Innovative Research Award: Riad Hossain

Riad Hossain is a machine learning researcher affiliated with East Delta University, Bangladesh. The supplied Scopus record lists 21 documents, 14 citations, and an h-index of 2. His publication record includes research on explainable machine learning for Parkinson’s disease detection, Bengali speech analysis, and WiFi-based occupancy detection using CSI today.

Abstract

Riad Hossain is identified in the supplied profile as a Machine Learning researcher associated with East Delta University, Bangladesh. His listed Scopus record contains 21 documents, 14 citations, and an h-index of 2. His research outputs include explainable machine learning for Parkinson’s disease detection from Bengali conversational speech, machine learning analysis of Bengali voice recordings, and CSI-based people counting in WiFi networks. These studies apply feature extraction, feature selection, classification, deep learning, and explainability across healthcare and wireless sensing applications. The publication record provides documented evidence of interdisciplinary research activity relevant to contemporary computer science and machine learning research domains. [1] [2] [3]

Keywords

• Machine Learning
• Explainable Artificial Intelligence
• Parkinson’s Disease Detection
• Bengali Speech Analysis
• Voice-Based Machine Learning
• WiFi Sensing
• Channel State Information
• Deep Learning

Introduction

Hossain’s documented research is situated within machine learning and its application to practical computational problems. The available publications address Bengali speech-based health analysis and WiFi sensing, demonstrating applications of computational methods to biomedical and networking contexts. These studies illustrate how machine learning techniques can process complex speech and wireless signal data. [1] [2] [3]

Research Profile

Hossain’s research profile centers on machine learning applications involving speech, healthcare analytics, and wireless sensing. His listed subject area is Machine Learning, while his publications demonstrate interdisciplinary work connecting artificial intelligence with biomedical signal analysis and computer networking. The record combines methodological development, feature engineering, classification, and data-driven evaluation domains. [1] [2] [3]

Research Contributions

The reported contributions include development of BenSParX, an explainable machine learning framework using Bengali conversational speech for Parkinson’s disease detection, and machine learning approaches for Bengali voice recordings. His work also addresses WiFi channel-state-information sensing for people counting, applying CNN and LSTM architectures to sequential wireless data environments. [1] [2] [3]

Publications

The supplied publication set comprises three research works. BenSParX presents a Bengali conversational speech framework for Parkinson’s disease detection; the ICCIT paper studies Parkinson’s detection from Bengali voice recordings; and the COMPAS paper investigates CSI-based people counting. Together, these publications represent applications of machine learning across healthcare and wireless sensing. [1] [2] [3]

Research Impact

The publications indicate research activity across two application domains: machine-learning-assisted health assessment and privacy-oriented wireless occupancy sensing. The reported studies use feature selection, acoustic measurements, classification algorithms, explainability, and deep learning methods. Their documented results provide research outputs that can support further investigation into Bengali speech analytics and WiFi sensing. [1] [2] [3]

Award Suitability

For the Global CSE Awards context, the supplied profile documents a machine-learning-focused research record, identifiable Scopus metrics, and publications with DOI-linked scholarly records. The evidence includes work in explainable artificial intelligence, speech-based disease detection, and wireless sensing. Award assessment should additionally follow the event’s published eligibility, nomination, and evaluation criteria. [1] [2] [3]

Conclusion

Riad Hossain’s supplied academic profile presents research activity in machine learning with publications spanning Bengali speech analysis, Parkinson’s disease detection, explainable models, and WiFi-based occupancy detection. The documented Scopus metrics and cited scholarly outputs provide identifiable evidence of research activity. The record can be reviewed alongside the award’s official criteria. [1] [2] [3]

External Links

• Scopus Author Profile:
  Scopus Author ID 59521054900
• Award Website:
  https://cseawards.com/

References

1. 1. Hossain, R., Kabir, M. A., Mowla, A. I. G., Roy, A. C., & Ghosh, R. K. (2026). BenSParX: A robust explainable machine learning framework for Parkinson’s disease detection from Bengali conversational speech. Artificial Intelligence in Medicine, 182, 103538.DOI:
      https://www.sciencedirect.com/science/article/pii/S0933365726001909
   2. Mowla, A. I. G., Hossain, R., & Asaduzzaman. (2024). CSI-based people counting in WiFi networks: Leveraging occupancy detection. In 2024 IEEE International Conference on Computing, Applications and Systems (COMPAS).DOI:
      https://ieeexplore.ieee.org/abstract/document/10796139
   3. Hossain, R., Roy, A. C., & Mowla, A. I. G. (2024). Parkinson’s disease detection from Bengali voice recordings using machine learning approach. In 2024 27th International Conference on Computer and Information Technology (ICCIT).DOI:
      https://ieeexplore.ieee.org/abstract/document/11021761