Results 61 to 70 of about 1,570 (213)

Predictable Internal Brain Dynamics in EEG and Its Relation to Conscious States

Frontiers in Neurorobotics, 2014
Consciousness is a complex and multi-faceted phenomenon defying scientific explanation. Part of the reason why this is the case is due to its subjective nature.
Jaewook eYoo, Jaerock eKwon, Yoonsuck eChoe   +2 more
doaj   +1 more source

Visualizing Neuroevolution in Neural Network Learning

, 2023
This thesis examines options for neural network learning achieved by means of neuroevolution, examines general functioning of neuroevolution, design and implementation of neuroevolution and marginally deals with design and implementation of feed-forward ...
Bednář, Martin
core   +1 more source

Class binarization to neuroevolution for multiclass classification

, 2022
Multiclass classification is a fundamental and challenging task in machine learning. The existing techniques of multiclass classification can be categorized as (1) decomposition into binary (2) extension from binary and (3) hierarchical classification ...
Lan, Gongjin; id_orcid, Gao, Zhenyu, Liu, Ting; id_orcid, Tong, Lingyao; id_orcid, Tong, Lingyao, Lan, Gongjin, Liu, Ting   +6 more
core   +1 more source

Hybridizing Deep Learning and Neuroevolution: Application to the Spanish Short-Term Electric Energy Consumption Forecasting

Applied Sciences, 2020
The electric energy production would be much more efficient if accurate estimations of the future demand were available, since these would allow allocating only the resources needed for the production of the right amount of energy required.
Federico Divina, José Francisco Torres Maldonado, Miguel García-Torres, Francisco Martínez-Álvarez, Alicia Troncoso   +4 more
doaj   +1 more source

Efficient Moth-Flame-Based Neuroevolution Models

, 2019
This chapter proposes a new efficient moth-flame-embedded multilayer perceptrons (MLP) neuroevolution model to deal with classification problems. Moth-flame optimizer (MFO) is one of the effective swarm-based metaheuristic methods inspired by the natural
Seyed Mohammad Jafar Jalali, Ali Asghar Heidari, Heidari, Ali Asghar, Mafarja, Majdi, Seyedali Mirjalili, Majdi Mafarja, Jin Song Dong, Yin, Yingyu, Dong, Jin Song, Yingyu Yin, Jalali, Seyed Mohammad Jafar, Mirjalili, Seyedali   +11 more
core   +1 more source

Neuroevolution-based Inverse Reinforcement Learning [PDF]

2017 IEEE Congress on Evolutionary Computation (CEC), 2017
The problem of Learning from Demonstration is targeted at learning to perform tasks based on observed examples. One approach to Learning from Demonstration is Inverse Reinforcement Learning, in which actions are observed to infer rewards. This work combines a feature based state evaluation approach to Inverse Reinforcement Learning with neuroevolution,
Karan K. Budhraja, Tim Oates 0001
openaire   +2 more sources

Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments

Sensors, 2018
Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc.
Alejandro Baldominos, Yago Saez, Pedro Isasi   +2 more
doaj   +1 more source

Face Patches Designed Through Neuroevolution for Face Recognition With Large Pose Variation

IEEE Access, 2023
Face Recognition (FR) has been a widely used biometric technique for identity authentication in various domains. Despite the remarkable progress in the field of FR during the past few years, there are still challenges that need to be addressed, including
Juan P. Perez, Claudio A. Perez
doaj   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

GPUMDkit: A User‐Friendly Toolkit for GPUMD and NEP

Materials Genome Engineering Advances, EarlyView.
GPUMDkit is a comprehensive and user‐friendly toolkit for GPUMD and NEP programs, integrating format conversion, structure sampling, property calculation, and visualization into a unified interface, substantially lowering the barrier to entry for machine‐learning molecular dynamics simulations with GPUMD and NEP.
Zihan Yan, Denan Li, Xin Wu, Zhoulin Liu, Chen Hua, Boyi Situ, Hao Yang, Shengjie Tang, Benrui Tang, Ziyang Wang, Shangzhao Yi, Huan Wang, Dian Huang, Ke Li, Qilin Guo, Zherui Chen, Ke Xu, Yanzhou Wang, Ziliang Wang, Gang Tang, Shi Liu, Zheyong Fan, Yizhou Zhu   +22 more
wiley   +1 more source