Snehal Laddha | Medical Image Analysis | Best Researcher Award

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Prof. Snehal Laddha | Medical Image Analysis | Best Researcher Award

University of Bradford | United Kingdom

Publication Profile

Google Scholar

INTRODUCTION 🌟

Ms. Snehal Laddha is a highly accomplished and dynamic educator and professional, with an extensive background in Electronics and Computer Science, specializing in AI, Data Analysis, and Deep Learning. With over 16 years of experience in the academic field, she has significantly contributed to student engagement, research innovation, and global academic exposure through teaching roles in India, Australia, and the UK.

EARLY ACADEMIC PURSUITS 📚

Snehal’s academic journey began with a keen interest in electronics and computer science. She completed her formal education, setting the foundation for her research in emerging fields such as Artificial Intelligence (AI) and data analysis. Her academic pursuit took her across various top institutions, where she honed her skills in programming, image analysis, and deep learning, fueling her desire to make a significant impact on both the academic and technological landscapes.

PROFESSIONAL ENDEAVORS 👩‍🏫

Snehal’s career includes prestigious teaching roles such as Assistant Professor at Ramdeobaba University, H.V.P.M.C.O.E.T., and S.R.K.N.E.C. in India, as well as a Teaching Tutor position at the University of Technology, Sydney, Australia. She has also demonstrated remarkable versatility, contributing in non-teaching roles such as Customer Service at Sydney CBD METCENTER, where she gained invaluable international exposure and insights into multicultural education systems.

CONTRIBUTIONS AND RESEARCH FOCUS  ON MEDICAL IMAGE ANALYSIS💡

A passionate researcher, Snehal’s work primarily revolves around AI, computer vision, and Internet of Things (IoT) applications. Her efforts in data analysis, deep learning, and image analysis have led to several publications in both national and international journals. Her most notable invention is the IoT-powered smart trash bin (patent granted in 2024). She is deeply committed to improving academic practices by leading initiatives to organize Faculty Development Programs (FDPs) and research projects sponsored by AICTE.

IMPACT AND INFLUENCE 🌍

Snehal’s global exposure and significant contributions in both teaching and research have shaped the careers of many students and academics. She consistently fosters innovation, critical thinking, and leadership in her students by incorporating advanced technologies into the curriculum. Through her work, she has become a vital figure in the academic community, nurturing the next generation of scientists and engineers.

ACADEMIC CITATIONS AND PUBLICATIONS 📑

Snehal has published numerous papers in international and national journals, covering a wide range of topics, from embedded systems to AI applications. Her citations reflect her deep expertise and recognition in the academic community. She remains dedicated to advancing her field and continues to contribute valuable insights through her research endeavors.

HONORS & AWARDS 🏆

Ms. Snehal Laddha has been recognized for her exceptional academic and professional contributions with multiple prestigious honors:

  • Australian Qualified Professional Engineer certification by Engineers Australia
  • Best Paper Award at Cardiff Metropolitan University, UK
  • Grants for organizing Faculty Development Programs and SPICES from AICTE
  • IELTS Score of 6.5/10 from the British Council

These awards reflect her dedication to excellence in both teaching and research, cementing her as a leader in her field.

LEGACY AND FUTURE CONTRIBUTIONS 🌱

Looking forward, Snehal plans to continue her research and academic endeavors, focusing on the evolution of AI and its practical applications in various industries. She is determined to contribute to the development of innovative solutions that can address societal challenges, particularly through IoT, deep learning, and image processing.

FINAL NOTE ✨

Ms. Snehal Laddha’s career is a testament to her unwavering commitment to education, research excellence, and global academic collaboration. With a rich background spanning teaching, research, and innovative solutions, she continues to inspire and lead in the realm of AI and data science, ensuring her legacy as an influential figure in the academic and technological spheres.

TOP NOTES PUBLICATIONS 📚

Liver Segmentation from MR T1 In-Phase and Out-Phase Fused Images Using U-Net and Its Modified Variants
    • Authors: SVL Siddhi Chourasia, Rhugved Bhojane
    • Journal: Intelligent Systems. ICMIB 2024. Lecture Notes in Networks and Systems
    • Year: 2025
Deep-Dixon: Deep-Learning frameworks for fusion of MR T1 images for fat and water extraction
    • Authors: SV Laddha, RS Ochawar, K Gandhi, YD Zhang
    • Journal: Multimedia Tools and Applications
    • Year: 2024
Addressing Class Imbalance in Diabetic Retinopathy Segmentation: A Weighted Ensemble Approach with U-Net, U-Net++, and DuckNet
    • Authors: E Gawate, S Laddha
    • Journal: International Conference on Intelligent Systems for Cybersecurity (ISCS)
    • Year: 2024
A Novel Method of Enhancing Skin Lesion Diagnosis Using Attention Mechanisms and Weakly-Supervised Learning
    • Author: SV Laddha
    • Journal: World Conference on Artificial Intelligence: Advances and Applications
    • Year: 2024
Automated Liver Segmentation in MR T1 In-Phase Images Transfer Learning Technique
    • Authors: SV Laddha, AH Harkare
    • Journal: Proceedings of Eighth International Conference on Information System Design
    • Year: 2024
Automated Segmentation of Liver from Dixon MRI Water-Only Images Using Unet, ResUnet, and Attention-Unet Models
    • Authors: E Gawate, SV Laddha, RS Ochawar
    • Journal: Information System Design: AI and ML Applications: Proceedings of Eighth International Conference on Information System Design
    • Year: 2024

Omar Haddad | Artificial Intelligence | Best Researcher Award

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Dr. Omar Haddad l Artificial Intelligence | Best Researcher Award

MARS Research Lab, Tunisia

Author Profile

Google Scholar

🎓 Early Academic Pursuits

Dr. Omar Haddad began his academic journey with a solid foundation in mathematics, earning his Bachelor in Mathematics from Mixed High School, Tataouine North, Tunisia, in 2008. Building on this, he pursued a Fundamental License in Computer Science at the Faculty of Sciences of Monastir, University of Monastir, Tunisia, graduating with honors in 2012. He further honed his skills in advanced topics through a Research Master in Computer Science specializing in Modeling of Automated Reasoning Systems (Artificial Intelligence) at the same institution, completing his thesis on E-Learning, NLP, Map, and Ontology in 2016. This laid the groundwork for his doctoral studies in Big Data Analytics for Forecasting Based on Deep Learning, which he completed with highest honors at the Faculty of Economics and Management of Sfax, University of Sfax, Tunisia, in 2023.

💼 Professional Endeavors

Currently, Dr. Haddad is an Assistant Contractual at the University of Sousse, Tunisia, where he contributes to the academic and technological growth of the institution. He is a dedicated member of the MARS Research Laboratory at the University of Sousse, where he collaborates on cutting-edge projects in Artificial Intelligence and Big Data Analytics. His professional expertise extends to teaching across various levels, including preparatory, license, engineer, and master levels, with over 2,000 hours of teaching experience in state and private institutions.

📚 Contributions and Research Focus

Dr. Haddad’s research spans several transformative domains, including:

  • Artificial Intelligence (AI) and its application in solving complex problems.
  • Machine Learning (ML) and Deep Learning (DL) for advanced predictive modeling.
  • Generative AI and Large Language Models (LLMs) for innovation in Natural Language Processing (NLP).
  • Big Data Analytics for informed decision-making and forecasting.
  • Computer Vision for visual data interpretation and insights.

He has published three significant contributions in Q1 and Q2 journals and participated in Class C conferences, highlighting his commitment to impactful and high-quality research.

🌟 Impact and Influence

As a peer reviewer for prestigious journals like The Journal of Supercomputing, Journal of Electronic Imaging, SN Computer Science, and Knowledge-Based Systems, Dr. Haddad ensures the integrity and quality of academic contributions in his field. His work has influenced a diverse range of domains, from computer science education to applied AI, establishing him as a thought leader in his areas of expertise.

🛠️ Technical Skills

Dr. Haddad possesses a robust set of technical skills, including:

  • Proficiency in Deep Learning frameworks and Big Data tools.
  • Expertise in programming languages such as Python and C.
  • Knowledge of Database Engineering and algorithms.
  • Application of Natural Language Processing (NLP) in academic and industrial projects.

👨‍🏫 Teaching Experience

Dr. Haddad has an extensive teaching portfolio, covering a wide range of topics:

  • Database Engineering, Algorithms, and Programming, taught across license and engineer levels.
  • Advanced topics such as Big Data Frameworks, Foundations of AI, and Object-Oriented Programming.
  • Specialized courses on Information and Communication Technologies in Teaching and Learning.

His dedication to education is evident in his ability to adapt teaching strategies to different academic levels and institutional needs.

🏛️ Legacy and Future Contributions

Dr. Haddad’s legacy lies in his ability to bridge academia and industry through innovative research and teaching. As he continues to expand his expertise in Generative AI and LLMs, his future contributions will likely shape the next generation of intelligent systems and predictive analytics. His goal is to inspire students and researchers to harness the transformative potential of AI and Big Data to address global challenges.

🌐 Vision for the Future

Dr. Omar Haddad envisions a future where AI and Big Data technologies are seamlessly integrated into various sectors to enhance decision-making, foster innovation, and empower global communities. By combining his teaching, research, and technical skills, he aims to leave a lasting impact on the academic and technological landscape.

📖 Top Noted Publications
Toward a Prediction Approach Based on Deep Learning in Big Data Analytics
    • Authors: O. Haddad, F. Fkih, M.N. Omri
    • Journal: Neural Computing and Applications
    • Year: 2022
A Survey on Distributed Frameworks for Machine Learning Based Big Data Analysis
    • Authors: O. Haddad, F. Fkih, M.N. Omri
    • Journal: Proceedings of the 21st International Conference on New Trends in Intelligent Software Systems
    • Year: 2022
An Intelligent Sentiment Prediction Approach in Social Networks Based on Batch and Streaming Big Data Analytics Using Deep Learning
    • Authors: O. Haddad, F. Fkih, M.N. Omri
    • Journal: Social Network Analysis and Mining
    • Year: 2024
Big Textual Data Analytics Using Transformer-Based Deep Learning for Decision Making
    • Authors: O. Haddad, M.N. Omri
    • Journal: Proceedings of the 16th International Conference on Computational Collective Intelligence
    • Year: 2024

Jing An | Artificial Intelligence | Best Researcher Award

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Dr. Jing An | Artificial Intelligence | Best Researcher Award

Yancheng Institute of Technology, China

👨‍🎓Professional Profile

Scopus Profile

👨‍🏫 Summary

Dr. Jing An is a distinguished professor and master tutor at Yancheng Institute of Technology, China. He specializes in intelligent manufacturing engineering, industrial big data fault diagnosis, and residual life prediction. With numerous patents and software copyrights, Dr. An has published over 20 SCI/EI indexed papers and contributed to key academic texts. His pioneering research in artificial intelligence-based fault diagnosis has earned him prestigious awards, including first-place recognition in the China Commerce Federation Science and Technology Award .

🎓 Education

Dr. An holds a Ph.D. in Computer Science from Hohai University (2021) and a Master’s degree in Computer Science from Harbin University of Science and Technology (2006). His academic foundation has strongly influenced his research in AI and fault diagnosis.

💼 Professional Experience

Having joined Yancheng Institute of Technology in 2006, Dr. An is also the Vice President of Science and Technology for Jiangsu Province’s “Double Innovation Plan.” He has led numerous provincial-level projects, and his expertise has extended to more than 10 industry partnerships 🚀.

📚 Academic Citations

Dr. An has authored over 20 peer-reviewed papers in prestigious journals such as IEEE Access and Mathematical Problems in Engineering. His impactful research on AI-based fault diagnosis methods is frequently cited within the academic community .

🔧 Technical Skills

Dr. An is skilled in AI-based fault diagnosis, deep learning, machine learning, and industrial big data analytics. He has expertise in Convolutional Neural Networks (CNNs) and intelligent manufacturing systems, focusing on improving machinery reliability and efficiency .

🧑‍🏫 Teaching Experience

Dr. An has been recognized as an outstanding teacher twice at Yancheng Institute of Technology. He teaches courses in intelligent manufacturing engineering and industrial big data fault diagnosis, preparing students to advance in the field of AI and data science .

🔍 Research Interests

Dr. An’s research is focused on developing intelligent systems for fault diagnosis in rotating machinery, predictive maintenance, and the application of deep learning techniques in industrial big data. His work aims to enhance manufacturing processes and equipment reliability .

📖Top Noted Publications

Hybrid Mechanism and Data-Driven Approach for Predicting Fatigue Life of MEMS Devices by Physics-Informed Neural Networks

Authors: Cheng, J., Lu, J., Liu, B., An, J., Shen, A.

Journal: Fatigue and Fracture of Engineering Materials and Structures

Year: 2024

Bearing Intelligent Fault Diagnosis Based on Convolutional Neural Networks

Authors: An, J., An, P.

Journal: International Journal of Circuits, Systems and Signal Processing

Year: 2022

Deep Clustering Bearing Fault Diagnosis Method Based on Local Manifold Learning of an Autoencoded Embedding

Authors: An, J., Ai, P., Liu, C., Xu, S., Liu, D.

Journal: IEEE Access

Year: 2021

Deep Domain Adaptation Model for Bearing Fault Diagnosis with Riemann Metric Correlation Alignment

Authors: An, J., Ai, P.

Journal: Mathematical Problems in Engineering

Year: 2020

Deep Domain Adaptation Model for Bearing Fault Diagnosis with Domain Alignment and Discriminative Feature Learning

Authors: An, J., Ai, P., Liu, D.

Journal: Shock and Vibration

Year: 2020

Sangeeta Parshionikar – Deep Learning – Best Researcher Award

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Mrs Sangeeta Parshionikar - Deep Learning - Best Researcher Award 

Fr. Conceicao Rodrigues College of Engineering - India

Author Profile

Early Academic Pursuits

Mrs. Sangeeta Parshionikar began her academic journey by obtaining her Bachelor's degree in Electronics and Power Engineering from Shri. Ramdeobaba Kamla Nehru Engineering College, Nagpur University, where she graduated with a commendable 72.8%. She further pursued her Master's in Electronics from Mumbai University, where she secured a distinction with 75.56%. Her interest in the field led her to specialize further with a diploma in Oracle 8.0 with Visual Basic 6.0 from SSI Nagpur and VLSI Design from CDAC Hyderabad.

Professional Endeavors

Mrs. Parshionikar's professional journey spans over 16 years in the field of education and 3 years in the industry, focusing on VLSI Design. Her industry experience includes roles at esteemed organizations like PowaiLabs Pvt. Ltd., Taray Technology Pvt. Ltd., and STRATeL Microsystems Pvt. Ltd. in Mumbai, Hyderabad, and Delhi, respectively. Currently, she serves as an Assistant Professor at Fr. Conceicao Rodrigues College of Engineering, Mumbai, teaching subjects like IoT, Basic VLSI Design, and ASIC Verification.

Contributions and Research Focus

Her contributions to academia are vast and varied. Mrs. Parshionikar has published several research papers, notably in areas such as deep learning applications for healthcare, VLSI design techniques, and IoT innovations. She has been recognized with multiple minor research grants from the University of Mumbai for her innovative projects, including Smart Air Pollution Monitoring and Stock Market Forecasting using Neural Networks. Additionally, she has filed several patents, including a groundbreaking concept for verbal agreement signing using blockchain and speech recognition.

Accolades and Recognition

Mrs. Parshionikar's dedication to her field has earned her numerous accolades, including appreciations from her college for successfully coordinating workshops and FDPs. She has delivered talks on various topics, from Verilog Programming to Branding for Startups, showcasing her versatility and expertise. Moreover, she has been an active member of several professional bodies, including ISTE, and has served as a reviewer for multiple international conferences.

Impact and Influence

With over 20 years of experience in the academic and industry sectors, Mrs. Parshionikar's influence extends beyond her immediate role. Her research and innovations have the potential to reshape industries, particularly in areas such as healthcare, finance, and environmental monitoring. Her leadership in coordinating various initiatives, including being a convocation coordinator and B.E. project coordinator, reflects her commitment to fostering excellence in education and research.

Legacy and Future Contributions

Mrs. Parshionikar's legacy lies in her relentless pursuit of knowledge dissemination, innovation, and mentorship. As she continues her Ph.D. journey and expands her research endeavors, she aims to contribute further to the scientific community. Her focus on emerging technologies like deep learning, IoT, and VLSI design positions her at the forefront of academic research. Moving forward, she aspires to develop more groundbreaking solutions, mentor future generations, and collaborate on global research initiatives.

In summary, Mrs. Sangeeta Parshionikar's multifaceted career, from her early academic pursuits to her current role as an esteemed educator and researcher, exemplifies dedication, expertise, and a passion for innovation. Her contributions to the field of Computer Engineering, VLSI Design, and Deep Learning make her a distinguished figure, poised to make significant advancements in the scientific community.

Notable Publication

 

Chengjie Sun – Natural Lanauage processing – Best Researcher Award 

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Assoc Prof Dr Chengjie Sun - Natural Lanauage processing - Best Researcher Award 

Harbin Institute of Technology - China

Author Profile:

Early Academic Pursuits

Assoc Prof Dr Chengjie Sun began his academic journey at Yantai University, where he pursued a Bachelor's degree in Computer Science and Technology from 1998 to 2002. Subsequently, he continued his education at Harbin Institute of Technology, achieving a Master's degree in Computer Science and Technology from 2002 to 2004. Later, he furthered his academic pursuits at the same institute, obtaining his Doctorate in Computer Application Technology from 2004 to 2008.

Professional Endeavors

After completing his doctoral studies, Assoc Prof Dr Chengjie Sun embarked on an illustrious professional journey. He joined the Harbin Institute of Technology's School of Computer Science and Technology as a Lecturer from January 2009 to December 2014. In 2013, he expanded his academic horizons by serving as a Visiting Scholar at the Massachusetts Institute of Technology's Computer Science and Artificial Intelligence Laboratory (CSAIL). Since January 2015, he has held the position of Associate Professor at the Harbin Institute of Technology.

Additionally, Assoc Prof Dr Chengjie Sun enriched his professional experience by collaborating with renowned institutions and organizations. He served as a Visiting Scholar at Microsoft Research Asia's Natural Language Computing Group from September 2011 to July 2012.

Contributions and Research Focus

Assoc Prof Dr Chengjie Sun's research interests primarily revolve around natural language processing technologies and their applications, recommender systems, and machine learning. His scholarly contributions encompass a wide range of topics, including offensive message identification, explainable dialogue systems, fake news detection, response generation, user modeling, and named entity recognition, among others. Through his extensive peer-reviewed publications in reputable journals and conferences, Chengjie Sun has significantly advanced the field of computer science and technology.

Accolades and Recognition

Assoc Prof Dr Chengjie Sun's exemplary contributions to academia have earned him prestigious accolades and recognition. In 2011, he received the First Prize of the Heilongjiang Science and Technology Award for his work on Sentence Level Pinyin Input Method in a Network Environment. His research endeavors and innovative approaches have consistently garnered acknowledgment from peers and experts in the field.

Impact and Influence

Assoc Prof Dr Chengjie Sun's research and teaching endeavors have had a profound impact on the academic community and industry alike. His publications in leading journals and conferences have contributed to the advancement of natural language processing, machine learning, and recommender systems. Furthermore, his collaborative efforts with esteemed institutions and professional memberships have facilitated knowledge exchange and interdisciplinary collaborations, fostering innovation and growth in the field.

Legacy and Future Contributions

As an Associate Professor at the Harbin Institute of Technology, Chengjie Sun continues to inspire and mentor future generations of computer scientists and researchers. His dedication to advancing knowledge and fostering excellence in research and teaching serves as a testament to his commitment to the academic community. Moving forward, Chengjie Sun's ongoing research endeavors and contributions are poised to shape the future of natural language processing technologies, recommender systems, and machine learning, leaving a lasting legacy in the field.

Citations

Notable Publication