21 citations
Accumulated across the listed Scholar publications.
Applied research in machine learning, NLP and AI systems.
My publications examine deep learning, natural language processing, bioinformatics, healthcare AI, LLM-driven classification, and automation-first ML development.
Scholar Snapshot
Current public Google Scholar metrics.
h-index 3
Reported directly by the public Scholar profile.
Research areas
Machine Learning, Deep Learning, and Natural Language Processing.
Publications
Research works indexed on Google Scholar.
Comparison of Deep Learning Approaches for DNA-Binding Protein Classification Using CNN and Hybrid Models
Compared CNN and hybrid architectures for biological sequence classification · World Conference on Artificial Intelligence: Advances and Applications · 2023 · 9 citations
Transformer-Based Models for Named Entity Recognition: A Comparative Study
Benchmarked transformer models for NER performance · 2023 14th International Conference on Computing Communication and Networking Technologies · 2023 · 6 citations
Transformer-based Models for Language Identification: A Comparative Study
Evaluated transformer architectures for language identification · 2023 International Conference on System, Computation, Automation and Networking · 2023 · 4 citations
Empirical Evaluation of Large Language Models in Resume Classification
Assessed LLM performance for resume-classification workflows · 2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies · 2024 · 2 citations
Enhancing Breast Cancer Detection in Mammography Images: A Comprehensive Analysis
Analyzed ML and deep-learning strategies for mammography-based detection · Proceedings of International Conference on Recent Innovations in Computing · 2024
Revolutionizing Talent Acquisition: A Comparative Study of Large Language Models in Resume Classification
Investigated LLM-based talent acquisition and resume-screening behavior · 2024 5th International Conference on Innovative Trends in Information Technology · 2024
Automating Machine Learning Model Development: An OperationalML Approach with PyCARET and Streamlit
Presented an automation-first approach for ML model development · 2023 Innovations in Power and Advanced Computing Technologies · 2024
Research Themes
Recurring technical themes across the publication record.
LLM and NLP Evaluation
Benchmarked NER, language identification, resume classification, and transformer-model behavior across applied NLP tasks.
NLPLLMsTransformers
Applied ML Systems
Designed OperationalML to simplify model development through PyCaret, Streamlit, and workflow automation.
MLOpsPyCaretStreamlit
AI for Science and Healthcare
Applied deep learning to DNA-binding protein classification and mammography-based breast cancer detection.
BioinformaticsHealthcare AIDeep Learning