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Dr. Sheilla Ann Bangoy Pacheco is an Assistant Professor at North Eastern Mindanao State University, specializing in machine learning, image processing, and adversarial AI. Her research focuses on robust facial recognition, privacy-preserving federated learning, and healthcare analytics. Her work on SARGAN-based face recognition and adversarial defense contributes to the development of secure and resilient biometric systems. Actively collaborating with international researchers, she adopts interdisciplinary approaches to address real-world challenges, particularly in healthcare and data privacy. Her contributions reflect growing societal relevance and a commitment to advancing trustworthy and secure artificial intelligence systems.
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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
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
Profile: Google Scholar | Scopus | ORCID
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.
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.
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.
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.
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.
SummaryMr. Andrews Tang is a passionate Computer Engineering graduate and a cutting-edge researcher with a focus on Deep Learning and Computer Vision. With strong expertise in machine learning, blockchain, and IoT, he has contributed to high-impact research in agriculture, aviation safety, and food traceability. Currently, he is working as a Research Associate at KNUST’s DIPPER Lab, leading projects that utilize AI for real-world problem-solving.
EducationBachelor of Science in Computer Engineering (2018-2022)
Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana
Professional Experience
Machine Learning Engineer, Aviation Safety Analysis
DIPPER Lab, KNUST (Jan 2024 – Present)
Computer Vision Researcher, Red Palm Oil Adulteration Detection
RAIL & DIPPER Lab, KNUST (Jan 2023 – Oct 2023)
Blockchain Researcher, Food Traceability System
DIPPER Lab, KNUST (Mar 2022 – Nov 2022)
Research Interests
Deep Learning
Computer Vision
AI for Safety & Systems
Technical Skills
Python, PyTorch, OpenAI Gym, MobileNetV2, YOLO, Wandb, Deep Learning, Computer Vision
Honours & AwardsBest Poster Award: Deep Learning Indaba (2023)
Member: Black in AI Fellowship (2024)
Publications
Early Academic PursuitsProf. Yang Ling’s academic journey began with a solid foundation in engineering, leading to her doctorate in 2022 from China Agricultural University. During her doctoral studies, she focused on applying advanced technologies to agricultural automation and machine vision, laying the groundwork for her future research in computer vision and image processing. Her educational background was enriched by a strong interdisciplinary approach, combining engineering with artificial intelligence, which would later fuel her impactful research career.
Professional EndeavorsAfter completing her Ph.D., Prof. Yang joined the School of Information Engineering and Automation at Kunming University of Science and Technology, where she quickly established herself as a key faculty member. Her role as a lecturer has allowed her to share her expertise in deep learning, computer vision, and automation with the next generation of engineers and researchers. Prof. Yang has also made significant contributions through her leadership in several national and provincial research projects, further solidifying her position in academia and research communities.
Contributions and Research FocusProf. Yang’s research spans a wide range of topics in the field of computer vision and image processing. Her primary research interests include:
Her research has not only contributed to advancing artificial intelligence in these areas but also has practical applications in agricultural machinery and automation, which aligns with the core goals of her academic institute.
Impact and InfluenceProf. Yang’s work has had a significant impact on the field of artificial intelligence and computer vision, both academically and practically. As the principal investigator in multiple high-profile projects, she has contributed to improving agricultural automation systems through advanced imaging and detection technologies. Her work in behavior recognition and three-dimensional reconstruction has applications beyond agriculture, influencing industries such as robotics, surveillance, and healthcare.
Prof. Yang’s publications in journals such as Computers and Electronics in Agriculture, Artificial Intelligence Review, and Archives of Computational Methods in Engineering have not only raised the profile of her work but have also shaped ongoing discussions in her research areas.
Technical SkillsProf. Yang has mastered a diverse set of technical skills, making her an interdisciplinary expert in her field. Her areas of proficiency include:
Her ability to bridge theoretical knowledge with practical application makes her an invaluable asset to her research team and to the wider scientific community.
Teaching ExperienceAs a lecturer at Kunming University of Science and Technology, Prof. Yang is committed to fostering the next generation of engineers and computer scientists. She teaches courses on computer vision, deep learning, and image processing, inspiring students with real-world applications and cutting-edge research. Her approach combines theoretical knowledge with practical insights, providing students with the skills needed to excel in today’s rapidly evolving tech landscape.
Prof. Yang’s mentorship extends beyond the classroom as she actively guides postgraduate students in their research endeavors, particularly in the areas of AI and computer vision.
Legacy and Future ContributionsLooking to the future, Prof. Yang’s contributions to deep learning and computer vision are poised to influence even more diverse industries. With ongoing participation in large-scale national projects, such as the National Key Research and Development Project and Yunnan Provincial Basic Research Program, she continues to shape the future of AI in agricultural automation and beyond.
Her leadership in pioneering technologies such as three-dimensional reconstruction and behavior analysis will further bridge the gap between academia and industry. Prof. Yang’s future research endeavors will undoubtedly focus on expanding AI applications to new frontiers, from enhancing human-computer interaction to advancing autonomous systems in agriculture and other fields.
Future Directions and CollaborationProf. Yang is dedicated to exploring new avenues for collaboration, especially in interdisciplinary research. Her vision includes expanding her work on target detection, image segmentation, and three-dimensional modeling to solve pressing global challenges. With her leadership, Kunming University of Science and Technology will continue to be at the forefront of AI-driven innovation, further solidifying its position as a hub for cutting-edge computer vision research.
Prof. Yang’s continued collaboration with international research teams, as well as her involvement in national technology projects, ensures her ongoing influence in the field of deep learning and artificial intelligence.
Publications
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: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.
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.
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.
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.
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.
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.