Faisal Alamri | Object Detection for Security and Surveillance | Best Researcher Award

Published on by Computer Vision Awards

Dr. Faisal Alamri | Object Detection for Security and Surveillance | Best Researcher Award

Chairperson of the Department of Computer Science and Information Technology | Jubail Industrial College (JIC) | Saudi Arabia

Dr. Faisal Alamri is an accomplished artificial intelligence researcher specializing in computer vision, machine learning, object detection, classification, segmentation, similarity search, adversarial perturbation, and zero-shot learning. He holds a Ph.D. in Computer Science with a focus on computer vision and machine learning from the University of Exeter, and completed his undergraduate and master’s degrees in computer systems engineering and networking. He currently serves as the Computer Science Department Chairperson at Jubail Industrial College, where he oversees academic and administrative activities and leads departmental initiatives. Previously, he worked as a machine learning engineer developing practical AI solutions, a postdoctoral research fellow, and a teaching assistant, and has also contributed as an online tutor and teaching volunteer. His research interests include developing innovative approaches for object detection, image analysis, and real-world AI applications. Dr. Alamri has been recognized for his achievements through multiple certifications and active participation in international conferences, workshops, and professional communities such as IEEE, Kaggle, NVIDIA, and MATLAB. He possesses strong technical skills in Python, MATLAB, C#, SPSS, AWS, Google Cloud ML Engine, and other platforms, and has completed various professional courses in deep learning, AI, cybersecurity, and digital analytics. His dedication to research, education, and community engagement reflects his commitment to advancing both science and society. He has a total of 49 citations, 7 documents, and an h-index of 5.

Profiles: Google Scholar | Scopus | ORCID | LinkedIn

Featured Publications

  1. Alamri, F., & Dutta, A. (2021). Multi-head self-attention via vision transformer for zero-shot learning. arXiv preprint arXiv:2108.00045.

  2. Alamri, F., & Pugeault, N. (2020). Improving object detection performance using scene contextual constraints. IEEE Transactions on Cognitive and Developmental Systems, 14(4), 1320–1330.

  3. Alamri, F., & Dutta, A. (2021). Implicit and explicit attention for zero-shot learning. In DAGM German Conference on Pattern Recognition (pp. 467–483).

  4. Alamri, F., & Dutta, A. (2023). Implicit and explicit attention mechanisms for zero-shot learning. Neurocomputing, 534, 55–66.

  5. Alamri, F., Kalkan, S., & Pugeault, N. (2021). Transformer-encoder detector module: Using context to improve robustness to adversarial attacks on object detection. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 9577–9584). IEEE.

Xinrong Hu | Object Detection and Recognition | Women Researcher Award

Published on by Computer Vision Awards

Prof. Xinrong Hu | Object Detection and Recognition | Women Researcher Award

Dean of Computer Science and Artificial Intelligence | Wuhan Textile University | China

Prof. Xinrong Hu is a distinguished researcher and academic leader in computer vision, natural language processing, virtual reality, and machine learning. She serves as Dean of the School of Computer and Artificial Intelligence at Wuhan Textile University and is a doctoral supervisor, leading an innovative research team at the Hubei Provincial Engineering Technology Research Center for Garment Informatization. She holds a Ph.D. and has extensive experience in guiding research projects, including over 30 funded initiatives, some with national and international significance. Her research interests focus on advancing artificial intelligence applications in real-world scenarios, combining theoretical innovation with practical solutions. She has authored more than 100 academic papers, edited six textbooks, translated a book, and holds 26 invention patents, demonstrating her strong research skills and contribution to knowledge dissemination. Prof. Hu has been recognized with multiple awards and honors, including provincial and ministerial-level scientific research awards, teaching achievement awards, and prestigious titles such as Hubei Provincial Distinguished Teacher and recipient of the Special Government Allowance from the State Council. Her professional engagement includes leadership in academic communities, mentorship of young researchers, and active participation in advancing the field of AI through both education and research initiatives. Her comprehensive expertise, innovative contributions, and dedication to fostering academic excellence make her a leading figure in her field. Her research impact is reflected in 1,044 citations, 209 documents, and an h-index of 16.

Profiles: Scopus | ResearchGate 

Featured Publications

  1. Hu, X., et al. (2025). CDPMF-DDA: Contrastive deep probabilistic matrix factorization for drug-disease association prediction. BMC Bioinformatics.

  2. Hu, X., et al. (2025). Source-free cross-modality medical image synthesis with diffusion priors. Journal of King Saud University – Computer and Information Sciences.

  3. Hu, X., et al. (2025). TADUFMA: Transformer-based adaptive denoising and unified feature modeling for multi-condition anomaly detection in computerized flat knitting machines. Measurement Science and Technology.

  4. Hu, X., et al. (2025). ViT-BF: Vision transformer with border-aware features for visual tracking. Visual Computer.

  5. Hu, X., et al. (2025). Adaptive debiasing learning for drug repositioning. Journal of Biomedical Informatics.

Madhuri Rao | Machine Learning | Best Researcher Award

Published on by Computer Vision Awards

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.

Fatma Zahra Sayadi | Deep Learning | Best Innovation Award

Published on by Computer Vision Awards

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.

Profile: Google Scholar | Scopus | ORCID 

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.

Puja Gupta | Computer Vision | Excellence in Research

Published on by Computer Vision Awards

Dr. Puja Gupta | Computer Vision | Excellence in Research

Asst Professor at Shri G.S. Institute of Technology & Science | India

Dr. Puja Gupta is a dedicated researcher and academic with expertise in artificial intelligence, machine learning, IoT, and smart computing technologies. She has contributed significantly to the field through her high-quality publications in reputed journals, patents, and innovative product development. Her work has addressed real-world challenges in healthcare, security, and sustainable technologies, bridging the gap between research and practical applications. With a strong academic foundation, she has successfully guided students in research and projects, fostering innovation and academic growth. She has been actively involved in international collaborations, research projects, and academic leadership roles, contributing to the advancement of her field. She is also a committed member of professional organizations, demonstrating her engagement in the broader research community. Her impactful contributions, leadership potential, and dedication to continuous professional development make her a valuable asset to both academia and society.

Professional Profile 

Google Scholar | Scopus Profile | ORCID Profile 

Education

Dr. Puja Gupta holds a strong academic background in computer science and engineering, culminating in a doctoral degree specializing in artificial intelligence and smart systems. Her Ph.D. research focused on the integration of machine learning techniques and IoT frameworks to design intelligent solutions that address complex societal problems. Prior to her doctoral studies, she earned her master’s and bachelor’s degrees in computer science, gaining a solid foundation in algorithms, data structures, and system design. Throughout her academic journey, she demonstrated exceptional commitment to learning, consistently achieving top ranks and recognition for her research contributions. Her advanced education has equipped her with in-depth knowledge of computational intelligence, optimization techniques, and applied research methodologies, enabling her to contribute effectively to both theoretical advancements and practical applications in the field. Her academic background continues to support her innovative research and teaching excellence in the areas of AI, IoT, and emerging technologies.

Professional Experience

Dr. Puja Gupta has extensive professional experience in both academic and research domains, with a focus on artificial intelligence, IoT, and smart computing solutions. She has worked as a faculty member at prestigious institutions, where she has taught and mentored students at undergraduate and postgraduate levels, guiding them in research projects and fostering innovation. Alongside teaching, she has been actively involved in funded research projects, many of which involved international collaborations and multidisciplinary teams. She has successfully published her findings in reputed journals and conferences indexed in IEEE and Scopus, and her work has also resulted in patents and prototypes with practical applications. Beyond academia, she has contributed to the research community by serving as a reviewer, participating in editorial activities, and organizing academic events. Her leadership roles in academic programs and community-driven initiatives further highlight her commitment to advancing knowledge and supporting the development of future researchers.

Research Interest

Dr. Puja Gupta’s research interests revolve around artificial intelligence, machine learning, IoT, big data analytics, and smart system design. She is particularly focused on developing intelligent solutions that address pressing societal challenges in areas such as healthcare, security, and sustainability. Her work often integrates computational intelligence with real-world applications, such as predictive healthcare models, smart monitoring systems, and secure communication frameworks for IoT devices. She is also keen on advancing research in explainable AI and optimization algorithms to ensure reliability and transparency in machine learning systems. Another area of interest is the development of resource-efficient AI models for deployment in edge and cloud environments. Her multidisciplinary approach allows her to collaborate across domains, leveraging data-driven techniques to innovate practical solutions. By combining theoretical knowledge with applied research, she aims to contribute to technological advancements that enhance the quality of life and create sustainable, impactful outcomes for society.

Award and Honor

Dr. Puja Gupta has been recognized with numerous awards and honors that highlight her academic excellence, research contributions, and leadership in the field of computer science and engineering. Her achievements include recognition for publishing impactful research in reputed journals, presenting at leading international conferences, and securing patents that demonstrate the practical value of her work. She has also been honored for her contributions to student mentoring and academic program development, reflecting her dedication to nurturing young talent. Several of her awards acknowledge her innovative approaches in AI and IoT research, particularly for developing solutions with direct societal impact. In addition, she has received appreciation for her involvement in community-driven initiatives and leadership in professional organizations. These honors not only recognize her past accomplishments but also serve as a testament to her commitment, perseverance, and ability to inspire others in the academic and research communities.

Research Skill

Dr. Puja Gupta possesses advanced research skills in artificial intelligence, machine learning, IoT systems, and computational modeling, enabling her to conduct impactful and interdisciplinary research. She is proficient in applying data analysis techniques, optimization algorithms, and predictive modeling to design intelligent solutions for real-world applications. Her expertise includes working with various programming languages, simulation tools, and research frameworks that support scalable and innovative problem-solving. She has developed strong skills in experimental design, result validation, and research dissemination through high-quality publications and conference presentations. Beyond technical expertise, she excels in collaborative research, often working with international teams and multidisciplinary groups to drive innovation. She is also skilled in project management, proposal writing, and securing research funding, which have been instrumental in the successful execution of her projects. Her research skills, combined with her commitment to continuous learning, position her as a versatile and resourceful academic and researcher in her field.

Publications Top Notes

Title: Impact of knowledge management practices on innovative capacity: A study of telecommunication sector
Authors: J Jyoti, P Gupta, S Kotwal
Year: 2011
Citation: 56

Title: A Novel Algorithm for Mask Detection and Recognizing Actions of Human
Authors: P Gupta, V Sharma, S Varma
Year: 2022
Citation: 48

Title: Transcriptional mechanisms underlying sensitization of peripheral sensory neurons by granulocyte-/granulocyte-macrophage colony stimulating factors
Authors: KK Bali, V Venkataramani, VP Satagopam, P Gupta, R Schneider, …
Year: 2013
Citation: 42

Title: Minimally invasive plate osteosynthesis (MIPO) for proximal and distal fractures of the tibia: a biological approach
Authors: P Gupta, A Tiwari, A Thora, JK Gandhi, VP Jog
Year: 2016
Citation: 41

Title: SUMOylation of enzymes and ion channels in sensory neurons protects against metabolic dysfunction, neuropathy, and sensory loss in diabetes
Authors: N Agarwal, FJ Taberner, DR Rojas, M Moroni, D Omberbasic, C Njoo, …
Year: 2020
Citation: 39

Title: An introduction of soft computing approach over hard computing
Authors: P Gupta, N Kulkarni
Year: 2013
Citation: 31

Title: People detection and counting using YOLOv3 and SSD models
Authors: P Gupta, V Sharma, S Varma
Year: 2021
Citation: 30

Title: Challenges in the adaptation of IoT technology
Authors: Neha, P Gupta, MA Alam
Year: 2021
Citation: 20

Title: Role of fine needle aspiration cytology in preoperative diagnosis of ameloblastoma
Authors: S Bisht, SA Kotwal, P Gupta, R Dawar
Year: 2009
Citation: 13

Title: Let the Blind See: An AIIoT based device for real-time object recognition with the voice conversion
Authors: P Gupta, M Shukla, N Arya, U Singh, K Mishra
Year: 2022
Citation: 9

Title: The impact of artificial intelligence on renewable energy systems
Authors: P Gupta, S Kumar, YB Singh, P Singh, SK Sharma, NK Rathore
Year: 2022
Citation: 8

Title: Simultaneous feature selection and clustering of micro-array and RNA-sequence gene expression data using multiobjective optimization
Authors: AK Alok, P Gupta, S Saha, V Sharma
Year: 2020
Citation: 8

Title: Activity detection and counting people using mask-RCNN with bidirectional ConvLSTM
Authors: P Gupta, U Singh, M Shukla
Year: 2022
Citation: 7

Title: Study of cloud providers (azure, amazon, and oracle) according to service availability and price
Authors: A Rajput, P Gupta, P Ghodeshwar, S Varma, KK Sharma, U Singh
Year: 2023
Citation: 6

Title: Machine learning approaches for IoT-data classification
Authors: O Farooq, P Gupta
Year: 2020
Citation: 5

Title: Evaluation of AI system’s voice recognition performance in social conversation
Authors: SK Barnwal, P Gupta
Year: 2022
Citation: 4

Title: Analysis of CNN Model with Traditional Approach and Cloud AI based Approach
Authors: U Kushwaha, P Gupta, S Airen, M Kuliha
Year: 2022
Citation: 4

Title: Analysis of crowd features based on deep learning
Authors: P Gupta, V Sharma, S Varma
Year: 2022
Citation: 4

Title: Acknowledgment of patient in sense behaviors using bidirectional ConvLSTM
Authors: U Singh, P Gupta, M Shukla, V Sharma, S Varma, SK Sharma
Year: 2023
Citation: 3

Title: Study on the NB-IoT based smart medical system
Authors: P Gupta, AK Pandey
Year: 2023
Citation: 3

Conclusion

Dr. Puja Gupta is highly deserving of the Best Researcher Award for her significant contributions to advancing research in artificial intelligence, IoT, and smart technologies, as well as her role in mentoring students and fostering innovation. Her impactful work, including patents, high-quality publications, and practical product development, has addressed societal challenges in healthcare, security, and sustainability. With her strong academic background, leadership in academic and community initiatives, and commitment to continuous growth, she holds great potential to further excel in future research, expand global collaborations, and take on greater leadership roles in the academic and research community.

Mohamed Hebaishy | Computer Vision | Excellence in Computer Vision Award

Published on by Computer Vision Awards

Assoc. Prof. Dr. Mohamed Hebaishy | Computer Vision | Excellence in Computer Vision Award

Associate Prof. in ERI at Electronics Research Institute, Egypt

Dr. Mohamed Ahmed Hebaishy is a distinguished researcher with extensive expertise in biometrics, iris recognition, image processing, computer vision, and satellite imaging. He has made remarkable contributions through his work in human identification systems, advanced image representation, and security technologies. His career spans academia, research institutions, and international collaborations, combining theoretical innovation with real-world applications in areas such as space research and remote sensing. He has published in reputed journals and conferences, including IEEE and Springer platforms, and actively engages in research that bridges science and technology. Beyond his research output, he has held significant leadership roles, mentored graduate students, and reviewed research projects for universities and conferences. His diverse professional experiences, strong academic foundation, and continuous pursuit of impactful research highlight his commitment to advancing scientific knowledge and addressing global challenges, making him a valuable contributor to the academic and research community.

Professional Profile 

Google Scholar | Scopus Profile | ORCID Profile 

Education

Dr. Mohamed Ahmed Hebaishy completed his Bachelor of Science in Electronic Engineering with a focus on automatic control and measurements at Menoufia University, where he built a strong foundation in control systems and electronics. He later pursued a Master of Science degree in Electronics and Communication at Cairo University, with his thesis centered on developing a fuzzy controller for flexible joint manipulators, reflecting his early focus on control and automation. His academic journey culminated in earning a Doctor of Philosophy in Information Technology from Vladimir State University in the Russian Federation, specializing in control system analysis and data processing. His doctoral thesis focused on using iris image processing in human identification systems, marking the beginning of his long-term contributions to the field of biometrics. Through these academic achievements, he has combined expertise in engineering, computing, and data-driven technologies, equipping him with the knowledge and skills to contribute meaningfully to interdisciplinary research.

Professional Experience

Dr. Mohamed Ahmed Hebaishy has built a rich professional career across academia and research institutions, holding positions that span lecturer, assistant professor, and department head roles. He has served as a researcher at the Electronics Research Institute, contributing to significant projects in informatics and computer science. His work extended to leadership in national space programs, where he played a key role in satellite image processing and payload command systems for EgyptSat missions. He also gained international academic experience as an assistant professor at Shaqra University in Saudi Arabia, where he later became head of the computer science department. His contributions include guiding research projects, supervising theses, and leading academic initiatives. Additionally, he has been a reviewer for major universities and scientific conferences, reflecting his involvement in shaping the academic community. His experience demonstrates a balance of teaching, research, and leadership, making him a well-rounded academic and professional.

Research Interest

Dr. Mohamed Ahmed Hebaishy’s research interests lie at the intersection of biometrics, image processing, computer vision, and artificial intelligence, with a strong emphasis on human identification systems and security technologies. He has worked extensively on iris recognition, exploring innovative approaches to enhance accuracy and efficiency in biometric applications. His interests also extend to satellite imaging and remote sensing, where he has contributed to projects in national space programs, including the development of image processing systems for EgyptSat satellites. In recent years, his focus has broadened to include advanced methods in pattern recognition, machine learning, and computer-aided automation systems. He is also engaged in applied research addressing real-world challenges such as waste sorting, wireless communication, and medical applications of imaging. His diverse interests reflect a commitment to advancing cutting-edge technologies that improve security, automation, and sustainability, while also fostering new interdisciplinary pathways in computer science and engineering.

Award and Honor

Throughout his career, Dr. Mohamed Ahmed Hebaishy has received recognition for his contributions to research, teaching, and leadership within the fields of biometrics, image processing, and space technology. His involvement in the EgyptSat satellite programs and ITIDA-funded security projects demonstrated his ability to translate research into impactful applications, earning him acknowledgment within the scientific community. He has also been invited as a reviewer for universities, research conferences, and scientific committees, reflecting trust in his expertise and judgment. His leadership as head of the computer science department at Shaqra University further highlights his role in shaping academic excellence and guiding student development. While his curriculum vitae does not list specific awards, his record of sustained contributions, successful project leadership, and active engagement in international research platforms stands as a form of recognition in itself. His ongoing publications in reputed journals further strengthen his professional standing as a dedicated and accomplished researcher.

Research Skill

Dr. Mohamed Ahmed Hebaishy possesses a broad set of research skills that reflect his deep expertise in both theoretical and applied aspects of computer science and engineering. He is skilled in biometric system design, with specialization in iris recognition, image processing algorithms, and human identification technologies. His technical capabilities extend to satellite image analysis, data processing, and control systems, where he has led projects involving payload command systems for national space programs. He is proficient in developing and applying advanced algorithms, including fuzzy logic, wavelet transforms, and optimization techniques, to solve complex research problems. His experience also covers interdisciplinary areas such as wireless communication systems, security applications, and automated testing tools. Beyond technical expertise, he has strong skills in project leadership, academic supervision, and research collaboration, enabling him to contribute effectively to both academic and applied research communities. His skill set demonstrates adaptability, innovation, and problem-solving ability.

Publications Top Notes

Title: A comparative study of QTP and load runner automated testing tools and their contributions to software project scenario
Authors: M Imran, M Hebaishy, AS Alotaibi
Year: 2016
Citation: 12

Title: Road extraction from high resolution satellite images by morphological direction filtering and length filtering
Authors: TM Talal, MI Dessouky, A El-Sayed, M Hebaishy, FA El-Samie
Year: 2008
Citation: 12

Title: Increasing the Efficiency of Iris Recognition Systems by Using Multi-Channel Frequencies of Gabor Filter
Authors: AS Alotaibi, MA Hebaishy
Year: 2014
Citation: 7

Title: Extraction of roads from high-resolution satellite images with the discrete wavelet transform
Authors: TM Talal, A El-Sayed, M Hebaishy, MI Dessouky, SA Alshebeili
Year: 2013
Citation: 4

Title: Optimized Daugman’s algorithm for iris localization
Authors: MA Hebaishy
Year: 2008
Citation: 4

Title: Sibs: A sparse encoder utilizing self-inspired bases for efficient image representation
Authors: AN Omara, MA Hebaishy, MS Abdallah, YI Cho
Year: 2024
Citation: 3

Title: Poster: Optimized Daugman’s algorithm for iris localization
Authors: M Hebaishy
Year: 2008
Citation: 3

Title: Fast Fingerprint Identification based on the DoG Filter
Authors: MA Hebaishy, FA Syam
Year: 2025

Title: S-shaped patch antenna array for automotive applications in X-band for wireless communications
Authors: MA Hebaishy
Year: 2024

Title: Building an automatic waste sorting system with controller based wireless sensor smart segregation system
Authors: MA Hebaishy
Year: 2024

Title: Security system based on human iris
Authors: HS Ahmed, MA Hebaishy
Year: 2014

Title: Attitude determination for geostationary satellite using optimized real time image registration algorithm
Authors: AE OA Elsayed, A Farrag, M Hebaishy
Year: 2009

Title: Texture analysis of the human iris for high authentication
Authors: MA Hebaishy, BV Gerkov
Year: 2002

Title: Using phase demodulator for encoding iris
Authors: AS Alotaibi, MA Hebaishy

Conclusion

Dr. Mohamed Ahmed Hebaishy is highly deserving of the Best Researcher Award for his significant contributions to biometrics, image processing, and satellite imaging, which have advanced both scientific understanding and practical applications in security and space research. His extensive academic career, impactful publications, leadership roles, and dedication to mentoring students highlight his commitment to advancing knowledge and fostering innovation. With his proven expertise and strong foundation in applied research, he is well positioned to continue driving advancements in computer vision, human identification systems, and international collaborations, further strengthening his role as a leader in research and society.

Dibyalekha Nayak | Computer vision | Women Researcher Award

Published on by Computer Vision Awards

Dr . Dibyalekha Nayak | Computer vision | Women Researcher Award

Assistant professor at Shah and Anchor Kutchhi Engineering College, India

Dr. Dibyalekha Nayak is a dedicated academician and emerging researcher with deep expertise in image processing, adaptive compression, and VLSI design. Her professional journey is marked by a strong commitment to teaching, scholarly research, and technological advancement. With over a decade of teaching experience and a recently completed Ph.D. from KIIT University, Bhubaneswar, her research has produced several publications in SCI-indexed journals and international conferences. Dr. Nayak’s contributions reflect an interdisciplinary approach, combining deep learning techniques with low-power hardware design to address complex challenges in wireless sensor networks and multimedia systems. She has actively participated in faculty development programs and technical workshops, continuously upgrading her knowledge. Her professional philosophy emphasizes ethics, hard work, and continuous learning. Currently serving as an Assistant Professor at Shah and Anchor Kutchi Engineering College in Mumbai, she aspires to make impactful contributions to the field of electronics and communication through research, innovation, and collaboration.

Professional Profile 

Education🎓

Dr. Dibyalekha Nayak holds a Ph.D. in Image Processing from the School of Electronics at KIIT University, Bhubaneswar, where she completed her research between September 2018 and May 2024. Her doctoral work focused on advanced techniques in image compression and saliency detection using deep learning and compressive sensing. She completed her Master of Technology (M.Tech) in VLSI Design from Satyabhama University, Chennai, in 2011, graduating with a commendable CGPA of 8.33. Prior to that, she earned her Bachelor of Engineering (B.E.) in Electronics and Telecommunication from Biju Patnaik University of Technology (BPUT), Odisha, in 2008, with a CGPA of 6.5. Her academic background provides a strong foundation in both theoretical electronics and practical applications in image processing and circuit design. The combination of image processing and VLSI design throughout her academic journey has enabled her to engage in cross-disciplinary research and foster innovation in both hardware and software domains.

Professional Experience📝

Dr. Dibyalekha Nayak has accumulated over 12 years of rich academic experience in various reputed engineering institutions across India. Currently, she serves as an Assistant Professor at Shah and Anchor Kutchi Engineering College, Mumbai, affiliated with Mumbai University, where she joined in July 2024. Prior to this, she worked as a Research Scholar at KIIT University (2018–2024), contributing significantly to image processing research. Her earlier roles include Assistant Professor positions at institutions such as College of Engineering Bhubaneswar (2016–2018), SIES Graduate School of Technology, Mumbai (2014), St. Francis Institute of Technology, Mumbai (2013), and Madha Engineering College, Chennai (2011–2012). Across these roles, she has taught a variety of undergraduate and postgraduate courses, supervised student projects, and contributed to departmental development. Her teaching areas span digital electronics, VLSI design, image processing, and communication systems, demonstrating a strong alignment between her teaching and research activities.

Research Interest🔎

Dr. Dibyalekha Nayak’s research interests lie at the intersection of image processing, deep learning, and VLSI design, with a special focus on adaptive compression, saliency detection, and compressive sensing. Her doctoral research addressed the development of innovative, low-complexity algorithms for image compression using techniques like block truncation coding and DCT, tailored for wireless sensor network applications. She is also deeply interested in integrating deep learning frameworks into image enhancement and compression tasks to improve performance in real-world environments. Additionally, her background in VLSI design supports her interest in low-power hardware architectures for efficient implementation of image processing algorithms. Dr. Nayak is particularly motivated by research problems that bridge the gap between theoretical innovation and practical implementation, especially in the fields of embedded systems and multimedia communication. Her interdisciplinary research aims to create scalable, energy-efficient, and intelligent solutions for future communication and sensing technologies.

Award and Honor🏆

While Dr. Dibyalekha Nayak’s profile does not explicitly mention formal awards or honors, her scholarly achievements speak volumes about her academic excellence and dedication. She has published multiple research articles in prestigious SCI and Web of Science indexed journals such as Multimedia Tools and Applications, Mathematics, and Computers, reflecting the quality and impact of her research. She has been actively involved in reputed international conferences including IEEE and Springer Lecture Notes, where she has presented and published her research findings. Her work on saliency-based image compression and fuzzy rule-based adaptive block compressive sensing has received commendation for its innovation and applicability. Furthermore, her selection and sustained work as a Research Scholar at KIIT University for over five years highlights the recognition she has earned within academic circles. Her consistent participation in technical workshops, faculty development programs, and collaborations also demonstrate her growing reputation and standing in the field of electronics and image processing.

Research Skill🔬

Dr. Dibyalekha Nayak possesses a versatile and robust set of research skills aligned with modern-day challenges in image processing and electronics. She is proficient in developing image compression algorithms, saliency detection models, and adaptive techniques using block truncation coding, fuzzy logic, and DCT-based quantization. Her technical expertise extends to deep learning architectures tailored for image enhancement and compressive sensing in wireless sensor networks. Additionally, she has a strong command of VLSI design methodologies, enabling her to work on low-power circuit design and hardware implementation strategies. Dr. Nayak is also skilled in scientific programming, using tools such as MATLAB and Python, along with LaTeX for research documentation. She has a clear understanding of research methodologies, simulation frameworks, and performance analysis metrics. Her experience in preparing manuscripts for SCI-indexed journals and conference presentations showcases her technical writing abilities. Overall, her analytical mindset and hands-on skills make her a competent and impactful researcher.

Conclusion💡

Dr. Dibyalekha Nayak is a highly dedicated and emerging researcher in the fields of Image Processing, Deep Learning, and VLSI. Her academic journey reflects perseverance, scholarly depth, and a clear focus on impactful research. Her SCI-indexed publications, teaching experience, and cross-domain knowledge make her a deserving candidate for the Best Researcher Award.

Publications Top Noted✍

  • Title: Fuzzy Rule Based Adaptive Block Compressive Sensing for WSN Application
    Authors: D. Nayak, K. Ray, T. Kar, S.N. Mohanty
    Journal: Mathematics, Volume 11, Issue 7, Article 1660
    Year: 2023
    Citations: 6

  • Title: A novel saliency based image compression algorithm using low complexity block truncation coding
    Authors: D. Nayak, K.B. Ray, T. Kar, C. Kwan
    Journal: Multimedia Tools and Applications, Volume 82, Issue 30, Pages 47367–47385
    Year: 2023
    Citations: 4

  • Title: Walsh–Hadamard Kernel Feature-Based Image Compression Using DCT with Bi-Level Quantization
    Authors: D. Nayak, K. Ray, T. Kar, C. Kwan
    Journal: Computers, Volume 11, Issue 7, Article 110
    Year: 2022
    Citations: 3

  • Title: Sparsity based Adaptive BCS color image compression for IoT and WSN Application
    Authors: D. Nayak, T. Kar, K. Ray
    Journal: Signal, Image and Video Processing, Volume 19, Issue 8, Pages 1–7
    Year: 2025

  • Title: Hybrid Image Compression Using DCT and Autoencoder
    Authors: D. Nayak, T. Kar, K. Ray, J.V.R. Ravindra, S.N. Mohanty
    Conference: 2024 IEEE Pune Section International Conference (PuneCon), Pages 1–6
    Year: 2024

  • Title: Performance Comparison of Different CS based Reconstruction Methods for WSN Application
    Authors: D. Nayak, K.B. Ray, T. Kar
    Conference: 2021 IEEE 2nd International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC)
    Year: 2021

  • Title: A Comparative Analysis of BTC Variants
    Authors: D. Nayak, K.B. Ray, T. Kar
    Conference: Proceedings of International Conference on Communication, Circuits, and Systems (LNEE, Springer)
    Year: 2021

  • Title: Low Power Error Detector Design by using Low Power Flip Flops Logic
    Authors: D. Chaini, P. Malgi, S. Lopes
    Journal: International Journal of Computer Applications, ISSN 0975-8887
    Year: 2014

Prof. Vaclav Skala | Computer Vision | Best Researcher Award

Published on by Computer Vision Awards

Prof. Vaclav Skala | Computer Vision | Best Researcher Award

Professor at University of West Bohemia, Czech Republic

👨‍🎓 Publication Profiles

Scopus

Orcid

Publications

A new fully projective O(lg N) line convex polygon intersection algorithm

  • Authors: Václav V. Skala
    Journal: Visual Computer
    Year: 2025

A new fully projective O(log N) point-in-convex polygon algorithm: a new strategy

  • Authors: Václav V. Skala
    Journal: Visual Computer
    Year: 2024

Meshfree Interpolation of Multidimensional Time-Varying Scattered Data

  • Authors: Václav V. Skala, Eliska E. Mourycova
    Journal: Computers
    Year: 2023

Multispectral Image Generation from RGB Based on WSL Color Representation: Wavelength, Saturation, and Lightness

  • Authors: Václav V. Skala
    Journal: Computers
    Year: 2023

Robust Line-Convex Polygon Intersection Computation in E2 using Projective Space Representation

  • Author: Václav V. Skala
    Journal: Machine Graphics and Vision
    Year: 2023

Prof Dr. Amar Hassan Khamis | Machine Learning for Computer Vision | Best Researcher Award

Published on by Computer Vision Awards

Prof Dr. Amar Hassan Khamis | Machine Learning for Computer Vision | Best Researcher Award

Prof Dr. Amar Hassan Khamis | Mohammed Bin Rashid University of Medicine and Health Sciences | United Arab Emirates

Dr. Amar Hassan Khamis holds a Ph.D. in Biostatistics & Genetic Epidemiology (2003) from the University of Méditerranée AIX Marseille and the University of Gazira under a sandwich program. He also earned a DEA in Biostatistics from the University of Paris XI (1994) and a certificate in Medical and Biological Studies with a focus on epidemiology and biostatistics (1991).

Professional Profiles

Google Scholar

Scopus

Orcid

🎓Academic  Qualifications 

Dr. Khamis boasts a robust academic background, having completed a PhD in Biostatistics & Genetic Epidemiology through a sandwich program between University of Méditerranée AIX Marseille, France, and University of Gazira, Sudan in 2003. His other qualifications include a DEA in Biostatistics from the University Paris XI, France, and a Certificate in Medical and Biological Studies with a focus on Epidemiology and Biostatistics. Additionally, he holds a B.Sc. in Statistics & Computer Science from the University of Khartoum, Sudan.

🏢Professional Career Highlights  

Dr. Amar Hassan Khamis is a distinguished Professor of Biostatistics, currently serving at the Hamdan Bin Mohammed College of Dental Medicine, part of the Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU) in Dubai since January 17, 2018. Renowned for his expertise, he has also contributed as an Adjunct Professor at Ajman University, teaching Biostatistics and Research Methods for postgraduate dentistry programs. Over his extensive career, Dr. Khamis has held key academic roles at esteemed institutions like the University of Dammam, KSA, University of Khartoum, Sudan, and the Ahfad University for Women, Sudan, demonstrating unwavering commitment to the field of Biostatistics and health sciences.

📚🧑‍🏫Teaching and Mentorship 

Dr. Khamis has a prolific teaching portfolio, having taught a variety of courses across undergraduate and postgraduate levels, including Mathematics, Biostatistics, Research Methodology, Clinical Trials, and Epidemiology. His professional workshops on Meta-Analysis, Advanced Biostatistics, and Respondent Driven Sampling are highly acclaimed. Moreover, he has supervised numerous higher diploma, MSc, and PhD theses, playing a pivotal role in advancing biostatistical research and application.

🌐🤝Global Collaboration and Leadership 

Dr. Khamis has played significant roles in global health initiatives, including consulting for WHO EMRO and conducting missions across the Eastern Mediterranean region. As a member of the Board of Research Committee of ALBASAR International Foundation and other international scientific associations, he has facilitated cross-border collaborations. His contributions to achieving the Millennium Development Goals (MDGs) in Africa highlight his dedication to improving public health outcomes.

🛠️💻Training and Skill Development 

An expert in statistical computing, Dr. Khamis is proficient in tools like SPSS, Stata, R-language, and Comprehensive Meta-Analysis (CMA). He has attended several advanced training programs worldwide, including courses on Meta-Analysis, Health Management, and Population Surveys at renowned institutions such as Johns Hopkins Bloomberg School of Public Health and Oxford University.

🏅🌟Recognition and Honors 

Dr. Khamis has been acknowledged as a pioneer in biostatistics, playing a transformative role in his academic and professional engagements. He has served as an external examiner for universities across Africa and the Middle East and as a member of research ethics committees in Sudan, Saudi Arabia, and the UAE.

Publications Top Noted 📝

Three-dimensional computed tomography analysis of airway volume in growing class II patients treated with Frankel II appliance

Authors: Ahmed, M.J.; Diar-Bakirly, S.; Deirs, N.; Hassan, A.; Ghoneima, A.

Journal: Head and Face Medicine

Year: 2024

Comparative Assessment of Pharyngeal Airway Dimensions in Skeletal Class I, II, and III Emirati Subjects: A Cone Beam Computed Tomography Study

Authors: AlAskar, S.; Jamal, M.; Khamis, A.H.; Ghoneima, A.

Journal: Dentistry Journal

Year: 2024

High-fidelity simulation versus case-based tutorial sessions for teaching pharmacology: Convergent mixed methods research investigating undergraduate medical students’ performance and perception

Authors: Kaddoura, R.; Faraji, H.; Otaki, F.; Khamis, A.H.; Jan, R.K.

Journal: PLoS ONE

Year: 2024

Enamel demineralization around orthodontic brackets bonded with new bioactive composite (in-vitro study)

Authors: Ali, N.A.M.; Nissan, L.M.K.; Al-Taai, N.; Khamis, A.H.

Journal: Journal of Baghdad College of Dentistry

Year: 2024

Do Hall Technique Crowns Affect Intra-arch Dimensions? A Split-mouth Quasi-experimental Non-randomized Feasibility Pilot Study

Authors: Alramzi, B.; Alhalabi, M.; Kowash, M.; Ghoneima, A.; Hussein, I.

Journal: International Journal of Clinical Pediatric Dentistry

Year: 2024

Assoc Prof Dr. Peixian Zhuang | Image Processing | Best Researcher Award

Published on by Computer Vision Awards

Assoc Prof Dr. Peixian Zhuang | Image Processing | Best Researcher Award

Peixian Zhuang at University of Science and Technology Beijing, China

👨‍🎓 Profiles

Scopus

Google Scholar

Publications

CVANet: Cascaded visual attention network for single image super-resolution

  • Authors: Weidong Zhang, Wenyi Zhao, Jia Li, Peixian Zhuang, Haihan Sun, Yibo Xu, Chongyi Li
  • Journal: Neural Networks
  • Year: 2024

Underwater image enhancement via piecewise color correction and dual prior optimized contrast enhancement

  • Authors: Weidong Zhang, Songlin Jin, Peixian Zhuang, Zheng Liang, Chongyi Li
  • Journal: IEEE Signal Processing Letters
  • Year: 2023

Non-uniform illumination underwater image restoration via illumination channel sparsity prior

  • Authors: Guojia Hou, Nan Li, Peixian Zhuang, Kunqian Li, Haihan Sun, Chongyi Li
  • Journal: IEEE Transactions on Circuits and Systems for Video Technology
  • Year: 2023

Gacnet: Generate adversarial-driven cross-aware network for hyperspectral wheat variety identification

  • Authors: Weidong Zhang, Zexu Li, Guohou Li, Peixian Zhuang, Guojia Hou, Qiang Zhang, Chongyi Li
  • Journal: IEEE Transactions on Geoscience and Remote Sensing
  • Year: 2023

Underwater image enhancement via minimal color loss and locally adaptive contrast enhancement

  • Authors: Weidong Zhang, Peixian Zhuang, Hai-Han Sun, Guohou Li, Sam Kwong, Chongyi Li
  • Journal: IEEE Transactions on Image Processing
  • Year: 2022