Due to prevalent availability of wireless networks, there is enormous usage of mobile devices among the public in all sectors. Locating the mobile devices using the wireless signal strength received enables transparent and immediate services for both indoor and outdoor users. For indoor users, a smart environment could be provided by giving smart facilities like automating switching on/off of room lights, air conditioning, etc., by finding the user's exact location using their mobile device. Similarly, outdoor users can get services from the mobile service providers based on their current location. In this paper, we consider indoor user localisation as a pattern classification problem and propose to apply fuzzy decision tree (FDT) for locating users. As far as our knowledge is concerned, this is the first attempt to apply FDT for locating users in indoor environment using wireless signal strength received by user's handheld devices. Copyright © 2016 Inderscience Enterprises Ltd.

Swathi Jamjala Narayanan

Department of Software Systems

School of Computer Science and Engineering

Vellore Campus

Bhatt R.B

Paramasivam I.

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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VIT University

User localisation using wireless signal strength - an application for pattern classification using fuzzy decision tree

International Journal of Internet Protocol Technology

Home automation-controlling the fans, lights and other electrical appliances in a house using Internet of things is widely preferred in recent days. In this paper, we propose a web application using which the fans, lights and other electrical appliances can be controlled over the Internet. The important features of the web application is that firstly, we have a chatbot algorithm such that the user can text information to control the functioning of the electrical appliances at home. The messages sent using the chatbot is processed using Natural Language processing techniques. Secondly, any device connected to the local area network of the house can control the devices and other appliances in the house. Thirdly, the web application used to enable home automation also has a security feature that only enables certain users to access the application. And finally, it also has a functionality of sending an email alert when intruder is detected using motion sensors. © 2017 IEEE.

2017 Innovations in Power and Advanced Computing Technologies (i-PACT)

Home automation using IoT and a chatbot using natural language processing

Fuzzy decision tree (FDT) is a powerful top-down, hierarchical search methodology to extract human interpretable classification rules. The performance of FDT depends on initial fuzzy partitions and other parameters like alpha-cut and leaf selection threshold. These parameters are decided either heuristically or by trial-and-error. For given set of parameters, FDT is constructed using any standard induction algorithms like Fuzzy ID3. Due to the greedy nature of induction process, there is a chance of FDT resulting in poor classification accuracy. To further improve the accuracy of FDT, in this paper, the authors propose the strategy called Improved Second Order- Neuro- Fuzzy Decision Tree (ISO-N-FDT). ISO-N-FDT tunes parameters of FDT from leaf node to roof node starting from left side of tree to its right and attains better improvement in accuracy with less number of iterations exhibiting fast convergence and powerful search ability.

International Journal of Fuzzy System Applications

An Improved Second Order Training Algorithm for Improving the Accuracy of Fuzzy Decision Trees

AbstractThis paper investigates the Triangular, Trapezoidal and Gaussian approximation methods for the purpose of induction of Fuzzy Decision Trees (FDT). The generation of FDT is done using a Fuzzy ID3 induction algorithm. In this work three fuzzy partitioning techniques which form the basis for our investigation are given attention, namely Fuzzy C Means clustering (FCM), Grid partitioning and Subtractive clustering (Subclust). Our contribution lies in studying the effect of various approximations on the generation of FDT giving specific attention to the classification accuracy of FDT. In this study we show that the accuracy levels of FDT generated using FCM clustered raw data, bypassing the approximation step, is acceptable and this method has several advantages too. Several computational experiments are conducted and non parametric statistical tests are performed to find if any significant differences exist between the method of bypassing the approximation step and the other methods which include approximation. Ten FDTs are developed and used in this study. These FDT’s differ in their fuzzy partitioning techniques and the approximation methods used.

Fulltext

Cybernetics and Information Technologies

A Study on the Approximation of Clustered Data to Parameterized Family of Fuzzy Membership Functions for the Induction of Fuzzy Decision Trees

Fuzzy decision tree (FDT) induction is a powerful methodology to extract human interpretable classification rules. Due to the greedy nature of FDT, there is a chance of FDT resulting in poor classification accuracy. To improve the accuracy of FDT, Bhatt and Gopal (2006) have proposed a back propagation strategy, where the interpretability of derived fuzzy rules is affected, as the certainty factor of the rules does not lie within the theoretical bounds of 0 and 1. To retain the human interpretability of fuzzy rules, and to make rules consistent with fuzzy set theory, we restrict the values of certainty factor to lie within theoretical bounds using the concept of gradient projection over neuro fuzzy decision tree and the model is named as Gradient Projected-Neuro-Fuzzy Decision Tree (GP-N-FDT). Here, the parameters of FDT developed using Fuzzy ID3 algorithm are fine tuned using GP-N-FDT strategy to improve the classification accuracy. Copyright © 2014 Inderscience Enterprises Ltd.

International Journal of Advanced Intelligence Paradigms

Induction of fuzzy decision trees and its refinement using gradient projected-neuro-fuzzy decision tree

Clustering is the process of identifying groups of similar data fulfilling certain criteria. Fuzzy c-means clustering algorithm generates cluster centers and degree of memberships of each pattern to each fuzzy cluster. However, this clustered raw data is not of much benefit for the symbolic representation of fuzzy rule base which can be generated using standard algorithms like fuzzy decision trees and others. Also human interpretability is improved when clustered raw data is represented by Triangular, Trapezoidal, or Gaussian kind of membership functions, rather than representing as matrix of raw membership values. The convex hull method for the estimation of trapezoidal membership functions from the clustered raw data is of limited use and many a times generates membership functions that are either highly overlapped or highly separated. In this article, two heuristic algorithms are presented for the estimation of parameterized family of membership functions, namely, triangular and trapezoidal. Each of these algorithms has been explained formally and then stated in pseudo code form and illustrated with a sample dataset. Finally, the practical application of these algorithms is given in the context of our recent research. © 2012 IEEE.

Journal	International Journal of Internet Protocol Technology
Publisher	Inderscience Publishers
ISSN	1743-8209
Open Access	0