transformative shift in the landscape of learning and teaching across diverse educational settings. This paper explores the influence of generative AI technologies, such as large language models and content creation tools including AI-powered virtual assistants, ChatGPT, adaptive learning platforms, and automated assessment systems, on pedagogical strategies, student engagement, and educational outcomes. We examine how generative AI facilitates personalized learning experiences by providing adaptive content, real-time feedback, and automated support for both students and instructors. Furthermore, ethical issues such as biasness, confidentiality and data privacy as well as artificial intelligence tendency to enhance the already established inequalities in the education sector are investigated. The current study examines the possibility of generative artificial intelligence in refining traditional methods of teaching and its innovative techniques in assessment, curriculum design and skill development as we leveraged on some case studies and empirical research conducted in literature. As part of our findings, the integration of artificial intelligence must be systematically handled so as to obtain fairness and equity in the outcomes even when generative artificial intelligence has a great potential in enhancing access to and effectiveness in education. In conclusion, in order to reap the full promises and capability of generative artificial intelligence in education, a deliberate thoughtful policy development, implementation and continuing research are suggested while the future direction of artificial intelligence in education is also presented.