Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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Classification of imbalanced data is a vastly explored issue of the last and present decade and still keeps the same importance because data are an essential term today and it becomes crucial when data are distributed into several classes. The term imbalance refers to uneven distribution of data into classes that severely affects the performance of traditional classifiers, that is, classifiers become biased toward the class having larger amount of data. The data generated from wireless sensor networks will have several imbalances. This review article is a decent analysis of imbalance issue for wireless sensor networks and other application domains, which will help the community to understand WHAT, WHY, and WHEN of imbalance in data and its remedies. © The Author(s) 2020.

Department of Information Technology

School of Information Technology and Engineering

Vellore Campus

World University root node

Fulltext

International Journal of Distributed Sensor Networks

A review on classification of imbalanced data for wireless sensor networks

The security and schedule management of an aircraft system has become much more important and very complicated issue because of information age's advances to solve this complication. We are using fuzzy soft set with the decision-making problem in aircraft system. This research uses the Fuzzy Soft Set Decision-Making Method (FSS_DMM) to determine the weight of assessment criteria and to synthesize the score of aircraft. This concept is used to acquire the complete performance value for each alternative, to make the final choice. © 2019 Association for Computing Machinery. All rights reserved.

Department of Software and Systems Engineering

ACM International Conference Proceeding Series

Security threat assessment of aircraft system using FSS

Fuzzy classification is a widely explored research solution of objects in data sciences and engineering. With the span of time, it got new heights with significant improvements according to the needs. Still there are some issues to be discussed and solved in a fuzzy manner; fuzzy classification of imbalanced data is one of them. Consequently, the importance of fuzzy nearest neighbor came into the scenario and deployed in many applications. Various improved crisp nearest neighbor approaches are performing well on imbalanced data-sets, but not much work has done on the fuzzy nearest neighbor for imbalanced data. In this paper, we propose to find out correct memberships of test instances from imbalanced data by merging an adaptive K-nearest neighbor approach to deal with the imbalanced issue and then join it with fuzzy K-nearest neighbor. © 2019, © 2019 IETE.

IETE Journal of Research

An Improved Fuzzy K-Nearest Neighbor Algorithm for Imbalanced Data using Adaptive Approach

With the wide use of smart devices, smart phones, sensors and wireless technology, the Internet of things has changed the perspective of communication and handling of information over the Internet. The devices are used to measure health attributes, to automate the health therapies or to monitor and track real time health attributes. Since all the devices are connected to high speed Internet and smart-phones, people have started to monitor health attributes and medical needs through mobile applications. Such integration of smart devices, Internet and mobile applications results an emerging use of telemedicine and telehealth through Internet of Medical Things (IoMT).Security is concern as IoT application area is evolving vastly long with attacks on devices. Interconnected devices has many application in smart grid, health, military, computing area, daily day-today life like smart vehicle, home, security application etc. Intelligent nodes and platform are working together without human interference in them. Enormous security and privacy threads of IoT applications arise due to ubiquitous and dynamic nature of IoT. Protocols like 6LoWPAN, providing security on communication level that is machine-to-machine communication security through authentication and authorization to void the security attacks on network. To provide the security as communication level, to nullify the security breaches on network, proposed system provides the mutual authentication between M2M devices. Registration, authentication, and key establishment can be done for communication of M2M devices in 6LoWPAN. © 2018 IEEE.

Proceedings - 2018 8th International Conference on Communication Systems and Network Technologies, CSNT 2018

Towards securing real time data in IoMT environment

The paper presents the analytic hierarchy process (AHP) as a potential decision making method for use in the selection of the most suitable material handling (MH) system in an iron and steel industry. In this study, AHP is used in assessing the various material transportation systems employed in a steel manufacturing industry and to decide the best equipment to be used. Information on the use of AHP in evaluating MH equipment is provided and an AHP model is proposed to guide the management of an iron and steel Industry, i.e., JSW Steel Ltd. Most important factors while selecting material transportation equipment and their relative influence on the objective of decision-making model are found. A total of seven decision criteria and five different alternatives are considered for this purpose. Each alternative is evaluated in terms of the decision criteria and the relative importance (or weight) of each criterion is estimated. From the obtained pairwise comparison matrices, the best alternative is chosen. This paper provides a good insight into a decision-making model to guide managers for assessing the various material transportation equipment that are commonly employed in a steel manufacturing plant. © Published under licence by IOP Publishing Ltd.

VIT Unitversity

Department of Manufacturing Engineering

School of Mechanical Engineering

IOP Conference Series: Materials Science and Engineering

Evaluation and selection of material handling equipment in iron and steel industry using analytic hierarchy process

Learning from imbalanced data is one of the burning issues of the era. Traditional classification methods exhibit degradation in their performances while dealing with imbalanced data sets due to skewed distribution of data into classes. Among various suggested solutions, instance based weighted approaches secured the space in such cases. In this paper, we are proposing a new fuzzy weighted nearest neighbor method that optimally handle the imbalance issue of data. Use of optimal weights improve the performance of fuzzy nearest neighbor algorithm for default balanced distribution of data, for the classification of imbalanced data concept of adaptive K is merged with it that apply large K, number of nearest neighbors for large class and small K for small class. We deploy this combination to classify imbalanced data with better accuracy for different evaluation measures. Experimental results affirm that our proposed method perform well than the traditional fuzzy nearest neighbor classification for these type of data sets.

International Journal of Intelligent Engineering and Systems

Improved fuzzy-optimally weighted nearest neighbor strategy to classify imbalanced data

Classification of imbalanced datasets is one of the widely explored challenges of the decade. The imbalance occurs in many real world datasets due to uneven distribution of data into classes, i.e. one class has more instances while others have a few that results in the biased performances of traditional classifiers towards the majority class with large number of instances and ignorance of other classes with less data. Many solutions have been proposed to deal with this issue in various crisp and fuzzy methods. This paper proposes a new hybrid fuzzy weighted nearest neighbor approach to find better overall classification performance for both minority and majority classes of imbalanced data. Benefits of neighbor weighted K nearest neighbor approach i.e. assignment of large weights to small classes and small weights to large classes are merged with fuzzy logic. Fuzzy classification helps in classifying objects more adequately as it determines that how much an object belongs to a class. Experimental results exhibit the improvements in classification of imbalanced data of different imbalance ratios in comparison with other methods.

Classification of imbalanced data using a modified fuzzy-neighbor weighted approach

The fast technological era facilitated with various computational devices leads to the exponential growth of data, need proper treatment; well known data warehousing and data mining terms are directly imply to produce informational processing of these huge datasets. Apart from storing and updating routine data, data mining provide the extraction of valuable knowledge, actually needed for decision making. From scientific data to organizational details, sports to banking data or library to security concerns every field dealing with large amount of data require data mining tools to dig strategic information for their respective requirements. In this study, researchers will study some data mining application areas. © Medwell Journals, 2011.