Fatma Zahra Sayadi | Deep Learning | Best Innovation Award

Prof. Fatma Zahra Sayadi | Deep Learning | Best Innovation Award

Associate Professor | University of Sousse | Tunisia

Fatma Elzahra Sayadi is a highly accomplished researcher and academic specializing in electronics and microelectronics, with current research focused on video surveillance systems, real-time processing, and signal compression. She earned her PhD in electronics for real-time systems from the University of Bretagne Sud in collaboration with the University of Monastir and has also completed her engineering and master’s studies in electrical and electronic systems. She has extensive professional experience as a maître de conférences and previously as a maître assistante and assistant technologist, teaching courses in microprocessors, multiprocessors, programming, circuit testing, and industrial electronics. Her research interests include signal processing, parallel architectures, microelectronics, real-time systems, and communication networks. She has actively participated in national and international research projects and collaborations with institutions in France, Italy, Germany, and Morocco. Her work has been published in over 37 journal articles, 40 conference papers, and six book chapters, and she has supervised several doctoral and master’s theses. She has been recognized with awards such as the first prize at the Women in Research Forum at the University of Sharjah and contributes to professional communities as a reviewer, evaluator, and organizer of academic events. She is skilled in research methodologies, signal and data analysis, electronic system design, and digital education innovation. Her academic contributions have been cited by 395 documents, with 69 documents contributing to her citations, and she has an h-index of 13.

Featured Publications

  1. Basly, H., Ouarda, W., Sayadi, F. E., Ouni, B., & Alimi, A. M. (2020). CNN-SVM learning approach based human activity recognition. In International Conference on Image and Signal Processing (pp. 271–281). 77 citations.

  2. Bouaafia, S., Khemiri, R., Sayadi, F. E., & Atri, M. (2020). Fast CU partition-based machine learning approach for reducing HEVC complexity. Journal of Real-Time Image Processing, 17(1), 185–196. 53 citations.

  3. Haggui, O., Tadonki, C., Lacassagne, L., Sayadi, F., & Ouni, B. (2018). Harris corner detection on a NUMA manycore. Future Generation Computer Systems, 88, 442–452. 48 citations.

  4. Basly, H., Ouarda, W., Sayadi, F. E., Ouni, B., & Alimi, A. M. (2022). DTR-HAR: Deep temporal residual representation for human activity recognition. The Visual Computer, 38(3), 993–1013. 40 citations.

  5. Bouaafia, S., Khemiri, R., Messaoud, S., Ben Ahmed, O., & Sayadi, F. E. (2022). Deep learning-based video quality enhancement for the new versatile video coding. Neural Computing and Applications, 34(17), 14135–14149. 35 citations.

Ahmad Reza Naghsh Nilchi | Deep Learning | Best Researcher Award

Prof. Ahmad Reza Naghsh Nilchi | Deep Learning | Best Researcher Award

Faculty Member | University of Isfahan | Iran

Prof. Ahmad Reza Naghsh-Nilchi is a distinguished researcher in computer vision, artificial intelligence, and medical image processing with a strong academic and professional background. He completed his PhD in Electrical and Computer Engineering at Michigan State University, where he specialized in digital image processing, and has since built an influential career in both academia and research. Over the years, he has served in multiple leadership positions including department chair, dean of research, and head of research laboratories, while also supervising numerous PhD and master’s students in advanced AI and imaging topics. His professional experience extends internationally through collaborations with leading institutions such as UC Irvine, University of Toronto, York University, and University of Ireland, contributing significantly to global research initiatives. His research interests span robust deep learning, adversarial defense, trustworthy AI, multimodal action recognition, image captioning, retinal analysis, and robot-camera pose estimation, reflecting both theoretical innovation and practical applications. He has published more than 70 papers in prestigious journals and conferences indexed by IEEE and Scopus, and his work has received more than 2,200 citations. His excellence has been recognized through multiple honors, including awards as University Researcher of the Year and Industrial Researcher of the Year. He possesses advanced research skills in AI model development, medical imaging, digital signal processing, and multimodal data analysis, complemented by editorial roles, conference organization, and active memberships in professional associations such as IEEE and ACM. His career demonstrates a commitment to advancing science, mentoring the next generation, and fostering impactful interdisciplinary collaborations. His Scopus output reflects international impact, with 1,319 citations by 1,214 documents, 65 published documents, and an h-index of 21.

Profile: Google Scholar | Scopus Profile | ORCID Profile

Featured Publications

Fathi, A., & Naghsh-Nilchi, A. R. (2012). Noise tolerant local binary pattern operator for efficient texture analysis. Pattern Recognition Letters, 33(9), 1093–1100.

Fathi, A., & Naghsh-Nilchi, A. R. (2012). Efficient image denoising method based on a new adaptive wavelet packet thresholding function. IEEE Transactions on Image Processing, 21(9), 3981–3990.

Fathi, A., & Naghsh-Nilchi, A. R. (2013). Automatic wavelet-based retinal blood vessels segmentation and vessel diameter estimation. Biomedical Signal Processing and Control, 8(1), 71–80.

Amirgholipour, S. K., & Ahmad, R. (2009). Robust digital image watermarking based on joint DWT-DCT. International Journal of Digital Content Technology and its Applications, 3(2), 42–48.*

Kasmani, S. A., & Naghsh-Nilchi, A. (2008). A new robust digital image watermarking technique based on joint DWT-DCT transformation. In 2008 Third International Conference on Convergence and Hybrid Information Technology (pp. 539–544). IEEE.

Zahra Yahyaoui | Deep Learning | Women Researcher Award

Dr. Zahra Yahyaoui | Deep Learning | Women Researcher Award

Teacher-Researcher at Higher Institute of Applied Sciences and Technology of Kasserine, Kairouan University | Tunisia

Dr. Zahra Yahyaoui is a dedicated researcher and educator with expertise in electronics, microelectronics, renewable energy systems, and artificial intelligence. She has established herself as an active contributor to the advancement of intelligent fault detection and diagnosis methods for photovoltaic and wind energy conversion systems. Her work bridges theory and practice, combining advanced machine learning techniques with embedded hardware implementation, ensuring her research is both academically rigorous and industrially relevant. Alongside her research activities, she has been deeply involved in teaching, supervision, and mentoring, helping to shape the academic and professional development of students in electronics and applied sciences. Her publications in high-impact journals and participation in international conferences highlight her growing recognition in the global research community. With technical versatility, adaptability, and strong teamwork skills, she continues to contribute to sustainable solutions in energy systems while promoting innovation, academic excellence, and interdisciplinary collaboration.

Professional Profiles 

Scopus Profile | ORCID Profile 

Education

Dr. Zahra Yahyaoui pursued her academic path in Tunisia, beginning with a bachelor’s degree in industrial computing with a specialization in embedded systems. She then advanced to a master’s research degree in nanomaterials and embedded electronics, where she specialized in embedded electronics and conducted important research on fault detection and diagnosis in wind energy systems using machine learning. Building on this foundation, she completed her doctoral studies in electronics and microelectronics at the Higher Institute of Applied Sciences and Technology of Kasserine, Kairouan University. Her PhD research focused on developing enhanced intelligent data-driven paradigms for fault detection and diagnosis in power systems, with practical applications on embedded architectures. She carried out her doctoral work within the Research Unit of Advanced Materials and Nanotechnologies, furthering her expertise at the intersection of artificial intelligence, renewable energy, and electronic systems. This strong academic background reflects her commitment to innovative, multidisciplinary research.

Professional Experience

Dr. Zahra Yahyaoui has built a solid academic and professional career through her teaching and research activities. She started as a part-time teacher at the Higher Institute of Applied Sciences and Technology of Kasserine, where she gained experience delivering courses and tutorials in electronics, microprocessor and microcontroller architectures, and embedded systems. Her role expanded to contractual teacher at the same institute under Kairouan University, where she was responsible for teaching system-on-chip design, combinational and sequential logic circuits, and analog signal processing, covering both theoretical and practical sessions. In addition to her teaching duties, she has co-supervised master’s theses on advanced topics such as interval-valued machine learning, deep learning for fault detection in renewable systems, and photovoltaic installation design. Through her academic contributions, she has combined teaching excellence with mentoring, ensuring students receive both theoretical knowledge and practical insights. Her professional journey highlights her commitment to education, innovation, and applied research.

Research Interest

Dr. Zahra Yahyaoui’s research interests lie at the intersection of electronics, artificial intelligence, and renewable energy systems. She focuses on developing intelligent data-driven approaches for fault detection and diagnosis, aiming to enhance the reliability and efficiency of power systems such as photovoltaic and wind energy converters. Her work emphasizes the use of advanced machine learning and deep learning techniques, including BiLSTM, GRU, and optimization algorithms, to address uncertainty in renewable energy conversion and monitoring. She is also interested in the implementation of these algorithms on embedded architectures, integrating software with hardware platforms like FPGA, Raspberry Pi, and microcontrollers for real-world applications. Beyond fault diagnosis, she explores forecasting methods for solar irradiance and power output, contributing to the broader field of sustainable energy management. By combining theoretical modeling, algorithm development, and embedded system integration, her research supports innovation in intelligent renewable energy technologies.

Research Skill

Dr. Zahra Yahyaoui has developed a diverse set of research skills that enable her to carry out impactful and interdisciplinary work. She is proficient in programming languages such as MATLAB and Python, which she uses extensively for data analysis, machine learning model development, and algorithm implementation. She is skilled in simulation tools like ISE and Simplorer, supporting her expertise in circuit and system design. Her hardware-related skills include working with Siemens S7-1200, FPGA boards, Raspberry Pi, and Arduino microcontrollers, allowing her to translate theoretical models into practical embedded system solutions. She has strong problem-solving abilities, adaptability, and teamwork skills, which contribute to successful research collaborations and academic projects. Her research methodology combines theoretical analysis with experimental validation, ensuring robust and application-oriented results. With certifications in artificial intelligence and embedded systems, she brings an advanced skillset for developing intelligent monitoring and diagnostic systems, particularly for renewable energy applications.

Publications Top Notes

Title: Fault detection and diagnosis in grid-connected PV systems under irradiance variations
Authors: Hajji, M.; Yahyaoui, Z.; Mansouri, M.; Nounou, H.; Nounou, M.
Year: 2023

Title: One-Class Machine Learning Classifiers-Based Multivariate Feature Extraction for Grid-Connected PV Systems Monitoring under Irradiance Variations
Authors: Yahyaoui, Z.; Hajji, M.; Mansouri, M.; Bouzrara, K.
Year: 2023

Title: Effective Fault Detection and Diagnosis for Power Converters in Wind Turbine Systems Using KPCA-Based BiLSTM
Authors: Yahyaoui, Z.; Hajji, M.; Mansouri, M.; Abodayeh, K.; Bouzrara, K.; Nounou, H.
Year: 2022

Title: Kernel PCA based BiLSTM for Fault Detection and Diagnosis for Wind Energy Converter Systems
Authors: Yahyaoui, Z.; Hajji, M.; Mansouri, M.; Bouzrara, K.; Nounou, H.; Nounou, M.
Year: 2022

Title: Efficient fault detection and diagnosis of wind energy converter systems
Authors: Yahyaoui, Z.; Hajji, M.; Mansouri, M.; Harkat, M.-F.; Kouadri, A.; Nounou, H.; Nounou, M.
Year: 2020

Conclusion

Dr. Zahra Yahyaoui is a deserving candidate for the Best Researcher Award due to her significant contributions in advancing intelligent data-driven techniques for renewable energy systems, fault detection, and embedded architectures. Her research has produced valuable publications in reputed international journals and conferences, with practical applications that support sustainable energy and technological innovation. Through her teaching, mentorship, and active participation in the academic community, she has demonstrated a strong commitment to knowledge sharing and capacity building. With her proven expertise, dedication, and potential for future leadership, she is well positioned to continue making impactful contributions to both research and society.

Ms. Linjing Wei | Deep Learning | Best Researcher Award

Ms. Linjing Wei | Deep Learning | Best Researcher Award

Linjing Wei at Gansu Agricultural University, China

Profile

Scopus

Academic Background:

Ms. Linjing Wei is a distinguished female professor at Gansu Agricultural University, specializing in Grassland Science with a research focus on Grassland Informatics. Born in July 1977, she has made significant contributions to her field through her extensive research, academic guidance, and numerous publications.

Education:

Ms. Wei earned her PhD in Grassland Science from Gansu Agricultural University in June 2015. Her educational background has provided a strong foundation for her academic and research pursuits.

Professional Experience:

Ms. Wei teaches several courses for master’s students, including Introduction to Cloud Computing, Case Analysis of Software Engineering, Information Systems and Information Resource Management, and Distributed Systems and Cloud Computing Technology. As the first supervisor, she has guided numerous master’s students in various majors, particularly in Agricultural Engineering and Information Technology.

Research Interests:

Ms.Wei's research interests lie in Grassland Informatics. Over the past five years, she has led several key research projects with significant funding, focusing on areas such as data resource integration, intelligent cloud platforms for agricultural logistics, ecosystem restoration and monitoring, sustainable development planning, and trustworthy traceability systems for agricultural products. Her published works include papers in prestigious journals like Sensors and the Canadian Journal of Remote Sensing, as well as contributions to national-level textbooks and academic monographs.

📝 Academic Achievements:

Ms. Wei has an impressive list of published papers, including "Fine Segmentation of Chinese Character Strokes Based on Co-ordinate Awareness and Enhanced BiFPN" in Sensors (2024), "Enhanced Wheat Head Detection in Images Using Fourier Domain Adaptation and Random Guided Filter" in Canadian Journal of Remote Sensing (2024), and "Feature Selection in High Dimensional Biomedical Data Based on BF-SFLA" in Neurogenetics (2022).

 Publications:

Fine Segmentation of Chinese Character Strokes Based on Coordinate Awareness and Enhanced BiFPN
  • Authors:Mo, H., Wei, L.
  • Journal: Sensors
  • Year: 2024
A Smart Chicken Farming Platform for Chicken Behavior Identification and Feed Residual Estimation
  • Authors: Yang, J., Gao, J., Li, Y., Lu, Q., Zheng, H.
  • Journal: Proceedings - 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
  • Year: 2023
Feature Selection in High Dimensional Biomedical Data Based on BF-SFLA
  • Authors: Dai, Y., Niu, L., Wei, L., Tang, J.
  • Journal: Frontiers in Neuroscience
  • Year: 2022
Jointly Learning Topics in Sentence Embedding for Document Summarization
  • Authors: Gao, Y., Xu, Y., Huang, H., Wei, L., Liu, L.
  • Journal: IEEE Transactions on Knowledge and Data Engineering
  • Year: 2020
Study on the Matching Algorithm of Turf Grass Introduction Features Based on Big Data Analysis
  • Authors: Wei, L., Dong, W., Gan, S., Wang, Y.
  • Year: 2019

Dr. Shivanshu Shrivastava | Deep Learning | Best Researcher Award

Dr. Shivanshu Shrivastava, Deep Learning, Best Researcher Award

Doctorate at Rajiv Gandhi Institute of Petroleum Technology, India

Profiles

Scopus

Google Scholar

🌍 Academic Background:

Dr. Shivanshu Shrivastava is an Assistant Professor in the Department of Electrical & Electronics Engineering at Rajiv Gandhi Institute of Petroleum Technology (RGIPT), Amethi, Uttar Pradesh, India. He has been contributing to the field of electrical and electronics engineering with a focus on artificial intelligence and communications since September 2021.

🎓 Education:

Dr. Shrivastava earned his Ph.D. from IIT Guwahati in August 2017, specializing in Wireless Communication with a thesis on “Security Issues in Cognitive Radios,” under the guidance of Prof. A. Rajesh and Prof. P. K. Bora. He completed his Postdoctoral Fellowships at Shenzhen University, China, and IIT Kanpur from August 2017 to December 2020, focusing on “Artificial Intelligence and Deep Learning Applications in 5G Communications” under Prof. Bin Chen. He holds a Bachelor of Engineering degree in Electronics and Telecommunication Engineering from CSVTU, Bhilai, with a CPI of 8.13/10.

💼 Work Experience:

Before joining RGIPT, Dr. Shrivastava worked as a Postdoctoral Fellow at Shenzhen University from January 2019 to December 2020 and as a SERB-NPDF at IIT Kanpur from August 2017 to October 2018. His current role involves advancing research in deep learning and AI applications in communications.

🔬 Research Areas:

His research interests encompass artificial intelligence and deep learning applications in communications, cognitive radio systems, wireless communications, visible light communications (VLC), and security issues in cognitive radios.

📝 Research Experience:

At RGIPT, Dr. Shrivastava leads research on deep learning and AI applications in wireless communication. His previous projects include optimizing achievable rates in hybrid RF/VLC systems and designing energy-efficient hybrid RF/VLC systems for 5G communications. He has supervised Ph.D. students and undergraduate project students in these areas.

🏆 Honors, Awards, and Memberships:

Dr. Shrivastava has received the International Travel Support (ITS) from SERB for attending the IEEE ICCCAS conference in Xiamen, China, and the Best Teacher Award from Union Bank of India at RGIPT. He was also honored with postdoctoral fellowships from Shenzhen University and IIT Kanpur.

📖 Publications:

A lightweight group-based SDN-driven encryption protocol for smart home IoT devices
  • Authors: Raza, A., Khan, S., Shrivastava, S., Wu, K., Wang, L.
  • Journal: Computer Networks
  • Year: 2024
Collision Penalty-Based Defense Against Collusion Attacks in Cognitive Radio Enabled Smart Devices
  • Authors: Shrivastava, S., John, S., Rajesh, A., Bora, P.K.
  • Journal: IEEE Transactions on Consumer Electronics
  • Year: 2024
Transfer learning for resource allotment in dynamic hybrid WiFi/LiFi communication systems
  • Authors: Verma, T., Shrivastava, S., Dwivedi, U.D., Kothari, D.P.
  • Journal: Optics Communications
  • Year: 2023
Asset Allotment in Hybrid RF/VLC Communication in the 400-700 THz Band
  • Authors: Shrivastava, S., Agarwal, S., Chen, B.
  • Journal: Terahertz Wireless Communication Components and System Technologies
  • Year: 2022
A survey on security issues in cognitive radio based cooperative sensing
  • Authors: Shrivastava, S., Rajesh, A., Bora, P.K., Lin, X., Wang, H.
  • Journal: IET Communications
  • Year: 2021