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    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.
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    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.
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    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.
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    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 Anthony
    Climate 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.
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    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 Anthony
    Adapting 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.