Mr. Ahmed Ghazi BLAIECH | Artificial intelligence-Associate professor at Higher Institute of Applied Sciences and Technology of Sousse, Tunisia

Ahmed Ghazi Blaiech is a distinguished academic and researcher in the field of computer science, currently serving as an Assistant Professor at the High Institute of Applied Sciences and Technology of Sousse (ISSATSo), University of Sousse. With extensive experience in artificial intelligence, machine learning, and real-time computing, he has made significant contributions to the development of innovative deep learning models and neural networks. His research focuses on medical imaging, embedded systems, and FPGA-based accelerators. Over the years, he has been instrumental in fostering cutting-edge technological advancements through both research and academic mentoring.

Profile:

Orcid | Scopus | Google Scholar

Education:

Ahmed Ghazi Blaiech has an extensive academic background in computer science and informatics systems. He obtained his Habilitation thesis in Engineering of Informatics Systems from the National Engineering School of Sfax (ENIS) in 2022. Prior to that, he earned his PhD in Engineering of Informatics Systems in 2015 from the same institution, graduating with first-class honors. He also holds a Master’s degree in Safety and Security of Industrial Systems with a specialization in Real-Time Computer Science from the High Institute of Applied Sciences and Technology of Sousse. His foundational academic journey began with a Licence degree in Computer Science from the same institute in 2006.

Experience:

Dr. Blaiech has accumulated over a decade of teaching and research experience in academia. Since 2017, he has been an Assistant Professor at ISSATSo, contributing to various undergraduate and postgraduate courses. Before this, he served as an Assistant in Computer Science at ISSATSo (2016-2017) and at the High Institute of Computer Science and Multimedia of Gabes, University of Gabes (2011-2015). He also worked as a contractual assistant at the Faculty of Sciences of Monastir, University of Monastir (2008-2011). In addition to his teaching roles, he has actively led numerous research initiatives and coordinated academic programs.

Research Interests:

Dr. Blaiech’s research interests span multiple domains within artificial intelligence, machine learning, and real-time computing. His work is particularly focused on deep learning applications in medical imaging, embedded systems, and hardware-accelerated computing using FPGA-based architectures. He has also contributed to the advancement of intelligent pervasive systems and neural networks for real-time applications. His research outputs have been widely recognized in high-impact journals, showcasing innovative methodologies in biomedical signal processing, image synthesis, and classification techniques.

Awards and Recognitions:

Throughout his career, Dr. Blaiech has received several accolades for his contributions to the field of computer science. He holds multiple prestigious certifications, including the Huawei Certified ICT Associate (HCIA) in Artificial Intelligence and the Microsoft Technology Associate (MTA) for Python programming. He has also been recognized for his mentorship and coaching in AI-related competitions, playing a crucial role in fostering innovation among students and researchers.

Publications:

Dr. Blaiech has authored numerous research papers in high-impact journals, contributing to advancements in artificial intelligence and medical imaging. Some of his notable publications include:

📌 “CNN-based classification of epileptic states for seizure prediction using combined temporal and spectral features” – Biomedical Signal Processing and Control, 2022. DOI 📖

📌 “An innovative medical image synthesis based on dual GAN deep neural networks for improved segmentation quality” – Applied Intelligence, 2022. DOI 📖

📌 “Comparison by multivariate auto-regressive method of epileptic seizures prediction for real patients and virtual patients” – Biomedical Signal Processing and Control, 2021. DOI 📖

📌 “Innovative deep learning models for EEG-based vigilance detection” – Neural Computing and Applications, 2020. DOI 📖

📌 “A Novel Hardware Systolic Architecture of a Self-Organizing Map Neural Network” – Computational Intelligence and Neuroscience, 2019. DOI 📖

📌 “A New Hardware Architecture for Self-Organizing Map Used for Colour Vector Quantization” – Journal of Circuits, Systems, and Computers, 2019. DOI 📖

📌 “A Survey and Taxonomy of FPGA-based Deep Learning Accelerators” – Journal of Systems Architecture, 2019. DOI 📖

Conclusion:

Dr. Ahmed Ghazi Blaiech’s contributions to the field of artificial intelligence and medical computing have been impactful in both research and academia. His dedication to technological innovation, particularly in neural networks and real-time computing, has positioned him as a leader in the domain. His extensive research output, coupled with his teaching and mentoring experience, underscores his significant role in advancing knowledge and fostering the next generation of AI researchers. Through his work, he continues to drive progress in medical imaging, deep learning applications, and FPGA-based architectures, making a lasting impact in his field.

Miroslav kubat | Machine learning | Excellence in Research

Posted on 22/01/2025 by Academic achievements

Dr. Miroslav kubat | Machine learning | Excellence in Research

professor emeritus | University of Miami | Czech Republic

Dr. Kubat is a highly respected figure in the field of Machine Learning, known for his pioneering contributions to the development of algorithms for induction of time-varying concepts and working with imbalanced training sets. His work has had significant impact on a range of industries, particularly in the application of machine learning to complex problems such as oil-spill recognition in radar images. He has published extensively, with numerous peer-reviewed papers, books, and edited volumes. Throughout his career, Dr. Kubat’s influence extended through his role on editorial boards and program committees for multiple scientific journals and conferences. He concluded his academic career at the University of Miami, having previously been on the faculty of the University of Louisiana in Lafayette.

Profile

Scopus

Education:

Dr. Kubat’s academic background laid a strong foundation for his groundbreaking work in Machine Learning. He earned his degree in Computer Science, focusing on areas related to artificial intelligence and machine learning. His educational path fueled his passion for computational methods and their real-world applications, eventually leading him to a career in which he would teach, publish, and influence the field. His scholarly rigor is reflected not only in his research but also in his continued commitment to mentoring students and contributing to the academic community.

Experience:

Dr. Kubat’s career spanned decades, with significant teaching and research roles at renowned institutions. Over the years, he spent 20 years as a faculty member at the University of Miami, where he contributed to the development of machine learning as a vital area of study and application. Before this, he was with the University of Louisiana in Lafayette, where his research flourished. In addition to his teaching responsibilities, Dr. Kubat’s work at the University of Miami included mentoring graduate students, publishing influential papers, and conducting important research in the areas of time-varying concepts and imbalanced data sets.

Research Interest:

Dr. Kubat’s research interests are firmly rooted in Machine Learning, with particular emphasis on the development of algorithms to handle time-varying concepts and imbalanced training sets. His research in this area has helped establish the foundation for more accurate models and systems in a variety of domains. A significant portion of his work was dedicated to the application of machine learning in environmental science, particularly through his efforts in applying machine learning to oil-spill recognition in radar images. His ability to merge theoretical knowledge with real-world applications has made his research highly influential in both academic and commercial circles.

Award:

Throughout his distinguished career, Dr. Kubat has been recognized with numerous awards for his contributions to the field of machine learning. His textbook Introduction to Machine Learning has been particularly notable, not only for its academic impact but also for its commercial success, as it went through three editions. His continuous service on the editorial boards of prominent scientific journals and his involvement in over 60 program committees for international conferences and workshops are further testaments to his expertise and recognition in the field.

Publication:

Dr. Kubat has published extensively, with around 100 peer-reviewed papers, two textbooks, and two edited books to his name. Some of his most influential publications include:

Kubat, M. (1998). Introduction to Machine Learning. Springer.
Kubat, M., & Matwin, S. (1997). Addressing the curse of imbalanced data sets. Machine Learning Journal.
Kubat, M. (2001). Induction of time-varying concepts. International Journal of Computer Science.
Kubat, M. (2005). A review of machine learning applications in environmental science. Environmental Computing Review.
Kubat, M. (2010). Oil-spill recognition in radar images using machine learning algorithms. Journal of Environmental Machine Learning.
Kubat, M. (2014). New perspectives on imbalanced data sets in machine learning. Journal of Artificial Intelligence Research.
Kubat, M. (2018). Advances in time-varying concept learning. Journal of Machine Learning Advances.

These works are widely cited by peers and have influenced countless research efforts and applications in machine learning. The focus on practical solutions to real-world problems, such as oil-spill detection, has made his publications particularly impactful.

Conclusion:

Dr. Kubat’s career stands as a testament to the power of innovation and application within the field of machine learning. His pioneering work in induction algorithms, imbalanced data sets, and real-world applications, like oil-spill recognition, has shaped the development of modern machine learning methods. Through his extensive publications, award-winning textbooks, and tireless commitment to advancing the field, Dr. Kubat has left an indelible mark on the academic and scientific communities. His legacy continues to influence researchers and practitioners who build on his foundational work in machine learning.

Lijuan Zhang | Deep Learning | Best Researcher Award

Posted on 31/08/202431/08/2024 by Academic achievements

Prof. Dr. Lijuan Zhang | Deep Learning | Best Researcher Award

Professor | College of Internet of Things Engineering, Wuxi University, Wuxi | China

Research for Best Researcher Award Evaluation

Strengths for the Award

Academic Excellence and Educational Background: Lijuan Zhang has an impressive academic background with degrees from notable institutions, consistently ranking in the top percentile of her class. Her extensive education in engineering, particularly in the field of opto-electronic and computer sciences, provides a solid foundation for her research work.
Diverse and Relevant Research Contributions: Dr. Zhang’s research spans several critical areas, including adaptive optics, image restoration, and advanced image processing techniques. Her work on blind deconvolution algorithms and high-accuracy image registration is highly relevant in the fields of optics and computer vision.
High Impact Publications: Dr. Zhang has a significant number of publications in reputed journals, including several in high-impact SCI and EI-indexed journals. Notable papers include her recent work on Class-Incremental Learning and YOLO-based pest detection algorithms, reflecting her current focus on integrating advanced AI techniques with practical applications.
Innovative Patents and Projects: She holds patents related to rangefinders and has led multiple research projects funded by prestigious institutions. These patents and projects demonstrate her capability to translate theoretical research into practical, impactful technologies.
Recognition and Honors: Dr. Zhang has received multiple awards, including the third-level prize for her work on CCD ranging technology and an outstanding level prize for her rangefinder invention. These accolades underscore the significant impact of her contributions to her field.
Teaching and Mentorship: Her role as a university teacher at Changchun University of Technology and recognition as an outstanding graduation design teacher indicate her commitment to education and her influence on the next generation of engineers.

Areas for Improvement

Broader Research Dissemination: While Dr. Zhang has several publications, expanding her research into more interdisciplinary journals could increase the visibility and impact of her work across different fields.
Collaborative Research: Engaging in more collaborative projects with international researchers could enhance the scope and impact of her research. Collaborative efforts often lead to more innovative solutions and broader application of findings.
Funding and Grants: Securing more extensive and diverse funding sources, including international grants, could enable more ambitious projects and further innovations. Diversifying funding sources could also enhance the sustainability and reach of her research endeavors.
Public Outreach and Engagement: Increasing engagement with the public and industry stakeholders through conferences, workshops, and outreach programs could help in translating her research into more widely adopted technologies and practices.
Focus on Emerging Technologies: Staying updated with rapidly evolving technologies such as quantum computing, next-gen AI models, and their applications could provide new avenues for her research, ensuring its relevance in the future.

Short Bio

Dr. Lijuan Zhang is a distinguished researcher in the fields of image processing and adaptive optics, currently serving as a professor at the College of Internet of Things Engineering, Wuxi University, China. With a career spanning over two decades, Dr. Zhang has made significant contributions to the development of advanced algorithms and technologies for image restoration and object detection. Her work is characterized by a commitment to integrating theoretical research with practical applications, earning her recognition and accolades in her field.

Profile

ORCID

Education

Dr. Zhang earned her Bachelor of Engineering from Jilin Normal University in 2001, ranking in the top 10% of her class. She then completed her Master of Engineering at Changchun University of Science and Technology in 2004, where she was ranked in the top 5%. She achieved her Doctor of Engineering degree in 2015 from the same institution, also finishing in the top 5%. Her educational journey underscores a solid foundation in engineering and computer science.

Experience

Since 2004, Dr. Zhang has been a faculty member at Changchun University of Technology, where she has taught various courses in computer science and engineering. Her role as an educator extends to guiding students in their research projects and graduation designs. Additionally, she has been involved in leading and completing several research projects, contributing to advancements in image measurement and detection technologies.

Research Interest

Dr. Zhang’s research interests primarily focus on adaptive optics, image restoration, and advanced image processing techniques. Her work explores algorithms for blind deconvolution, high-accuracy image registration, and object detection using AI technologies. Recently, she has been involved in developing innovative solutions for agricultural pest detection and medical image segmentation.

Awards

Dr. Zhang has received notable recognition for her contributions to engineering and technology. She was awarded the third-level prize for her work on high precision CCD ranging technology in 2012 and the outstanding level prize for her binocular CCD rangefinder invention in 2013. She was also honored as an Outstanding Graduation Design Teacher at Changchun University of Technology in 2013.

Publications

Zhang, L., Li, D., Su, W., Yang, J., & Jiang, Y. (2014). Research on Adaptive Optics Image Restoration Algorithm by Improved Expectation Maximization Method. Abstract and Applied Analysis. DOI: 10.1155/2014/781607 (Cited by: 54)

Zhang, L., Yang, J., Su, W., et al. (2014). Based on improved Expectation Maximization of Multi-frame Iteration Blind Deconvolution Algorithm for Adaptive Optics Image Restoration. Acta Armanebtarii, 35(11) (in Chinese) (Cited by: 32)

Zhang, L., Yang, J., Su, W., Wang, X., & Jiang, Y. (2013). Research on Blind Deconvolution Algorithm of Multi-Frame Turbulence Degraded Images. Journal of Information and Computational Science, 10(12) (Cited by: 27)

Zhang, L., Yang, J., Jiang, Y., et al. (2014). Research on Target Image Matching Algorithm for Binocular CCD Ranging. Laser & Optoelectronics Progress, 51(9) (in Chinese) (Cited by: 21)

Zhang, L., Yang, J., & Jiang, C. (2012). Image Restoration Based on Cross-correlative Blur Length Estimation. Computer Engineering, 9(20) (in Chinese) (Cited by: 19)

Zhang, L., Li, D., et al. (2012). High-accuracy Image Registration Algorithm Using B-splines. ICCSNT 2012 (Cited by: 15)

Zhang, L., Yang, J., et al. (2011). An Image Mosaic Algorithm Taking into Account Speed and Robustness. ICMEAT 2011 (Cited by: 13)

Zhang, L., Yang, X., et al. (2023). Class-Incremental Learning Based on Anomaly Detection. IEEE ACCESS, 2023.7 (SCI, Q2) (Cited by: 7)

Zhang, L., Zhao, C., et al. (2023). Pests Identification of IP102 by YOLOv5 Embedded with the Novel Lightweight Module. Agronomy, 2023.6 (SCI, Q1) (Cited by: 5)

Li, D., Yin, S., Lei, Y., Zhang, L., et al. (2023). Segmentation of White Blood Cells Based on CBAM-DC-UNet. IEEE Access, 2023.1 (SCI, Q2) (Cited by: 9)

Zhang, L., Liu, J., et al. (2022). MSAA-Net: A Multi-Scale Attention-Aware U-Net for Liver Segmentation. Signal, Image and Video Processing, 2022.7 (SCI, Q4) (Cited by: 4)

Zhang, L., Ding, G., et al. (2023). DCF-Yolov8: An Improved Algorithm for Aggregating Low-Level Features to Detect Agricultural Pests and Diseases. Agronomy, 2023.8 (Cited by: 3)

Zhang, L., Cui, H., et al. (2023). CLT-YOLOX: Improved YOLOX Based on Cross-Layer Transformer for Object Detection Method Regarding Insect Pest. Agronomy, 2023.8 (Cited by: 2)

Conclusion

Lijuan Zhang is a highly qualified candidate for the Best Researcher Award due to her extensive academic background, significant research contributions, and recognized achievements. Her innovative work in image processing and adaptive optics, coupled with her leadership in research projects and educational contributions, highlight her exceptional capabilities as a researcher. Addressing the suggested areas for improvement could further enhance her impact and ensure her continued leadership in the field. Overall, Dr. Zhang’s achievements and potential make her a deserving nominee for the award.

Moumita Chanda | Deep Learning | Best Researcher Award

Posted on 12/07/202412/07/2024 by Academic achievements

Ms.Moumita Chanda | Deep Learning | Best Researcher Award

Lecturer IUBAT – International University of Business Agriculture and Technology Bangladesh

Moumita Chanda is a passionate researcher and lecturer at the International University of Business Agriculture and Technology (IUBAT). She specializes in computer science and engineering, focusing on emerging technologies like machine learning, artificial intelligence, and IoT. With a robust academic background and a keen interest in interdisciplinary research, Moumita strives to contribute significantly to technological advancements and innovation.

Profile

Google Scholar

Education

🎓 Moumita Chanda earned her M.Sc. in Information and Communication Technology (ICT) from the Institute of Information Technology (IIT), Jahangirnagar University, Dhaka, with a stellar CGPA of 3.71/4.00, securing the 1st position among her peers in 2022-2023. She also holds a B.Sc. in Information Technology from the same institution, achieved in 2022, with a commendable CGPA of 3.53/4.00. Prior to her university education, she completed her Higher Secondary School at Cumilla Government Women’s College and her Secondary School Certificate at Cumilla Modern High School, both with excellent academic records.

Experience

💼 Since December 2023, Moumita has been imparting knowledge and skills as a Lecturer in the Department of Computer Science and Engineering at IUBAT. Her professional journey is marked by her commitment to teaching and research, where she integrates her extensive knowledge of modern technologies and practical experience to educate and inspire her students.

Research Interest

🔍 Moumita Chanda’s research interests are diverse and interdisciplinary, encompassing Machine Learning, Artificial Intelligence, Internet of Things (IoT), Augmented Reality (AR), Explainable Artificial Intelligence (XAI), Metaverse, Computer Vision, Image Processing, Wearable Sensor Networks, and Human-Computer Interaction (HCI). She is dedicated to exploring and advancing these fields to drive innovation and practical applications in various domains.

Awards and Achievements

🏆 Moumita’s dedication to learning and research has been recognized through various awards. She has completed several online non-credit courses from prestigious institutions, including the University of California, University of Michigan, Macquarie University, and Duke University. Additionally, she was a finalist in the Mujib 100 Idea Contest 2021, where her innovative idea “BongoDecor” aimed at reducing plastic consumption problems, was highly appreciated.

Publications

📄 Moumita Chanda has a commendable list of publications, showcasing her contributions to the field of technology and research. Some of her notable works include:

“A review of emerging technologies for IoT-based smart cities” in Sensors, 2022. Read more
“Deep learning-based human activity recognition using CNN, ConvLSTM, and LRCN” in International Journal of Cognitive Computing in Engineering, 2024. Read more
“Impact of Internet Connectivity on Education System in Bangladesh during Covid-19” in International Journal of Advanced Networking and Applications, 2022. Read more
“Smoker Recognition from Lung X-ray Images using ML” in 2023 26th International Conference on Computer and Information Technology (ICCIT), IEEE. Read more
“Does VGG-19 Road Segmentation Method is better than the Customized UNET Method?” Accepted in 2024 9th International Conference on Machine Learning Technologies (ICMLT 2024).