Shanmugatha "Shan" Suthaharan

Dr. Suthaharan received a B.S. in Statistics and Computing with first class honors from University of Jaffna, Sri Lanka in 1981, an M.S. in Computer Science from Dundee University, United Kingdom in 1987, and a Ph.D. in Computer Science from Monash University, Australia in 1996. Before joining University of North Carolina at Greensboro (UNCG) in fall 2001, he was the Chairman of the Computer Science Department at the Tennessee State University (TSU) between spring 1999 and spring 2001 and a lecturer at Monash University between January 1996 and December 1998. During 1995 and 1996, he was a postdoctoral fellow at Monash University. He has been the Director of Computer Science Program and was an elected member of the University Undergraduate Curriculum Committee at UNCG. He teaches undergraduate and graduate courses, Principles of Computer Networks, Cryptography and Security in Computing, Art and Code (an interdeciplinary course) and Senior Project. He taught the following undergraduate and graduate courses in the past: Elementary Data Structures and Algorithm, Operating Systems, Computer Vision and Computer Graphics. He has more than 25 years of international and US teaching experience in Computer Science and related fields. He taught at Jaffna University, Monash University, TSU and UNCG.

There are 11 included publications by Shanmugatha "Shan" Suthaharan :

TitleDateViewsBrief Description
Big data analytics: Machine learning and Bayesian learning perspectives—What is done? What is not? 2018 11 Big data analytics provides an interdisciplinary framework that is essential to support the current trend for solving real-world problems collaboratively. The progression of big data analytics framework must be clearly understood so that novel approa...
Big Data Classification: Problems and Challenges in Network Intrusion Prediction with Machine Learning 2014 11138 This paper focuses on the specific problem of Big Data classification of network intrusion traffic. It discusses the system challenges presented by the Big Data problems associated with network intrusion prediction. The prediction of a possible intru...
Characterization of Differentially Private Logistic Regression 2018 144 The purpose of this paper is to present an approach that can help data owners select suitable values for the privacy parameter of a differentially private logistic regression (DPLR), whose main intention is to achieve a balance between privacy streng...
FLaSKU - A classroom experience with teaching computer networking: Is it useful to others in the field? 2014 50 In general, every educator has a classroom experience that he or she wants to share for the benefit of other educators and students in the field. This paper presents a classroom experience with teaching a computer networking course to both undergradu...
Logistic Map-Based Fragile Watermarking for Pixel Level Tamper Detection and Resistance 2010 2293 An efficient fragile image watermarking technique for pixel level tamper detection and resistance is proposed. It uses five most significant bits of the pixels to generate watermark bits and embeds them in the three least significant b...
Modeling of class imbalance using an empirical approach with spambase dataset and random forest classification 2014 120 Classification of imbalanced data is an important research problem as most of the data encountered in real world systems is imbalanced. Recently a representation learning technique called Synthetic Minority Over-sampling Technique (SMOTE) has been pr...
No-reference visually significant blocking artifact metric for natural scene images 2009 1987 Quantifying visually annoying blocking artifacts is essential for image and video quality assessment. This paper presents a no-reference technique that uses the multi neural channels aspect of human visual system (HVS) to quantify visual impairment b...
Optimization: A Journal of Mathematical Programming and Operations Research 2014 1739 In this article we study support vector machine (SVM) classifiers in the face of uncertain knowledge sets and show how data uncertainty in knowledge sets can be treated in SVM classification by employing robust optimization. We present knowledge-base...
Reduction of queue oscillation in the next generation Internet routers 2007 1027 The Internet routers employing the random early detection (RED) algorithm for congestion control suffer from the problem of chaotic queue oscillation. It is well known that the slowly varying nature of the average queue size computed using an exponen...
Security problems and challenges in a machine learning-based hybrid big data processing network systems 2014 162 The data source that produces data continuously in high volume and high velocity with large varieties of data types creates Big Data, and causes problems and challenges to Machine Learning (ML) techniques that help extract, analyze and visualize impo...
A Software Engineering Schema for Data Intensive Applications 2018 200 The features developed by a software engineer (system specification) for a software system may significantly differ from the features required by a user (user requirements) for their envisioned system. These discrepancies are generally resulted from ...