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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...
Currently, There are many E-commerce websites around the internet world. These E-commerce websites can be categorized into many types which one of them is C2C (Customer to Customer) websites such as eBay and Amazon. The main objective of C2C websites is an online market place that everyone can buy or sell anything at any time. Since, there are a lot of products in the E-commerce websites and each...
In this paper we investigate the role of different temporal windows in classification of functional near-infrared spectroscopy (fNIRS) signals corresponding to mental arithmetic and mental counting for development of a brain-computer interface. Signals are acquired from the prefrontal cortex of four healthy subjects during mental arithmetic and mental counting tasks using a continuous-wave fNIRS system,...
To add more value on YouTube, a popular portal of social media clips, it is worth recognizing automatically the mood of a media clip using the comments given to such clip. This paper presents a method to classify emotion of a Thai media clip on YouTube using the comments given to the clip. Six basic emotions considered are Anger, Disgust, Fear, Happiness, Sadness and Surprise. Performances using three...
The paper proposes cognitive learning technique for predicting the destination in a video conference being held over an organizational network. The dataset comprised of 22801 connectivity records of video conferences held during the year 2010–2013. Naive Bayes, k-NN and decision tree were trained on the dataset and the performance of the learning algorithms were evaluated. The destination has been...
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...
Word level Script and language identification is a process of separating the script and language of each word present in a printed or handwritten multi-script document. It is an essential part of a multi-lingual Optical Character Recognizer (OCR). Most of the OCRs are solely designed for a single script. So it can't convert a document which is written in more than one script. This paper explained...
Stress is a mental condition that can effects the brain electrical activity to be different from the normal state. This brain cognitive change can be measured using EEG. The objective of this paper is to classify stress subjects based on EEG signal using SVM. The data which are used to represent stress subjects were taken from the residents of Pusat Darul Wardah; a shelter centre for troubled women...
Good performance of pedestrian detection in an automatic driving system is a necessary task. Many pedestrian detection algorithm use Histogram Oriented Gradient (HOG) for feature extraction and Support Vector Machine (SVM) for classification. Some papers use additional features with HOG, such as Local Binary Pattern (HOG-LBP), to improve the performance. Neural Network and Extreme Learning Machine...
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...
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...
People play different roles in various social networks. Even in a single network, people may interact with others based on different roles, and there are various relationships among them. However, current research usually treats all relationships homogeneously (i.e. friendship). In this paper, we try to identify different types of relationship (family, colleague, and social) within social networks...
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...
Breast cancer is the second of the deadliest cancers causing women mortality around the world. The early prediction of breast cancer is the key to reduce women mortality. The major sign of breast cancer is the occurrence of microcalcification clusters in the breast. To efficiently diagnose the breast cancer, an efficient classification system for microcalcification in digital mammogram image is proposed...
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...
We propose in this paper, Face2Mus, a mobile application that streams music from online radio stations after identifying the user's emotions, without interfering with the device's usage. Face2Mus streams songs from online radio stations and classifies them into emotion classes based on audio features using an energy aware support vector machine (SVM) classifier. In parallel, the application captures...
This paper focuses on respiratory phase detection and classification without the help of the airflow measurements. Instead of using the airflow measurements to identify breathing phases, the proposed work depends on advanced digital signal processing techniques to process the acoustic signal of respiration that was collected using a microphone placed in front of the subject's nose. The recorded signal...
The Pedestrian detection using Histograms of Oriented Gradients (HOG) is the most popular method to detect a human from a picture. However, it, calculates the HOG description, will cost too much time and can't meet the real-time request for detecting pedestrian from the video surveillance system. In this paper we present a novel algorithm for detecting a human from a video. Firstly, The improved approach...
The use of digital technology is growing at a very fast pace which led to the emergence of systems based on the cognitive infocommunications. The expansion of this sector impose the use of combining methods in order to ensure the robustness in cognitive systems.
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