Research within a larger, multi-faceted risk assessment project for the Intelligence Community (IC) combines Natural Language Processing (NLP) and Machine Learning techniques to detect potentially malicious shifts in the semantic content of information either accessed or produced by insiders within an organization. Our hypothesis is that the use of fewer, more discriminative linguistic features...

Applications of non-linear kernel Support Vector Machines (SVMs) to large datasets is seriously hampered by its excessive training time. We propose a modification, called the approximate extreme points support vector machine (AESVM), that is aimed at overcoming this burden. Our approach relies on conducting the SVM optimization over a carefully selected subset, called the representative set, of...

After the major reasons of water bloom were analyzed, using the rough set theory and principal component analysis respectively to identify the main factors affecting the forecast algal blooms. On this basis, to take advantage of the Libsvm water bloom prediction model and Elman water bloom prediction model for the shortterm prediction of algal blooms phenomenon respectively. Obtained through th...

Support Vector Machines (SVMs) are a widely used technique for classification, clustering and data analysis. While efficient algorithms for training SVMs are available, dealing with large datasets makes training and classification a computationally challenging problem. In this paper we exploit modern processor architectures to improve the training speed of LIBSVM, a well known software tool whi...

There have been continuous researches in the field of emotion detection through faces of biological species in the last few decades. This was further fuelled by the rise of artificial intelligence which has added a new paradigm to its ongoing research. This paper discusses the role of one of the artificial intelligence techniques, Support vector machines for efficient emotion detection. This st...

This work emphasises on the development of Assamese online character recognition system using HMM and SVM and performs a recognition performance analysis for both models. Recognition models using HTK (HMM Toolkit) and LIBSVM (SVM Toolkit) are generated by training 181 different Assamese Stokes. Stroke and Akshara level testing are performed separately. In stroke level testing, the confusion pat...

Abstract The long-term use of a piezoelectric smart structure make it difficult to judge whether the or lead zirconate titanate (PZT) is damaged when signal changes. If sensor fault occurs, cases and degrees are unknown based on electromechanical impedance method. Therefore, after principal component analysis (PCA) six characteristic indexes, two-component solution that could explain 99.2% vari...