Qichuan Tian | Computer Science | Best Researcher Award

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Prof. Dr. Qichuan Tian | Computer Science | Best Researcher Award

Prof. Dr. at  Beijing University of Civil Engineering and Architecture, China

Qichuan Tian, born in 1971, is a distinguished professor and technical expert specializing in artificial intelligence, pattern recognition, and computer vision. He holds a Ph.D. in Engineering from Northwestern Polytechnical University (2006) and currently serves as a professor and master’s supervisor at Beijing University of Civil Engineering and Architecture (BUCEA). As the Director of the Department of Artificial Intelligence at the School of Intelligent Science and Technology, he leads research in biometrics, human-computer interaction, and deep learning. He is a member of multiple prestigious organizations, including the National Information Technology Standardization Technical Committee and the Chinese Society of Biomedical Engineering. His career spans academia and industry, with significant contributions in developing national standards, publishing books, and mentoring graduate students. Tian has also played a key role in over 20 research projects funded by national and provincial foundations, solidifying his reputation as a thought leader in AI and computational sciences.

Professional Profile

Education

Qichuan Tian has an extensive academic background in engineering. He obtained his Bachelor of Engineering (1993) and Master of Engineering (1996) from Taiyuan University of Science and Technology. In 2006, he completed his Doctor of Engineering at Northwestern Polytechnical University, specializing in artificial intelligence and computer vision. His academic training laid a strong foundation for his later contributions to AI, biometrics, and deep learning. His studies focused on integrating computational intelligence into practical applications, a theme that continues to define his research and professional endeavors.

Professional Experience

Tian has a diverse career in academia and research. Since 2012, he has served as the Head of the Department of Artificial Intelligence at BUCEA, where he spearheads innovative AI programs. From 2009 to 2010, he was a Visiting Scholar at Auburn University, USA, gaining international exposure in computer science. Between 2006 and 2008, he conducted postdoctoral research at Tianjin University. Previously, he held various roles at Taiyuan University of Science and Technology (1993–2012), where he advanced from Assistant Professor to Associate Professor and later became the Chief Leader of Circuits and Systems. His leadership has been instrumental in shaping AI research and education in China.

Research Interests

Tian’s research interests focus on artificial intelligence, pattern recognition, image processing, and deep learning. He specializes in biometric recognition, computer vision, and human-computer natural interaction. His work extends to security authentication, big data analysis, and IoT-based embedded systems. Tian has published over 100 journal and conference papers, authored six books, and contributed significantly to national standards in AI applications. His interdisciplinary research bridges theoretical advancements with practical AI implementations, making substantial contributions to the field.

Research Skills

With expertise in artificial intelligence and computer vision, Tian possesses strong research skills in deep learning algorithms, biometric recognition systems, and real-time image processing. He has successfully led projects in autonomous driving, green building AI integration, and complex object detection. His experience includes handling large-scale datasets, implementing machine learning frameworks, and designing AI-driven applications. Additionally, he has obtained over 50 invention patents and software copyrights, showcasing his ability to translate theoretical research into impactful technological innovations.

Awards and Honors

Tian’s contributions to academia and AI research have earned him multiple accolades. In 2024, he was recognized among CNKI’s Highly Cited Scholars (Top 5). He received the First Prize for Teaching Achievements at BUCEA in 2021 and was honored for developing a National First-Class Blended Online and Offline Course in 2020. Additionally, he was awarded the Outstanding Master’s Thesis Advisor Award in 2012. His accolades highlight his commitment to education, research, and AI-driven innovations, reinforcing his influence in the field of intelligent science and technology.

Conclusion

Qichuan Tian is a prominent scholar and AI expert dedicated to advancing artificial intelligence and biometric research. His leadership in academia, combined with his extensive research portfolio, underscores his impact on technological advancements in pattern recognition, computer vision, and human-computer interaction. With a career spanning over two decades, Tian has played a pivotal role in shaping AI education, national standards, and industry collaborations. His legacy continues to influence emerging AI technologies and inspire the next generation of researchers in intelligent computing.

Publications Top Notes

  • Title: An improved framework for breast ultrasound image segmentation with multiple branches depth perception and layer compression residual module

    • Authors: K. Cui, Qichuan Tian, Haoji Wang, Chuan Ma
    • Year: 2025

  • Title: Mobile Robot Path Planning Algorithm Based on NSGA-II

    • Authors: Sitong Liu, Qichuan Tian, Chaolin Tang
    • Year: 2024
    • Citations: 1

  • Title: OcularSeg: Accurate and Efficient Multi-Modal Ocular Segmentation in Non-Constrained Scenarios

    • Authors: Yixin Zhang, Caiyong Wang, Haiqing Li, Qichuan Tian, Guangzhe Zhao
    • Year: 2024

  • Title: Convolutional Neural Network–Bidirectional Gated Recurrent Unit Facial Expression Recognition Method Fused with Attention Mechanism

    • Authors: Chaolin Tang, Dong Zhang, Qichuan Tian
    • Year: 2023
    • Citations: 4

 

 

 

Dagne Walle | Computer Science | Best Scholar Award

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Mr. Dagne Walle | Computer Science | Best Scholar Award

Haramaya at Haramaya university, Ethiopia

Dagne Walle Girmaw is a lecturer, researcher, and programmer at Haramaya University in Ethiopia, with a strong academic background in Information Technology. His expertise lies in applying machine learning and deep learning techniques to solve critical challenges in agriculture. Dagne’s work focuses on developing automated systems to detect crop diseases at an early stage, utilizing advanced AI models to improve food security and agricultural sustainability. His passion for using technology to bridge the gap between agriculture and innovation has led to impactful research that can potentially transform the agricultural landscape in Ethiopia and beyond. Dagne is committed to making a difference by empowering farmers with actionable insights that can enhance crop yields and reduce losses. As an educator, Dagne also plays a pivotal role in nurturing the next generation of IT professionals in Ethiopia, providing them with the necessary tools to apply advanced technologies in real-world scenarios.

Professional Profile

Education:

Dagne Walle Girmaw holds a Master’s degree in Information Technology from the University of Gondar, completed in 2021. He also earned his Bachelor’s degree in Information Technology from Haramaya University in 2017. His academic journey has been focused on acquiring a deep understanding of IT systems, with a particular emphasis on machine learning and deep learning. The combination of his education and technical skills has enabled him to pioneer research in applying these advanced technologies to agricultural challenges. His education from two reputable institutions in Ethiopia has provided him with both theoretical knowledge and practical experience in addressing real-world issues in agriculture, particularly the detection of crop diseases using AI.

Professional Experience:

Since 2018, Dagne has been a lecturer and researcher at Haramaya University, where he imparts knowledge on Information Technology and leads research initiatives focused on AI applications in agriculture. As a lecturer, he has played a key role in shaping the education of students, particularly those interested in IT, by teaching courses and supervising academic projects. His research experience spans over six years, during which he has developed several deep learning-based models for detecting crop diseases such as stem rust in wheat, livestock skin diseases, and common bean leaf diseases. His academic and research endeavors at Haramaya University have allowed him to make meaningful contributions to the field of agricultural technology and provide students with cutting-edge insights into the intersection of IT and agriculture.

Research Interest:

Dagne Walle Girmaw’s research interests are primarily centered around the application of deep learning and machine learning techniques in agriculture. He is particularly focused on developing systems for early disease detection in crops, which can significantly improve agricultural productivity and food security. His research has led to the development of various models, such as those for detecting and classifying diseases in crops like wheat, beans, and peas, using deep convolutional neural networks (CNNs). Additionally, Dagne’s work includes using AI for the detection of counterfeit Ethiopian banknotes. His interest in machine learning-driven solutions highlights his desire to use technology to solve some of the most pressing challenges in the agricultural sector, with the ultimate goal of empowering farmers and enhancing food systems in Ethiopia and other developing countries.

Research Skills:

Dagne possesses strong research skills in machine learning, deep learning, and computer vision, which are central to his work on agricultural disease detection. He is proficient in using deep learning frameworks such as TensorFlow and Keras to develop complex models that can process and analyze agricultural data, including images of crops. His research skills also include data preprocessing, model evaluation, and optimization techniques, all of which are essential for creating accurate and reliable models. Furthermore, Dagne has experience in implementing algorithms for image classification and pattern recognition, which are key components in his work on disease detection. His ability to integrate AI technologies into real-world applications demonstrates a high level of proficiency in his field and a commitment to advancing agricultural technologies through research.

Awards and Honors:

Dagne Walle Girmaw has earned multiple Reviewer Contribution Certificates, recognizing his active participation in the academic and research community. These certificates highlight his role in reviewing academic papers, further cementing his reputation as a respected contributor to the field of Information Technology and machine learning. While specific awards for his research have not been mentioned, his work’s impact on agricultural technology has gained recognition, particularly in Ethiopia, where his research has the potential to improve the lives of farmers and contribute to national food security. His certifications and recognition as a reviewer reflect his dedication to advancing knowledge in both the academic and applied research fields.

Conclusion:

Dagne Walle Girmaw is a promising researcher and academic in the field of Information Technology, with a focus on using AI and deep learning to address challenges in agriculture. His work is particularly impactful in the realm of crop disease detection, where he has developed models that could potentially transform agricultural practices in Ethiopia and beyond. With a strong educational background, extensive professional experience, and a passion for solving agricultural problems through technology, Dagne is well-positioned to make significant contributions to both the academic and practical aspects of agricultural innovation. His research holds the potential to not only advance technology but also improve the livelihoods of farmers, enhance food security, and contribute to sustainable agricultural practices.

Publication Top Notes

  1. Title: Livestock animal skin disease detection and classification using deep learning approaches
    • Authors: Walle Girmaw, D.
    • Journal: Biomedical Signal Processing and Control
    • Year: 2025
    • Volume: 102
    • Article Number: 107334
  2. Title: Deep convolutional neural network model for classifying common bean leaf diseases
    • Authors: Girmaw, D.W., Muluneh, T.W.
    • Journal: Discover Artificial Intelligence
    • Year: 2024
    • Volume: 4(1)
    • Article Number: 96
  3. Title: A novel deep learning model for cabbage leaf disease detection and classification
    • Authors: Girmaw, D.W., Salau, A.O., Mamo, B.S., Molla, T.L.
    • Journal: Discover Applied Sciences
    • Year: 2024
    • Volume: 6(10)
    • Article Number: 521
  4. Title: Field pea leaf disease classification using a deep learning approach
    • Authors: Girmaw, D.W., Muluneh, T.W.
    • Journal: PLoS ONE
    • Year: 2024
    • Volume: 19(7)
    • Article Number: e0307747
  5. Title: Development of a Model for Detection and Grading of Stem Rust in Wheat Using Deep Learning
    • Authors: Nigus, E.A., Taye, G.B., Girmaw, D.W., Salau, A.O.
    • Journal: Multimedia Tools and Applications
    • Year: 2024
    • Volume: 83(16)
    • Pages: 47649–47676
    • Citations: 4

 

 

M Sinthuja | Machine Learning | Best Researcher Award

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M Sinthuja | Machine Learning | Best Researcher Award

Assistant Professor at M S Ramaiah Institute of Technology, India

M. Sinthuja is a dedicated academic and researcher specializing in data mining and information technology. With over a decade of teaching experience across various prestigious institutions, she has made significant contributions to the field through her innovative research and commitment to student development. Sinthuja’s career began as an Assistant Professor at Sri Ramakrishna Institute of Technology, followed by positions at other esteemed colleges, where she has played a pivotal role in disseminating theoretical and practical knowledge to students. Her research focuses on applying data mining techniques to analyze frequent patterns within online shopping databases, a field increasingly relevant in today’s data-driven world. Sinthuja has authored numerous papers published in recognized journals, showcasing her ability to contribute valuable insights to the academic community. Additionally, she actively engages in mentoring students to identify their interests and achieve academic excellence. Her work has garnered recognition, including sponsorship from the University Grants Commission (UGC) of India. Sinthuja’s passion for research and teaching positions her as a noteworthy candidate for accolades such as the Best Researcher Award, reflecting her potential for continued contributions to her field.

Professional Profile

Education

M. Sinthuja’s educational background has laid a strong foundation for her career in academia and research. She pursued her higher studies in Computer Science and Engineering, culminating in a research thesis submitted in December 2018 at Annamalai University. Her research, titled “Application of Data Mining Techniques for Finding Frequent Patterns using Online Shopping Database,” showcases her expertise in data mining, a critical area in the modern technological landscape. Sinthuja’s academic journey includes her undergraduate studies, where she developed a solid understanding of computer science fundamentals. Additionally, her commitment to lifelong learning is evident in her various professional development activities and participation in academic workshops. This educational trajectory not only equips her with a robust theoretical framework but also enhances her practical skills in programming and data analysis. Her academic achievements demonstrate a blend of theoretical knowledge and practical application, making her a proficient educator and researcher. Sinthuja’s academic background, combined with her dedication to teaching and research, positions her as a valuable contributor to the field of computer science and data mining.

Professional Experience

M. Sinthuja possesses a rich and diverse professional experience that spans over a decade in the field of information technology and computer science education. Beginning her career as an Assistant Professor at Sri Ramakrishna Institute of Technology in Coimbatore, she played a pivotal role in shaping the academic journey of numerous students. Following this, she held similar positions at SBM College of Engineering and Technology and Presidency University in Bangalore, further expanding her expertise and influence in the academic community. Since 2020, she has been an Assistant Professor at M. S. Ramaiah Institute of Technology, recognized as one of the top engineering colleges in Karnataka. Throughout her career, Sinthuja has emphasized the importance of disseminating theoretical and practical knowledge, motivating students to excel academically, and fostering a culture of self-learning. Her teaching methodologies incorporate current industry trends, preparing students for real-world challenges. Sinthuja’s commitment to education is evident in her proactive engagement in curriculum development and student mentorship, establishing her as a respected figure in the academic realm. This breadth of experience underscores her capability as an educator and her dedication to advancing the field of information technology.

Research Interest

M. Sinthuja’s primary research interest lies in the field of data mining, particularly in the application of data mining techniques to uncover frequent patterns in large datasets. Her doctoral research focused on analyzing online shopping databases, which is crucial in today’s e-commerce-driven economy. She is particularly interested in the development and evaluation of algorithms that enhance the efficiency of data mining processes. Sinthuja’s work encompasses a variety of data mining methodologies, including association rule mining and frequent itemset mining, which are essential for extracting valuable insights from complex datasets. Her research not only contributes to theoretical advancements in data mining but also has practical implications for businesses seeking to leverage data for strategic decision-making. Additionally, she aims to explore interdisciplinary applications of data mining in fields such as healthcare, finance, and social media analysis. By integrating her findings with real-world applications, Sinthuja seeks to bridge the gap between academic research and industry needs. This commitment to applying theoretical knowledge to practical challenges reflects her dedication to advancing the field of data science and her desire to contribute positively to societal advancements through technology.

Research Skills

M. Sinthuja possesses a comprehensive skill set that enhances her research capabilities in the field of data mining and information technology. She is proficient in several programming languages, including C, C++, Java, and Python, which are essential for developing algorithms and implementing data analysis techniques. Additionally, her knowledge of scripting languages such as HTML and JavaScript allows her to create user interfaces and enhance data visualization in her projects. Sinthuja is adept at utilizing various database management tools and operating systems, enabling her to work with diverse datasets and perform complex analyses. Her research skills extend to the design and evaluation of algorithms, particularly in association rule mining, where she has conducted extensive comparative studies on algorithm performance. Sinthuja’s ability to analyze data, draw meaningful conclusions, and present findings clearly has resulted in numerous publications in reputable journals. Furthermore, she excels in mentoring students and collaborating with peers, demonstrating her ability to work effectively in research teams. Overall, her technical proficiency, analytical thinking, and collaborative spirit make her a valuable asset to any research endeavor in the domain of data mining and computer science.

Awards and Honors

M. Sinthuja has received several accolades throughout her academic and research career, recognizing her contributions to the field of data mining and information technology. A notable achievement is the sponsorship of her research by the University Grants Commission (UGC) of India, which underscores the significance and relevance of her work in the academic community. This endorsement not only validates her research efforts but also highlights her potential to make impactful contributions to the field. Additionally, her research has been published in several respected journals, showcasing her commitment to disseminating knowledge and advancing academic discourse in data mining. The recognition of her work in indexed journals, such as SCOPUS and UGC-listed publications, reflects her dedication to high-quality research output. Sinthuja’s involvement in collaborative research projects and her active participation in academic conferences further illustrate her commitment to professional development and networking within her field. These honors and recognitions serve as a testament to her expertise and influence as a researcher and educator, positioning her favorably for future accolades, such as the Best Researcher Award.

Conclusion

In conclusion, M. Sinthuja stands out as a remarkable candidate for the Best Researcher Award, owing to her extensive contributions to the field of data mining and her commitment to academic excellence. Her solid educational background, combined with over a decade of professional experience, underscores her qualifications as both an educator and a researcher. Sinthuja’s research focus on data mining techniques, particularly in analyzing online shopping databases, highlights her ability to address relevant and pressing issues in the digital age. Her proficiency in various programming languages and her analytical skills further enhance her capacity to contribute to the academic community meaningfully. While there are opportunities for growth in expanding her research scope and increasing her academic visibility, her achievements and dedication to student development are commendable. With the support and recognition that the Best Researcher Award could provide, Sinthuja is well-positioned to continue her impactful work, inspire future generations of researchers, and contribute significantly to the advancement of knowledge in the field of information technology.

Yunxiang Lu | neural network dynamics | Best Researcher Award

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Dr. Yunxiang Lu | neural network dynamics | Best Researcher Award 

at Nanjing University of Posts and Telecommunications, China.

Dr. Yunxiang Lu is an accomplished scholar in Control Science and Engineering, currently pursuing a combined Master and Ph.D. program at the College of Automation and Artificial Intelligence at Nanjing University of Posts and Telecommunications, China. His research focuses on nonlinear dynamic systems, bifurcation theory, and the application of control systems in ecological and biological networks. Throughout his academic career, Yunxiang has demonstrated his proficiency through numerous publications in high-impact journals and participation in prestigious conferences. His work contributes significantly to the understanding of neural networks, eco-epidemiological systems, and cyber-physical systems. In addition, Yunxiang has industry experience as a technical engineer, applying advanced control theories in real-world projects like smart factories powered by 5G technology.

Profile

Scopus

ORCID

Education 

Yunxiang Lu is currently pursuing a combined Master and Ph.D. degree in Control Science and Engineering at Nanjing University of Posts and Telecommunications. His studies cover diverse areas such as matrix theory, bifurcation of nonlinear dynamic systems, and adaptive control. Throughout his education, Yunxiang has excelled in courses like Image Analysis and Understanding, Nonlinear Systems and Chaos Control, and Optimization Methods, reflecting his deep understanding of advanced control theories. His exceptional academic performance includes top grades in Matrix Theory (100), Linear System Theory (95), and Image Analysis and Understanding (95), indicating his strong analytical and mathematical capabilities. His educational background equips him to analyze complex networks and systems, which are fundamental to his research in ecological competition networks and neural systems.

Experience 

Yunxiang Lu has gained practical experience through his role as an IT Technical Engineer at China Telecom Corporation’s Nanjing Branch. In this position, he contributed to the 5G+MEC smart factory project, where he applied his knowledge in telecommunications and control systems to enhance smart factory operations. Yunxiang participated in developing a 5G+MEC virtual private network, integrating 5G wireless scanning guns and machine vision systems, which underscores his ability to apply cutting-edge technologies in real-world environments. In academia, Yunxiang presided over the Postgraduate Research and Practice Innovation Program of Jiangsu Province, leading research on bifurcation control in fractional-order ecological networks. His ability to balance academic research with practical engineering projects reflects his diverse expertise and versatility.

Research Interests 

Yunxiang Lu’s research is primarily focused on control theory, bifurcation dynamics, and ecological and biological systems. He is particularly interested in the dynamical behavior of complex networks, such as ecological competition networks and neural networks, under various influences like fractional orders and time delays. His work explores how network topology and control strategies affect the stability and evolution of these systems. Yunxiang has also ventured into cyber-physical systems, investigating tipping points and bifurcation mechanisms in networks. His research aims to develop optimized control strategies for managing the dynamics of anomalous diffusion systems, which include neural networks and ecological competition networks, contributing to both theoretical advancements and practical applications in system stability and control.

Awards 

Yunxiang Lu has received multiple prestigious awards for his academic excellence. In 2022, he was honored as an Excellent Graduate by Nanjing University of Posts and Telecommunications, a reflection of his outstanding performance throughout his Ph.D. program. He was also recognized as an Excellent Postgraduate in both 2021 and 2020, receiving second prizes in the university’s Postgraduate Academic Scholarship competition during those years. These accolades underscore his dedication to academic success and research excellence. Yunxiang’s continuous recognition over the years highlights his consistency and high academic standards, making him a standout student in the College of Automation and Artificial Intelligence.

Publications 

Dr. Yunxiang Lu has contributed extensively to high-impact research in nonlinear systems and control theory. His key publications include:

 

  1. “Stability and bifurcation exploration of delayed neural networks with radial-ring configuration and bidirectional coupling”, IEEE Transactions on Neural Networks and Learning Systems, 2023, in press.
    • Cited by: 10
  2. “A delayed eco-epidemiological competition network with reaction-diffusion terms: Tipping anticipation”, Applied and Computational Mathematics, 2023, accepted.
    • Cited by: 7
  3. “Hybrid control synthesis for Turing instability and Hopf bifurcation of marine planktonic ecosystems with diffusion”, IEEE Access, 2021, 9: 111326-111335.
    • Cited by: 15
  4. “Hopf bifurcation of biological competition network with independent non-cross propagation characteristics”, Complex System and Complexity Science, 2022, 19(1): 1-11.
    • Cited by: 5

Conclusion

Yunxiang Lu, Ph.D., is a strong candidate for the Best Researcher Award, given his extensive contributions to the fields of control science, nonlinear systems, and neural network modeling. His technical expertise, research leadership, and publication record in high-impact journals demonstrate his commitment to advancing scientific knowledge. With a focus on expanding his research’s practical and interdisciplinary impact, he would be a highly deserving recipient of this award.