Naourez Benhadj | Deep Learning | Excellence in Research

Published on 12/12/202512/12/2025 by Computer Vision Awards

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

Eccentricity faults diagnosis in permanent magnet synchronous motors: A finite element-based approach.

– International Journal on Energy Conversion (2019). Cited By : 7

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

Published on 08/11/202508/11/2025 by Computer Vision Awards

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.

Nagaraj | Deep Learning for Computer Vision | Excellence in Research

Published on 28/10/202528/10/2025 by Computer Vision Awards

Dr. P. Nagaraj | Deep Learning for Computer Vision | Excellence in Research

Associate Professor | SRM Institute of Science and Technology | India

Dr. P. Nagaraj is an esteemed Associate Professor at the SRM Institute of Science and Technology, Tiruchirappalli, Tamil Nadu, India. With research expertise spanning Artificial Intelligence, Data Science, Data Analytics, Machine Learning, and Recommender Systems, he has made substantial contributions to intelligent computing and healthcare analytics. His innovative work focuses on applying deep learning, fuzzy inference, and explainable AI (XAI) techniques to real-world challenges in medical diagnosis, cybersecurity, and sustainable automation.Dr. Nagaraj has an impressive research portfolio, with over 208 indexed publications, 2,736 citations, and an h-index of 32, reflecting the global relevance and scholarly influence of his work. His notable publications include advancements in diabetes prediction, brain tumor classification, Alzheimer’s disease analysis, and cyberattack detection using AI-driven frameworks. His studies on distributed denial-of-service (DDoS) detection, IoT-based healthcare systems, and intelligent recommendation models have been widely cited and applied across multiple interdisciplinary domains.In recognition of his outstanding research, Dr. Nagaraj has been consecutively listed among the World’s Top 2% Scientists (2023–2025), highlighting his sustained impact in computer science and data-driven innovation. He is also a two-time recipient of the prestigious India AI Fellowship (Ministry of Electronics and Information Technology, MeitY), each worth ₹1 Lakh, for his pioneering projects titled AgriTech of Next-Gen Automation for Sustainable Crop Production and A Deep Learning Approach to Improve Pulmonary Cancer Diagnosis Using CNN.Through collaborations with national and international scholars, Dr. Nagaraj continues to advance the frontier of intelligent data analytics for societal benefit. His research contributes significantly to sustainable digital transformation, healthcare improvement, and agricultural innovation, positioning him as a leading figure in India’s AI research landscape and a global advocate for technology-driven social progress.

Profiles: Google Scholar | ORCID | Scopus

Featured Publications

1.Sudar, K. M., Beulah, M., Deepalakshmi, P., Nagaraj, P., & Chinnasamy, P. (2021). Detection of distributed denial of service attacks in SDN using machine learning techniques. In Proceedings of the 2021 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1–6). IEEE. Cited By : 158

2.Nagaraj, P., & Deepalakshmi, P. (2022). An intelligent fuzzy inference rule‐based expert recommendation system for predictive diabetes diagnosis. International Journal of Imaging Systems and Technology, 32(4), 1373–1396. Cited By : 100

3.Nagaraj, P., Muneeswaran, V., Reddy, L. V., Upendra, P., & Reddy, M. V. V. (2020). Programmed multi-classification of brain tumor images using deep neural network. In Proceedings of the 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 1–6). IEEE. Cited By : 85

4.Nagaraj, P., Deepalakshmi, P., & Romany, F. M. (2021). Artificial flora algorithm-based feature selection with gradient boosted tree model for diabetes classification. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 14, 2789–2802. Cited By : 79

.5.Nagaraj, P., & Deepalakshmi, P. (2020). A framework for e-healthcare management service using recommender system. Electronic Government, an International Journal, 16(1–2), 84–100. Cited By : 70

Dr. P. Nagaraj’s research advances global innovation by integrating artificial intelligence and data analytics to address critical challenges in healthcare, agriculture, and cybersecurity. His vision is to harness intelligent automation and explainable AI to create sustainable, data-driven solutions that enhance human well-being, industrial efficiency, and societal resilience.

Ahmad Reza Naghsh Nilchi | Deep Learning | Best Researcher Award

Published on 15/09/202515/09/2025 by Computer Vision Awards

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.

Prof. Kalfalla Awedat | Deep Learning | Best Researcher Award

Published on 16/04/202516/04/2025 by Computer Vision Awards

Early Academic Pursuits

Prof. Kalfalla Awedat laid a strong academic foundation in Electrical and Communication Engineering, earning his B.Sc. and M.Sc. degrees from Tripoli University in Libya in 2001 and 2008, respectively. Driven by a passion for research and innovation, he pursued and completed his Ph.D. in Electrical and Computer Engineering at Western Michigan University in 2016. His doctoral studies honed his expertise in biomedical signal processing and electrical systems, setting the stage for a diverse and impactful career in academia and engineering.

Professional Endeavors

Prof. Awedat has cultivated a distinguished academic career with a focus on engineering education and hands-on instruction. He currently serves as a Tenure-Track Assistant Professor at SUNY Morrisville, where he teaches computer information technology and leads the development of a cutting-edge Virtual Reality/Augmented Reality (VR/AR) lab. Previously, he held a non-tenure track Assistant Professor position at Pacific Lutheran University, where he taught a broad spectrum of undergraduate courses including C++, Python, Java, microelectronics, data mining, and computer networking. His early academic appointments include serving as a Teaching Assistant at Western Michigan University and as a Lecturer at Aljabal Algrabi University in Libya. Beyond academia, he worked as a Communications Engineer at GECOL Company, where he applied fiber optic technologies and automated diagnostic systems in high-voltage environments.

Contributions and Research Focus

Prof. Awedat’s research spans across several innovative domains. His work focuses on applying deep learning networks for fault detection in solar panel systems, integrating compressive sensing with AI for big data analytics, and utilizing machine learning for cancer data classification and medical image modeling. He has developed supervised machine learning techniques for early cancer detection, contributed to the modeling and classification of medical images, and conducted significant research on signal and image processing, including object detection, face recognition, and data augmentation. His passion for solving real-world problems through intelligent systems continues to drive his academic output.

Impact and Influence

With a diverse academic and industry background, Prof. Awedat has significantly impacted both students and the broader scientific community. His leadership in developing new courses and laboratories—such as Biomedical Signal Processing, Microelectronics, and Engineering with 3D printing—has enhanced student learning and lab-based education. He also secured a $10,000 research grant for efficient face recognition methods and has applied for a $484,000 NSF grant in collaboration with North Carolina State University and Benedict College, highlighting his growing influence in the fields of AI and engineering education.

Academic Citations and Recognitions

Prof. Awedat’s scholarly work has been cited in areas related to biomedical signal processing, computer vision, and big data analytics. His research outputs are reflected in multiple funded projects and collaborative research initiatives. Notably, his contribution to the project titled “Efficient Face Recognition Using Regularized Adaptive Non-Local Coding” has received peer recognition and funding support, demonstrating the academic relevance and potential societal impact of his work.

Technical Skills

Prof. Awedat possesses a broad skill set across various software and platforms including MATLAB, OrCAD, PSpice, Python, C++, and Java. His practical knowledge extends to tools like LabChart, PowerLab, and advanced lab equipment such as FLUKE systems. He has undergone professional training in fiber optics, project management (PMP), and laboratory techniques, further enhancing his capacity to lead research and teaching innovation.

Teaching Experience

With more than a decade of academic experience, Prof. Awedat has taught and developed courses across the domains of engineering, programming, and data science. From digital logic and electronic circuits to data mining and biomedical engineering, his teaching portfolio is both deep and diverse. He has a proven track record of mentoring students, supervising design projects, and fostering a culture of inquiry and innovation in classroom settings.

Legacy and Future Contributions

Prof. Awedat’s legacy lies in his dedication to bridging theoretical knowledge with practical applications. Through curriculum development, research mentorship, and technological innovation, he continues to shape future-ready engineers. His ongoing work in autonomous vehicle object detection, AI-powered medical diagnostics, and immersive VR/AR education tools positions him as a forward-thinking leader in academic and technological spaces. Looking ahead, his collaborative efforts and interdisciplinary research promise to make substantial contributions to the fields of smart healthcare, renewable energy, and AI-enhanced engineering education

Publications

Advanced fault detection in photovoltaic panels using enhanced U-Net architectures

Authors: Khalfalla Awedat, Gurcan Comert, Mustafa Ayad, Abdulmajid Mrebit
Journal: Machine Learning with Applications
Year: 2025

COVID-CLNet: COVID-19 Detection With Compressive Deep Learning Approach

Authors: Khalfalla Awedat, Almabrok Essa
Journal: International Journal of Computer Vision and Image Processing (IJCVIP)
Year: 2022

Novel Robust Augmentation Approach Based on Sensing Features for Data Classification

Authors: Masoud M Alajmi, Khalfalla A Awedat
Journal: IEEE Access
Year: 2021

COVID-CLNet: COVID-19 Detection with Compressive Deep Learning Approaches

Authors: Khalfalla Awedat, Almabrok Essa
Journal: arXiv preprint arXiv:2012.02234
Year: 2020

Efficient face recognition using regularized adaptive non-local sparse coding

Authors: Masoud Alajmi, Khalfalla Awedat, Almabrok Essa, Fawaz Alassery, Osama S Faragallah
Journal: IEEE Access
Year: 2019

Dr. Aiai Wang | Deep Learning | Best Researcher Award

Published on 15/04/202515/04/2025 by Computer Vision Awards

Early Academic Pursuits

Dr. Aiai Wang began her academic journey with a Bachelor of Science in Mining Engineering from the School of Mining Engineering, North China University of Science and Technology (2017–2021). Her solid foundation in mining principles led her to pursue a Master’s degree in Civil Engineering at the University of Science and Technology Beijing (USTB), School of Civil and Resource Engineering (2021–2024). Her postgraduate studies were marked by rigorous research and an emphasis on digital mining and intelligent mining technologies.

Professional Endeavors

In her current role as Secretary of the 16th Party Branch, Dr. Wang exemplifies leadership in academic and organizational settings. She is actively engaged in digital transformation projects in mining, bringing innovation to tailings sand cementation and filling physical dynamics. Her work bridges academic research with practical applications, emphasizing smart mining technologies for safe and efficient resource extraction.

Contributions and Research Focus

Dr. Aiai Wang has significantly contributed to the field of cementitious tailings fills and mining support systems. Her research interests include pore structure characterization, CT image reconstruction, nano-cellulose reinforcement, and intelligent modeling of mine systems. Notably, she co-authored several peer-reviewed articles exploring dynamic behaviors of backfills, including:

“Effect of height to diameter ratio on dynamic characteristics…” in Construction and Building Materials (2022).
“Influence of nano cellulose on cementitious tailings backfill…” in Construction and Building Materials (2022).
“Quantitative analysis of pore characteristics…” in Journal of Materials Research and Technology (2023).

Impact and Influence

Her scientific contributions are well-recognized, garnering 10+ citations and influencing sustainable and safe practices in mining engineering. Through co-authored patents and software copyrights, Dr. Wang has developed intelligent systems for mining tunnel support, lithology identification, and strength prediction of fill media. These innovations are revolutionizing safety measures and process optimization in deep mining operations.

Academic Cites and Honors

Dr. Aiai Wang is a decorated scholar. She was named one of USTB’s “Top Ten Academic Stars” in 2023 and has received multiple accolades, including the National Scholarship for Master’s Students, Taishan Iron and Steel Scholarship, and First-Class Academic Scholarships for consecutive academic years. Her consistent performance has also earned her recognition as an Outstanding Three-Good Graduate Student at USTB.

Technical Skills and Certifications

Dr. Aiai Wang holds technical certifications in English proficiency, including CET4 and CET6, and is proficient in developing predictive models and intelligent systems for mining processes. She has co-developed several copyrighted software systems, such as:

Macro Strength Prediction System for Tail Sand Cementation
Comprehensive Tunnel Roof Support Classifier
Automatic Lithology Identifier & Flexible Support Designer

Teaching and Mentorship

While primarily research-focused, Dr. Wang is involved in guiding junior peers within the School of Civil and Resource Engineering. Her involvement in academic committees and student organizations reflects her mentorship spirit and dedication to nurturing future mining engineers.

Legacy and Future Contributions

Dr. Aiai Wang’s work lays a crucial foundation for the integration of AI, digital modeling, and nanomaterials in mine engineering. Her patented methodologies for non-blasting mining, mesh-supported fill reinforcement, and automated support design are paving the way for next-generation, sustainable, and high-precision mining operations. As she advances her academic career, Dr. Wang is poised to be a key thought leader in intelligent and green mining technologies.

Publications

Quantitative Analysis of Pore Characteristics of Nanocellulose Reinforced Cementitious Tailings Fills Using 3D Reconstruction of CT Images

Authors: Wang, Aiai; Cao, Shuai; Yilmaz, Erol
Journal: Journal of Materials Research and Technology
Year: 2023

Prof. Haigen Hu | Deep Learning | Best Researcher Award

Published on 12/04/202512/04/2025 by Computer Vision Awards

Summary

Prof. Haigen Hu is a seasoned academic and researcher with a Ph.D. in Control Theory and Engineering. With extensive experience in deep learning, computer vision, and medical image processing, he is currently serving as a professor at Zhejiang University of Technology. His work bridges academia and industry, having led multiple national and provincial research projects.

Education

Ph.D. in Control Theory and Control Engineering, Tongji University, Shanghai, China (2013)
Postdoctoral Research, LITIS Laboratory, University of Rouen Normandy, France (Completed in 2019)

Professional Experience

Professor, College of Computer Science and Technology, Zhejiang University of Technology, China
Former Postdoctoral Fellow, LITIS Laboratory, University of Rouen Normandy, France

Research Interests

Deep Learning
Computer Vision
Medical Image Processing

Publications

Multipath and noise resilient direction of arrival method for low-cost mechanical arm calibration

Authors: Hanmo Chen, Qianwei Zhou, Haigen Hu, Baoqing Li
Journal: Computers and Electrical Engineering
Year: 2025

Pruning Networks only Using Few-shot Pre-training Based on Gradient Similarity Frequency

Authors: Haigen Hu, Huihuang Zhang, Qianwei Zhou, Tieming Chen
Journal: IEEE Transactions on Artificial Intelligence
Year: 2025

An anchor-free instance segmentation method for cells based on mask contour

Authors: Qi Chen, Huihuang Zhang, Qianwei Zhou, Qiu Guan, Haigen Hu
Journal: Applied Intelligence
Year: 2025

RMFDNet: Redundant and Missing Feature Decoupling Network for salient object detection

Authors: Qianwei Zhou, Jintao Wang, Jiaqi Li, Haigen Hu, Keli Hu
Journal: Engineering Applications of Artificial Intelligence
Year: 2025

A comprehensive survey on contrastive learning

Authors: Haigen Hu, Xiaoyuan Wang, Yan Zhang, Qi Chen, Qiu Guan
Journal: Neurocomputing
Year: 2024

Ms. Beenish Khalid | Deep Learning | Best Researcher Award

Published on 12/04/202512/04/2025 by Computer Vision Awards

Summary

Ms. Beenish Khalid is a seasoned Research Software Engineer with a Ph.D. in Computer Engineering and over 10 years of experience in developing advanced software solutions. At Hecta Solutions, she has spearheaded projects in AI, machine learning, and web development, bringing cutting-edge innovations to life. With deep proficiency in numerous programming languages, database systems, and tools, she is passionate about technological advancement and collaborative innovation.

Education

Ph.D. in Computer Engineering – NUST, Islamabad (2025)
MS in Computer Engineering – UET Taxila (2018)
B.Sc. in Computer Engineering – UET Taxila (2015)

Professional Experience

Research Software Engineer – Hecta Solutions (2022–2024)
Developed AI/ML-based BCI systems using React, Django, and TensorFlow.
Junior Research Officer – Air Headquarters (2018–2021)
Focused on cryptography, secure architectures, and defense-grade applications.
Freelance Developer – Freelancer Site (2018)
Delivered high-performance software using Assembly, C++, Python, and MATLAB.
Software Developer (Intern) – National Development Center (2014)
Built robust Android applications in Java using Eclipse IDE.

Research Interests

Brain-Computer Interface (BCI) systems
Artificial Intelligence & Deep Learning
Cybersecurity and Secure Systems
Signal Processing and EEG Compression
Web Technologies and UI/UX Design

Publications

A triple-shallow CNN with genetic algorithm channel selection method for classifying EEG complex limb movements

Author: Beenish Khalid, Ali Hassan, Muhammad Yasin, Muhammad Salman, Muhammad Fasih Uddin Butt, Wadood Abdul, Imran Khan Niazi
Journal: Biomedical Signal Processing and Control
Year: 2025

EMD and VMD in Pre-Movement EEG Signal Analysis: A Hybrid Mode Selection to Classify Upper Limb Complex Movements Using Statistical Features

Author: Beenish Khalid, Ali Hassan, Ehsan Ullah Munir, Imran Khan Niazi
Journal: 2023 IEEE 20th International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET)
Year: 2023

EEG Compression Using Motion Compensated Temporal Filtering and Wavelet Based Subband Coding

Author: Beenish Khalid, Muhammad Majid, Imran Fareed Nizami, Syed Muhammad Anwar, Majdi Alnowamii
Journal: IEEE Access
Year: 2020

Ms. Chetna Kwatra | Deep Learning | Women Researcher Award

Published on 04/04/202504/04/2025 by Computer Vision Awards

Early Academic Pursuits

Ms. Chetna Vaid Kwatra embarked on her academic journey with a strong foundation in engineering and technology. She earned a Diploma in Digital Electronics & Microprocessor System Design from Kasturba Polytechnic for Women, Delhi, in 2002. Building upon this, she pursued a B.Tech. in Information Technology from Maharaja Surajmal Institute of Technology, Guru Gobind Singh Indraprastha University, Delhi, in 2005. Her commitment to academic excellence led her to attain an M.Tech. in Computer Science Engineering from Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, in 2014, solidifying her expertise in computer science and engineering.

Professional Endeavors

Ms. Kwatra has amassed extensive experience in academia and industry. She commenced her career as a Software Engineer at L&T Infotech, Mumbai (2005–2007), gaining valuable industry exposure before transitioning into academia. Her teaching career spans multiple institutions, having served as an Assistant Professor in the Department of Computer Science & Engineering at Lovely Professional University in various tenures (2008–2015, 2017–2018, 2022–Present). She also contributed to Guru Nanak Dev University – Regional Campus, Jalandhar, as an Assistant Professor (2015–2016), shaping the next generation of engineers and researchers.

Contributions and Research Focus

Ms. Kwatra’s research is deeply rooted in Artificial Intelligence (AI), Network Security, and Cryptography, with a focus on Intrusion Detection Systems, Cybersecurity, and AI applications in Healthcare. Her contributions to Machine Learning in Cybersecurity and Healthcare have led to multiple high-impact publications. Her works include “Harnessing Ensemble Deep Learning Models for Precise Detection of Gynaecological Cancers” (2025) and “Early Detection of Gynecological Malignancies Using Ensemble Deep Learning Models: ResNet50 and Inception V3” (2025), showcasing her dedication to AI-driven healthcare solutions. Additionally, she has delved into cryptography and security, with significant publications such as “Exploring State-of-the-Art Cryptography: A Systematic Exploration of Advanced Approaches for IoT Device Authentication” (ICAIHC 2023) and “Artificial Intelligence Application for Security Issues and Challenges in IoT” (IC3I 2022).

Impact and Influence

Ms. Kwatra’s contributions to cybersecurity, AI, and healthcare research have made a profound impact on academic and professional communities. She has received notable recognitions and awards, including the Best Paper Award in Cloud Computing at ICRITO’14, Amity University, Noida. Her role as a committee member at the 7th International Joint Conference on Computing Sciences (ICCS-2023) reflects her influence in the research domain. She has also been acknowledged for conducting guest lectures, such as the Cyber Security session at Government ITI, Talwandi Choudrian, Kapurthala (2023), further extending her expertise beyond academia.

Academic Citations and Book Chapters

Her scholarly works have gained recognition in prestigious international journals and conferences. She has also contributed book chapters, such as “IoMT—Applications, Benefits, and Future Challenges in the Healthcare Domain” in Advances in Fuzzy-Based Internet of Medical Things (IoMT), 2024. With multiple papers published in IEEE and other reputed platforms, her research has significantly contributed to AI applications in cybersecurity and healthcare.

Technical Skills and Certifications

. Kwatra has continuously honed her technical proficiency through certifications and faculty development programs. She holds expertise in cryptography, machine learning, deep learning models, and cybersecurity. Her credentials include:

Certifications in Cryptography (Asymmetric, Symmetric, Hash & Integrity) from the University of Colorado (2024)
Faculty Development Programs in AI for IoT, Data Analysis, and AI Security Workshops (2022–2025)
Completion of Deep Learning workshops and AI-based curriculum design programs

Teaching and Mentorship

Ms. Kwatra has been actively involved in teaching a variety of undergraduate and postgraduate courses, including Java Programming, C++, Computer Networks, Cybersecurity, Network Security & Cryptography, and Intrusion Detection Systems. With a passion for mentoring, she has successfully guided three dissertation students, fostering innovation and critical thinking among aspiring researchers.

Legacy and Future Contributions

With an impressive career spanning academia, research, and industry, Ms. Kwatra continues to drive innovation in AI, cybersecurity, and healthcare applications. Her commitment to research, teaching, and professional training sets a benchmark for future scholars and researchers. As she advances in her academic pursuits, her contributions to AI-driven disease detection, cryptography, and security systems are poised to make lasting impacts in both research and real-world applications.

Publications

Harnessing ensemble deep learning models for precise detection of gynaecological cancers

Authors: C.V. Kwatra, Chetna Vaid, H. Kaur, Harpreet, S.P. Potharaju, Sai Prasad, D.B. Jadhav, Devyani Bhamare, S.B. Tambe, Sagar B.
Journal: Clinical Epidemiology and Global Health
Year: 2025

Dr. Meng Wang | Deep Learning | Best Researcher Award

Published on 02/04/202502/04/2025 by Computer Vision Awards

Early Academic Pursuits

Dr. Meng Wang embarked on his academic journey at Xidian University, earning a Bachelor of Science in Communication Engineering in 2004. He later pursued a Master of Engineering in Software Engineering from Xi’an Jiaotong University under the guidance of Prof. Xiaojun Wu in 2012. His passion for artificial intelligence and data science led him to complete his PhD in Computer Science at Xidian University in 2018, where he was mentored by Prof. Jiangtao Cui and Prof. Hui Li.

Professional Endeavors

Dr. Wang has had a progressive academic career at Xi’an Polytechnic University. He joined as an Assistant Professor in 2019 and quickly advanced to Associate Professor in 2023. His leadership capabilities were recognized, and he was appointed Deputy Dean of the School of Computer Science in 2024. Additionally, since 2020, he has been actively supervising Master’s students, guiding them in cutting-edge research areas.

Contributions and Research Focus

Dr. Wang’s research spans artificial intelligence, deep learning, time series analysis, and spatio-temporal big data. His work focuses on optimizing data mining and management techniques to enhance computational efficiency in large-scale systems. His contributions to intelligent scheduling, power load forecasting, and competitive location selection have led to significant advancements in AI-driven decision-making processes.

Impact and Influence

His research excellence has been acknowledged with numerous prestigious awards. Notably, he received the “Sa Shixuan” Best Paper Award at the 40th CCF National Database Conference in 2023, the Rising Star Award by ACM China in 2020, and the Best Paper Award Runner-Up at IEEE MDM 2019. He also earned the Natural Science Award of Shaanxi Province (Second Prize) in 2020 for his impactful contributions to AI-driven data analysis.

Academic Cites

Dr. Wang has an extensive publication record in top-tier journals and conferences, including IEEE Transactions on Knowledge and Data Engineering (TKDE), ACM Transactions on Intelligent Systems and Technology (TIST), and Bioinformatics. His research has been widely cited, underscoring the influence of his work in AI, data science, and computational intelligence. His recent contributions include innovative methods for electricity theft detection, time series forecasting, and multi-view graph neural networks for biomedical applications.

Technical Skills and Expertise

With a strong technical foundation, Dr. Wang specializes in AI model development, deep learning frameworks, spatio-temporal data processing, and intelligent scheduling algorithms. His expertise also extends to data-driven optimization techniques, predictive analytics, and big data management, making his research highly applicable to real-world AI challenges.

Teaching and Mentorship

Dr. Wang is committed to academic excellence, having received multiple teaching awards, including the “Light of Textile” Higher Education and Teaching Achievement Award (First Prize) in 2023. He also won the Excellence Award at the Shaanxi Undergraduate Colleges’ Classroom Teaching Innovation Competition in 2022. His ability to integrate research with education has made a profound impact on his students and the broader academic community.

Legacy and Future Contributions

As a leading AI researcher, Dr. Wang continues to push the boundaries of artificial intelligence, big data, and predictive analytics. His ongoing research in power load forecasting, tariff prediction, and intelligent scheduling is poised to revolutionize energy and data management industries. Through his leadership at Xi’an Polytechnic University, he aims to cultivate the next generation of AI experts, driving technological advancements for a smarter and more data-driven future.

Publications

Place Your Next Branch with MILE-RUN: Min-dist Location Selection over User Movement

Author: Meng Wang
Journal: Information Sciences
Year: 2018

PINOCCHIO: Probabilistic Influence-Based Location Selection over Moving Objects

Author: Meng Wang
Journal: IEEE Transactions on Knowledge and Data Engineering (TKDE)
Year: 2016

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

Featured Publications

Profiles: Google Scholar | Scopus | ResearchGate

Featured Publications

Profiles: Google Scholar | ORCID | Scopus

Featured Publications

Featured Publications

Professor at SUNY Morrisville College, United States

Profiles

Early Academic Pursuits

Professional Endeavors

Contributions and Research Focus

Impact and Influence

Academic Citations and Recognitions

Technical Skills

Teaching Experience

Legacy and Future Contributions

Advanced fault detection in photovoltaic panels using enhanced U-Net architectures

COVID-CLNet: COVID-19 Detection With Compressive Deep Learning Approach

Novel Robust Augmentation Approach Based on Sensing Features for Data Classification

COVID-CLNet: COVID-19 Detection with Compressive Deep Learning Approaches

Efficient face recognition using regularized adaptive non-local sparse coding

Doctorate at University of Science and Technology Beijing, China

Profiles

Early Academic Pursuits

Professional Endeavors

Contributions and Research Focus

Impact and Influence

Academic Cites and Honors

Technical Skills and Certifications

Teaching and Mentorship

Legacy and Future Contributions

Quantitative Analysis of Pore Characteristics of Nanocellulose Reinforced Cementitious Tailings Fills Using 3D Reconstruction of CT Images

Professor at Zhejiang University of Technology, China

Profiles

Summary

Education

Professional Experience

Research Interests

Multipath and noise resilient direction of arrival method for low-cost mechanical arm calibration

Pruning Networks only Using Few-shot Pre-training Based on Gradient Similarity Frequency

An anchor-free instance segmentation method for cells based on mask contour

RMFDNet: Redundant and Missing Feature Decoupling Network for salient object detection

A comprehensive survey on contrastive learning

National University of Sciences and Technology , Islamabad, Sweden

Profiles

Summary

Education

Professional Experience

Research Interests

A triple-shallow CNN with genetic algorithm channel selection method for classifying EEG complex limb movements

EMD and VMD in Pre-Movement EEG Signal Analysis: A Hybrid Mode Selection to Classify Upper Limb Complex Movements Using Statistical Features

EEG Compression Using Motion Compensated Temporal Filtering and Wavelet Based Subband Coding

Lovely Professional University, India

Profiles

Early Academic Pursuits

Professional Endeavors

Contributions and Research Focus

Impact and Influence

Academic Citations and Book Chapters

Technical Skills and Certifications

Teaching and Mentorship

Legacy and Future Contributions

Harnessing ensemble deep learning models for precise detection of gynaecological cancers

Doctorate at Xi’an Polytechnic University, China

Profiles

Early Academic Pursuits

Professional Endeavors

Contributions and Research Focus

Impact and Influence

Academic Cites

Technical Skills and Expertise

Teaching and Mentorship

Legacy and Future Contributions

Place Your Next Branch with MILE-RUN: Min-dist Location Selection over User Movement

PINOCCHIO: Probabilistic Influence-Based Location Selection over Moving Objects