Fareeha Anwar | Computer Science | Best Researcher Award

Published on by Environmental Scientists

Best Researcher Award

Fareeha Anwar
Imam Mohammad Ibn Saud Islamic University, Saudi Arabia

Fareeha Anwar
Affiliation Imam Mohammad Ibn Saud Islamic University
Country Saudi Arabia
Scopus ID 36197820200
Documents 5
Citations 145
h-index 5
Subject Area Computer Science
Event Environmental Scientists
ORCID 0000-0002-6993-7761

Fareeha Anwar is a researcher affiliated with Imam Mohammad Ibn Saud Islamic University whose scholarly work contributes to the field of Computer Science. Her publication record, citation profile, and international research visibility demonstrate continuing academic engagement and provide a foundation for professional recognition within multidisciplinary scientific communities. reflecting a commitment to solving complex real-world challenges through innovative technologies. Her publication record, citation profile, and international research visibility demonstrate sustained academic engagement and an active contribution to multidisciplinary scientific research.[1]

Abstract

This article summarizes the academic profile of Fareeha Anwar and highlights her research achievements, scholarly publications, and citation performance. The profile reflects measurable scientific activity in Computer Science and recognizes contributions through internationally indexed research outputs.[2]

Keywords

Computer Science, Artificial Intelligence, Academic Research, Scopus, Scholarly Publications, Citation Analysis, Research Excellence, Best Researcher Award.

Introduction

Academic excellence is evaluated through research quality, publication impact, and continued scholarly engagement. International indexing services provide transparent indicators that support the recognition of researchers across scientific disciplines.[1]

Research Profile

Fareeha Anwar has established a research profile supported by indexed publications and growing citation metrics. Her academic activities demonstrate sustained participation in Computer Science research while contributing to collaborative scientific advancement.[3]

Research Contributions

Her publications address contemporary computational topics and reflect an emphasis on knowledge development within Computer Science. These contributions support the dissemination of research findings through peer-reviewed scholarly communication.[4]

Publications

The available publication record includes five Scopus-indexed documents with accumulated citations that demonstrate academic visibility. Persistent citation activity indicates continued relevance and accessibility within the international research community.[1]

Research Impact

With 145 citations and an h-index of 5, the available bibliometric indicators suggest measurable research influence. These metrics provide objective evidence of scholarly recognition and support broader academic evaluation processes.[2]

Award Suitability

Based on her documented publication history, citation performance, and institutional affiliation, Fareeha Anwar demonstrates characteristics commonly considered during research excellence evaluations. These achievements align with the objectives of the Environmental Scientists Best Researcher Award program.[5]

Conclusion

Fareeha Anwar’s academic profile reflects continued scholarly productivity and measurable research impact within Computer Science. Her documented achievements represent a solid foundation for academic recognition and future research development.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Fareeha Anwar, Author ID 36197820200. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=36197820200
  2. ORCID. (n.d.). Fareeha Anwar ORCID Record.
    https://orcid.org/0000-0002-6993-7761
  3. Google Scholar. (n.d.). Fareeha Anwar Citation Profile.
    https://scholar.google.com/citations?hl=en&user=7zlArcAAAAAJ
  4. Abdullah, S., Anwar, F., & Fatima, M. (2026). Spiking neural networks for real-time mapping of EBV-infected B cells in neuroinflammatory lesions. Annals of Medicine & Surgery. Advance online publication.
    https://doi.org/10.1097/MS9.0000000000005088
  5. Mohammad, U. G., Imtiaz, S., Shakya, M., Almadhor, A., & Anwar, F. (2022). An optimized feature selection method using ensemble classifiers in software defect prediction for healthcare systems. Wireless Communications and Mobile Computing, 2022, Article 1028175
    https://onlinelibrary.wiley.com/doi/10.1155/2022/1028175

Mahdi Aliyari-Shoorehdeli | Data Science and Analytics | Best Researcher Award

Published on by Environmental Scientists

Best Researcher Award

Mahdi Aliyari-Shoorehdeli
Affiliation K. N. Toosi University of Technology
Country Iran
Scopus ID 16178561500
Documents 243
Citations 3315
h-index 29
Subject Area Data Science and Analytics
Event Environmental Scientists
ORCID 0000-0002-9985-510X

Mahdi Aliyari-Shoorehdeli
K. N. Toosi University of Technology

Mahdi Aliyari-Shoorehdeli is an academic researcher whose work contributes to data science, intelligent systems, computational modeling, and analytics. His publication record, citation impact, and interdisciplinary collaborations demonstrate sustained scholarly activity across internationally recognized scientific platforms.His research spans artificial intelligence, adaptive neuro-fuzzy inference systems (ANFIS), particle swarm optimization, machine learning, optimization algorithms, control engineering, and computational intelligence. [1]

Abstract

This article summarizes the academic profile of Mahdi Aliyari-Shoorehdeli and highlights measurable research achievements in data science and analytics. His scholarly activities demonstrate consistent publication output, recognized citation performance, and active participation in international research collaborations.[2]

Keywords

Data Science, Artificial Intelligence, Machine Learning, Analytics, Intelligent Systems, Computational Modeling, Optimization, Research Impact.

Introduction

The research activities of Mahdi Aliyari-Shoorehdeli focus on analytical methodologies that combine computational intelligence with practical engineering applications. His publications contribute to advancing modern data-driven approaches across multiple scientific disciplines.[3]

Research Profile

With 243 indexed publications, more than 3,300 citations, and an h-index of 29, the researcher has established a strong international academic presence. These indicators reflect sustained productivity and consistent scholarly influence within the research community.[1]

Research Contributions

His studies emphasize intelligent control, optimization techniques, machine learning algorithms, and advanced analytical frameworks. These contributions support the development of efficient computational solutions for complex engineering and scientific problems.

Publications

Research findings have appeared in peer-reviewed journals and conference proceedings covering artificial intelligence, automation, optimization, and computational science. Many publications are indexed in internationally recognized academic databases with DOI identification.[5]

Research Impact

Citation statistics indicate that the research has been referenced by scholars across diverse disciplines, demonstrating continuing academic relevance. The combination of publication quality and interdisciplinary collaboration strengthens the overall research influence.[2]

Award Suitability

The documented publication record, citation performance, and established research profile provide evidence supporting recognition through the Best Researcher Award. These achievements align with academic standards commonly considered during scholarly evaluation processes.

Conclusion

Mahdi Aliyari-Shoorehdeli continues to contribute to data science and analytics through impactful publications and collaborative research. His academic record reflects sustained scholarly engagement and measurable contributions to international scientific literature.

External Links

References

  1. Elsevier. Scopus Author Details: Mahdi Aliyari-Shoorehdeli, Author ID 16178561500.
    https://www.scopus.com/authid/detail.uri?authorId=16178561500
  2. ORCID. Researcher Profile.
    https://orcid.org/0000-0002-9985-510X
  3. Khanesar, M. A., Teshnehlab, M., & Aliyari-Shoorehdeli, M. (2007). A novel binary particle swarm optimization. In Proceedings of the 2007 Mediterranean Conference on Control & Automation (pp. 1–6).
    https://ieeexplore.ieee.org/document/4433821
  4. Aliyari-Shoorehdeli, M., Teshnehlab, M., Sedigh, A. K., & Khanesar, M. A. (2009). Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods. Applied Soft Computing

Jianan Chen | Computer Science | Best Researcher Award

Published on by Environmental Scientists

Best Researcher Award

Jianan Chen
Affiliation Purdue University
Country United States
Scopus ID 57259732400
Documents 6
Citations 27
h-index 3
Subject Area Computer Science
Event Environmental Scientists

Jianan Chen
Purdue University, United States

Jianan Chen recognizes scholars who demonstrate promising academic achievement, scholarly integrity, and measurable research contributions within their respective disciplines. Jianan Chen, affiliated with Purdue University, has established a growing research profile in Computer Science through publications addressing privacy-preserving machine learning, federated learning, and intelligent distributed systems. His published work, citation record, and participation in contemporary computing research indicate an emerging contribution to data privacy and secure artificial intelligence, supporting consideration for academic recognition.[1]

Abstract

Jianan Chen’s scholarly activities primarily focus on secure machine learning, hierarchical federated learning, distributed intelligence, and privacy-preserving computation. His research aims to improve communication efficiency, model personalization, and privacy protection while maintaining reliable performance in collaborative learning environments. These topics have become increasingly important as artificial intelligence systems expand into healthcare, mobile computing, and cloud-based infrastructures.[2]

Keywords

Computer Science, Federated Learning, Privacy Preservation, Machine Learning, Artificial Intelligence

Introduction

Modern distributed artificial intelligence requires solutions that protect user privacy without compromising analytical performance. Jianan Chen contributes to this evolving field through studies investigating secure collaborative learning frameworks capable of addressing communication constraints and heterogeneous data environments. His work aligns with global research efforts aimed at building trustworthy and scalable intelligent systems suitable for real-world applications.[3]

Research Profile

According to available scholarly databases, Jianan Chen has authored six indexed publications with twenty-seven citations and an h-index of three. His affiliation with Purdue University reflects engagement within a leading academic environment that supports interdisciplinary computing research. His publications demonstrate consistent interest in federated optimization, intelligent communication strategies, and privacy-aware learning architectures.[1]

Research Contributions

His research contributions include personalized privacy preservation, utility-enhanced hierarchical federated learning, and communication-efficient distributed optimization. These investigations contribute to improving scalability and security in collaborative machine learning systems while addressing practical deployment challenges. Such research has relevance across mobile computing, cloud services, and intelligent cyber-physical systems.[4]

Publications

Recent publications include studies published in peer-reviewed journals such as IEEE Transactions on Mobile Computing, emphasizing utility-enhanced personalized privacy preservation in hierarchical federated learning. These publications reflect current research interests in secure artificial intelligence and distributed computing methodologies.

Research Impact

Although still in the early stages of his academic career, Jianan Chen’s citation metrics and publication record demonstrate increasing scholarly visibility. His work addresses practical issues associated with privacy-preserving machine learning, an area of growing significance for academia and industry. Continued publication and collaboration may further expand the influence of his research within Computer Science.[1]

Award Suitability

Based on available academic indicators, Jianan Chen demonstrates qualities commonly associated with emerging research excellence, including peer-reviewed publications, measurable citation impact, and research focused on contemporary technological challenges. His contributions to secure federated learning and privacy-aware artificial intelligence support consideration for recognition through the Environmental Scientists Best Researcher Award program.[6]

Conclusion

Jianan Chen represents an emerging researcher whose work contributes to advancing secure, privacy-preserving artificial intelligence. His scholarly output, citation profile, and focus on distributed learning technologies reflect meaningful engagement with contemporary Computer Science research. Continued development of these investigations is expected to strengthen both academic impact and interdisciplinary collaboration.

References

  1. Elsevier. (n.d.). Scopus author details: Jianan Chen, Author ID 57259732400. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57259732400
  2. Google Scholar. (n.d.). Jianan Chen publication profile.
    https://scholar.google.com/citations?user=9cql4fcAAAAJ&hl=en
  3. Chen, J., et al. (2025). Utility-Enhanced Personalized Privacy Preservation in Hierarchical Federated Learning. IEEE Transactions on Mobile Computing.
    https://ieeexplore.ieee.org/document/10847868
  4. Chen, J., Hu, Q., & Jiang, H. (2024). Alliance makes difference? Maximizing social welfare in cross-silo federated learning. IEEE Transactions on Vehicular Technology, 73(2), 2786–2798.
  5. Chen, J., Hu, Q., Zhong, F., Zhuang, Y., & Xu, M. (2024). Upcycling noise for federated unlearning. arXiv.
    https://arxiv.org/abs/2412.05529

Mohaddeseh Esmaeili Farsani | Data Science and Analytics | Best Researcher Award

Published on by Environmental Scientists

Best Researcher Award

Mohaddeseh Esmaeili Farsani
Isfahan University, Iran

Mohaddeseh Esmaeili Farsani
Affiliation Isfahan University
Country Iran
Scopus ID aGHJtpkAAAAJ
Documents 4
Citations 1
h-index 1
Subject Area Data Science and Analytics
Event International Environmental Scientists Award

The Best Researcher Award nomination profile of Mohaddeseh Esmaeili Farsani highlights emerging scholarly contributions in the interdisciplinary domains of data science, artificial intelligence, biomedical signal processing, and intelligent healthcare systems. Through collaborative research activities, the researcher has contributed to studies involving clinical artificial intelligence governance, electrocardiogram interpretation, electroencephalography analytics, and contactless physiological monitoring technologies. These works demonstrate engagement with contemporary scientific challenges related to trustworthy AI implementation and healthcare innovation.[1]

Abstract

Mohaddeseh Esmaeili Farsani has participated in research addressing advanced analytical methods for healthcare and biomedical engineering applications. The publication portfolio reflects interests in artificial intelligence governance, physiological signal interpretation, medical imaging analytics, and machine learning methodologies. The research emphasizes responsible AI deployment, regulatory compliance, and evidence-based healthcare technologies while contributing to interdisciplinary scientific knowledge development.[2]

Keywords

Data Science, Artificial Intelligence, Biomedical Engineering, Machine Learning, EEG Analysis, ECG Interpretation, Clinical Informatics, Signal Processing, Healthcare Analytics, Responsible AI.

Introduction

Recent advancements in data science have accelerated the integration of artificial intelligence into healthcare systems. Researchers working at this intersection contribute to the development of predictive models, clinical decision-support tools, and intelligent monitoring platforms. Mohaddeseh Esmaeili Farsani’s scholarly activities align with these developments through collaborative investigations focused on trustworthy and effective AI utilization within biomedical contexts.[3]

Research Profile

The research profile demonstrates engagement with emerging themes in computational healthcare and intelligent biomedical systems. Published works explore clinical evaluation frameworks, AI regulation, human-machine interaction technologies, and standardized methods for physiological data analysis. These areas contribute to improving transparency, reliability, and operational effectiveness in healthcare-oriented artificial intelligence applications.[4]

Research Contributions

  • Contribution to AI governance frameworks for clinical decision-making systems.
  • Participation in systematic reviews of contactless vital-sign monitoring technologies.
  • Research involving deep learning applications for cardiac signal interpretation.
  • Development of methodologies supporting EEG data acquisition and machine learning integration.
  • Promotion of interdisciplinary collaboration between data science and biomedical engineering.

Publications

  1. TRIAGE: Trustworthy Reporting and Assessment for Clinical Gain and Effectiveness of AI Models (Diagnostics, 2026).
  2. Contactless Vital Sign Monitoring Through Intelligent Human–Machine Interaction: A Systematic Review (2026).
  3. CARDIO-AI: Compliance and Artificial Intelligence Regulation for Deep Learning in Electrocardiogram Interpretation (2026).
  4. AI-EEG: Advanced Integration and Machine Learning Standards for EEG Data Acquisition and Processing (2025).

Research Impact

The documented research output contributes to ongoing discussions regarding ethical AI deployment, healthcare data governance, and intelligent biomedical analytics. Although the publication record is at an early stage, the thematic focus addresses areas of substantial scientific and societal relevance. The integration of data science methods with clinical applications supports future advancements in healthcare technology and evidence-driven decision making.[5]

Award Suitability

Based on the available scholarly record, Mohaddeseh Esmaeili Farsani demonstrates active participation in research addressing significant contemporary challenges in artificial intelligence and healthcare analytics. The interdisciplinary nature of the work, together with contributions to responsible AI, biomedical signal processing, and clinical technology evaluation, aligns with the objectives of the International Environmental Scientists Award’s recognition of emerging research excellence and scientific innovation.[6]

Conclusion

Mohaddeseh Esmaeili Farsani’s academic contributions reflect growing engagement with data-driven healthcare innovation and intelligent biomedical systems. The publication portfolio demonstrates participation in research focused on trustworthy AI, advanced signal processing, and clinical technology assessment. These activities support continued scholarly development and provide a foundation for future scientific contributions within data science and analytics.

References

  1. Elsevier. (n.d.). Scopus author details: Mohaddeseh Esmaeili Farsani, Author ID Google Scholar.
    https://scholar.google.com/citations?hl=en&user=aGHJtpkAAAAJ
  2. Fazilati, F., et al. (2026). TRIAGE: Trustworthy Reporting and Assessment for Clinical Gain and Effectiveness of AI Models. Diagnostics.
    https://doi.org/10.3390/diagnostics16050666
  3. Alihosseini, N., et al. (2026). Contactless Vital Sign Monitoring Through Intelligent Human–Machine Interaction.
  4. Rajabi, M.Z., et al. (2026). CARDIO-AI: Compliance and Artificial Intelligence Regulation for Deep Learning in ECG Interpretation.
  5. Marateb, H.R., et al. (2025). AI-EEG: Advanced Integration and Machine Learning Standards for EEG Data Acquisition and Processing.
  6. International Environmental Scientists Award. (n.d.). Award nomination and evaluation framework.
    environmentalscientists.org

Khalil Abdelnaby | Computer Science | Research Excellence Award

Published on by Environmental Scientists

Mr. Vivek Dwivedi | Computer Science | Research Excellence Award

Al-Ahliyya Amman university | Jordan

Dr. Khalil Mohamed Khalil AbdElnaby is a researcher in Systems and Computers Engineering with expertise in Artificial Intelligence, cloud robotics, cybersecurity, embedded systems, IoT, and intelligent communication technologies. His research contributions focus on deep learning, network intrusion detection, hardware trojan detection, cloud computing, FPGA systems, and optimization techniques for intelligent engineering applications. He has authored and co-authored more than 10 scientific publications in reputable international journals and conferences. His research profile has achieved over 100 citations with an h-index of 5, reflecting the growing academic impact and relevance of his contributions to advanced engineering and AI-driven technologies.

Professional Profiles 

Education Background

Vivek Dwivedi | Computer Science | Research Excellence Award

Published on by Environmental Scientists

Mr. Vivek Dwivedi | Computer Science | Research Excellence Award

Research Scholar | The University of Slovak University of Technology | Slovakia

Mr. Vivek Dwivedi is an emerging researcher in the field of Computer Science, specializing in machine learning, robotics, and intelligent computational systems. His research emphasizes the development of real-time applications using computer vision, natural language processing, and advanced programming frameworks. He has worked on innovative solutions such as adaptive multi-camera systems for virtual environments and intelligent robotic mechanisms, showcasing strong technical expertise and research potential. With 12 published documents, 25 citations, and an h-index of 3, his contributions reflect steady academic growth and relevance. His work aims to bridge the gap between theoretical research and practical implementation, contributing to advancements in automation, smart technologies, and next-generation digital systems that address real-world challenges.

                            Citation Metrics ( Scopus )

60

50

40

30

20

10

0

 

Citations
25
documents
12
h-index
3

Citations

Documents

h-index

 

Li Mingxuan | Engineering | Research Excellence Award

Published on by Environmental Scientists

Mr. Li Mingxuan | Engineering | Research Excellence Award

Artificial Intelligence Division | The University of  Beijing Smart-Chip Microelectronics Technology Company Ltd | China

Mr. Li Mingxuan is an emerging author contributing to the advancement of artificial intelligence applications in modern power systems. His research focuses on integrating machine learning techniques with energy infrastructure to improve system efficiency, reliability, and intelligent monitoring. His published work explores innovative approaches such as enhanced image processing algorithms for transmission line inspection and intelligent fault detection methodologies. With a growing academic presence, he has authored 11 research documents, receiving 2 citations and achieving an h-index of 1. His contributions emphasize the practical implementation of AI-driven solutions in complex engineering environments, particularly in optimizing distributed energy systems and smart grid technologies. His research reflects a commitment to advancing intelligent automation and supporting the evolution of sustainable and resilient power networks through engineering innovation and interdisciplinary collaboration.

                            Citation Metrics ( Scopus )

11

10

8

6

4

2

0

 

Citations
2
documents
11
h-index
1

Citations

Documents

h-index

 

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.