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Int. J. Fuzzy Syst. Appl.最新文献

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Modified K-Nearest Neighbour Using Proposed Similarity Fuzzy Measure for Missing Data Imputation on Medical Datasets (MKNNMBI) 基于相似度模糊测度的改进k近邻医疗数据缺失数据补全
Int. J. Fuzzy Syst. Appl. Pub Date : 2022-07-01 DOI: 10.4018/ijfsa.306278
B. Bai, N. Mangathayaru, B. Rani
{"title":"Modified K-Nearest Neighbour Using Proposed Similarity Fuzzy Measure for Missing Data Imputation on Medical Datasets (MKNNMBI)","authors":"B. Bai, N. Mangathayaru, B. Rani","doi":"10.4018/ijfsa.306278","DOIUrl":"https://doi.org/10.4018/ijfsa.306278","url":null,"abstract":"Early disease diagnosis is a burning problem in health sector, medical domain and disease management. During analysis, quality of the data can be achieved only if the data is complete. Missing values reduces the efficiency of data analysis task. Researchers proposed various imputation methods but always there was a need for a better imputation method. This paper objective is to propose a method for imputation using proposed similarity fuzzy measure through which we can impute missing values by finding k similar instances called as Modified k-Nearest Neighbour for imputation of missing data (MKNNMBI). The proposed imputation method outperformed when compared with other existing imputation methods MV EM, MV BPCA, MV Ignore, MV KMeans, MV FKMeans, MV KNN, MV MC, MV WKNNimpute, MV SVDimpute, MV SVMimpute, CBC-IM-FUZZY. These imputation methods were studied on different benchmark datasets and tested for performance on different classifiers like C4.5, SVM, kNN, NB and found that the proposed method leads to accurate imputation and improves the accuracy.","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120958380","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
IoT Analytics and ERP Interoperability in Automotive SCM: ANN-Fuzzy Logic Technique for Designing Decision Support Systems 汽车SCM中的物联网分析和ERP互操作性:决策支持系统设计的ann -模糊逻辑技术
Int. J. Fuzzy Syst. Appl. Pub Date : 2022-07-01 DOI: 10.4018/ijfsa.306282
Paul Jayender, G. Kundu
{"title":"IoT Analytics and ERP Interoperability in Automotive SCM: ANN-Fuzzy Logic Technique for Designing Decision Support Systems","authors":"Paul Jayender, G. Kundu","doi":"10.4018/ijfsa.306282","DOIUrl":"https://doi.org/10.4018/ijfsa.306282","url":null,"abstract":"Abstract Objective – The objective of this paper is to understand the potential of Interoperability between ERP and IOT Analytics in enabling the agile performance in Automotive supply chain by exploring the influence between Interoperability, SC Visibility and SCM agile performance and propose design for decision making system using Artificial neural network integration with fuzzy logic technique. Design/methodology/approach – TOE view was used to develop theoretical framework in addition to the elaborate literature review. Empirical analysis on the collected data from professionals in the automotive industry used to conclude on the findings. Findings – The IOT-Analytics and ERP interoperability identified as an enabler of SCM agile performance. Originality/value – The research article provides theoretical and empirical evidence over the IOT analytics and ERP interoperability potential impact in the Automotive SCM with novel approach towards designing effective decision support system using artificial neural network-fuzzy logic integration technique.","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125301032","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
Designing a Fuzzy Q-Learning Power Energy System Using Reinforcement Learning 基于强化学习的模糊q -学习能量系统设计
Int. J. Fuzzy Syst. Appl. Pub Date : 2022-07-01 DOI: 10.4018/ijfsa.306284
J. Avanija, Suneetha Konduru, Vijetha Kura, G. NagaJyothi, Bhanuprakash Dudi, S. ManiNaidu
{"title":"Designing a Fuzzy Q-Learning Power Energy System Using Reinforcement Learning","authors":"J. Avanija, Suneetha Konduru, Vijetha Kura, G. NagaJyothi, Bhanuprakash Dudi, S. ManiNaidu","doi":"10.4018/ijfsa.306284","DOIUrl":"https://doi.org/10.4018/ijfsa.306284","url":null,"abstract":"Modern power and energy systems are becoming more complicated and uncertain as distributed energy resources (DERs), flexible loads, and other developing technologies become more integrated. This brings great challenges to the operation and control. Furthermore, the deployment of modern sensor and smart metres generates a considerable amount of data, which opens the door to fresh data-driven ways for dealing with complex operation and control difficulties. One of the most commonly touted strategies for control and optimization problems is reinforcement learning (RL). Designing a fuzzy Q-learning power energy system using RL technique will control and reduce the problems arranging in the energy system.","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114617619","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
Design and Optimization of Fuzzy-Based FIR Filters for Noise Reduction in ECG Signals Using Neural Network 基于模糊的心电信号FIR滤波器的设计与优化
Int. J. Fuzzy Syst. Appl. Pub Date : 2022-07-01 DOI: 10.4018/ijfsa.312215
V. Tallapragada, D. V. Reddy, V. SureshVarmaK.N., N. BharathiD.V.
{"title":"Design and Optimization of Fuzzy-Based FIR Filters for Noise Reduction in ECG Signals Using Neural Network","authors":"V. Tallapragada, D. V. Reddy, V. SureshVarmaK.N., N. BharathiD.V.","doi":"10.4018/ijfsa.312215","DOIUrl":"https://doi.org/10.4018/ijfsa.312215","url":null,"abstract":"Cardiovascular disease (CVD) has been identified as a threat to human life for decades, with the majority of individuals dying as a result of delayed diagnosis and treatment. An electrocardiogram (ECG) plays a vital role in the prognosis of such an ailment. The presence of noise and artifacts complicates the accurate detection and identification of CVD. As a result, reliable signal recovery tasks necessitate noise removal, which is an inverse problem. The main noises present in electrocardiogram (ECG) signals are EMG noise, electrode motion artifact noise. In this paper, radial basis function (RBF) and multi swarm optimization neural network (MSONN) are used to denoise the ECG signal. The cut-off frequency is calculated using a low-pass filter. By using, fuzzy FIR filtering technique baseline wander noises can be removed. Results show that MOS based approach outperforms existing approaches in terms of accuracy and is observed to be 87% even when the dataset size is small. Further, noises if any exists are also removed by the use of cascaded multiplier less Fuzzy FIR filters","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122253202","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
Fuzzy-Based Social Relationship-Aware Routing Scheme for Opportunistic Networks 机会主义网络中基于模糊的社会关系感知路由方案
Int. J. Fuzzy Syst. Appl. Pub Date : 2022-07-01 DOI: 10.4018/ijfsa.306273
Rani, Amita Malik
{"title":"Fuzzy-Based Social Relationship-Aware Routing Scheme for Opportunistic Networks","authors":"Rani, Amita Malik","doi":"10.4018/ijfsa.306273","DOIUrl":"https://doi.org/10.4018/ijfsa.306273","url":null,"abstract":"Opportunistic network is the current area of research which facilitates message delivery between the nodes where there is no end-to-end connectivity. The most of the existing routing schemes in Opportunistic networks consume high resources and leads to decline in network efficiency. So, designing an efficient routing scheme for OppNets always remains a stimulating task due to high mobility of nodes, absence of an end-to-end connectivity and the lack of further knowledge about the network topology. In this research paper we propose a novel Fuzzy based Social relationshipAware Routing (F-SAR) scheme in which the decision of the best forwarder to forward the message depend on fuzzy inference system considering residual energy, buffer availability and social relationship of nodes. This routing scheme is simulated in “The ONE” simulator and the results shows that it outperforms SnW, Prophet, and FLDEAR in terms of delivery rate, message transmission overhead, and average latency","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115376763","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
Secure Energy-Efficient Load Balancing and Routing in Wireless Sensor Networks With Mediative Micro-ANN Fuzzy Logic 基于中介微神经网络模糊逻辑的无线传感器网络安全节能负载均衡与路由
Int. J. Fuzzy Syst. Appl. Pub Date : 2022-07-01 DOI: 10.4018/ijfsa.306277
Laxmaiah Kocharla, B. Veeramallu
{"title":"Secure Energy-Efficient Load Balancing and Routing in Wireless Sensor Networks With Mediative Micro-ANN Fuzzy Logic","authors":"Laxmaiah Kocharla, B. Veeramallu","doi":"10.4018/ijfsa.306277","DOIUrl":"https://doi.org/10.4018/ijfsa.306277","url":null,"abstract":"The security and life time of network are most import factors in wireless sensor networks (WSN). In this research work Mediative Micro ANN Fuzzy logic(MMAFL) has been proposed, along with load-balance routing algorithm. The designed method can increase WSN’s additional feature like securing packet from message capture and tampering attacks. The aim of the proposed routing algorithm is reduce the overall energy consumption and ensure fair use of node energy, so that lifetime of the network has been automatically increased. Experimentation of the proposed Mediative Micro ANN Fuzzy logic is implemented in MATLAB 2018b software and compared with the existing techniques to prove the effectiveness of the approach. The MMAFL model is most useful for medical, Mobile, and industrial applications for data transferring and network security. The performance measures like accuracy 97.45%, F measure 97.34%, overhead 11.10%, delay 8.96ms, throughput 90%, lifetime 27%, and packet-delivery ration 86.11% had been attained, which are most prominent compared to earlier models.","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115960089","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
Fuzzy Sematic Segmentation and Efficient Classification of Lung Cancer Multi-Dimensional Datasets 肺癌多维数据集的模糊语义分割与高效分类
Int. J. Fuzzy Syst. Appl. Pub Date : 2022-07-01 DOI: 10.4018/ijfsa.306276
Patil Prabhu Dev, S. Patil, Vishwanath R. Hulipalled, Kirankumari Patil
{"title":"Fuzzy Sematic Segmentation and Efficient Classification of Lung Cancer Multi-Dimensional Datasets","authors":"Patil Prabhu Dev, S. Patil, Vishwanath R. Hulipalled, Kirankumari Patil","doi":"10.4018/ijfsa.306276","DOIUrl":"https://doi.org/10.4018/ijfsa.306276","url":null,"abstract":"Lung cancer is one of the leading cause of cancer death around the world. Lung cancer has been the most common cancer worldwide since 1985, both in terms of incidence and mortality. Recognition and prediction of lung cancer at the earliest stage can be very useful to improve the survival rate of patients. Effective and early diagnosis of cancer is one the major challenging task for medical practitioners. In this research work, we propose a novel technique on lung MRI image based segmentation and classification is using fuzzy logic and deep learning. The proposed technique considers multi-dimensional medical dataset modeling and representation for effective diagnosis and prediction. A fuzzy based sematic segmentation with relevance to Region of Interest (RoI) extraction and append deep learning models to customized RoI selection under segmented patches. The multi-layer classification approach is viewed to be an effective and accurate diagnosis method for the prediction of disease at early stage.","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116436401","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
Fuzzy Joint Gaussian-Impulsive Noise Removal Using Joint Distribution Modelling in Sparse Domain 基于稀疏域联合分布建模的模糊联合高斯-脉冲噪声去除
Int. J. Fuzzy Syst. Appl. Pub Date : 2022-07-01 DOI: 10.4018/ijfsa.312216
V. Tallapragada, D. V. Reddy, V. SureshVarmaK.N.
{"title":"Fuzzy Joint Gaussian-Impulsive Noise Removal Using Joint Distribution Modelling in Sparse Domain","authors":"V. Tallapragada, D. V. Reddy, V. SureshVarmaK.N.","doi":"10.4018/ijfsa.312216","DOIUrl":"https://doi.org/10.4018/ijfsa.312216","url":null,"abstract":"Image denoising is trivial. It is considered that when multiple sources of noise act simultaneously such a task tends to be more critical. The distribution of resulting noise will possess irregular structure with heavy tail leading to fuzzy in detection and removal of noise from images. Most mixed noise removal schemes first detect the pixels with noise attack and then attempt to remove the noise. The proposed scheme is a single phase mechanism where the noise detection phase is absent. The proposed scheme uses sparse coding as a base and modifies the weight of the fidelity term so that the heavy tail of mixed noise distribution is approximated to Gaussian distribution. The simulation results prove the superiority of the proposed scheme using peak signal to noise ratio and feature similarity index. Results show that in the severe mixed noise case a PSNR improvement of 1% is achieved, whereas in the intermediate and little mixed noise cases a PSNR improvement of about 4% and 5% ae achieved.","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123732155","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
An Advanced Unscented Kalman Filter and Fuzzy-Based Approach for GPS Position Estimation Real-Time Applications 一种先进的无气味卡尔曼滤波和基于模糊的GPS定位实时估计方法
Int. J. Fuzzy Syst. Appl. Pub Date : 2022-07-01 DOI: 10.4018/ijfsa.306279
K. U. Kiran, S. Rao, K. Ramesh
{"title":"An Advanced Unscented Kalman Filter and Fuzzy-Based Approach for GPS Position Estimation Real-Time Applications","authors":"K. U. Kiran, S. Rao, K. Ramesh","doi":"10.4018/ijfsa.306279","DOIUrl":"https://doi.org/10.4018/ijfsa.306279","url":null,"abstract":"Currently, the necessity for GPS is evolved in each stage throughout several applications, due to the increasing number of applications related to GPS, the need for GPS receiver positioning is increasing in almost every field. This process is a bit like a nonlinear process. To get the exact position of the GPS receiver, the received signal is corrupted due to the many factors that must be rectified. Also, the error of satellite orbit is very important to determine the exact position of GPS device. These errors are minimized statistical signal processing and Adaptative filtering techniques are commonly applied to estimate the GPS receiver position. In this work, estimation of the receiver position is done through the Extended Kalman filter (EKF). The result of this study projects the efficiency of the Unscented Kalman filter (UKF) method which is better than EKF in tracking the GPS receiver position than the EKF.","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130717444","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
Interdependent Attribute Interference Fuzzy Neural Network-Based Alzheimer Disease Evaluation 基于相互依存属性干扰模糊神经网络的阿尔茨海默病评价
Int. J. Fuzzy Syst. Appl. Pub Date : 2022-07-01 DOI: 10.4018/ijfsa.306275
Syed Thouheed Ahmed, M. S. Koti, Muthukumaran Venkatesan, Rose Bindu Joseph, S. S. Kumar
{"title":"Interdependent Attribute Interference Fuzzy Neural Network-Based Alzheimer Disease Evaluation","authors":"Syed Thouheed Ahmed, M. S. Koti, Muthukumaran Venkatesan, Rose Bindu Joseph, S. S. Kumar","doi":"10.4018/ijfsa.306275","DOIUrl":"https://doi.org/10.4018/ijfsa.306275","url":null,"abstract":"Alzheimer’s disease is associated with a fragmental protein deposits termed as biomarkers. These biomarkers are studied and researched with various techniques in improving the performance and accuracy of diagnosis. In this research article, a technique is proposed to extract the attribute of brain MRI datasets. The attributes are processed and computed using a neural networking technique to categorize attribute mapping based on Interdependent Attribute Interference (IAI). The categorized data is teamed with a fuzzy logic to provide a reliable computation rule in decision making. The proposed technique has outperformed the accuracy of disease evaluation and diagnosis with a categorization sensitivity of 89.27% and an accuracy of 93.91%.","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128190695","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
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