Vasuki | Deep Learning for Computer Vision | Women Researcher Award

Published on 15/10/202516/10/2025 by Computer Vision Awards

Dr. R. Vasuki | Deep Learning for Computer Vision | Women Researcher Award

Assistant Professor | Mannar Thirumalai Naicker College | India

Dr. R. Vasuki is an Assistant Professor in the Department of Artificial Intelligence at Mannar Thirumalai Naicker College, Madurai. She holds a Ph.D. in Computer Science from Karpagam Academy of Education, along with M.Phil, MCA, and BCA degrees from Bharathidasan University and Cauvery College for Women. She has over fourteen years of academic experience and previously served as an Assistant Professor at Annai Fathima College and as a Website Developer at LM Technologies, Chennai. Her research interests include biometrics, cryptography, database management systems, web development, and artificial intelligence. She has published several papers in reputed international journals and conferences such as IEEE, Springer, and Scopus-indexed publications, with notable work in biometric template protection, image encryption, and machine learning applications. Dr. Vasuki has organized and participated in numerous faculty development programs, workshops, and seminars, and has contributed as a reviewer for reputed journals. She received the first prize for a paper presentation from the Madurai Productivity Council and has authored a book titled Internet of Things along with a book chapter on conversational AI applications. Her research skills include data analysis, model optimization, and AI-driven system development, supported by certifications in deep learning, cybersecurity, and cloud computing. She actively mentors students in technical skill development and promotes innovation in higher education. Her research has received 1 citation by 3 documents with an h-index of 1.

Profile: Scopus

Featured Publications

1. Vasuki, R. (2024). Iris biometric template identification and recognition scheme using a novel parallel fused encoder.

Osman Yildirim | Deep Learning | Best Researcher Award

Published on 12/09/202513/09/2025 by Computer Vision Awards

Prof. Osman Yildirim | Deep Learning | Best Researcher Award

Head of the Department | Istanbul Aydın University | Turkey

Prof. Osman Yildirim is a distinguished academic and researcher recognized for his contributions at the intersection of engineering, business, sustainability, and biomedical applications. He holds dual doctoral degrees in Engineering and Business Administration, a unique combination that has enabled him to approach research challenges with a strong interdisciplinary perspective. Over the course of his career, he has taken on significant academic leadership roles, including serving as Head of Department at Istanbul Aydin University, while also guiding doctoral students and fostering collaborative research projects. His professional experience spans teaching across engineering and business disciplines, coordinating research initiatives, and contributing to institutional development through mentorship and administrative leadership. His primary research interests focus on green transformation, sustainable supply chains, carbon policy impacts, energy management systems in universities, and AI-based medical imaging applications for improved diagnostics. These areas reflect his commitment to aligning research with both technological advancements and societal needs, particularly in the context of sustainable development and healthcare innovation. He has published widely in reputed Q1 and Q2 indexed journals such as Scopus and SCI, showcasing the impact of his work in both technical and applied fields. His achievements have been recognized through awards and honors that acknowledge his contributions to advancing interdisciplinary research and education. In addition, he has built valuable collaborations with international teams, integrating expertise from engineering, business, and medicine to deliver impactful solutions with global relevance. His research skills include expertise in machine learning, AI-driven image analysis, sustainable system design, and computational modeling for optimization under carbon constraints. These technical strengths, combined with his leadership and mentorship, position him as a leading scholar dedicated to advancing academic excellence and addressing global challenges through innovative and socially relevant research.

Profile: Google Scholar | Scopus Profile | ORCID Profile

Featured Publications

Ozturk, A. I., Yıldırım, O., İdman, E., & İdman, E. (2025). A comparative study of hybrid decision tree–deep learning models in the detection of intracranial arachnoid cysts. Neuroscience Informatics, 100234.

Ozturk, A. I., Yildirim, O., Kaygusuz, K., Idman, E., & Idman, E. (2025). Brain cyst detection using deep learning models. International Journal of Innovative Research and Scientific Studies, 8(5), 8974.

Borhan Elmi, M. M., & Yıldırım, O. (2025). Improve MPPT in organic photovoltaics with chaos-based nonlinear MPC. Balkan Journal of Electrical and Computer Engineering, 13(1), 1418574.

Ozturk, A. I., Yıldırım, O., & Deryahanoglu, O. (2025). A comprehensive strategy for the identification of arachnoid cysts in the brain utilizing image processing segmentation methods. International Journal of Innovative Technology and Exploring Engineering, 14(2), 1031.

Borhan Elmi, M. M., & Yıldırım, O. (2024). Improve LVRT capability of organic solar arrays by using chaos-based NMPC. International Journal of Energy Studies, 4(3), 1449558.

Yildirim, O., Khaustova, V. Y., & Ilyash, O. I. (2023). Reliability and validity adaptation of the hospital safety climate scale. The Problems of Economy, 4(1), 207–216.

Yildirim, O. (2023). Multidimensional and strategic outlook in digital business transformation: Human resource and management recommendations for performance improvement. In Book chapter.

Yildirim, O. (2023). Health professionals’ perspective in the context of social media, paranoia, and working autonomy during the COVID-19 pandemic period. Archives of Health Science Research, 10(1), 30–37.

Yildirim, O. (2023). The personified model for supply chain management. In Multidimensional and strategic outlook in digital business transformation: Human resource and management recommendations for performance improvement.

Yildirim, O., Ilyash, O. I., Khaustova, V. Y., & Celiksular, A. (2022). The effect of emotional intelligence and work-related strain on the employee’s organizational behavior factors. The Problems of Economy, 2(1), 124–131.

Yildirim, O. (2022). Investigation of the electrical conductivity of pernigranilin with carbon monoxide and nitrogen monoxide doping. Mathematical Statistician and Engineering Applications, 9(4).

Yildirim, O. (2022). Cyst segmentation using filtering technique in computed tomography abdominal kidney images. Mathematical Statistician and Engineering Applications, 9(4).

Yildirim, O. (2022). Design of flyback converter by obtaining the characteristics of polymer based R2R organic PV panels. International Journal of Renewable Energy Research, 12(4).

Avdullahi, A., & Yildirim, O. (2021). The mediating role of emotional stability between regulation of emotion and overwork. In Book chapter.

Tunç, P., Yıldırım, O., Göktepe, E. A., & Çapuk, S. (2021). Investigation of the relationship between personality, organizational identification and turnover in competitive flight model. TroyAcademy, 6(1), 894141.

Sivanagireddy Kalli | Deep Learning for Computer Vision | Academic Excellence Distinction Award

Published on 15/07/202516/07/2025 by Computer Vision Awards

Dr. Sivanagireddy Kalli | Deep Learning for Computer Vision | Academic Excellence Distinction Award

Professor at Sridevi Women’s Engineering College, India

Dr. K. Sivanagireddy is a seasoned academician and researcher with over 20 years of experience in teaching, research, and administration. He has served in key academic leadership roles including Dean Academics, Head of Department, and Principal across reputed engineering institutions in Telangana and Andhra Pradesh. His extensive contributions include the publication of more than 60 research papers in SCI, Scopus, and UGC CARE-listed journals, along with participation in over 20 international conferences. He has been a driving force in innovation, holding eight patents—both national and international—and authoring nine technical books. He recently completed a Postdoctoral Fellowship at the University of South Florida (2024) and earned a Ph.D. in Electronics and Communication Engineering from JNTU Hyderabad (2019). His expertise spans areas like VLSI Design, IoT, AI, Embedded Systems, and Medical Image Processing. Recognized nationally and internationally, Dr. Sivanagireddy is also an active member of professional bodies such as IEEE, IAENG, and IAOE.

Professional Profile

Education🎓

Dr. K. Sivanagireddy has a strong academic foundation rooted in electronics, communication, and embedded systems. He earned his Ph.D. in Electronics and Communication Engineering from Jawaharlal Nehru Technological University, Hyderabad, in 2019. Recently, in 2024, he completed his Postdoctoral Fellowship at the University of South Florida, USA, further enriching his research exposure and global academic outlook. His earlier postgraduate education includes an M.Tech in Embedded Systems from JNTUK, Kakinada (2014), and an M.E in VLSI Design from Vinayaka Missions University, Tamil Nadu (2006). He began his academic journey with a B.Tech in Electronics and Communication Engineering from Bharathidasan University, Tiruchirappalli, in 2002. His education reflects a clear emphasis on digital design, embedded computing, and system optimization, which laid the groundwork for his multifaceted contributions in academia and research. He has also pursued various NPTEL and FDP certifications from top IITs, demonstrating his commitment to lifelong learning and skill enhancement.

Professional Experience📝

Dr. K. Sivanagireddy brings over two decades of professional academic experience, with an emphasis on leadership, research, and teaching. Currently serving as Dean Academics and Professor at Sridevi Women’s Engineering College, Hyderabad since 2019, he previously held the positions of Head of Department and Associate Professor at the same institute. Earlier in his career, he worked at Arjun College of Technology and Science and LITAM, Guntur, where he mentored undergraduate and postgraduate students and handled administrative responsibilities. His contributions extend to coordinating academic accreditations like NAAC and NBA, overseeing student projects, counseling, and organizing technical paper contests. His strategic leadership has helped align institutional goals with academic excellence and research development. With a deep understanding of educational systems, faculty management, and curriculum design, Dr. Sivanagireddy has played a pivotal role in shaping the academic structure of the institutions he served. His professionalism and experience continue to influence engineering education in India.

Research Interest🔎

Dr. Sivanagireddy’s research interests are broad, multidisciplinary, and highly application-oriented. His primary focus lies in Medical Image Processing, Artificial Intelligence, Deep Learning, and IoT-enabled systems, especially for healthcare diagnostics and smart surveillance. He has conducted advanced research in brain tumor detection, cancer classification, heart disease prediction, and autonomous medical devices, often leveraging CNN, LSTM, and hybrid deep learning models. Additionally, his work spans VLSI Design, Embedded Systems, Cybersecurity, Video Surveillance, and Signal Processing, reflecting his versatility. His contributions also extend to developing IoT-integrated intelligent systems, machine learning-based prediction models, and hardware optimization techniques. Many of his projects are focused on societal needs, such as fall detection for the elderly, counterfeit currency detection, and remote health monitoring. His research is rooted in real-world impact, bridging engineering with life sciences and computing. This interdisciplinary approach allows him to explore innovative solutions across both theoretical and applied research domains.

Award and Honor🏆

Dr. K. Sivanagireddy’s scholarly achievements have been widely recognized through multiple national and international honors. He received the International Academic Excellence Award from I2OR in 2022, acknowledging his impactful global research footprint. In 2021, he was conferred with the National Faculty Excellency Award by the International Journal of MC Square Scientific Research, reflecting his outstanding contributions to teaching and innovation. He also earned the National Certificate of Excellence from the Telangana Engineering Colleges Faculty Association in 2020, further emphasizing his role in academic leadership. In addition to these awards, his editorial engagement with the Asian Council of Science Editors and professional memberships with IEEE, IAENG, and IAOE signify his active participation in international scholarly communities. His commitment to excellence, innovation, and quality research has made him a role model in engineering academia, and these accolades underscore his dedication to elevating academic standards at both institutional and national levels.

Research Skill🔬

Dr. Sivanagireddy possesses a diverse and robust set of research skills that span both theoretical modeling and practical application. He is adept in machine learning algorithms, deep learning frameworks, IoT development, and VLSI simulation tools. His proficiency in tools like MATLAB, Python, Verilog, and FPGA platforms has enabled him to develop and deploy intelligent systems for healthcare, security, and automation. He has expertise in image processing techniques, including segmentation, classification, and feature extraction using CNNs, Bi-LSTM, and hybrid models. Additionally, he demonstrates advanced knowledge in medical diagnostics, pattern recognition, and cloud computing integration. His research skillset is not only confined to software but extends to hardware optimization, including CMOS and ASIC design. Through his participation in over 20 conferences and completion of NPTEL certifications from IITs, he maintains up-to-date technical competence. These diverse skills allow him to drive interdisciplinary research, publish impactful papers, and mentor future innovators effectively.

Conclusion💡

Dr. K. Sivanagireddy is highly deserving and well-qualified for the Best Researcher Award. With a prolific publication record, leadership roles, multiple patents, academic books, and contributions to multiple domains in engineering and technology, he stands out as a multidisciplinary scholar and innovator. A stronger emphasis on research impact, international projects, and focused thematic expertise would further elevate his candidacy.

Publications Top Noted✍

Title: An effective motion object detection using adaptive background modeling mechanism in video surveillance system
Authors: SNR Kalli
Year: 2021
Citations: 54
Title: Early lung cancer prediction using correlation and regression
Authors: K Sivanagireddy, S Yerram, SSN Kowsalya, SS Sivasankari, J Surendiran, RG Vidhya
Year: 2022
Citations: 24
Title: Image Compression and reconstruction using a new approach by artificial neural network
Authors: KSN Reddy, BR Vikram, LK Rao, BS Reddy
Year: 2012
Citations: 21
Title: A Fast Curvelet Transform Image Compression Algorithm using with Modified SPIHT
Authors: KSN Reddy, BRS Reddy, G Rajasekhar, KC Rao
Year: 2012
Citations: 14
Title: A nanoplasmonic branchline coupler for subwavelength wireless networks
Authors: K Thirupathaiah, KS Reddy, GRS Reddy
Year: 2021
Citations: 11
Title: Generative Adversarial Networks based Approach for Intrusion Detection System
Authors: S Kalli, BN Kumar, S Jagadeesh
Year: 2022
Citations: 8
Title: IMPLEMENTATION OF OBJECT TRACKING AND VELOCITY DETERMINATION
Authors: SNR Kalli
Year: 2012
Citations: 5
Title: Image compression by discrete curvelet wrapping technique with simplified SPIHT
Authors: KSN Reddy, L Rao, P Ravikanth
Year: 2012
Citations: 4
Title: Identification of criminal & non-criminal faces using deep learning and optimization of image processing
Authors: K Sivanagireddy, S Jagadeesh, A Narmada
Year: 2024
Citations: 3
Title: Low memory low complexity image compression using DWT and HS-SPIHT encoder
Authors: K Sivanagireddy, M Saipravallika, PKC Tejaswini
Year: 2012
Citations: 3
Title: Reconstruction Using a New Approach By Artificial Neural Network
Authors: SNRKI Compression
Year: 2012
Citations: 3
Title: Early Lung Cancer Prediction using Correlation and Regression
Authors: K Sivanagireddy
Year: 2022
Citations: 2
Title: Smart Door Lock to Avoid Robberies in ATM
Authors: VS Reddy, S Kalli, H Gebregziabher, BR Babu
Year: 2021
Citations: 2
Title: Image Segmentation by Using Modified Spatially Constrained Gaussian Mixture Model
Authors: S Kalli, BM Bhaskara
Year: 2016
Citations: 2
Title: Efficient Memory and Low Complexity Image Compression Using DWT with Modified SPIHT Encoder
Authors: KSN Reddy, VS Reddy, DBR Vikram
Year: 2012
Citations: 2
Title: Brain Tumor Detection through Image Fusion Using Cross Guided Filter and Convolutional Neural Network
Authors: MV Srikanth, S Kethavath, S Yerram, SNR Kalli, JB Naik
Year: 2024
Citations: 1
Title: Autoencoder-based Deep Learning Approach for Intrusion Detection System using Firefly Optimization Algorithms
Authors: N Kumar Bukka, S Jagadeesh, KS Reddy
Year: 2024
Citations: 1

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

Dr. Sageengrana S | Deep Learning | Best Researcher Award

Published on 26/03/202526/03/2025 by Computer Vision Awards

Early Academic Pursuits

Dr. Sageengrana is currently pursuing a Ph.D. in Information and Communication Engineering part-time at Anna University, Chennai, with his thesis already submitted. He completed his Master of Engineering (M.E.) in Multimedia Technology from Anna University, where he secured a CGPA of 9.0, earning the distinction of University Second Rank. His journey in academia began with a Bachelor of Engineering (B.E.) in Computer Science and Engineering from the Institute of Road and Transport Technology, Erode, graduating with a First Class CGPA of 8.28. Dr. Sageengrana’s academic foundation was built upon solid early education, with a Higher Secondary School percentage of 83% and Secondary School Leaving percentage of 84.2%.

Professional Endeavors

With over 10 years of professional experience, Dr. Sageengrana has made significant contributions to the field of Computer Science and Engineering. He has served as an Assistant Professor in various esteemed institutions, including VelTech High Tech Dr. Rangarajan Dr. Sankuthala Engineering College, Adhi College of Engineering and Technology, Sri Sairam Institute of Technology, and currently, Sathyabama Institute of Science and Technology. Throughout his career, he has taught diverse subjects like Python Programming, Deep Learning, Compiler Design, Computer Networks, Operating Systems, and more, shaping the minds of the next generation of engineers.

Contributions and Research Focus

Dr. Sageengrana’s research interests are deeply rooted in Data Structures, Object-Oriented Programming, Cloud Computing, Big Data, Deep Learning, and Augmented Reality. His contributions include several publications and patents, particularly in the fields of machine learning, deep learning, and AI applications. Notably, his work on “Optimized RB-RNN: Development of Hybrid Deep Learning for Analyzing Student’s Behaviors in Online Learning Using Brain Waves and Chatbots” was featured in the Expert Systems with Applications journal in 2024, with an impact factor of 8.67. Furthermore, he has contributed to several book chapters on AI and machine learning topics, focusing on applications in finance, cyber security, and environmental intelligence.

Impact and Influence

Dr. Sageengrana has made a profound impact through his research and collaborations. His funded project on “Smart Medication Management and Dispensing System” received ₹55,000 in seed funding from Sathyabama University. Additionally, he has worked closely with the Metropolitan Transport Corporation to develop a purchase order website, showcasing his practical expertise. His innovative patents, such as the design of an unmanned aerial vehicle for emergency medication delivery and systems for water quality management, address pressing global issues and demonstrate his potential for transformative contributions to society.

Academic Cites

Dr. Sageengrana’s scholarly work has garnered significant attention in the academic community, with publications indexed in reputable databases like Scopus. His papers, such as the one on “Automatic Face Recognition System to Mark Student Attendance Using Convolutional Neural Networks” and “Smart Phone-Based Bidirectional Communication System for Hearing & Speaking Impaired Community,” have been presented at international conferences and published in high-impact journals, further elevating his profile in the research world.

Technical Skills

Programming Languages: Python, C, C++, Java, JavaScript, HTML, CSS
Operating Systems: Windows, UNIX, Linux
Database Management: MySQL
Research Tools & Techniques: Machine Learning, Deep Learning, Augmented Reality, Virtual Reality, Cloud Computing, Big Data

Teaching Experience

Dr. Sageengrana’s teaching career spans a wide range of subjects, from foundational topics like Programming in C to advanced fields such as Deep Learning and Augmented Reality. His commitment to high-quality education is reflected in his participation in various faculty development programs (FDPs) on cutting-edge topics such as Quantum Computing, Machine Learning, and Design Thinking. He has delivered guest lectures on emerging technologies like Deep Learning and Neural Networks, fostering deeper understanding among students and peers alike.

Legacy and Future Contributions

Dr. Sageengrana’s dedication to education, research, and innovation positions him as a key figure in the fields of Computer Science and Engineering. His legacy will likely continue through his mentorship, his influential publications, and his future contributions to AI, deep learning, and smart technologies. As he progresses in his Ph.D. and furthers his research on intelligent systems, his work will likely shape the next generation of tech innovations, especially in online learning, healthcare, and environmental management.

Professional Memberships

Life Member of ISTE (Indian Society for Technical Education)
Life Member of CSTA (Computer Science Teachers Association)
Life Member of CSI (Computer Society of India)
IEEE Member (Institute of Electrical and Electronics Engineers)

Publications

Optimized RB-RNN: Development of hybrid deep learning for analyzing student’s behaviours in online-learning using brain waves and chatbots

Authors: S. Sageengrana, S. Subramanian Selvakumar, S. Selvaraj Srinivasan
Journal: Expert Systems with Applications
Year: 2024

Featured Publications

Education🎓

Professional Experience📝

Research Interest🔎

Award and Honor🏆

Research Skill🔬

Conclusion💡

Publications Top Noted✍

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

Doctorate at SRM Institute of Science and Technology, India

Profiles

Early Academic Pursuits