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.