ICT Innovations is one of the leading international conferences in the region that serves as a platform for presenting novel ideas and fundamental advances in the fields of computer science and engineering.
The ICT Innovations conference series has established itself as an international forum for presenting scientific results related to innovative fundamental and applied research in ICT. The conference aims to bring together academics as well as industrial practitioners, to share their most recent research, practical solutions and experiences and to discuss the trends, opportunities and challenges.
The conference will consist of regular sessions with technical contributions (regular papers) reviewed and selected by an international program committee, as well as invited talks presented by leading scientists. Different workshops will be held in line with the main conference. The official language of the conference will be English.
The focal point for this year’s conference is Machine Learning and Applications.
The need for machine learning is ever growing due to the increased pervasiveness of data analysis tasks in almost every area of life, including business, science and technology. Not only is the pervasiveness of data analysis tasks increasing, but so is their complexity. These tasks usually need to handle massive datasets which can be partially labelled. The datasets can be with many input and output dimensions, streaming at very high rates. Furthermore the data can be placed in a spatio-temporal or network context.
Machine learning is continuously unleashing its power in a wide range of applications including marketing, e-commerce, software systems, networking, telecommunications, banking, finance, economics, social science, computer vision, speech recognition, natural-language processing, robotics, biology, transportation, health care and medicine. The information explosion has resulted in the collection of massive amounts of data. This amount of data, coupled with the rapid development of processor power and computer parallelization, has made it possible to build different predictive models. These predictive models can study and analyze complex data in various application areas to discover relationships in data to inspire insights and create opportunities, identify anomalies and solve problems, anticipate outcomes and make better decisions.
Conference topics of interest include but are not limited to:
Prof. Vesna Dimitrova, PhD
Prof. Ivica Dimitrovski, PhD
A Decade of Machine Learning in Profiled Side-channel Analysis
Deep learning for machinery fault diagnosis
Generic acceleration schemes for large-scale optimization in machine learning
The Machine Learning of Time and Applications