Madhuri Rao | Machine Learning | Best Researcher Award

Dr. Madhuri Rao | Machine Learning | Best Researcher Award

Senior Assistant Professor | MIT World Peace University | India

Dr. Madhuri Rao is a dedicated researcher and academic in computer science with expertise in wireless sensor networks, Internet of Things, artificial intelligence, blockchain, and cybersecurity, with her current work focusing on deep learning, cloud security, and healthcare applications. She earned her Ph.D. in Computer Science and Engineering from Biju Patnaik University of Technology, where her research emphasized energy-efficient object tracking in wireless sensor networks. Over her career, she has gained extensive professional experience as a faculty member, academic coordinator, research supervisor, and editorial board member, contributing significantly to both teaching and research. She has authored and co-authored numerous publications in reputed journals and conferences, including IEEE, Springer, Elsevier, and Scopus-indexed platforms, along with patents and book chapters that highlight her innovative approach. Her research interests span interdisciplinary applications of advanced technologies to address challenges in security, healthcare, and sustainability, with ongoing involvement in collaborative projects and international initiatives. She has received recognition through awards such as best paper honors and a best research scholar award, underscoring her contributions to the academic community. Her research skills include problem-solving, experimental design, data analysis, and guiding students at undergraduate, postgraduate, and doctoral levels, coupled with active roles as session chair, track chair, and guest lecturer in international conferences. She is also a life member of professional societies and holds certifications that strengthen her academic profile. Her impactful contributions are reflected in 116 citations and an h-index of 7.

Profile: Google Scholar | ORCID | ResearchGate | LinkedIn

Featured Publications

  1. Rao, M., & Kamila, N. K. (2021). Cat swarm optimization based autonomous recovery from network partitioning in heterogeneous underwater wireless sensor network. International Journal of System Assurance Engineering and Management, 1–15.

  2. Rao, M., Kamila, N. K., & Kumar, K. V. (2016). Underwater wireless sensor network for tracking ships approaching harbor. 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), 1098–1102. IEEE.
  3. Rao, M., & Kamila, N. K. (2018). Spider monkey optimisation based energy efficient clustering in heterogeneous underwater wireless sensor networks. International Journal of Ad Hoc and Ubiquitous Computing, 29(1–2), 50–63.

  4. Chaudhury, P., Rao, M., & Kumar, K. V. (2009). Symbol based concatenation approach for text to speech system for Hindi using vowel classification technique. 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 1393–1396. IEEE.

  5. Kumar, K. V., Kumari, P., Rao, M., & Mohapatra, D. P. (2022). Metaheuristic feature selection for software fault prediction. Journal of Information and Optimization Sciences, 43(5), 1013–1020.

Ahmet Kayabaşı| Artificial Intelligence | Best Researcher Award

Prof. Dr. Ahmet Kayabaşı | Artificial Intelligence | Best Researcher Award

Professor | Karamanoglu Mehmetbey University | Turkey

Prof. Dr. Ahmet Kayabaşı is a distinguished academic in electrical-electronics engineering with expertise in artificial intelligence, antennas, biomedical signal processing, image processing, fuzzy logic, and power electronics. He earned his PhD in Electrical-Electronics Engineering from Selcuk University and has since built a strong academic career combining teaching, research, and leadership. His professional experience includes serving as Head of Department, Director of the Institute of Graduate Studies, and Senate Member, along with mentoring numerous MSc and PhD students. His research interests span interdisciplinary fields, applying advanced AI techniques in UAV swarm algorithms, smart agriculture, biomedical diagnostics, and energy-efficient power systems. He has been actively involved in TÜBİTAK and institutional projects, contributing to impactful solutions for both academia and industry. Recognized for his excellence, he has received awards such as Best Presenter Award at ICAT and has played vital roles in academic conferences and scientific communities. His research skills include developing intelligent systems, applying machine learning to engineering challenges, and designing novel antenna and biomedical applications. He has published widely in leading international journals indexed in IEEE, Scopus, and Web of Science, with notable contributions in Applied Thermal Engineering, Swarm and Evolutionary Computation, and Computers and Electronics in Agriculture. His academic excellence is reflected in 609 citations by 522 documents, 47 publications, and an h-index of 13.

Profile: Google Scholar | Scopus | ORCID

Featured Publications

  1. Sabanci, K., Kayabasi, A., & Toktas, A. (2017). Computer vision‐based method for classification of wheat grains using artificial neural network. Journal of the Science of Food and Agriculture, 97(8), 2588–2593.

  2. Yigit, E., Sabanci, K., Toktas, A., & Kayabasi, A. (2019). A study on visual features of leaves in plant identification using artificial intelligence techniques. Computers and Electronics in Agriculture, 156, 369–377.

  3. Kayabasi, A., Toktas, A., Yigit, E., & Sabanci, K. (2018). Triangular quad-port multi-polarized UWB MIMO antenna with enhanced isolation using neutralization ring. AEU-International Journal of Electronics and Communications, 85, 47–53.

  4. Sabanci, K., Toktas, A., & Kayabasi, A. (2017). Grain classifier with computer vision using adaptive neuro‐fuzzy inference system. Journal of the Science of Food and Agriculture, 97(12), 3994–4000.

  5. Yildiz, B., Aslan, M. F., Durdu, A., & Kayabasi, A. (2024). Consensus-based virtual leader tracking swarm algorithm with GDRRT*-PSO for path-planning of multiple-UAVs. Swarm and Evolutionary Computation, 88, 101612.

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.

Mr. Andrews Tang | Deep Learning | Best Researcher Award

Mr. Andrews Tang | Deep Learning | Best Researcher Award

Andrews Tang at Kwame Nkrumah University of Science and Technology, Ghana

👨‍🎓 Profiles

Scopus

Google Scholar

Publications

Assessing blockchain and IoT technologies for agricultural food supply chains in Africa: A feasibility analysis

  • Authors: Andrews Tang, Eric Tutu Tchao, Andrew Selasi Agbemenu, Eliel Keelson, Griffith Selorm Klogo, Jerry John Kponyo
  • Journal: Heliyon
  • Year: 2024

An Open and Fully Decentralised Platform for Safe Food Traceability

  • Authors: Eric Tutu Tchao, Elton Modestus Gyabeng, Andrews Tang, Joseph Barnes Nana Benyin, Eliel Keelson, John Jerry Kponyo
  • Year: 2022

Prof. Ling Yang | Deep Learning | Women Researcher Award

Prof. Ling Yang | Deep Learning | Women Researcher Award

Professor at Kunming University of Science and Technology, China

👨‍🎓 Profiles

Scopus

Orcid

Publications

Enhancing Panax notoginseng Leaf Disease Classification with Inception-SSNet and Image Generation via Improved Diffusion Model

  • Authors: Wang, R., Zhang, X., Yang, Q., Liang, J., Yang, L.
  • Journal: Agronomy
  • Year: 2024

Deep learning implementation of image segmentation in agricultural applications: a comprehensive review

  • Authors: Lei, L., Yang, Q., Yang, L., Wang, R., Fu, C.
  • Journal: Artificial Intelligence Review
  • Year: 2024

Alternate micro-sprinkler irrigation and organic fertilization decreases root rot and promotes root growth of Panax notoginseng by improving soil environment and microbial structure in rhizosphere soil

  • Authors: Zang, Z., Yang, Q., Liang, J., Guo, J., Yang, L.
  • Journal: Industrial Crops and Products
  • Year: 2023

A BlendMask-VoVNetV2 method for quantifying fish school feeding behavior in industrial aquaculture

  • Authors: Yang, L., Chen, Y., Shen, T., Yu, H., Li, D.
  • Journal: Computers and Electronics in Agriculture
  • Year: 2023

An FSFS-Net Method for Occluded and Aggregated Fish Segmentation from Fish School Feeding Images

  • Authors: Yang, L., Chen, Y., Shen, T., Li, D.
  • Journal: Applied Sciences (Switzerland)
  • Year: 2023

Mr. Xiaoyu Li | Deep Learning | Best Researcher Award

Mr. Xiaoyu Li, Deep Learning, Best Researcher Award

Xiaoyu Li at Beijing Forestry University, China

Professional Profile

🌟 Summary:

Xiaoyu Li is a university student at Beijing Forestry University’s School of Soil and Water Conservation. His research focuses on Remote Sensing & GIS, Image Processing, Land Use, Transportation, UAV utilization, and Ecology. He has contributed to national-level scientific projects, including the Qinghai-Tibet Plateau expedition, and has authored publications in prestigious journals. His work includes assessing human living environments, controlling soil erosion, and studying sediment connectivity and erosion dynamics. Xiaoyu Li has pioneered large-scale land use classification in northwestern China using UAV remote sensing and has contributed to understanding vegetation changes in the Qinghai-Tibet Plateau.

🎓 Education:

Currently pursuing studies at Beijing Forestry University, College of Soil and Water Conservation.

💼 Professional Experience:

Engaged in multiple national-level research projects focusing on environmental assessment, soil erosion control, and watershed dynamics.

🔬 Research Interests:

  • Remote Sensing & GIS
  • Image Processing and Analysis
  • Land Use and Transportation
  • UAV (drone) utilization and Ecology

📖 Publications Top Noted:

Paper Title: Land-Use Composition, Distribution Patterns, and Influencing Factors of Villages in the Hehuang Valley, Qinghai, China, Based on UAV Photogrammetry
  • Authors: Xiaoyu Li, Zhongbao Xin
  • Journal: Remote Sensing
  • Year: 2024

MUDASSIR-KHAN-Artificial Intelligence-Best Researcher Award

Assist.Prof.Dr. MUDASSIR-KHAN-Artificial Intelligence-Best Researcher Award

King Khalid University-Saudi Arabia

Author Profile

Early Academic Pursuits

Dr. Mudassir Khan embarked on his academic journey with a solid foundation, earning his BSc. (Hons) from Aligarh Muslim University in June 2007. His passion for computer science led him to pursue a Master's in Computer Applications (MCA) from the same university, where he delved into the intricacies of software development and computing. His academic pursuits reached their zenith with a Ph.D. in Computer Science from Noida International University in February 2022, showcasing his commitment to continuous learning and scholarly pursuits.

Professional Endeavors

With over 13 years of experience in the teaching field, Dr. Khan currently serves as an Assistant Professor in the Computer Science Department at King Khalid University. His multifaceted role encompasses a wide array of subjects, including Programming Languages, Machine Learning, Python Programming, Operating Systems, Computing Ethics, Cyber Defense Technology, Computer Networking, Multimedia & Graphics, and various core computer science disciplines. Dr. Khan's expertise extends beyond the classroom, where he actively contributes as an organizing committee member, advisory committee member, technical session chair, reviewer, and editor in various conferences and international peer-reviewed journals.

As an administrator, Dr. Khan has taken on the responsibility of Department Head, demonstrating his leadership skills in handling departmental affairs, faculty management, student guidance, task allocation, and course updates in alignment with industry standards.

Contributions and Research Focus On Artificial Intelligence

Dr. Mudassir Khan's academic journey is marked by significant contributions to the field of computer science. His research focus spans a wide spectrum, from operating systems and computing ethics to cutting-edge areas like Machine Learning, Python Programming, and Cyber Defense Technology. With 13+ years of experience, he has consistently implemented innovative teaching pedagogies, reflecting his dedication to providing an enriching learning experience for his students.

Accolades and Recognition

Dr. Khan's contributions to academia are underscored by his impressive publication record. He boasts 13 SCI publications, 22 Scopus publications, 5 book publications, and 1+ patents/copyrights. His research has not only garnered attention on national and international platforms but has also positioned him as a thought leader in his field. With 31 publications in total and active participation in 9 national and international conferences, Dr. Khan's work has received well-deserved accolades and recognition.

In the ever-evolving landscape of technology, Artificial Intelligence (AI) stands at the forefront, revolutionizing the way we perceive and interact with machines. Mudassir Khan, a trailblazing researcher in this dynamic field, has made indelible contributions that redefine the boundaries of AI. His work spans a spectrum of key areas, from machine learning to neural networks, showcasing a commitment to unraveling the potential of intelligent systems.

Impact and Influence

As an academician and administrator, Dr. Mudassir Khan has made a lasting impact on the institutions he has served. His role as Department Head has seen him introduce innovative courses, update existing ones to meet current industry standards, and ensure a balanced workload allocation. His commitment to academic excellence has influenced students, faculty, and the academic community at large.

Mudassir Khan's journey in Artificial Intelligence is marked by relentless pursuit and innovation. As a recipient of the prestigious Best Researcher Award, his impact on the field is not only acknowledged but celebrated. With a focus on ethical AI practices, he has emerged as an advocate for responsible and conscientious development, ensuring that advancements in technology align with societal values and expectations.

Legacy and Future Contributions

Dr. Khan's legacy is one of continuous learning, leadership, and research excellence. His diverse expertise and commitment to staying at the forefront of technological advancements position him as a catalyst for positive change. Looking ahead, Dr. Mudassir Khan envisions contributing further to the realms of academia, research, and administration, leaving an enduring legacy for future generations of scholars.

Notable Publication