Irenilza De Alencar Nääs | Object Detection and Recognition | Women Researcher Award

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Prof. Irenilza De Alencar Nääs | Object Detection and Recognition | Women Researcher Award

Professor | Universidade Paulista | Brazil

Prof. Irenilza de Alencar Nääs is a leading researcher at Universidade Paulista, São Paulo, Brazil, specializing in precision livestock farming, agricultural engineering, and AI-driven animal welfare assessment. She has authored over 339 peer-reviewed publications with more than 3,311 citations h-index 32, reflecting strong international impact and extensive collaboration with more than 400 co-authors worldwide. Her recent work integrates thermography, computer vision (YOLOv8), and machine learning to improve broiler welfare, postharvest quality, and occupational health in agri-food systems. Dr. Nääs’s research significantly advances sustainable agriculture and data-driven decision-making for global food security.

Citation Metrics (Scopus)

4000

3000

2000

1000

0

Citations
3,311

Documents
339

h-index
32

🟦 Citations 🟥 Documents 🟩 h-index

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Featured Publications


Princípios de conforto térmico na produção animal .

– Ícone Editora.. (1989). Cited By : 251

Infrared thermal image for assessing animal health and welfare.

-Journal of Animal Behaviour and Biometeorology. (2014). Cited By: 143

Impact of lameness on broiler well-being.

– Journal of Applied Poultry Research. (2009). Cited By: 116

Real time computer stress monitoring of piglets using vocalization analysis.

– Computers and Electronics in Agriculture. (2025). Cited By: 108

Steven Sheng-Uei Guan | Machine Learning for Computer Vision | Research Excellence Award

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Prof. Dr. Steven Sheng-Uei Guan | Machine Learning for Computer Vision | Research Excellence Award

Professor | Xi’an Jiaotong-Liverpool University | Australia

Prof. Dr. Steven Sheng Uei Guan is an accomplished researcher at Xi’an Jiaotong-Liverpool University, China, with a Scopus h-index of 25, over 244 publications, and more than 2,362 citations. His research expertise spans human–object interaction detection, graph neural networks, continual learning, human–robot interaction, blockchain-enabled data trading, and intelligent healthcare systems. Dr. Guan has collaborated with over 200 international co-authors, reflecting his strong global research network. His work contributes significantly to advancing artificial intelligence for real-world perception, secure data sharing, and socially beneficial intelligent systems, impacting domains such as robotics, medical informatics, and computational social systems.

Citation Metrics (Scopus)

4000

3000

2000

1000

0

Citations
2362

Documents
244

h-index
25

🟦 Citations 🟥 Documents 🟩 h-index

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Featured Publications


Encyclopedia of information science and technology.

– IGI Global. (2018). Cited By : 454

Parameter estimation of photovoltaic models via cuckoo search.

-Parameter estimation of photovoltaic models via cuckoo search. (2013). Cited By: 303

An incremental approach to genetic-algorithms-based classification.

-Multimedia Tools and Applications. (2005). Cited By: 124

Investigation of neural networks for function approximation.

– Procedia Computer Science. (2013). Cited By: 111

Yanli Shi | Deep Learning for Computer Vision | Best Innovation Award

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Dr. Yanli Shi | Deep Learning for Computer Vision | Best Innovation Award

Jilin University of Chemical Technology | China

Dr. Yanli Shi is a researcher at the Jilin Institute of Chemical Technology, Jilin, China, with recognized contributions in image processing, computer vision, and intelligent information technologies. As a first author, Dr. Shi has published nearly 20 high-quality SCI and EI-indexed journal articles, including three papers in JCR Zone 1 journals, reflecting strong research impact and international visibility. According to Scopus, Dr. Shi’s work has received 160 citations, with an h-index of 7, demonstrating consistent scholarly influence.Dr. Shi has led and successfully completed several competitive research projects, including one project funded by the Jilin Provincial Natural Science Foundation, one project under the “13th Five-Year Plan” Science and Technology Program of the Jilin Provincial Department of Education, and one vertical project supported by the Jilin Municipal Science and Technology Bureau, which also included the Outstanding Young Talent Cultivation Program. These projects have advanced both fundamental research and applied technological development.With a strong emphasis on technology transfer and practical innovation, Dr. Shi holds one national invention patent and has actively translated research outcomes into industrial solutions. Through extensive collaboration, Dr. Shi has participated in over 100 horizontal projects with Inner Mongolia University and local enterprises, generating more than 1.6 million yuan in research funding. These collaborations have addressed real-world technical challenges and promoted regional industrial and technological development.Dr. Shi’s recent publications in leading journals such as Pattern Recognition and Scientific Reports further highlight expertise in fine-grained visual classification, deep learning, and image super-resolution. Overall, Dr. Shi’s work demonstrates a balanced integration of academic excellence, cross-sector collaboration, and measurable societal and economic impact.

Profile: Scopus 

Featured Publications

1.Shi, Y., et al. (2025). Multi-scale adversarial diffusion network for image super-resolution. Scientific Reports.  Cited By: 1

2.Shi, Y., et al. (2025). LDH-ViT: Fine-grained visual classification through local concealment and feature selection. Pattern Recognition. Cited By : 1

Dr. Yanli Shi research advances state-of-the-art computer vision and image intelligence technologies, bridging fundamental algorithms with real-world industrial applications. Through high-impact publications, patented innovations, and extensive university–industry collaborations, the work delivers scalable solutions to practical technical challenges. This integration of scientific excellence and technology transfer contributes meaningfully to societal development and global innovation.

Xuewen Zhou | Machine Learning for Computer Vision | Young Scientist Award

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Mr. Xuewen Zhou | Machine Learning for Computer Vision | Young Scientist Award

Master of Engineering | Hubei Normal University | China

Mr. Xuewen Zhou is a developing researcher in medical signal processing, medical image segmentation, and intelligent optimization algorithms, with growing contributions to the fields of biomedical engineering and computational intelligence. Affiliated with Hubei Normal University, his research focuses on designing advanced fractional-order and optimization-driven neural network models to enhance the analysis of physiological signals such as ECG and EEG as well as dermatological image segmentation. With 5 scientific publications, 4 citations, and an h-index of 1, Dr. Zhou is steadily establishing a strong academic presence.Dr. Zhou’s notable achievements include the publication of multiple SCI-indexed journal papers and active participation in leading international conferences. His recent SCI Q2 paper Adaptive Fractional Order Pulse Coupled Neural Networks with Multi-Scale Optimization for Skin Image Segmentation introduces an innovative segmentation framework integrating fractional order optimization with pulse coupled neural networks. The method employs a novel entropy–edge fitness function significantly improving accuracy in skin lesion delineation.Another key contribution is the SCI Q2 paper Improved Sparrow Search Based on Temporal Convolutional Network for ECG Classification where Dr. Zhou explores hybrid fractional order algorithms to optimize ECG recognition. His work rigorously analyzes the influence of positive and negative fractional orders on optimization stability offering valuable insights into next-generation fractional learning systems.In the EI indexed China Automation Congress Dr. Zhou proposed an ECG classification model combining spatial–channel attention networks with an improved RIME optimization algorithm enhancing hyperparameter tuning for complex biomedical patterns. He also contributed to neuromorphic computing through the ICNC  paper on FRMAdam iTransformer KAN presenting a fractional order momentum optimizer for EEG and ECG prediction.Dr. Zhou maintains strong collaborations with researchers including Jiejie Chen Ping Jiang Xinrui Zhang Zhiwei Xiao and Zhigang Zeng contributing to interdisciplinary advancements across medical AI fractional order theory and neural computation. His research demonstrates meaningful societal impact by improving early disease detection supporting intelligent diagnostic tools and advancing clinical decision making technologies on a global scale.

Profiles: Scopus | ORCID | ResearchGate

Featured Publications

1.Zhou, X., Chen, J., Jiang, P., Zhang, X., & Zeng, Z. (2026). Adaptive fractional-order pulse-coupled neural networks with multi-scale optimization for skin image segmentation. Biomedical Signal Processing and Control, (February 2026).

2.Zhou, X., Chen, J., Xiao, Z., Zhang, X., Jiang, P., & Zeng, Z. (2026). Improved sparrow search based on temporal convolutional network for ECG classification. Biomedical Signal Processing and Control, (February 2026).

3.Xiao, Z., Chen, J., Zhou, X., Wei, B., Jiang, P., & Zeng, Z. (2025). Monotonic convergence of adaptive Caputo fractional gradient descent for temporal convolutional networks. Neurocomputing, (December 2025).

4.Zhang, X., Chen, J., Zhou, X., & Jiang, P. (2024, December 13). FRMAdam-iTransformer KAN: A fractional order RMS momentum Adam optimized iTransformer with KAN for EEG and ECG prediction. In 2024 International Conference on Neuromorphic Computing (ICNC).

5.Zhou, X., Chen, J., Jiang, P., & Zhang, X. (2024, November 1). Electrocardiogram classification based on spatial-channel networks and optimization algorithms. In 2024 China Automation Congress (CAC).

Dr. Xuewen Zhou’s work advances science and society by developing fractional-order neural systems that significantly enhance the accuracy of biomedical signal and image analysis. His innovations support earlier disease detection, improved diagnostic reliability, and broader global access to intelligent healthcare technologies.

Shakil Hossain | Multi-Modal and Cross-Modal Vision | Young Scientist Award

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Mr. Md. Shakil Hossain | Multi-Modal and Cross-Modal Vision | Young Scientist Award

Research Assistant | Bangladesh University of Business and Technology | Bangladesh 

Md. Shakil Hossain is an emerging researcher and academic specializing in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, Natural Language Processing (NLP), and Multimodal Learning. He currently serves as a Research Assistant at the Advanced Machine Intelligence Research (AMIR) Lab, where his work focuses on hybrid deep learning architectures, large language models (LLMs), and multimodal fusion systems for real-world AI applications. His research aims to bridge the gap between intelligent computation and societal needs, with contributions spanning sentiment analysis, mental health assessment, and cross-lingual text processing.Before joining AMIR Lab, he worked as a Market Research Analyst at Gram Ltd., where he conducted in-depth market and competitive analyses to support the launch of Dhopa Elo, an innovative startup product revolutionizing laundry services. He also utilized data analytics, customer segmentation, and ROI optimization to strengthen marketing strategies and business performance.Md. Hossain received a research grant from the Bangladesh University of Business and Technology (BUBT) for his project, Smart Agro-Monitor: IoT-Based Precision Farming for Enhanced Crop Management.” This initiative leveraged IoT and AI to improve water management, pest control, and crop health monitoring, empowering farmers with data-driven insights for sustainable agriculture.He has authored and co-authored 16 research papers in leading journals and conferences such as Scientific Reports, IEEE Access, Knowledge-Based Systems, and Neural Networks. His publications have collectively received 31 citations, with an h-index of 3 and an i10-index of 1, reflecting his growing academic impact. Collaborating with renowned scholars including Prof. Dr. A. B. M. Shawkat Ali, Md. Hossain continues to pursue interdisciplinary AI research that promotes innovation, ethics, and societal advancement through intelligent technologies.

Profiles: Google Scholar | ORCID  | Scopus

Featured Publications

1.Hossain, M. M., Hossain, M. S., Mridha, M. F., Safran, M., & Alfarhood, S. (2025). Multi-task opinion enhanced hybrid BERT model for mental health analysis.  Cited By: 13

2.Hossain, M. M., Hossain, M. S., Hossain, M. S., Mridha, M. F., & Safran, M. (2024). TransNet: Deep attentional hybrid transformer for Arabic posts classification. IEEE Access. Cited By: 7

3.Hossain, M. M., Hossain, M. S., Safran, M., Alfarhood, S., & Alfarhood, M. (2024). A hybrid attention-based transformer model for Arabic news classification using text embedding and deep learning. IEEE Access. Cited By: 6

4.Hossain, M. M., Hossain, M. S., Chaki, S., Hossain, M. R., Rahman, M. S., & Ali, A. B. M. (2025). CrosGrpsABS: Cross-attention over syntactic and semantic graphs for aspect-based sentiment analysis in a low-resource language.  Cited By: 2

5.Hossain, M. S., Hossain, M. M., Hossain, M. S., Mridha, M. F., & Safran, M. (2025). EmoNet: Deep attentional recurrent CNN for X (formerly Twitter) emotion classification. IEEE Access. Cited By: 2

Md. Shakil Hossain’s research advances the integration of AI, NLP, and IoT to solve real-world problems in healthcare, agriculture, and digital communication. His work promotes human-centered, sustainable, and data-driven innovation, empowering industries and societies to harness intelligent technologies for global progress.

Simy Baby | Applications of Computer Vision | Best Researcher Award

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Mrs. Simy Baby | Applications of Computer Vision | Best Researcher Award

Researcher | National Institute of Technology | India

Mrs. Simy Baby is a pioneering researcher at the National Institute of Technology, Tiruchirappalli, with extensive expertise in machine learning, semantic communication, computer vision, and mmWave radar signal processing. Her research bridges the gap between radar sensing and intelligent communication frameworks, focusing on efficient feature extraction, complex-valued encoding, and task-oriented inference.Her seminal work, “Complex Chromatic Imaging for Enhanced Radar Face Recognition” (Computers and Electrical Engineering,  introduced a novel representation that preserves amplitude and phase information of mmWave radar signals, achieving an exceptional recognition accuracy. Another significant contribution, “Complex-Valued Linear Discriminant Analysis on mmWave Radar Face Signatures for Task-Oriented Semantic Communication” (IEEE Transactions on Cognitive Communications and Networking ), proposed a CLDA-based encoding framework enhancing feature interpretability and robustness under channel variations. Current investigations include Data Fusion Discriminant Analysis (DFDA) for multi-view activity recognition and Semantic Gaussian Process Regression (GPR) for vehicular pose estimation, highlighting her commitment to multitask semantic communication systems.Dr. Baby has 21 publications with 20 citations and an h-index of 3.  demonstrating a rapidly growing impact in her field. She is an active member of the Indian Society for Technical Education (ISTE) and contributes to the scientific community through innovative research that combines theory and practical applications. Her work on radar-based recognition, semantic feature transmission, and multi-task inference frameworks holds significant potential for intelligent transportation systems, human activity recognition, and bandwidth-efficient communication technologies.Through her research, Dr. Baby has established herself as a leading figure in advancing radar imaging and semantic communication, providing scalable solutions that merge high-performance computing with real-world societal applications. Her vision continues to shape the future of intelligent sensing and communication systems globally.

Profiles: Google Scholar | ORCID | Scopus 

Featured Publications

1. Ansal, K. A., Rajan, C. S., Ragamalika, C. S., & Baby, S. M. (2022). A CPW fed monopole antenna for UWB/Ku band applications. Materials Today: Proceedings, 51, 585–590. Cited By : 5

2. Ansal, K. A., Ragamalika, C. S., Rajan, C. S., & Baby, S. M. (2022). A novel ACS fed antenna with comb shaped radiating strip for triple band applications. Materials Today: Proceedings, 51, 332–338. Cited By : 4

3. Ansal, K. A., Kumar, A. S., & Baby, S. M. (2021). Comparative analysis of CPW fed antenna with different substrate material with varying thickness. Materials Today: Proceedings, 37, 257–264. Cited By : 4

4. Baby, S. M., & Gopi, E. S. (2025). Complex chromatic imaging for enhanced radar face recognition. Computers and Electrical Engineering, 123, 110198. Cited By : 3

5.Ansal, K. A., Shanmuganatham, T., Baby, S. M., & Joy, A. (2015). Slot coupled microstrip antenna for C and X band application. International Journal of Advanced Research Trends in Engineering and Technology.Cited By : 3

Dr. Simy M. Baby’s research advances the integration of semantic communication and computer vision, enabling high-accuracy radar-based recognition and task-oriented inference. Her work has significant implications for intelligent transportation, human activity monitoring, and bandwidth-efficient communication, driving innovation in both science and industry globally.

Vasuki | Deep Learning for Computer Vision | Women Researcher Award

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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.

 

Dibyalekha Nayak | Computer vision | Women Researcher Award

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Dr . Dibyalekha Nayak | Computer vision | Women Researcher Award

Assistant professor at Shah and Anchor Kutchhi Engineering College, India

Dr. Dibyalekha Nayak is a dedicated academician and emerging researcher with deep expertise in image processing, adaptive compression, and VLSI design. Her professional journey is marked by a strong commitment to teaching, scholarly research, and technological advancement. With over a decade of teaching experience and a recently completed Ph.D. from KIIT University, Bhubaneswar, her research has produced several publications in SCI-indexed journals and international conferences. Dr. Nayak’s contributions reflect an interdisciplinary approach, combining deep learning techniques with low-power hardware design to address complex challenges in wireless sensor networks and multimedia systems. She has actively participated in faculty development programs and technical workshops, continuously upgrading her knowledge. Her professional philosophy emphasizes ethics, hard work, and continuous learning. Currently serving as an Assistant Professor at Shah and Anchor Kutchi Engineering College in Mumbai, she aspires to make impactful contributions to the field of electronics and communication through research, innovation, and collaboration.

Professional Profile 

Education🎓

Dr. Dibyalekha Nayak holds a Ph.D. in Image Processing from the School of Electronics at KIIT University, Bhubaneswar, where she completed her research between September 2018 and May 2024. Her doctoral work focused on advanced techniques in image compression and saliency detection using deep learning and compressive sensing. She completed her Master of Technology (M.Tech) in VLSI Design from Satyabhama University, Chennai, in 2011, graduating with a commendable CGPA of 8.33. Prior to that, she earned her Bachelor of Engineering (B.E.) in Electronics and Telecommunication from Biju Patnaik University of Technology (BPUT), Odisha, in 2008, with a CGPA of 6.5. Her academic background provides a strong foundation in both theoretical electronics and practical applications in image processing and circuit design. The combination of image processing and VLSI design throughout her academic journey has enabled her to engage in cross-disciplinary research and foster innovation in both hardware and software domains.

Professional Experience📝

Dr. Dibyalekha Nayak has accumulated over 12 years of rich academic experience in various reputed engineering institutions across India. Currently, she serves as an Assistant Professor at Shah and Anchor Kutchi Engineering College, Mumbai, affiliated with Mumbai University, where she joined in July 2024. Prior to this, she worked as a Research Scholar at KIIT University (2018–2024), contributing significantly to image processing research. Her earlier roles include Assistant Professor positions at institutions such as College of Engineering Bhubaneswar (2016–2018), SIES Graduate School of Technology, Mumbai (2014), St. Francis Institute of Technology, Mumbai (2013), and Madha Engineering College, Chennai (2011–2012). Across these roles, she has taught a variety of undergraduate and postgraduate courses, supervised student projects, and contributed to departmental development. Her teaching areas span digital electronics, VLSI design, image processing, and communication systems, demonstrating a strong alignment between her teaching and research activities.

Research Interest🔎

Dr. Dibyalekha Nayak’s research interests lie at the intersection of image processing, deep learning, and VLSI design, with a special focus on adaptive compression, saliency detection, and compressive sensing. Her doctoral research addressed the development of innovative, low-complexity algorithms for image compression using techniques like block truncation coding and DCT, tailored for wireless sensor network applications. She is also deeply interested in integrating deep learning frameworks into image enhancement and compression tasks to improve performance in real-world environments. Additionally, her background in VLSI design supports her interest in low-power hardware architectures for efficient implementation of image processing algorithms. Dr. Nayak is particularly motivated by research problems that bridge the gap between theoretical innovation and practical implementation, especially in the fields of embedded systems and multimedia communication. Her interdisciplinary research aims to create scalable, energy-efficient, and intelligent solutions for future communication and sensing technologies.

Award and Honor🏆

While Dr. Dibyalekha Nayak’s profile does not explicitly mention formal awards or honors, her scholarly achievements speak volumes about her academic excellence and dedication. She has published multiple research articles in prestigious SCI and Web of Science indexed journals such as Multimedia Tools and Applications, Mathematics, and Computers, reflecting the quality and impact of her research. She has been actively involved in reputed international conferences including IEEE and Springer Lecture Notes, where she has presented and published her research findings. Her work on saliency-based image compression and fuzzy rule-based adaptive block compressive sensing has received commendation for its innovation and applicability. Furthermore, her selection and sustained work as a Research Scholar at KIIT University for over five years highlights the recognition she has earned within academic circles. Her consistent participation in technical workshops, faculty development programs, and collaborations also demonstrate her growing reputation and standing in the field of electronics and image processing.

Research Skill🔬

Dr. Dibyalekha Nayak possesses a versatile and robust set of research skills aligned with modern-day challenges in image processing and electronics. She is proficient in developing image compression algorithms, saliency detection models, and adaptive techniques using block truncation coding, fuzzy logic, and DCT-based quantization. Her technical expertise extends to deep learning architectures tailored for image enhancement and compressive sensing in wireless sensor networks. Additionally, she has a strong command of VLSI design methodologies, enabling her to work on low-power circuit design and hardware implementation strategies. Dr. Nayak is also skilled in scientific programming, using tools such as MATLAB and Python, along with LaTeX for research documentation. She has a clear understanding of research methodologies, simulation frameworks, and performance analysis metrics. Her experience in preparing manuscripts for SCI-indexed journals and conference presentations showcases her technical writing abilities. Overall, her analytical mindset and hands-on skills make her a competent and impactful researcher.

Conclusion💡

Dr. Dibyalekha Nayak is a highly dedicated and emerging researcher in the fields of Image Processing, Deep Learning, and VLSI. Her academic journey reflects perseverance, scholarly depth, and a clear focus on impactful research. Her SCI-indexed publications, teaching experience, and cross-domain knowledge make her a deserving candidate for the Best Researcher Award.

Publications Top Noted✍

  • Title: Fuzzy Rule Based Adaptive Block Compressive Sensing for WSN Application
    Authors: D. Nayak, K. Ray, T. Kar, S.N. Mohanty
    Journal: Mathematics, Volume 11, Issue 7, Article 1660
    Year: 2023
    Citations: 6

  • Title: A novel saliency based image compression algorithm using low complexity block truncation coding
    Authors: D. Nayak, K.B. Ray, T. Kar, C. Kwan
    Journal: Multimedia Tools and Applications, Volume 82, Issue 30, Pages 47367–47385
    Year: 2023
    Citations: 4

  • Title: Walsh–Hadamard Kernel Feature-Based Image Compression Using DCT with Bi-Level Quantization
    Authors: D. Nayak, K. Ray, T. Kar, C. Kwan
    Journal: Computers, Volume 11, Issue 7, Article 110
    Year: 2022
    Citations: 3

  • Title: Sparsity based Adaptive BCS color image compression for IoT and WSN Application
    Authors: D. Nayak, T. Kar, K. Ray
    Journal: Signal, Image and Video Processing, Volume 19, Issue 8, Pages 1–7
    Year: 2025

  • Title: Hybrid Image Compression Using DCT and Autoencoder
    Authors: D. Nayak, T. Kar, K. Ray, J.V.R. Ravindra, S.N. Mohanty
    Conference: 2024 IEEE Pune Section International Conference (PuneCon), Pages 1–6
    Year: 2024

  • Title: Performance Comparison of Different CS based Reconstruction Methods for WSN Application
    Authors: D. Nayak, K.B. Ray, T. Kar
    Conference: 2021 IEEE 2nd International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC)
    Year: 2021

  • Title: A Comparative Analysis of BTC Variants
    Authors: D. Nayak, K.B. Ray, T. Kar
    Conference: Proceedings of International Conference on Communication, Circuits, and Systems (LNEE, Springer)
    Year: 2021

  • Title: Low Power Error Detector Design by using Low Power Flip Flops Logic
    Authors: D. Chaini, P. Malgi, S. Lopes
    Journal: International Journal of Computer Applications, ISSN 0975-8887
    Year: 2014

Dr. Zhou Zhang | Computer Vision | Best Researcher Award

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Dr. Zhou Zhang | Computer Vision | Best Researcher Award

Doctorate at SUNY Farmingdale State College, United States

👨‍🎓 Profiles

Scopus

Orcid

Google Scholar

🎓 Early Academic Pursuits

Dr. Zhou (Joe) Zhang embarked on his academic journey with a strong foundation in mechanical and electrical engineering. His early education culminated in a Ph.D. in Mechanical Engineering from Stevens Institute of Technology, where he was awarded the prestigious James Harry Potter Award in 2018 for outstanding doctoral performance. During his doctoral studies, Dr. Zhang explored virtual reality applications in engineering education, including camera pose tracking, data fusion, and the development of virtual laboratories—an area that would become a cornerstone of his future research.

🏫 Professional Endeavors

Dr. Zhang’s academic career is marked by progressive teaching and research roles. He currently serves as an Assistant Professor at SUNY Farmingdale State College, where he teaches Tool Design and Electronics Packaging. Previously, he held key positions including Associate Professor at Middle Tennessee State University and Assistant Professor at CUNY’s New York City College of Technology, where he played a central role in launching and coordinating the Robotics Concentration. His professional journey also includes roles as a Visiting Research Scholar at NYU, a Research Associate at Southeast University, and an Electrical Engineer at CRRC Nanjing Puzhen Co., Ltd, as well as a Mechanical Engineer at CETC’s 14th Research Institute.

🔬 Contributions and Research Focus

Dr. Zhang’s research bridges academic theory and practical implementation. His major contributions span virtual reality (VR) and augmented reality (AR) for engineering education, AI and machine learning applications in robotics, force-feedback robotics, and bio-inspired virtual assembly systems. His work has been funded by institutions such as CUNY GRTI and CUNY Research Awards, including notable projects like the AI and Machine Learning in Co-Robotics and the Virtual Assembly Platform for Engineering Education. Earlier in his career, he was also involved in state-funded research in China, including a $5 million smart controller project backed by the State Grid Corporation of China.

🌍 Impact and Influence

Dr. Zhang has made a tangible impact on student development, workforce readiness, and interdisciplinary education. His initiatives include establishing co-op and internship collaborations with industry, mentoring undergraduate research, and leading programs like the Virtual Reality and Artificial Intelligence Club. He also contributed to maintaining ABET accreditation, aligning curriculum development with institutional and industry standards. His mentorship has supported student participation in key events such as the Brooklyn Navy Yard Competition, Maker Faire, and the CUNY Black Male Initiative Conference.

📚 Academic Citations & Publications

Dr. Zhang’s scholarly work is extensively cited in the domains of VR-based education, 3D reconstruction, force-feedback robotics, and embedded systems. His contributions have not only advanced academic research but also enriched applied engineering education. As one of the main investigators in several NSF-funded projects, his research continues to influence both academic curricula and practical engineering tools.

💻 Technical Skills

Dr. Zhang is proficient in a variety of engineering and programming tools, including virtual reality system design, computer-aided engineering, middleware integration, finite element methods (FEM), and AI/machine learning applications in robotics. His skills encompass real-time 3D reconstruction, electromagnetic field simulation, and embedded systems design, with applications extending to DSP, ARM-based controls, and semiconductor converters.

🧑‍🏫 Teaching Experience

With over two decades of teaching experience, Dr. Zhang has taught a wide array of courses across institutions like SUNY Farmingdale, CUNY, NJIT, and Middle Tennessee State University. His teaching portfolio includes Mechanical Measurement, Stress Analysis, Rapid Prototyping, Programmable Logic Controllers, and AI-integrated robotics courses. He has served in diverse capacities—course designer, club advisor, curriculum developer, and research mentor—demonstrating his commitment to academic excellence and student engagement.

🏆 Awards and Honors

Dr. Zhang has received multiple accolades for his dedication to academic and research excellence. In addition to the James Harry Potter Award, he earned graduate travel grants from Stevens Institute of Technology, recognizing his contributions to engineering research and academic dissemination.

🚀 Legacy and Future Contributions

Dr. Zhang’s legacy lies in his ability to blend innovative research with effective teaching, transforming traditional mechanical engineering education through technology. His future goals include advancing interdisciplinary robotics education, expanding virtual learning platforms, and fostering global academic-industry collaborations. With a career devoted to bridging theoretical knowledge and real-world applications, Dr. Zhang continues to inspire students and colleagues alike, shaping the future of engineering education and technological innovation.

 

Publications

The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection

  • Authors: Momina Liaqat Ali, Zhou Zhang
    Journal: Computers
    Year: 2024

Project-Based Courses for B.Tech. Program of Robotics in Mechanical Engineering Technology

  • Authors: Zhang Z., Zhang A.S., Zhang M., Esche S.
    Journal:     Computers in Education Journal
    Year:     2020

A Virtual laboratory system with biometric authentication and remote proctoring based on facial recognition

  • Authors: Zhang, Z.; Zhang, M.; Chang, Y.; Esche, S.K.; Chassapis, C.
    Journal: Computers in Education
    Year: 2016

Real-time 3D reconstruction for facilitating the development of Game-based virtual laboratories

  • Authors: Zhang, Z.; Zhang, M.; Chang, Y.; Esche, S.K.; Chassapis, C.
    Journal:     Computers in Education
    Year:     2016

Usability evaluation of a virtual educational laboratory platform

  • Authors: Chang, Y.; Aziz, E.-S.S.; Zhang, Z.; Zhang, M.; Esche, S.K.
    Journal: Computers in Education
    Year: 2016

Dr. Recai Yilmaz | Applications of Computer Vision | Best Researcher Award

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Dr. Recai Yilmaz | Applications of Computer Vision | Best Researcher Award

Doctorate at Children’s National Hospital, Washington, D.C, United States

👨‍🎓 Profiles

Scopus

Orcid

Google Scholar

🎓 Early Academic Pursuits

Dr. Recai Yilmaz’s academic journey began with a strong foundation in medicine, earning his M.D. from Istanbul Faculty of Medicine in 2017. His passion for medical innovation led him to pursue a Ph.D. in Experimental Surgery at McGill University, focusing on neurosurgical simulation and artificial intelligence. His early education at Private Anafen Gaye High School in Istanbul, where he was a full-scholarship student, demonstrated his academic excellence from a young age.

💼 Professional Endeavors

Dr. Yilmaz has amassed extensive experience at the intersection of medicine, artificial intelligence, and computer vision. As a Postdoctoral Research Fellow at Children’s National Medical Center, Washington, D.C., he applies computer vision and machine learning to intraoperative surgical video analysis, aiming to improve real-time surgical performance assessment. His tenure at MultiCIM Technologies Inc. (CareChain) further reflects his leadership in integrating AI into patient triage and clinical decision-making systems.

🔬 Contributions and Research Focus

Dr. Yilmaz’s research is centered on AI-driven surgical assessment, medical data organization, and neurosurgical simulation. At McGill University’s Neurosurgical Simulation and Artificial Intelligence Learning Centre, he developed virtual reality surgical simulation models, advanced AI-based assessment tools, and real-time feedback mechanisms for neurosurgical expertise evaluation. His research also includes cloud-based medical data management and optical flow analysis in surgical procedures.

🌍 Impact and Influence

His pioneering work has significantly influenced AI applications in surgery and clinical decision-making. By integrating computer vision and deep learning into medical practice, he has improved the efficiency and accuracy of surgical skill evaluation, patient triage, and clinical outcome prediction. His projects have not only enhanced surgical education but also contributed to safer and more effective surgical procedures worldwide.

📚 Academic Citations and Recognitions

Dr. Yilmaz has been recognized with numerous awards and grants, including the prestigious Innovator of the Year Award (2023) by the Congress of Neurological Surgeons and research funding from the Brain Tumour Foundation of Canada and the Royal College of Physicians and Surgeons of Canada. His work has been published in high-impact journals and conferences, advancing the field of AI in medicine.

💻 Technical Expertise

  • Artificial Intelligence & Machine Learning (Medical AI applications, Neural Networks)
  • Computer Vision & Image Processing (Surgical video analysis, Optical flow)
  • Programming Languages (Python, MATLAB, C++, IBM SPSS)
  • Statistical Analysis & Data Science (AI-driven performance assessment, Data modeling)

🎓 Teaching and Mentorship

Dr. Yilmaz has actively mentored graduate students, medical researchers, and undergraduate students in AI, neurosurgical simulation, and data analysis. His mentorship spans institutions such as McGill University and Marianopolis College, where he has guided students in machine learning applications, research methodologies, and clinical AI integration.

🌟 Legacy and Future Contributions

Dr. Yilmaz’s legacy lies in his commitment to bridging AI and medicine. His contributions to surgical performance evaluation, AI-driven triage systems, and neurosurgical education continue to shape the future of AI-assisted medical practice. Moving forward, he aims to expand AI integration in real-time surgical decision-making, enhance global accessibility to AI-driven surgical training, and pioneer intelligent healthcare solutions.

 

Publications

AI in surgical curriculum design and unintended outcomes for technical competencies in simulation training

  • Authors:Ali M Fazlollahi, Recai Yilmaz, Alexander Winkler-Schwartz, Nykan Mirchi, Nicole Ledwos, Mohamad Bakhaidar, Ahmad Alsayegh, Rolando F Del Maestro
  • Journal: JAMA network open
  • Year: 2023

Utilizing artificial intelligence and electroencephalography to assess expertise on a simulated neurosurgical task

  • Authors: Sharif Natheir, Sommer Christie, Recai Yilmaz, Alexander Winkler-Schwartz, Khalid Bajunaid, Abdulrahman J Sabbagh, Penny Werthner, Jawad Fares, Hamed Azarnoush, Rolando Del Maestro
  • Journal: Computers in Biology and Medicine
  • Year: 2023

O022 real-time artificial intelligence instructor vs expert instruction in teaching of expert level tumour resection skills–a randomized controlled trial

  • Authors: R Yilmaz, M Bakhaidar, A Alsayegh, R Del Maestro
  • Journal: British Journal of Surgery
  • Year: 2023

Effect of artificial intelligence tutoring vs expert instruction on learning simulated surgical skills among medical students: a randomized clinical trial

  • Authors: Ali M Fazlollahi, Mohamad Bakhaidar, Ahmad Alsayegh, Recai Yilmaz, Alexander Winkler-Schwartz, Nykan Mirchi, Ian Langleben, Nicole Ledwos, Abdulrahman J Sabbagh, Khalid Bajunaid, Jason M Harley, Rolando F Del Maestro
  • Journal: JAMA network open
  • Year: 2022

Assessment of learning curves on a simulated neurosurgical task using metrics selected by artificial intelligence

  • Authors: Nicole Ledwos, Nykan Mirchi, Recai Yilmaz, Alexander Winkler-Schwartz, Anika Sawni, Ali M Fazlollahi, Vincent Bissonnette, Khalid Bajunaid, Abdulrahman J Sabbagh, Rolando F Del Maestro
  • Journal: Journal of neurosurgery
  • Year: 2022