Conference Information

LA-CCI 2018: IEEE Latin American Conference on Computational Intelligence

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Submission Date:
2018-06-04 Extended
Notification Date:
2018-07-02
Conference Date:
2018-11-07
Location:
Guadalajara, Mexico
Years:
5
Viewed: 14445   Tracked: 0   Attend: 0

Conference Partner Index (CP-I)

41.7 / 100
Ranked #4,622 of 5,684 conferences · Top 82%

#590 of 741 in Artificial Intelligence & Machine Learning

Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
48
Community attention (10%)
8
Public record completeness (15%)
25

Inputs used: Editions on record: 5 · Researchers who opened this page in the past 24 months: 2

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-24

Call For Papers

LA-CCI 2018 (IEEE Latin American Conference on Computational Intelligence) is an academic conference held in Guadalajara, Mexico on 2018-11-07. The paper submission deadline is 2018-06-04 (extended). Acceptance notifications are sent on 2018-07-02.

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.
Last updated by Dou Sun on

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