Browsing by Author "Monsurat Omolara Balogun"

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    Mitigating the Challenge of Energy Crisis via Energy Audit and Efficiency Measures: A Case of a Household in Nigeria
    (The 7th International Conference on Green Energy and Applications, 2023-11-26) Adeyemi Abdulhameed, Jimoh; Olatunji Ahmed, Lawal; Zinat Alabi, Abdulkadir; Adewale Bashir, Bello; Monsurat Omolara Balogun; Alao Rasaq Atanda
    Approximately 90 million of Nigeria's residents are without electricity access; this represents 12.5% of the world’s population without access. Nigeria is challenged in meeting goal 7 of the sustainable development goals (SDGs) due to inadequate power generation to meet demands, poor transmission networks and insufficient investment. Proposing energy audits and efficiency measures, such as proper housekeeping, retrofitting electrical systems and installing renewable energy sources, will help reduce energy consumption and mitigate the country's energy crisis. This study conducted on a residential building in Nigeria found that retrofitting the building lighting system could achieve annual savings of as much as 230kWh and have a payback period of 9 months with a return on investment (ROI) of 126%. The retrofit of the pump system is expected to save 182kWh within a payback period of 2 years and have an ROI of 50%. The installation of solar panels and retrofit of batteries is expected to save 3MWh at a capital cost of N300,000, with an ROI of 26% and a net present value of over N690,000. These measures would also reduce a total of 1.97 tons of greenhouse gas emissions annually
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    Optimized Negative Selection Algorithm for Image Classification in Multimodal Biometric System
    (2023) Monsurat Omolara Balogun; Latifat Adeola Odeniyi; Elijah Olusola Omidiora; Stephen Olatunde Olabiyisi; Adeleye Samuel Falohun
    Classification is a crucial stage in identification systems, most specifically in biometric identification systems. A weak and inaccurate classification system may produce false identity, which in turn impacts negatively on delicate decisions. Decision making in biometric systems is done at the classification stage. Due to the importance of this stage, many classifiers have been developed and modified by researchers. However, most of the existing classifiers are limited in accuracy due to false representation of image features, improper training of classifier models for newly emerging data (over-fitting or under-fitting problem) and lack of an efficient mode of generating model parameters (scalability problem). The Negative Selection Algorithm (NSA) is one of the major algorithms of the Artificial Immune System, inspired by the operation of the mammalian immune system for solving classification problems. However, it is still prone to the inability to consider the whole self-space during the detectors/features generation process. Hence, this work developed an Optimized Negative Selection Algorithm (ONSA) for image classification in biometric systems. The ONSA is characterized by the ability to consider whole feature spaces (feature selection balance), having good training capability and low scalability problems. The performance of the ONSA was compared with that of the standard NSA (SNSA), and it was discovered that the ONSA has greater recognition accuracy by producing 98.33% accuracy compared with that of the SNSA which is 96.33%. The ONSA produced TP and TN values of 146% and 149%, respectively, while the SNSA produced 143% and 146% for TP and TN, respectively. Also, the ONSA generated a lower FN and FP rate of 4.00% and 1.00%, respectively, compared to the SNSA, which generated FN and FP values of 7.00% and 4.00%, respectively. Therefore, it was discovered in this work that global feature selection improves recognition accuracy in biometric systems. The developed biometric system can be adapted by any organization that requires an ultra-secure identification system.