A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Filters
A Fault Diagnosis Method Based on Semi - Supervised Fuzzy C - Means Cluster Analysis
2015
International Journal on Cybernetics & Informatics
Preserved Fulltext
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]
2019
Damped Harmonic Oscillator [Working Title]
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2022
Intelligent Automation and Soft Computing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2008
International Journal of Approximate Reasoning
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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
2019
International Journal for Research in Applied Science and Engineering Technology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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]
2012
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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
2021
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2014
Communications in statistics. Simulation and computation
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
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
2021
Converter
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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]
2019
Pattern Recognition [Working Title]
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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
2021
Mathematics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2018
Journal of Intelligent & Fuzzy Systems
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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
2017
Discoveries
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
« Previous
Showing results 1 — 15 out of 3,491 results