AIMC Topic: Cognition
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Analysis of the Effect of Artificial Intelligence on Role Cognition in the Education System.
Occupational therapy international
May 30, 2022
Taking the entire education system in Taiyuan City, Shanxi Province, Central China, as an example, this paper uses the questionnaire survey method to analyze the effect of artificial intelligence (AI) on role cognition in the education system. The ed...
Fuzzy-Rough Cognitive Networks: Theoretical Analysis and Simpler Models.
IEEE transactions on cybernetics
May 19, 2022
Fuzzy-rough cognitive networks (FRCNs) are recurrent neural networks (RNNs) intended for structured classification purposes in which the problem is described by an explicit set of features. The advantage of this granular neural system relies on its t...
Hybrid Model-Based Emotion Contextual Recognition for Cognitive Assistance Services.
IEEE transactions on cybernetics
May 19, 2022
Endowing ubiquitous robots with cognitive capabilities for recognizing emotions, sentiments, affects, and moods of humans in their context is an important challenge, which requires sophisticated and novel approaches of emotion recognition. Most studi...
Impairments in Cognitive Control Using a Reverse Visually Guided Reaching Task Following Stroke.
Neurorehabilitation and neural repair
May 16, 2022
BACKGROUND: Cognitive and motor function must work together quickly and seamlessly to allow us to interact with a complex world, but their integration is difficult to assess directly. Interactive technology provides opportunities to assess motor acti...
Uncovering shape signatures of resting-state functional connectivity by geometric deep learning on Riemannian manifold.
Human brain mapping
May 10, 2022
Functional neural activities manifest geometric patterns, as evidenced by the evolving network topology of functional connectivities (FC) even in the resting state. In this work, we propose a novel manifold-based geometric neural network for function...
Brain-Inspired Experience Reinforcement Model for Bin Packing in Varying Environments.
IEEE transactions on neural networks and learning systems
May 2, 2022
Bin-packing problem (BPP) is a typical combinatorial optimization problem whose decision-making process is NP-hard. This article examines BPPs in varying environments, where random number and shape of items are to be packed in different instances. Th...
Toward Cognitive Navigation: Design and Implementation of a Biologically Inspired Head Direction Cell Network.
IEEE transactions on neural networks and learning systems
May 2, 2022
As a vital cognitive function of animals, the navigation skill is first built on the accurate perception of the directional heading in the environment. Head direction cells (HDCs), found in the limbic system of animals, are proven to play an importan...
Task-State EEG Signal Classification for Spatial Cognitive Evaluation Based on Multiscale High-Density Convolutional Neural Network.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Apr 25, 2022
In this study, a multi-scale high-density convolutional neural network (MHCNN) classification method for spatial cognitive ability assessment was proposed, aiming at achieving the binary classification of task-state EEG signals before and after spati...
Socially assistive robots for people with dementia: Systematic review and meta-analysis of feasibility, acceptability and the effect on cognition, neuropsychiatric symptoms and quality of life.
Ageing research reviews
Apr 21, 2022
BACKGROUND: There is increasing interest in using robots to support dementia care but little consensus on the evidence for their use. The aim of the study is to review evidence about feasibility, acceptability and clinical effectiveness of socially a...
Brain Functional Connectivity Analysis via Graphical Deep Learning.
IEEE transactions on bio-medical engineering
Apr 21, 2022
OBJECTIVE: Graphical deep learning models provide a desirable way for brain functional connectivity analysis. However, the application of current graph deep learning models to brain network analysis is challenging due to the limited sample size and c...
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