Naourez Benhadj | Deep Learning | Excellence in Research

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Prof. Naourez Benhadj | Deep Learning | Excellence in Research

Associate Professor | Ecole Nationale d’Ingénieurs de Sfax | Tunisian

Dr. Naourez Benhadj is a researcher at the Ecole Nationale d’Ingénieurs de Sfax (ENIS), Tunisia, specializing in electric machines, PMSM design, hybrid/electric vehicle energy management, and intelligent optimization techniques. With 32 scientific publications, 243 citations, and an h-index of 9, he has contributed significantly to fault detection, finite-element modeling, and advanced optimization algorithms, including recent work on transformer-based solar power prediction and PMSM design using chaotic PSO. Collaborating with over 30 international co-authors, his research supports sustainable mobility, smart energy systems, and high-efficiency electric transportation, fostering technological advancement and environmental impact on a global scale.

 

Citation Metrics (Scopus)

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Citations
243

Documents
32

h-index
9

🟦 Citations 🟥 Documents 🟩 h-index

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Featured Publications


Comparison of fuel consumption and emissions of two hybrid electric vehicle configurations.

-International Conference on Sciences and Techniques of Automatic Control and Computer Engineering. (2018) Cited By: 4

Design simulation and realization of solar battery charge controller using Arduino Uno..

-International Conference on Sciences and Techniques of Automatic Control and Computer Engineering . (2017) Cited By: 21

Torque ripple and harmonic density current study in induction motor: Two rotor slot shapes.

– International Review on Modelling and Simulations.(2007). Cited By: 5

Thermal modeling of permanent magnet motor with finite element method.

– International Conference on Sciences and Techniques of Automatic Control and Computer Engineering. (2014). Cited By: 5

Abrar Alajlan | Deep Learning for Computer Vision | Best Researcher Award

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Dr. Abrar Alajlan | Deep Learning for Computer Vision | Best Researcher Award

Associate professor | King Saud University | Saudi Arabia

Dr. Abrar Alajlan is an Associate Professor of Computer Science at King Saud University  Saudi Arabia, renowned for his multidisciplinary research contributions across Artificial Intelligence (AI), Machine Learning, Wireless Sensor Networks  Expert Systems, Robotics, and Cloud Computing Security. His academic and scientific work integrates computational intelligence with practical problem-solving, contributing to the advancement of smart adaptive and secure digital ecosystems. Dr. Alajlan has authored 28 peer-reviewed scientific publications and a scholarly book titled Cryptographic Methods His research outputs have achieved over 412 citations, with an h-index of 10 and i10-index of 11, reflecting his consistent impact and scholarly excellence in computer science and AI applications.Among his notable achievements, his paper ESOA-HGRU: Egret Swarm Optimization Algorithm-Based Hybrid Gated Recurrent Unit for Classification of Diabetic Retinopathy published in Artificial Intelligence Review is ranked in the Top 5% of ISI journals, showcasing his pioneering efforts in applying optimization-based deep learning for medical diagnostics. His other influential works, including A Novel-Cascaded ANFIS-Based Deep Reinforcement Learning for the Detection of Attacks in Cloud IoT-Based Smart City Applications Concurrency and Computation: Practice and Experience and Artificial Intelligence-Based Multimodal Medical Image Fusion Using Hybrid S2 Optimal CNN demonstrate his commitment to bridging AI with cybersecurity healthcare and intelligent automation.Earlier in his career Dr. Alajlan’s significant contributions to robotics and sensor-based systems notably  Trajectory Planning and Collision Avoidance Algorithm for Mobile Robotics Systems IEEE Sensors Journal and Sensor Fusion-Based Model for Collision-Free Mobile Robot Navigation earned substantial citations and remain foundational in the field of autonomous robotic navigation and path optimization.Dr. Alajlan’s extensive collaborations with leading researchers such as M. M. Almasri, K. M. Elleithy and A. Razaque have resulted in high-impact publications addressing challenges in smart cities network security and intelligent automation. His research stands out for its societal relevance, focusing on AI-driven healthcare solutions, sustainable IoT systems, and secure digital transformation. Through his scholarly excellence, mentorship, and interdisciplinary approach, Dr. Alajlan continues to advance the frontiers of intelligent computing for global scientific and technological progress.

Profiles: Google Scholar | Scopus | ResearchGate

Featured Publications

1.Almasri, M. M., Alajlan, A. M., & Elleithy, K. M. (2016). Trajectory planning and collision avoidance algorithm for mobile robotics system. IEEE Sensors Journal, 16(12), 5021–5028. Cited By : 89

2.Almasri, M., Elleithy, K., & Alajlan, A. (2015). Sensor fusion-based model for collision-free mobile robot navigation. Sensors, 16(1), 24. Cited By : 76

3.Almasri, M. M., Elleithy, K. M., & Alajlan, A. M. (2016, May). Development of efficient obstacle avoidance and line following mobile robot with the integration of fuzzy logic system in static and dynamic environments. In 2016 IEEE Long Island Systems, Applications and Technology Conference (LISAT) (pp. 1–6). IEEE. Cited By : 30

4.Alajlan, A. M., Almasri, M. M., & Elleithy, K. M. (2015, May). Multi-sensor based collision avoidance algorithm for mobile robot. In 2015 Long Island Systems, Applications and Technology Conference (pp. 1–6). IEEE. Cited By : 30

5.Almasri, M. M., & Alajlan, A. M. (2022). Artificial intelligence-based multimodal medical image fusion using hybrid S2 optimal CNN. Electronics, 11(14), 2124. Cited By : 25

Dr. Abrar M. Alajlan’s pioneering research in Artificial Intelligence and secure computational systems bridges scientific innovation with real-world applications, advancing intelligent healthcare, smart city resilience, and cyber-secure digital infrastructures. His vision centers on harnessing AI to create adaptive, safe, and sustainable technologies that empower global innovation and societal well-being.

Ahmad Reza Naghsh Nilchi | Deep Learning | Best Researcher Award

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Prof. Ahmad Reza Naghsh Nilchi | Deep Learning | Best Researcher Award

Faculty Member | University of Isfahan | Iran

Prof. Ahmad Reza Naghsh-Nilchi is a distinguished researcher in computer vision, artificial intelligence, and medical image processing with a strong academic and professional background. He completed his PhD in Electrical and Computer Engineering at Michigan State University, where he specialized in digital image processing, and has since built an influential career in both academia and research. Over the years, he has served in multiple leadership positions including department chair, dean of research, and head of research laboratories, while also supervising numerous PhD and master’s students in advanced AI and imaging topics. His professional experience extends internationally through collaborations with leading institutions such as UC Irvine, University of Toronto, York University, and University of Ireland, contributing significantly to global research initiatives. His research interests span robust deep learning, adversarial defense, trustworthy AI, multimodal action recognition, image captioning, retinal analysis, and robot-camera pose estimation, reflecting both theoretical innovation and practical applications. He has published more than 70 papers in prestigious journals and conferences indexed by IEEE and Scopus, and his work has received more than 2,200 citations. His excellence has been recognized through multiple honors, including awards as University Researcher of the Year and Industrial Researcher of the Year. He possesses advanced research skills in AI model development, medical imaging, digital signal processing, and multimodal data analysis, complemented by editorial roles, conference organization, and active memberships in professional associations such as IEEE and ACM. His career demonstrates a commitment to advancing science, mentoring the next generation, and fostering impactful interdisciplinary collaborations. His Scopus output reflects international impact, with 1,319 citations by 1,214 documents, 65 published documents, and an h-index of 21.

Profile: Google Scholar | Scopus Profile | ORCID Profile

Featured Publications

Fathi, A., & Naghsh-Nilchi, A. R. (2012). Noise tolerant local binary pattern operator for efficient texture analysis. Pattern Recognition Letters, 33(9), 1093–1100.

Fathi, A., & Naghsh-Nilchi, A. R. (2012). Efficient image denoising method based on a new adaptive wavelet packet thresholding function. IEEE Transactions on Image Processing, 21(9), 3981–3990.

Fathi, A., & Naghsh-Nilchi, A. R. (2013). Automatic wavelet-based retinal blood vessels segmentation and vessel diameter estimation. Biomedical Signal Processing and Control, 8(1), 71–80.

Amirgholipour, S. K., & Ahmad, R. (2009). Robust digital image watermarking based on joint DWT-DCT. International Journal of Digital Content Technology and its Applications, 3(2), 42–48.*

Kasmani, S. A., & Naghsh-Nilchi, A. (2008). A new robust digital image watermarking technique based on joint DWT-DCT transformation. In 2008 Third International Conference on Convergence and Hybrid Information Technology (pp. 539–544). IEEE.