Assoc. Prof. Dr. Entesar Eliwa | Deep Learning for Robotic Vision | Excellence in Research Award

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Assoc. Prof. Dr. Entesar Eliwa | Deep Learning for Robotic Vision | Excellence in Research Award

King Faisal University | Saudi Arabia

Dr. Entesar Hamed I. Eliwa is an Associate Professor at King Faisal University, Faculty of Science, Department of Mathematics and Statistics. She holds a B.Sc. in Computer Science from Minia University, where she also served as a Teaching Assistant before completing her M.Sc. and Ph.D. in the Computer Science Department. After joining King Faisal University as an Assistant Lecturer, her strong research productivity and academic contributions led to her promotion to Associate Professor. Her work focuses on data mining, knowledge discovery, predictive modeling, supervised learning, classification, association rule mining, Deep Learning for Robotic Vision and artificial intelligence. She has successfully completed 28 research projects and is currently leading 6 ongoing studies. Her scholarly influence is reflected in her most recent citation metrics, with 443 total citations across 373 citing documents, demonstrating a solid and expanding research footprint. She has produced 22 research documents contributing to advancements in computational intelligence, and she maintains an h-index of 8, underscoring the depth and consistency of her academic impact. Through her research, publications, and academic service, Dr. Eliwa continues to strengthen the fields of computer science, data analytics, and artificial intelligence within both regional and global research communities.

Profile: Scopus | Orcid | Google Scholar

Featured Publications

El Koshiry, A., Eliwa, E., Abd El-Hafeez, T., & Tony, M. A. A. (2026). The effectiveness of an e-learning platform in developing digital citizenship skills among blind students.  https://doi.org/10.1007/978-3-031-94770-4_21

Hamed, E., & Abd El-Hafeez, T. (2025). Deep learning for sustainable agriculture: Automating rice and paddy ripeness classification for enhanced food security. Egyptian Informatics Journal. https://doi.org/10.1016/j.eij.2025.100785

Amr, A., Eliwa, E., Tony, A. A., Shalgham, A., & Contributors from King Faisal University; Minia University; Arish University. (2025). The effectiveness of using Box-to-Box technology to develop some of the composite physical and technical capabilities of footballers. Fusion: Practice and Applications. https://doi.org/10.54216/fpa.170224

Eliwa, E. H. I., & Abd El-Hafeez, T. (2025). A robust deep learning pipeline for multi-class cervical cancer cell identification. Egyptian Informatics Journal. https://doi.org/10.1016/j.eij.2025.100787

Eliwa, E. H. I., & Abd El-Hafeez, T. (2025). A novel YOLOv11 framework for enhanced tomato disease detection. PeerJ Computer Science. https://doi.org/10.7717/peerj-cs.3200

Mr. Angelos Athanasiadis | Deep Learning for Robotic Vision | Research Excellence Award

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Mr. Angelos Athanasiadis | Deep Learning for Robotic Vision | Research Excellence Award

Aristotle University of Thessaloniki | Greece

Mr. Angelos Athanasiadis is a Ph.d. candidate in electrical and computer engineering at the Aristotle University of Thessaloniki, specializing in fpga-based acceleration of convolutional neural networks and heterogeneous computing systems. he holds an M.Eng. in electronics and computer systems and an mba with high distinction, combining strong technical expertise with strategic insight. his research focuses on full-precision CNN acceleration, FPGA architectures, cyber-physical systems, Deep Learning for Robotic Vision and distributed embedded system emulation. Angelos has contributed to major eu-funded research projects, including the adviser and redesign projects, and has completed industrial internships at cadence design systems in Munich. He has also worked in r&d and embedded development roles at exapsys and seems pc, strengthening his applied engineering experience. Academically, he has collaborated with Professor Ioannis papaefstathiou and assistant professor nikolaos tampouratzis, contributing to innovations in energy-efficient cnn inference and high-fidelity system emulation. his open-source framework, fusion, integrates qemu and omnet++ using hla/certi for deterministic, timing-accurate, multi-node execution. Although early in his publication journey, angelos has 1 citation, 1 scopus-listed document, and an h-index of 1, reflecting the initial impact of his contributions. Driven by interdisciplinary research, he aims to advance reconfigurable computing for next-generation autonomous and embedded intelligent systems.

Profiles: Orcid | Google Scholar

Featured Publications

Athanasiadis, A., Tampouratzis, N., & Papaefstathiou, I. (2025). An efficient open-source design and implementation framework for non-quantized CNNs on FPGAs. Integration, 102625.

Athanasiadis, A., Tampouratzis, N., & Papaefstathiou, I. (2024). An open-source HLS fully parameterizable matrix multiplication library for AMD FPGAs. WiPiEC Journal – Works in Progress in Embedded Computing, Article 62.

Katselas, L., Athanasiadis, A., Jiao, H., Papameletis, C., Hatzopoulos, A., & Marinissen, E. J. (2017). Embedded toggle generator to control the switching activity during test of digital 2D-SoCs and 3D-SICs. In 2017 27th International Symposium on Power and Timing Modeling, Optimization and Simulation (PATMOS) (pp. 1–8). IEEE.

Mr. Emmanuel Ebikabowei Enemugha | AI-Based Robot Perception | Best Researcher Award

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Mr. Emmanuel Ebikabowei Enemugha | AI-Based Robot Perception | Best Researcher Award

University Malaya Department of Mechanical Engineering | Nigeria

Mr. Enemugha Emmanuel Ebikabowei is a dedicated mechanical engineer and researcher, currently a Ph.d. Candidate in mechanical engineering at the University of Malaya, Malaysia, with specialization in computational fluid dynamics, gas-turbine performance, pump-impeller blade design, and energy systems optimization. He also serves as a lecturer in the department of mechanical engineering at Nigeria maritime university. His publication record on researchgate lists 5 documents with 69 reads, though his citation and h-index metrics are not publicly indicated. His scholarly work includes notable contributions such as a hybrid optimization of mixed-axial flow pump impellers using taguchi method, genetic algorithms, AI-Based Robot Perception and neural networks. additionally, He has conducted experimental analyses on firewood combustion efficiency for sustainable cooking in bayelsa state, Nigeria. His research is shaping the future of efficient pump systems and clean energy solutions for both industrial and community-scale applications.

Profile: Orcid

Featured Publications

Enemugha, E. E., Ab Karim, M. S. B., & Nik Ghazali, N. N. B. (2025). Hybrid optimisation of mixed-axial flow pump impellers parameter using Taguchi, genetic algorithms, and artificial neural networks. Next Research.

Enemugha, E. E., & Munuakuro, A. E. (2025). Experimental analysis of firewood combustion efficiency and fuel consumption patterns for sustainable cooking in Bayelsa State, Nigeria. International Journal for Research in Applied Science and Engineering Technology, 13(4).

Enemugha, E. E. (2025). The effects of impeller blade count on centrifugal pump performance and efficiency under different operating conditions: A comparison of numerical prediction. International Journal for Research in Applied Science and Engineering Technology, 13(4).

Bratua, I., Burubai, W., & Enemugha, E. E. (2025). Comparative analysis of fuelwood weight loss and energy efficiency in Bayelsa State, Nigeria. World Journal of Advanced Engineering Technology and Sciences, 14(3).

Enemugha, E. E., Ab Karim, M. S., & Nik Ghazali, N. N. (2025). Comprehensive optimization of centrifugal pump performance through the integration of the Taguchi method and polynomial regression models. Global Journal of Engineering and Technology Advances, 22(2).

Mr. Gajraj Singh | 3D Vision and Sensing | Best Researcher Award

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Mr. Gajraj Singh | 3D Vision and Sensing | Best Researcher Award

IILM University Greater Noida UP | India

Author Profile

SCOPUS

ORCID

Summary

Mr. Gajraj Singh is an experienced academic and researcher with a strong background in Artificial Intelligence, Machine Learning, and Electronics Engineering. With qualifications from premier institutions like NIT Kurukshetra and SVNIT Surat, he has dedicated over a decade to teaching and guiding students. His research spans deep learning applications in medical imaging, underwater image processing,  and AI-enabled diagnostics, often intersecting with power electronics. He has published in reputed SCOPUS-indexed journals and IEEE conferences and has earned professional certifications in AI/ML. Currently, he serves as an Assistant Professor at IILM University, Greater Noida, where he continues to contribute to interdisciplinary research and student mentorship.

Early academic pursuits

Mr. Gajraj Singh began his academic journey with a Bachelor of Technology (B.Tech.) from BBDIT Ghaziabad, laying a strong foundation in engineering principles. His interest in advanced electronics led him to pursue a Master of Technology (M.Tech.) from the prestigious National Institute of Technology (NIT) Kurukshetra. Currently, he is enrolled in a Ph.D. program in Electronics Engineering at Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat. His doctoral research focuses on deep learning-based heart function assessment using echocardiography, 3D Vision and Sensing a domain bridging healthcare and electronics engineering, with supporting applications in power electronics diagnostics.

Professional endeavors

With over a decade of academic and teaching experience, Mr. Singh has established himself as a committed educator and mentor. From 2014 to July 2025, he served as an Assistant Professor at RAIT, D. Y. Patil Group, Navi Mumbai, where he contributed to curriculum development and student projects in AI and 3D Vision and Sensing embedded systems. He now holds the position of Assistant Professor in the School of Computer Science and Engineering at IILM University, Greater Noida. His teaching experience includes hands-on guidance in areas like deep learning, power electronics, and real-time systems.

Contributions and research focus

Mr. Singh's research trajectory is rooted in practical applications of Artificial Intelligence (AI) and Machine Learning (ML). He has made notable contributions to medical imaging, particularly using deep learning for 3D Vision and Sensing cardiac diagnostics via echocardiographic data. His work also spans underwater image enhancement—an area relevant for autonomous systems and robotics. He has published in SCOPUS-indexed journals and IEEE conferences, and integrates knowledge of power electronics in developing AI-driven solutions for biomedical and environmental challenges.

Impact and influence

As a certified AI/ML practitioner with training from COURSERA and NPTEL, Mr. Singh has influenced many budding engineers and researchers through his innovative teaching style and project mentoring. His interdisciplinary approach—bridging electronics, healthcare, 3D Vision and Sensing and AI—has encouraged students to explore solutions that impact both industry and society. His leadership in guiding academic projects has led to student participation in national and international innovation platforms.

Academic cites

Mr. Singh’s publications in SCOPUS and IEEE have garnered academic attention in the domains of medical imaging and AI-based diagnostics. His citation count is gradually increasing as his work on deep learning-based echocardiography and 3D Vision and Sensing underwater image processing gains recognition within the scientific community.

Legacy and future contributions

Mr. Gajraj Singh envisions building a strong AI research culture at IILM University. He aims to develop AI-driven platforms for predictive healthcare, leveraging real-time imaging and intelligent automation. His long-term goal includes establishing a research lab focused on power electronics, medical signal processing, and AI for social good. By continuing collaborations and mentoring research scholars, 3D Vision and Sensing he plans to leave a lasting legacy in both academia and applied technology.

Publications

Title: Preprocessing and Frame Level Classification Framework for Cardiac Phase Detection in 2D Echocardiography
Authors: Singh, G.; Darji, A.D.; Sarvaiya, J.N.; Patnaik, S.
Journal: Biomedical Signal Processing and Control
Publication Date: 2025

Title: EchoPhaseFormer: A Transformer Based Echo Phase Detection and Analysis in 2D Echocardiography
Authors: Singh, G.; Darji, A.D.; Sarvaiya, J.N.; Patnaik, S.
Journal: SN Computer Science
Publication Date: 2024

Title: Preprocessing and Frame Level Classification Framework for Cardiac Phase Detection in 2D Echocardiography
Authors: Singh, G.; Darji, A.D.; Sarvaiya, J.N.; Patnaik, S.
Journal: Research Square
Publication Date: 2023

Title: Underwater Image/Video Enhancement Using Wavelet Based Color Correction (WBCC) Method
Authors: Singh, G.; Jaggi, N.; Vasamsetti, S.; Sardana, H.K.; Kumar, S.; Mittal, N.
Journal: 2015 IEEE Underwater Technology (UT)
Publication Date: 2015

Conclusion

Mr. Gajraj Singh stands as a progressive educator and researcher with a forward-looking vision. His integrated expertise in AI, deep learning, and power electronics positions him to make lasting contributions in smart healthcare and intelligent systems. Through academic excellence, impactful research, and consistent mentorship, he is shaping the next generation of innovators in engineering and technology.