Akeem Kareem | Mechanical Engineering | Best Researcher Award

Mr. Akeem Kareem, Kumoh National Institute of Technology, South Korea.

Akeem Kareem, MNSE, MIAENG, is a dynamic researcher and engineer specializing in R&D on Digital Twin, Metaverse (VR & AR), and Prognostics and Health Management (PHM). Currently pursuing a Doctor of Science in Mechanical Engineering at Kumoh National Institute of Technology, South Korea, he brings extensive expertise in data-driven AI processes and 3D animation. With professional stints in mechanical engineering, facilities management, and data science, Akeem combines technical precision with a passion for innovation. His multilingual fluency in Yoruba and English, certifications in Machine Learning, and published works highlight his commitment to leveraging technology for groundbreaking solutions.

Publication profile

Orcid

Education

Doctor of Science, Mechanical Engineering – Kumoh National Institute of Technology (2023–2026)
Masters, Mechanical Engineering – Kumoh National Institute of Technology (2021–2023)
BEng, Mechanical Engineering (Thermofluid Option) – University of Ilorin, Nigeria (2013–2017)
OND, Mechanical Engineering – Kwara State Polytechnic, Nigeria (2009–2011)

Experience

Research and Development Specialist – FD&P (2023–Present)
Graduate Research Assistant – Kumoh National Institute of Technology (2021–2023)
Lead Project Manager (Facilities) – James Cubitt Facilities Managers (2018–2021)
Graduate Mechanical Engineer – Dangote Group (2012–2013)
Automotive Technician – CFAO CICA Nig Limited (2011–2012)

Suitability For The Award

Mr. Akeem Kareem is a strong candidate for the Best Researcher Award, showcasing a multidisciplinary expertise in semiconductor engineering, data-driven processes, and mechanical systems. His innovative contributions span Digital Twins, Metaverse technologies, reinforcement learning, and predictive health monitoring (PHM). With an impressive academic and professional track record, including publications in advanced areas of fault diagnostics and computational analysis, Mr. Kareem demonstrates exceptional research acumen and industry impact.

Professional Development

Akeem Kareem is dedicated to professional growth, showcasing expertise in machine learning, electromechanical engineering, and data science. With certifications in Machine Learning with Python, Introduction to Data Science, and Electromechanical Engineering, he bridges engineering solutions with data-driven insights. Akeem actively contributes to publications in AI-based diagnostics, leveraging supervised learning and FEA assessments. His proficiency in developing robust AI frameworks and commitment to continuous learning position him as a catalyst for innovative solutions in engineering, digital twin technology, and AR/VR applications.

Research Focus

Akeem Kareem’s research emphasizes advanced engineering systems integrating Digital Twin, Metaverse technologies (VR & AR), and Prognostics and Health Management (PHM). His expertise lies in developing data-driven fault diagnostics, reliability enhancement for electronic components, and AI-powered frameworks for autonomous systems. With a focus on machine learning and reinforcement deep learning, he seeks to revolutionize industries through predictive analytics and system optimization. By combining academic rigor with real-world applications, Akeem is driving innovation at the intersection of engineering and digital transformation.

Awards and Honors

Certified Graduate Mechanical Engineer – Dangote Group
Recognized for contributions to facilities management – James Cubitt Facilities Managers
Multiple publications in AI and diagnostics frameworks
Certifications in Machine Learning, Electromechanical Engineering, and Data Science
Research contributions to enhancing AI reliability in harsh environments

Publications

“A Comparative Data Augmentation-Assisted Diagnostic Framework for Industrial Centrifugal Pumps”
“A Comparative Study of Deep-Learning Autoencoders (DLAEs) for Vibration Anomaly Detection in Manufacturing Equipment”
“Enhancing Transformer Core Fault Diagnosis and Classification through Hilbert Transform Analysis of Electric Current Signals”
“Transformer Core Fault Diagnosis via Current Signal Analysis with Pearson Correlation Feature Selection”
“Hyperelastic and Stacked Ensemble-Driven Predictive Modeling of PEMFC Gaskets Under Thermal and Chemical Aging”
“Advanced Data Augmentation Techniques for Enhanced Fault Diagnosis in Industrial Centrifugal Pumps”
“ANN-based Reliability Enhancement of SMPS Aluminium Electrolytic Capacitors in Cold Environments” (2023)
“ANN-Based Reliability Enhancement of SMPS Aluminum Electrolytic Capacitors in Cold Environments” (2023)