231 Hits in 8.6 sec

Credit Assessment of Bank Customers by a Fuzzy Expert System Based on Rules Extracted from Association Rules

Hamid Eslami Nosratabadi, Ahmad Nadali, Sanaz Pourdarab
2012 International Journal of Machine Learning and Computing  
data using a SOM based method and Then design a contextual classifier by incorporating the fuzzy k-nn rule is with it for better classification [1] .  ...; method of building credit-scoring models using fuzzy rule based classifiers has been suggested in another research which the rule base is learned from the training  ...  ACKNOWLEDGEMENT Here, we appreciate from the Credit Experts of Saman Iranian bank which has given the data and their knowledge and the authority to use them to us as the researchers.  ... 
doi:10.7763/ijmlc.2012.v2.210 fatcat:o2ywvtmop5gp7iy5uydogy22ky

Development of Dialogue Management System for Banking Services

Samir Rustamov, Aygul Bayramova, Emin Alasgarov
2021 Applied Sciences  
All NLU set-ups were followed by the Dialogue Management module that contains a Rule-based Policy to handle FAQs and chitchats as well as a Transformer Embedding Dialogue (TED) Policy to handle more complex  ...  We investigated both built-in LI methods such as fastText and custom machine learning (ML) models trained on the domain-based dataset.  ...  We would like to thank Agasalim Mammadov, Nurana Hasanbayova, Ali Nasrullayev, Emil Alasgarov, and Igbal Huseynov for helping during the data preparation and research process.  ... 
doi:10.3390/app112210995 fatcat:sllrqgr3yrhv3pj4ueqbwpuike

Machine Learning in Banking Risk Management: A Literature Review

Martin Leo, Suneel Sharma, K. Maddulety
2019 Risks  
or problems in risk management that have been inadequately explored and are potential areas for further research.  ...  This paper, through a review of the available literature seeks to analyse and evaluate machine-learning techniques that have been researched in the context of banking risk management, and to identify areas  ...  NN, Bayesian Classifier, DA, Logistic Regression, KNN, Decision tree, Survival Analysis, Fuzzy Rule based system, SVM, Hybrid mode SVM, Classification Trees, Ensemble Learning, CART, C4.5, Bayesian belief  ... 
doi:10.3390/risks7010029 fatcat:laddvv3hxbaxzau5zgjrv5pkhe

Machine Learning for Anomaly Detection: A Systematic Review

Ali Bou Nassif, Manar Abu Talib, Qassim Nasir, Fatima Mohamad Dakalbab
2021 IEEE Access  
NN 1 fuzzy 2 fuzzy + SVM 1 RF + Entropy 1 GA 2 fuzzy K-Means Clustering + ANN 1 RF + LR 1 Gaussian model 2 GA + SOM + SVM 1 RF + RT 1 HTM 2 GA + SVM 1 RLS + ELM + NN 1 IF 2  ...  detection method using the CSI-KNN algorithm" Conf. 2008 [140] A114 "K-Means+ID3: A Novel Method for Supervised Anomaly Detection by Cascading K-Means Clustering and ID3 Decision Tree Learning Methods  ... 
doi:10.1109/access.2021.3083060 fatcat:vv7qthbvqjdz7ksm3yosulk22q

Fuzzy particle swarm optimization (FPSO) based feature selection and hybrid kernel distance based possibilistic fuzzy local information C-means (HKD-PFLICM) clustering for churn prediction in telecom industry

C. K. Praseeda, B. L. Shivakumar
2021 SN Applied Sciences  
The process of information gain and fuzzy particle swarm optimization (FPSO) has been executed by the method of feature selection, besides the divergence kernel-based support vector machine (DKSVM) classifier  ...  The performance of churn prediction has been improved by applying artificial intelligence and machine learning techniques.  ...  , and μ ac 2 i is membership function value of fuzzy rule i for ac 2 .  ... 
doi:10.1007/s42452-021-04576-7 fatcat:oku6amh57jdlljqcgtp5pwjzfu

Network Anomaly Detection: Methods, Systems and Tools

Monowar H. Bhuyan, D. K. Bhattacharyya, J. K. Kalita
2014 IEEE Communications Surveys and Tutorials  
We categorize existing network anomaly detection methods and systems based on the underlying computational techniques used.  ...  Within this framework, we briefly describe and compare a large number of network anomaly detection methods and systems.  ...  It is also partially supported by NSF (US) grants CNS-0851783 and CNS-1154342. The authors are thankful to the funding agencies.  ... 
doi:10.1109/surv.2013.052213.00046 fatcat:nevvj3lcovgllkbhrl5zasfu7m

Hybrid recommender systems: A systematic literature review

Erion Çano, Maurizio Morisio
2017 Intelligent Data Analysis  
Recommender systems are software tools used to generate and provide suggestions for items and other entities to the users by exploiting various strategies.  ...  Based on our findings, most of the studies combine collaborative filtering with another technique often in a weighted way.  ...  Acknowledgments This work was supported by a fellowship from TIM 12 .  ... 
doi:10.3233/ida-163209 fatcat:rqskvan7lrhmtcncsid2dpdata

A Survey of Outlier Detection Methods in Network Anomaly Identification

P. Gogoi, D. K. Bhattacharyya, B. Borah, J. K. Kalita
2011 Computer journal  
In this paper, we present a comprehensive survey of well known distance-based, density-based and other techniques for outlier detection and compare them.  ...  Indeed, for many applications the discovery of outliers leads to more interesting and useful results than the discovery of inliers.  ...  Petrovskiy introduces a fuzzy kernel-based method for real-time network intrusion detection [61] . It involves a kernel-based fuzzy clustering technique.  ... 
doi:10.1093/comjnl/bxr026 fatcat:smdimqftezaufcdgcebjz5exmq

Machine Learning Applied to Banking Supervision a Literature Review

Pedro Guerra, Mauro Castelli
2021 Risks  
The most relevant ML techniques encompass k-nearest neighbours (KNN), support vector machines (SVM), tree-based models, ensembles, boosting techniques, and artificial neural networks (ANN).  ...  Results are presented in a timeline according to the publication date and categorised by time slots.  ...  deep CCAE, fuzzy rules, fuzzy rough nn, fuzzy nn, random tree, random forest public: enterprise financial statement information from Taiwan securities market-Taiwan Economic Journal (2008-2013)  ... 
doi:10.3390/risks9070136 fatcat:gqjub6czvjao3fbqwf34otgwre

Six Sigma Applied Throughout the Lifecycle of an Automated Decision System

Angie Patterson, Piero Bonissone, Marc Pavese
2005 Quality and Reliability Engineering International  
In our approach we have used soft computing (SC) techniques, a collection of computational paradigms (probabilistic, fuzzy, neural, and evolutionary) in which the equation 'model = structure + parameters  ...  The model's accuracy is further improved by using global or local data-driven search methods to tune the structure and/or parameters.  ...  However, both classifiers (fuzzy-rule-based engines and case-based engines) and their common underlying process have a much broader applicability.  ... 
doi:10.1002/qre.629 fatcat:tlq5xy5sbneynev3bjs5dhfdbq

Systematic Literature Review over IDPS, Classification and Application in its Different Areas

Shehroz Afzal, Jamil Asim
In this Survey paper focuses on Classifying various kinds of IDS with the major types of attacks based on intrusion methods.  ...  Numerous intrusion detection methods have been proposed in the literature to tackle computer security threats, which can be broadly classified into Signature-based Intrusion Detection Systems (SIDS) and  ...  Acknowledgement Authors are thankful to the Editorial Team for their constructive support.  ... 
doi:10.52700/scir.v3i2.58 fatcat:xrczlxjg5ncclf2ftxyw3y5zce

Improved classification of large imbalanced data sets using rationalized technique: Updated Class Purity Maximization Over_Sampling Technique (UCPMOT)

Sachin S. Patil, Shefali P. Sonavane
2017 Journal of Big Data  
Availability of data and materials The datasets supporting the conclusions of this article are available in the UCI repository, the causality workbench under pharmacology base and at ProgrammableWeb. https  ...  Consent for publication Not applicable. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.  ...  In [27] , fuzzy rule classification is anticipated as a solution for the multi-class dilemma by merging the pairwise learning with preprocessing.  ... 
doi:10.1186/s40537-017-0108-1 fatcat:khtlj6jgazazrfb6iynydjbbtm

A Survey on Trust Evaluation Based on Machine Learning

Jingwen Wang, Xuyang Jing, Zheng Yan, Yulong Fu, Witold Pedrycz, Laurence T. Yang
2020 ACM Computing Surveys  
Researchers have proposed many methods to use machine learning for trust evaluation. However, the literature still lacks a comprehensive literature review on this topic.  ...  In this article, we perform a thorough survey on trust evaluation based on machine learning. First, we cover essential prerequisites of trust evaluation and machine learning.  ...  It is difficult to formulate the rules of game. Cloud-based It fully integrates ambiguity and randomness and describes the uncertainty of trust.  ... 
doi:10.1145/3408292 fatcat:fem3px673bcfdltackc7bstxji

A Literature Review on Application of Sentiment Analysis Using Machine Learning Techniques

Anvar Shathik J, Krishna Prasad K
2020 Zenodo  
Machine Learning (ML) is a multidisciplinary field, a mixture of statistics and computer science algorithms that are commonly used in predictive and classification analyses.  ...  The goal and primary objectives of this article are to analytically categorize and analyze the prevalent research techniques and implementations of Machine Learning techniques to Sentiment Analysis on  ...  Panoptical view of sentiment analysis technique Different methods and techniques such as machine learning techniques, Lexicon based methods, hybrid methods, Rules-based techniques, and on to logic methods  ... 
doi:10.5281/zenodo.3977576 fatcat:djsvzgiypnfibcvj6swo3pw75u

Outlier Detection Strategies for WSNs: A Survey

Bhanu Chander, G. Kumaravelan
2021 Journal of King Saud University: Computer and Information Sciences  
Hence, this paper presents a comprehensive overview of the state-of-the-art Statistical and Artificial Intelligence (AI) based techniques used in WSNs to detect outliers in the view of architecture, type  ...  Furthermore, each aforementioned outlier detection approach is presented with detailed discussions and future scope for developments.  ...  Typically clustering-based techniques can be classified from unlabeled or unsupervised methods with many defined clustering set-of-rules analyzing by one data point to all available clusters.  ... 
doi:10.1016/j.jksuci.2021.02.012 fatcat:rpgswasszzbgdbkziskhqrqjam
« Previous Showing results 1 — 15 out of 231 results