Jiawen Xu | Engineering | Best Researcher Award

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Assoc Prof Dr. Jiawen Xu | Engineering | Best Researcher Award

Southeast University | China

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Early Academic Pursuits 📚

Dr. Jiawen Xu's academic journey began with his undergraduate studies at the University of Science and Technology of China, where he pursued a Bachelor’s degree in Precision Machinery and Precision Instrumentation from 2005 to 2009. His interest in advanced engineering led him to continue his studies at the same institution for his Master’s degree, focusing on the same field under the guidance of Professor Zhihua Feng. Dr. Xu's pursuit of higher knowledge took him to the University of Connecticut, Storrs, where he completed his Ph.D. in Mechanical Engineering in 2017, working under Professor Tang Jiong. His early academic pursuits laid a strong foundation for his research in mechanical piezoelectric metamaterials and structural health monitoring.

Professional Endeavors 🛠️

Since 2018, Dr. Xu has served as an Associate Professor at the School of Instrument Science and Engineering, Southeast University, Nanjing, China. His professional career is distinguished by his involvement in cutting-edge research and development projects. Dr. Xu has led several key projects funded by national and provincial programs, including research on piezoelectric metamaterials and energy harvesting systems. His role in these projects underscores his expertise in vibration energy harvesting, structural health monitoring, and mechanical metamaterials.

Contributions and Research Focus 🔬

Dr. Xu's research is characterized by his innovative work in mechanical piezoelectric metamaterials and structural health monitoring. His contributions include:

  • Mechanical Piezoelectric Metamaterials: Dr. Xu has developed signal processing methods for studying these materials, designed vibration modes, and explored vibration suspension using differential piezoelectric metamaterials.
  • Piezoelectric Impedance Structural Health Monitoring: His research involves advanced techniques such as tunable inductance enhanced 1D-CNN, deep learning/transformer-based monitoring, and temperature decoupling using piezoelectric impedance.
  • Gravity Wave Detection: Dr. Xu has worked on structural dynamics analysis and key technologies for mechanical differential measurement, utilizing deep learning for signal processing and denoising.
  • Piezoelectric Vibration Energy Harvesting: His work includes broadband energy harvesting, multi-directional harvesting by cantilever-pendulum systems, and enhancing power output density through strain smoothing effects.

Accolades and Recognition 🏅

Dr. Xu has been recognized for his contributions to the field of mechanical engineering through various prestigious awards and roles. He is a Fellow of the Jiangsu Instrumental Society and serves as the Deputy Director of the Youth Committee of the Jiangsu Instrumental Society. Additionally, he is an expert reviewer for several high-impact journals, including the IEEE Transactions on Industrial Electronics and the Journal of Applied Physics. His extensive publication record in leading journals further attests to his significant impact in the field.

Impact and Influence 🌟

Dr. Xu's research has had a profound impact on the fields of mechanical metamaterials and energy harvesting. His work on piezoelectric metamaterials and structural health monitoring has advanced the understanding and application of these technologies in various engineering contexts. His innovative approaches to energy harvesting and structural analysis have contributed to advancements in sustainable and efficient engineering solutions. Dr. Xu’s role as a reviewer and expert in several scientific communities highlights his influence in shaping the future of mechanical engineering research.

Legacy and Future Contributions 🔮

Dr. Xu’s ongoing research and leadership in the field of mechanical engineering continue to shape future advancements. His projects, such as those related to piezoelectric metamaterials and gravity wave detection, promise to push the boundaries of current technology and engineering practices. As he continues to explore new methodologies and applications, Dr. Xu is poised to leave a lasting legacy in the field, influencing both academic research and practical engineering solutions. His dedication to innovative research and his active role in professional societies ensure that his contributions will have a lasting impact on the engineering community.

 

Publications 📚

  • 📄 Modeling and Experimental Study of Vibration Energy Harvester with Triple-Frequency-Up Voltage Output by Vibration Mode Switching
    Authors: Jiawen Xu, Zhikang Liu, Wenxing Dai, Ru Zhang, Jianjun Ge
    Journal: Micromachines
    Year: 2024
  • 📄 Graded metamaterial with broadband active controllability for low-frequency vibration suppression
    Authors: Jian, Y., Hu, G., Tang, L., Huang, D., Aw, K.
    Journal: Journal of Applied Physics
    Year: 2024
  • 📄 Robustness analysis and prediction of topological edge states in topological elastic waveguides
    Authors: Tong, S., Sun, W., Xu, J., Li, H.
    Journal: Physica Scripta
    Year: 2024
  • 📄 Deep residual shrinkage network with multichannel VMD inputs for noise reduction of micro-thrust measurement
    Authors: Liu, Z., Chen, X., Xu, J., Zhao, L.
    Journal: AIP Advances
    Year: 2024
  • 📄 LiteFormer: A Lightweight and Efficient Transformer for Rotating Machine Fault Diagnosis
    Authors: Sun, W., Yan, R., Jin, R., Yang, Y., Chen, Z.
    Journal: IEEE Transactions on Reliability
    Year: 2024

 

Soopil Kim | Computer Science | Best Researcher Award

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Dr. Soopil Kim | Computer Science | Best Researcher Award

Daegu Gyeongbuk Institute of Science and Technology | South Korea

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Early Academic Pursuits 🎓

Dr. Soopil Kim's academic journey began with a Bachelor of Engineering in Robotics and Mechatronics Engineering from Daegu Gyeongbuk Institute of Science & Technology (DGIST), where he graduated Cum Laude. He continued his studies at DGIST, pursuing a Master’s and Ph.D. in the same field, focusing on medical image segmentation. His research during these years emphasized label-efficient segmentation models and limited pixel-level annotation, laying a strong foundation for his future work in deep learning and computer vision.

Professional Endeavors 💼

Dr. Kim's career has seen significant milestones, including a role as a Visiting Student at Stanford University's CNSLAB under the supervision of Prof. Kilian M. Pohl and Ehsan Adeli. Currently, he is a Post-Doctoral Research Fellow at the Medical Image & Signal Processing Lab (MISPL) at DGIST, where he works under Prof. Sang Hyun Park. His professional trajectory reflects a commitment to advancing the field of computer vision through innovative research and collaboration.

Contributions and Research Focus 🔬

Dr. Kim’s research is at the forefront of deep learning and computer vision. His work addresses the challenges of image segmentation with partially labeled datasets by developing federated learning strategies and few-shot segmentation techniques. His notable contributions include the creation of a medical image segmentation model that integrates meta-learning and bi-directional recurrent neural networks, a semi-supervised segmentation model based on uncertainty estimation, and a transductive segmentation model for industrial imaging. These advancements aim to improve the efficiency and accuracy of image segmentation processes.

Accolades and Recognition 🏆

Dr. Kim has received several awards that highlight his exceptional contributions to the field. Notably, he was ranked 3rd among 40 teams in the SNUH Sleep AI Challenge in 2021 and was honored with the Outstanding Student Award from the Department of Robotics and Mechatronics Engineering at DGIST in 2022. In 2024, he was recognized at the KCCV Oral/Poster Presentation Doctoral Colloquium for his work on label-efficient segmentation models.

Impact and Influence 🌍

Dr. Kim's research has made a significant impact on the field of computer vision, particularly in the area of image segmentation. His innovative approaches to handling partially labeled datasets and federated learning have the potential to advance both academic research and practical applications in medical imaging and beyond. His work on few-shot learning and uncertainty-aware models addresses critical challenges in the field, contributing to more robust and adaptable segmentation solutions.

Legacy and Future Contributions 🚀

As Dr. Kim continues his research, his focus on improving segmentation models and developing new methodologies promises to shape the future of computer vision. His commitment to exploring federated learning and few-shot learning techniques will likely drive further innovations in the field, offering solutions to complex challenges and enhancing the accuracy of image analysis across various applications.

 

Publications 📘

📄Few-shot anomaly detection using positive unlabeled learning with cycle consistency and co-occurrence features
Authors: Sion An, Soopil Kim, Philip Chikontwe, Jiwook Jung, Hyejeong Jeon, Jaehong Kim, Sang Hyun Park
Journal: Expert Systems with Applications
Year: 2024

📄Federated learning with knowledge distillation for multi-organ segmentation with partially labeled datasets
Authors: Soopil Kim, Haejun Park, Myeongju Kang, Kilian M. Pohl, Sang Hyun Park
Journal: Medical Image Analysis
Year: 2024

📄FedNN: Federated learning on concept drift data using weight and adaptive group normalizations
Authors: Myeongju Kang, Soopil Kim, Kwang-Hyun Jin, Kilian M. Pohl, Sang Hyun Park
Journal: Pattern Recognition
Year: 2024

📄Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection
Authors: Soopil Kim, Sion An, Philip Chikontwe, Kilian M. Pohl, Sang Hyun Park
Conference: Proceedings of the AAAI Conference on Artificial Intelligence
Year: 2024

📄Uncertainty-aware semi-supervised few shot segmentation
Authors: Soopil Kim, Philip Chikontwe, Sion An, Sang Hyun Park
Journal: Pattern Recognition
Year: 2023

 

Wojciech M Kozlowski | Mathematics | Best Researcher Award

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Assoc Prof Dr. Wojciech M Kozlowski | Mathematics | Best Researcher Award

University of New South Wales | Australia

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Early Academic Pursuits 📚

Dr. Walter (Wojciech M.) Kozlowski's journey into academia began at the prestigious Jagiellonian University in Krakow, Poland. He earned his Master of Mathematics in 1977, specializing in Hammerstein operators in Orlicz spaces under the mentorship of Prof. J. Szarski. Continuing his studies at the same institution, he completed his Doctorate in Mathematics in 1981 with a focus on nonlinear operators in spaces of measurable vector-valued functions, guided by Prof. J. Musielak. His deep dive into functional analysis and fixed point theory set the stage for a distinguished career.

Professional Endeavors 🌐

Dr. Kozlowski's professional career is as diverse as it is distinguished. He has held academic posts at universities worldwide, including Jagiellonian University, Southwest Missouri State University, and the University of Adelaide. His role as a Fulbright Scholar at the California Institute of Technology and the University of Southern California further solidified his research credentials. Parallel to his academic career, he has amassed over 20 years of experience in Information and Communication Technology, currently serving as the Principal Network Cloud Architect at Telstra Group. His previous roles include significant positions at Hewlett Packard Enterprise, IBM, and Cable & Wireless Optus.

Contributions and Research Focus 🧪

Dr. Kozlowski's contributions to mathematics and technology are substantial. His research interests span modular function spaces, fixed point theory, nonlinear analysis, semigroups of nonlinear operators, and the geometry of Banach spaces and operator theory. Recently, he has extended his expertise to deep learning and its applications to telecommunications and the digital economy. His monographic book "Modular Function Spaces" and numerous scientific papers are testaments to his profound impact on these fields.

Accolades and Recognition 🏆

Throughout his career, Dr. Kozlowski has received numerous awards and honors. Notably, he was a Fulbright Scholar from 1986 to 1988, and he was honored as an Honorary Professor at the University of Adelaide from 1992 to 1997. His academic achievements include a Habilitation in Mathematics from A. Mickiewicz University in 2016. In the professional realm, he has been recognized as a Distinguished Certified IT Architect by The Open Group and has received certifications from AWS and the Linux Foundation Networking.

Impact and Influence 🌟

Dr. Kozlowski's work has had a lasting impact on both academia and industry. His research in functional analysis and fixed point theory has influenced numerous scholars and researchers. In the technology sector, his leadership in network transformation at Telstra Group is shaping the future of telecommunications. His contributions to large-scale transformation projects have set new standards for operational efficiency and technological innovation.

Legacy and Future Contributions 🌍

Dr. Kozlowski's legacy is characterized by his dual expertise in mathematics and technology. His ongoing research collaborations, particularly with institutions like UNSW and CARMA, ensure that his influence will continue to grow. As a mentor and leader, he has inspired countless students and professionals. His future contributions, especially in the evolving fields of deep learning and telecommunications, promise to further his impact on both scientific knowledge and technological advancement.

 

Publications

 📰On approximation by rational functions in Musielak–Orlicz spaces
Journal: Journal of Approximation Theory
Year: 2024

 📰MODULAR VERSION OF GOEBEL–KIRK THEOREM
Authors: Kozlowski, W.M.
Journal: Topological Methods in Nonlinear Analysis
Year: 2024

 📰CONTRACTIVE SEMIGROUPS IN TOPOLOGICAL VECTOR SPACES, ON THE 100TH ANNIVERSARY OF STEFAN BANACH'S CONTRACTION PRINCIPLE
Authors: Kozlowski, W.M.
Journal: Bulletin of the Australian Mathematical Society
Year: 2023

 📰NOTES ON MODULAR PROJECTIONS
Authors: Kozlowski, W.M.
Journal: Applied Set-Valued Analysis and Optimization
Year: 2022

 📰On modular approximants in sequential convergence spaces
Authors: Kozlowski, W.M.W.
Journal: Journal of Approximation Theory 📰
Year: 2021

 

 

Bipin Kumar | Earth and Planetary Sciences | Best Researcher Award

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Dr. Bipin Kumar | Earth and Planetary Sciences | Best Researcher Award

Indian Institute of Tropical Meteorology | India

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Early Academic Pursuits

Dr. Bipin Kumar's academic journey began with a Bachelor of Science in Physical Sciences from the University of Allahabad in 1995, followed by a Master of Science in Mathematics from IIT Kanpur in 1998. He then pursued an MS in Research (Mathematics) at the National University of Singapore in 2006, focusing on computational methods for phase-field models. His quest for deeper expertise led him to earn a Ph.D. in Computing from Dublin City University in 2009, with a thesis on high-performance computing for multiphase fluid flows.

Professional Endeavors

Dr. Kumar’s career spans over two decades, encompassing roles in academia, research institutions, and industry. His notable positions include:

  • Scientist and Research Guide at IITM, Pune, India.
  • Associate Professor at Savitribai Phule Pune University.
  • Visiting Scientist at NCAR, Boulder, USA, and Visiting Faculty at McGill University, Canada.
  • Associate Faculty at the International Center for Theoretical Sciences, Bengaluru, India.
  • Scientist at Max-Planck-Institute for Meteorology, Germany.

He has held various teaching and research positions, contributing to advancements in high-performance computing, data science, and atmospheric physics.

Contributions and Research Focus

Dr. Kumar's research is centered around:

  • Data Science and AI/ML: Developing parallel Python routines and deep learning algorithms for weather forecasting, data downscaling, fire forecasting, and more.
  • Atmospheric Physics: Studying cloud droplet and aerosol dynamics using DNS.
  • High-Performance Computing (HPC): Enhancing parallel code for CFD problems, 3D visualization, and parallel I/O optimization.
  • Numerical Linear Algebra: Creating parallel algorithms for solving large linear systems of equations.

Accolades and Recognition

Dr. Kumar has received several prestigious awards:

  • DCU Teaching Excellence Nominee Award (2008)
  • Microsoft Postgraduate Research Scholarship (Ireland, 2007-08)
  • DCU Dean’s Connect Scholarship (Ireland, 2006-09)
  • NUS Research Scholarship (Singapore, 2004-06)
  • CSIR Senior Research Fellowship (India, 2004)

Impact and Influence

Dr. Kumar’s work has significantly influenced fields such as HPC, data science, and atmospheric physics. His contributions to developing computational methods for complex fluid flows and forecasting systems have advanced our understanding of cloud dynamics and weather patterns. His research has impacted both theoretical and practical aspects of meteorology and data analysis.

Legacy and Future Contributions

Dr. Kumar aims to broaden his impact through continued research and teaching. By leveraging his expertise in HPC, data science, and cloud microphysics, he aspires to address critical challenges in earth science and contribute to the development of innovative solutions for climate and environmental issues.

 

   Publications

  • Deep learning-based bias correction of ISMR simulated by GCM
    Authors: Sumanta Chandra Mishra Sharma, Bipin Kumar, Adway Mitra, Subodh Kumar Saha
    Journal: Atmospheric Research
    Year: 2024

 

  • Harnessing deep learning for forecasting fire-burning locations and unveiling PM2.5 emissions
    Authors: Gaikwad, S., Kumar, B., Yadav, P.P., Rao, S.A., Ghude, S.D.
    Journal: Modeling Earth Systems and Environment
    Year: 2024

 

  • Machine learning based quantification of VOC contribution in surface ozone prediction
    Authors: Kalbande, R., Kumar, B., Maji, S., Rathore, D.S., Beig, G.
    Journal: Chemosphere
    Year: 2023

 

  • On the modern deep learning approaches for precipitation downscaling
    Authors: Kumar, B., Atey, K., Singh, B.B., Nanjundiah, R.S., Rao, S.A.
    Journal: Earth Science Informatics
    Year: 2023

 

  • A modified deep learning weather prediction using cubed sphere for global precipitation
    Authors: Singh, M., Acharya, N., Patel, P., Nanjundiah, R.S., Niyogi, D.
    Journal: Frontiers in Climate
    Year: 2023

 

 

 

 

 

 

Akash Sharma | Engineering | Best Researcher Award

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Mr. Akash Sharma | Engineering | Best Researcher Award

Malaviya National Institute of Technology | India

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Early Academic Pursuits 🎓

Mr. Akash Sharma’s academic journey began with a solid foundation in Electrical Engineering. He earned his Bachelor of Technology (B-Tech) in Electrical Engineering from Arya College of Engineering & IT, RTU Kota in 2016, achieving a commendable 68.5%. He further pursued a Master of Technology (M-Tech) in Power Systems from Malaviya National Institute of Technology (MNIT), Jaipur in 2021, with a CGPA of 7.72. His quest for knowledge continued as he completed his PhD in Power Systems at MNIT, Jaipur in 2022, with a CGPA of 7.6. His doctoral research focused on the performance analysis of smart grids, utilizing data-driven methods and machine learning.

Professional Endeavors 💼

Mr. Sharma's professional experience includes diverse roles. He served as a guest faculty at the College of Dairy Science and Technology, Jobner, from 2021 to 2022, where he contributed to the academic environment. Prior to this, he worked as a Graduate Engineer Trainee (GET) at IRB Infrastructure Ltd., handling electrical aspects of various plants and overseeing staff welfare. Additionally, Mr. Sharma gained valuable experience as a Public Relations Officer (PRO) with Indian Business Pages in 2016.

Contributions and Research Focus 🔍

Mr. Sharma's research is centered on the performance analysis of smart grids, integrating deep learning and machine learning techniques. His PhD work emphasizes cybersecurity in energy consumption, aiming to develop advanced models for detecting and mitigating cyber-attacks on smart grid infrastructures. His work also explores the seamless integration of renewable energy sources and optimization of smart grid performance. He has published a notable research paper on voltage profile enhancement using FACTS devices and has worked on solar tracking systems.

Accolades and Recognition 🏅

While Mr. Sharma has not yet received major awards, his active participation in co-curricular activities and his impactful research reflect his dedication. His work on smart grids and renewable energy has been well-received in academic circles, demonstrating his commitment to advancing the field of electrical engineering.

Impact and Influence 🌟

Mr. Sharma's contributions to smart grid technology and renewable energy integration are shaping the future of power systems. His work in enhancing grid performance and addressing cybersecurity concerns is crucial in the evolving landscape of energy management. His involvement in both academic and professional settings highlights his influence on the next generation of engineers and researchers.

Legacy and Future Contributions 🚀

Looking ahead, Mr. Sharma's ongoing research and professional activities will continue to impact the field of electrical engineering. His focus on smart grids and renewable energy positions him to contribute significantly to advancements in these areas. As he builds on his experiences and research, he is poised to leave a lasting legacy in the realm of power systems and sustainable energy solutions.

 

Publications

  • Title: Anomaly detection in smart grid using optimized extreme gradient boosting with SCADA system
  • Authors: Sharma, A., Tiwari, R.
  • Journal: Electric Power Systems Research
  • Year: 2024

 

  • Title: Load Shedding Technique for Maintaining Voltage Stability
  • Authors: Sharma, P.K., Sharma, A., Tiwari, R.
  • Journal: Lecture Notes in Electrical Engineering
  • Year: 2024

 

 

 

 

Sasank V.V.S | Computer Science | Best Researcher Award

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Assist Prof Dr. Sasank V.V.S | Computer Science | Best Researcher Award

K L University | India

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Early Academic Pursuits

Dr. Sasank V.V.S. exhibited a strong academic foundation from the outset. He completed his secondary education at Jassver English Medium School in 2007 with a First Class distinction, scoring 72.66%. He continued to excel in his Intermediate studies at Mega Junior College, graduating in 2009 with an 84.1% mark, also achieving First Class. His academic journey progressed to higher education at Gitam Institute of Technology, GITAM University, where he obtained his B.Tech in Information Technology in 2013 with a CGPA of 8.15, earning a Distinction. He further advanced his education with an M.Tech in Computer Science and Technology from the same institution, graduating in 2016 with a remarkable 9.11 CGPA, securing the top rank in his department. Dr. Sasank completed his Ph.D. at K.L. University in 2023, marking a significant milestone in his academic career.

Professional Endeavors

Dr. Sasank has a rich professional background in both academia and industry. He began his teaching career as a Teaching Assistant in the CSE Department at Gitam University from October 2015 to April 2016. He then served as an Assistant Professor at the Lendi Institute of Engineering & Technology, VIZIANAGARAM, from June 2016 to April 2017. Following this, he joined Anil Neerukonda Institute of Technology & Sciences (ANITS) as an Assistant Professor and Placement Officer from June 2017 to April 2019. He has been affiliated with K L University since July 2019, initially in the CSE Department and later in the CSIT Department, where he also served as the ERP Registration In-charge. His teaching repertoire includes subjects such as DBMS, Software Engineering, Computer Architecture & Organization, Term Paper, UI/UX Design, and DevOps.

Contributions and Research Focus

Dr. Sasank's research primarily focuses on advanced topics in computer science and engineering. His areas of interest include brain tumor classification, real-time traffic management using IoT and machine learning techniques, and the evolution of modern women in literature. He has published a significant number of papers in reputed journals, including  SCI papers and several Scopus-indexed articles. His notable publications include works on hybrid deep neural networks, automatic tumor growth prediction, and brain tumor classification using modified kernel-based softplus extreme learning machines. Additionally, he has guided numerous B.Tech and M.Tech project batches, contributing to the academic growth of his students.

Accolades and Recognition

Dr. Sasank has received several accolades for his academic and research achievements. He was the top ranker in his M.Tech program at Gitam University in 2016. He has published 18 papers, including SCI, Scopus, and WOS-indexed journals, and has contributed to two book chapters. His innovative research has led to the publication of two patents: one on real-time traffic management using IoT and machine learning techniques, and another on the evolution of modern women in Manju Kapur’s novels. Additionally, he has earned global certifications, including Google Associate Cloud Engineer and AWS Cloud Practitioner, and has presented his research at various international conferences.

Impact and Influence

Dr. Sasank's contributions to the field of computer science and engineering have had a significant impact on both academic and practical applications. His research on brain tumor classification and real-time traffic management has potential real-world implications, advancing the fields of medical imaging and smart city technologies. As an educator, he has influenced many students through his teaching and mentorship, guiding them in their academic and research endeavors.

Legacy and Future Contributions

Dr. Sasank's ongoing research and academic activities are expected to leave a lasting legacy in the field of computer science and engineering. His contributions to brain tumor classification and IoT-based traffic management are poised to influence future research and development in these areas. As he continues to publish and present his work, Dr. Sasank is likely to inspire and mentor the next generation of engineers and researchers, ensuring continued innovation and excellence in his field.

 

Notable Publications

Prostate cancer classification using adaptive swarm Intelligence based deep attention neural network 2024

Effective Segmentation and Brain Tumor Classification Using Sparse Bayesian ELM in MRI Images 2023

Hybrid deep neural network with adaptive rain optimizer algorithm for multi-grade brain tumor classification of MRI images 2022 (14)

An automatic tumour growth prediction based segmentation using full resolution convolutional network for brain tumour 2022 (27)

Hate Speech & Offensive Language Detection Using ML &NLP 2022 (4)

 

 

 

 

Debapriya Banik | Computer Science | Best Researcher Award

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Dr. Debapriya Banik | Computer Science | Best Researcher Award

ICFAI University | India

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Early Academic Pursuits

Dr. Debapriya Banik embarked on his academic journey with a solid foundation in secondary and higher secondary education at Holy Cross School in Agartala, achieving commendable scores in both ICSE and ISC examinations. His passion for Computer Science led him to pursue a Bachelor of Technology (B.Tech) in Computer Science and Engineering at the National Institute of Technology Agartala, where he graduated with a CGPA of 7.59. His pursuit of higher education continued at Tripura University, where he excelled in his Master of Technology (M.Tech) in Computer Science and Engineering, earning a gold medal for securing the highest percentage. Dr. Banik's academic endeavors culminated in a Doctor of Philosophy (Ph.D.) in Computer Science and Engineering from Jadavpur University, where he focused on developing computer-aided techniques for the early prediction of colorectal cancer based on diagnostic image analysis.

Professional Endeavors

Dr. Banik's professional career is marked by diverse and impactful roles in academia and industry. He began his career as a consultant at Polaris Financial Technology Ltd in Chennai, gaining valuable industry experience. Transitioning to academia, he served as a Junior Research Fellow (JRF) on a DBT Twinning Project at Jadavpur University, working on techniques for pain management and breast cancer using IR imaging. He then became a Senior Research Fellow (SRF)-Direct at Jadavpur University under the sponsorship of the Council for Scientific and Industrial Research, Government of India. His academic career continued to flourish as he took on roles as an Assistant Professor at Techno India in Kolkata and later at ICFAI University in Tripura, where he currently teaches in the Department of Computer Science & Engineering.

Contributions and Research Focus

Dr. Banik's research has significantly contributed to the field of computer science, particularly in medical image analysis. His Ph.D. thesis focused on developing computer-aided techniques for the early prediction of colorectal cancer, leveraging diagnostic image analysis. Additionally, he has worked on the design and development of techniques for pain management and breast cancer using IR imaging. His innovative research approaches have not only advanced the field but also provided practical solutions for critical medical challenges. Dr. Banik's work is characterized by a strong focus on applying machine learning and computational techniques to enhance diagnostic accuracy and efficiency.

Accolades and Recognition

Dr. Banik's academic excellence and research contributions have earned him numerous awards and recognitions. He was awarded a gold medal for his outstanding performance in M.Tech at Tripura University. He received the DST-Inspire fellowship from the Department of Science and Technology, Government of India, and the North Eastern Council (NEC) Scheme fellowship for postgraduate studies. Dr. Banik secured the first position in the Workshop on Machine Learning for Medical Image Analysis (WMLMIA) - Fetal Ultrasound Censor (FUC) Grand Challenge, organized by the Department of Electrical Engineering at IIT Kharagpur. Additionally, he had the opportunity for a short-term research visit to the Medical University of Vienna, Austria, sponsored by DST, Government of India, where he worked on developing a computer-assisted diagnosis system for segmenting and detecting abnormalities and diseases under the supervision of Prof. Christian Kollman.

Impact and Influence

Dr. Banik's work has had a profound impact on the field of medical image analysis, particularly in the early detection and management of diseases such as colorectal and breast cancer. His research has paved the way for more accurate and efficient diagnostic tools, improving patient outcomes and contributing to the advancement of medical technology. As an educator, he has influenced and mentored numerous students, fostering a new generation of computer science professionals who are equipped with cutting-edge knowledge and skills.

Legacy and Future Contributions

Dr. Banik's legacy lies in his dedication to advancing the field of computer science through innovative research and his commitment to education. His work continues to inspire researchers and students alike, and his contributions to medical image analysis have set a high standard for future research in the field. Looking ahead, Dr. Banik aims to further his research on computer-aided diagnosis systems, exploring new applications and techniques to address emerging challenges in healthcare. His future contributions are expected to continue making significant strides in improving diagnostic accuracy and patient care through advanced computational methods.

 

Notable Publications

Robust medical and color image cryptosystem using array index and chaotic S-box 2024

dHBLSN: A diligent hierarchical broad learning system network for cogent polyp segmentation 2024

2pClPr: A Two-Phase Clump Profiler for Segmentation of Cancer Cells in Fluorescence Microscopic Images 2023 (2)

RBECA: A regularized Bi-partitioned entropy component analysis for human face recognition 2022 (1)

LwMLA-NET: A Lightweight Multi-Level Attention-based NETwork for Segmentation of COVID-19 Lungs Abnormalities from CT Images 2022 (39)

 

 

 

Elaheh Yaghoubi | Energy | Best Researcher Award

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Dr. Elaheh Yaghoubi | Energy | Best Researcher Award

Karabuk University | Turkey

Author profile

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Early Academic Pursuits

Dr. Elaheh Yaghoubi's academic journey began with an Associate's degree in Electrical Engineering from University College of Rouzbahan, Iran, where she graduated with a GPA of 3.5. She then pursued a Bachelor's degree in Electrical Engineering at Aryan Institute of Science and Technology University, Iran, achieving a perfect GPA of 4. Following this, she completed her Master's degree in Electrical Engineering at Islamic Azad University in Qaemshahr, Mazandaran, Iran, again with a perfect GPA of 4. Her Master's thesis focused on developing a routing algorithm for a proposed topology for a grid on a large-scale chip to detect errors. Dr. Yaghoubi is currently a Ph.D. candidate in Electronic and Electrical Engineering at Karabuk University in Turkey, where she is working on her thesis titled "Optimal power control of grid-connected distributed generation in a hierarchical framework based on Model Predictive Control."

Professional Endeavors

Dr. Yaghoubi has a diverse professional background that complements her academic achievements. From 2015 to 2018, she served as a Senior Manager at Kati Kabl Tabarestan Factory in Mazandaran, Iran, where she was responsible for quality assurance, inspecting products to ensure high quality, and troubleshooting technical issues. She then worked as a Senior Manager at Rico Electronics Company in Mazandaran, Iran, overseeing product quality assurance and implementing design modifications. From 2019 to 2021, she worked as a Website Designer at WebCore Company in Mazandaran, designing front-end interfaces with HTML, CSS, and JavaScript, and back-end systems with PHP and Laravel. Currently, Dr. Yaghoubi is a Principal Researcher at the Power Electrical Developing Advanced Research (PEDAR) group, focusing on investigation, teaching, and designing.

Contributions and Research Focus

Dr. Yaghoubi's research interests are broad and interdisciplinary, encompassing power system analysis, power system stability, power management, microgrids, smart grids, renewable energies, model predictive controllers (MPC), artificial neural networks, machine learning, deep learning, plasmonic applications, and nano-electronic devices. Her current research work involves optimal power control of grid-connected distributed generation using model predictive control, a topic that is crucial for the advancement of smart grids and renewable energy systems. She has also contributed to the understanding and development of routing algorithms for large-scale chips and has experience in quality control and product management in industrial settings.

Accolades and Recognition

Throughout her academic and professional career, Dr. Yaghoubi has been recognized for her excellence and contributions. She successfully passed her Ph.D. qualification exam with a perfect grade of 4 out of 4. Her consistent academic performance, marked by perfect GPAs during her Bachelor's and Master's studies, reflects her dedication and expertise in her field.

Impact and Influence

Dr. Yaghoubi's work has had a significant impact on both academic and industrial fields. Her research on smart grids, optimization techniques, and model predictive control contributes to the development of more efficient and reliable power systems. Her practical experience in quality control and product management ensures that her research is grounded in real-world applications and industrial standards.

Legacy and Future Contributions

Dr. Yaghoubi's legacy lies in her interdisciplinary approach to electronic and electrical engineering, integrating theoretical research with practical applications. Her work in power systems, renewable energy, and advanced control techniques positions her as a key contributor to the future of smart grid technology and sustainable energy solutions. As she continues her research and professional activities, Dr. Yaghoubi is likely to make further significant contributions to the field, driving innovation and excellence in electronic and electrical engineering.

 

Notable Publications

A systematic review and meta-analysis of machine learning, deep learning, and ensemble learning approaches in predicting EV charging behavior 2024

A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering 2024

Controlling and tracking the maximum active power point in a photovoltaic system connected to the grid using the fuzzy neural controller 2023 (1)

Tunable band-pass plasmonic filter and wavelength triple-channel demultiplexer based on square nanodisk resonator in MIM waveguide 2022 (9)

Triple-channel glasses-shape nanoplasmonic demultiplexer based on multi nanodisk resonators in MIM waveguide 2021 (11)

 

 

 

Yunyoung Nam | Computer Science | Best Researcher Award

Published on by Book of Award

Prof. Yunyoung Nam | Computer Science | Best Researcher Award

Soonchunhyang University | South Korea

Author Profile

Scopus

Early Academic Pursuits

Prof. Yunyoung Nam began his academic journey with a focus on Information and Communication. He achieved significant recognition early in his career, earning the Presidential Award for Excellence in Graduate School of Information and Communication in both 2003 and 2007. He also received the Dasan Fellowship from Ajou University in 2000, demonstrating his potential and commitment to his field from the start.

Professional Endeavors

Prof. Nam's professional career is marked by a series of progressive roles in academia and research. He served as a Senior Researcher at the Ubiquitous Convergence Research Institution from 2007 to 2010, followed by multiple postdoctoral positions at Stony Brook University-SUNY and Worcester Polytechnic Institute. He transitioned to academia as a Research Assistant Professor at Ajou University before joining Soonchunhyang University, where he has held positions as Assistant Professor, Associate Professor, and currently, Professor since 2023.

Contributions and Research Focus

Prof. Nam's research interests span a wide range of cutting-edge topics, including multimedia information retrieval, digital signal processing, machine learning methods for multimedia applications, and biomedical engineering. His contributions to these fields are substantial, with notable projects such as the development of a 3D deep-learning-based diagnosis platform for retinal disease, and a smartphone-based diagnosis and referral platform for chronic diseases. His research has been supported by prestigious organizations, including the National Research Foundation (NRF) and the Ministry of Science, ICT, and Future Planning (MSIP).

Accolades and Recognition

Throughout his career, Prof. Nam has been recognized for his exceptional contributions to research and academia. He has received the Presidential Award for Best Researcher at Soonchunhyang University three times (2015, 2018, 2020), highlighting his sustained excellence in research. Additionally, he was included in the 2011 edition of Who's Who in America, further acknowledging his influence and impact in his field.

Impact and Influence

Prof. Nam's impact extends beyond his research contributions. As an educator, he has taught a wide range of courses at Soonchunhyang University, including C#, AI, algorithms, data structure, discrete mathematics, and software engineering. His commitment to teaching and mentorship has influenced many students and upcoming researchers. Moreover, his membership in several professional associations such as IEEE and the Korea Information Processing Society, and his editorial roles in various journals, underscore his active involvement in the academic community.

Legacy and Future Contributions

Prof. Nam's legacy is defined by his relentless pursuit of knowledge and innovation in multimedia applications and biomedical engineering. His research projects, such as the RNA Innovation Human Resources Project for the Hyper-connected Convergence Industry, and the training of experts for the intelligent home care industry, are paving the way for future advancements. As he continues to lead and innovate, Prof. Nam's future contributions are expected to significantly influence both academic research and practical applications in his areas of expertise.

 

Notable Publications

Localization and grading of NPDR lesions using ResNet-18-YOLOv8 model and informative features selection for DR classification based on transfer learning 2024

Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets 2024

Federated Learning for Computational Offloading and Resource Management of Vehicular Edge Computing in 6G-V2X Network 2024 (5)

A gamified cognitive behavioral therapy for Arabs to reduce symptoms of depression and anxiety: A case study research 2024

Sleep Posture Classification Using RGB and Thermal Cameras Based on Deep Learning Model 2024

 

 

 

Lin Guo | Computer Science | Excellence in Innovation Award

Published on by Book of Award

Mr. Lin Guo | Computer Science | Excellence in Innovation Award

Huazhong University of Science and Technology | China

Author Profile

Scopus

Early Academic Pursuits

Mr. Lin Guo embarked on his academic journey with a strong foundation in Computer Science and Technology at Zhengzhou University, where he graduated with distinction as one of the top students. Building on this success, he pursued postgraduate studies in Artificial Intelligence at Huazhong University of Science and Technology, demonstrating a keen interest in advanced technologies and research methodologies.

Professional Endeavors

Mr. Guo's professional career is marked by significant contributions in the field of artificial intelligence and computer vision. His internship at Megvii Technology's Shanghai Research Institute focused on developing cutting-edge algorithms for AVP parking semantic mapping, addressing challenges in SLAM optimization and multi-frame fusion mapping. His role as a key engineer underscored his ability to innovate and implement complex solutions in real-world applications.

Contributions and Research Focus

Lin Guo has made substantial contributions to the field through his research publications and project involvements. His research spans point cloud registration, 3D registration efficiency, and advanced methods in SLAM and VIO positioning. His work on optimizing point cloud feature learning and overcoming feature ambiguity in different reference systems has been acknowledged for its innovation and practical relevance.

Accolades and Recognition

His academic achievements and research prowess have been recognized with numerous honors, including being an Outstanding Graduate of Henan Province and receiving prestigious scholarships from Zhengzhou University and Huazhong University of Science and Technology. His contributions to accepted and submitted papers in leading conferences and journals highlight his growing influence in the academic community.

Impact and Influence

Lin Guo's research has made a significant impact on the fields of computer vision and robotics, particularly in enhancing the accuracy and efficiency of point cloud registration and SLAM technologies. His methods have set benchmarks in performance on diverse datasets, demonstrating their applicability across indoor and outdoor environments.

Legacy and Future Contributions

Looking ahead, Lin Guo aims to continue pushing the boundaries of artificial intelligence and robotics. His future contributions are expected to further advance state-of-the-art techniques in SLAM optimization, 3D registration, and autonomous systems. By bridging theoretical insights with practical applications, he seeks to foster advancements that positively impact industries and society at large.

 

Notable Publications

Learning compact and overlap-biased interactions for point cloud registration 2024

SC 2-PCR++: Rethinking the Generation and Selection for Efficient and Robust Point Cloud Registration 2023 (9)

One-Inlier is First: Towards Efficient Position Encoding for Point Cloud Registration 2022 (5)