School of Electrical Engineering and Technology (SEET)
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School of Electrical Engineering and Technology (SEET)
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Item IoT Based Security Management Framework for Heterogeneous Network Environment(2020) Ajibo, C. A.; Chinaeke-Ogbuka, I. M.; Dogo, E. M.; Ogbuka C. U.In an effort to curb the potential losses associated with the event of security bridge, admitting the uneven bandwidth support that characterizes most developing smart cities, we propose a neural inspired Multimodal Security Management System (MSMS) that is bandwidth-tolerant. The proposed system leverages on a Next-Generation Network (NGN) architecture in catering for the challenges associated with the provisioning of ubiquitous broadband access for IoT support in a heterogeneous morphological network environment. In order to evaluate the MSMS, we simulated the proposed cloud-based system on a Next Generation Network (NGN) architecture which utilizes Internet Protocol/Multi-Protocol Label Switching (IP/MPLS) as transport technique in a Long Term Evolution (LTE) backbone infrastructure. We then compare its performance over a competitive alternative transport technique: "Internet Protocol Asynchronous Transfer Mode (IP/ATM)". Thus, we further evaluated the MEMS on the latter architecture. While, our proposed system is able to capture both textual, aural, and visual information of individuals in security vulnerable environments via installed smart microphones and cameras, it is also able to integrate this information's in predicting security threats. When compared with the popular Security Management System (SMS) "ShotSpotter", results show that our proposed system outperforms the ShotSpotter system by 0.87 and 0.45 in terms of efficiency and response time respectively. Finally, simulation of our proposed system on an IP/MPLS transport schemes shows that the former outperforms the latter with respect to overall network bandwidth utilization and average traffic loss in the ratio of 0.098 and 0.087 respectively.Item Artificial intelligence model for prediction of cardiovascular disease: An empirical study(AccScience Publishing, 2024) Umar, B. U.; Ajao, L. A.; Dogo, E. M.; Ajao, F. J.; Atama, M.Cardiovascular disease (CVD) is a disease related to the heart and blood vessels. Prediction of CVD is essential for early detection and diagnosis, which is however compounded by the complex interplay between medical history, physical examination outcomes, and imaging results. While the existing automated systems are fraught with the usage of irrelevant and redundant attributes, artificial intelligence (AI) helps in the identification of potential CVD populations by prediction models. This work aims at developing an AI model for predicting CVD using different classifications of machine learning techniques. The CVD dataset was obtained from the UCI repository containing about 76 cardiac attributes for training in various machine learning models, which include a hybrid of artificial neural network genetic algorithm (ANN-GA), artificial neural network, support vector machine (SVM), K-means, K-nearest neighbor (KNN), and decision tree (DT). The performance of the models was measured in terms of accuracy, means square error, sensitivity, specificity, and precision. The results showed that the hybrid model of ANN-GA performs better with an accuracy of 86.4%, compared to the SVM, K-means, KNN, and DT measured at 84.0%, 59.6%, 79.0%, and 77.8%, respectively. It was observed that the system performs better as the number of datasets increases in the database, with a fewer selection of attributes using genetic algorithm for selection. Thus, the ANN-GA model is recommended for CVD prediction and diagnosis.Item Investigating the Thresholding Effect and Fingerprint Transformation Using Cross-Correlation Similarity Matching(2024) Garuba, O. R.; Abdullahi, I. M.; Dogo, E. M.; Maliki, D.This research presents a cross-correlation similarity matching method to study the fingerprint transformation and thresholding impact. This work directly compares the impact of various transformations (rotation, translation, elastic deformation, and scaling) on the fingerprint matching performance at different threshold values, in contrast to the standard minutiae-based systems. In order to compare the template positions of the two fingerprints using plots, the cross-correlation similarity matching of fingerprints first selects suitable templates in the primary fingerprint and then uses template matching to assess the impact of each transformation on matching accuracy, FRR, and FAR in the secondary print. The findings highlight the potential of thresholding in developing reliable and practical fingerprint recognition systems.Item A decade bibliometric analysis of underwater sensor network research on the Internet of Underwater Things: An African perspective(Springer, Cham, 2020) Salami, A. F.; Dogo, E. M.; Makaba, T; Adedokun, E. A.; Muazu, M. B.; Sadiq, B. O.; Salawudeen, A. T.Recent advancements in cloud computing (CC) and the rapid growth of the Internet of Things (IoT) have tremendously revolutionized terrestrial wireless sensor networks (TWSN) communication. These have resultantly paved the way for the practical realization of underwater wireless sensor networks (UWSN) and the emergence of the Internet of Underwater Things (IoUT). The need for better environmental monitoring within the context of smart cities and the recent spate of global natural disasters has further aroused research interest in IoUT which has motivated a number of UWSN innovations, such as the development of tethered remotely operated underwater vehicles (ROUVs), untethered autonomous underwater vehicles (AUVs), unmanned/autonomous surface vehicles (USVs/ASVs) and other smart underwater technologies. While these inventions hold promising prospects for technologically advanced countries, the same assertion cannot be made for most African countries due to challenges inherent in research and development activities into critical IoUT/UWSN projects in the region. This chapter conducts a systematic bibliometric analysis that highlights the knowledge base for core research works in UWSN globally and within the African region. This research discovered 1025 peer-reviewed articles in 5 Scopus-indexed document sources published between 2008 and July 2019. Microsoft Excel and VOSviewer science mapping software tool was used to analyse the retrieved data from Scopus repository. The bibliometric analysis was used to evaluate specific criteria, namely, major subject area, document sources, most cited and productive authors, countries, institutions, funding institutions and most used keywords. The findings of this research indicated that UWSN/IoUT research is still in its infancy in the African region. This chapter concludes by highlighting vital missing links, essential research directions and unique technical recommendations that will be of relevance in helping the successful actualization of IoUT/UWSN research projects in Africa.