Le Yao | Computer Science | Best Researcher Award

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Prof. Le Yao | Computer Science | Best Researcher Award

Prof. Le Yao, Hangzhou Normal University, China

Le Yao is an accomplished Associate Professor at the School of Mathematics, Hangzhou Normal University, China. With a strong background in control science and engineering, he specializes in data-driven process modeling, soft sensor development, quality-related fault diagnosis, and industrial causal analysis. His research focuses on deep learning, interpretable modeling, and causal analysis for industrial applications. Le Yao has been actively involved in multiple funded projects supported by NSFC and the China Postdoctoral Science Foundation. He has an impressive academic record, with numerous high-impact publications in IEEE Transactions and other renowned journals. Recognized for his contributions, he has received prestigious awards, including the National Scholarship for Ph.D. and Outstanding Dissertation Awards. His innovative work bridges the gap between theoretical advancements and practical applications in industrial processes, making significant contributions to smart manufacturing and intelligent systems.

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Summary of Suitability for the ‘Research for Best Researcher Award’

Le Yao is an exceptional candidate for the ‘Research for Best Researcher Award,’ given his impressive academic journey, extensive research contributions, and leadership in the field of industrial data-driven modeling. His work focuses on crucial areas such as soft sensor modeling, quality prediction, fault diagnosis, and causal analysis, with significant contributions to process control in industrial settings. His innovations in deep learning, causal analysis, and interpretable process modeling have greatly advanced the application of machine learning techniques to complex, large-scale industrial systems.

Notably, his research on scalable and distributed parallel modeling for big process data, combined with his exploration of probabilistic modeling and causal discovery methods, reflects a profound understanding of both theoretical and practical aspects of industrial systems. His ability to fuse domain knowledge with data-driven techniques has led to breakthroughs in process quality prediction and fault detection, impacting industries significantly. Furthermore, Le Yao has successfully secured competitive research funding from prestigious sources, such as the National Natural Science Foundation of China (NSFC) and the China Postdoctoral Science Foundation, demonstrating his capability to lead high-level research initiatives.

🎓 Education

Le Yao holds a Ph.D. in Control Science and Engineering from Zhejiang University (2019), where he specialized in big process data modeling, quality prediction, and process monitoring. His doctoral studies were pivotal in advancing soft sensor modeling techniques for industrial applications. Prior to his Ph.D., he earned an M.S. (2015) from Jiangnan University, where he focused on soft sensor modeling and system identification. His bachelor’s degree (2012) was also from Jiangnan University, where he developed a strong foundation in control science and engineering. Throughout his academic journey, Le Yao has consistently demonstrated excellence, securing prestigious scholarships and honors. His multidisciplinary expertise enables him to develop innovative solutions for industrial automation, smart manufacturing, and data-driven decision-making. His research contributions have influenced numerous industrial applications, bridging the gap between academic advancements and real-world implementations.

💼 Professional Experience 

Le Yao is currently an Associate Professor at Hangzhou Normal University (2022–present), where he leads research on deep learning, causal analysis, and interpretable modeling for industrial systems. Prior to this, he served as a Postdoctoral Researcher (2019–2022) at Zhejiang University’s Institute of Industrial Process Control, focusing on deep learning-driven process modeling and process knowledge fusion. During his postdoctoral tenure, he was awarded research grants from NSFC and the China Postdoctoral Science Foundation. His expertise spans scalable and distributed parallel modeling, soft sensor applications, and quality prediction in large-scale industrial systems. Le Yao’s research integrates advanced computational techniques with practical industrial challenges, driving innovation in smart manufacturing. His leadership in industrial data analytics and AI-driven process control has positioned him as a key contributor to the field, influencing both academic research and industry practices.

🏅 Awards and Recognition

Le Yao has been recognized with numerous prestigious awards for his academic and research contributions. He received the 2020 Outstanding Dissertation Award from the Chinese Institute of Electronics and was named an Outstanding Graduate by Zhejiang University and Zhejiang Province in 2019. His research excellence has been acknowledged through multiple National Scholarships for Ph.D. students (2017, 2018). His work has been featured in top-tier conferences, earning him Best Paper Finalist awards at IEEE DDCLS (2018) and China Process Control Conferences (2016, 2017, 2018). These accolades reflect his outstanding contributions to industrial process modeling, soft sensing, and causal analysis. His innovative approaches to quality prediction and fault diagnosis have significantly impacted the field, earning him recognition from both academic institutions and industry leaders. Le Yao’s commitment to excellence continues to drive his research endeavors, making him a prominent figure in data-driven industrial applications.

🌍 Research Skills On Computer Science

Le Yao’s research expertise spans multiple domains, including data-driven process modeling, soft sensor development, quality-related fault diagnosis, and industrial causal analysis. He specializes in deep learning techniques for process optimization and interpretable modeling to enhance decision-making in industrial environments. His work on scalable and distributed parallel modeling has introduced novel methodologies for handling big process data efficiently. His causal analysis research integrates process knowledge with data-driven approaches, improving anomaly detection and fault diagnosis. He has developed advanced deep learning models incorporating hierarchical extreme learning machines and probabilistic latent variable regression. His research contributions have been implemented in real-world industrial applications, optimizing quality prediction and process control. With a strong foundation in control engineering, statistics, and artificial intelligence, Le Yao continues to advance the field by bridging theoretical research with industrial needs.

📖 Publication Top Notes

  • Deep learning of semisupervised process data with hierarchical extreme learning machine and soft sensor application

    • Authors: L Yao, Z Ge
    • Citation: 295
    • Year: 2017
    • Journal: IEEE Transactions on Industrial Electronics, 65 (2), 1490-1498

  • Big data quality prediction in the process industry: A distributed parallel modeling framework

    • Authors: L Yao, Z Ge
    • Citation: 108
    • Year: 2018
    • Journal: Journal of Process Control, 68, 1-13

  • Nonlinear probabilistic latent variable regression models for soft sensor application: From shallow to deep structure

    • Authors: B Shen, L Yao, Z Ge
    • Citation: 102
    • Year: 2020
    • Journal: Control Engineering Practice, 94, 104198

  • Scalable semisupervised GMM for big data quality prediction in multimode processes

    • Authors: L Yao, Z Ge
    • Citation: 90
    • Year: 2018
    • Journal: IEEE Transactions on Industrial Electronics, 66 (5), 3681-3692

  • Locally weighted prediction methods for latent factor analysis with supervised and semisupervised process data

    • Authors: L Yao, Z Ge
    • Citation: 80
    • Year: 2016
    • Journal: IEEE Transactions on Automation Science and Engineering, 14 (1), 126-138

  • Distributed parallel deep learning of hierarchical extreme learning machine for multimode quality prediction with big process data

    • Authors: L Yao, Z Ge
    • Citation: 62
    • Year: 2019
    • Journal: Engineering Applications of Artificial Intelligence, 81, 450-465

  • Moving window adaptive soft sensor for state shifting process based on weighted supervised latent factor analysis

    • Authors: L Yao, Z Ge
    • Citation: 62
    • Year: 2017
    • Journal: Control Engineering Practice, 61, 72-80

  • Cooperative deep dynamic feature extraction and variable time-delay estimation for industrial quality prediction

    • Authors: L Yao, Z Ge
    • Citation: 61
    • Year: 2020
    • Journal: IEEE Transactions on Industrial Informatics, 17 (6), 3782-3792

  • Online updating soft sensor modeling and industrial application based on selectively integrated moving window approach

    • Authors: L Yao, Z Ge
    • Citation: 60
    • Year: 2017
    • Journal: IEEE Transactions on Instrumentation and Measurement, 66 (8), 1985-1993

  • Parallel computing and SGD-based DPMM for soft sensor development with large-scale semisupervised data

    • Authors: W Shao, L Yao, Z Ge, Z Song
    • Citation: 53
    • Year: 2018
    • Journal: IEEE Transactions on Industrial Electronics, 66 (8), 6362-6373

SAMEERCHAND PUDARUTH | Artificial Intelligence | Academic Excellence Award

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Assoc. Prof. Dr. SAMEERCHAND PUDARUTH | Artificial Intelligence | Academic Excellence Award

Assoc. Prof. Dr. SAMEERCHAND PUDARUTH, University of Mauritius, Mauritius

Dr. Sameerchand Pudaruth is an Associate Professor in the Faculty of Information, Communication & Digital Technologies at the University of Mauritius. With a PhD in Artificial Intelligence, his expertise spans machine learning, computational linguistics, and data science. His academic journey reflects a deep commitment to technological advancement and knowledge dissemination. Having served as Head of the ICT Department (2019–2021), he played a key role in shaping academic policies and research initiatives. Dr. Pudaruth is an accomplished author and a sought-after researcher, contributing significantly to AI, distributed systems, and digital transformation. His membership in prestigious organizations such as IEEE, ACM, and IAENG highlights his standing in the global research community. Beyond academia, he is dedicated to mentoring students, fostering innovation, and driving interdisciplinary collaboration in AI. His work continues to bridge the gap between theoretical research and practical AI applications, making significant strides in Mauritius and beyond.

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Suitability for the Research for Best Researcher Award – Associate Professor Dr. Sameerchand Pudaruth

Dr. Sameerchand Pudaruth is a distinguished academic and researcher in Artificial Intelligence, Computer Science, and related fields, with a strong background in teaching, research, and leadership. With over two decades of experience, he has contributed significantly to the advancement of AI, distributed systems, multimedia, and computational linguistics. His PhD in Artificial Intelligence, combined with his legal studies, positions him uniquely at the intersection of AI and law, demonstrating an interdisciplinary approach to research.

His extensive publication record, including book chapters, conference proceedings, and journal articles, highlights his dedication to pushing the boundaries of AI, machine learning, and computational methodologies. His research contributions in legal text classification, intelligent systems, and IT support services optimization have real-world applications, making his work both academically rigorous and practically relevant. His leadership roles, including serving as Head of the ICT Department and holding senior positions in IEEE and ACM, reflect his commitment to advancing research and fostering collaboration in the global research community.

🎓 Education

Dr. Sameerchand Pudaruth holds a PhD in Artificial Intelligence from the University of Mauritius, awarded in 2020, focusing on AI-driven decision-making systems. His academic portfolio includes an MSc in Computer Science with a specialization in Distributed Systems and Multimedia (2004–2006) and a BSc (Hons) in Computer Science and Engineering (First Class) from the same institution (2000–2003). In addition, he pursued legal studies, earning an LLB from the University of London (2009–2012) and completing the Law Practitioner’s Vocational Course (2013). This diverse educational background equips him with a unique interdisciplinary perspective, blending AI, computational intelligence, and legal analytics. His continuous professional development is evident through active participation in research forums, conferences, and international collaborations. Dr. Pudaruth’s academic journey reflects a commitment to lifelong learning, innovation, and thought leadership in artificial intelligence and digital technologies.

💼 Professional Experience

Dr. Sameerchand Pudaruth has been an integral part of the University of Mauritius since 2007. Currently serving as an Associate Professor (since 2022), he previously held the positions of Senior Lecturer (2016–2022) and Lecturer (2007–2016). He also served as Head of the ICT Department (2019–2021), where he spearheaded academic advancements and research collaborations. His career is marked by expertise in AI, machine learning, and data-driven technologies, leading various projects focused on intelligent systems. He has mentored numerous postgraduate students and contributed extensively to curriculum development. Dr. Pudaruth’s industry collaborations have reinforced his reputation as a pioneer in applied AI research. His leadership in conferences and symposiums underscores his commitment to advancing knowledge in computational sciences. As an educator, researcher, and innovator, he continuously pushes the boundaries of AI applications, influencing both academia and industry.

🏅 Awards and Recognition

Dr. Sameerchand Pudaruth’s contributions to AI and computational research have earned him numerous accolades. He has been a Senior IEEE Member since 2020, recognizing his significant professional achievements. His role as a founding member and Vice-Chair of IEEE Mauritius Section (2018–2021) highlights his leadership in fostering AI research communities. He has received research grants and recognition for outstanding publications in AI, machine learning, and computational linguistics. His book “Python in One Week” (2010) and multiple Springer book chapters have cemented his reputation in AI education. His work on machine learning applications in law and data science has been widely cited, further amplifying his global impact. His membership in ACM, IAENG, and the Association for Computational Linguistics reflects his standing in the international AI research community. Through groundbreaking research, academic leadership, and scholarly contributions, Dr. Pudaruth continues to be a driving force in AI innovation.

🌍 Research Skills On Artificial Intelligence

Dr. Sameerchand Pudaruth specializes in artificial intelligence, machine learning, and computational linguistics. His expertise spans natural language processing, legal informatics, and AI-driven decision-making models. He has pioneered research in AI applications for law, healthcare, and distributed systems. With a deep understanding of neural networks, deep learning, and intelligent automation, he has contributed significantly to AI-driven knowledge systems. His work includes feature selection using evolutionary algorithms, predictive modeling, and sentiment analysis. He has led interdisciplinary collaborations to advance AI ethics, data privacy, and algorithmic fairness. His ability to translate complex AI concepts into real-world applications has led to impactful research publications. As a member of international AI organizations, he remains at the forefront of technological advancements, constantly exploring innovative solutions. Dr. Pudaruth’s research continues to influence academia, industry, and public policy, making AI more accessible and applicable across diverse domains.

 📖 Publication Top Notes

  1. Plant leaf recognition using shape features and colour histogram with K-nearest neighbour classifiers
    • Authors: T. Munisami, M. Ramsurn, S. Kishnah, S. Pudaruth
    • Citations: 262
    • Year: 2015
  2. People factors in agile software development and project management
    • Authors: V. Lalsing, S. Kishnah, S. Pudaruth
    • Citations: 195
    • Year: 2012
  3. Predicting the price of used cars using machine learning techniques
    • Authors: S. Pudaruth
    • Citations: 183
    • Year: 2014
  4. Automatic Recognition of Medicinal Plants using Machine Learning Techniques
    • Authors: A. Begue, V. Kowlessur, U. Singh, F. Mahomoodally, S. Pudaruth
    • Citations: 123
    • Year: 2017
  5. A review of mobile ad hoc NETwork (MANET) Protocols and their Applications
    • Authors: D. Ramphull, A. Mungur, S. Armoogum, S. Pudaruth
    • Citations: 98
    • Year: 2021
  6. Authorship attribution using stylometry and machine learning techniques
    • Authors: H. Ramnial, S. Panchoo, S. Pudaruth
    • Citations: 71
    • Year: 2016
  7. Sentiment analysis from Facebook comments using automatic coding in NVivo 11
    • Authors: S. Pudaruth, S. Moheeputh, N. Permessur, A. Chamroo
    • Citations: 53
    • Year: 2018
  8. Forgotten, excluded or included? Students with disabilities: A case study at the University of Mauritius
    • Authors: U. Singh, S. Pudaruth, R. Gunputh
    • Citations: 30
    • Year: 2017
  9. Resource allocation in 4G and 5G networks: A review
    • Authors: L.N. Degambur, A. Mungur, S. Armoogum, S. Pudaruth
    • Citations: 29
    • Year: 2021
  10. SuperFish: A Mobile Application for Fish Species Recognition using Image Processing Techniques and Deep Learning
  • Authors: P. Sameerchand, N. Nadeem, A. Chandani, K. Somveer, V. Munusami, S. Pudaruth
  • Citations: 26
  • Year: 2021

Sai Venkatesh Chilukoti | Computer Science | Best Researcher Award

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Mr. Sai Venkatesh Chilukoti | Computer Science | Best Researcher Award

Mr. Sai Venkatesh Chilukoti, University of Louisiana at Lafayette, United States

Sai Venkatesh Chilukoti is a dedicated researcher in Computer Engineering, currently pursuing a Ph.D. at the University of Louisiana at Lafayette under Dr. Xiali Hei. With a stellar academic record and a CGPA of 4.0, he specializes in Deep Learning, Network Security, and Cyber-Physical Systems. His research interests span Federated Learning, Differential Privacy, and Machine Learning applications in healthcare and security. Sai Venkatesh has contributed to multiple peer-reviewed journals and conferences, focusing on privacy-preserving AI and identity recognition. As a Research Assistant in the Wireless Embedded Device Security (WEDS) Lab, he has worked on cutting-edge projects integrating AI, privacy-enhancing techniques, and embedded security. With experience as a Teaching Assistant, he mentors students in Neural Networks, Python, and AI-related fields. His passion lies in translating research into real-world applications, particularly in medical imaging and cybersecurity. Sai is fluent in English, Hindi, and Telugu, and has strong technical skills in Python, PyTorch, and TensorFlow.

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Suitability for the Research for Best Researcher Award – Sai Venkatesh Chilukoti

Sai Venkatesh Chilukoti demonstrates an impressive academic and research portfolio, making him a strong contender for the Research for Best Researcher Award. Currently pursuing a Ph.D. in Computer Engineering at the University of Louisiana at Lafayette with a perfect 4.0/4.0 CGPA, he has developed expertise in deep learning, distributed computing, network security, and cyber-physical systems. His academic credentials are further strengthened by a solid foundation in electronics and communication engineering at the undergraduate level.

His research contributions are notable, particularly in privacy-preserving deep learning, federated learning, and medical AI applications. His work on diabetic retinopathy classification, gastrointestinal cancer prediction, and differential privacy models for healthcare data showcases both technical depth and real-world impact. His involvement in cutting-edge machine learning techniques, including LSTMs, Transformers, and convolutional networks, highlights his ability to innovate within the field. Furthermore, his research assistantship in Wireless Embedded Device Security (WEDS) Lab and role as a teaching assistant demonstrate both research rigor and mentorship capabilities.

🎓 Education 

Sai Venkatesh Chilukoti is currently pursuing a Ph.D. in Computer Engineering at the University of Louisiana at Lafayette (2021-2025, expected), under the supervision of Dr. Xiali Hei, with a perfect CGPA of 4.0. His coursework includes Deep Learning, Network Security, Distributed Computing, and Cyber-Physical Systems. His research focuses on privacy-preserving AI, federated learning, and deep learning model optimization.

He completed his B.Tech. in Electronics and Communication Engineering at Velagapudi Ramakrishna Siddhartha Engineering College (2017-2021), earning a CGPA of 8.53/10. His undergraduate studies encompassed AI, Python, Artificial Neural Networks, and Digital Signal Processing.

Throughout his education, Sai has actively engaged in research projects, including identity recognition using mmWave radar sensors, privacy-aware medical imaging, and deep learning applications in cybersecurity. His academic journey reflects a strong foundation in computational intelligence and a commitment to solving real-world challenges through innovative AI techniques.

💼 Professional Experience

Sai Venkatesh Chilukoti has extensive research and teaching experience, specializing in Deep Learning, Cybersecurity, and Federated Learning. As a Research Assistant at the Wireless Embedded Device Security (WEDS) Lab (2021-present), he has worked on privacy-preserving AI models, security solutions for embedded devices, and deep learning-based medical imaging applications. His work includes designing federated learning frameworks for decentralized AI and developing privacy-aware deep learning techniques.

As a Teaching Assistant for Neural Networks (2024-present), Sai mentors students in probability, calculus, and AI programming using PyTorch and Scikit-learn.

He has led numerous projects, such as statistical analysis of COVID-19 data, AI-driven financial forecasting, and deep learning applications in 3D printing quality control. His expertise extends to programming in Python, C, SQL, and MATLAB, along with experience in cloud computing and AI model deployment. He has also reviewed papers for IEEE Access and other reputed journals.

🏅 Awards and Recognition 

Sai Venkatesh Chilukoti has been recognized for his outstanding contributions to AI research, deep learning, and cybersecurity. He has received multiple conference paper acceptances, including at the Hawai’i International Conference on System Sciences (HICSS-56) and CHSN2021. His work on privacy-preserving AI has been published in high-impact journals like BMC Medical Informatics and Decision Making and Electronic Commerce Research and Applications.

He has also earned certifications in Deep Learning Specialization (Coursera), AI for Everyone (deeplearning.ai), and Applied Machine Learning in Python (University of Michigan). His research in Federated Learning has gained attention for its innovative approach to privacy protection in healthcare AI models. Additionally, Sai has contributed as a reviewer for IEEE Access and Euro S&P, demonstrating his expertise in computer security and AI ethics. His contributions to machine learning, cybersecurity, and privacy-aware AI continue to impact both academic and industrial domains.

🌍 Research Skills On Computer Science

Sai Venkatesh Chilukoti specializes in Federated Learning, Differential Privacy, and Deep Learning model optimization. His expertise spans AI-driven cybersecurity, identity recognition using mmWave radar sensors, and privacy-preserving medical imaging. He has worked extensively with machine learning frameworks such as PyTorch, TensorFlow, and Scikit-learn.

Sai has developed AI models for secure collaborative learning, utilizing techniques like DP-SGD for privacy preservation. His research also explores transformer-based architectures, convolutional networks, and ensemble learning methods to enhance predictive performance. He has integrated advanced optimization techniques, including adaptive gradient clipping and label smoothing, into deep learning pipelines.

He has hands-on experience with federated learning platforms like Flower and privacy-preserving AI models in medical data analysis. His work in statistical modeling, computer vision, and neural networks has contributed to breakthroughs in security and healthcare AI. Sai’s research aims to advance AI applications while maintaining ethical and privacy standards.

📖 Publication Top Notes

  • A reliable diabetic retinopathy grading via transfer learning and ensemble learning with quadratic weighted kappa metric
      • Authors: Sai Venkatesh Chilukoti, Liqun Shan, Vijay Srinivas Tida, Anthony S. Maida, Xiali Hei
      • Journal: BMC Medical Informatics and Decision Making
      • Volume: 24, Issue 1
      • Article Number: 37
      • Year: 2024
  • Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection
      • Authors: Vijay Srinivas Tida, Sai Venkatesh Chilukoti, Sonya H. Y. Hsu, Xiali Hei
      • Conference: 56th Hawaii International Conference on System Sciences
      • Year: 2023
  • Single Image Multi-Scale Enhancement for Rock Micro-CT Super-Resolution Using Residual U-Net
    • Authors: Liqun Shan, Chengqian Liu, Yanchang Liu, Yazhou Tu, Sai Venkatesh Chilukoti
    • Journal: Applied Computing and Geosciences
    • Year: 2024
  • Kernel-Segregated Transpose Convolution Operation
    • Authors: Vijay Srinivas Tida, Sai Venkatesh Chilukoti, Sonya H. Y. Hsu
    • Conference: 56th Hawaii International Conference on System Sciences
    • Year: 2023
  • Modified ResNet Model for MSI and MSS Classification of Gastrointestinal Cancer
    • Authors: Sai Venkatesh Chilukoti, C. Meriga, M. Geethika, T. Lakshmi Gayatri, V. Aruna
    • Book Title: High Performance Computing and Networking: Select Proceedings of CHSN 2021
    • Year: 2022
  • Enhancing Unsupervised Rock CT Image Super-Resolution with Non-Local Attention
    • Authors: Chengqian Liu, Yanchang Liu, Liqun Shan, Sai Venkatesh Chilukoti, Xiali Hei
    • Journal: Geoenergy Science and Engineering
    • Volume: 238
    • Article Number: 212912
    • Year: 2024
  • Method for Performing Transpose Convolution Operations in a Neural Network
    • Inventors: Vijay Srinivas Tida, Sonya Hsu, Xiali Hei, Sai Venkatesh Chilukoti, Yazhou Tu
    • Patent Application: US Patent App. 18/744,260
    • Year: 2024
  • IdentityKD: Identity-wise Cross-modal Knowledge Distillation for Person Recognition via mmWave Radar Sensors
    • Authors: Liqun Shan, Rujun Zhang, Sai Venkatesh Chilukoti, Xingli Zhang, Insup Lee
    • Conference: ACM Multimedia Asia
    • Year: 2024
  • Facebook Report on Privacy of fNIRS Data
    • Authors: M. I. Hossen, Sai Venkatesh Chilukoti, Liqun Shan, Vijay Srinivas Tida, Xiali Hei
    • Preprint: arXiv preprint arXiv:2401.00973
    • Year: 2024

Ghulam Mohi-ud-din | AI | Best Researcher Award

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Prof. Ghulam Mohi-ud-din | AI | Best Researcher Award

👤 Prof. Ghulam Mohi-ud-din, Northwestern Polytechnical University, China

Ghulam Mohi-ud-din is a highly skilled and accomplished professional in Software Engineering and Computer Science. Born in Rawalpindi, Pakistan, he has garnered extensive international experience in academia and industry. Holding a PhD in Software Engineering from the University of Florida, he has contributed to the field through teaching, research, and product development roles. With experience at prestigious institutions such as IBM, Oracle, and Nanchang Hangkong University, Ghulam’s work focuses on Artificial Intelligence, Machine Learning, and Software Engineering. His expertise in developing and coordinating large-scale projects and mentoring students has earned him recognition in the tech community.

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🌟  Summary of Suitability for the Research for Best Researcher Award:

Ghulam Mohi-ud-din is highly suitable for the Research for Best Researcher Award due to his extensive academic background, international exposure, and remarkable contributions to the fields of software engineering, artificial intelligence, and computer science. He has obtained a Ph.D. in Software Engineering from the prestigious University of Florida, USA, further solidifying his academic expertise. His earlier degrees, including a Master’s in Computer Science from the University of Florida and a Bachelor’s in Software Engineering from the University of Arizona, reflect his strong foundation in both theoretical and practical aspects of software development.

His professional journey demonstrates a unique combination of teaching, research, and industry experience. As a faculty member at Nanchang Hangkong University in China, he has significantly impacted students’ learning in areas such as Artificial Intelligence, Machine Learning, and Software Engineering.

🎓 Education

Ghulam Mohi-ud-din’s educational journey is marked by excellence in software engineering and computer science. He completed his PhD in Software Engineering from the University of Florida, USA, in January 2022, furthering his research in Artificial Intelligence. Prior to this, he obtained his Master of Science in Computer Systems Networking and Telecommunications from the same university in 2010. His academic career began with a Bachelor of Science in Software Engineering from the University of Arizona, USA, in 2007. Throughout his studies, Ghulam consistently demonstrated a strong aptitude for problem-solving and innovative thinking, especially in areas involving AI and data communication. His educational background laid a robust foundation for his subsequent professional achievements.

 💼  Professional Experience

Ghulam Mohi-ud-din’s career spans academia and industry, where he has made significant contributions. He currently serves as a faculty member at Nanchang Hangkong University in China, teaching courses in Artificial Intelligence, Machine Learning, Software Engineering, and Networking. Before this, he was a Product Development Coordinator at ResearchEx Ltd. in London, managing project costs, schedules, and performance. His previous roles at IBM as a Data Administrator and at Oracle Corporation as a Database Administrator allowed him to develop expertise in database management, system administration, and project coordination. Additionally, Ghulam has extensive experience in mentoring students, overseeing research projects, and securing funding for various academic initiatives. His work reflects a blend of technical knowledge and practical application, making him a key player in both academia and the technology industry.

🏅 Awards and Recognition

Throughout his career, Ghulam Mohi-ud-din has earned numerous accolades and recognition for his exceptional work in software engineering and academia. His research contributions in Artificial Intelligence and Machine Learning have been widely recognized in both academic and professional circles. He has successfully secured funding for various research projects, including a $25,000 grant for his work at Oracle Corporation. His leadership in the academic community, particularly in teaching and mentoring students, has made a lasting impact. Additionally, Ghulam’s innovative work in database management and system administration at IBM and Oracle garnered praise for its strategic insights and efficiency. His dedication to advancing technology and education has positioned him as a respected figure in the field of computer science.

🌍 Research Skills On AI

Ghulam Mohi-ud-din possesses a robust skill set in software engineering and artificial intelligence research. His expertise spans data analysis, machine learning, AI algorithms, and system architecture. He is proficient in applying research methodologies to develop innovative solutions in complex technological fields, including network communications and software testing. Throughout his academic and professional career, he has demonstrated an exceptional ability to design and execute research projects that lead to meaningful outcomes, including published papers and conference presentations. His work involves creating security protocols, optimizing system performance, and contributing to cutting-edge AI research. Ghulam is also skilled at managing interdisciplinary teams, coordinating projects, and mentoring students to foster research excellence. His research is characterized by a blend of theoretical knowledge and practical application, making him a versatile and accomplished researcher in his field.

📖 Publication Top Notes

  • A Wireless Sensor Network for Coal Mine Safety Powered by Modified Localization Algorithm
    • Authors: Hafiz Zameer ul Hassan, Anyi Wang, Ghulam Mohi-ud-din
    • Citation: Heliyon (2024-12)
  • Click-level Supervision for Online Action Detection Extended from SCOAD
    • Authors: Xiang Zhang, Yuhan Mei, Ye Na, Xia Ling Lin, Genqing Bian, Qingsen Yan, Ghulam Mohi-ud-din, Chen Ai, Zhou Li, Wei Dong
    • Citation: Future Generation Computer Systems (2024-12)
  • Unmanned Aerial Vehicle Intrusion Detection: Deep-meta-heuristic System
    • Authors: Shangting Miao, Quan Pan, Dongxiao Zheng, Ghulam Mohi-ud-din
    • Citation: Vehicular Communications (2024-04)
  • Real-time Portrait Image Retouching Extended from DualBLN
    • Authors: Xiang Zhang, Dawei Yan, Genqing Bian, Chengzhe Lu, Sifei Wang, Ghulam Mohi-ud-din, Qingsen Yan, Wei Dong
    • Citation: Expert Systems with Applications (2024-03)
  • Intrusion Detection Using Hybrid Enhanced CSA-PSO and Multivariate WLS Random-Forest Technique
    • Authors: Ghulam Mohi-ud-din, Liu Zhiqiang, Zheng Jiangbin, Wang Sifei, Lin Zhijun, Muhammad Asim, Yuxuan Zhong, Yuxin Chen
    • Citation: IEEE Transactions on Network and Service Management (2023-12)
  • Intrusion Detection Using Hybridized Meta-heuristic Techniques with Weighted XGBoost Classifier
    • Authors: Ghulam Mohiuddin, Zhijun Lin, Jiangbin Zheng, Junsheng Wu, Weigang Li, Yifan Fang, Sifei Wang, Jiajun Chen, Xinyu Zeng
    • Citation: Expert Systems with Applications (2023-12)
  • Intrusion Detection in Wireless Sensor Network Using Enhanced Empirical Based Component Analysis
    • Authors: Liu Zhiqiang, Ghulam Mohiuddin, Zheng Jiangbin, Muhammad Asim, Wang Sifei
    • Citation: Future Generation Computer Systems (2022-10)
  • NIDS: Random Forest Based Novel Network Intrusion Detection System for Enhanced Cybersecurity in VANET’s
    • Authors: Ghulam Mohi-ud-din, Jiangbin Zheng, Zhiqiang Liu, Muhammad Asim, Jiajun Chen, Jinjing Liu, Zhijun Lin
    • Citation: 2022 International Conference on Virtual Reality, Human-Computer Interaction and Artificial Intelligence (VRHCIAI) (2022-10)
  • A Novel Deep Learning-Based Security Assessment Framework for Enhanced Security in Swarm Network Environment
    • Authors: Zhiqiang Liu, Ghulam Mohi-ud-din, Jiangbin Zheng, Sifei Wang, Muhammad Asim
    • Citation: International Journal of Critical Infrastructure Protection (2022-09)
  • Modeling Network Intrusion Detection System Using Feed-Forward Neural Network Using UNSW-NB15 Dataset
    • Authors: Liu Zhiqiang, Ghulam Mohi-ud-din, Li Bing, Luo Jianchao, Zhu Ye, Lin Zhijun
    • Citation: 2019 IEEE 7th International Conference on Smart Energy Grid Engineering (SEGE) (2019-08)

Dr. Dezhang Lu | Veterinary Science and Veterinary Medicine | Best Researcher Award

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Dr. Dezhang Lu | Veterinary Science and Veterinary Medicine | Best Researcher Award 🏆

 Doctor. Dezhang Lu , Northwest A&F University, China,🎓

Professional Profile

🌟 Dr. De-zhang Lu: Pioneer in Veterinary Anesthesia🐾

📚Early Academic Pursuits 

De-Zhang Lu embarked on his academic journey at the College of Veterinary Medicine, Northeast Agriculture University. He earned his Bachelor’s degree in agronomy in July 2006, followed by a Master’s degree in Veterinary Medicine in July 2009. His passion for veterinary sciences led him to pursue a Ph.D. at the same institution, which he successfully completed in July 2011. These formative years laid a solid foundation for his future contributions to veterinary medicine.

👨‍🏫Professional Endeavors

Upon completing his Ph.D., Dr. De-Zhang Lu joined the College of Veterinary Medicine at Northwest A&F University as a lecturer in July 2011. His role involves teaching veterinary surgery and surgical operations to undergraduates. Additionally, he is actively engaged in clinical veterinary medicine at the university’s affiliated small-animal teaching hospital. His dedication to education and clinical practice has made him a vital part of the faculty.

🔬Contributions and Research Focus

Dr. Lu’s research interests are deeply rooted in clinical veterinary medicine, veterinary anesthesia and analgesia, and stem cell therapy in veterinary science. He has made significant contributions to these fields through various research projects and publications. Notably, he secured a grant from the Foundation for Talent of Northwest A&F University for his research on new anesthesia methods for pigs.

🏅Accolades and Recognition 

Dr. Lu’s work has been widely recognized within the veterinary community. His research has been published in reputable journals such as the Pakistani Veterinary Journal, ACTA VET BRNO, Medycyna Weterynaryjna, and the Journal of Integrative Agriculture. These publications have cemented his reputation as an expert in veterinary anesthesia and analgesia.

🌟Impact and Influence 

Through his teaching and research, Dr. Lu has significantly impacted the field of veterinary medicine. His work on anesthesia techniques has improved the welfare and treatment outcomes for animals. Additionally, his role as an educator has influenced countless veterinary students, shaping the next generation of veterinarians.

🔮Legacy and Future Contributions 

Dr. De-Zhang Lu continues to push the boundaries of veterinary science. His ongoing research and commitment to clinical practice ensure that he remains at the forefront of veterinary medicine. His future contributions are expected to further enhance veterinary surgical techniques and animal welfare, leaving a lasting legacy in the field.

📖Publications : 

Assoc Prof Dr. Chao Bian | Computer Science | Best Researcher Award

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Assoc Prof Dr. Chao Bian | Computer Science | Best Researcher Award 🏆

Associate Professor Doctor. Chao Bian , Yinchuan University of Science and Technology, China  🎓

Professional Profile

🌟Early Academic Pursuits 📚

Dr. Chao Bian’s academic journey began with a robust foundation in management and engineering sciences. Currently, he is an Associate Professor at Yinchuan University of Science and Technology while pursuing a PhD in Management Science and Engineering, with a specialization in Management Information Systems at Xi’an University of Architecture and Technology. His academic focus and dedication to the field of artificial intelligence and its applications to environmental issues mark the early stages of his distinguished career.

💼Professional Endeavors

Dr. Bian has seamlessly blended his academic pursuits with practical applications in his professional career. As an Associate Professor, he imparts knowledge and guides students at Yinchuan University of Science and Technology. Simultaneously, he is deeply involved in research, focusing on the integration of AI with environmental monitoring systems to tackle air pollution. His dual roles as an educator and researcher underscore his commitment to advancing both theoretical knowledge and practical solutions.

🔬Contributions and Research Focus 

Dr. Bian’s research primarily revolves around artificial intelligence and its application to air pollution issues. His significant contributions include: PM2.5 Prediction Model: Utilizing state trend awareness concepts and advanced big data analysis techniques, Dr. Bian developed a model combining Long Short-Term Memory (LSTM) neural networks with bagging ensemble learning algorithms. This model achieved a 12% reduction in error compared to traditional methods, enhancing prediction accuracy and generalizability. Fuzzy Evaluation Method: He introduced an innovative fuzzy evaluation method integrating evidence theory with the K-nearest neighbor (KNN) algorithm. This method provides a holistic assessment of atmospheric pollution, enhancing precision and reliability by synthesizing multifaceted, uncertain, and ambiguous environmental data.

🏆Accolades and Recognition 

Dr. Bian has led and participated in numerous regional and national research projects, including those funded by the Shaanxi Provincial Natural Science Foundation and the National Natural Science Foundation. His work has not only garnered academic recognition but also facilitated practical advancements in air pollution control technology. His paper, “Air Pollution Concentration Fuzzy Evaluation Based on Evidence Theory and the K-nearest Neighbor Algorithm,” published in Frontiers in Environmental Science, showcases his contributions to the field.

🌍Impact and Influence 

Dr. Bian’s innovative approaches have significantly impacted environmental monitoring and public health decision-making. His predictive models and fuzzy evaluation methods provide powerful analytical tools for assessing and addressing air pollution. These advancements aid policymakers and researchers in making informed decisions, thereby contributing to cleaner air and improved public health outcomes.

🔮Legacy and Future Contributions

Dr. Chao Bian continues to push the boundaries of artificial intelligence applications in environmental sciences. His ongoing research and development efforts aim to refine predictive models and enhance the precision of pollution assessments. By fostering collaborations between academia and industry, Dr. Bian ensures that his research translates into practical solutions, leaving a lasting legacy in the field of environmental monitoring and AI.

📖Publications : 

Dr. Shiney Jeyaraj | Computer Science | Best Researcher Award

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Dr. Shiney Jeyaraj | Computer Science | Best Researcher Award 🏆

Doctor. Shiney Jeyaraj , Shark AI Solutions, India  🎓

Professional Profile

🌟Early Academic Pursuits 📚

Dr. Shiney Jeyaraj’s journey in the realm of computer science began with her Bachelor’s in Computer Science Engineering from PSG College of Technology, Coimbatore, India, where she graduated with a CGPA of 8.74 in 2011. Her passion for artificial intelligence and machine learning led her to pursue a Master’s in Software Engineering at the College of Engineering, Guindy, Anna University, Chennai, India, graduating in 2017 with a CGPA of 8.67. Driven by a deep interest in Natural Language Processing (NLP), she continued her academic journey, earning a PhD in Information and Communication Engineering with a specialization in NLP from the same institution, submitting her thesis in 2023.

💼Professional Endeavors 

Dr. Shiney Jeyaraj is the founder of Shark AI Solutions, a pioneering company based in Chennai, India, specializing in data science, data analytics, NLP, machine learning, and related areas. Since its inception in July 2022, she has led numerous innovative projects, including developing predictive analytics and visualizations for industrial safety, contextual recommendation services, AI sentiment analysis engines, travel recommendation engines, and conversational chatbots.

Prior to founding Shark AI Solutions, Dr. Jeyaraj gained extensive experience as an NLP Researcher and Graduate Researcher at the College of Engineering, Guindy, where she worked on cutting-edge projects such as query identification systems using BERT embeddings, Siamese LSTM classifiers for semantic similarity, and domain-adaptive text summarization models.

🏆Accolades and Recognition 

Throughout her academic and professional career, Dr. Jeyaraj has received several prestigious awards and fellowships, including the Anna Centenary Research Fellowship for her meritorious PhD candidacy. Her innovative work and contributions to the field of NLP have been recognized and celebrated in various conferences and academic forums.

🌍Impact and Influence 

Dr. Jeyaraj’s work in NLP and AI has had a significant impact on various industries, from healthcare to travel and beyond. Her development of systems for predictive analytics, sentiment analysis, and recommendation engines has improved efficiency and decision-making processes across these sectors. Additionally, her contributions to academic research have advanced the understanding and application of NLP technologies.

🔮Legacy and Future Contributions 

As a thought leader in the field of AI and NLP, Dr. Shiney Jeyaraj continues to push the boundaries of technology and innovation. Through her company, Shark AI Solutions, she aims to address complex challenges and create impactful solutions that drive progress and enhance human capabilities. Her dedication to research and development ensures that she will remain at the forefront of advancements in AI and NLP, leaving a lasting legacy in the field.

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