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All Journal Journal of Computer Science and Engineering (JCSE) Journal of Computing Theories and Applications International Journal of Research in STEM Education
Moses, Timothy
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Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Environmental Science Mathematics Physics

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A fault-tolerance model for Hadoop rack-aware resource management system Moses, Timothy; Abiodun, Oladunjoye John
Journal of Computer Science and Engineering (JCSE) Vol 4, No 1: February (2023)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jcse.v4i1.651

Abstract

The central resource manager of Hadoop Yet Another Resource Manager (YARN) has posed a major concern to big data analysis and exploration. The central arbiter is overwhelmed whenever there are resource requests by application masters and heartbeat communication from several name nodes in the Hadoop cluster; thereby, degrading the performance of the framework. An attempt to decentralize the resource manager's responsibilities by introducing a new layer in the cluster named the Rack Unit Resource Manager (RU_RM) layer increased cluster performance but introduced a fault-tolerance concern. This work, therefore, developed a fault-tolerant model to allow for efficient and effective data analysis in the Hadoop cluster. A pseudo-distributed computation was set up with the help of the YARN Scheduler Load Simulator (SLS) and WordCount operation performed with varying input sizes. Two fault scenarios were presented and the results obtained showed that with an increase in input size (workload), the running time of the developed fault-tolerant model though slightly higher than that of the existing model, is significantly negligible when compared to the computation bottleneck incurred anytime RU_RM fails. The developed model, therefore, has good performance in the presence of failure of a unit (RU_RM) in the cluster.
Big Data Indexing: Taxonomy, Performance Evaluation, Challenges and Research Opportunities Othman, Abubakar Usman; Moses, Timothy; Aisha, Umar Yahaya; Gital, Abdulsalam Ya’u; Souley, Boukari; Adeleke, Badmos Tajudeen
Journal of Computer Science and Engineering (JCSE) Vol 3, No 2: August (2022)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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Abstract

In order to efficiently retrieve information from highly huge and complicated datasets with dispersed storage in cloud computing, indexing methods are continually used on big data. Big data has grown quickly due to the accessibility of internet connection, mobile devices like smartphones and tablets, body-sensor devices, and cloud applications. Big data indexing has a variety of problems as a result of the expansion of big data, which is seen in the healthcare industry, manufacturing, sciences, commerce, social networks, and agriculture. Due to their high storage and processing requirements, current indexing approaches fall short of meeting the needs of large data in cloud computing. To fulfil the indexing requirements for large data, an effective index strategy is necessary. This paper presents the state-of-the-art indexing techniques for big data currently being proposed, identifies the problems these techniques and big data are currently facing, and outlines some future directions for research on big data indexing in cloud computing. It also compares the performance taxonomy of these techniques based on mean average precision and precision-recall rate.
A Study of Computer Literacy Among Stm Teachers in Colleges of Education in Nigeria Moses, Timothy; Yakubu, Sani
International Journal of Research in STEM Education Vol. 2 No. 1 (2020): May Issue
Publisher : Universitas Terbuka

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Abstract

Information and Communication Technology has changed instructional conveyance. Advancement recorded in information assembling, teaching, and learning collection has given teachers new devices to work with, subsequently the upheaval in the field of training. This study examined computer literacy among Science, Technology, and Mathematics (STM) instructors in Colleges of Education. Discoveries show that the degree of computer proficiency among STM instructors is low. The explanation behind this incorporates few available skillful instructors, insufficient ICT tools in schools, computer phobia, access to Personal Computers (PCs)/PC labs, and absence of ICT devices for STM training. Proposals were anyway made that will help improve the degree of proficiency among STM instructors.
A Machine Learning Based Approach to Course and Career Recommendation System: A Systematic Literature Review Iorzua, Joseph Tersoo; Moses, Timothy; Eke, Christopher Ifeanyi; Agushaka, Ovre Jeffery; Kwaghtyo, Dekera Kenneth; Godswill, Theophilus
Journal of Computing Theories and Applications Vol. 3 No. 1 (2025): JCTA 3(1) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.12603

Abstract

Learners are continually faced with choosing appropriate courses or making career choices due to increased educational opportunities. The emergence of machine learning-based course and career recommender systems has the potential to address this issue, offering personalized course recommendations tailored to individual learning pathways, preferences, and learning history. The optimization and feature engineering techniques and practical deployment environments have not been collectively examined in the previous research, despite the significant advancements in this area of research. Furthermore, previous research has rarely synthesized how these technical components help students choose appropriate courses and careers. This systematic review was carried out to investigate the current state of machine learning-based course and career recommender systems, focusing on key elements, such as primary data sources, feature engineering methods, algorithms, optimization techniques, evaluation metrics, and the environments where the existing course recommendation models are deployed. The PRISMA method for conducting a systematic review was used to choose studies that met the requirements for inclusion and exclusion. The study findings show significant reliance on interpretable and traditional machine learning algorithms, such as K-Nearest Neighbor and Random Forest, to develop recommender models. Feature engineering remains basic, as most studies rely on normalization, while optimization processes are often underreported. Also, evaluation metrics varied widely, impeding comparability, while most of the recommender models are deployed in an e-learning environment, leaving the traditional learning environment underrepresented. Furthermore, the study findings identified issues including data sparsity and diversity, data security and privacy, and changes in learner preferences that may have an impact on the performance of recommender systems while recommending further studies to make use of standardized optimization methods, and automated domain-informed feature engineering frameworks, benchmark and annotated datasets in developing models the gives priority to learners’ success and educational relevance.
Co-Authors Abiodun, Oladunjoye John Adeleke, Badmos Tajudeen Agushaka, Ovre Jeffery Aisha, Umar Yahaya Eke, Christopher Ifeanyi Gital, Abdulsalam Ya’u Godswill, Theophilus Iorzua, Joseph Tersoo Kwaghtyo, Dekera Kenneth Othman, Abubakar Usman Souley, Boukari Yakubu, Sani