Muhammed Mohaimin Sadiq Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Ph.D Candidate
grad_student
Research Areas
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Biography and Research Information
OverviewAI-generated summary
Muhammed Mohaimin Sadiq's research focuses on the application of advanced computational techniques for biomedical data analysis, specifically in the detection of neurological disorders. His work investigates novel methods for classifying electroencephalogram (EEG) data, aiming to improve the accuracy and efficiency of diagnosing conditions such as epilepsy. Sadiq has published research on utilizing Hellinger distance as a feature selection metric for EEG-based seizure detection. His scholarly contributions include a h-index of 2, with 5 publications and 33 citations. He collaborates with other researchers at the University of Arkansas at Little Rock, including Mariofanna Milanova, with whom he has shared one publication.
Metrics
- h-index: 2
- Publications: 5
- Citations: 35
Selected Publications
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Novel EEG feature selection based on hellinger distance for epileptic seizure detection (2025)
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Novel EEG Classification Based on Hellinger Distance for Seizure Epilepsy Detection (2024)
Collaboration Network
Top Collaborators
- Novel EEG Classification Based on Hellinger Distance for Seizure Epilepsy Detection
- Novel EEG feature selection based on hellinger distance for epileptic seizure detection
- Novel EEG Classification Based on Hellinger Distance for Seizure Epilepsy Detection
- Novel EEG feature selection based on hellinger distance for epileptic seizure detection
- Novel EEG Classification Based on Hellinger Distance for Seizure Epilepsy Detection
- Novel EEG feature selection based on hellinger distance for epileptic seizure detection
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