Topics of Interest



Any topic related to Computational Intelligence, mainly but not limited to the following:

 

Problems

Single List for all tracks:

– Foundations of Computational Intelligence;

– Analysis & Identification;

– Modeling & Design;

– Representation & Interfacing;

– Methods & techniques of CI Hybridization;

– Simulation & Estimation;

– Signal & Sensor Processing;

– Machine learning and pattern recognition;

– Time series forecasting;

– Classification & Clustering;

– Search & Optimization (combinatorial, stochastic, dynamic, multimodal, multi-objective, etc);

– Constraint handling (Routing, Scheduling, Timetabling, allocation Neighboring, Placement, etc);

– Information retrieval & Ambient intelligence;

– Decision Problems;

– Cognitive Robotics;

– Image Processing & Computer Vision;

– Bio & Medical Informatics – Computational biology;

– Information & Network security;

– Data & Web Mining;

– E-commerce, E-Procurement & E-Government;

– Telecommunications and Networking;

– Energy generation and energy dispatch;

– Parallelization & Hardware Implementations.

Approaches

(1) Evolutionary & Swarm Computation:

– Evolutionary computation;

– Swarm intelligence;

– Artificial immune systems;

– Novel metaheuristics and hyperheuristics;

– Memetic and Collective intelligence;

– Nature and Bio-inspired methods;

– Artificial life.

 

(2) Neural & Learning Systems:

– Machine learning;

– Neural Computation (+Weightless Systems);

– Complex systems;

– Wavelets;

– Molecular and quantum computing;

– Brain-machine interfaces;

– Network Sciences;

– Local search methods.

 

(3) Fuzzy & Stochastic Modeling:

– Fuzzy logic;

– Fuzzy optimization and design;

– Fuzzy pattern recognition;

– Fuzzy control & decision making/support;

– Rough sets;

– Uncertainty analysis;

– Fractals;

– Game theory;

– Social Simulation;

– Multi-agent systems;

– Symbolic Systems;

– Grey systems.

 

Technologies

(1) Evolutionary & Swarm Computation:

– Ant colony optimization;

– Particle swarm optimization;

– Fish School Search;

– Bee colony optimization;

– Genetic programming and Genetic algorithms;

– Cultural Algorithms and Co-Evolution;

– Evolutionary Strategy and Differential Evolution;

– Evolutionary design & scheduling;

– Danger theory and network immune systems;

– Firefly optimization;

– Glowsworm Swarm Optimization;

– Cuckoo search;

– Herd optimization; etc.

(2) Neural & Learning Systems:

– Cognitive systems and applications;

– Computer vision;

– Hardware Implementations;

– Web intelligence;

– Natural language processing & Speech understanding;

– Supervised and Unsupervised Learning;

– Semi-supervised and Weakly Supervised Learning;

– Support vector machines;

– Local Search Methods (Tabu search, iterated search, etc.)

– Reinforcement Learning

– Neuroscience and biologically inspired control;

– Distributed intelligent systems; etc.

(3) Fuzzy & Stochastic Modeling:

– Fuzzy sets & Type-2 fuzzy logic;

– Approximate reasoning;

– Rough sets & data analysis;

– Case-Based Reasoning;

– Expert systems;

– Knowledge engineering.

– Adaptive Dynamic Programing & Control;

– Convergence and performance analysis;

– Bayesian methods;

– Monte-Carlo methods and variations;

– Markov decision processes; and,

– Other Statistical learning.