A M Arefin Khaled Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
unknown
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
A M Arefin Khaled's research investigates the application of machine learning and advanced computational techniques to address complex problems across various domains. His work includes developing frameworks for accurate disease classification, such as Parkinson's disease, by integrating hybrid convolutional and unified graph neural networks. Khaled also explores smart investment strategies in sustainable energy, utilizing machine learning to optimize decision-making processes. Additionally, his research addresses cellular network efficiency, focusing on detecting sleeping cells through methods like one-class support vector machines and deep autoencoders.
With a recent publication in 2025, Khaled has authored four publications and garnered 70 citations, holding an h-index of 2. His recent activity indicates ongoing contributions to the field.
Metrics
- h-index: 2
- Publications: 4
- Citations: 70
Selected Publications
-
Smart Investment Strategies in Sustainable Energy via Machine Learning (2025)
-
A Comparative Framework Integrating Hybrid Convolutional and Unified Graph Neural Networks for Accurate Parkinson’s Disease Classification (2024)
Collaboration Network
Top Collaborators
- A Comparative Framework Integrating Hybrid Convolutional and Unified Graph Neural Networks for Accurate Parkinson’s Disease Classification
- Smart Investment Strategies in Sustainable Energy via Machine Learning
- Detecting Sleeping Cells in Cellular Networks Based on One-Class Support Vector Machines Algorithm and Deep Autoencoders
- Detecting Sleeping Cells in Cellular Networks Based on One-Class Support Vector Machines Algorithm and Deep Autoencoders
- Detecting Sleeping Cells in Cellular Networks Based on One-Class Support Vector Machines Algorithm and Deep Autoencoders
- Detecting Sleeping Cells in Cellular Networks Based on One-Class Support Vector Machines Algorithm and Deep Autoencoders
- Detecting Sleeping Cells in Cellular Networks Based on One-Class Support Vector Machines Algorithm and Deep Autoencoders
- Detecting Sleeping Cells in Cellular Networks Based on One-Class Support Vector Machines Algorithm and Deep Autoencoders
- A Comparative Framework Integrating Hybrid Convolutional and Unified Graph Neural Networks for Accurate Parkinson’s Disease Classification
- A Comparative Framework Integrating Hybrid Convolutional and Unified Graph Neural Networks for Accurate Parkinson’s Disease Classification
- Smart Investment Strategies in Sustainable Energy via Machine Learning
- Smart Investment Strategies in Sustainable Energy via Machine Learning
- Smart Investment Strategies in Sustainable Energy via Machine Learning
- Smart Investment Strategies in Sustainable Energy via Machine Learning
Similar Researchers
Based on overlapping research topics