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An Analysis of Social Networking for E-learning in Institutions of Higher Learning using Perceived Ease of use and Perceived Usefulness
(Phcog.Net, 2022) Ajibade, Samuel-Soma M.; Adhikari, Nirmal; Ngo-Hoang, Dai-Long
Higher education students and faculty use Facebook and Twitter. Researchers have also looked at social networking platforms in higher education. Social media has facilitated student-professor communication, collaboration, and engagement. To embrace students and teachers who utilize technology to learn and teach, it must be determined what influences their readiness to do so. This report tests the adoption of social networking media for e-learning in Nigerian utilizing the Technology Acceptance Model (TAM), which emphasizes perceived ease of use, perceived usefulness, and behavioural intention to utilize new technologies. Surveys were utilized for quantitative research. This study polled teachers and students from 4 Nigerian schools. Structural Equation Modeling was used to anticipate the model’s recommended factors (SEM). The study indicated that students’ and teachers’ behavioral intentions to use social media for e-learning in Nigerian universities are influenced by perceived ease of use and perceived usefulness.
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 Anthony
The 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.
Analysis of ımproved evolutionary algorithms using students' datasets
(IEEE, 2022) Ajibade, Samuel-Soma M.; Ayaz, Muhammad; Ngo-Hoang, Dai-Long; Tabuena, Almighty C.; Rabbi, Fazle; Tilaye, Getahun Fikadu; Bassey, Mbiatke Anthony
Evolutionary Algorithms (EAs) are powerful heuristic search approaches which relies on Darwinian evolution that capture global solutions to complex optimization problems which has powerful features of reliability and versatility. (EAs) such as 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, chaotic map and dynamic-weight Particle Swarm Optimization (CHDPSOA) are combined with PSO to enhance the search strategy through adjusting the inertia weight of PSO and changing the position update formula in the (CHDPSOA), resulting in efficient balancing for local and global PSO feature selection processes. The performance of CHDPSOA was compared to that of three metaheuristic techniques: Differential Evolution (DE) and the original PSO, using eight numerical functions. The validation of this technique is carried out on four different datasets. The results show that the CHDPSOA is a good feature selection technique that balances the exploration and exploitation search processes to produce good results. The proposed CHDPSOA method performed well in correctly categorizing features using the KNN Classifier for all four datasets.
Application of Artificial Intelligence in Healthcare Systems: A Scientometric Analysis
(Institute of Electrical and Electronics Engineers Inc., 2024) Ajibade, Samuel-Soma M.; Lee, Angela Siew Hoong; Jasser, Muhammed Basheer; Akinola, Tomiwa Faith
This bibliometric study examines the worldwide progression of machine learning applications in medical and healthcare research between 2010 and 2023. A large dataset of published articles pertaining to machine learning applications in the medical and healthcare sectors is mined for useful information in this study. The data extraction procedure involves retrieving pertinent information from primary sources such as journals, books, and conference proceedings. Subsequently, the retrieved data is subjected to analysis to discern the patterns and tendencies in the use of machine learning in medical and healthcare research. A total of 1,220 publications were found in the Scopus database over the past 14 years. In addition, the study demonstrated that most AIHS research has concentrated on artificial intelligence applications to address a wide range of problems, including patient data security and chronic medical difficulties (such as cardiovascular disorders). Policymakers, healthcare practitioners, and researchers around the world may find this study's conclusions helpful. Emerging ethical concerns, integration, and real-world uses in smart healthcare systems, and the Internet of Things (IoT), could be the focus of future research. © 2024 IEEE.
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 Oluwatosin
The 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.
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.
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 Oluwatosin
Solar 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.
Bibliometric survey on particle swarm optimization algorithms (2001-2021)
(HINDAWI LTD, 2022) Ajibade, Samuel-Soma M.; Ojeniyi, Adegoke
Particle swarm optimization algorithms (PSOA) is a metaheuristic algorithm used to optimize computational problems using candidate solutions or particles based on selected quality measures. Despite the extensive research published, studies that critically examine its recent scientific developments and research impact are lacking. Therefore, the publication trends and research landscape on PSOA research were examined. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and bibliometric analysis techniques were applied to identify and analyze the published documents indexed in Scopus from 2001 to 2021. The published documents on PSOA increased from 8 to 1,717 (21,362.50%) due to the growing applications of PSOA in solving computational problems. "Conference papers" is the most common document type, whereas the most prolific researcher on PSOA is Andries P. Engelbrecht (South Africa). The most active affiliation (Ministry of Education) and funding organization (National Natural Science Foundation) are based in China. The research landscape on PSOA revealed high levels of publications, citations, and collaborations among the top authors, institutions, and countries worldwide. Keywords co-occurrence analysis revealed that "particle swarm optimization (PSO)" occurred more frequently than others. The findings of the study could provide researchers and policymakers with insights into the prospects and challenges of PSOA research relative to similar algorithms in the literature.
Boosting enabled efficient machine learning technique for accurate prediction of crop yield towards precision agriculture
(Springer Nature, 2024) Nagesh, O. Sri; Budaraju, Raja Rao; Kulkarni, Shriram S.; Vinay M.; Ajibade, Samuel-Soma M.; Chopra, Meenu; Jawarneh, Malik; Kaliyaperumal, Karthikeyan
Due to the limited availability of natural resources, it is essential that agricultural productivity keep pace with population growth. Despite unfavorable weather circumstances, this project's major objective is to boost production. As a consequence of technological advancements in agriculture, precision farming as a way for enhancing crop yields is gaining appeal and becoming more prevalent. When it comes to predicting future data, machine learning employs a number of methods, including the creation of models and the acquisition of prediction rules based on past data. In this manuscript, we examine various techniques to machine learning, as well as an automated agricultural yield projection model based on selecting the most relevant features. For the purpose of selecting features, the Grey Level Co-occurrence Matrix method is utilised. For classification, we make use of the AdaBoost Decision Tree, Artificial Neural Network (ANN), and K-Nearest Neighbour (KNN) algorithms. The data set that was used in this study is simply a compilation of information about a variety of topics, including yield, pesticide use, rainfall, and average temperature. This data collection consists of 33 characteristics or qualities in total. The crops soya beans, maze, potato, rice, paddy, wheat, and sorghum are included in this data collection. This data collection was made possible through the collaboration of the Food and Agriculture Organisation (FAO) and the World Data Bank, both of which make their data available to the public. The AdaBoost decision tree has achieved the highest level of accuracy possible when used to anticipate agricultural yield. Both the accuracy rate and the recall rate are quite high at 99 percent.
Computational model of recommender system intervention
(Hindawi Limited, 2022) Ojeniyi, Adegoke; Ajibade, Samuel-Soma M.; Obafunmiso, Christiana Kehinde; Adegbite-Badmus, Tawakalit
A recommender system is an information selection system that offers preferences to users and enhances their decision-making. This system is commonly implemented in human-computer-interaction (HCI) intervention because of its information filtering and personalization. However, its success rate in decision-making intervention is considered low and the rationale for this is associated with users' psychological reactance which is causing unsuccessful recommender system interventions. This paper employs a computational model to depict factors that lead to recommender system rejection by users and how these factors can be enhanced to achieve successful recommender system interventions. The study made use of design science research methodology by executing a computational analysis based on an agent-based simulation approach for the model development and implementation. A total of sixteen model concepts were identified and formalized which were implemented in a Matlab environment using three major case conditions as suggested in previous studies. The result of the study provides an explicit comprehension on interplaying of recommender system that generate psychological reactance which is of great importance to recommender system developers and designers to depict how successful recommender system interventions can be achieved without users experiencing reactance and rejection on the system.
A computer-aided feature-based encryption model with concealed access structure for medical Internet of Things
(Elsevier Inc., 2023) Vaidya, Sumit; Suri, Ashish; Batla, Vishnu; Keshta, Ismail; Ajibade, Samuel-Soma M.; Safarov, Giyosiddin
One of the Internet of Things (IoT) security issues is the secure sharing and granular management of data access. This study recommends a feature-based encryption scheme with a hidden access structure for medical IoT data security. While establishing fine-grained access control of ciphertext data, the system can guarantee clinical client data privacy. First, it is recommended to convert identity-based encryption (IBE) into a feature-based encryption model (FBEM) using a universal conversion technique that supports multi-valued attributes and gates. IBE characteristics could be inherited by the converted FBEM. The conversion method is then used to change the receiver anonymous IBE scheme into the FBEM scheme with concealed access structure. The FBEM model is then used to construct the IoT scenario for the smart medical application. Theoretical analysis and experimental findings reveal that the suggested system provides advantages over prominent systems regarding computing efficiency, storage load, and security when the access structure is disguised.
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.
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 Oluwatosin
Data 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.
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 Oluwatosin
Evaluating 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.
Evolution of machine learning applications in medical and healthcare analytics research: A bibliometric analysis
(Elsevier B.V., 2024) Ajibade, Samuel-Soma M.; Alhassan, Gloria Nnadwa; Zaidi, Abdelhamid; Oki, Olukayode Ayodele; Awotunde, Joseph Bamidele; Ogbuju, Emeka; Akintoye, Kayode A.
This bibliometric research explores the global evolution of machine learning applications in medical and healthcare research for 3 decades (1994 to 2023). The study applies data mining techniques to a comprehensive dataset of published articles related to machine learning applications in the medical and healthcare sectors. The data extraction process includes the retrieval of relevant information from the source sources such as journals, books, and conference proceedings. An analysis of the extracted data is then conducted to identify the trends in the machine learning applications in medical and healthcare research. The Results revealed the publications published and indexed in the Scopus and PubMed database over the last 30 years. Bibliometric Analysis revealed that funding played a more significant role in publication productivity compared to collaboration (co-authorships), particularly at the country level. Hotspots analysis revealed three core research themes on MLHC research hence demonstrating the importance of machine learning applications to medical and healthcare research. Further, the study showed that the MLHC research landscape has largely focused on ML applications to tackle various issues ranging from chronic medical challenges (e.g., cardiological diseases) to patient data security. The findings of this research may be useful to policy makers and practitioners in the medical and healthcare sectors and to global research endeavours in the field. Future studies could include addressing issues such as growing ethical considerations, integration, and practical applications in wearable technology, IoT, and smart healthcare systems. © 2024 The Author(s)
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.
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 Anthony
Firefly 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.
Improvement of Population Diversity of Meta-heuristics Algorithm Using Chaotic Map
(Springer Science and Business Media Deutschland GmbH, 2022) Ajibade, Samuel-Soma M.; Ogunbolu, Mary O.; Chweya, Ruth; Fadipe, Samuel
Particle swarm optimization (PSO) is a global optimization and nature-inspired algorithm known for its good quality and easily applied in various real-world optimization challenges. Nevertheless, PSO has some weaknesses such as slow convergence, converging prematurely and simply gets stuck at local optima. This study aims to solve the problem of deprived population diversity in the search process of PSO which causes premature convergence. Therefore, in this research, a method is brought to PSO to keep away from early stagnation which explains premature convergence. The aim of this research is to propose a chaotic dynamic weight particle swarm optimization (CHPSO) wherein a chaotic logistic map is utilized to enhance the populace diversity within the search technique of PSO with the aid of editing the inertia weight of PSO in an effort to avoid premature convergence. This study additionally investigates the overall performance and feasibility of the proposed CHPSO as a function selection set of rules for fixing problems of optimization. 8 benchmark functions had been used to assess the overall performance and seek accuracy of the proposed (CHPSO) algorithms and as compared with a few other meta-heuristics optimization set of rules. The outcomes of the experiments show that the CHPSO achieves correct consequences in fixing an optimization and has established to be a dependable and green metaheuristics algorithm for selection of features.
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 Oluwatosin
This 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.
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, Fazle
The 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.

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