Computational intelligence and neuroscience
-
Comput Intell Neurosci · Jan 2021
ReviewReview on the Application of Metalearning in Artificial Intelligence.
In recent years, artificial intelligence supported by big data has gradually become more dependent on deep reinforcement learning. However, the application of deep reinforcement learning in artificial intelligence is limited by prior knowledge and model selection, which further affects the efficiency and accuracy of prediction, and also fails to realize the learning ability of autonomous learning and prediction. Metalearning came into being because of this. ⋯ Then, four typical applications of metalearning in the field of artificial intelligence are summarized: few-shot learning, robot learning, unsupervised learning, and intelligent medicine. Then, the challenges and solutions of metalearning are analyzed. Finally, a systematic summary of the full text is made, and the future development prospect of this field is assessed.
-
Comput Intell Neurosci · Jan 2019
Modulation Classification of Underwater Communication with Deep Learning Network.
Automatic modulation recognition has successfully used various machine learning methods and achieved certain results. As a subarea of machine learning, deep learning has made great progress in recent years and has made remarkable progress in the field of image and language processing. Deep learning requires a large amount of data support. ⋯ However, the extensive application of deep learning in the field of communication has not yet been fully developed, especially in underwater acoustic communication. In this paper, we mainly discuss the modulation recognition process which is an important part of communication process by using the deep learning method. Different from the common machine learning methods that require feature extraction, the deep learning method does not require feature extraction and obtains more effects than common machine learning.
-
Hyperspectral imaging (HSI) technology can be used to detect human emotions based on the power of material discrimination from their faces. In this paper, HSI is used to remotely sense and distinguish blood chromophores in facial tissues and acquire an evaluation indicator (tissue oxygen saturation, StO2) using an optical absorption model. This study explored facial analysis while people were showing spontaneous expressions of happiness during social interaction. ⋯ The affective data (StO2) of 23 young adults were acquired by HSI while the participants expressed emotions (calm or happy), and these were used to compare the significant differences in the variations of StO2 between the different ROIs through repeated measures analysis of variance. Results demonstrate that happiness causes different distributions in the variations of StO2 for the above ROIs; these are explained in depth in the article. This study establishes that facial tissue oxygen saturation is a valid and reliable physiological indicator of happiness and merits further research.
-
Comput Intell Neurosci · Jan 2018
Retrieval of Semantic-Based Inspirational Sources for Emotional Design.
In the conceptual design stage, inspirational sources play an important role in designers' creative thinking. This paper proposes a retrieval method for semantic-based inspirational sources, which helps designers obtain inspirational images in the conceptual design stage of emotional design. ⋯ The KanseiNet for Chinese is first constructed, and the Sources of Inspiration Retrieval System for Emotional Design (SIRSED) is developed. An experiment comparing the existing image retrieval systems with SIRSED proved the latter to be a more comprehensive and accurate way for designers to access inspirational sources.
-
Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein.