Guest Editor(s)
Dr. Narinder Kaur
Email: er.narinder@gmail.com
Affiliation: Chandigarh University, Mohali, India
Homepage:
Research Interests: AI in healthcare
Dr. Jawad Khan
Email: jkhanbk1@gachon.ac.kr
Affiliation: Gachon University, South Korea
Homepage:
Research Interests: machine learning, text mining, NLP and data mining, AI and computer vision, IoTs
Summary
Image recognition has become one of the most influential areas of artificial intelligence, driving transformative advancements across healthcare, autonomous systems, agriculture, industrial automation, surveillance, robotics, smart cities, and multimedia analytics. The rapid evolution of deep learning architectures, including convolutional neural networks (CNNs), vision transformers (ViTs), generative AI models, self-supervised learning frameworks, and multimodal foundation models, has significantly enhanced image understanding capabilities.
Despite remarkable progress, several challenges remain, such as limited annotated datasets, model interpretability, computational efficiency, robustness against adversarial attacks, domain adaptation, real-time deployment, and ethical considerations. Recent developments in intelligent automation further demand efficient image recognition systems capable of autonomous decision-making in dynamic environments.
This Special Issue aims to provide a platform for researchers, practitioners, and industry experts to present cutting-edge research, innovative methodologies, and real-world applications that advance the state-of-the-art in deep learning-based image recognition and intelligent automation.
The objectives of this Special Issue are:
· To explore recent advances in deep learning architectures for image recognition.
· To highlight novel attention mechanisms, transformer-based approaches, and hybrid deep learning models.
· To investigate explainable and trustworthy AI solutions for image understanding.
· To showcase intelligent automation applications powered by image recognition technologies.
· To promote interdisciplinary research integrating computer vision, machine learning, and automation systems.
· To discuss challenges, opportunities, and future directions in image recognition research.
Keywords
deep learning, image recognition, computer vision, explainable AI, medical imaging, intelligent automation, image classification, object detection, multimodal learning, industrial AI, smart systems.
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