Riad Hossain | Machine Learning | Innovative Research Award

Published on by Global CSE Awards

Innovative Research Award: Riad Hossain

Riad Hossain — East Delta University, Bangladesh

Riad Hossain
Name Riad Hossain
Affiliation East Delta University
Country Bangladesh
Scopus ID 59521054900
Documents 21
Citations 14
h-index 2
Subject Area Machine Learning
Event Global CSE Awards

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]

 

References

    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

Nawazish Alvi | Machine Learning | Innovative Research Award

Published on by Global CSE Awards

Innovative Research Award

                    Nawazish Alvi
Affiliation Beijing University of Posts and Telecommunications
Country Pakistan
Google Scholar ID IdC_it0AAAAJ
Documents 1
Citations 1
Subject Area Machine Learning
Event Global CSE Awards
ORCID 0009-0008-2396-8811

Nawazish Alvi

Beijing University of Posts and Telecommunications

The Innovative Research Award recognizes researchers who demonstrate scholarly commitment through emerging scientific contributions and academic engagement. Nawazish Alvi’s research activities in Machine Learning reflect an interest in advancing intelligent computational methods while contributing to the broader objectives of modern computer science research.[1]

Abstract

This academic profile summarizes the scholarly activities of Nawazish Alvi within the domain of Machine Learning. The article highlights research interests, publication record, research influence, and the relevance of these achievements to the Innovative Research Award presented through the Global CSE Awards platform.[1][2]

Keywords

Machine Learning, Artificial Intelligence, Data Science, Academic Research, Scientific Publications, Citation Analysis, Research Recognition, Global CSE Awards, Innovative Research Award, Scholarly Impact.[2]

Introduction

Machine Learning has become a significant area of modern computing, enabling intelligent systems to analyze data and support decision making. Academic researchers contribute to this field by developing algorithms, validating models, and sharing findings through scholarly publications that encourage scientific collaboration and innovation.[1][3]

Research Profile

Nawazish Alvi is associated with Beijing University of Posts and Telecommunications and has developed an academic profile centered on Machine Learning. The available scholarly metrics indicate active participation in research dissemination, reflecting an emerging contribution to computational intelligence and data-driven technologies.[1][2]

Research Contributions

The research activities associated with this profile demonstrate engagement with Machine Learning methodologies and analytical approaches. Such contributions support the advancement of intelligent computing by expanding understanding, encouraging reproducible research practices, and providing a foundation for future scientific investigations.[2][3]

Publications

The documented publication record currently includes one scholarly work indexed through the researcher’s academic profile. Publications serve as measurable evidence of scientific communication, enabling peer evaluation, knowledge dissemination, and future citation within the global research community.[1][4]

Research Impact

Citation metrics provide an initial indication of scholarly visibility and engagement. Although the available citation count remains modest, it reflects interaction with the academic community and establishes a foundation for future influence through continued publication and collaborative research activities.[1][2]

Award Suitability

The Innovative Research Award acknowledges researchers demonstrating promising academic engagement and dedication to scientific advancement. Based on the available research profile, publication activity, and focus on Machine Learning, this academic record aligns with the objectives of recognizing emerging scholarly excellence.[1]

Conclusion

Nawazish Alvi’s academic profile represents a developing contribution to Machine Learning research through scholarly publication and scientific participation. Continued research activity, collaboration, and dissemination of knowledge are expected to strengthen future academic impact and support sustained professional recognition.[1]

References

  1. Google Scholar. (n.d.). Scholar profile: Nawazish Alvi.
    https://scholar.google.com/citations?user=IdC_it0AAAAJ&hl=en
  2. ORCID. (n.d.). Researcher identifier profile.
    https://orcid.org/0009-0008-2396-8811
  3. Alvi, N. M., Alvi, W. M., Zhou, X., Li, J., & Wei, Y. (2026). Constrained soft actor–critic for joint computation offloading and resource allocation in UAV-assisted edge computing. Sensors, 26(4), 1149.
    https://www.mdpi.com/1424-8220/26/4/1149
  4. Global CSE Awards. (n.d.). Innovative Research Award Information.
    https://cseawards.com/

Yuan Xiaolin | Machine Learning | Editorial Board Member

Published on by Global CSE Awards

Dr. Yuan Xiaolin | Machine Learning | Editorial Board Member

Professor | Hefei Institute of Physical Sciences, Chinese Academy of Sciences | China

Xiao Lin Yuan is an Associate Professor at the Institute of Plasma Physics, Chinese Academy of Sciences, and an expert in fusion engineering systems, with particular specialization in vacuum pumping, fueling systems, and intelligent diagnostics for fusion devices. He earned a doctoral degree in Nuclear Science and Engineering, following comprehensive academic training that laid a strong foundation in plasma physics and large-scale scientific instrumentation. His professional experience includes long-term research and technical roles at a national fusion research institute, where he has contributed to the design, integration, and optimization of critical subsystems for advanced tokamak facilities, as well as participation in nationally and internationally funded collaborative projects. His research focuses on vacuum system design, leak detection technologies, molecular pump fault diagnosis, and the application of artificial intelligence methods such as support vector machines and deep learning models to enhance reliability and predictive maintenance in fusion devices. He has published extensively in leading peer-reviewed journals and international conference proceedings in the fields of fusion engineering, nuclear science, and vacuum technology, demonstrating both methodological rigor and practical impact. Through his sustained research output, project involvement, and academic leadership, he has earned professional recognition within the fusion research community and actively contributes to the advancement of intelligent control and diagnostic technologies for next-generation fusion systems.

Profile : ORCID

Featured Publications

Yuan, X.-L., Chen, Y., Hu, J.-S., et al. (2016). Development and implementation of flowing liquid lithium limiter control system for EAST. Fusion Engineering and Design, 112, 332–337.

Yuan, X.-L., Chen, Y., Hu, J.-S., et al. (2018). 10 Hz pellet injection control system integration for EAST. Fusion Engineering and Design, 126, 130–138.

Yuan, X.-L., Chen, Y., et al. (2018). Development and implementation of supersonic molecular beam injection for EAST tokamak. Fusion Engineering and Design, 134, 62–67.

Yuan, X.-L., Chen, Y., et al. (2023). A support vector machine framework for fault detection in molecular pump. Journal of Nuclear Science and Technology, 60, 72–82.

Zhou, Y., Jiang, M., Yuan, X.-L., et al. (2024). Fault prediction of molecular pump based on DE-Bi-LSTM. Fusion Science and Technology, 80, 1001–1011.