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In Data Mining classification plays prominent role in predicting outcomes. One of the best supervised classification techniques in Data Mining is Naive Bayes Classification. Naive Bayes Classification is good at predicting outcomes and often outperforms other classification techniques. One of the reasons behind the strong performance of Naive Bayes Classification is due to the assumption of conditional...
In this paper, we propose a novel approach for reader-emotion categorization using word embedding learned from neural networks and an SVM classifier. The primary objective of such word embedding methods involves learning continuous distributed vector representations of words through neural networks. It can capture semantic context and syntactic cues, and subsequently be used to infer similarity measures...
This study presents a small part of the major study, involved in categorizing EEG calmness. The kNN classifier was used to classify EEG features named as asymmetry index (AsI) which was extracted during relaxed state and non-relaxed state. Results from the previous study showed that the EEG behaviour during both states appear to have more than two groups. The group of four EEG behaviours and three...
In this paper, an epileptic seizure event detection algorithm utilizing five features namely singular values, total average power, delta band average power, variance and mean, is proposed. Using CHB-MIT Scalp EEG Database, the calculations of the features are performed over a sliding window of one second. The algorithm was evaluated in terms of accuracy, sensitivity, specificity and failure rate....
We present synthesized findings from a systematic study of user mobility based on a well grounded data set through mining attributes of place-to-place transitions. Next place predictions are the atomic units in constructing end-to-end user mobility trajectories based on historical trace data. These trajectories in turn form models for opportunistic networks to be utilized for providing location and...
Ultra WideBand(UWB) Impulse Radio (IR) has huge advantages in target identification for its broad band, good concealment and strong ability to penetrate obstacles. We apply the UWB-IR technology theory in target identification under the foliage environment. It is different from the traditional radar and wireless sensor network (WSN), for it combines forward communication and target identification...
Developing efficient and usable brain-computer interfaces (BCIs) requires well-designed trade-off between accuracy and computational time. This paper presents a very fast and accurate method to classify asynchronous brain signals from a multi-class mental tasks dataset using time-domain features. Five different statistical time-domain features were extracted to characterize various properties of three...
This paper presents an evaluation of characteristic frequency features in healthy and diseased ECG via k-NN classifier. Initially, a total of 264 segment samples are obtained for healthy, bundle branch blocks, dysrhythmia cardiomyopathy conditions from the PTB Diagnostic ECG database. The signal is preprocessed to obtain the power spectral density. The characteristic frequency for each segment sample...
This paper presents an intelligent system for the classification of ischemic stroke severity. The application of Artificial Neural Network (ANN) is proposed in this study to classify ischemic stroke severity using EEG sub bands Relative Power Ratio (RPR). There were 100 subjects from National Stroke Association of Malaysia NASAM, Petaling Jaya, Selangor, Malaysia divided into Early Group (EG), Intermediate...
This paper addresses two contributions for improving the accuracy and speed of preceding car detection systems. First, it proposes a feature description using Scalable Histogram of Oriented Gradient (SHOG) to solve scale problem of car region on the image. Without resizing the images to a fixed size, it is capable to extract a high-discriminated features with on the same feature space. Second, instead...
Object recognition is a very important task in the field of computer vision. We present a new method for object recognition. The image content is described by the image gradient. Then, the intersection distance is proposed to measure the similarities of the images of different objects. Our method demonstrates good performances on three face data sets.
Users in social networks use hashtags for various reasons, some of them being serving search purposes, gaining attention or popularity or starting new conversation - thus, creating viral memes. In this paper we address the problem of classifying these hashtags in different categories, based on whether they represent a real life event or a social network generated meme. We compute a set of language-agnostic...
We propose a medical named entity recognition for medical question answering system with Indonesian language. The aim is to provide a good medical named entity grammar by only using the available language resource. Our strategy here is to build the features most often used for the recognition and classification of medical named entities. We organize them along two different axes: word-level and list...
This paper presents a real-time human action recognition method based on a modified Deep Belief Network (DBN) model. To recognize human actions, the positions of human joints are taken into account. Each action is made of a sequence of human joint positions. Since the classic DBN cannot deal with temporal information, the proposed method employs the conditional Restricted Boltzmann Machine (cRBM)...
Vehicle Tracking and positioning in GSM networkwith greater accuracy is one of the major popular research topics of Intelligence transportation System and it pass on with the evolution of techniques and methods which enable the data processor to learn and execute activities with the help of Machine learning. Support Vector Machine (SVM) is an isolated classifier which deals with both linear and nonlinear...
Classifier fusion methods are usually used to combine multiple classification decisions and generate better classification results than any single classifier. In order to improve object classification accuracy, it is a common method to assign weights to classifiers based on their importance in a multiple decision system. In this paper we put forward a method to weight different classifiers in classifier...
The cost associated with manually labeling every individual instance in large datasets is prohibitive. Significant labeling efforts can be saved by assigning a collective label to a group of instances (a bag). This setup prompts the need for algorithms that allow labeling individual instances (instance annotation) based on bag-level labels. Probabilistic models in which instance-level labels are latent...
Automatic facial point detection plays arguably the most important role in face analysis. Several methods have been proposed which reported their results on databases of both constrained and unconstrained conditions. Most of these databases provide annotations with different mark-ups and in some cases the are problems related to the accuracy of the fiducial points. The aforementioned issues as well...
Machine Fault Diagnosis and condition monitoring using Acoustic Emission and Vibration Signature is an active research area of much industrial importance. Pre-Processing is an important stage after data acquisition. In this paper we have presented a preprocessing scheme which includes a filter, a smoothing algorithm, a novel segmentation technique and a normalization algorithm which is less affected...
Brain-Computer Interaction (BCI) is a technology developed with the purpose of building a pathway between the brain and computer which is independent of neuromuscular functions. Potential applications in rehabilitation of patients with motor disabilities and video gaming make BCI an important field of research. A task like controlling a prosthetic limb using BCI is challenging. Performing this with...
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