LA-CCI is an acronym that refers to the Latin America Conference on Computational Intelligence (CI) that currently includes Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Mexico, Peru, and Venezuela. All of them with active research groups in CI eager to develop the area and exchange experiences/personnel. LA-CCI is a structuring endeavor from LA-CIS (Latin American Computational Intelligence Society) that is encouraging research groups of these participating Countries to teams up around the exciting topic of Computational Intelligence.
The fourth LA-CCI will be held in Arequipa, Peru from November 8th to 10th of 2017. The objective of LA-CCI 2017 is to provide a high-level international forum for scientists, researchers, engineers, and educators to disseminate their latest research results and exchange views of the future research directions on Neural & Learning Systems, Fuzzy & Stochastic Modeling, Evolutionary & Swarm Computation, and their related applications.
In the previous editions of LA-CCI (i.e. 2016 – Cartagena de Indias/Colombia, 2015 – Curitiba/Brazil, and in 2014 – San Carlos de Bariloche/Argentina), it was supported by respective national and several sister societies, especially the prestigious IEEE and IEEE-CIS. However, as a natural result of the growth of the event, the organizing team and steering committee are very happy to announce that in 2017, for the second time, IEEE will be a full sponsor of our Conference as maintaining IEEE-CIS as technical co-sponsor!
The local organizers of 2017 IEEE LA-CCI and steering committee of LA-CIS care much about future researchers that will integrate the organization, thus the congress will include thematic sessions, schools, and discussions-panels specially tailored for them. As an additional self-imposed responsibility, the organizers found important to gather other scholars, government officials, and funding agencies representatives to discuss institutional approaches, internationalization policies and funding mechanisms that could be proposed especially for Latin America Countries.
The joint organization team would like to encourage the participation of established scientists, young researchers and advanced students, not limited of Latin America Countries. It is important to notice that our Congress is organized by Latin Countries but aims at the international scientific community. Last, but not least, it is worth to state our main goals, namely:
(i) To be a notch scientific forum in Latin America, this time held in Arequipa, Peru;
(ii) To be a regional hub of CI practitioners and professionals;
(iii) To be a privileged showcase of CI to industry;
(iv) To be a springboard for students;
(v) To publicize CI research going on within LA-CCI Countries;
(vi) To encourage CI research links to the LA-CCI Countries;
(vii) To foster academic mobility in LA-CCI and beyond; and,
(viii) To promote Science & Culture for Latin America.
This manifesto was drafted on November, 08th of 2016, and subsumes (as the predecessors) the rationale of the local organizer of 2017 IEEE LA-CCI alongside the steering committees of LA-CCI, who signed respectively in Peru and in Brazil.
Yván Jesús Túpac Valdivia-Peru / Fernando Buarque de Lima Neto-Brazil.
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.
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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. |
11月08日
2017
11月10日
2017
初稿截稿日期
初稿录用通知日期
终稿截稿日期
注册截止日期
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