Výsledky vyhledávání - "N. Badrinath"

Customer Purchase Prediction and Potential Customer Identification for Digital Marketing Using Machine Learning

Autor: Malla Sudhakara, Bhavya K. R., M Rudra Kumar, N. Badrinath, K. Rangaswamy

Publikováno v: Advances in Business Information Systems and Analytics ISBN: 9781668442463

In recent years, digital marketing has surpassed traditional marketing as the preferred technique of reaching customers. Researchers and academics may utilize it for social media marketing and for predicting client buy intent, among other application

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1297f5b015f813e9e456928ebbaa2220
https://doi.org/10.4018/978-1-6684-4246-3.ch006

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Akademický článek

"Sustaining Real Estate Sector through Technological Interventions: Purchase Intention of Residential Apartment Buyers". (German)

Autor: S Shastry, Venkatesh, M N, Badrinath, N, Dinesh

Publikováno v: European Property Law Journal; Nov2022, Vol. 11 Issue 1/2, p98-104, 7p

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Classification and Prediction of Erythemato-Squamous Diseases Through Tensor-Based Learning

Autor: K. S. Ravichandran, R. Krishankumar, G. Gopinath, N. Badrinath, J. Premaladha

Publikováno v: Proceedings of the National Academy of Sciences, India Section A: Physical Sciences. 90:327-335

The paper proposes a classification algorithm based on support tensor machines which finds the maximum margin between the tensor spaces. The proposed algorithm has been deployed to classify erythemato-squamous diseases (ESDs) with the help of its fea

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b71196f5d34a32c9bf61a03f78b166fb
https://doi.org/10.1007/s40010-018-0563-x

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Analysis and design of axially loaded columns with ternary mineral admixtures and glass fiber for sustainable concrete composite

Autor: N Badrinath, J Bramha Chari Kanneganti

Publikováno v: IOP Conference Series: Earth and Environmental Science. 796:012060

Concrete is a composition of cement, aggregates, and H2O. An increase in the usage of cement for concrete leads to an increase in CO2 emissions and non-renewable resource usage is also increasing, which is very problematic for future generations. So,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d8d8ad51d0f5fcdd2bf4ce6858129c4f
https://doi.org/10.1088/1755-1315/796/1/012060

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Estimation of automatic detection of erythemato-squamous diseases through AdaBoost and its hybrid classifiers

Autor: K. S. Ravichandran, N. Badrinath, R. Girish Soundhar, G. Gopinath

Publikováno v: Artificial Intelligence Review. 45:471-488

This paper focuses on efficient techniques based on AdaBoost and its hybrid (AdaBoost-SVM) classifiers for an automatic detection of the erythemato-squamous diseases (ESD). The classification of ESD requires an enormous amount of computational effort

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::30e8a5ee97cae640fbc974efec321b73
https://doi.org/10.1007/s10462-015-9436-8

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A Survey on Utilization of the Machine Learning Algorithms for the Prediction of Erythemato Squamous Diseases

Autor: N. Badrinath, G. Gopinath

Publikováno v: Research Journal of Applied Sciences, Engineering and Technology. 7:3883-3887

The aim of this study list the contributions of various machine learning algorithms for the prediction of Erythemato Squamous Diseases (ESDs) and it is very useful for the budding researchers to do research in this field. In the advent of ozone deple

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3e438bf4314ecb18cca3b90fe8fc717e
https://doi.org/10.19026/rjaset.7.746

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Review of Meta-Heuristic Optimization based Artificial Neural Networks and its Applications

Autor: K. Vetrivel, N. Badrinath, D. Devikanniga

Publikováno v: Journal of Physics: Conference Series. 1362:012074

There are several meta-heuristic optimization algorithms developed on inspiration from nature. Artificial neural network proves to be efficient among other machine learning techniques. The efficiency of classification and prediction is improved by op

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3b95c13295af69e2e53c96eca03a59a6
https://doi.org/10.1088/1742-6596/1362/1/012074

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