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.

Mohsen Edalat | Machine Learning for Computer Vision | Editorial Board Member

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Assoc. Prof. Dr. Mohsen Edalat | Machine Learning for Computer Vision | Editorial Board Member

Associate Professor | Shiraz University | Iran

Dr. Mohsen Edalat an accomplished researcher from Shiraz University, Iran, has made notable contributions to the fields of machine learning geospatial modeling and smart agriculture. With an impressive research record comprising 39 scientific publications and over 614 citations Dr. Edalat has demonstrated sustained academic productivity and influence in computational and environmental sciences. His research emphasizes the integration of advanced data-driven algorithms with ecological and agricultural systems to enhance sustainability and decision-making processes.Among his recent works Dr. Edalat has explored diverse applications of machine learning for ecological and agricultural optimization. His 2025 publications include studies on predicting nepetalactone accumulation in Nepeta persica through machine learning and geospatial analysis modeling ecological preferences of Kentucky bluegrass under varying water conditions (Water Switzerland)  and mapping early-season dominant weeds using UAV-based imagery to support precision farming. These investigations reflect his innovative approach to merging remote sensing artificial intelligence and environmental modeling to address complex agroecological challenges.With an h-index of 11 and collaborations with more than 60 co-authors  Dr. Edalat’s work highlights strong interdisciplinary engagement and a commitment to advancing data-driven sustainability. His studies contribute not only to the scientific community but also to practical agricultural applications that promote resource efficiency and ecological resilience. Through his ongoing research Dr. Edalat continues to shape the evolving landscape of smart agriculture and environmental informatics demonstrating the global relevance and societal value of computational intelligence in natural systems.

Profiles:  Scopus | ORCID

Featured Publications

1. Edalat, M., et al. (2025). Predicting nepetalactone accumulation in Nepeta persica using machine learning algorithms and geospatial analysis. Scientific Reports.

2. Edalat, M., et al. (2025). Modeling the ecological preferences and adaptive capacities of Kentucky bluegrass based on water availability using various machine learning algorithms. Water (Switzerland).

3. Edalat, M., et al. (2025). Early season dominant weed mapping in maize field using unmanned aerial vehicle (UAV) imagery: Towards developing prescription map. Smart Agricultural Technology.

Dr. Mohsen Edalat’s research integrates machine learning, geospatial analytics, and agricultural science to enhance crop management and environmental sustainability. His innovative work advances precision agriculture, supporting data-driven decisions that improve resource efficiency, boost food security, and promote sustainable development at a global scale.

Varsha Singh | Deep Learning for Computer Vision | Best Researcher Award

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Ms. Varsha Singh | Deep Learning for Computer Vision | Best Researcher Award

Research Scholar (Ph.D.) | National Institute of Technology | India

Ms. Varsha Singh is a dedicated researcher at the National Institute of Technology, Tiruchirappalli, specializing in deep learning, computer vision, and efficient image super-resolution architectures. Her research is centered on developing lightweight yet high-performing neural models that enhance perceptual image quality through advanced multi-scale feature extraction, attention mechanisms, and dense connectivity designs.Her notable contribution, Optimized and Deep Cross Dense Skip Connected Network for Single Image Super-Resolution (DCDSCN) published in SN Computer Science introduced a cross-dense skip-connected framework that effectively balances computational efficiency and reconstruction accuracy. The proposed Cross Dense-in-Dense Convolution Block (CDDCB) leverages multi-branch feature fusion and short-path gradient propagation, achieving superior PSNR and SSIM performance across benchmark datasets such as Set5, Set14, BSD100, and Urban100. Building on this foundation, her subsequent work Multi-Scale Attention Residual Convolution Neural Network for Single Image Super-Resolution (MSARCNN) published in Digital Signal Processing Elsevier  advances the field through the integration of Squeeze-and-Excitation and Pixel Attention modules within a multi-scale residual framework, enabling fine-grained texture recovery while maintaining low model complexity.With two international journal publications, Ms. Singh’s work demonstrates a strong emphasis on hierarchical feature fusion, adaptive attention modeling, and efficient neural design for real-time visual intelligence. She actively contributes to the scholarly community as a reviewer for the International Research Journal of Multidisciplinary Technovation (Scopus Indexed), where she has evaluated research papers in deep learning and image processing.Ms. Singh’s contributions bridge theoretical innovation and practical deployment, particularly in resource-constrained imaging and enhancement systems, fostering advancements in next-generation super-resolution and perceptual image restoration. Her research continues to strengthen the global discourse on AI-driven visual computing, supporting the development of intelligent and sustainable imaging solutions for diverse real-world applications.

Profiles: Google Scholar | ResearchGate

Featured Publications

1.Singh, V., Vedhamuru, N., Malmathanraj, R., & Palanisamy, P. (2025). Multi-scale attention residual convolution neural network for single image super-resolution (MSARCNN). Digital Signal Processing, 146, 105614.

2.Singh, V., Vedhamuru, N., Malmathanraj, R., & Palanisamy, P. (2025). Optimized and deep cross dense skip connected network for single image super-resolution (DCDSCN). SN Computer Science, 6(5), 495.

Ms. Varsha Singh’s research advances efficient deep learning and image super-resolution, enabling high-quality visual reconstruction with minimal computational cost. Her innovations contribute to scientific progress in AI-driven imaging, with potential applications in medical diagnostics, remote sensing, and real-time visual enhancement, driving global innovation in sustainable and intelligent vision technologies.

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

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

Associate professor | King Saud University | Saudi Arabia

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

Profiles: Google Scholar | Scopus | ResearchGate

Featured Publications

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

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

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

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

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

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

Nagaraj | Deep Learning for Computer Vision | Excellence in Research

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

Associate Professor | SRM Institute of Science and Technology  | India 

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

Profiles: Google Scholar | ORCID  | Scopus

Featured Publications

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

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

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

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

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

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

Venkataraman Thangadurai | 3D Computer Vision | Best Researcher Award

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Prof. Dr. Venkataraman Thangadurai | 3D Computer Vision | Best Researcher Award

Professor | University of St Andrews | United Kingdom

Prof. Dr. Venkataraman Thangadurai is a globally renowned expert in solid-state chemistry, electrochemical energy storage, and advanced battery technologies. With a research focus on fast ion conductors, solid electrolytes, lithium- and sodium-based batteries, and fuel cell materials, he has made pioneering contributions to both fundamental science and practical energy solutions. Prof. Thangadurai has authored over 278 peer-reviewed journal articles, 6 book chapters, and 21 conference proceedings, and has delivered 180 conference presentations, 83 posters, and 80 invited talks at top universities, institutes, and companies worldwide. His work has resulted in 13 patents/patent applications and has placed him among the top 1% of authors in Royal Society of Chemistry journals by citations in 2020. As of March 2025, his research has received 25,991 citations with an h-index of 69, reflecting the high impact of his work globally.He is the Founder and Advisor of Ions Storage Systems, Maryland, USA (2012–present) and Founder and Director of Superionics, Calgary, Canada (2021–present), translating cutting-edge research into commercial energy storage technologies. His research highlights include optimizing lithium nucleation overpotentials in garnet-based hybrid solid-state batteries, developing doped sodium gadolinium silicate ceramics for fast Na⁺ conduction, and enhancing electrocatalysts for lithium–sulfur batteries.Prof. Thangadurai collaborates extensively with leading international researchers and institutions, including the University of Calgary, University of Maryland, University of St Andrews, University of Kiel, Yale University, and ANSTO, advancing cross-disciplinary solutions in energy materials. Beyond his scientific contributions, he mentors emerging scientists and actively promotes innovation that addresses global energy challenges. His work has significant societal impact, enabling safer, high-performance, and sustainable energy storage solutions critical for electric mobility, grid storage, and renewable energy integration.

Profiles: Google Scholar | ORCID | Scopus

Featured Publications

1. Murugan, R., Thangadurai, V., & Weppner, W. (2007). Fast lithium ion conduction in garnet-type Li₇La₃Zr₂O₁₂. Angewandte Chemie International Edition, 46(41), 7778–7781.
Cited By : 3691

2.Han, X., Gong, Y., Fu, K., He, X., Hitz, G. T., Dai, J., Pearse, A., Liu, B., Wang, H., … Thangadurai, V. (2017). Negating interfacial impedance in garnet-based solid-state Li metal batteries. Nature Materials, 16(5), 572–579. Cited By : 2088

3.Thangadurai, V., Narayanan, S., & Pinzaru, D. (2014). Garnet-type solid-state fast Li ion conductors for Li batteries: Critical review. Chemical Society Reviews, 43(13), 4714–4727. Cited By : 1712

4.Pal, B., Yang, S., Ramesh, S., Thangadurai, V., & Jose, R. (2019). Electrolyte selection for supercapacitive devices: A critical review. Nanoscale Advances, 1(10), 3807–3835.
Cited By : 1229

5.Wang, C., Fu, K., Kammampata, S. P., McOwen, D. W., Samson, A. J., Zhang, L., … Thangadurai, V. (2020). Garnet-type solid-state electrolytes: Materials, interfaces, and batteries. Chemical Reviews, 120(10), 4257–4300. Cited By : 1130

Prof. Dr. Venkataraman Thangadurai  pioneering research in solid-state chemistry and advanced battery technologies drives global innovation in energy storage, enabling safer, high-performance, and sustainable batteries that power electric mobility, renewable energy integration, and next-generation clean technologies.

Prof. Vaclav Skala | Computer Vision | Best Researcher Award

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Prof. Vaclav Skala | Computer Vision | Best Researcher Award

Professor at University of West Bohemia, Czech Republic

👨‍🎓 Publication Profiles

Scopus

Orcid

Publications

A new fully projective O(lg N) line convex polygon intersection algorithm

  • Authors: Václav V. Skala
    Journal: Visual Computer
    Year: 2025

A new fully projective O(log N) point-in-convex polygon algorithm: a new strategy

  • Authors: Václav V. Skala
    Journal: Visual Computer
    Year: 2024

Meshfree Interpolation of Multidimensional Time-Varying Scattered Data

  • Authors: Václav V. Skala, Eliska E. Mourycova
    Journal: Computers
    Year: 2023

Multispectral Image Generation from RGB Based on WSL Color Representation: Wavelength, Saturation, and Lightness

  • Authors: Václav V. Skala
    Journal: Computers
    Year: 2023

Robust Line-Convex Polygon Intersection Computation in E2 using Projective Space Representation

  • Author: Václav V. Skala
    Journal: Machine Graphics and Vision
    Year: 2023