Computational Intelligence

Craniometric Points Measurement

Anthropometric studies focusing on facial metrics and their proportions form an important research area devoted to observations of the appearance of the human skull. Many different applications include the use of craniometry for maxillofacial reconstruction and surgery. The paper and the associated dataset explores the possibility of using selected craniometric points and associated metric to observe  spatial changes during the maxillofacial surgery treatment.  The experimental dataset includes observations of 27 individuals.

Categories:
Digital signal processing
Machine Learning
45 Views

Competition on Evolutionary Computation in the Energy Domain: Smart Grid Applications

Following the success of the previous editions at WCCI 2018 (Rio de Janeiro, Brazil) and CEC/GECCO 2019 (New Zealand and Prague, Czechia) we are launching a more challenging algorithm competition at major international conferences in the field of computational intelligence. This WCCI & GECCO 2020 competition proposes two testbeds in the energy domain:

Categories:
Energy
Computational Intelligence
204 Views

2021 Competition on Evolutionary Computation in the Energy Domain: Smart Grid Applications

Following the success of the previous editions (CEC, GECCO, WCCI), we are launching a more challenging competition at major conferences in the field of computational intelligence. This GECCO 2021 competition proposes two tracks in the energy domain:

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Energy
Computational Intelligence
Submitted On: 
Mon, 02/08/2021 - 15:32
Last Updated On: 
Wed, 02/24/2021 - 10:38
Citation Author(s): 
Fernando Lezama, Joao Soares, Bruno Canizes, Zita Vale, Ruben Romero

FUZZ-IEEE Competition on Explainable Energy Prediction

Predicting energy consumption is currently a key challenge for the energy industry as a whole.  Predicting the consumption in a certain area is massively complicated due to the sudden changes in the way that energy is being consumed and generated at the current point in time. However, this prediction becomes extremely necessary to minimise costs and to enable adjusting (automatically) the production of energy and better balance the load between different energy sources.

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Artificial Intelligence
IoT
Machine Learning
Energy
Computational Intelligence
Submitted On: 
Wed, 11/25/2020 - 09:55
Last Updated On: 
Wed, 12/23/2020 - 12:16
Citation Author(s): 
Isaac Triguero

IEEE-CIS Technical Challenge on Energy Prediction from Smart Meter Data

Imagine you just moved to your brand-new home and hired your energy provider. They tell you that based on the provided information they will set up a direct debit of €50/month. However, at the end of the year, that prediction was not quite accurate, and you end up paying a settlement amount of €300, or if you are lucky, they give you back some money. Either way, you will probably be disappointed with your energy provider and might consider moving on to another one. Predicting energy consumption is currently a key challenge for the energy industry as a whole.

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Artificial Intelligence
IoT
Machine Learning
Energy
Computational Intelligence
Submitted On: 
Wed, 05/27/2020 - 08:45
Last Updated On: 
Tue, 07/20/2021 - 06:35