Výsledky vyhledávání

Facial gender recognition using Gabor-DCT feature extraction

Autor: Vibhor Kant, Shubh Lakshmi Agrwal, Ayushi Jhanwar, Sandeep Kumar Gupta, Kuldeep Goswami

Publikováno v: Journal of Statistics and Management Systems. 22:719-728

Facial Gender Identification has vast application in human computer interaction, determines customer profile in shopping centers, and restricted permission to enter in prohibited zone, crim...

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9717168437bc130014e8d9a567375d59
https://doi.org/10.1080/09720510.2019.1609728

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A hybrid method based on neural network and improved environmental adaptation method using Controlled Gaussian Mutation with real parameter for short-term load forecasting

Autor: Vibhor Kant, Pragya Dwivedi, Priyanka Singh

Publikováno v: Energy. 174:460-477

Load forecasting is a challenging task in power markets that require attention in generating accurate and stable load to deal with planning and management strategies. In past few years, several intelligence-based models have been introduced for preci

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::4221e6fb69a56ab527a0063d18a61fdc
https://doi.org/10.1016/j.energy.2019.02.141

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A Review and Classification of Multi-Criteria Recommender Systems

Autor: Vibhor Kant, Shweta Gupta

Publikováno v: 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS).

Recommender systems (RSs) are personalization tools that gives recommendations for items to users by exploiting various methods. Conventional collaborative filtering (CF) based RSs provide suggestions to users based on overall rating of items which i

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::aac8d6164e358e5dafc5732754fd0aa4
https://doi.org/10.1109/iciccs48265.2020.9120983

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Recommendation to Group of Users Using the Relevance Concept

Autor: Saumya Jain, Vibhor Kant, Vivek Kumar

Publikováno v: Machine Intelligence and Signal Processing ISBN: 9789811513657

Group recommender systems (GRSs) have played an important role in numerous online applications by providing recommendation to the group of users where satisfaction of the entire group is a major concern. In traditional GRSs, the relevance of all the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8b841bee34e387e79a4460dd552222d3
https://doi.org/10.1007/978-981-15-1366-4_25

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Tags and Item Features as a Bridge for Cross-Domain Recommender Systems

Autor: Vibhor Kant, Pragya Dwivedi, Ashish Kumar Sahu

Publikováno v: Procedia Computer Science. 125:624-631

Collaborative filleting is one of the widely implemented techniques in the area of recommender systems. But it suffers from data sparsity problem. To address that problem, cross-domain recommender systems (CDRSs) have been emerged to solve the data s

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::6c127749adcca8c4fbd3590c47c14e1a
https://doi.org/10.1016/j.procs.2017.12.080

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Learning path recommendation based on modified variable length genetic algorithm

Autor: Kamal Kant Bharadwaj, Pragya Dwivedi, Vibhor Kant

Publikováno v: Education and Information Technologies. 23:819-836

With the rapid advancement of information and communication technologies, e-learning has gained a considerable attention in recent years. Many researchers have attempted to develop various e-learning systems with personalized learning mechanisms for

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::cd196c32b376bb649e5de33cacc08e17
https://doi.org/10.1007/s10639-017-9637-7

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Enhanced multi-criteria recommender system based on fuzzy Bayesian approach

Autor: Vibhor Kant, Pragya Dwivedi, Tanisha Jhalani

Publikováno v: Multimedia Tools and Applications. 77:12935-12953

In the area of recommender systems, collaborative filtering is widely used technique for recommending appropriate items to a user based on the available ratings given by similar users. Most recommender systems (RSs) work only on the single criterion

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::5cbbe8ac844dd2e51bbfa36d7c6e00b9
https://doi.org/10.1007/s11042-017-4924-2

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