Dibyalekha Nayak | Computer vision | Women Researcher Award

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

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