This track focuses on the latest developments in convolutional neural networks (CNNs) for computer vision applications. Researchers are invited to present novel architectures, optimization techniques, and performance evaluations of CNNs in various domains.

This session will explore cutting-edge methods for image recognition and classification, emphasizing the role of machine learning algorithms. Contributions that highlight real-world applications and comparative studies are particularly encouraged.

This track aims to discuss state-of-the-art object detection techniques, including both traditional and deep learning approaches. Papers that address challenges in real-time detection and applications in autonomous systems are welcome.

This session will delve into advanced feature extraction and selection methodologies crucial for enhancing machine learning performance. Contributions that propose innovative techniques or frameworks for feature engineering are highly sought after.

This track will cover the latest segmentation algorithms, focusing on their applications in various fields such as medical imaging and autonomous driving. Researchers are encouraged to present novel approaches and comparative analyses of segmentation techniques.

This session will focus on methodologies for video analysis, including motion detection, tracking, and event recognition. Contributions that explore the integration of machine learning with video processing are particularly encouraged.