ICMLA 2023

ICMLA'23 aims to bring together researchers and practitioners to present their latest achievements and innovations in the area of machine learning (ML). 

The conference provides a leading international forum for the dissemination of original research in ML, with emphasis on applications as well as novel algorithms and systems. Following the success of previous ICMLA conferences, the conference aims to attract researchers and application developers from a wide range of ML related areas, and the recent emergence of Big Data processing brings an urgent need for machine learning to address these new challenges. The conference will cover both machine learning theoretical research and its applications. Contributions describing machine learning techniques applied to real-world problems and interdisciplinary research involving machine learning, in fields like medicine, biology, industry, manufacturing, security, education, virtual environments, games, are especially encouraged. 

Accepted papers will be submitted for inclusion into IEEE Xplore subject to meeting IEEE Xplore’s scope and quality requirements. The conference is technically co-sponsored by IEEE.

It is planned that ICMLA'23 will be organized as in pre-pandemics time, with participants attending the conference in person, unless Covid rules will change towards the end of the year and this will not be possible. However, facilities will be in place so that people can attend and present their papers online, although we encourage in-person attendance. 

Contributions describing applications of machine learning (ML)techniques to real-world problems, interdisciplinary research involving machine learning, experimental and/or theoretical studies yielding new insights into the design of ML systems, and papers describing development of new analytical frameworks that advance practical machine learning methods are especially encouraged.

The technical program will consist of, but is not limited to, the following topics of interest:
• statistical learning • neural network learning • learning through fuzzy logic • learning through evolution (evolutionary algorithms) • reinforcement learning • multi-strategy learning • cooperative learning • planning and learning • multi-agent learning • online and incremental learning • scalability of learning algorithms • inductive learning • inductive logic programming • Bayesian networks • support vector machines • case-based reasoning • machine learning for bioinformatics and computational biology • multi-lingual knowledge acquisition and representation • grammatical inference • knowledge acquisition and learning • knowledge discovery in databases • knowledge intensive learning • knowledge representation and reasoning • machine learning and information retrieval • machine learning for web navigation and mining • learning through mobile data mining • text and multimedia mining through machine learning • distributed and parallel learning algorithms and applications • feature extraction and classification • theories and models for plausible reasoning • computational learning theory • cognitive modeling • deep and transfer learning • federated learning • machine learning on the edge • machine learning for computer vision • hybrid learning algorithms

Applications of machine learning in:
• medicine, health, bioinformatics and systems biology • industrial and engineering applications • security applications • smart cities and autonomous driving • game playing and problem solving • intelligent virtual environments • economics, business and forecasting applications, etc.