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A Fault Diagnosis Method Based on Semi - Supervised Fuzzy C - Means Cluster Analysis

Su-Qun Cao, Xinggang Ma, Youfu Zhang, Limin Luo, Fupeng Yi
2015 International Journal on Cybernetics & Informatics  
With the rapid development of science and technology, the monitoring signal data is numerous and keeps growing fast. Only typical fault samples can be obtained and labeled.  ...  According to this, a novel fault diagnosis method based on semi-supervised fuzzy C-means(SFCM) cluster analysis is proposed.  ...  [13] proposed an ensemble fault diagnosis algorithm based on fuzzy c-means algorithm (FCM) with the Optimal Number of Clusters (ONC) and probabilistic neural network (PNN), called FCM-ONC-PNN.  ... 
doi:10.5121/ijci.2015.4227 fatcat:yt7cv4turrcnhi5xvh37khx26a

Data Mining Technology for Structural Control Systems: Concept, Development, and Comparison [chapter]

Meisam Gordan, Zubaidah Ismail, Zainah Ibrahim, Huzaifa Hashim
2019 Damped Harmonic Oscillator [Working Title]  
According to this categorization, the applications of statistical, machine learning, and artificial intelligence techniques with respect to vibration control system research area are compared.  ...  Structural control systems are classified into four categories, that is, passive, active, semi-active, and hybrid systems.  ...  Acknowledgements This research was funded by the University of Malaya (UM) and the Ministry of Higher Education (MOHE), Malaysia (Grant numbers: IIRG007A, GPF015A-2018 and RG561-18HTM).  ... 
doi:10.5772/intechopen.88651 fatcat:gcdilq4dcjh23njbzdhbevldbm

Optimized Fuzzy Enabled Semi-Supervised Intrusion Detection System for Attack Prediction

Gautham Praveen Ramalingam, R. Arockia Xavier Annie, Shobana Gopalakrishnan
2022 Intelligent Automation and Soft Computing  
This model increases the accuracy of intrusion detection using Machine Learning Methodologies and fuzziness has been used to identify various categories of hazards, and a machine learning approach has  ...  The combined use of fuzziness-based and RNN-IDS is therefore highly suited to high-precision classification, and its efficiency is better compared to that of conventional machine learning approaches.  ...  Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.  ... 
doi:10.32604/iasc.2022.022211 fatcat:u4jmyoynyzfv3mjvq23mneoywm

The Last State of Artificial Intelligence in Project Management [article]

Mohammad Reza Davahli
2020 arXiv   pre-print
However, the most popular PM processes among included papers were project effort prediction and cost estimation, and the most popular AI techniques were support vector machines, neural networks, and genetic  ...  Of the 652 articles found, 58 met the predefined criteria and were included in the review.  ...  ML can be categorized into supervised learning, reinforcement learning, semi-supervised learning, and unsupervised learning [73] .  ... 
arXiv:2012.12262v1 fatcat:q3qxxsb6rbailg7leuoputgphy

Preface

Vito Di Gesù, Roberto Tagliaferri
2008 International Journal of Approximate Reasoning  
We are especially grateful to Thierry Denoeux for his advices, debates, and encouragements during our efforts in preparing and organizing the special issue.  ...  Michele Ceccarelli and Antonio Maratea in their paper ''Improving Fuzzy Clustering of Biological Data by Metric Learning with Side Information'' introduced Semi Supervised methods as a guide in the learning  ...  Fuzzy Soft Nearest Prototype Classification and Fuzzy Labelled Neural GAS algorithms for the construction of nearest prototype classifiers are deeply illustrated and compared in the context of clinical  ... 
doi:10.1016/j.ijar.2007.04.002 fatcat:cpefp3o72nc4bevtabioijbko4

A Survey on Crop Prediction using Machine Learning Approach

Sriram Rakshith. K
2019 International Journal for Research in Applied Science and Engineering Technology  
We consider Artificial Neural Network, Information Fuzzy Network and other Data Mining Techniques.  ...  The complete research comes up to a conclusion that Artificial Neural Network is the suitable technique for our project.  ...  The three methods of data mining are Supervised learning, Unsupervised learning and Semi-supervised learning.  ... 
doi:10.22214/ijraset.2019.4542 fatcat:kjpthue2gnczphjpkb43q5minq

Hypergraph and protein function prediction with gene expression data [article]

Loc Tran
2012 arXiv   pre-print
Thus, in this paper, the three un-normalized, random walk, and symmetric normalized hypergraph Laplacian based semi-supervised learning methods applied to hypergraph constructed from the gene expression  ...  Experiment results show that the average accuracy performance measures of these three hypergraph Laplacian based semi-supervised learning methods are the same.  ...  the full topology of the network and the Artificial Neural Networks and Support Vector Machine are supervised learning methods.  ... 
arXiv:1212.0388v1 fatcat:sf2aopcbpbcptdfughqvbyqnei

Feature Evaluation of Emerging E-Learning Systems Using Machine Learning: An Extensive Survey

Shabnam Mohamed Aslam, Abdul Khader Jilani, Jabeen Sultana, Laila Almutairi
2021 IEEE Access  
The motivation behind this research analysis is to separate the potential outcomes of assessing e-learning models utilizing AI strategies such as Supervised, Semi Supervised, Reinforced Learning advances  ...  As of late, with the progression of AI and man-made brainpower, there has been a developing spotlight on versatile e-learning.  ...  The element selected is the one with the most elevated data gain. 4) ARTIFICIAL NEURAL NETWORKS An artificial neural network (ANN) is a well-known directed characterization method.  ... 
doi:10.1109/access.2021.3077663 fatcat:avwqkzxvufauvjjyebbm3twnpe

The Integration of Artificial Neural Networks and Text Mining to Forecast Gold Futures Prices

Hsin-Hung Chen, Mingchih Chen, Chun-Cheng Chiu
2014 Communications in statistics. Simulation and computation  
For example, we apply OPECDS (available from Appendix) with artificial neural networks (ANNs) and then the numerical results show the proposed OPECDS can be effectively used for data-mining in OPEC oil-dependant  ...  The economic parameters of the proposed dataset include inflation rate, interest rate, OPEC oil production level, silver price, gold price, market index and U.S. dollar index.  ...  ,-The Integration of Artificial Neural Networks and Text Mining to Forecast Gold Futures Prices‖. . The input variables include Inf, Int, Opl, Gol, Sil, Dji, Din, Od and Op at ith period.  ... 
doi:10.1080/03610918.2013.786780 fatcat:hw3jvichb5b7ppihokwfgkczge

Research on the Application of Machine Learning Algorithms in Credit Risk Assessment of Minor Enterprises

Huichao Mi
2021 Converter  
Support Vector Machine and Deep Neural Network, and adopts SMOTE and Undersampling to process imbalanced data.  ...  Under the influence of COVID-19, minor enterprises, especially the manufacturing industry, are facing greater financial pressure and the possibility of non-performing loans is increasing.  ...  Artificial Neural Networks Artificial Neural Networks (ANN) is an important method in machine learning.  ... 
doi:10.17762/converter.220 fatcat:miafkis7vzcwpjfe4kzym36wi4

Pattern Recognition and Its Application in Solar Radiation Forecasting [chapter]

Mahmoud Ghofrani, Rasool Azimi, Mastaneh Youshi
2019 Pattern Recognition [Working Title]  
The neural network learning process can be disrupted by anomalies of wind/solar time-series data, which results in less accurate forecasting.  ...  Accurate forecasting of these sources facilitates planning and operating the electric grid to integrate wind/solar power more reliably and efficiently.  ...  The ANFIS method is a hybrid artificial intelligence method combining learning and generalization ability of neural network with characteristics of fuzzy inference system.  ... 
doi:10.5772/intechopen.83503 fatcat:pynv6e3snndrflaycylutamjga

Machine Learning (ML) in Medicine: Review, Applications, and Challenges

Amir Masoud Rahmani, Efat Yousefpoor, Mohammad Sadegh Yousefpoor, Zahid Mehmood, Amir Haider, Mehdi Hosseinzadeh, Rizwan Ali Naqvi
2021 Mathematics  
unsupervised learning, supervised learning, semi-supervised learning, and reinforcement learning), evaluation methods (simulation-based evaluation and practical implementation-based evaluation in real  ...  The purpose of this paper is to help researchers gain a proper understanding of machine learning and its applications in healthcare.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/math9222970 fatcat:rkdhwxlw4zhsxbtoayzaxoqmwu

Editorial

Srikanta Patnaik, Srikanta Patnaik
2018 Journal of Intelligent & Fuzzy Systems  
Some of the most widely used tools include various frameworks, libraries, and methods are (i) decision tree learning (ii) association rule learning (iii) deep learning (iv) artificial neural networks (  ...  They have used ensemble methods for better performance and demonstrated the comparative experimental results of the proposed algorithm against other familiar semi-supervised learning techniques on benchmark  ... 
doi:10.3233/jifs-169562 fatcat:2qknpgafxjdaza2ppepzpwxreu

Text Classification Techniques: A Literature Review

2018 Interdisciplinary Journal of Information, Knowledge, and Management  
The automation of text classification process is required, with the increasing amount of data and need for accuracy.  ...  However, in spite of the growth and spread of AI in all fields of research, its role with respect to text mining is not well understood yet.  ...  Fuzzy c-means, k-means clustering and Hierarchical clustering are unsupervised learning approaches and co-training, self-training, transductive SVM and graph based methods form the constituents of semi-supervised  ... 
doi:10.28945/4066 fatcat:6dio5bpajjf77lkrs7xdtciveu

Artificial Intelligence versus Doctors' Intelligence: A Glance on Machine Learning Benefaction in Electrocardiography

Victor Ponomariov, Liviu Chirila, Florentina-Mihaela Apipie, Raffaele Abate, Mihaela Rusu, Zhuojun Wu, Elisa A. Liehn, Ilie Bucur
2017 Discoveries  
This review summarizes and cross-compares the current machine learning algorithms applied to electrocardiogram interpretation.  ...  limitations of certain interventions, thus reducing the hospitalization costs and physicians' workload.  ...  Conflict of Interest Liviu Chirila and Raffaele Abate are co-founders of ECUORE LTD, a company developing machine learning solutions for telemonitoring and artificial intelligent analysis in cardiology  ... 
doi:10.15190/d.2017.6 pmid:32309594 pmcid:PMC6941587 fatcat:hleylzko55emllz7letxwgtmgy
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