Yuhuan Fei | Object Detection | Best Researcher Award

Assist Prof Dr. Yuhuan Fei | Object Detection | Best Researcher Award

Qufu Normal University | China

PUBLICATION PROFILE

Scopus

👤 Summary

Dr. Yuhuan Fei is an Associate Professor at Qufu Normal University, China, with a strong background in Machine Learning, Intelligent Manufacturing, and Ceramic Cutting Tools. He holds a Ph.D. in Mechanical Manufacturing and Automation and focuses on the development and performance of advanced ceramic cutting tools, material microstructure, and mechanical properties. Dr. Fei has made substantial contributions to scientific research, including numerous journal publications, patents, and participation in national research projects.

🎓 Education

Dr. Fei completed his Ph.D. in Mechanical Manufacturing and Automation at Shandong University in 2012, with a thesis on fabrication and cutting performance of Al2O3-TiC-TiN ceramic cutting tools. Prior to that, he earned a Master’s degree in Materials Processing Engineering from Northeastern University in 2007, focusing on the phase analysis of Ti-700 alloy. He also holds a Bachelor’s degree in Material Forming and Controlling Engineering, completed in 2004 at Northeastern University.

🧑‍🏫 Professional Experience

Dr. Fei has held various academic and professional positions. He served as a Visiting Scholar at McGill University from 2017 to 2018. Since 2013, he has been an Associate Professor at Qufu Normal University in Shandong Province. Before that, he worked as a Patent Examiner at the Patent Examination Cooperation Center (SIPO) in Jiangsu Province, focusing on intellectual property evaluations related to material sciences.

📚 Academic Citations

Dr. Fei has authored numerous papers in high-impact journals. Some of his most cited works include those published in the Journal of Real-Time Image Processing (2025), Journal of Ceramic Science and Technology (2021, 2019), and Ceramics International (2014). His research also appears in Materials Science and Engineering A, Rare Metal Materials and Engineering, and several other prestigious journals, significantly contributing to the fields of material science and engineering.

🛠️ Technical Skills

Dr. Fei’s technical expertise includes:

  • Advanced ceramic material development and cutting tool fabrication

  • Phase composition analysis and microstructure characterization of materials

  • Machine learning applications in intelligent manufacturing systems

  • Patent analysis and intellectual property research

👨‍🏫 Teaching Experience

Dr. Fei has an active teaching role at Qufu Normal University, where he imparts knowledge on material science and engineering, particularly focusing on ceramic cutting tools and intelligent manufacturing. He supervises graduate students, guiding them through research projects and theses, while also leading research seminars and educational workshops.

🔬 Research Interests

Dr. Fei’s primary research interests include:

  • Development of ceramic cutting tools for advanced manufacturing

  • Study of material processing techniques and phase transitions in cutting materials

  • Application of machine learning to optimize manufacturing processes

  • Investigation of mechanical properties and microstructural characteristics of ceramics

📚 Top Notes Publications 

The Influence of Ni Addition on the Microstructures and Mechanical Properties of Al2O3–TiN–TiC Ceramic Materials
    • Authors: Yuhuan Fei, Chuanzhen Huang, Hanlian Liu, Bin Zou

    • Journal: Not mentioned in the provided text

    • Year: 2018

Study on the Cutting Performance of Al2O3-TiC-TiN Ceramic Tool Materials
    • Authors: Yuhuan Fei, Chuan Zhen Huang, Han Lian Liu, Bin Zou

    • Journal: Not mentioned in the provided text

    • Year: 2016

On the Formation Mechanisms of Fine Granular Area (FGA) on the Fracture Surface for High Strength Steels in the VHCF Regime
    • Authors: Yong-De Li, Limin Zhang, Yuhuan Fei, Mei-Xia Li

    • Journal: Not mentioned in the provided text

    • Year: 2015

Mechanical Properties of Al2O3–TiC–TiN Ceramic Tool Materials
    • Authors: Yuhuan Fei, C.Z. Huang, H.L. Liu, Bin Zou

    • Journal: Not mentioned in the provided text

    • Year: 2014

Establishment of the Low Defect Ceramic Cutting Tool Database
    • Authors: Chang Bin Zou, Chuan Zhen Huang, Bin Zou, Jun Wang

    • Journal: Not mentioned in the provided text

    • Year: 2013

The Effects of Sintering Process on Microstructure and Mechanical Properties of TiB2-Ti(C0.5N0.5)-WC Composite Tool Materials
    • Authors: Yan Zhao, Chuan Zhen Huang, Bin Zou, Jun Wang

    • Journal: Not mentioned in the provided text

    • Year: 2012

 

Diksha Kumar | Deep learning | Women Researcher Award

Mrs. Diksha Kumar | Deep learning | Women Researcher Award

Ramrao Adik Institute of Technology (RAIT) | India

Publication Profile

Scopus
Orcid

BIOGRAPHY OF Mrs. Diksha Kumar 🌱📚

INTRODUCTION 🌍

Diksha Kumar is an accomplished research scholar with over 10 years of experience in higher education, currently pursuing her PhD in Computer Engineering at Ramrao Adik Institute of Technology (RAIT), India. Specializing in machine learning, deep learning, and generative AI, she has made significant strides in both research and innovation. She has secured funding for her research through a grant from Mumbai University, India, and holds a patent for an innovative aquatic animal-friendly ocean cleaning system. Through her research endeavors, Diksha has consistently pushed the boundaries of technology to contribute to a cleaner, smarter world. 🌍🔬

EARLY ACADEMIC PURSUITS 🎓

Diksha Kumar’s academic journey began with her intense passion for computer science and technology. As a student, she exhibited a deep curiosity and enthusiasm for computational methods and their real-world applications. Pursuing her undergraduate and master’s degrees, Diksha excelled in every academic pursuit, solidifying her foundation in computer engineering. Her rigorous academic training at Ramrao Adik Institute of Technology (RAIT) laid the groundwork for her current doctoral research, enabling her to delve into cutting-edge areas such as deep learning and machine learning.

PROFESSIONAL ENDEAVORS 👩‍💼

her professional career, Diksha Kumar has worked across various dimensions of academia and research. As a Research Scholar at RAIT, she has engaged in multiple industry-linked research projects. Her pioneering work in image classification and processing has garnered her attention in international academic circles. She has secured prestigious research grants and has become a member of the IEEE, further bolstering her professional credibility. Her practical contributions in the fields of AI and remote sensing have not only garnered attention but are making impactful changes in technology and environmental sustainability.

CONTRIBUTIONS AND RESEARCH FOCUS  ON  Deep learning 🔬

Diksha’s research work is marked by several innovative contributions to deep learning and AI. Her work on adapting Attention U-Net for image classification and combining it with XGBoost to enhance classification accuracy stands out. Additionally, she developed a hybrid model fusing VGG19 with XGBoost for improved image analysis. Moreover, her current focus on a novel cloud removal technique combining Homomorphic Filtering and Gamma Correction holds promise for enhancing image processing techniques. These contributions demonstrate her skill in addressing complex challenges in AI and image processing.

IMPACT AND INFLUENCE 🌟

Diksha Kumar’s research has had a substantial impact on both academia and industry. Her work, which fuses theoretical AI techniques with real-world applications, is paving the way for further advancements in image classification, remote sensing, and environmental protection. The patented aquatic animal-friendly ocean cleaning system showcases her commitment to using technology for environmental conservation, setting an example for researchers seeking to combine innovation with sustainability. Her pioneering work continues to inspire others in the research community and has led to significant strides in the application of deep learning to environmental issues.

ACADEMIC CITATIONS AND PUBLICATIONS 📑

Diksha Kumar has earned recognition through her impressive academic citations, reflecting the importance and relevance of her work. With a citation index of 19 and multiple publications in SCI and Scopus-indexed journals, her contributions to image processing, machine learning, and deep learning are well-regarded. Her academic output includes four journal articles, highlighting her prolific and impactful research work. Each publication has been a step forward in the development of AI-driven solutions that address both scientific and practical problems.

HONORS & AWARDS 🏆

Diksha’s commitment to excellence has earned her several accolades and recognitions in her field. Notably, she has been awarded a research grant from Mumbai University, which enabled her to pursue her innovative projects. Additionally, her research achievements are acknowledged through her patent for the aquatic animal-friendly ocean cleaning system. As she continues to make strides in her field, Diksha is a strong contender for the Women Researcher Award, which would recognize her outstanding contributions to research and technology.

LEGACY AND FUTURE CONTRIBUTIONS 🔮

Diksha Kumar’s legacy is being shaped by her relentless pursuit of excellence in research and her dedication to the practical applications of AI and deep learning. With future projects focused on advancing image processing techniques and addressing environmental issues, Diksha is positioning herself as a leader in these fields. Her future contributions will not only advance the frontiers of AI but also have a direct and positive impact on society, ensuring that her work continues to inspire and influence the next generation of researchers.

FINAL NOTE ✨

Diksha Kumar is a dynamic researcher whose work in AI, deep learning, and image classification is leaving a lasting impact on the academic and scientific communities. Her innovative solutions and commitment to using technology for the betterment of the environment showcase her ability to combine technical expertise with real-world challenges. As she moves forward with her PhD and her current research projects, Diksha’s work promises to make an even greater impact in the years to come.

TOP NOTES PUBLICATIONS 📚

AUXG: Deep Feature Extraction and Classification of Remote Sensing Image Scene Using Attention Unet and XGBoost
  • Authors: D.G. Kumar, Diksha Gautam, S.Z. Chaudhari, Sangita Zope
  • Journal: Journal of the Indian Society of Remote Sensing
  • Year: 2024

Snehal Laddha | Medical Image Analysis | Best Researcher Award

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