Kuldeep Singh

Work place: Guru Nanak Dev University Regional Campus Fattu Dhinga, Kapurthala, Punjab, India

E-mail: kuldeepsinghbrar87@gmail.com


Research Interests: Computational Learning Theory, Computer Networks


Kuldeep Singh received his B. tech degree in Electronics & Communication Engineering from Punjab Technical University, Jalandhar, Punjab, India in 2009 & Master’s degree in Electronics & Communication Engineering from Panjab University, Chandigarh, India in 2011. He is working as assistant professor in Guru Nanak Dev University Regional Campus Fattu Dhinga, Kapurthala, Punjab, India. His areas of interest include Wireless Communications, Machine Learning Techniques etc. He has published 25 research paper in various national & International conferences and International Journals.

Author Articles
Fuzzy Logic Based Modified Adaptive Modulation Implementation for Performance Enhancement in OFDM Systems

By Kuldeep Singh

DOI: https://doi.org/10.5815/ijisa.2016.05.07, Pub. Date: 8 May 2016

Adaptive modulation is one of the recent technologies used to improve future communication systems. Many adaptive modulation techniques have been developed for the improving the performance of Orthogonal Frequency Division Multiplexing (OFDM) system in terms of high data rates and error free delivery of data. But uncertain nature of wireless channel reduces the performance of OFDM system with fixed modulation techniques. In this paper, modified adaptive modulation technique has been proposed which adapts to the nature of communication channel based upon present modulation order, code rate, BER and SNR characterizing uncertain nature of communication channel by using a Fuzzy Inference System which further enhances the performance of OFDM systems in terms of high transmission data rate and error free delivery of data.

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A Near Real-time IP Traffic Classification Using Machine Learning

By Kuldeep Singh S. Agrawal B.S. Sohi

DOI: https://doi.org/10.5815/ijisa.2013.03.09, Pub. Date: 8 Feb. 2013

With drastic increase in internet traffic over last few years due to increase in number of internet users, IP traffic classification has gained significant importance for research community as well as various internet service providers for optimization of their network performance and for governmental intelligence organizations. Today, traditional IP traffic classification techniques such as port number and payload based direct packet inspection techniques are rarely used because of use of dynamic port number instead of well-known port number in packet headers and various cryptographic techniques which inhibit inspection of packet payload. Current trends are use of machine learning (ML) techniques for IP traffic classification. In this research paper, a real time internet traffic dataset has been developed using packet capturing tool for 2 second packet capturing duration and other datasets have been developed by reducing number of features of 2 second duration dataset using Correlation and Consistency based Feature Selection (FS) Algorithms. Then, five ML algorithms MLP, RBF, C4.5, Bayes Net and Naïve Bayes are employed for IP traffic classification with these datasets. This experimental analysis shows that Bayes Net is an effective ML technique for near real time and online IP traffic classification with reduction in packet capture duration and reduction in number of features characterizing each application sample with Correlation based FS Algorithm.

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Other Articles