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In this paper, we analyse the emotion of children's stories in sentence level by considering the context information. We demonstrate that the emotion of a sentence is not only dependent on its content, but also affected by its neighbours in a story. A Hidden Markov Model (HMM) based method is proposed to model the emotion sequence and to detect whether a sentence is neutral or not. We show the important...
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...
Disaggregating total household's energy data down to individual appliances via non-intrusive appliance load monitoring (NALM) has generated renewed interest with ongoing or planned large-scale smart meter deployments worldwide. Of special interest are NALM algorithms that are of low complexity and operate in near real time, supporting emerging applications such as in-home displays, remote appliance...
Previous works on outdoor traffic sign recognition and classification have been demonstrated useful to the driver assistant system and the possibility to the autonomous vehicles. This motivates our research on the assistance for visual impairment or visual disabled pedestrians in the indoor environment. In this paper, we build an indoor sign database and investigate the recognition and classification...
Logo identification and classification have received considerable attention from both the machine learning and computer vision communities. Vehicle logo recognition (VLR) is used to recognise accurately the manufacturer of a vehicle by using its iconic logo. A VLR system in addition to license plate recognition aims to increase the confidence of vehicle monitoring systems in private environments such...
Handwriting recognition is the ability of a computer to understand handwritten inputs from users. Generally it includes preprocessing, feature extraction, and classifier training. In this paper, we will develop a handwriting digit recognition system by using Deep Boltzmann Machine (DBM) together with the Support Vector Machine (SVM). DBM is a deep learning technique to learn high level features from...
The prevalent use of Online Social Networks (OSN) and the anonymity and lack of accountability they inherent from being online give rise to many problems related to finding the connection between the massive amount of text data on OSN and the people who actually wrote them. Analyzing text data for such purposes is called authorship analysis. This work is focused on one specific type of authorship...
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...
With the growing use of biometric authentication systems in the past years, spoof fingerprint detection has become increasingly important. In this work, we implement and evaluate two different feature extraction techniques for software-based fingerprint liveness detection: Convolutional Networks with random weights and Local Binary Patterns. Both techniques were used in conjunction with a Support...
A method is proposed to distinguish patients with depression from healthy persons using data measured by Functional Near Infrared Spectroscopy (FNIRS) during a cognitive task. Firstly, General Linear Model (GLM) is used to extract features from 52-channel FNIRS data of patients with depression and normal healthy persons. Then a Support Vector Machine (SVM) classifier is designed for classification...
Several methods for object category recognition in RGB-D images have been reported in literature. These methods are typically tested under the same conditions (which we can consider a “domain” in a restricted sense) such as viewing angles, distances to the object as well as lightening conditions on which they are trained. However, in practical applications one often has to deal with previously unseen...
Quran is the holy book for Muslims around the world. Since it was revealed to the Prophet Muhammad (PBUH) before about 14 hundreds years, Quran is preserved in all imaginable ways from distortion. The rapid and huge growth of digital media and internet usage, cause a wide spread of the Quranic knowledge as well as Quranic Verses, scripts, Translations, and many other Quranic sciences in its digital...
An MQDF-CNN hybrid model is presented for offline handwritten Chinese character recognition. The main idea behind MQDF-CNN hybrid model is that the significant difference on features and classification mechanisms between MQDF and CNN can complement each other. Linear confidence accumulation and multiplication confidence criteria are used for fusion outputs of MQDF and CNN. Experiments have been conducted...
This paper introduces new handwritten databases of selected words in the five Middle-Eastern languages of Arabic, Dari, Farsi, Pashto and Urdu. The databases share a common lexicon of forty words that are related to finance and are used in daily life. The five databases have been collected from over 1600 native writers located in four countries. Recognition results for each of the databases are also...
Sentiment analysis is treated as a classification task as it classifies the orientation of a text into either positive or negative. This paper describes experimental results that applied Support Vector Machine (SVM) on benchmark datasets to train a sentiment classifier. N-grams and different weighting scheme were used to extract the most classical features. It also explores Chi-Square weight features...
This paper proposes an automated non-invasive system for skin cancer (melanoma) detection based on Support Vector Machine classification. The proposed system uses a number of features extracted from the Wavelet or the Curvelet decomposition of the grayscale skin lesion images and color features obtained from the original color images. The dataset used include both digital images and Dermoscopy images...
An automatic Language Identification (LID) is a system designed to recognize a language from a given spoken utterance. The spoken utterances are classified according to the pre-defined set of languages. In this paper a LID system is designed for two different languages namely English and French. The classification of an audio sample is done by extracting Mel frequency cepstral coefficients (MFCCs)...
Finding out an effective way to score Chinese written essays automatically remains challenging for researchers. Several methods have been proposed and developed but limited in the character and word usage levels. As one of the scoring standards, however, content or topic perspective is also an important and necessary indicator to assess an essay. Therefore, in this paper, we propose a novel perspective...
This paper proposes a statistical framework for intrusion detection system based on sampling with Least Square Support Vector Machine (LS-SVM). Decision making is performed in two stages. In the first stage, the whole dataset is divided into some predetermined arbitrary subgroups. The proposed algorithm selects representative samples from these subgroups such that the samples reflect the entire dataset...
A crucial feature of a good scene recognition algorithm is its ability to generalize. Scene categories, especially those related to human made indoor places or to human activities like sports, do present a high degree of intra-class variability, which in turn requires high robustness and generalization properties. Such features are amongst the distinctive characteristics of the Naive Bayes Nearest...
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