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Öğe Application of Machine Learning in Energy Storage: A Scientometric Research of a Decade(Springer Science and Business Media Deutschland GmbH, 2024) Ajibade, Samuel-Soma M.; Bashir, Faizah Mohammed; Dodo, Yakubu Aminu; Dayupay, Johnry P.; De La Calzada, Limic M.; Adediran, Anthonia OluwatosinThe publication trends and bibliometric analysis of the research landscape on the applications of machine/deep learning in energy storage (MES) research were examined in this study based on published documents in the Elsevier Scopus database between 2012 and 2022. The PRISMA technique employed to identify, screen, and filter related publications on MES research recovered 969 documents comprising articles, conference papers, and reviews published in English. The results showed that the publications count on the topic increased from 3 to 385 (or a 12,733.3% increase) along with citations between 2012 and 2022. The high publications and citations rate was ascribed to the MDLES research impact, co-authorships/collaborations, as well as the source title/journals’ reputation, multidisciplinary nature, and research funding. The top/most prolific researcher, institution, country, and funding body on MDLES research are; is Yan Xu, Tsinghua University, China, and the National Natural Science Foundation of China, respectively. Keywords occurrence analysis revealed three clusters or hotspots based on machine learning, digital storage, and Energy Storage. Further analysis of the research landscape showed that MDLES research is currently and largely focused on the application of machine/deep learning for predicting, operating, and optimising energy storage as well as the design of energy storage materials for renewable energy technologies such as wind, and PV solar. However, future research will presumably include a focus on advanced energy materials development, operational systems monitoring and control as well as techno-economic analysis to address challenges associated with energy efficiency analysis, costing of renewable energy electricity pricing, trading, and revenue prediction.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 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 Data mining analysis of online drug reviews(Institute of Electrical and Electronics Engineers Inc., 2022) Ajibade, Samuel-Soma M.; Zaidi, Abdelhamid; Tapales, Catherine P.; Ngo-Hoang, Dai-Long; Ayaz, Muhammad; Dayupay, Johnry P.; Aminu Dodo, Yakubu; Chaudhury, Sushovan; Adediran, Anthonia OluwatosinData mining methods like sentiment analysis provide useful information. This paper examines drug online user reviews. This research predicts user satisfaction with sentiments and applied drugs on effectiveness and side effects using sentiment analysis based on classification and analyzes model transfer across data sources like Emzor and May & Baker data. Online medication review data. Web crawlers was used to collect the ratings and comments of forum members. Emzor Pharmaceutical Company had 463 reviews and May & Baker Pharmaceutical Company had 421 reviews. Data was split 70% for training and 30% for testing. We used sentiment analysis to predict user ratings on overall satisfaction, side effects, and drug efficacy. Emzor data performs better 89.1% in-domain sentiment analysis, while May & Baker data accuracy is 86.90% overall. In cross-data sentiment analysis, the Emzor and May & Baker data performed well when the trained model was applied to side effects. This study acquired data by trawling an internet drug review forum. This study shows that transfer learning can leverage cross-domain similarities to analyze cross-domain sentiment.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.Öğe Teacher’s Attitudes Towards Improving Inter-professional Education and Innovative Technology at a Higher Institution: A Cross-Sectional Analysis(Springer Science and Business Media Deutschland GmbH, 2023) Ajibade, Samuel-Soma M.; Mejarito, Cresencio; Chin, Dindo M.; Dayupay, Johnry P.; Gido, Nathaniel G.; Tabuena, Almighty C.; Chaudhury, Sushovan; Bassey, Mbiatke AnthonyAdapting health professional curriculum and training to evolving requirements and exponential expansion in healthcare awareness and knowledge is vital. As an example of this uniformity, interprofessional education can be found. Teachers’ willingness to participate in interprofessional education is closely linked to their attitude about it. The goal of this research is to investigate teacher attitudes toward interprofessional education (IPE) at Ekiti State College of Health and Technology (EKCHT), Ijero Ekiti, Nigeria. Cross-sectional research involving 85 teachers was used. In order to collect data, a five-point Likert scale with three subscales on IPE was utilized, which was stratified sampling. Positive attitude was defined as having a cut-off percentage of more than seventy-five percent. At a 96% confidence level, SPSS version 21 was used to analyze the Bio-demographic data and teacher attitudes were correlated using logistic regression. There are a greater number of male teachers than females that took part in the survey. Attitudes of teacher's IPE in academic contexts were found to be negative (30.82 < 75%) in the total attitude score (121.45 > 75%). Teacher’s attitudes were not influenced by their age, gender, academic rank, or level of competence. Academics with positive opinions toward interprofessional education were more likely to have used it at the college (P = 0.147). As a result, while teachers have a generally positive view of interprofessional education, they have a negative view of subscale 3-interprofessional education in academic contexts. Training in behavior change and IPE awareness for teachers is suggested to avoid negative attitudes.
