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Electronics
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Applications of Bioinspired Neural Network
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Applications of Bioinspired Neural Network

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 13273

Special Issue Editors

Prof. Dr. Hiroki Tamura

E-Mail Website
Guest Editor
Department of Environmental Robotics, Faculty of Engineering, University of Miyazaki, Miyazaki, Japan
Interests: SoftComputing; neural networks; interfaces (computer)
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tang Zheng

E-Mail Website
Guest Editor
Faculty of Engineering, Department of Intellectual Information Engineering, University of Toyama, Toyama, Japan
Interests: intelligent informatics; soft computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Neural networks based on computational and engineering approaches have been successfully applied to a wide variety of fields. Academia generally takes it for granted that the findings in neural networks have greatly promoted the development of artificial intelligence. Research that focuses on interdisciplinary fields includes areas like artificial intelligence, computer vision, perception, pattern recognition, brain models, and neural computing. Neural networks are models that can be merged with other ideas and concepts. In particular, improvements in bioinspired concepts are expected to improve the possibilities of neural networks. Thus, more and more attention has been paid to research progress in neural networks and their potential applications in many fields.

Topics of interest include but are not limited to the following:

  • Speech recognition system using the human hearing system;
  • Modeling the relationship between the human gaze and perception;
  • Recognition system using the immune network system and neural networks.

Prof. Dr. Hiroki Tamura
Prof. Dr. Tang Zheng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (5 papers)

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Research

15 pages, 1358 KiB  
Article
Emotional Variability Analysis Based I-Vector for Speaker Verification in Under-Stress Conditions
by Barlian Henryranu Prasetio, Hiroki Tamura and Koichi Tanno
Electronics 2020, 9(9), 1420; https://doi.org/10.3390/electronics9091420 - 01 Sep 2020
Cited by 4 | Viewed by 1858
Abstract
Emotional conditions cause changes in the speech production system. It produces the differences in the acoustical characteristics compared to neutral conditions. The presence of emotion makes the performance of a speaker verification system degrade. In this paper, we propose a speaker modeling that [...] Read more.
Emotional conditions cause changes in the speech production system. It produces the differences in the acoustical characteristics compared to neutral conditions. The presence of emotion makes the performance of a speaker verification system degrade. In this paper, we propose a speaker modeling that accommodates the presence of emotions on the speech segments by extracting a speaker representation compactly. The speaker model is estimated by following a similar procedure to the i-vector technique, but it considerate the emotional effect as the channel variability component. We named this method as the emotional variability analysis (EVA). EVA represents the emotion subspace separately to the speaker subspace, like the joint factor analysis (JFA) model. The effectiveness of the proposed system is evaluated by comparing it with the standard i-vector system in the speaker verification task of the Speech Under Simulated and Actual Stress (SUSAS) dataset with three different scoring methods. The evaluation focus in terms of the equal error rate (EER). In addition, we also conducted an ablation study for a more comprehensive analysis of the EVA-based i-vector. Based on experiment results, the proposed system outperformed the standard i-vector system and achieved state-of-the-art results in the verification task for the under-stressed speakers. Full article
(This article belongs to the Special Issue Applications of Bioinspired Neural Network)
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15 pages, 4196 KiB  
Article
Gaze-Based Vehicle Driving Evaluation of System with an Actual Vehicle at an Intersection with a Traffic Light
by Takumi Shimauchi, Keiko Sakurai, Lindsey Tate and Hiroki Tamura
Electronics 2020, 9(9), 1408; https://doi.org/10.3390/electronics9091408 - 01 Sep 2020
Cited by 1 | Viewed by 2018
Abstract
Due to the population aging in Japan, more elderly people are retaining their driver’s licenses and the increase in the number of car accidents by elderly drivers is a social problem. To address this problem, an objective data-based method to evaluate whether elderly [...] Read more.
Due to the population aging in Japan, more elderly people are retaining their driver’s licenses and the increase in the number of car accidents by elderly drivers is a social problem. To address this problem, an objective data-based method to evaluate whether elderly drivers can continue driving is needed. In this paper, we propose a car driving evaluation system based on gaze as calculated by eye and head angles. We used an eye tracking device (TalkEye Lite) made by the Takei Scientific Instruments Cooperation. For our image processing technique, we propose a gaze fixation condition using deep learning (YOLOv2-tiny). By using an eye tracking device and the proposed gaze fixation condition, we built a system where drivers could be evaluated during actual car operation. We describe our system in this paper. In order to evaluate our proposed method, we conducted experiments from November 2017 to November 2018 where elderly people were evaluated by our system while driving an actual car. The subjects were 22 general drivers (two were 80–89 years old, four were 70–79 years old, six were 60–69 years old, three were 50–59 years old, five were 40–49 years old and two were 30–39 years old). We compared the subjects’ gaze information with the subjective evaluation by a professional driving instructor. As a result, we confirm that the subjects’ gaze information is related to the subjective evaluation by the instructor. Full article
(This article belongs to the Special Issue Applications of Bioinspired Neural Network)
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13 pages, 4851 KiB  
Article
Detecting Predictable Segments of Chaotic Financial Time Series via Neural Network
by Tianle Zhou, Chaoyi Chu, Chaobin Xu, Weihao Liu and Hao Yu
Electronics 2020, 9(5), 823; https://doi.org/10.3390/electronics9050823 - 16 May 2020
Cited by 6 | Viewed by 2388
Abstract
In this study, a new idea is proposed to analyze the financial market and detect price fluctuations, by integrating the technology of PSR (phase space reconstruction) and SOM (self organizing maps) neural network algorithms. The prediction of price and index in the financial [...] Read more.
In this study, a new idea is proposed to analyze the financial market and detect price fluctuations, by integrating the technology of PSR (phase space reconstruction) and SOM (self organizing maps) neural network algorithms. The prediction of price and index in the financial market has always been a challenging and significant subject in time-series studies, and the prediction accuracy or the sensitivity of timely warning price fluctuations plays an important role in improving returns and avoiding risks for investors. However, it is the high volatility and chaotic dynamics of financial time series that constitute the most significantly influential factors affecting the prediction effect. As a solution, the time series is first projected into a phase space by PSR, and the phase tracks are then sliced into several parts. SOM neural network is used to cluster the phase track parts and extract the linear components in each embedded dimension. After that, LSTM (long short-term memory) is used to test the results of clustering. When there are multiple linear components in the m-dimension phase point, the superposition of these linear components still remains the linear property, and they exhibit order and periodicity in phase space, thereby providing a possibility for time series prediction. In this study, the Dow Jones index, Nikkei index, China growth enterprise market index and Chinese gold price are tested to determine the validity of the model. To summarize, the model has proven itself able to mark the unpredictable time series area and evaluate the unpredictable risk by using 1-dimension time series data. Full article
(This article belongs to the Special Issue Applications of Bioinspired Neural Network)
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22 pages, 2150 KiB  
Article
Validation of Large-Scale Classification Problem in Dendritic Neuron Model Using Particle Antagonism Mechanism
by Dongbao Jia, Yuka Fujishita, Cunhua Li, Yuki Todo and Hongwei Dai
Electronics 2020, 9(5), 792; https://doi.org/10.3390/electronics9050792 - 11 May 2020
Cited by 12 | Viewed by 1957
Abstract
With the characteristics of simple structure and low cost, the dendritic neuron model (DNM) is used as a neuron model to solve complex problems such as nonlinear problems for achieving high-precision models. Although the DNM obtains higher accuracy and effectiveness than the middle [...] Read more.
With the characteristics of simple structure and low cost, the dendritic neuron model (DNM) is used as a neuron model to solve complex problems such as nonlinear problems for achieving high-precision models. Although the DNM obtains higher accuracy and effectiveness than the middle layer of the multilayer perceptron in small-scale classification problems, there are no examples that apply it to large-scale classification problems. To achieve better performance for solving practical problems, an approximate Newton-type method-neural network with random weights for the comparison; and three learning algorithms including back-propagation (BP), biogeography-based optimization (BBO), and a competitive swarm optimizer (CSO) are used in the DNM in this experiment. Moreover, three classification problems are solved by using the above learning algorithms to verify their precision and effectiveness in large-scale classification problems. As a consequence, in the case of execution time, DNM + BP is the optimum; DNM + CSO is the best in terms of both accuracy stability and execution time; and considering the stability of comprehensive performance and the convergence rate, DNM + BBO is a wise choice. Full article
(This article belongs to the Special Issue Applications of Bioinspired Neural Network)
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20 pages, 1068 KiB  
Article
Automatic Sleep Disorders Classification Using Ensemble of Bagged Tree Based on Sleep Quality Features
by Edita Rosana Widasari, Koichi Tanno and Hiroki Tamura
Electronics 2020, 9(3), 512; https://doi.org/10.3390/electronics9030512 - 20 Mar 2020
Cited by 38 | Viewed by 4469
Abstract
Sleep disorder is a medical disease of the sleep patterns, which commonly suffered by the elderly. Sleep disorders diagnosis and treatment are considered to be challenging due to a time-consuming and inconvenient process for the patient. Moreover, the use of Polysomnography (PSG) in [...] Read more.
Sleep disorder is a medical disease of the sleep patterns, which commonly suffered by the elderly. Sleep disorders diagnosis and treatment are considered to be challenging due to a time-consuming and inconvenient process for the patient. Moreover, the use of Polysomnography (PSG) in sleep disorder diagnosis is a high-cost process. Therefore, we propose an efficient classification method of sleep disorder by merely using electrocardiography (ECG) signals to simplify the sleep disorders diagnosis process. Different from many current related studies that applied a five-minute epoch to observe the main frequency band of the ECG signal, we perform a pre-processing technique that suitable for the 30-seconds epoch of the ECG signal. By this simplification, the proposed method has a low computational cost so that suitable to be implemented in an embedded hardware device. Structurally, the proposed method consists of five stages: (1) pre-processing, (2) spectral features extraction, (3) sleep stage detection using the Decision-Tree-Based Support Vector Machine (DTB-SVM), (4) assess the sleep quality features, and (5) sleep disorders classification using ensemble of bagged tree classifiers. We evaluate the effectiveness of the proposed method in the task of classifying the sleep disorders into four classes (insomnia, Sleep-Disordered Breathing (SDB), REM Behavior Disorder (RBD), and healthy subjects) from the 51 patients of the Cyclic Alternating Pattern (CAP) sleep data. Based on experimental results, the proposed method presents 84.01% of sensitivity, 94.17% of specificity, 86.27% of overall accuracy, and 0.70 of Cohen’s kappa. This result indicates that the proposed method able to reliably classify the sleep disorders merely using the 30-seconds epoch ECG in order to address the issue of a multichannel signal such as the PSG. Full article
(This article belongs to the Special Issue Applications of Bioinspired Neural Network)
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