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Nidhika Yadav

Bio: Nidhika Yadav is an academic researcher. The author has contributed to research in topics: Rough set & Fuzzy classification. The author has an hindex of 1, co-authored 1 publications receiving 8 citations.

Papers
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Posted ContentDOI
03 Jul 2015-viXra
TL;DR: This paper discusses about rough sets and fuzzy rough sets with its applications in data mining that can handle uncertain and vague data so as to reach at meaningful conclusions.
Abstract: Rough set theory is a new method that deals with vagueness and uncertainty emphasized in decision making. The theory provides a practical approach for extraction of valid rules fromdata.This paper discusses about rough sets and fuzzy rough sets with its applications in data mining that can handle uncertain and vague data so as to reach at meaningful conclusions.

8 citations


Cited by
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Journal ArticleDOI
TL;DR: This research study applies the combinative theories of rough fuzzy sets and rough fuzzy digraphs to extract granular structures and develops and implements some algorithms of the proposed models to granulate these social networks.
Abstract: An extraction of granular structures using graphs is a powerful mathematical framework in human reasoning and problem solving. The visual representation of a graph and the merits of multilevel or multiview of granular structures suggest the more effective and advantageous techniques of problem solving. In this research study, we apply the combinative theories of rough fuzzy sets and rough fuzzy digraphs to extract granular structures. We discuss the accuracy measures of rough fuzzy approximations and measure the distance between lower and upper approximations. Moreover, we consider the adjacency matrix of a rough fuzzy digraph as an information table and determine certain indiscernible relations. We also discuss some general geometric properties of these indiscernible relations. Further, we discuss the granulation of certain social network models using rough fuzzy digraphs. Finally, we develop and implement some algorithms of our proposed models to granulate these social networks.

13 citations

Journal ArticleDOI
TL;DR: The data mining of the traffic vehicles with rough set theory was made and it was shown that it is possible to generate the decision rules of the number of vehicles at the specific points in the city.
Abstract: Often, it is difficult to interpret and use the large size of data obtained from the experiment. In addition, the generated information can be unprecise. The rough set theory besides probability theory, fuzzy set theory and many others in recent years is very often used by scientists to solve problems of data mining. In the paper the data mining of the traffic vehicles with rough set theory was made. With this theory it was shown that it is possible to generate the decision rules of the number of vehicles at the specific points in the city. On the basis of 120 objects 16 well-defined linguistic decision rules were obtained.

7 citations

Proceedings ArticleDOI
01 Aug 2017
TL;DR: This paper briefly explains the abstract of fuzzy sets, advanced fuzzy sets and their hybrids, which include rough sets, intuitionistic fuzzy set, interval-valued fuzzy setsand soft sets.
Abstract: Fuzzy Sets were proposed several years ago with various extensions in later years. Each extension has advantages over the fuzzy sets. Rough sets are used to handle incomplete information. Interval-valued fuzzy sets deal with uncertainty and vagueness. Intuitionistic fuzzy sets contain a sub-interval hesitation degree that lies between membership and non-membership degrees. Soft sets overcome the problem of insufficiency of parameterization. Advanced fuzzy sets have myriad number of advantages due to their applications in realistic examples. This paper briefly explains the abstract of fuzzy sets, advanced fuzzy sets and their hybrids. Advanced fuzzy sets include rough sets, intuitionistic fuzzy sets, interval-valued fuzzy sets and soft sets.

5 citations

Book ChapterDOI
06 Dec 2018
TL;DR: This paper proposes a hybrid novel method for handling the handoff mechanism based on Fuzzy rough set theory (FRST) with Support Vector machine (SVM), which enables the decision making stage of the handoffs process more tenable and productive.
Abstract: Spectrum handoff management is an important issue which needs to be addressed in Cognitive Radio Networks (CRN) for interminable connectivity and productive usage of unallocated spectrum for the unlicensed users. Spectrum handoff which comes under the phase of Spectrum mobility in CRN plays a vital role in ensuring seamless connectivity which is quite exigent. Handoff process in general comes under active and proactive types. The intelligent and hybrid handoff methods which combines both these types based on the network conditions proves to be quite satisfactory in the recent works. This paper proposes a hybrid novel method for handling the handoff mechanism based on Fuzzy rough set theory (FRST) with Support Vector machine (SVM), which enables the decision making stage of the handoff process more tenable and productive. The implied method predicts the node wherein handoff is to be initiated in the lead through which the handoff delay time and number of handoffs are minimized. The experimental results are compared with the previously proposed hybrid schemes including Fuzzy genetic algorithm (FGA) based handoff, FGA with cuckoo search (CS) optimization technique, FRS with CS and the findings portray the suggested methodology attains better prediction mechanism with minimal handoffs.

4 citations

Journal ArticleDOI
TL;DR: The developed S2M-WSN system would definitely bridge the information gap between end-users and domain experts for sustainable growth of agriculture.
Abstract: This paper presents the design of a (i) soil sensing and monitoring wireless sensor network (S2M-WSN) that can sense and monitor farmland with distributed and networked motes equipped with multiple soil sensors and (ii) a prototype decision support system (DSS) that analyses the soil data received from S2M-WSN using statistical methods, stored rules and knowledge base (KB) and auto-transmits alerts on the mobile phones of end-users. Both S2M-WSN and the DSS are integrated through (iii) predefined data frame format and interface protocol. The developed system has been deployed both in the laboratory and real crop conditions for verification, validation and performance evaluation. The system is evaluated by considering various performance metrics such as ambiguities associated with data, data packet delivery rates, support delivery performance ratio, system response time, and resultant plant growth. The developed system empowers the end users with assistance in on-field decision making through relevant advisories and alerts at right time on their mobile phones. The authors believe that the developed system would definitely bridge the information gap between end-users and domain experts for sustainable growth of agriculture.

4 citations