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Hybrid Event

7th - 8th December 2026 | Oistins, Barbados

International Conference on Artificial Intelligence, Machine Learning and Soft Computing (ICAIMLSC - 26)

4

Days

4

Hrs

07

Min

02

Sec

Conference Program

Session Tracks

SDG Wheel

Aligned with

UN Sustainable Development Goals

This conference contributes to global sustainability by aligning its research discussions and academic sessions with key United Nations Sustainable Development Goals. It fosters knowledge exchange, innovation, and collaborative engagement.

Why it matters

SDG 4 — Quality Education
SDG 9 — Industry, Innovation and Infrastructure
SDG 10 — Reduced Inequalities
SDG 11 — Sustainable Cities and Communities
Explore All Session Tracks
Track 01
Intelligent Search Techniques in Art History

This track focuses on the development and application of intelligent search algorithms tailored for the exploration of art historical data. Participants will discuss innovative methods for enhancing search efficiency and accuracy in art databases.

Track 02
Automated Reasoning in Artistic Contexts

This session explores the use of automated reasoning and logic programming in analyzing and interpreting art. Contributions will highlight how these techniques can facilitate deeper insights into artistic movements and styles.

Track 03
Machine Learning Applications in Art Analysis

This track emphasizes the implementation of machine learning algorithms in the analysis of artworks and artistic trends. Researchers will present case studies demonstrating the impact of these technologies on art historical research.

Track 04
Intelligent Planning for Art Restoration Projects

This session investigates the role of intelligent planning systems in the management of art restoration projects. Discussions will include methodologies for optimizing resource allocation and project timelines in the context of preserving cultural heritage.

Track 05
Visual and Linguistic Perception in Art Interpretation

This track examines the intersection of visual and linguistic perception theories with art interpretation. Participants will explore how these cognitive processes can be modeled and applied to enhance understanding of artistic expression.

Track 06
Evolutionary and Swarm Algorithms in Creative Processes

This session focuses on the application of evolutionary and swarm algorithms in creative processes within the arts. Researchers will discuss how these algorithms can inspire new artistic methodologies and practices.

Track 07
Fuzzy Logic and Rough Sets in Art Classification

This track delves into the use of fuzzy sets and rough sets for the classification and categorization of artworks. Contributions will highlight the advantages of these approaches in dealing with the ambiguity inherent in art.

Track 08
Neural Computing in Art Generation

This session explores the application of neural computing techniques in the generation of art. Participants will showcase innovative projects that utilize neural networks to create original artistic works.

Track 09
Multi-Agent Systems in Collaborative Art Creation

This track investigates the use of multi-agent systems in collaborative art creation. Discussions will focus on how these systems can facilitate interaction among artists and enhance the creative process.

Track 10
Data and Web Mining for Art Historical Research

This session addresses the role of data and web mining techniques in uncovering insights from vast art historical datasets. Researchers will present methodologies for extracting valuable information from online art repositories.

Track 11
Hybrid Intelligent Models for Art Evaluation

This track emphasizes the development of hybrid intelligent models that integrate various algorithms for art evaluation. Participants will discuss the effectiveness of these models in assessing artistic quality and value.

2026 UPDATE

Consistent Academic Support

Science Net ensures that research activities continue without interruption in the current global situation. Participants can engage through digital and hybrid conference formats.