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Int. J. Fuzzy Log. Intell. Syst.最新文献

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DNN based Classification of ADHD fMRI Data using Functional Connectivity Coefficient 基于DNN的ADHD fMRI数据的功能连接系数分类
Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2020-12-31 DOI: 10.5391/ijfis.2020.20.4.255
N. Chauhan, Byung-Jae Choi
{"title":"DNN based Classification of ADHD fMRI Data using Functional Connectivity Coefficient","authors":"N. Chauhan, Byung-Jae Choi","doi":"10.5391/ijfis.2020.20.4.255","DOIUrl":"https://doi.org/10.5391/ijfis.2020.20.4.255","url":null,"abstract":"Functional magnetic resonance imaging (fMRI) has emerged as a popular research topic in neuroimaging for automated classification and recognition of different brain disorders. Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common behavioral disorders in young children because its underlying mechanism is still not completely under-stood. The use of fMRI data in ADHD research is utilized to reflect the neural mechanism and functional integration of the brain. Alteration in the functional connectivity of the brain is expected to provide useful information for classifying or predicting brain disorders. In this study, a deep neural network (DNN) approach was applied to classify ADHD using functional connectivity-based fMRI data. The functional connectivity coefficient was extracted between regions determined by independent component analysis (ICA) and used to feed the DNN for classification. The DNN model demonstrated an accuracy of 95% with the preprocessed fMRI data from Nilearn, which is a Python module for neuroimaging data.","PeriodicalId":354250,"journal":{"name":"Int. J. Fuzzy Log. Intell. Syst.","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124403671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Neurological Measurement of Human Trust in Automation Using Electroencephalogram 脑电图对自动化信任的神经学测量
Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2020-12-31 DOI: 10.5391/ijfis.2020.20.4.261
Seeung Oh, Younho Seong, Sun Yi, Sangsung Park
{"title":"Neurological Measurement of Human Trust in Automation Using Electroencephalogram","authors":"Seeung Oh, Younho Seong, Sun Yi, Sangsung Park","doi":"10.5391/ijfis.2020.20.4.261","DOIUrl":"https://doi.org/10.5391/ijfis.2020.20.4.261","url":null,"abstract":"In modern society, automation is sufficiently complex to conduct advanced tasks. The role of the human operator in controlling a complex automation is crucial for avoiding failures, reducing risk, and preventing unpredictable situations. Measuring the level of trust of human operators is vital in predicting their acceptance and reliance on automation. In this study, an electroencephalogram (EEG) is used to identify specific brainwaves under trusted and mistrusted cases of automation. A power spectrum analysis was used for a brainwave analysis. The results indicate that the power of the alpha and beta waves is stronger for a trusted situation, whereas the power of gamma waves was stronger for a mistrusted situation. When the level of human trust in automation increases, the use of automatic control increases. Therefore, the findings of this research will contribute to utilizing a neurological technology to measure the level of trust of the human operator, which can affect the decision-making and the overall performance of automation used in industries.","PeriodicalId":354250,"journal":{"name":"Int. J. Fuzzy Log. Intell. Syst.","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133538868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Isolated Spoken Word Recognition Using One-Dimensional Convolutional Neural Network 基于一维卷积神经网络的孤立口语单词识别
Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2020-12-31 DOI: 10.5391/ijfis.2020.20.4.272
J. A. Qadir, Abdulbasit K. Al-Talabani, Hiwa A. Aziz
{"title":"Isolated Spoken Word Recognition Using One-Dimensional Convolutional Neural Network","authors":"J. A. Qadir, Abdulbasit K. Al-Talabani, Hiwa A. Aziz","doi":"10.5391/ijfis.2020.20.4.272","DOIUrl":"https://doi.org/10.5391/ijfis.2020.20.4.272","url":null,"abstract":"Isolated uttered word recognition has many applications in human–computer interfaces. Feature extraction in speech represents a vital and challenging step for speech-based classification. In this work, we propose a one-dimensional convolutional neural network (CNN) that extracts learned features and classifies them based on a multilayer perceptron. The proposed models are tested on a designed dataset of 119 speakers uttering Kurdish digits (0–9). The results show that both speaker-dependent (average accuracy of 98.5%) and speaker-independent (average accuracy of 97.3%) models achieve convincing results. The analysis of the results shows that 9 of the speakers have a bias characteristic, and their results are outliers compared to the other 110 speakers.","PeriodicalId":354250,"journal":{"name":"Int. J. Fuzzy Log. Intell. Syst.","volume":"191 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123338989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Powerset Theory of Fuzzy Soft Sets 模糊软集的幂集理论
Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2020-12-31 DOI: 10.5391/ijfis.2020.20.4.298
J. Močkoř
{"title":"Powerset Theory of Fuzzy Soft Sets","authors":"J. Močkoř","doi":"10.5391/ijfis.2020.20.4.298","DOIUrl":"https://doi.org/10.5391/ijfis.2020.20.4.298","url":null,"abstract":"Fuzzy powerset theory is defined by a monad, and therefore it can be applied in computer science. Fuzzy soft sets generalize fuzzy sets and have considerable application potential in, for instance, decision-making and optimization. In this study, we prove that fuzzy soft sets also give rise to a powerset theory, which is also defined by a monad. As in the case of fuzzy sets, in fuzzy soft set theory, it is possible to use several theoretical constructions requiring the existence of a powerset theory and monads. We describe the construction of fuzzy soft relations as an example of the use of monads in fuzzy soft sets.","PeriodicalId":354250,"journal":{"name":"Int. J. Fuzzy Log. Intell. Syst.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130625789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Intuitionistic Fuzzy Ideals of Ternary Near-Rings 三元近环的直觉模糊理想
Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2020-12-31 DOI: 10.5391/ijfis.2020.20.4.290
W. Nakkhasen
{"title":"Intuitionistic Fuzzy Ideals of Ternary Near-Rings","authors":"W. Nakkhasen","doi":"10.5391/ijfis.2020.20.4.290","DOIUrl":"https://doi.org/10.5391/ijfis.2020.20.4.290","url":null,"abstract":"We define the concept of intuitionistic fuzzy ideals of ternary near-rings as a generalization of fuzzy ideals, and we investigate some of their properties. Moreover, we characterize the notions of Noetherian and Artinian ternary near-rings using their intuitionistic fuzzy ideals.","PeriodicalId":354250,"journal":{"name":"Int. J. Fuzzy Log. Intell. Syst.","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128435838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Development and Analysis of Models for Assessing Predicted Mean Vote Using Intelligent Technologies 利用智能技术评估预测平均投票的模型开发与分析
Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2020-12-31 DOI: 10.5391/ijfis.2020.20.4.324
L. Sansyzbay, B. Orazbayev, W. Wójcik
{"title":"Development and Analysis of Models for Assessing Predicted Mean Vote Using Intelligent Technologies","authors":"L. Sansyzbay, B. Orazbayev, W. Wójcik","doi":"10.5391/ijfis.2020.20.4.324","DOIUrl":"https://doi.org/10.5391/ijfis.2020.20.4.324","url":null,"abstract":"One of the approaches toward determining the degree of microclimate comfort is measuring its individual components: temperature, air velocity, relative humidity, and air quality. A significant disadvantage of this approach is the neglect of the mutual influence of microclimate parameters on each other. To improve the accuracy of determining microclimate comfort, it is necessary to use a complex predicted mean vote (PMV) indicator. The PMV equation is complex and computationally consuming; simplified solutions can be obtained using Fanger’s diagrams, Excel calculation programs, and specialized computer applications. With the development of technology, intelligent microclimate systems are gaining popularity. In this article, for selecting one of the most effective intelligent technologies, models have been developed for assessing the PMV indicator using the frameworks of fuzzy logic and neural networks. The data obtained using the calculation program of the researchers of the Federal State Unitary Enterprise Research Institute (Russia) were used as input parameters for the models’ development. The program’s performance was validated against the PMV parameter values in the ISO 7730:2005 standard, and a good agreement was found. The PMV index values produced by the considered models were compared to the values calculated using the program, to determine the operability and efficiency of the developed models. Our analysis suggests that neural networks perform better on the assessment of thermal comfort, compared with fuzzy systems.","PeriodicalId":354250,"journal":{"name":"Int. J. Fuzzy Log. Intell. Syst.","volume":"354 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125639606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Strong Law of Large Numbers for Fuzzy Random Variables in Fuzzy Metric Space 模糊度量空间中模糊随机变量的强大数律
Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2020-12-31 DOI: 10.5391/ijfis.2020.20.4.278
Reza Ghasemi, M. Rabiei, A. Nezakati
{"title":"Strong Law of Large Numbers for Fuzzy Random Variables in Fuzzy Metric Space","authors":"Reza Ghasemi, M. Rabiei, A. Nezakati","doi":"10.5391/ijfis.2020.20.4.278","DOIUrl":"https://doi.org/10.5391/ijfis.2020.20.4.278","url":null,"abstract":"","PeriodicalId":354250,"journal":{"name":"Int. J. Fuzzy Log. Intell. Syst.","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127539587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Principal Component Analysis for Heart Rate Measurement using UWB Radar 超宽带雷达心率测量的主成分分析
Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2020-09-30 DOI: 10.5391/IJFIS.2020.20.3.211
Le Dang Khanh, Pham Xuan Duong
{"title":"Principal Component Analysis for Heart Rate Measurement using UWB Radar","authors":"Le Dang Khanh, Pham Xuan Duong","doi":"10.5391/IJFIS.2020.20.3.211","DOIUrl":"https://doi.org/10.5391/IJFIS.2020.20.3.211","url":null,"abstract":"This paper proposes a signal processing approach based on principal component analysis (PCA) for monitoring heart rate using an ultra-wideband impulse (UWB) radar. Vital signals including respiration and heart rate is measured by a UWB radar, and then compressed and projected on the main principal component. This projection helps to significantly improve the signal-to-noise ratio in comparison to other conventional methods such as direct fast Fourier transform and complex signal decomposition. Thus, an accurate measurement of heart rate can be obtained. The proposed approach can help improve about 10 dB of heartbeat signal.","PeriodicalId":354250,"journal":{"name":"Int. J. Fuzzy Log. Intell. Syst.","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133482164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
A Fuzzy Logic Expert System for Pricing Digital Services: The Case of Price Adjustment for a Mobile Service Provider 数字服务定价的模糊逻辑专家系统——以某移动服务提供商价格调整为例
Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2020-09-30 DOI: 10.5391/IJFIS.2020.20.3.227
Adeolu O. Dairo, Krisztián Szücs
{"title":"A Fuzzy Logic Expert System for Pricing Digital Services: The Case of Price Adjustment for a Mobile Service Provider","authors":"Adeolu O. Dairo, Krisztián Szücs","doi":"10.5391/IJFIS.2020.20.3.227","DOIUrl":"https://doi.org/10.5391/IJFIS.2020.20.3.227","url":null,"abstract":"As connected smart devices and terminals continue to grow along with digital content, data traffic of mobile service providers is also growing, and the price war in mobile markets is driving traffic growth without a commensurable increase in revenue. As a result, network capital expenditure (CAPEX) investment, quality of experience, and customer experience are under enormous pressure. In a competitive mobile market, strategic pricing may play an essential role in managing this pressure only if appropriate tools are available for the service providers. In this paper, a fuzzy knowledge-based expert pricing system was developed with a focus on solving network traffic, price war, and business revenue challenges in a competitive mobile market. Its core lay in its ability to recommend digital- and data services-related price points within a competitive and price war mobile environment. The proposed pricing system was experimentally evaluated through a pilot conducted on a few segments of a mobile service provider’s customer base in an emerging market and later scaled up to a broader base. Upon implementation, data services revenue increased, and overall gross margin increased with a reduction in data traffic, resulting in better throughput and network quality and, consequently, better customer experience with improved net promoter score.","PeriodicalId":354250,"journal":{"name":"Int. J. Fuzzy Log. Intell. Syst.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124236696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
On Mixed b-Fuzzy Topological Spaces 关于混合b-模糊拓扑空间
Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2020-09-30 DOI: 10.5391/IJFIS.2020.20.3.242
W. Al-Omeri
{"title":"On Mixed b-Fuzzy Topological Spaces","authors":"W. Al-Omeri","doi":"10.5391/IJFIS.2020.20.3.242","DOIUrl":"https://doi.org/10.5391/IJFIS.2020.20.3.242","url":null,"abstract":"This paper describes the construction of a topological space from two different fuzzy topologies using the fuzzy b -q-nbd of a fuzzy point with respect to one topology and the fuzzy b closure of a fuzzy set with respect to another topology. Additionally, some relationships are established between the two adopted topologies and the resulting mixed topology. Finally, we define countability on mixed fuzzy topological spaces and investigate the various quasi-type properties of such spaces.","PeriodicalId":354250,"journal":{"name":"Int. J. Fuzzy Log. Intell. Syst.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129851653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
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