New Trends in Computer Vision, Pattern Recognition and Machine Learning

Dear Colleagues,

The study of artificial intelligence (AI) is expanding at a lightning pace. Many fields related to artificial intelligence have advanced from simple machine learning applications to some of the sophisticated applications including Industry 4.0, robotics, healthcare and social media. AI is a broad and ever-evolving discipline where innovative advances are made on a regular basis. This Special Issue on "Advances in Computer Vision, Pattern Recognition, and Machine Learning" has been proposed to present a comprehensive exploration of the latest breakthroughs in the interconnected fields of computer vision, pattern recognition and machine learning. The collection brings together cutting-edge research and innovative methodologies that have reshaped the understanding of visual data processing and AI-enabled intelligent systems.

Researchers, practitioners, and enthusiasts will find this Special Issue to be a valuable resource for staying abreast of the rapidly evolving landscape in computer vision, pattern recognition, and machine learning. As technology continues to advance, the work presented here highlights the potential for these fields to revolutionize industries ranging from healthcare to autonomous systems. This collection not only celebrates the achievements made so far but also serves as an inspiration for future innovation at the crossroads of visual data analysis and AI-enabled intelligent algorithms.

This Special Issue will accept high-quality papers containing original research results and survey articles of exceptional merit spanning computer vision, pattern recognition, and machine learning. The topics of interest include, but are not limited to:

• Machine learning and deep learning models for computer vision and pattern recognition;
• Deep generative models for the generation of visual virtual data;
• Automated deep and machine learning models for visual data pre-processing and mining;
• Novel and innovative processes for object detection, object classification and object tracking;
• Unsupervised, semi-supervised, and supervised learning frameworks in pattern recognition;
• Real-time computer vision and pattern recognition applications;
• Multi-modal solutions for pattern recognition problems (such as multimodal detection, retrieval, fusion and analysis);
• Communication pattern recognition in autonomous systems;
• Ethical and privacy issues in computer vision and pattern recognition;
• Zero-shot learning in computer vision and pattern recognition;
• Application of computer vision, pattern recognition and machine learning in (but not limited to) healthcare, astronomy, gaming, finance, robotics, agriculture, industry 4.0 and education.

Prof. Dr. Jonathan Wu
Prof. Dr. Gaurav Bhatnagar
Guest Editors

Manuscript Submission Information

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