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pro vyhledávání: '"M.A.H. Farquad"'
Akademický článek
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Publikováno v:
IET Software. 13:187-194
Cloud computing is used to connect several number of remote servers through Internet to accumulate and recover large data anywhere and anytime. As of the conventional privacy defending process, there is a possibility for malevolent assault on the sen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a698ed052e2696c0d353465329da3eec
https://doi.org/10.1049/iet-sen.2018.5258
https://doi.org/10.1049/iet-sen.2018.5258
Akademický článek
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Publikováno v:
Learning and Analytics in Intelligent Systems ISBN: 9783030469382
Deep learning is a subset of machine learning, also known as hierarchical learning. It is based on artificial neural network with various stages of representative transforms. Deep neural networks have been applied in different applications like image
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4d9746c5161113b6ea968b3c4d2c81f7
https://doi.org/10.1007/978-3-030-46939-9_13
https://doi.org/10.1007/978-3-030-46939-9_13
Publikováno v:
International Journal of Business Intelligence and Data Mining. 16:1
In this personalised web search (PWS), we utilise a kernel-based FCM for clustering a web pages. For effective personalised web search, queries are optimised using GSA with respect to clustered que...
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https://explore.openaire.eu/search/publication?articleId=doi_________::2a222e53cbb06629c327d04d8d39304d
https://doi.org/10.1504/ijbidm.2020.103848
https://doi.org/10.1504/ijbidm.2020.103848
Publikováno v:
Applied Soft Computing. 19:31-40
Support vector machine (SVM) is currently state-of-the-art for classification tasks due to its ability to model nonlinearities. However, the main drawback of SVM is that it generates “black box” model, i.e. it does not reveal the knowledge learnt
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0a894f235cd624b98bfd19823acdfc01
https://doi.org/10.1016/j.asoc.2014.01.031
https://doi.org/10.1016/j.asoc.2014.01.031
Autor:
M.A.H. Farquad, Indranil Bose
Publikováno v:
Decision Support Systems. 53:226-233
This paper deals with the application of support vector machine (SVM) to deal with the class imbalance problem. The objective of this paper is to examine the feasibility and efficiency of SVM as a preprocessor. Our study analyzes different classifica
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https://explore.openaire.eu/search/publication?articleId=doi_________::702bbb643243b8773870cfa6c7908934
https://doi.org/10.1016/j.dss.2012.01.016
https://doi.org/10.1016/j.dss.2012.01.016
Publikováno v:
Advanced Materials Research. :6527-6533
Credit Scoring is the use of statistical/intelligent models to transform relevant data into numerical measures that guide the management and decision makers to make decisions such as accept/reject, pricing, pay/no pay and collections. This study focu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::57b95c7525f4b950f1c64705225ba9bf
https://doi.org/10.4028/www.scientific.net/amr.433-440.6527
https://doi.org/10.4028/www.scientific.net/amr.433-440.6527
Publikováno v:
Expert Systems with Applications. 37:5577-5589
Support Vector Regression (SVR) solves regression problems based on the concept of Support Vector Machine (SVM) introduced by Vapnik (1995). The main drawback of these newer techniques is their lack of interpretability. In other words, it is difficul
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7537a34d58beb51204b68a9813801475
https://doi.org/10.1016/j.eswa.2010.02.055
https://doi.org/10.1016/j.eswa.2010.02.055
Publikováno v:
ICIT
Support Vector Machines (SVMs) have proved to be good alternative compared to other machine learning techniques specifically for classification problems. The use of optimization methodologies plays a central role in finding solutions of SVMs. In this
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f911d5f4ea4ac68cb0168bbef77b3113
https://doi.org/10.1109/icit.2015.52
https://doi.org/10.1109/icit.2015.52