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Öğe An analysis of the impact of social media addiction on academic engagement of students(ResearchTrentz Academy Publishing Education Services, 2022) Ajibade, Samuel-Soma M.; Mejarito, Cresencio; Egere, Odafe Martin; Adediran, Anthonia Oluwatosin; Gido, Nathaniel G.; Bassey, Mbiatke AnthonyThe study's goal is to comprehend how internet addiction affects students' academic performance. However, very few research has been able to explain how excessive internet use causes students to lose interest in their academic work. Many studies have examined the detrimental association between addictions and academic performance. This research consists of two factors: internet addiction (emotional and cognitive preoccupation with internet and loss of control and interference with daily life) and academic engagement (enthusiasm and commitment). Through questionnaires, data was gathered from 186 students at a higher institution in Nigeria. Both correlation and regression were used to evaluate the data. The results of the investigation demonstrated that internet addiction significantly and unfavorably affects enthusiasm and commitment. It's interesting to note that internet obsession on an emotional or cognitive level was not shown to be a reliable indicator of internet addiction or loss of control.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 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 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 Enhancing Students' Learning Motivation and Comprehension by Reflecting on the Practical Applications of Learning Materials in an Education Learning Journal(Institute of Electrical and Electronics Engineers Inc., 2024) Ajibade, Samuel-Soma M.; Valle, Lislee; General, Edralin; Baclayon-Lim, Joshlen; Colina, Sarah Jane; Akintoye, Kayode Akinlekan; Adediran, Anthonia OluwatosinEvaluating the learning materials' personal utility and worth is vital for cultivating motivation and involvement in high-quality learning experiences. The researchers set out to find out how using a personal-utility prompt in journal writing affected the students' motivation to learn and their understanding of biological concepts in the classroom. Sixty high school students from a Nigerian school participated in the quasi-experimental field investigation. Students were asked to maintain a weekly learning journal starting from the first week of the semester for a total of eight weeks. Students were given short instructions on how to write their journal entries, which might or might not have included a suggestion about how the entries could benefit them individually. The results show that having students think about the educational resources' personal value was a great success with the personal-utility prompt. Consequently, students whose teachers allowed them to employ a personal-utility prompt had more interest in learning and had more success with comprehension than those whose teachers did not. A clear and effective strategy for improving students' motivation and understanding in secondary science education is to have them write reflective journals in which they consider the relevance and usefulness of various learning materials. © 2024 IEEE.Öğe Machine Learning Applications in Renewable Energy (MLARE) Research: A Publication Trend and Bibliometric Analysis Study (2012–2021)(MDPI, 2023) Ajibade, Samuel-Soma M.; Bekun, Festus Victor; Adedoyin, Festus Fatai; Gyamfi, Bright Akwasi; Adediran, Anthonia OluwatosinThis study examines the research climate on machine learning applications in renewable energy (MLARE). Therefore, the publication trends (PT) and bibliometric analysis (BA) on MLARE re search published and indexed in the Elsevier Scopus database between 2012 and 2021 were examined. The PT was adopted to deduce the major stakeholders, top-cited publications, and funding organi zations on MLARE, whereas BA elucidated critical insights into the research landscape, scientific developments, and technological growth. The PT revealed 1218 published documents compris ing 46.9% articles, 39.7% conference papers, and 6.0% reviews on the topic. Subject area analysis revealed MLARE research spans the areas of science, technology, engineering, and mathematics among others, which indicates it is a broad, multidisciplinary, and impactful research topic. The most prolific researcher, affiliations, country, and funder are Ravinesh C. Deo, National Renewable Energy Laboratory, United States, and the National Natural Science Foundation of China, respectively. The most prominent journals on the top are Applied Energy and Energies, which indicates that journal reputation and open access are critical considerations for the author’s choice of publication outlet. The high productivity of the major stakeholders in MLARE is due to collaborations and research funding support. The keyword co-occurrence analysis identified four (4) clusters or thematic areas on MLARE, which broadly describe the systems, technologies, tools/technologies, and socio-technical dynamics of MLARE research. Overall, the study showed that ML is critical to the prediction, operation, and optimization of renewable energy technologies (RET) along with the design and development of RE-related materials.Öğe New Insights into the Emerging Trends Research of Machine and Deep Learning Applications in Energy Storage: A Bibliometric Analysis and Publication Trends(Econjournals, 2023) Ajibade, Samuel-Soma M.; Zaidi, Abdelhamid; Al Luhayb, Asamh Saleh M.; Adediran, Anthonia Oluwatosin; Voumik, Liton Chandra; Rabbi, FazleThe publication trends and bibliometric analysis of the research landscape on the applications of machine and deep learning in energy storage (MDLES) 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 MDLES 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 A research landscape bibliometric analysis on climate change for last decades: Evidence from applications of machine learning(Elsevier, 2023) Ajibade, Samuel-Soma M.; Zaidi, Abdelhamid; Bekun, Festus Victor; Adediran, Anthonia Oluwatosin; Bassey, Mbiatke AnthonyClimate change (CC) is one of the greatest threats to human health, safety, and the environment. Given its current and future impacts, numerous studies have employed computational tools (e.g., machine learning, ML) to understand, mitigate, and adapt to CC. Therefore, this paper seeks to comprehensively analyze the research/publications landscape on the MLCC research based on published documents from Scopus. The high productivity and research impact of MLCC has produced highly cited works categorized as science, technology, and engineering to the arts, humanities, and social sciences. The most prolific author is Shamsuddin Shahid (based at Universiti Teknologi Malaysia), whereas the Chinese Academy of Sciences is the most productive affiliation on MLCC research. The most influential countries are the United States and China, which is attributed to the funding activities of the National Science Foundation and the National Natural Science Foundation of China (NSFC), respectively. Collaboration through co-authorship in high impact journals such as Remote Sensing was also identified as an important factor in the high rate of productivity among the most active stakeholders researching MLCC topics worldwide. Keyword co-occurrence analysis identified four major research hotspots/themes on MLCC research that describe the ML techniques, potential risky sectors, remote sensing, and sustainable development dynamics of CC. In conclusion, the paper finds that MLCC research has a significant.
