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102 results on '"Dwivedi, Pragya"'

9. Food Recipe and Nutritional Information Generator

Author

Mishra, Ayush, Gupta, Ayush, Sahu, Arvind, Kumar, Amit, Dwivedi, Pragya, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Singh, Pradeep, editor, Singh, Deepak, editor, Tiwari, Vivek, editor, and Misra, Sanjay, editor

Published
2023
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10. Very Short-Term Load Forecasting with Deep Learning Neural Network in Delhi, India

Author

Singh, Priyanka, Dwivedi, Pragya, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kumar, Rajesh, editor, Ahn, Chang Wook, editor, Sharma, Tarun K., editor, Verma, Om Prakash, editor, and Agarwal, Anand, editor

Published
2022
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11. Load Forecasting for EV Charging Stations Based on Artificial Neural Network and Long Short Term Memory

Author

Kumar, Naval, Kumar, Dinesh, Dwivedi, Pragya, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Woungang, Isaac, editor, Dhurandher, Sanjay Kumar, editor, Pattanaik, Kiran Kumar, editor, Verma, Anshul, editor, and Verma, Pradeepika, editor

Published
2022
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12. Short-Term Electricity Load Forecast Using Hybrid Model Based on Neural Network and Evolutionary Algorithm

Author

Singh, Priyanka, Dwivedi, Pragya, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Dutta, Debashis, editor, and Mahanty, Biswajit, editor

Published
2020
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17. Trust-Enhanced Multi-criteria Recommender System

Author

Goswami, Arpit, Dwivedi, Pragya, Kant, Vibhor, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Pant, Millie, editor, Ray, Kanad, editor, Sharma, Tarun K., editor, Rawat, Sanyog, editor, and Bandyopadhyay, Anirban, editor

Published
2018
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18. Learning Path Recommendation Based on Modified Variable Length Genetic Algorithm

Author

Dwivedi, Pragya, Kant, Vibhor, and Bharadwaj, Kamal K.

Abstract

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 assisting learners so that they can learn more efficiently. In this context, curriculum sequencing is considered as an important concern for developing more efficient personalized e-learning systems. A more effective personalized e-learning recommender system should recommend a sequence of learning materials called learning path, in an appropriate order with a starting and ending point, rather than a sequence of unordered learning materials. Further the recommended sequence should also match the learner preferences for enhancing their learning capabilities. Moreover, the length of recommended sequence cannot be fixed for each learner because these learners differ from one another in their preferences such as knowledge levels, learning styles, emotions, etc. In this paper, we present an effective learning path recommendation system (LPRS) for e-learners through a variable length genetic algorithm (VLGA) by considering learners' learning styles and knowledge levels. Experimental results are presented to demonstrate the effectiveness of the proposed LPRS in e-learning environment.

Published
2018
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29. A Linear Regression Approach to Multi-criteria Recommender System

Author

Jhalani, Tanisha, Kant, Vibhor, Dwivedi, Pragya, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tan, Ying, editor, and Shi, Yuhui, editor

Published
2016
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31. Digital Preservation of Indigenous Knowledge Through Artificial Neural Network: A Study

Author

Dwivedi, Pragya

Abstract

The aim of this study is to conserve the primordial knowledge for the reason that the binaries of indigenous knowledge are either orally-transmitted or transmitted through imitation and demonstration. Writing it down (with their equal graphical relevance) changes some of its fundamental properties, because indigenous knowledge is orally monopolized. The paradox is that, even in contemporary automated epistemology, the indigenous knowledge shuns any form of preservation through libraries or in raw manuscripts. Thus, it means that there are no formal mechanisms or no vital area to preserve this knowledge. In the phase of drowning resources scientists, researchers, practitioners (engrossed to unearth the value of indigenous stigmata) have no path to manage the things back to their ontological trace; they always need some guidance about how the things can be resolved, and this changes the chronological documentation. However, in the 21st century, due to the ascendancy of data centric technicity, there seems to be a way to preserve the orally commuted knowledge system through Artificial Neural Networking (ANN).  Design/ Methodology/ Approach: The design of this study frames out the preservation methods and the process of preservation where the researcher can identify the way to protect the primordial knowledge in view of the fact that the Artificial Neural Network is a set of Algorithms which deals with pattern recognition, image identification, and machine translation. But, its centricity lies in its assimilation of data-processing which mirrors the information processing of the human brain. Just as the human brain follows neuronal probing for its acquiring of new stimuli and the deletion of the old ones; the ANN also modifies itself by counting the variables and their practical usability. The Encoding of data in the neurons (nodes) according to which the processing of information performed by the different algorithms with distinct functioning or implementation of techniques will regulate the working and incremental data received.  Findings: This paper explains how to preserve the Indigenous knowledge in a digital manner with the help of the Artificial Neural Network (ANN). The applicability of ANN is not wholly developed and is still in progress. Therefore, by coming away from traditional normative translation of oral words into written form - the essence of Indigenous knowledge must be preserved through some other means. The Preservation of this knowledge will help to secure the ancient property with the help of technology through which we are able to secure our future.  Originality: This paper will present its mechanisms of ‘datapreservations’ ranging from clustering to feedback algorithms; which will be crucial in the approach of indigenous knowledge preservation. The preserved knowledge, as it is dynamically opposed to the traditional mode of preservations through physical manner – can be accessed to a wide era of civilization, contributing to the creation of an equal hemisphere with rich data sets.  Research Limitation/ Implication: The main limitation of this study is that it focuses the application of the Artificial Neural Network (ANN), but this study is not limited to the theoretical analysis of this mechanism. Therefore, the future implication of this study direct towards a clear practical view of this study which helps many researchers, scientists and the practitioners to change the reality.

Published
2023
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32. Enhancing Recommendation Quality of a Multi Criterion Recommender System Using Genetic Algorithm

Author

Parveen, Rubina, Kant, Vibhor, Dwivedi, Pragya, Jaiswal, Anant K., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Prasath, Rajendra, editor, Vuppala, Anil Kumar, editor, and Kathirvalavakumar, T., editor

Published
2015
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36. A Fuzzy Approach to Multidimensional Context Aware e-Learning Recommender System

Author

Dwivedi, Pragya, Bharadwaj, Kamal K., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Prasath, Rajendra, editor, and Kathirvalavakumar, T., editor

Published
2013
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38. Effective Trust-Aware E-learning Recommender System Based on Learning Styles and Knowledge Levels

Author

Dwivedi, Pragya and Bharadwaj, Kamal K.

Abstract

In the age of information explosion, e-learning recommender systems (ELRSs) have emerged as the most essential tool to deliver personalized learning resources to learners. Due to enormous amount of information on the web, learner faces problem in searching right information. ELRSs deal with the problem of information overload effectively and provide recommendations by taking into consideration the learners' preferences such as learning styles, goals, knowledge levels, learning paths etc. In this paper, we propose a weighted hybrid scheme to recommend right learning resources to a learner by incorporating both the learners' learning styles (LSs) and the knowledge levels (KLs). Further, by elicitation of trust values among learners, we develop a scheme such that for a given active learner, the trustworthy learners having greater knowledge and similar learning style patterns as that of the active learner have greater weightage in recommendation strategy. Experimental results are presented to demonstrate the effectiveness of the proposed scheme. (Contains 7 tables and 8 figures.)

Published
2013

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