Ming-Hsiang Su | Signal Processing | Best Researcher Award

Published on by Environmental Scientists

Prof. Ming-Hsiang Su | Signal Processing | Best Researcher Award

Prof. Ming-Hsiang Su | Soochow University | Taiwan

Prof. Ming-Hsiang Su is a prominent researcher and assistant professor specializing in the fields of deep learning, natural language processing, and speech signal processing, with a particular focus on spoken dialogue systems, emotion recognition, and personality trait perception. His work integrates advanced computational techniques with real-world applications, developing intelligent systems capable of understanding, interpreting, and generating human-like speech and dialogue. Prof. Ming-Hsiang Su has contributed to the advancement of speech emotion recognition by considering both verbal and nonverbal vocal cues, and has designed sophisticated models for empathetic dialogue generation, text-to-motion transformation, and mood disorder detection through audiovisual signals. He has published extensively in high-impact journals and conferences, addressing topics such as few-shot image segmentation, sound source separation, automatic ontology population, and speaker identification. His research also extends to applied systems, including automated crop disease detection, question-answering systems, and industrial defect detection using deep learning architectures. By combining theoretical insights with practical implementations, Prof. Ming-Hsiang Su work bridges the gap between computational intelligence and human-centered applications, enhancing machine understanding of complex speech, language, and affective behaviors. Through his interdisciplinary approach, he continues to advance innovative methods for human-computer interaction, intelligent dialogue systems, and multimodal data analysis, establishing a significant impact on both academic research and practical technological applications across various domains, with 791 citations by 684 documents, 83 documents, and an h-index of 15.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Huang, K. Y., Wu, C. H., Hong, Q. B., Su, M. H., & Chen, Y. H. (2019). Speech emotion recognition using deep neural network considering verbal and nonverbal speech sounds. ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech, and …, 138.

Su, M. H., Wu, C. H., Huang, K. Y., Hong, Q. B., & Wang, H. M. (2017). A chatbot using LSTM-based multi-layer embedding for elderly care. 2017 International Conference on Orange Technologies (ICOT), 70-74.

Hsu, J. H., Su, M. H., Wu, C. H., & Chen, Y. H. (2021). Speech emotion recognition considering nonverbal vocalization in affective conversations. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 29, 1675-1686.

Su, M. H., Wu, C. H., & Cheng, H. T. (2020). A two-stage transformer-based approach for variable-length abstractive summarization. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28, 2061-2072.

Su, M. H., Wu, C. H., Huang, K. Y., & Hong, Q. B. (2018). LSTM-based text emotion recognition using semantic and emotional word vectors. 2018 First Asian Conference on Affective Computing and Intelligent …, 78.

 

May El Barachi | Predictive Analytics and Machine Learning | Best Researcher Award

Published on by Environmental Scientists

Prof. May El Barachi | Predictive Analytics and Machine Learning | Best Researcher Award

Professor and Dean, University of Wollongong in Dubai, United Arab Emirates.

Prof. May El Barachi is a Canadian computer scientist and seasoned academic leader known for her transformative impact on education, research, and innovation. She is currently a Full Professor and Head of the School of Computer Science at the University of Wollongong in Dubai. Over her 15+ year career, she has secured over 5 million AED in research funding, published 110+ high-impact papers, and built a global network of collaborations across academia and industry. Her work bridges cutting-edge AI research with real-world applications in smart systems and sustainable development. A passionate advocate for diversity, inclusion, and lifelong learning, she holds a UAE Golden Visa and is fluent in English, Arabic, and French.

Profile

Scopus

Orcid

Google Scholar

🎓 Education

  • Ph.D. in Computer Science, Concordia University, Montréal, Canada (2004–2009)
    Specialization: Next Generation Networks, Service Engineering, Network Intelligence and Adaptation

  • M.A.Sc. in Electrical and Computer Engineering, Concordia University, Montréal, Canada (2002–2004)
    Specialization: Web Services, IP Telephony, Multimedia Communications

  • B.Sc. in Electronics and Communication Engineering, Arab Academy for Science, Technology & Maritime Transport, Alexandria, Egypt (1995–2000)
    Graduated Valedictorian with Honors; Specialization in Object Recognition, Remote Sensing, and Neural Networks

💼 Professional Experience

Prof. May El Barachi is a visionary academic leader and full professor of Computer Science with over 15 years of experience across the UAE, Canada, and Europe. She currently serves as the Head of the School of Computer Science at the University of Wollongong in Dubai (UOWD), where she has led transformational initiatives, including a 400% increase in enrollment, the development of innovative Master’s programs in AI and Cybersecurity, and the establishment of a widely recognized Executive Learning Program that has trained over 2,500 professionals.

Previously, she held the roles of Associate Dean of Research and Associate Professor at UOWD, where she restructured research clusters around Sustainable Development Goals, drove multi-million-dirham funding acquisition, and played a pivotal role in Ph.D. program development. At Zayed University, she served as Smart Lab Director and co-founder, pioneering research on smart cities and AI-driven systems, while also leading curriculum development and accreditation efforts. Her earlier experience includes postdoctoral research at the University of Quebec (ETS), and roles in industry-academic collaborations at Ericsson Canada and the Ambient Networks Project in Sweden, focusing on web services, context-aware networks, and next-generation telecom systems.

🔬 Research Interest

  • Artificial Intelligence and Machine Learning: including deep learning, computer vision, natural language processing (NLP), reinforcement learning, and ethical AI

  • Emerging Technologies & Applications: smart cities, smart healthcare systems, autonomous systems, and digital transformation

  • Next Generation Networks: context-aware networking, IoT integration, and cloud computing

  • Interdisciplinary Innovation: leveraging AI for societal challenges, particularly in sustainable development, cybersecurity, and educational technology

🏆Author Metrics

  • Publications: 110+ peer-reviewed papers

  • Google Scholar Profile: Google Scholar – Prof. May El Barachi

  • Research Funding Secured: 5.28+ million AED

  • Professional Training Delivered: 2,500+ industry professionals via executive education programs

  • Languages: English, Arabic, French

📚 Publications

1. Evaluating the Impact of COVID-19 on Multimodal Cargo Transport Performance: A Mixed-Method Study in the UAE Context

  • Authors: Rami Aljadiri, Balan Sundarakani, May El Barachi

  • Journal: Sustainability

  • Volume & Issue: 15(22)

  • Article Number: 15703

  • Publication Date: November 7, 2023

  • DOI: 10.3390/su152215703

  • Abstract: This study examines the challenges and opportunities of multimodal cargo transport in the UAE during the COVID-19 pandemic (2020–2022). Utilizing a mixed-method approach, the research involved qualitative interviews with five senior logistics executives and quantitative surveys with 120 participants. Findings indicate a significant relationship between geographical/geopolitical risks and increased shipping costs, emphasizing the need for secure and cost-effective multimodal solutions. The study offers insights for enhancing logistics performance in transit hubs during uncertain times.

2. E2DNE: Energy Efficient Dynamic Network Embedding in Virtualized Wireless Sensor Networks

  • Authors: Vahid Maleki Raee, Amin Ebrahimzadeh, Roch H. Glitho, May El Barachi, Fatna Belqasmi

  • Journal: IEEE Transactions on Green Communications and Networking

  • Volume & Issue: 7(3)

  • Pages: 1309–1325

  • Publication Year: 2023

  • DOI: 10.1109/TGCN.2023.3271230

  • Abstract: The paper introduces E2DNE, a novel approach for energy-efficient dynamic network embedding in virtualized wireless sensor networks (VWSNs). By optimizing resource allocation and reducing energy consumption, E2DNE enhances the performance and sustainability of VWSNs, making them more adaptable to varying network demands.

3. Secure Data Access Using Blockchain Technology Through IoT Cloud and Fabric Environment

  • Authors: Sangeeta Gupta, Premkumar Chithaluru, May El Barachi, Manoj Kumar

  • Journal: Security and Privacy

  • Publication Date: November 23, 2023

  • DOI: 10.1002/spy2.356

  • Abstract: This study addresses the challenges of secure data access in IoT environments by integrating blockchain technology with cloud computing. The proposed framework leverages the Hyperledger Fabric platform to ensure data integrity, confidentiality, and scalability, providing a robust solution for managing IoT data securely.

4. Combining Named Entity Recognition and Emotion Analysis of Tweets for Early Warning of Violent Actions

  • Authors: May El Barachi, Sujith Samuel Mathew, Manar AlKhatib

  • Conference: 2022 7th International Conference on Smart and Sustainable Technologies (SpliTech)

  • Publication Date: July 7–8, 2022

  • DOI: 10.23919/SpliTech55088.2022.9854231

  • Abstract: The paper presents a proactive framework that combines Named Entity Recognition (NER) and emotion analysis to detect early warning signs of potential violent actions from social media content. By analyzing tweets related to the 2020 US presidential election, the study demonstrates the framework’s effectiveness in identifying negative sentiments associated with specific entities, offering a tool for early intervention strategies.

5. A Green, Energy, and Trust-Aware Multi-Objective Cloud Coalition Formation Approach

  • Authors: Souad Hadjres, Fatna Belqasmi, May El Barachi, Nadjia Kara

  • Journal: Future Generation Computer Systems

  • Volume: 111

  • Pages: 52–67

  • Publication Date: October 2020

  • DOI: 10.1016/j.future.2020.04.030

  • Abstract: This research proposes a multi-objective approach for forming cloud coalitions that are energy-efficient and trust-aware. The algorithm considers factors like energy consumption, trust levels among providers, and service quality to form optimal coalitions. Experimental results show improvements in coalition size, provider payoff, and reduced mistrust costs, highlighting the approach’s potential for sustainable cloud computing.

🏁 Conclusion

Prof. May El Barachi is an outstanding and highly qualified nominee for the Best Researcher Award in Predictive Analytics and Machine Learning. Her portfolio exhibits the rare combination of technical depth, real-world applicability, international leadership, and a firm commitment to innovation in AI and societal impact. She not only advances predictive analytics through rigorous research but also through her systemic influence in academia and industry.

Given her publication volume, research funding, academic innovation, and practical AI implementations, she represents a paragon of excellence and leadership in predictive analytics and machine learning.

Moumita Ghosh | Computer Science | Best Researcher Award

Published on by Environmental Scientists

Dr. Moumita Ghosh | Computer Science | Best Researcher Award

Assistant Professor, Heritage Institute of Technology, India

Dr. Moumita Ghosh (PhD, Engg.) is a passionate researcher from Kolkata, India 🇮🇳, currently working as an Assistant Professor in the Department of Computer Science and Engineering at the Heritage Institute of Technology. Her core research interests lie at the intersection of Data Science and Computational Biodiversity. With a deep commitment to innovation and academia, she integrates machine learning and data mining techniques to address biodiversity conservation and complex ecological data analysis. 👩‍🏫🌿📊

Profile

Orcid

Education 🎓

Dr. Ghosh holds a Ph.D. in Engineering (2019–2024) from Jadavpur University, Kolkata, with her thesis focusing on “Algorithms for Data Mining: Applications in Biodiversity” 🧠🌱. She earned her M.E. in Multimedia Development from the same university (2011–2013) and completed her B.Tech. in Info Technology from WBUT in 2011. She also achieved outstanding academic performance in both her higher secondary and secondary education at Ichapur Girls’ High School. 🎓

Experience 💼

With over a decade of academic experience, Dr. Ghosh has served in key teaching roles at several premier institutions. She currently teaches Data Structures at Heritage Institute of Technology (2024–Present). Previously, she worked at Narula Institute of Technology (2022–2024), Jadavpur University (as a guest faculty and later PI for a DST-funded project), and held Assistant Professor roles at Institute of Engineering and Management (2015–2017) and Bengal College of Engineering and Technology (2013–2015). 💻📚

Research Interest 🔍

Dr. Ghosh’s research bridges Data Science and Ecology through Computational Biodiversity 🌍🧬. Her work includes pattern mining, remote sensing data, complex networks, and biodiversity modeling using advanced machine learning algorithms. She explores how AI and statistical methods can help mitigate biodiversity loss, emphasizing ecological data interpretation and predictive modeling. Her interests extend to deep learning, natural language processing, and ecological network analysis. 📈🌐

Awards 🏆

Dr. Ghosh is a UGC NET qualifier (2017 & 2018) and was awarded the prestigious DST Women Scientists Fellowship (2019–2022), where she led a ₹22 lakh project on biodiversity data mining. She collaborates internationally with Universitas Islam Indonesia and has served as a reviewer and TPC member for various global conferences. She is a proud member of the Computer Society of India (CSI) since 2021. 🏅🌟

Publications 📄

📖 Ghosh et al. (2023). “An Irregular CLA-based Novel Frequent Pattern Mining Approach.” International Journal of Data Mining, Modelling and Management. DOI

📖 Ghosh et al. (2022). “Recognition of Coexistence Pattern of Salt Marshes and Mangroves.” Ecological Informatics. DOI

📖 Ghosh et al. (2022). “Frequent itemset mining using FP-tree.” Innovations in Systems and Software Engineering.

📖 Ghosh et al. (2021). “Knowledge Discovery of Sundarban Mangrove Species.” SN Computer Science. DOI

📖 Ghosh et al. (2021). “Prediction of Interaction between SARS-CoV-2 and Human Protein.” Journal of The Institution of Engineers (India): Series B. DOI

📖 Mondal, Ghosh et al. (2022). “Suffix forest for mining tri-clusters from time-series data.” Innovations in Systems and Software Engineering.

📖 Ghosh & Parekh (2013). “Fish shape recognition using multiple shape descriptors.” International Journal of Computer Applications.

Conclusion

Dr. Moumita Ghosh is a highly suitable candidate for the Best Researcher Award. Her innovative integration of machine learning and biodiversity studies, coupled with a solid record of publications, a granted patent, and a DST fellowship, reflects both depth and societal relevance in her research. With continued international exposure and independent research leadership, she is poised to make significant contributions to science and sustainability.

Fernando Bruno Dovichi Filho | Engineering | Best Researcher Award

Published on by Environmental Scientists

Prof. Fernando Bruno Dovichi Filho | Engineering | Best Researcher Award

Professor, UNIFEI/UFSCAR, Brazil

Fernando Bruno Dovichi Filho 🇧🇷 is a Brazilian Mechanical Engineer with a Ph.D. in Mechanical Engineering, specializing in energy systems, renewable energy, and thermal modeling. With a rich blend of academic and research experience, he is currently a Substitute Professor at the Federal Institute of São Paulo (IFSP – Piracicaba campus). His work focuses on computational modeling, biomass energy, and sustainability-driven technologies, actively contributing to Brazil’s bioenergy development. Fernando’s background includes hands-on research in high-precision machining, hybrid propulsion, and energy conversion systems.

Profile

Orcid

Education 🎓

Fernando completed his Ph.D. in Mechanical Engineering (2017–2022) at the Federal University of Itajubá (UNIFEI), where he analyzed the technical and economic potential of electricity generation from biomass in Minas Gerais 🌱⚡. He earned his Master’s degree (2013–2015) at the same institution, refining thermal property estimation methods. His Bachelor’s in Industrial Mechanical Engineering (2008–2012) from ETEP Faculdades included a project on optical glass machining 🔧📐, showcasing his early inclination toward precision engineering and energy systems.

Experience 💼

With teaching and research roles across premier institutions, Fernando’s career spans from academia to aerospace research. Currently a full-time Substitute Professor at IFSP – Piracicaba (2023–present), he develops curricula, teaches engineering courses, and guides research and extension projects 🧑‍🏫📊. He previously served as a Substitute Professor at IFMS in 2016. As a PBIC/CNPq Research Fellow, he contributed to advanced propulsion research at both IAE and IEAv from 2009 to 2012, specializing in hybrid rocket engines, high-voltage discharges, and detonation studies using NASA CEA software 🚀💻.

Research Interest 🔍

Fernando’s research integrates renewable energy, thermal systems, and decision-making methodologies. His main focus is on biomass-based electricity generation, thermophysical property modeling, and multi-criteria decision analysis (MCDA) with GIS integration 🌍🧪. He is also keen on advancing thermal estimation techniques, applying hybrid modeling tools like MATLAB and EES, and evaluating the technology readiness of green energy solutions in Brazil and globally.

Awards 🏆

Fernando’s research integrates renewable energy, thermal systems, and decision-making methodologies. His main focus is on biomass-based electricity generation, thermophysical property modeling, and multi-criteria decision analysis (MCDA) with GIS integration 🌍🧪. He is also keen on advancing thermal estimation techniques, applying hybrid modeling tools like MATLAB and EES, and evaluating the technology readiness of green energy solutions in Brazil and globally.

Publications 📄

📖 Evaluation of TRL for biomass electricity technologies, Journal of Cleaner Production, 2021
DOI Link – Cited in renewable energy feasibility studies worldwide.

📖 GIS-MCDM methodology for biomass selection, Agriculture, 2025
DOI Link – A key reference for geo-spatial biomass planning.

📖 An approach to technology selection, Energy, 2023
DOI Link – Cited in works addressing clean technology prioritization.

📘 Book Chapter: From Crops and Wastes to Bioenergy, Woodhead Publishing, 2025

Publisher Link – Cited by authors in sustainable agriculture and energy.

Conclusion

Based on his research achievements, publications, and experience, Fernando Bruno Dovichi Filho is a suitable candidate for the Best Researcher Award. His contributions to sustainable energy solutions and his expertise in thermal systems optimization and renewable energy systems demonstrate his potential to make a significant impact in the field. With some further emphasis on international collaborations and publishing in top-tier journals, he is well-positioned to continue making meaningful contributions to research.

Sarah Di Grande | Analytics | Best Researcher Award

Published on by Environmental Scientists

Ms. Sarah Di Grande | Analytics | Best Researcher Award

PhD student, University of Catania, Italy

Sarah Di Grande is a driven researcher and data scientist currently pursuing a PhD in Systems, Energy, Computer, and Telecommunications Engineering at the University of Catania, Italy. With expertise in machine learning and a focus on sustainable water-energy optimization, she has contributed extensively to data science applications in renewable energy and smart city initiatives.

Profile

Orcid

Education 🎓

Sarah completed a Master’s in Data Science for Management at the University of Catania in 2022, graduating summa cum laude with a thesis on unsupervised machine learning for photovoltaic systems. She also holds a Bachelor’s degree in Business Economics from the same institution and graduated from Liceo Megara with top honors in 2017. Her studies have centered on advanced machine learning, big data, and data security.

Experience 💼

Currently, Sarah is a PhD student and researcher at the University of Catania, working in collaboration with Darwin Technologies on machine learning-based water-energy optimization. She previously interned as a data scientist at BaxEnergy, where she applied predictive maintenance techniques for photovoltaic panels, gaining hands-on experience in industrial data science applications.

Research Interests 🔬

Her research is dedicated to leveraging artificial intelligence for sustainable energy systems, focusing on machine learning applications in hydropower forecasting, urban traffic prediction, and water distribution network optimization. Sarah’s work aims to enhance resource management and promote sustainability in smart cities.

Awards 🏆

Sarah has received recognition for her innovative contributions, winning the Start-Cup Sicilia 2023 for her work on the “Smart Knee Project,” a device aimed at diagnosing knee osteoarthritis. She also secured second place in the University of Catania’s Start-Cup competition for the same project.

Publications Top Notes📚

Sarah has contributed numerous papers to international conferences and journals, exploring AI in hydropower, water distribution, and urban traffic management. Some key publications include:

“A Proactive Approach for the Sustainable Management of Water Distribution Systems” (2023) in 12th International Conference on Data Science, Technology and Applications – DATA [cited by 10 articles].

“Detection and Prediction of Leakages in Water Distribution Networks” (2023) in DATA 2023 [cited by 7 articles]

“A Machine Learning Approach for Hydroelectric Power Forecasting” (2023) in 14th International Renewable Energy Congress – IREC [cited by 5 articles].

“Data Science for the Promotion of Sustainability in Smart Water Distribution Systems” (2024) in Communications in Computer and Information Science, Springer [cited by 12 articles].