Symmetry

Research

37 pages, 9357 KiB

Open AccessArticle

Improving Functional Connectivity in Developmental Dyslexia through Combined Neurofeedback and Visual Training

by Tihomir Taskov and Juliana Dushanova

Symmetry 2022, 14(2), 369; https://doi.org/10.3390/sym14020369 - 13 Feb 2022

Cited by 1 | Viewed by 2943

This study examined the effects of combined neurofeedback (NF) and visual training (VT) on children with developmental dyslexia (DD). Although NF is the first noninvasive approach to support neurological disorders, the mechanisms of its effects on the brain functional connectivity are still unclear. [...] Read more.

This study examined the effects of combined neurofeedback (NF) and visual training (VT) on children with developmental dyslexia (DD). Although NF is the first noninvasive approach to support neurological disorders, the mechanisms of its effects on the brain functional connectivity are still unclear. A key question is whether the functional connectivities of the EEG frequency networks change after the combined NF–VT training of DD children (postD). NF sessions of voluntary α/θ rhythm control were applied in a low-spatial-frequency (LSF) illusion contrast discrimination, which provides feedback with visual cues to improve the brain signals and cognitive abilities in DD children. The measures of connectivity, which are defined by small-world propensity, were sensitive to the properties of the brain electrical oscillations in the quantitative EEG-NF training. In the high-contrast LSF illusion, the z-NF reduced the α/θ scores in the frontal areas, and in the right ventral temporal, occipital–temporal, and middle occipital areas in the postD (vs. the preD) because of their suppression in the local hub θ-network and the altered global characteristics of the functional θ-frequency network. In the low-contrast condition, the z-NF stimulated increases in the α/θ scores, which induced hubs in the left-side α-frequency network of the postD, and changes in the global characteristics of the functional α-frequency network. Because of the anterior, superior, and middle temporal deficits affecting the ventral and occipital–temporal pathways, the z-NF–VT compensated for the more ventral brain regions, mainly in the left hemispheres of the postD group in the low-contrast LSF illusion. Compared to pretraining, the NF–VT increased the segregation of the α, β (low-contrast), and θ networks (high-contrast), as well as the γ2-network integration (both contrasts) after the termination of the training of the children with developmental dyslexia. The remediation compensated more for the dorsal (prefrontal, premotor, occipital–parietal connectivities) dysfunction of the θ network in the developmental dyslexia in the high-contrast LSF illusion. Our findings provide neurobehavioral evidence for the exquisite brain functional plasticity and direct effect of NF–VT on cognitive disabilities in DD children. Full article

(This article belongs to the Special Issue Simulation and Modelling in Natural Sciences, Economics, Biomedicine and Engineering II)

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15 pages, 901 KiB

Open AccessArticle

Enhance Contrast and Balance Color of Retinal Image

by Jessada Dissopa, Supaporn Kansomkeat and Sathit Intajag

Symmetry 2021, 13(11), 2089; https://doi.org/10.3390/sym13112089 - 4 Nov 2021

Cited by 9 | Viewed by 3068

Abstract

This paper proposes a simple and effective retinal fundus image simulation modeling to enhance contrast and adjust the color balance for symmetric information in biomedicine. The aim of the study is for reliable diagnosis of AMD (age-related macular degeneration) screening. The method consists [...] Read more.

This paper proposes a simple and effective retinal fundus image simulation modeling to enhance contrast and adjust the color balance for symmetric information in biomedicine. The aim of the study is for reliable diagnosis of AMD (age-related macular degeneration) screening. The method consists of a few simple steps. Firstly, local image contrast is refined with the CLAHE (Contrast Limited Adaptive Histogram Equalization) technique by operating CIE L*a*b* color space. Then, the contrast-enhanced image is stretched and rescaled by a histogram scaling equation to adjust the overall brightness offsets of the image and standardize it to Hubbard’s retinal image brightness range. The proposed method was assessed with retinal images from the DiaretDB0 and STARE datasets. The findings in the experimentation section indicate that the proposed method results in delightful color naturalness along with a standard color of retinal lesions. Full article

(This article belongs to the Special Issue Simulation and Modelling in Natural Sciences, Economics, Biomedicine and Engineering II)

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12 pages, 698 KiB

Open AccessArticle

Determining the Tiers of a Supply Chain Using Machine Learning Algorithms

by Kyoung Jong Park

Symmetry 2021, 13(10), 1934; https://doi.org/10.3390/sym13101934 - 14 Oct 2021

Cited by 10 | Viewed by 2610

Abstract

Companies in the same supply chain influence each other, so sharing information enables more efficient supply chain management. An efficient supply chain must have a symmetry of information between participating entities, but in reality, the information is asymmetric, causing problems. The sustainability of [...] Read more.

Companies in the same supply chain influence each other, so sharing information enables more efficient supply chain management. An efficient supply chain must have a symmetry of information between participating entities, but in reality, the information is asymmetric, causing problems. The sustainability of the supply chain continues to be threatened because companies are reluctant to disclose information to others. If companies participating in the supply chain do not disclose accurate information, the next best way to improve the sustainability of the supply chain is to use data from the supply chain to determine each enterprise’s information. This study takes data from the supply chain and then uses machine learning algorithms to find which enterprise the data refer to when new data from unknown sources arise. The machine learning algorithms used are logistic regression, random forest, naive Bayes, decision tree, support vector machine, k-nearest neighbor, and multi-layer perceptron. Indicators for evaluating the performance of multi-class classification machine learning methods are accuracy, confusion matrix, precision, recall, and F1-score. The experimental results showed that LR and MLP accurately predicted companies (tiers), but NB, DT, RF, SVM, and K-NN did not accurately predict companies. In addition, the performance similarity of machine learning algorithms through experiments was classified into LR and MLP groups, NB and DT groups, and RF, SVM, and K-NN groups. Full article

(This article belongs to the Special Issue Simulation and Modelling in Natural Sciences, Economics, Biomedicine and Engineering II)

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16 pages, 6691 KiB

Open AccessArticle

Web-Browsing Application Using Web Scraping Technology in Korean Network Separation Application

by Won-Chi Jung, Jinsu Kim and Namje Park

Symmetry 2021, 13(8), 1550; https://doi.org/10.3390/sym13081550 - 23 Aug 2021

Cited by 4 | Viewed by 3020

Abstract

Attackers’ intrusion into the Enterprise LAN is increasing every year, and the method is becoming more intelligent and crafty. Various security measures against external network intrusions, such as firewalls, are being studied and applied to protect against external attacks, but it is difficult [...] Read more.

Attackers’ intrusion into the Enterprise LAN is increasing every year, and the method is becoming more intelligent and crafty. Various security measures against external network intrusions, such as firewalls, are being studied and applied to protect against external attacks, but it is difficult to respond to increasing attacks. Most institutions block access from the external network for the safety of the internal network and allow access from the internal network to the external network through some restricted ports. In particular, restricted ports in subject to a variety of security techniques to block intrusion into the internal network, but in the process, access to the internal network is only applied by restricted ports, making it inefficient to handle internal requests. Although various studies have been conducted on network isolation to address these challenges, it is difficult to perform tasks efficiently as security functions, such as detecting whether request data is attacked or not, during actual application. The proposed technique is a network-blocking-based network separation technique that converts data from the external network connected to the Internet into symmetry data from which malicious code is removed through an agent and delivers it to the client of the internal network. We propose a technique to provide access. Full article

(This article belongs to the Special Issue Simulation and Modelling in Natural Sciences, Economics, Biomedicine and Engineering II)

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14 pages, 4162 KiB

Open AccessArticle

An Effective Evaluation of Wavelength Scheduling for Various WDM-PON Network Designs with Traffic Protection Provision

by Rastislav Róka

Symmetry 2021, 13(8), 1540; https://doi.org/10.3390/sym13081540 - 23 Aug 2021

Cited by 10 | Viewed by 2275

Abstract

Recently, metropolitan and access communication networks have markedly developed by utilizing a variety of technologies. Their bearer communication infrastructures will be mostly exploiting the optical transmission medium where wavelength division multiplexing techniques will play an important role. This contribution discusses the symmetric sharing [...] Read more.

Recently, metropolitan and access communication networks have markedly developed by utilizing a variety of technologies. Their bearer communication infrastructures will be mostly exploiting the optical transmission medium where wavelength division multiplexing techniques will play an important role. This contribution discusses the symmetric sharing of common optical network resources in wavelength and time domains. Wavelength-Division Multiplexed Passive Optical Networks (WDM-PON) attract considerable attention regarding the next generation of optical metropolitan and access networks. The main purpose of this contribution is presented by the analysis of possible scheduling of wavelengths for our novel hybrid network topologies considered for WDM-PON networks. This contribution briefly deploys adequate Dynamic Wavelength Allocation (DWA) algorithms for selected WDM-PON network designs with the provision of traffic protection when only passive optical components in remote nodes are utilized. The main part of this study is focused on the use of wavelength scheduling methods for selected WDM-PON network designs. For evaluation of offline and online wavelength scheduling for novel hybrid network topologies, a simulation model realized in the Matlab programming environment allows to analyze interactions between various metropolitan and access parts in the Optical Distribution Network (ODN) related to advanced WDM-PON network designs. Finally, wavelength scheduling methods are compared from a viewpoint of utilization in advanced WDM-PON networks designs. Full article

(This article belongs to the Special Issue Simulation and Modelling in Natural Sciences, Economics, Biomedicine and Engineering II)

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19 pages, 3767 KiB

Open AccessArticle

Multilingual Conversational Systems to Drive the Collection of Patient-Reported Outcomes and Integration into Clinical Workflows

by Izidor Mlakar, Valentino Šafran, Daniel Hari, Matej Rojc, Gazihan Alankuş, Rafael Pérez Luna and Umut Ariöz

Symmetry 2021, 13(7), 1187; https://doi.org/10.3390/sym13071187 - 1 Jul 2021

Cited by 14 | Viewed by 3826

Abstract

Patient-reported outcomes (PROs) and their use in the clinical workflow can improve cancer survivors’ outcomes and quality of life. However, there are several challenges regarding efficient collection of the patient-reported outcomes and their integration into the clinical workflow. Patient adherence and interoperability are [...] Read more.

Patient-reported outcomes (PROs) and their use in the clinical workflow can improve cancer survivors’ outcomes and quality of life. However, there are several challenges regarding efficient collection of the patient-reported outcomes and their integration into the clinical workflow. Patient adherence and interoperability are recognized as main barriers. This work implements a cancer-related study which interconnects artificial intelligence (spoken language algorithms, conversational intelligence) and natural sciences (embodied conversational agents) to create an omni-comprehensive system enabling symmetric computer-mediated interaction. Its goal is to collect patient information and integrate it into clinical routine as digital patient resources (the Fast Healthcare Interoperability Resources). To further increase convenience and simplicity of the data collection, a multimodal sensing network is delivered. In this paper, we introduce the main components of the system, including the mHealth application, the Open Health Connect platform, and algorithms to deliver speech enabled 3D embodied conversational agent to interact with the cancer survivors in five different languages. The system integrates cancer patients’ reported information as patient gathered health data into their digital clinical record. The value and impact of the integration will be further evaluated in the clinical study. Full article

(This article belongs to the Special Issue Simulation and Modelling in Natural Sciences, Economics, Biomedicine and Engineering II)

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20 pages, 12797 KiB

Open AccessArticle

Preemptive Prediction-Based Automated Cyberattack Framework Modeling

by Sungwook Ryu, Jinsu Kim, Namje Park and Yongseok Seo

Symmetry 2021, 13(5), 793; https://doi.org/10.3390/sym13050793 - 3 May 2021

Cited by 3 | Viewed by 2876

Abstract

As the development of technology accelerates, the Fourth Industrial Revolution, which combines various technologies and provides them as one service, has been in the spotlight, and services using big data, Artificial Intelligence (AI) and Internet of Things (IoT) are becoming more intelligent and [...] Read more.

As the development of technology accelerates, the Fourth Industrial Revolution, which combines various technologies and provides them as one service, has been in the spotlight, and services using big data, Artificial Intelligence (AI) and Internet of Things (IoT) are becoming more intelligent and helpful to users. As these services are used in various fields, attacks by attackers also occur in various areas and ways. However, cyberattacks by attackers may vary depending on the attacking pattern of the attacker, and the same vulnerability can be attacked from different perspectives. Therefore, in this study, by constructing a cyberattack framework based on preemptive prediction, we can collect vulnerability information based on big data existing on the network and increase the accuracy by applying machine learning to the mapping of keywords frequently mentioned in attack strategies. We propose an attack strategy prediction framework. Full article

(This article belongs to the Special Issue Simulation and Modelling in Natural Sciences, Economics, Biomedicine and Engineering II)

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29 pages, 7786 KiB

Open AccessArticle

Functional Connectivity in Developmental Dyslexia during Speed Discrimination

by Tihomir Taskov and Juliana Dushanova

Symmetry 2021, 13(5), 749; https://doi.org/10.3390/sym13050749 - 25 Apr 2021

Cited by 8 | Viewed by 3063

Abstract

A universal signature of developmental dyslexia is literacy acquisition impairments. Besides, dyslexia may be related to deficits in selective spatial attention, in the sensitivity to global visual motion, speed processing, oculomotor coordination, and integration of auditory and visual information. Whether motion-sensitive brain areas [...] Read more.

A universal signature of developmental dyslexia is literacy acquisition impairments. Besides, dyslexia may be related to deficits in selective spatial attention, in the sensitivity to global visual motion, speed processing, oculomotor coordination, and integration of auditory and visual information. Whether motion-sensitive brain areas of children with dyslexia can recognize different speeds of expanded optic flow and segregate the slow-speed from high-speed contrast of motion was a main question of the study. A combined event-related EEG experiment with optic flow visual stimulation and functional frequency-based graph approach (small-world propensity ϕ) were applied to research the responsiveness of areas, which are sensitive to motion, and also distinguish slow/fast -motion conditions on three groups of children: controls, untrained (pre-D) and trained dyslexics (post-D) with visual intervention programs. Lower ϕ at θ, α, γ1-frequencies (low-speed contrast) for controls than other groups represent that the networks rewire, expressed at β frequencies (both speed contrasts) in the post-D, whose network was most segregated. Functional connectivity nodes have not existed in pre-D at dorsal medial temporal area MT+/V5 (middle, superior temporal gyri), left-hemispheric middle occipital gyrus/visual V2, ventral occipitotemporal (fusiform gyrus/visual V4), ventral intraparietal (supramarginal, angular gyri), derived from θ-frequency network for both conditions. After visual training, compensatory mechanisms appeared to implicate/regain these brain areas in the left hemisphere through plasticity across extended brain networks. Specifically, for high-speed contrast, the nodes were observed in pre-D (θ-frequency) and post-D (β2-frequency) relative to controls in hyperactivity of the right dorsolateral prefrontal cortex, which might account for the attentional network and oculomotor control impairments in developmental dyslexia. Full article

(This article belongs to the Special Issue Simulation and Modelling in Natural Sciences, Economics, Biomedicine and Engineering II)

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19 pages, 4496 KiB

Open AccessArticle

Effective Rotor Fault Diagnosis Model Using Multilayer Signal Analysis and Hybrid Genetic Binary Chicken Swarm Optimization

by Chun-Yao Lee and Guang-Lin Zhuo

Symmetry 2021, 13(3), 487; https://doi.org/10.3390/sym13030487 - 16 Mar 2021

Cited by 8 | Viewed by 1980

Abstract

This article proposes an effective rotor fault diagnosis model of an induction motor (IM) based on local mean decomposition (LMD) and wavelet packet decomposition (WPD)-based multilayer signal analysis and hybrid genetic binary chicken swarm optimization (HGBCSO) for feature selection. Based on the multilayer [...] Read more.

This article proposes an effective rotor fault diagnosis model of an induction motor (IM) based on local mean decomposition (LMD) and wavelet packet decomposition (WPD)-based multilayer signal analysis and hybrid genetic binary chicken swarm optimization (HGBCSO) for feature selection. Based on the multilayer signal analysis, this technique can reduce the dimension of raw data, extract potential features, and remove background noise. To compare the validity of the proposed HGBCSO method, three well-known evolutionary algorithms are adopted, including binary-particle swarm optimization (BPSO), binary-bat algorithm (BBA), and binary-chicken swarm optimization (BCSO). In addition, the robustness of three classifiers including the decision tree (DT), support vector machine (SVM), and naive Bayes (NB) was compared to select the best model to detect the rotor bar fault. The results showed that the proposed HGBCSO algorithm can obtain better global exploration ability and a lower number of selected features than other evolutionary algorithms that are adopted in this research. In conclusion, the proposed model can reduce the dimension of raw data and achieve high robustness. Full article

(This article belongs to the Special Issue Simulation and Modelling in Natural Sciences, Economics, Biomedicine and Engineering II)

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