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Durga Toshniwal
Indian Institute of Technology Roorkee
Machine learningData miningComputer scienceSocial mediaSentiment analysis
41Publications
6H-index
188Citations
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Publications 38
Newest
#1Shalini Jangra (IITR: Indian Institute of Technology Roorkee)
#2Durga Toshniwal (IITR: Indian Institute of Technology Roorkee)H-Index: 6
Abstract Collaborative frequent itemset mining involves analyzing the data shared from multiple business entities to find interesting patterns from it. However, this comes at the cost of high privacy risk. Because some of these patterns may contain business-sensitive information and hence are denoted as sensitive patterns. The revelation of such patterns can disclose confidential information. Privacy-preserving data mining (PPDM) includes various sensitive pattern hiding (SPH) techniques, which ...
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#1Jatin Bedi (IITR: Indian Institute of Technology Roorkee)H-Index: 3
#2Durga Toshniwal (IITR: Indian Institute of Technology Roorkee)H-Index: 6
Abstract With the increasing population and rising living standard, the demand for energy and materials have increased to a greater extent. The accurate estimation of increasing electricity demand is prerequisite for strategies planning, improving revenue, reducing power wastage and stable operation of the energy demand management system. Recent advancements in the field of electricity load forecasting provide powerful tools to capture non-linear energy demand trends and outperform conventional ...
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#1Shivani Sharma (IITR: Indian Institute of Technology Roorkee)H-Index: 3
#2Durga Toshniwal (IITR: Indian Institute of Technology Roorkee)H-Index: 6
Abstract Border based Knowledge hiding techniques (BB-KHT) are widely adopted form of privacy preservation techniques of data mining. These approaches are used to hide sensitive knowledge (confidential information) present in a dataset before sharing or analyzing it. BB-KHT primarily rely on border theory and maximum criterion method for preserving privacy and perpetuating good data quality of sanitized dataset but costs high computational complexity. Further, due to sequential nature, these app...
3 CitationsSource
#1Pratima Kumari (IITR: Indian Institute of Technology Roorkee)
#2Durga Toshniwal (IITR: Indian Institute of Technology Roorkee)H-Index: 6
A novel infectious coronavirus disease (COVID-19) identified in late 2019 has now been labelled as a global pandemic by World Health Organization (WHO). The COVID-19 outbreak has shown some positive impacts on the natural environment. In present work, India is taken as a case study to evaluate the effect of lockdown on air quality of three Indian cities. The variation in concentration of key air pollutants including [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see tex...
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#1Samarth Godara (IITR: Indian Institute of Technology Roorkee)
#2Durga Toshniwal (IITR: Indian Institute of Technology Roorkee)H-Index: 6
Abstract Agricultural policymakers use various types of expert systems to identify agricultural problems and to explore their potential solutions. However, in the current scenario, there is no robust system that can be used to collect and analyze information regarding the problems faced by farmers of the developing countries on a large scale. This article outlines the possible mechanisms through which information and communication technology (ICT) with the use of Knowledge Discovery in Databases...
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#1Udit Sharma (IITR: Indian Institute of Technology Roorkee)
#2Durga Toshniwal (IITR: Indian Institute of Technology Roorkee)H-Index: 6
Last. Shivani Sharma (IITR: Indian Institute of Technology Roorkee)H-Index: 3
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The process of collaborative data mining may sometimes expose the sensitive patterns present inside the data which may be undesirable to the data owner. Sensitive Pattern Hiding (SPH) is a subfield of data mining that addresses this problem. However, most of the existing approaches used for hiding sensitive patterns cause high side-effect on non-sensitive patterns which in-turn reduces the utility of the sanitized dataset. Furthermore, most of them are sequential in nature and are not able to co...
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#1Amit Agarwal (IITR: Indian Institute of Technology Roorkee)H-Index: 8
#1Amit Agarwal (IITR: Indian Institute of Technology Roorkee)H-Index: 2
Last. Durga Toshniwal (IITR: Indian Institute of Technology Roorkee)H-Index: 6
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With the availability of smart devices and affordable data plans, social media platforms have become the primary source of information dissemination across geographically dispersed users/locations. It has shown great potential across different application domains including event detection, opinion analysis, recommendation, and prediction. However, the process of extracting useful information from the collected voluminous social media data during natural hazards is a standing problem that needs s...
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#2Durga ToshniwalH-Index: 6
Last. Manoranjan ParidaH-Index: 12
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#1Narayan Chaturvedi (IITs: Indian Institutes of Technology)
#2Durga Toshniwal (IITs: Indian Institutes of Technology)H-Index: 6
Last. Manoranjan Parida (IITs: Indian Institutes of Technology)H-Index: 12
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#1Amit Agarwal (IITR: Indian Institute of Technology Roorkee)H-Index: 8
#1Amit AgarwalH-Index: 2
Last. Jatin Bedi (IITR: Indian Institute of Technology Roorkee)H-Index: 3
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Traditionally, elections polls have been widely used to analyse trend and to predict likely election results. However, these methods are very expensive and labour intensive. With the widespread development of several social media platforms, a large amount of unstructured data become easily available, which in turn could be processed and analysed to extract meaningful information about several topics and events such as election, sports, natural hazards etc. Hence, in this study, we utilise twitte...
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