Hong Wang | Artificial Intelligence | Best Researcher Award

Posted on 16/12/202416/12/2024 by ScienceFather

Prof. Hong Wang | Artificial Intelligence | Best Researcher Award

Prof. Hong Wang, Shandong Normal University, China

Prof. Wang earned his Ph.D. in Computer Science from the Chinese Academy of Sciences. His research focuses on Artificial Intelligence, Machine Learning, Healthcare Big Data, and Bioinformatics. 🧠 He has extensive teaching experience, with roles from Lecturer to Doctoral Supervisor. He has received multiple honors, including the Outstanding Graduate Tutor award and Shandong Province Science and Technology Progress prizes. 🏆 Prof. Wang has published widely, including papers on molecular property prediction and drug interactions. His current research includes cutting-edge AI applications in health. 💻

Publication Profile

Google Scholar

Education Background 🎓

Prof. Hong Wang completed his PhD in Computer Science from the Chinese Academy of Sciences in Beijing, China, from 1999 to 2002. Prior to that, he earned a Master of Science in Computer Science from Tianjin University in Tianjin, China, between 1988 and 1991. His academic journey began at Tianjin University, where he obtained his Bachelor of Science in Computer Science in 1988. His strong educational foundation has supported his exceptional career in AI, machine learning, and bioinformatics. 📚💻

Working Experience 👨‍🏫

Prof. Hong Wang has had a distinguished academic career at Shandong Normal University, starting as a Teaching Assistant from 1991 to 1995. He then served as a Lecturer from 1995 to 2000 and quickly advanced to the position of Associate Professor from 2000 to 2006. Since 2006, he has held the prestigious title of Professor, contributing significantly to the university’s academic growth. In 2009, Prof. Wang also became a Doctoral Supervisor, guiding the next generation of scholars and researchers. His career spans over three decades, focusing on teaching, research, and mentorship. 🎓📚👨‍🔬

Honors and Awards 🏅

Prof. Hong Wang has received numerous prestigious honors throughout his career, reflecting his dedication and contributions to academia. In March 2021, he was recognized as a March 8th Red Banner Holder. He was named Outstanding Graduate Tutor in September 2021 for his exceptional mentoring. In March 2019, he received the award for Outstanding Contribution to Achievement. His excellence in teaching was acknowledged with the University-Level Distinguished Teacher award in December 2014, followed by the Individual with Excellence in Teacher Ethics award in September 2014. Additionally, he was honored as a Good Teacher and Friend to College Students in January 2003. 🌟🎓👨‍🏫

Research Experience and Achievements 🔬

Prof. Hong Wang has led impactful research projects, including funding from the National Natural Science Foundation of China, with programs spanning from 2021 to 2024 (62072290) and 2017 to 2020 (61672329). He is also part of the Jinan City Science and Technology Bureau project from 2023 to 2024 (202228110). His outstanding contributions have earned him several prestigious awards, such as the Shandong Computer Society Science and Technology Progress Second Prize (First Place) in July 2024. Additionally, he received the Shandong Province Science and Technology Progress First Prize (7th place) in December 2022 and the Shandong Province Higher Education Outstanding Research Achievements Second Prize (First Place) in both 2020 and 2018. 🏆📚

Publication Top Notes

EDDINet: Enhancing drug-drug interaction prediction via information flow and consensus constrained multi-graph contrastive learning – 2024
EMPPNet: Enhancing Molecular Property Prediction via Cross-modal Information Flow and Hierarchical Attention – Cited by 3, 2023
GCNs–FSMI: EEG recognition of mental illness based on fine-grained signal features and graph mutual information maximization – Cited by 8, 2023
Detecting depression tendency with multimodal features – Cited by 9, 2023
A Soft-Attention Guidance Stacked Neural Network for neoadjuvant chemotherapy’s pathological response diagnosis using breast dynamic contrast-enhanced MRI – Cited by 1, 2023
Adaptive dual graph contrastive learning based on heterogeneous signed network for predicting adverse drug reactions – Cited by 6, 2023
Predicting drug-drug adverse reactions via multi-view graph contrastive representation model – Cited by 11, 2023
Explainable knowledge integrated sequence model for detecting fake online reviews – Cited by 9, 2023
CasANGCL: Pre-training and fine-tuning model based on cascaded attention network and graph contrastive learning for molecular property prediction – Cited by 19, 2023
Dual network contrastive learning for predicting microbe-disease associations – Cited by 2, 2022
Knowledge graph construction for computer networking course group in secondary vocational school based on multi-source heterogeneous data – Cited by 2, 2022
Test Paper Generation Based on Improved Genetic Simulated Annealing Algorithm – 2022
MS-ADR: Predicting drug–drug adverse reactions based on multi-source heterogeneous convolutional signed network – Cited by 6, 2022
Medical concept integrated residual short‐long temporal convolutional networks for predicting clinical events – Cited by 1, 2022

Debajyoti Dhar | Computer Science | Best Researcher Award

Posted on 22/11/202422/11/2024 by ScienceFather

Mr. Debajyoti Dhar | Computer Science | Best Researcher Award

Mr. Debajyoti Dhar, Atal Bihari Vajpayee Indian Institute of Information Technology and Management Gwalior, India

Debajyoti Dhar is an ambitious B.Tech student with a CGPA of 7.67/10, specializing in Computer Science. He has honed his skills through impactful internships, including as a Software Development Engineer at Defence Research and Development Establishment and a Full Stack Developer at Edilitics Private Limited. Debajyoti has contributed to projects like a Decentralized FPS Game with NFT Marketplace and a Ticket Management Platform, showcasing his expertise in blockchain, cloud systems, and machine learning. He has co-authored IEEE conference papers and a journal paper, demonstrating his strong research capabilities. 💻📊🔗

Publication Profile

Orcid

Education Background

Debajyoti Dhar is currently pursuing a Bachelor of Technology in Computer Science at the Indian Institute of Information Technology and Management Gwalior. He started his academic journey in December 2021 and is expected to graduate in July 2025. With a CGPA of 7.67/10.00, Debajyoti has demonstrated a strong academic performance, excelling in his coursework. His education has equipped him with a solid foundation in computer science, preparing him for advanced projects and research in areas such as software development, machine learning, and blockchain technology. 📚💻🚀

Professional Experience

Debajyoti Dhar has gained valuable experience through multiple internships, showcasing his expertise in software development. At Defence Research and Development Establishment (Dec 2022–Oct 2023), he developed a heavy gas detection model in Java and created a 2D plotter in Python for data visualization. During his time at Edilitics Private Limited (Apr–Jun 2023), he built a robust backend using FastAPI and enhanced development efficiency with CI/CD pipelines and Docker. At Mak Design Private Limited (May–Jul 2024), he created a real-time chat module with Django and ReactJS, ensuring end-to-end encryption. 💻🔧🚀

Achievements

Debajyoti Dhar has demonstrated exceptional skills through various achievements. As a freelance developer for Metarootz, he built a full-stack blockchain social media project using NodeJS, ExpressJS, and MongoDB for the backend, and NextJS with TailwindCSS for the frontend. He delivered a comprehensive 5-day training bootcamp on web app deployment automation with Docker, Kubernetes, and Github Actions for industry professionals. Debajyoti has also co-authored two IEEE conference papers on computer vision and deep learning and contributed to a machine learning paper in MDPI Sensors journal. Additionally, he solved 300+ DSA questions on GFG and LeetCode. 📈💻📚

Research Focus

Mr. Debajyoti Dhar has contributed significantly to machine learning and optimization techniques, particularly in the context of environmental prediction. His recent work, “Highly Efficient JR Optimization Technique for Solving Prediction Problem of Soil Organic Carbon on Large Scale”, published in Sensors, demonstrates his expertise in applying advanced algorithms to solve agricultural and environmental challenges. The research focuses on soil organic carbon prediction using machine learning models, emphasizing scalability and efficiency. This aligns with his broader focus on data science, AI-driven predictions, and sustainable technologies to address complex real-world problems in various domains. 🌍🤖📊

Publication Top Notes

Highly Efficient JR Optimization Technique for Solving Prediction Problem of Soil Organic Carbon on Large Scale (2024) 📚

Deepali Bhamare | Deep Learning | Best Researcher Award

Posted on 22/10/202422/10/2024 by ScienceFather

Ms. Deepali Bhamare | Deep Learning | Best Researcher Award

Ms. Deepali Bhamare, S.S.V.P.S.B.D’s COE Dhule, India

Deepali Bhamare is an accomplished educator and engineer with over two decades of experience in electronics and telecommunication. She holds a B.E. in Electronics and Telecommunication from NMU Jalgaon (2002), an M.E. in Digital Communication from R.G.P.V. Bhopal (2012), and is pursuing a PhD in Electronics Engineering. Deepali has worked in industry as a QC and Testing Engineer before transitioning into academia, where she currently serves as Assistant Professor at S.S.V.P.S. College of Engineering Dhule. She has actively contributed to various institutional committees and has attended numerous FDPs and workshops related to AI, machine learning, and research methodologies. 🎓📊📡

Publication Profile

Scopus

Educational Qualification

Ms. Deepali Bhamare has a robust educational background in Electronics and Telecommunications. She completed her H.S.C. in Science with 74.08%, followed by a B.E. in Electronics and Telecommunication Engineering from N.M.U. Jalgaon (64%) in 2002. She also holds a master’s degree in Digital Communication from R.G.P.V. Bhopal (75.03%), and is currently pursuing her PhD in Electronics Engineering from N.M.U., which showcases her commitment to advancing her expertise in the field.

Professional Experience

Her extensive work experience spans across both industry and academia. She worked as a Quality Control Engineer at renowned firms like Satronix India Pvt Ltd and Penguin Audio Products Ltd. Since 2008, she has transitioned into academia, holding roles such as Lecturer and Assistant Professor at S.S.V.P.S. College of Engineering, Dhule. Her academic career, coupled with her technical experience, demonstrates her comprehensive understanding of engineering principles and practical applications.

Training and Development

Ms. Bhamare has actively participated in various Faculty Development Programs (FDPs), Short-Term Training Programs (STTPs), and workshops. Notable topics include “Next Generation Artificial Intelligence,” “Python Programming with Django,” “Machine Learning and Deep Learning,” “Neural Networks and Fuzzy Logic,” and “Artificial Intelligence in Healthcare.” These programs indicate her continuous efforts to stay updated with emerging technologies, particularly in AI, machine learning, and data science.

Academic Involvement

In addition to teaching, she has held several key positions in her college, such as Member of the Anti-Ragging Committee, BC Cell, and Extra Curricular Cell, as well as Lab In-Charge. These roles highlight her dedication to both student welfare and the efficient management of college resources.

Conclusion

Ms. Deepali Bhamare’s well-rounded qualifications, research pursuits in electronics, and ongoing professional development through numerous FDPs and workshops position her as a strong candidate for the Best Researcher Award. Her blend of academic knowledge, research focus, and involvement in emerging technologies such as AI and machine learning, make her a notable contributor to the field of electronics engineering.

Publication Top Notes

A Review on Person Identification Using Periocular Biometrics

Person Identification System Using Periocular Biometrics Based on Hybrid Optimal Dense Capsule Network

Noor .A. Rashed | Computer Science Award | Women Researcher Award

Posted on 28/09/202417/10/2024 by ScienceFather

Dr . Noor .A. Rashed | Computer Science Award | Women Researcher Award

Dr. Noor Rashid, Iraq

Dr. Noor Rashid is a Ph.D. candidate at the University of Technology, Baghdad, specializing in Computer Science. She earned her master’s degree from the University of al-Anbar in 2018. Her research covers areas such as Artificial Intelligence, secure data systems, machine learning, data mining, image processing, and project management automation. Her current focus is on optimization algorithms, particularly multi-objective optimization (2022-2023). Dr. Rashid has contributed significantly to the field, including her recent publication on evolutionary and swarm-based algorithms. She continues to advance AI and optimization research in her academic journey.

Publication profile

Google Scholar

Orcid

Employment

Dr. Noor Rashid is currently employed at the University of Technology, Baghdad, Iraq, in the Department of Computer Science. As a dedicated researcher and educator, she contributes to the university’s mission by advancing studies in Artificial Intelligence, secure data systems, and optimization algorithms. Her role involves teaching and mentoring students while conducting innovative research in multi-objective optimization and machine learning. Dr. Rashid’s work continues to impact both the academic community and the broader technological landscape through her involvement in cutting-edge computer science projects.

Education and Qualifications 🎓📜

Dr. Noor Rashid is currently pursuing her Ph.D. in Computer Science at the University of Technology, Baghdad, Iraq, from November 2021 to November 2024. Her doctoral research focuses on advanced areas such as optimization algorithms and Artificial Intelligence, contributing to cutting-edge technological advancements. Prior to this, Dr. Rashid earned her master’s degree from the College of Computer Science and Information Technology at the University of al-Anbar in 2018. Her academic background equips her with a strong foundation in secure data, machine learning, and project management systems, preparing her for continued success in the field.

Research Focus 🎯🔬

Dr. Noor Rashid’s research primarily focuses on Artificial Intelligence (AI), particularly in machine learning, optimization algorithms, and data mining. Her studies delve into complex areas such as multi-objective optimization and evolutionary algorithms, aiming to solve real-world computational problems. Additionally, Dr. Rashid has worked extensively on medical image processing, applying AI techniques like ANN and SVM to detect and classify diseases like diabetic retinopathy. Her research bridges the gap between AI and healthcare, making significant contributions to secure data, networks, and advanced algorithmic developments. 🚀🧠

Publication Top Notes

Diagnosis retinopathy disease using GLCM and ANN – N. Rashed, S. Ali, A. Dawood – J. Theor. Appl. Inf. Technol 96, 6028-6040, 2018 (Cited by: 4) 📖
Unraveling the Versatility and Impact of Multi-Objective Optimization: Algorithms, Applications, and Trends for Solving Complex Real-World Problems – N.A. Rashed, Y.H. Ali, T.A. Rashid, A. Salih – arXiv preprint, 2024 (Cited by: 2) 🌐
Advancements in Optimization: Critical Analysis of Evolutionary, Swarm, and Behavior-Based Algorithms – Rashed, Y.H. Ali, T.A. Rashid – Algorithms 17(9), 416, 2024 📑
ANN and SVM to recognize Texture features for spontaneous Detection and Rating of Diabetic Retinopathy – Rashed (Upcoming) 🔍

Deepali Hirolikar | Machine Learning Award | Best Researcher Award

Posted on 23/07/202424/07/2024 by ScienceFather

Dr. Deepali Hirolikar | Machine Learning Award | Best Researcher Award

Dr. Deepali Hirolikar, PDEA,s College of Engineering, Manjari(Bk.), Pune, India

Dr. Deepali S. Hirolikar is the Head of the Department of Information Technology at PDEA’s College of Engineering, Pune, with 18 years of experience in academia. She holds a PhD in Information Technology from Shri JJT University, Rajasthan. Dr. Hirolikar has published numerous papers in national and international journals, focusing on topics such as IoT, cloud computing, and machine learning. She has also published a book on IoT security paradigms. As an active contributor to various workshops and conferences, she has received multiple accolades for her work. 🖥️📚🎓

Publication Profile

Orcid

Experience 🏆

Prof. Dr. Deepali S. Hirolikar has amassed over 18 years of experience in academia. She currently serves as the Head of the Information Technology Department and Assistant Professor at PDEA’s College of Engineering, Manjari, Pune, a position she has held since September 6, 2005. Before this, she was a Lecturer in the Computer Engineering Department at SRGSIOT, Hadapsar.

Education 📚

She completed her SSC at Keshavraj Vidyalaya, Latur in 1995 with distinction, and her HSC at Dayanand Science Junior College, Latur in 1997 with first class. She earned her Diploma in Computer Science Engineering from PLGP, Latur in 2000 with first class, and her BE in Computer Science and Engineering from Dr. BAMU, Aurangabad in 2004 with distinction. Prof. Dr. Hirolikar obtained her ME in Information Technology from UOP Pune, MIT College of Engineering, Pune in 2011 with first class, and her PhD in Information Technology from Shri JJT University, Rajasthan in 2021.

Research Focus

Deepali Hirolikar’s research primarily focuses on using metaheuristic methods and machine learning for efficiently predicting and classifying heart disease data. Her work includes the development and application of advanced algorithms to enhance the accuracy and efficiency of heart disease prediction models. By leveraging mathematical and engineering principles, she contributes to the field of medical data analysis, particularly in identifying patterns and improving diagnostic processes. Her research also spans the integration of machine learning techniques with medical datasets to facilitate better health outcomes.

Publication Top Notes

Metaheuristic Methods for Efficiently Predicting and Classifying Real Life Heart Disease Data Using Machine Learning

Qibin Zhao | Machine Learning Award | Best Researcher Award

Posted on 09/03/202409/03/2024 by ScienceFather

Prof Dr. Qibin Zhao | Machine Learning Award | Best Researcher Award

Prof Dr. Qibin Zhao, RIKEN, Japan

👨‍💼 Dr. Qibin Zhao is a prominent figure in the field of machine learning and deep learning, serving as the Team Leader at RIKEN Center for Advanced Intelligence Project in Tokyo, Japan. With a Ph.D. in Computer Science and Engineering from Shanghai Jiao Tong University, China, his expertise spans across tensor networks, computer vision, and brain imaging/signal processing. Dr. Zhao has received numerous research grants and awards, including the ICASSP Best Student Paper Award in 2019. He actively contributes to academic activities as an area chair and organizer in prestigious conferences like NeurIPS and ICML, while also serving as a reviewer for leading journals.

Publication Profile:

Scopus

Education

📚 Dr. Qibin Zhao’s academic journey is marked by excellence and dedication. He earned his Ph.D. in Computer Science and Engineering from Shanghai Jiao Tong University, China, from 2004 to 2009, laying the foundation for his future contributions to the field. Prior to this, he obtained his M.S. in Computer Science at Guangxi University, China, from 2001 to 2004, and his B.S. in Computer Science at Henan University of Science and Technology, China, from 1996 to 2000. This comprehensive educational background equipped him with the necessary skills and knowledge to excel in his career in research and academia. 🎓

Working Experience

👨‍💼 Dr. Qibin Zhao’s professional journey reflects a commitment to advancing the fields of artificial intelligence and computer science. Since 2020, he has held the position of Team Leader at the Tensor Learning Team within the RIKEN Center for Advanced Intelligence Project in Tokyo, Japan, guiding cutting-edge research initiatives. Concurrently, he serves as a Visiting Professor at Tokyo University of Agriculture and Technology and was a Part-time Lecturer at Waseda University, both in Tokyo. His leadership roles include being the Unit Leader of the Tensor Learning Unit at RIKEN from 2017 to 2020. Dr. Zhao’s international influence extends to his visiting professorships in China and Japan, alongside his impactful research scientist roles at RIKEN. 🌐

Awards and Honors:

🏆 Dr. Qibin Zhao’s contributions to signal processing and artificial intelligence have garnered significant recognition. Notable among his accolades is the 2019 ICASSP Best Student Paper Award for groundbreaking work presented by L. Yuan. His research excellence was further acknowledged with the 2018 IEEE Signal Processing Magazine Best Paper Award, authored by A. Cichocki and team. Dr. Zhao’s impact extends to Japan, where he received the 3rd IEEE Signal Processing Society Japan Best Paper Award in 2018. Additionally, he has been honored with the 5th Research Incentive Award by the RIKEN President in 2014, among other prestigious recognitions for his pioneering research in brain signal decoding and affective brain-computer interfaces. 🌟

Research Focus:

🔬 Dr. Qibin Zhao’s research primarily focuses on advanced techniques in tensor decomposition and multiway data analysis, leveraging the power of tensor networks in various applications. His work encompasses areas such as semi-supervised multi-view concept decomposition, robust kernel PCA for multidimensional data, Bayesian tensor factorization for scalable analysis, and noisy tensor completion methods. With expertise in tensor ring factorization, he explores innovative approaches for image completion, fusion, and analysis in hyperspectral and multispectral domains. Dr. Zhao’s contributions extend to exclusive and consistent NMF for multi-view representation learning, deep matrix factorization with hypergraph regularization, and novel tensorized transformer networks for medical image segmentation. 🧠

Publication Top Notes:

Semi-supervised multi-view concept decomposition – Jiang, Q., Zhou, G., Zhao, Q. (2024) Expert Systems with Applications 📝
- Citations: 0
Noisy Tensor Completion via Low-Rank Tensor Ring – Qiu, Y., Zhou, G., Zhao, Q., Xie, S. (2024) IEEE Transactions on Neural Networks and Learning Systems 📝
- Citations: 8, Cited by: Unknown
Exclusivity and consistency induced NMF for multi-view representation learning – Huang, H., Zhou, G., Zheng, Y., Yang, Z., Zhao, Q. (2023) Knowledge-Based Systems 📝
- Citations: 0, Cited by: Unknown
Diverse Deep Matrix Factorization with Hypergraph Regularization for Multi-View Data Representation – Huang, H., Zhou, G., Liang, N., Zhao, Q., Xie, S. (2023) IEEE/CAA Journal of Automatica Sinica 📝
- Citations: 3, Cited by: Unknown
TT-Net: Tensorized Transformer Network for 3D medical image segmentation – Wang, J., Qu, A., Wang, Q., Liu, J., Wu, Q. (2023) Computerized Medical Imaging and Graphics 📝
- Citations: Unknown, Cited by: Unknown

Fangxin Fang | Machine learning | Best Researcher Award

Posted on 27/02/202427/02/2024 by ScienceFather

Dr. Fangxin Fang | Machine learning | Best Researcher Award

Dr. Fangxin Fang, Imperial College London, United Kingdom

Dr. Fangxin Fang 🌐 is a pioneering researcher in numerical simulations of nonlinear fluid flows 🌊. With a PhD from James Cook University 🎓, Australia, and extensive experience at Imperial College London 🏛️, he integrates advanced techniques like Machine Learning and Data Assimilation for accurate predictions. Dr. Fang’s innovative work has led to software development 🖥️, numerous publications 📚, and successful grant applications totaling over £10M 💰. A leader in project management and collaboration, he fosters interdisciplinary research 🤝. His contributions extend to teaching 🎓, mentoring, and impactful engagement with industry and academia worldwide 🌍.

Publication Profile:

Scopus

Google scholar

Education:

🎓 Dr. Fangxin Fang’s educational journey reflects a commitment to academic excellence across diverse continents. Beginning with a Bachelor’s in Engineering from Hehai University, China, they laid the groundwork for their scholarly pursuits. This was followed by a Master of Science degree from the North China Institute of Water Conservancy and Hydropower, China, where they deepened their expertise in a specialized field. Undeterred by geographical boundaries, they pursued doctoral studies at James Cook University, Australia, earning a PhD. Each academic milestone represents their dedication to learning and growth, culminating in a comprehensive understanding of their field. 🌐

Employment:

🌟 Dr. Fangxin Fang’s professional journey spans continents, showcasing their invaluable contributions to the global scientific community. They commenced as an Engineering Assistant at the China Institute of Water Resources and Hydropower Research, Beijing, China, before transitioning to academia as a Lecturer at the North China Institute of Water Conservancy and Hydropower, Beijing. Following their passion, they pursued doctoral studies at James Cook University, Australia, setting the stage for a distinguished career. Over the years, they’ve held pivotal roles, from Research Assistant at the University of Auckland, New Zealand, to Postdoctoral Researcher in Toulouse, France. Currently, they serve as a Senior Research Fellow at Imperial College, London, UK, solidifying their legacy in Earth Science and Engineering. 🌍

Research Focus:

Dr. Fangxin Fang’s 🌊 research focus lies at the intersection of computational fluid dynamics and advanced data-driven methods. With a keen emphasis on numerical simulations, his work delves into understanding complex fluid dynamics in various domains such as oceanography, atmospheric science, and urban environments. Utilizing techniques like reduced order modeling, machine learning, and deep convolutional networks, Dr. Fang explores nonlinear spatio-temporal fluid flows. His contributions pave the way for enhanced predictions of phenomena like ocean currents, air pollution dispersion, and flood dynamics. Dr. Fang’s innovative research transcends traditional boundaries, offering insights crucial for environmental sustainability and disaster management 🌍.