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Öğe Application of Machine Learning in Renewable Energy: A Bibliometric Analysis of a Decade(Institute of Electrical and Electronics Engineers Inc., 2023) Ajibade, Samuel-Soma M.; Flores, Denis Dante Corilla; Ayaz, Muhammad; Dodo, Yakubu Aminu; Areche, Franklin Ore; Adediran, Anthonia Oluwatosin; Oyebode, Oluwadare Joshua; Dayupay, Johnry P.Machine learning studies in the field of renewable energy are analysed here (REML). So, from 2012 to 2021, we looked at the publication tendencies (PT) and bibliometric analysis (BA) of REML research that was indexed by Elsevier Scopus. Key insights into the research landscape, scientific discoveries, and technological advancement were revealed by BA, while PT highlighted REML's important players, top cited papers, and financing organisations. In total, the PT discovered 1,218 works, 397 of which were conference papers and 106 were reviews. Because it spans the disciplines of science, technology, engineering, and mathematics, REML research is exhaustive, varied, and consequential. The most productive researchers, countries, and sponsors include Ravinesh C. Deo, the United States' National Renewable Energy Laboratory, and China's National Natural Science Foundation. Journal prestige and open access are valued by contributors, as seen by the success of Applied Energy and Energies. Productivity among REML's key stakeholders is boosted by collaborations and research funding. Keyword co-occurrence analysis was used to categorise REML research into four broad topic areas: systems, technologies, tools/technologies, and socio-technical dynamics. According to the results, ML plays a crucial role in the prediction, operation, and optimisation of RET as well as the design and development of RE-related materials.Öğe Bibliographic Exploration of Application of Machine Learning and Artificial Intelligence in Solar Energy(Institute of Electrical and Electronics Engineers Inc., 2024) Ajibade, Samuel-Soma M.; Bashir, Faizah Mohammed; Dodo, Yakubu Aminu; Oyebode, Oluwadare Joshua; Culpable, Rex V.; De La Calzada, Limic M.; Adediran, Anthonia OluwatosinSolar energy could mitigate global warming and climate change. Solar energy faces economic, environmental, and technical challenges. Machine learning solves these technical issues. Despite several studies, machine learning in photovoltaics and solar energy is understudied. This study examines publishing patterns and bibliometrics to critically evaluate machine learning applications in photovoltaics and solar energy research. Scopus uses PRISMA. International publishing, citations, and collaboration are high. The Chinese Ministry of Education employs famous scholars like G. E. Georghiou and Haibo Ma. China is most active due to funding schemes like the National Natural Science Foundation and the National Key Research and Development Programme. This study examines publication patterns by country, institution, and funding organisation from 2014 to 2022, spanning topic categories and indicators. Examining author-keyword data to group publishing themes and identify influential journals. Increasing understanding of machine learning applications in photovoltaics and solar energy research. This project will examine the potential for significant development and the hurdles that must be overcome to leverage Cognitive Computing's benefits in cancer and tumour research. In response to the rising amount of malware, phishing, and intrusion attacks on global energy and grid infrastructure, photovoltaic and solar energy system cybersecurity may be studied. © 2024 IEEE.Öğe Data classification technique for assessing drug use in adolescents in secondary education(ResearchTrentz Academy Publishing Education Services, 2022) Ajibade, Samuel-Soma M.; Oyebode, Oluwadare Joshua; Dayupay, Johnry P.; Gido, Nathaniel G.; Tabuena, Almighty C.; Kilag, Osias Kit T.The reasons why students abuse drugs are crucial information. Knowledge of the difficulties associated with drug use can be improved by employing data mining techniques, which have many advantages. The focus of this study is to examine the causes of drug abuse among Lagos's high school students usingdata mining methods. In February of 2021, a cross-sectional study was conducted. Four hundred teenagers and young adults were present. They were given a questionnaire to fill out about their drug use habits, the types of drugs they take, and why they takethem. We found that 59.1% of students drank alcohol, 23.6 % smoked cigarettes, 15.4 % used cannabis, and 3.1% used cocaine. In addition, the performance of 5 classifiers is compared in terms of correctly classified instances (CCI), with all of them performing better than the simplest classifier (more frequent category: used drug/never used drugs) in terms of the percentage of correctly classified instances. KNN yielded the highest CCI across the board when various drugs were compared (alcohol: 82.40 percent, tobacco: 66.22 percent, cannabis: 91.16 percent, and cocaine: 94.24). Use motives obtained a higher classifier performance when it came to alcohol and tobacco use, but the opposite was true for cannabis and cocaine. Peer pressure and the community in which a teen lives are two major factors that we found to have a significantimpact on that teen's drug use.Öğe Feature Selection for Metaheuristics Optimization Technique with Chaos(Institute of Electrical and Electronics Engineers Inc., 2022) Chaudhury, Sushovan; Oyebode, Oluwadare Joshua; Ngo Hoang, Dai-Long; Rabbi, Fazle; Ajibade, Samuel-Soma M.Particle swarm optimization (PSO) is a global optimization method that is extremely effective. PSO's flaws include slow convergence, premature convergence, and getting stuck at local optima. In this paper, the chaos map and dynamic-weight Particle Swarm Optimization (CPSO) are combined with PSO to improve the search process by adjusting the inertia weight of PSO and changing the position update formula in the Chaos dynamic-weight Particle Swarm Optimization (CPSO), resulting in efficient balancing for local and global PSO feature selection processes. Using eight numerical functions, the performance of CPSO was compared to that of two metaheuristic techniques which are the original PSO and Differential Evolution (DE). The results reveal that the CPSO is an efficient feature selection technique that generates good results by balancing the exploration and exploitation search processes.Öğe Gaussian map to improve firefly algorithm performance(Institute of Electrical and Electronics Engineers Inc., 2022) Rabbi, Fazle; Ayaz, Muhammad; Dayupay, Johnry P.; Oyebode, Oluwadare Joshua; Gido, Nathaniel G.; Adhikari, Nirmal; Tabuena, Almighty C.; Ajibade, Samuel-Soma M.; Bassey, Mbiatke AnthonyFirefly Algorithm (FA) mimics firefly behavior by flashing and attracts them. Firefly's global search mobility is improved for dependable global optimization using chaotic maps in this work. Investigations of benchmark problems with chaotic maps are carried out in depth. The system uses eight separate chaotic maps to fine-tune the firefly's enticing movements. By using planned chaotic transmissions instead of fixed values, the new method beats classic firefly methods. According to statistical data and the success rates of FA, the new algorithms improve the solution's performance and the reliability of global optimality.
