Hind Hallabia | Data Science | Excellence in Research Award

Published on by Math Scientist

Dr. Hind Hallabia | Data Science | Excellence in Research Award

Teaching and Researcher Assistant | University Institute of Technology of Saint Etienne Jean Monnet University | France

Dr. Hind Hallabia, affiliated with the University Institute of Technology of Saint-Étienne at Jean Monnet University, France, specializes in remote sensing, pansharpening, and advanced image processing techniques for satellite data analysis. Her research focuses on developing graph-based segmentation methods, superpixel modeling, and data fusion frameworks to enhance multispectral and panchromatic imagery. Dr. Hallabia investigates latent low-rank decomposition, detail-injection mechanisms, and texture-based segmentation models to improve image quality, spatial–spectral fidelity, and analytic accuracy in Earth observation applications. Her work contributes to advances in hazardous-area monitoring, environmental assessment, and optical remote sensing technologies through methodological innovation, algorithm design, and computational enhancements.

Profiles: Scopus | Orcid

Featured Publications

  • Hallabia, H. (2025). A graph-based superpixel segmentation approach applied to pansharpening. Sensors, 25(16), Article 4992. https://doi.org/10.3390/s25164992
    Year: 2025

  • Hallabia, H. (2025). Land and aquatic spectral signatures analysis over a spatio-temporal hazardous area acquired by Worldview satellite. Annual International Congress on Electrical Engineering 2025.
    Year: 2025

  • Hallabia, H. (2025). Advanced trends in optical remotely sensed data fusion: Pansharpening case study. Iris Journal of Astronomy and Satellite Communications.
    Year: 2025

  • Hallabia, H., Hamam, H., & Ben Hamida, A. (2023). A novel detail injection framework using latent low-rank decomposition for multispectral pan-sharpening. Multimedia Tools and Applications, 82, 11971–11995. https://doi.org/10.1007/s11042-022-12770-x
    Year: 2023

  • Hallabia, H., & Hamam, H. (2021). A graph-based textural superpixel segmentation method for pansharpening application. Proceedings of IGARSS 2021. https://doi.org/10.1109/igarss47720.2021.9553304
    Year: 2021

Senthilkumar R | Data Science | Editorial Board Member

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Dr. Senthilkumar R | Data Science | Editorial Board Member

Assistant Professor / CSE | Hindustan Institute of Technology | India

Dr. Senthilkumar R is a researcher and academic specializing in Internet of Things (IoT), artificial intelligence, fog and edge computing, machine learning, and big data analytics. With over 12 years of academic experience, he has contributed to innovative system designs including AI-enabled monitoring systems, IoT-based automation, embedded intelligence, and deep learning–driven applications. His work spans smart environments, air quality monitoring, emergency alert systems, and AI-powered automation solutions. He has published in reputed international journals such as Elsevier and IOS Press, with research touching multiple domains including communication systems, intelligent sensing, and emotion detection. His achievements include industry-recognized innovation projects, government honors such as the Chief Minister’s Award of Excellence, and contributions to books and edited volumes in artificial intelligence and psychological computing.

Profiles: Scopus | Orcid 

Featured Publications

1. Senthilkumar. (2024). Performance analysis of multiple-input multiple-output orthogonal frequency division multiplexing system using arithmetic optimization algorithm. Computer Standards & Interfaces, Article 103934. Citation count: 0. Year: 2024.

2. Senthilkumar. (2024). Machine and deep learning techniques for emotion detection. In Advances in Psychology, Mental Health, and Behavioral Studies (Edited book). IGI Global. Citation count: 0. Year: 2024.

3. Senthilkumar, R., Venkatakrishnan, P., & Balaji, N. (2022). IoT-based artificial intelligence indoor air quality monitoring system using enabled RNN algorithm techniques. Journal of Intelligent & Fuzzy Systems. Citation count: 0. Year: 2022.

4. Senthilkumar, R., Venkatakrishnan, P., & Balaji, N. (2020). Intelligent-based novel embedded system: IoT-enabled air pollution monitoring system. Microprocessors and Microsystems, 103172. Citation count: 0. Year: 2020.