ICCPR 2026 covers a wide spectrum of computing topics, including computer vision and image processing, pattern recognition and data mining, machine learning, deep learning models, NLP and text analysis, HCI, big data, biometrics, time series analysis, reinforcement learning, and media content understanding. The conference aims to bring together researchers a…
Programmed at 2026, in alphabetical order.
Selected from 9 sessions.
This session focuses on novel machine learning architectures, optimization techniques, and deep learning models for various applications beyond standard neural networks.
Exploring the use of artificial intelligence and pattern recognition techniques in medical diagnosis, treatment planning, and healthcare analytics for improved patient outcomes.
Exploring the application of machine learning, deep learning, and data science techniques in financial markets, risk assessment, fraud detection, and algorithmic trading.
Focusing on advanced image processing techniques, including image enhancement, restoration, compression, and their applications in various domains like medical imaging and remote s…
This session addresses computer vision applications in industrial settings, including quality inspection, defect detection, robotic guidance, and automated manufacturing systems.
Machine learning techniques combined with Earth observation data are transforming the way we monitor and address global sustainability challenges. This track focuses on recent adva…
This session focuses on the latest advancements in multimodal foundational models that can process and understand multiple types of data (text, image, audio, video) simultaneously.
With the rapid development of artificial intelligence, computer vision, pattern recognition, and data science, an increasing amount of data exhibits complex geometric structures th…
With the continuous advancement of deep learning technologies, efficient data representation and cross-domain modeling capabilities have become core drivers in the field of intelli…
An audience of 5 companies, parsed from the program.
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