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Processing surges are fast and unexpected changes in the processing demand that commonly occur in cloud computing. The cloud elasticity enables to handle processing surges, increasing and decreasing resources as required. However, a surge can be very fast, so that the overhead to provide more resource is greater than the processing benefit. On the other hand, if the surge is slow and continuous, and...
Nowadays, there are a lot of online repositories containing thousands of very useful educational resources for the educational community. To take full advantage of these resources requires a simple, direct and effective access to those resources that are of interest, therefore, it is necessary that those resources are ordered or ranked based on some criteria? -- that is to say, they have to be classified...
The condition monitoring plays an important role for the system reliability and safety of aerospace engineering. Especially, to detect anomaly with the monitoring data is the very primary and critical task for the system health management (SHM). Due to the high efficiency, easy usage, and predictive performance, the predicted model is applied to realize anomaly detection for monitoring data of complex...
Capturing the type of physical activity a person is performing thorough his daily life, can inspire the development of new and innovative applications. Examples include monitoring patients' health and physical activity performance, reasoning upon the observed activity to recommend better training strategy, new therapeutic programs, etc. In this work we propose an algorithm for Human Activity Recognition...
This article proposed a new smart diagnosis algorithm of the open-circuit fault in a PV generator. For the faults conventional diagnosis, it used the analysis of the actual operation parameters of the PV generator. For the faults smart diagnosis, it based on the optimization of SVM technique by the neural network for the classification of observations located on its margin. The resulting algorithm...
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,...
In this article a new scheme is proposed to use mean supervector in text-prompted speaker verification system. In this scheme, for each month name a subsystem is constructed and a final score based on passphrase is computed by the combination of the scores of these subsystems. Results from the telephony dataset of Persian month names show that the proposed method significantly reduces EER in comparison...
I-vectors have proved to be the most effective features for text-independent speaker verification in recent researches. In this article a new scheme is proposed to utilize i-vectors in text-prompted speaker verification in a simple while effective manner. In order to examine this scheme empirically, a telephony dataset of Persian month names is introduced. Experiments show that the proposed scheme...
Abnormal activity detection plays an important role in many areas such as surveillance, military installations, and sports. Existing abnormal activity detectors mostly rely on motion data obtained over a number of frames to characterize abnormality. However, only motion may not be able to capture all forms of abnormality, in particular, poses that do not amount to motion "outliers". In this...
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 building energy consumption represent 60% of total primary energy consumption in the world. In order to control the demand response schemes for residential users, it is crucial to be able to predict the different components of the total power consumption of a household. This work provide a non intrusive identification model of devices with a sample frequency of one hertz. The identification results...
The entire visualization of GastroIntestinal (GI) tract is not possible with conventional endoscopic exams. Wireless Capsule Endoscopy (WCE) is a low risk, painless, noninvasive procedure for diagnosing diseases such as bleeding, polyps, ulcers, and Crohns disease within the human digestive tract, especially the small intestine that was unreachable using the traditional endoscopic methods. However,...
Web spam is one of the recent problems of search engines because it powerfully reduced the quality of the Web page. Web spam has an economic impact because spammers provide a large free advertising data or sites on the search engines and so an increase in the web traffic. In this paper we have implemented spam detection system based on a SVM classifier that combines new link features with content...
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...
In this paper, we proposed a biased support vector machine (Biased-SVM) with self-constructed Universum (termed as U-BSVM) to solve the PU learning problem. We first treat the PU problem as an imbalanced binary classification problem by labeling all the unlabeled inputs as negative with noise, then inspired by the Universum-SVM (U-SVM), introduce the Universum data set which is constructed from the...
Pedestrian detection is an important area in computer vision with key applications in intelligent vehicle and surveillance systems. One of the main challenges in pedestrian detection is occlusion. In this paper, we propose a novel pedestrian detection approach capable of handling partial occlusion. Three stage cascaded classifier is used in the proposed approach. Global classifier based on HOG features...
In recent years, many vehicle detection algorithms have been proposed. However, a lot of challenges still remain. Local Binary Pattern (LBP) is one of the most popular texture descriptors which has shown its superiority in face recognition and pedestrian detection. But the original LBP pattern is sensitive to noise especially in flat region where gray levels change rarely. To solve this problem, Local...
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