Conférences  >  Informatique  >  Evénement multidisciplinaires ou générales en informatique  >  Thaïlande

Stamford International University

In this digital era, the world is heading towards intelligent machines, which are expected to be as intelligent as people; Machine Intelligence. Researchers and scientists are striving to make smart machines that are sophisticated enough to automate the entire system. Machine learning is widely adopted as a method of data analysis that automates analytical model building. In recent times, Machine learning offers a wide range of applications, including healthcare, security, agriculture and even in day-to-day applications. On the other hand, Machine learning plays a significant role in sustainable computing. The increasing adoption of machine learning intelligence and even its exponentially increasing consequences on various markets need a detailed examination of its implications for achieving long-term innovation success. The 2nd International Conference on Trends in Sustainable Computing and Machine Intelligence [ICTSM 2024] welcomes papers on broad aspects of AI, machine learning and sustainable computing that constitute advances in various real-time applications. ICTSM 2024 aims to report the novel results achieved in addition to proposals for developing new perspectives on Machine Intelligence [MI] and computing problems, both of which should include demonstrations of real-time value and effectiveness. Papers detailing the applications of MI and computing are also accepted, although the emphasis should be on how the novel and innovative MI and sustainable approaches enhance the performance in application domains, rather than presenting another application of established methods. The submitted papers should explain a novel solution, originality, and provide an in-depth review of the technique.

Email.: ictsmet.confdesk@gmail.com

Track - 1 Decision support systems Neural networks and applications Machine Learning Natural language processing Automated problem solving AI and evolutionary algorithms Intelligent information systems Distributed AI algorithms and models Knowledge discovery and acquisition Reinforcement learning Computational Intelligence Computer Vision and Image Processing Cognitive and Biologically-Inspired Vision Soft Computing and Applications Hybrid Intelligent Systems Track - 2 Distributed Computing AI driven Sustainable Cloud/Edge Systems Pattern Recognition and Analysis Ubiquitous and High-Performance Computing Sustainable computing for CPS and IoT Security, Trust and Privacy Sustainable Data Centers Reconfigurable Computing Big Data for Sustainable Computing Sustainable Informatics and Optimization Cognitive Science and Technology Sustainable Communication and Security Sustainable High-Performance Systems Sustainable Industries Energy-aware Machine Learning