R S Shaji | AI | Outstanding Educator Award

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Dr. R S Shaji | AI | Outstanding Educator Award

Professor at St. Xavier’s Catholic College of Engineering, India

Dr. R.S. Shaji is a distinguished academician, administrator, and researcher with over 26 years of teaching and 22 years of administrative experience in Computer Science and Engineering. Currently serving as Dean (Systems) and Professor at St. Xavier’s Catholic College of Engineering, Tamil Nadu, he is also a recognized NAAC Assessor and a doctoral supervisor at Anna University and Noorul Islam University. With extensive contributions to the domains of Machine Learning, Smart Grid Computing, Cyber Security, and Cloud Computing, he has successfully produced 8 Ph.D. graduates and is presently guiding 10 doctoral scholars.

🔹Professional Profile:

Scopus Profile

Orcid Profile

Google Scholar Profile

🎓Education Background

  • Ph.D. in Computer Science and Engineering (2012) – Manonmaniam Sundaranar University, Tirunelveli

  • M.Tech. in Computer Science and Engineering (2002) – Pondicherry University (Central University), Puducherry

💼 Professional Development

Dr. Shaji has held prominent academic leadership roles including Dean (Research), Head of Department, and Director of Admissions across reputed institutions like St. Xavier’s Catholic College of Engineering and Noorul Islam University. He is a recognized faculty member and supervisor under AICTE, UGC, and Anna University. His experience also extends to three years in the software industry, and he has been deeply involved in curriculum design, institutional accreditation processes, and national missions such as Unnat Bharat Abhiyan and MHRD’s Institution Innovation Council.

🔬Research Focus

His core research domains include:

  • Machine Learning

  • Smart Grid Computing

  • Cyber Security

  • Cloud Computing

  • Blockchain Applications

  • Healthcare and Medical Informatics

📈Author Metrics:

  • Publications: 72 research articles in SCI, Scopus, Web of Science indexed journals, and Google Scholar

  • Conference Papers: 23 papers in reputed national and international conferences (IEEE, etc.)

  • Books & Chapters: 1 National Book, 5 Book Chapters

  • Patents: 1 Design Patent Granted, 1 Technology Patent Published, 1 Design Patent Examined

Awards & Honors

  • Recognized as a NAAC Peer Team Member

  • Reviewer for prestigious publishers: IEEE, Elsevier, Springer, Wiley, Taylor & Francis, IET, and Inderscience

  • Consultant for industry and academia in software and cloud architecture, cybersecurity, healthcare informatics, and e-governance systems

  • Editorial roles in 6 refereed journals (3 international, 3 national)

  • Institutional Coordinator and President for national innovation and safety programs

📝Publication Top Notes

🔐 1. Hybrid-CID: Securing IoT with Mongoose Optimization

  • Authors: SM Sheeba, R.S. Shaji
  • Journal: International Journal of Computational Intelligence Systems, Vol. 18(1), pp. 1–18
  • Year: 2025
  • Summary: Proposes a hybrid Cryptographic-Identification (Hybrid-CID) framework enhanced by Mongoose Optimization for robust IoT security.

🚘 2. Enhancing Security in VANETs: Adaptive Bald Eagle Search Optimization-Based Multi-Agent Deep Q Neural Network for Sybil Attack Detection

  • Authors: M. Ajin, R.S. Shaji
  • Journal: Vehicular Communications, Article ID: 100928
  • Year: 2025
  • Summary: Introduces an advanced Sybil attack detection mechanism in Vehicular Ad-Hoc Networks using Adaptive Bald Eagle Search Optimization with Multi-Agent Deep Q-Networks.

🎥 3. Design of Approximate Multiplier for Multimedia Application in Deep Neural Network Pre-Processing

  • Authors: M.D.S., R.S. Shaji, Nelmin Bathlin
  • Conference: 3rd Congress on Control, Robotics and Mechatronics (CCRM)
  • Year: 2025
  • Summary: Develops an energy-efficient approximate multiplier for DNN-based multimedia pre-processing.

➗ 4. Design of Approximate Multiplier Using Highly Compressed 5:2 Counter

  • Authors: R.S. Shaji, S. Hariprasad, S. Shettygari, J.K. Vasan, V. Vijayan
  • Conference: 6th International Conference on Mobile Computing and Sustainable Informatics
  • Year: 2025
  • Summary: Presents a high-performance 5:2 counter-based multiplier aimed at improving computational efficiency in mobile systems.

⚡ 5. Enhancing Smart Grid Security Using BLS Privacy Blockchain With Siamese Bi-LSTM for Electricity Theft Detection

  • Authors: G. Johncy, R.S. Shaji, T.M. Angelin Monisha Sharean, U. Hubert
  • Journal: Transactions on Emerging Telecommunications Technologies, Vol. 36(1), e70033
  • Year: 2025
  • Summary: Proposes a secure smart grid framework using BLS Privacy Blockchain and Siamese Bi-LSTM to detect electricity theft with improved precision.

.Conclusion:

Dr. R.S. Shaji emerges as a strong and deserving candidate for the Research for Outstanding Educator Award. His long-standing commitment to research, mentorship, education leadership, and recent impactful publications in futuristic domains mark him as a transformative academician.

With minor enhancements in global research footprint, commercialization, and metrics transparency, he can not only justify this award but also aspire for national/international fellowships and innovation recognitions.

✔️ Verdict: Highly Suitable and Strongly Recommended for the award.

Rania Loukil | Deep Learning | Best Scholar Award

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Mr. Rania Loukil | Deep Learning | Best Scholar Award

Maitre Assistant at Ecole Nationale d’Ingenieurs de Tunis, Tunisia

Dr. Rania Loukil is a Tunisian researcher and academic specializing in Artificial Intelligence, Embedded Systems, and Control Engineering. Currently serving as a Maître Assistant (Assistant Professor) at the Higher Institute of Technology and Computer Science (ISTIC), University of Carthage, she has over a decade of experience in teaching, research, and interdisciplinary collaboration. Her research merges deep learning with practical domains like IoT, smart grids, and fault diagnosis, reflecting a strong commitment to innovation and applied AI solutions.

🔹Professional Profile:

Scopus Profile

Orcid Profile

🎓Education Background

  • Ph.D. in Electrical Engineering, National Engineering School of Sfax (ENIS), University of Sfax, Tunisia | 2010–2014

  • Master Project, INRIA Paris / ENIS | 2008–2009

  • Engineering Degree in Electrical Engineering, ENIS, Sfax | 2005–2008

  • Preparatory Classes (MP), IPEIS, Sfax | 2003–2005

  • Baccalaureate in Mathematics, Tunisia | 2002–2003 – Mention Bien

💼 Professional Development

  • Maître Assistant in Artificial Intelligence, ISTIC, University of Carthage | Jan 2018–Present

  • Coach Junior, BIAT Foundation | Nov 2018–Present

  • Maître Assistant in AI, ISI Gabes | Sep 2015–Dec 2017

  • Head of Electrical Engineering Department, Ecole Polytechnique Centrale Privée de Tunis | Feb 2015–Aug 2015

  • Permanent Faculty, Ecole Polytechnique Centrale Privée de Tunis | Oct 2014–Jan 2015

🔬Research Focus

  • Artificial Intelligence & Deep Learning (RNNs, Transformers, Bayesian Networks)

  • Fault Diagnosis and Nonlinear Control (Sliding Mode, Observers)

  • IoT and Embedded Systems

  • Smart Grids and Microgrid Energy Management

  • Nanocomposite Classification and Materials Informatics

📈Author Metrics:

  • Published in leading journals including Expert Systems with Applications and Scientific Reports

  • Recent works involve hybrid deep learning approaches for nanocomposite classification and smart energy systems

  • Selected publications:

    • Classification of Nanocomposites using RNN Transformer & Bayesian Network, ESWA, 2025

    • Probabilistic and Deep Learning Approaches for Conductivity-Driven Nanocomposite Classification, Scientific Reports, 2025

    • IoT Solution for Energy Management, IREC 2023

🏆Awards and Honors:

  • Recognized contributor to interdisciplinary AI projects

  • Regular presenter at international conferences on AI, control systems, and energy informatics

  • Acknowledged for excellence in education and mentorship through BIAT Foundation coaching initiatives

📝Publication Top Notes

1. Classification of a Nanocomposite Using a Combination Between Recurrent Neural Network Based on Transformer and Bayesian Network for Testing the Conductivity Property

Journal: Expert Systems with Applications
Publication Date: April 2025
DOI: 10.1016/j.eswa.2025.126518
ISSN: 0957-4174
Authors: Wejden Gazehi, Rania Loukil, Mongi Besbes
Abstract: This study presents a hybrid AI model combining Transformer-based RNN and Bayesian Networks to classify nanocomposites based on conductivity, demonstrating improved interpretability and predictive accuracy.

2. Probabilistic and Deep Learning Approaches for Conductivity-Driven Nanocomposite Classification

Journal: Scientific Reports
Publication Date: March 7, 2025
DOI: 10.1038/s41598-025-91057-1
ISSN: 2045-2322
Authors: Wejden Gazehi, Rania Loukil, Mongi Besbes
Abstract: This paper explores probabilistic learning and deep learning methods for classifying nanocomposites with a focus on electrical conductivity, emphasizing model generalizability.

3. Enhanced Nanoparticle Classification Through Optimized Artificial Neural Networks

Conference: 2024 International Conference on Decision Aid Sciences and Applications (DASA)
Presentation Date: December 11, 2024
DOI: 10.1109/dasa63652.2024.10836425
Authors: Wejden Gazehi, Rania Loukil, Mongi Besbes
Abstract: The paper demonstrates how optimized ANN architectures can significantly improve nanoparticle classification in terms of conductivity profiling, offering an efficient pipeline for smart material characterization.

4. Improving the Classification of a Nanocomposite Using Nanoparticles Based on a Meta-Analysis Study, Recurrent Neural Network and Recurrent Neural Network Monte-Carlo Algorithms

Journal: Nanocomposites
Publication Date: July 8, 2024
DOI: 10.1080/20550324.2024.2367181
ISSN: 2055-0324, 2055-0332
Authors: Rania Loukil, Wejden Gazehi, Mongi Besbes
Abstract: Through a comparative analysis using RNN and Monte-Carlo RNN algorithms, this work proposes a robust framework for classifying nanocomposites, supported by meta-analytical insights.

5. Design and Implementation of an IoT Solution for Energy Management\

Conference: 14th International Renewable Energy Congress (IREC 2023)
Presentation Date: December 16, 2023
Authors: Rania Loukil, Neila Bediou, Hatem Oueslati, Majdi Hazami
Abstract: This contribution introduces a practical IoT-based architecture for optimizing energy consumption and monitoring within renewable energy systems, aligning with smart grid principles.

.Conclusion:

Dr. Rania Loukil stands out as an exemplary scholar combining deep learning, embedded systems, and energy informatics. Her cross-disciplinary work addresses both academic challenges and societal needs, aligning well with the objectives of a Best Scholar Award. Given her solid track record, thematic relevance, and academic leadership, she is highly deserving of this recognition.

➡️ Recommendation: Strongly endorse her nomination for the Best Scholar Award, with suggestions to highlight international collaborations, quantitative metrics, and applied impacts during the award presentation or application.

Tzu-Chien Wang | AI | Best Researcher Award

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Assist. Prof. Dr. Tzu-Chien Wang | AI | Best Researcher Award

Tzu-Chien Wang at Department of Computer Science and Information Management Soochow University, Taiwan

Dr. Tzu-Chien Wang is an Assistant Professor in the Department of Computer Science and Information Management at Soochow University. He specializes in artificial intelligence, data mining, decision support systems, and process improvement techniques. With a strong background in machine learning, natural language processing, and predictive modeling, he has contributed significantly to both academia and industry by developing proof-of-concept models for operational processes.

Professional Profile:

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Education Background

Dr. Tzu-Chien Wang earned his Ph.D. in Business Administration from National Taiwan University, where he specialized in data-driven decision-making, artificial intelligence applications, and business intelligence. His doctoral research focused on leveraging machine learning, data mining, and optimization techniques to enhance decision support systems and operational efficiency. His academic training has provided him with a strong foundation in predictive modeling, natural language processing, and process improvement methodologies, which he has effectively applied in both research and industry settings.

Professional Development

Dr. Wang has a diverse professional background, spanning academia, industry, and research institutions. Before joining Soochow University in 2025, he served as an Assistant Professor at Mackay Junior College of Medicine, Nursing, and Management. He also held managerial roles in data development at VisualSoft Information System Co., Ltd. and worked as a Senior Data Analyst at Fubon Life Insurance Co., Ltd. Additionally, he contributed as an Assistant Research Fellow at the Commerce Development Research Institute, focusing on international digital commerce.

Research Focus

His research interests include artificial intelligence, data mining, decision support systems, natural language processing, optimization, clustering, classification, and predictive model building. He is particularly engaged in developing AI-driven solutions for business intelligence, healthcare applications, and digital transformation.

Author Metrics:

Dr. Wang has published extensively in AI, data analytics, and business intelligence. His research contributions can be found on Google Scholar, reflecting his impact on data science and AI applications.

Awards and Honors:

  • High-Age Health Smart Medical Care Industry-Academia Alliance, National Science and Technology Council, Taiwan (2025–2028)

  • AI+BI Agile Development Data Platform Project, Ministry of Economic Affairs, Taiwan (2022)

  • Consumer Data-Driven Precision R&D and Manufacturing (C2M) Promotion Project, Bureau of Energy, Taiwan (2021)

Publication Top Notes

1. Deep Learning-Based Prediction and Revenue Optimization for Online Platform User Journeys

  • Author: T.C. Wang
  • Journal: Quantitative Finance and Economics (2024)
  • Type: Research Article
  • Citations: 6
  • Summary: This study utilizes deep learning techniques to predict user behavior and optimize revenue generation on online platforms, improving personalized recommendations and business strategies.

2. An Integrated Data-Driven Procedure for Product Specification Recommendation Optimization with LDA-LightGBM and QFD

  • Authors: T.C. Wang, R.S. Guo, C. Chen
  • Journal: Sustainability (2023)
  • Type: Research Article
  • Citations: 5
  • Summary: This research presents a hybrid framework combining Latent Dirichlet Allocation (LDA), LightGBM, and Quality Function Deployment (QFD) to optimize product specification recommendations, improving efficiency in sustainable manufacturing.

3. Integrating Latent Dirichlet Allocation and Gradient Boosting Tree Methodology for Insurance Product Development Recommendation

  • Authors: W.Y. Chen, T.C. Wang, R.S. Guo, C. Chen
  • Conference: Proceedings of the 9th International Conference on Big Data Analytics (ICBDA) (2024)
  • Type: Conference Paper
  • Citations: 1
  • Summary: This paper integrates LDA and Gradient Boosting Trees to refine insurance product development recommendations, offering a data-driven approach for personalized insurance solutions.

4. Data Mining Methods to Support C2M Product-Service Systems Design and Recommendation System Based on User Value

  • Authors: T.C. Wang, R.S. Guo, C. Chen
  • Conference: 2022 Portland International Conference on Management of Engineering and Technology (PICMET)
  • Type: Conference Paper
  • Citations: 1
  • Summary: This study explores data mining techniques to enhance Consumer-to-Manufacturer (C2M) product-service system design, optimizing recommendation systems based on user value analysis.

5. Customer Demand Evaluation Method

  • Author: T.C. Wang
  • Patent: TW Patent TW202,414,306 A (2024)
  • Type: Patent
  • Summary: This patent presents a novel method for evaluating customer demand using AI-driven analytics, enhancing precision in product development and market segmentation.

Conclusion

Dr. Tzu-Chien Wang is a strong candidate for the Best Researcher Award, given his expertise in AI, machine learning, and business intelligence, along with his demonstrated contributions to academia and industry. His innovative research, patents, and funded projects underscore his impact. By expanding global collaborations, diversifying his research themes, and increasing engagement in AI policy and ethics, he can further solidify his standing as a leading researcher in artificial intelligence

An Zeng | Machine Learning | Best Researcher Award

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Prof. An Zeng | Machine Learning | Best Researcher Award

Professor at Guangdong University of Technology, China📖

Professor Zeng An is a distinguished researcher with extensive expertise in machine learning, data mining technologies, and their applications in medicine. Her work has significantly contributed to the advancement of deep learning, neural networks, probabilistic models, rough set theory, genetic algorithms, and other optimization methods. Since her postdoctoral research at the National Research Council of Canada and Dalhousie University (2008–2011) under the guidance of Professor Kenneth Rockwood, Professor Xiaowei Song, and Professor Arnold Mitnitski, she has been dedicated to applying these computational techniques to clinical research on Alzheimer’s Disease (AD).

Profile

Scopus Profile

Education Background🎓

Professor Zeng An completed her postdoctoral research at the National Research Council of Canada, collaborating with leading experts in medical AI applications. She holds a Ph.D. in Computer Science with a focus on machine learning and data mining techniques for medical applications. Her academic journey also includes a master’s and a bachelor’s degree in computer science or related fields (specific institutions and years can be added if available).

Professional Experience🌱

With a career spanning academia and research, Professor Zeng An has held key positions in leading universities and research institutions. During her postdoctoral tenure (2008–2011), she worked at Dalhousie University’s Faculty of Computer Science and Faculty of Medicine, contributing to AI-driven clinical research on neurodegenerative diseases. She has since continued her work in academia, conducting research on advanced machine learning techniques, medical data analysis, and clinical decision support systems.

Research Interests🔬

Professor Zeng An’s research focuses on developing intelligent algorithms for medical applications, particularly in Alzheimer’s Disease diagnostics and prediction. She specializes in deep learning, neural networks, probabilistic models, genetic algorithms, and optimization techniques. Her work extends to clinical data mining, patient risk assessment, and AI-driven medical decision-making, significantly impacting precision medicine.

Author Metrics

Professor Zeng An has a strong publication record in high-impact journals and conferences related to machine learning, AI in healthcare, and medical informatics. Her work has received substantial citations, reflecting her influence in the field. Key metrics such as H-index, i10-index, and total citations further highlight her academic contributions (specific numbers can be added if available).

Awards & Honors

Throughout her career, Professor Zeng An has received prestigious awards and recognitions for her contributions to AI and medical research. Her collaborations with renowned scientists in AI-driven healthcare innovations have led to groundbreaking advancements in the field. She continues to be a leading figure in interdisciplinary research, bridging computer science and medicine for improved healthcare outcomes.

Publications Top Notes 📄

1. Reinforcement Learning-Based Method for Type B Aortic Dissection Localization

  • Authors: Zeng An, Xianyang Lin, Jingliang Zhao, Baoyao Yang, Xin Liu
  • Journal: Journal of Biomedical Engineering (Shengwu Yixue Gongchengxue Zazhi), 2024
  • Citations: 0
  • Summary: This study presents a reinforcement learning-based approach for accurately localizing Type B aortic dissection, improving diagnostic precision in medical imaging.

2. Progressive Deep Snake for Instance Boundary Extraction in Medical Images (Open Access)

  • Authors: Zixuan Tang, Bin Chen, Zeng An, Mengyuan Liu, Shen Zhao
  • Journal: Expert Systems with Applications, 2024
  • Citations: 2
  • Summary: The research introduces a progressive deep snake model to enhance boundary extraction in medical images, facilitating precise segmentation for clinical applications.

3. Multi-Scale Quaternion CNN and BiGRU with Cross Self-Attention Feature Fusion for Fault Diagnosis of Bearing

  • Authors: Huanbai Liu, Fanlong Zhang, Yin Tan, Shenghong Luo, Zeng An
  • Journal: Measurement Science and Technology, 2024
  • Citations: 1
  • Summary: This paper develops a multi-scale quaternion CNN and BiGRU model integrating cross self-attention feature fusion to enhance the accuracy of bearing fault diagnosis in industrial applications.

4. An Ensemble Model for Assisting Early Alzheimer’s Disease Diagnosis Based on Structural Magnetic Resonance Imaging with Dual-Time-Point Fusion

  • Authors: Zeng An, Jianbin Wang, Dan Pan, Wenge Chen, Juhua Wu
  • Journal: Journal of Biomedical Engineering (Shengwu Yixue Gongchengxue Zazhi), 2024
  • Citations: 0
  • Summary: The study proposes an ensemble model utilizing dual-time-point fusion of MRI scans to improve early detection and diagnosis of Alzheimer’s Disease.

5. FedDUS: Lung Tumor Segmentation on CT Images Through Federated Semi-Supervised Learning with Dynamic Update Strategy

  • Authors: Dan Wang, Chu Han, Zhen Zhang, Zhenwei Shi, Zaiyi Liu
  • Journal: Computer Methods and Programs in Biomedicine, 2024
  • Summary: This research introduces a federated semi-supervised learning framework with a dynamic update strategy for effective lung tumor segmentation in CT imaging.

Conclusion

Professor An Zeng is a highly qualified candidate for the Best Researcher Award, given her outstanding contributions to AI in medicine, deep learning, and computational diagnostics. Her strong publication record, international research experience, and interdisciplinary approach make her an excellent nominee. While expanding clinical collaborations and citation impact would further enhance her profile, her cutting-edge research already positions her as a leader in medical AI applications.

Dongfang Zhao | Machine Learning | Best Researcher Award

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Prof. Dongfang Zhao | Machine Learning | Best Researcher Award

Prof. Dongfang Zhao at University of Washington, United States

🌟 Dongfang Zhao, Ph.D., is a Tenure-Track Assistant Professor at the University of Washington Tacoma and a Data Science Affiliate at the eScience Institute. With a Ph.D. in Computer Science from Illinois Institute of Technology (2015) and PostDoc from the University of Washington, Seattle (2017), Dr. Zhao’s career spans academic excellence and groundbreaking research in distributed systems, blockchain, and machine learning. His work, recognized with federal grants and best paper awards, has significantly impacted cloud computing, HPC systems, and AI-driven blockchain solutions. Dr. Zhao is an influential editor, reviewer, and committee member in prestigious venues. 📚💻✨

Professional Profile:

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Education and Experience 

🎓 Education:

  • Postdoctoral Fellowship, Computer Science, University of Washington, Seattle (2017)
  • Ph.D., Computer Science, Illinois Institute of Technology, Chicago (2015)
  • M.S., Computer Science, Emory University, Atlanta (2008)
  • Diploma in Statistics, Katholieke Universiteit Leuven, Belgium (2005)

💼 Experience:

  • Tenure-Track Assistant Professor, University of Washington Tacoma (2023–Present)
  • Visiting Professor, University of California, Davis (2018–2023)
  • Assistant Professor, University of Nevada, Reno (2017–2023)
  • Visiting Scholar, University of California, Berkeley (2016)
  • Research Intern, IBM Almaden Research Center (2015), Argonne National Laboratory (2014), Pacific Northwest National Laboratory (2013)

Professional Development

📊 Dr. Dongfang Zhao is a leading voice in distributed systems, blockchain technologies, and scalable machine learning. He contributes to academia as an Associate Editor for the Journal of Big Data and serves on the editorial board of IEEE Transactions on Distributed and Parallel Systems. A sought-after reviewer and conference organizer, Dr. Zhao actively shapes the future of AI and cloud computing. With a deep commitment to mentorship, he has guided doctoral students to successful careers in academia and industry. His collaborative initiatives reflect a passion for addressing real-world challenges through computational innovation. 🌐✨📖

Research Focus

🔬 Dr. Zhao’s research emphasizes cutting-edge developments in distributed systems, blockchain, machine learning, and HPC (high-performance computing). His work delves into creating energy-efficient, scalable blockchain platforms like HPChain and developing frameworks for efficient scientific data handling. His contributions include lightweight blockchain solutions for reproducible computing and innovations in AI-driven systems like HDK for deep-learning-based analyses. Dr. Zhao’s interdisciplinary approach fosters impactful collaborations, addressing pressing technological needs in cloud computing, scientific simulations, and data analytics. His research bridges the gap between theoretical insights and practical applications in modern computing ecosystems. 🚀📊🧠

Awards and Honors 

  • 🏆 2022 Federal Research Grant: NSF 2112345, $255,916 for a DLT Machine Learning Platform
  • 🌟 2020 Federal Research Grant: DOE SC0020455, $200,000 for HPChain blockchain research
  • 🏅 2019 Best Paper Award: International Conference on Cloud Computing
  • 🥇 2018 Best Student Paper Award: IEEE International Conference on Cloud Computing
  • 🎓 2015 Postdoctoral Fellowship: Sloan Foundation, $155,000
  • 🎖️ 2007 Graduate Fellowship: Oak Ridge Institute for Science and Education, $85,000

Publication Top Notes:

1. Regulated Charging of Plug-In Hybrid Electric Vehicles for Minimizing Load Variance in Household Smart Microgrid

  • Authors: L. Jian, H. Xue, G. Xu, X. Zhu, D. Zhao, Z.Y. Shao
  • Published In: IEEE Transactions on Industrial Electronics, Volume 60, Issue 8, Pages 3218-3226
  • Citations: 280 (as of 2012)
  • Abstract:
    This paper proposes a regulated charging strategy for plug-in hybrid electric vehicles (PHEVs) to minimize load variance in household smart microgrids. The method ensures that the charging process aligns with household power demand patterns, improving grid stability and efficiency.

2. ZHT: A Lightweight, Reliable, Persistent, Dynamic, Scalable Zero-Hop Distributed Hash Table

  • Authors: T. Li, X. Zhou, K. Brandstatter, D. Zhao, K. Wang, A. Rajendran, Z. Zhang, …
  • Published In: IEEE International Symposium on Parallel & Distributed Processing (IPDPS)
  • Citations: 212 (as of 2013)
  • Abstract:
    This paper introduces ZHT, a zero-hop distributed hash table designed for high-performance computing systems. It is lightweight, scalable, and reliable, making it suitable for persistent data storage in distributed environments.

3. Optimizing Load Balancing and Data-Locality with Data-Aware Scheduling

  • Authors: K. Wang, X. Zhou, T. Li, D. Zhao, M. Lang, I. Raicu
  • Published In: 2014 IEEE International Conference on Big Data (Big Data), Pages 119-128
  • Citations: 171 (as of 2014)
  • Abstract:
    This paper addresses the challenges of load balancing and data locality in big data processing systems. A novel data-aware scheduling algorithm is proposed to improve efficiency and performance in high-performance computing environments.

4. FusionFS: Toward Supporting Data-Intensive Scientific Applications on Extreme-Scale High-Performance Computing Systems

  • Authors: D. Zhao, Z. Zhang, X. Zhou, T. Li, K. Wang, D. Kimpe, P. Carns, R. Ross, …
  • Published In: 2014 IEEE International Conference on Big Data (Big Data), Pages 61-70
  • Citations: 154 (as of 2014)
  • Abstract:
    FusionFS is a distributed file system tailored for extreme-scale high-performance computing systems. It provides efficient data storage and retrieval, supporting data-intensive scientific applications and overcoming the bottlenecks in traditional storage systems.

5. Enhanced Data-Driven Fault Diagnosis for Machines with Small and Unbalanced Data Based on Variational Auto-Encoder

  • Authors: D. Zhao, S. Liu, D. Gu, X. Sun, L. Wang, Y. Wei, H. Zhang
  • Published In: Measurement Science and Technology, Volume 31, Issue 3, Article 035004
  • Citations: 105 (as of 2019)
  • Abstract:
    This study enhances fault diagnosis for machines using a data-driven approach. By leveraging variational auto-encoders (VAEs), the method effectively handles small and unbalanced datasets, achieving high diagnostic accuracy for industrial applications.

Manijeh Emdadi | Artificial Intelligence | Best Researcher Award

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Dr. Manijeh Emdadi | Artificial Intelligence | Best Researcher Award

Research Fellow at Islamic Azad University Science and Research Branch, Iran📖

Dr. Manijeh Emdadi is an accomplished Data Scientist and AI Specialist with 8 years of experience in designing, developing, and deploying machine learning models and data-driven solutions. Currently pursuing her Ph.D. in Artificial Intelligence at the Islamic Azad University, Tehran, her research focuses on exploring explainable AI models for healthcare decision support systems. Dr. Emdadi has a robust background in machine learning, neural networks, and deep learning, and she actively collaborates with cross-disciplinary teams to develop innovative AI solutions.

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Education Background🎓

  • Ph.D. in Artificial Intelligence (In Progress)
    Islamic Azad University Science and Research Branch, Tehran, Iran
    Research Focus: Exploring Explainable AI Models for Healthcare Decision Support Systems
  • Master of Science in Data Science / Artificial Intelligence
    Islamic Azad University Qazvin Branch, Qazvin, Iran
    Thesis: Optimizing Neural Network Architectures for Image Recognition Tasks
  • Bachelor of Science in Computer Engineering
    Iran University of Science and Technology (IUST), Tehran, Iran
    Relevant Courses: Advanced Algorithms

Professional Experience🌱

Dr. Emdadi has a strong professional background as a Data Scientist, collaborating with cross-functional teams to integrate predictive analytics into business workflows. Her expertise spans programming in Python, SQL, and Java, as well as working with data science tools such as Pandas, NumPy, Scikit-Learn, TensorFlow, and PyTorch. Additionally, she has experience deploying AI/ML models on cloud platforms like Google Cloud. She also serves as a teaching assistant for graduate-level courses on deep learning, sharing her knowledge and expertise with the next generation of AI professionals.

Research Interests🔬

Dr. Emdadi’s primary research interests lie in the intersection of Artificial Intelligence, Machine Learning, and healthcare applications. She is particularly focused on exploring explainable AI models for decision support systems in healthcare, using machine learning and neural networks to solve complex problems in medical data analysis. Her research also includes advancements in deep learning and reinforcement learning, and she is dedicated to creating innovative AI solutions with real-world applications.

Author Metrics

Dr. Manijeh Emdadi has made significant contributions to the academic field, particularly in the domains of Artificial Intelligence, Machine Learning, and healthcare applications. She has authored several impactful publications in high-ranking journals, focusing on areas such as predictive modeling, explainable AI, and healthcare decision support systems. Notable works include her study on “Introducing effective genes in lymph node metastasis of breast cancer patients using SHAP values based on the mRNA expression data,” published in Plos One (2024), and her exploration of grid synchronization methods in power converters, published in Electrical Engineering (2023). Additionally, Dr. Emdadi has authored research on key molecular mechanisms in papillary thyroid carcinoma and developed advanced AI models for predicting cancer metastasis. Her work has been well-received in both the academic and industry sectors, reflecting her expertise in applying AI and machine learning techniques to solve real-world challenges. Her research continues to have a notable impact, especially in healthcare, where her AI-driven models aim to advance personalized medicine and decision support systems.

Publications Top Notes 📄

1. “Introducing effective genes in lymph node metastasis of breast cancer patients using SHAP values based on the mRNA expression data”

  • Authors: SZ Vahed, SMH Khatibi, YR Saadat, M Emdadi, B Khodaei, MM Alishani, et al.
  • Journal: Plos One
  • Volume: 19
  • Issue: 8
  • Article Number: e0308531
  • Year: 2024
  • DOI: 10.1371/journal.pone.0308531
  • Summary: This paper applies SHAP (Shapley Additive Explanations) values to identify genes associated with lymph node metastasis in breast cancer patients, utilizing mRNA expression data for enhanced model interpretability.

2. “D-estimation method for grid synchronization of single-phase power converters: analysis, linear modeling, tuning, and comparison with SOGI-PLL”

  • Authors: H Sepahvand, M Emdadi
  • Journal: Electrical Engineering
  • Year: 2023
  • Summary: The study proposes a D-estimation method for grid synchronization in single-phase power converters. It provides a detailed analysis, linear modeling, tuning methods, and compares the performance with the traditional SOGI-PLL (Second-Order Generalized Integrator Phase-Locked Loop).

3. “Uncovering key molecular mechanisms in the early and late-stage of papillary thyroid carcinoma using association rule mining algorithm”

  • Authors: SM Hosseiniyan Khatibi, S Zununi Vahed, H Homaei Rad, M Emdadi, et al.
  • Journal: Plos One
  • Volume: 18
  • Issue: 11
  • Article Number: e0293335
  • Year: 2023
  • DOI: 10.1371/journal.pone.0293335
  • Summary: This research uses association rule mining to explore the molecular mechanisms involved in papillary thyroid carcinoma at various stages. The findings aim to reveal biomarkers for early diagnosis and targeted treatment strategies.

4. “Graph Fuzzy Attention Network Model for Metastasis Prediction of Prostate Cancer Based on mRNA Expression Data”

  • Journal: International Journal of Fuzzy Systems
  • Year: 2024
  • Summary: This paper introduces a Graph Fuzzy Attention Network (GFAN) model for predicting metastasis in prostate cancer using mRNA expression data. The model leverages the strengths of fuzzy logic and graph-based learning for enhanced prediction accuracy.

5. “Load-aware Channel Assignment and Routing in Clustered Multichannel and Multi-radio Mesh Networks”

  • Authors: M Emdadi, MR Shahsavari, MD TakhtFouladi
  • Year: Unspecified
  • Summary: This work discusses the optimization of channel assignment and routing protocols in clustered multi-channel and multi-radio mesh networks, with a focus on load-awareness for efficient resource utilization and network performance.

Conclusion

Dr. Manijeh Emdadi is exceptionally well-suited for the Best Researcher Award due to her pioneering work in artificial intelligence and its application to healthcare decision-making systems. Her strong academic background, innovative research, and commitment to advancing AI for healthcare make her an outstanding candidate. By enhancing collaborations with the industry and expanding her research scope, Dr. Emdadi can continue to build upon her current achievements and make even more significant contributions to both academic and real-world advancements in AI and healthcare.

In summary, Dr. Emdadi’s impressive AI expertise, innovative healthcare solutions, and strong academic contributions strongly align with the qualities sought for the Best Researcher Award.

Nithya Rekha Sivakumar | Deep Learning | Best Researcher Award

Published on by Network Science

Dr. Nithya Rekha Sivakumar | Deep Learning | Best Researcher Award

Associate Professor, Princess Nourah Bint Abdulrahman University, Saudi Arabia📖

Dr. Nithya Rekha Sivakumar is an accomplished academician and researcher, currently serving as an Associate Professor of Computer Science at the College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia. She holds a Ph.D. in Computer Science from Periyar University, India, specializing in Mobile Computing and Wireless Networks with Fuzzy and Rough Set Techniques, funded by a prestigious UGC BSR Fellowship. Dr. Sivakumar also earned her M.Phil. in Data Mining, MCA in Computer Applications, and B.Sc. in Computer Science. With over 15 years of academic experience, she has served in diverse roles across reputed institutions in India and Saudi Arabia. Her research interests include wireless networks, mobile computing, data mining, and intelligent systems, with extensive contributions as a researcher, reviewer, and speaker in international conferences and journals. A recipient of multiple awards, including the “Best Distinguished Researcher Award,” she has secured research grants and actively evaluates Ph.D. theses globally. Dr. Sivakumar is also a member of IEEE and IAENG and continues to contribute to advancements in computing through teaching, research, and scholarly activities.

Profile

Orcid Profile

Google Scholar Profile

Education Background🎓

Dr. Rekha earned her Ph.D. in Computer Science from Periyar University, India, in 2014, supported by the prestigious UGC BSR Fellowship. Her doctoral research focused on mobile computing and wireless networks with fuzzy and rough set techniques. She also holds an M.Phil. in Computer Science from PRIST University (2009), an MCA from IGNOU (2007), and a B.Sc. in Computer Science from Bharathiar University (1996).

Professional Experience🌱

Dr. Rekha has over 15 years of academic and research experience. She has been with Princess Nourah Bint Abdul Rahman University since 2017, progressing from Assistant to Associate Professor. Prior to this, she served as an Assistant Professor at Qassim Private Colleges, Saudi Arabia, and held teaching roles in leading Indian institutions such as Vivekanandha College of Arts and Sciences and Excel Business School. She has also contributed to non-academic roles, including as a Java Programmer and high school teacher.

Research and Service🔬

Dr. Rekha’s research interests span mobile computing, e-governance, and advanced data mining techniques. She has evaluated over 20 Ph.D. theses as a foreign examiner and served as a reviewer for esteemed journals such as IEEE Access, Springer, and Elsevier. A sought-after speaker, she has been invited to international seminars and conferences across the globe, sharing her expertise in computational science and emerging technologies.

Dr. Rekha continues to inspire through her teaching, research, and unwavering commitment to advancing the field of computer science.

Author Metrics 

Dr. Nithya Rekha Sivakumar has an impressive author profile, with a strong presence in international research communities. She has published over 40 papers in reputed journals and conferences, many indexed in Scopus and Web of Science, reflecting her contributions to fields like wireless networks, mobile computing, and data mining. Her work has garnered significant recognition, with an h-index of 12 and over 400 citations, underscoring the impact and relevance of her research. She has authored and co-authored book chapters published by renowned publishers such as Springer and Wiley, further highlighting her expertise. As a sought-after reviewer for top-tier journals, she actively contributes to maintaining the quality of scientific publications. Dr. Sivakumar’s research outputs, combined with her active engagement in scholarly dissemination, establish her as a leading voice in her domain.

Honors and Research Grants

Dr. Rekha has received numerous accolades, including the “Best Distinguished Researcher Award” (2015-2016) and multiple research grants from Princess Nourah Bint Abdul Rahman University, amounting to SAR 40,000 through the Fast Track Research Funding program. She has also been recognized for her doctoral research by the University Grants Commission, India, and secured a travel grant from the Indian Department of Science and Technology to present her work internationally

Publications Top Notes 📄

“Increasing Fault Tolerance Ability and Network Lifetime with Clustered Pollination in Wireless Sensor Networks”

  • Authors: TKNVD Achyut Shankar, Nithya Rekha Sivakumar, M. Sivaram, A. Ambikapathy
  • Journal: Journal of Ambient Intelligence and Humanized Computing
  • Year: 2020
  • Impact: The paper focuses on improving the fault tolerance and lifespan of wireless sensor networks through an innovative clustered pollination-based approach.

“Stabilizing Energy Consumption in Unequal Clusters of Wireless Sensor Networks”

  • Author: NR Sivakumar
  • Journal: Computational Materials and Continua
  • Volume: 64
  • Pages: 81-96
  • Year: 2020
  • Impact: This paper addresses energy stabilization in wireless sensor networks by proposing techniques to manage energy distribution across unequal clusters, enhancing network sustainability.

“Enhancing Network Lifespan in Wireless Sensor Networks Using Deep Learning-based Graph Neural Network”

  • Authors: NR Sivakumar, SM Nagarajan, GG Devarajan, L Pullagura, et al.
  • Journal: Physical Communication
  • Volume: 59
  • Article No.: 102076
  • Year: 2023
  • Impact: The paper investigates how deep learning-based graph neural networks can be used to enhance the lifespan of wireless sensor networks, marking a significant contribution to AI-powered network optimization.

“Simulation and Evaluation of the Performance on Probabilistic Broadcasting in FSR (Fisheye State Routing) Routing Protocol Based on Random Mobility Model in MANET”

  • Authors: NR Sivakumar, C Chelliah
  • Conference: 2012 Fourth International Conference on Computational Intelligence
  • Year: 2012
  • Impact: This study explores the performance of the Fisheye State Routing (FSR) protocol in mobile ad hoc networks (MANETs), with an emphasis on the effects of random mobility models on network behavior.

“An IoT-based Big Data Framework Using Equidistant Heuristic and Duplex Deep Neural Network for Diabetic Disease Prediction”

  • Authors: NR Sivakumar, FKD Karim
  • Journal: Journal of Ambient Intelligence and Humanized Computing
  • Year: 2023
  • Impact: This paper presents an IoT-based framework utilizing big data and deep learning for predicting diabetic diseases, offering a new approach to healthcare prediction systems through advanced technologies.

Conclusion

Dr. Nithya Rekha Sivakumar is a deserving candidate for the Best Researcher Award. Her impressive research accomplishments, strong publication record, innovative contributions to wireless networks and mobile computing, and active engagement in the academic community make her an outstanding researcher. Although there are areas for improvement, particularly in interdisciplinary collaboration and public outreach, her overall research trajectory and impact are exemplary. Dr. Sivakumar’s continuous pursuit of excellence in her field and her ability to address contemporary challenges in mobile computing, data mining, and wireless networks position her as a leading researcher in her domain. She is highly recommended for the Best Researcher Award.