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An ensemble consists of a set of individually trained classifiers (e.g., as neural networks or decision trees etc.) whose predictions are combined in some manner (e.g., averaging or voting etc.) to form the final prediction. In literature many previous study has shown that an ensemble is often more accurate than any of the single classifiers. Ensemble learning is primarily used to improve the (classification,...
Phishing is a criminal scheme to steal the user's personal data and other credential information. It is a fraud that acquires victim's confidential information such as password, bank account detail, credit card number, financial username and password etc. and later it can be misuse by attacker. We aim to use fundamental visual features of a web page's appearance as the basis of detecting page similarities...
Today iris recognition systems are extensively used for security and authentication purposes due to their simplicity and high reliability. But these systems face a major challenge of being spoofed by high quality printed iris images or pictures captured by camera. The problem is aggravated by use of varying illumination conditions in an attack access attempt. This paper investigates spoofing attempts...
Text-to-Speech (TTS), an astounding feature to assemble computer with intelligence and to induce sound is seemingly a challenging task as it is related to the propagation of uncertainty with the input. This is because TTS evolutes the input based on the probabilities and not with certainty ratios. TTS is accomplished by generating the sound structure/phoneme and then classifying these phonemes in...
Oral leukoplakia (OLK) is the most common pre malignant disorder (PMD) with highest malignant potentiality. It is clinically highly correlated with oral squamous cell carcinoma (OSCC). Painful biopsy is the gold standard till date for diagnosis of these diseases. Again for specific grading of such pathological states and mitigation of inter and intra observer variability and subjective disease classification,...
Human detection is vital to many applications, for example, human-robot interaction, unattended ground sensor systems, smart rooms, etc. In this paper we investigate the application of a one-class classifier to the problem of human detection using solely ultrasonic sensors. Our approach is based on fuzzy rules that are extracted from the signal features in time and frequency domains. The performance...
In this paper we address the problem of human activity recognition based only on acoustic modality. The ultimate goal is continuous acoustic monitoring of public places like parks and bus stops for detecting littering activities so that the people involved in such acts can be prompted to bin appropriately. We exploit the fact that when human interacts with objects, a characteristic sound is produced,...
In this paper we investigate the application of ensemble of one-class classifiers to the problem of acoustic event classification. We present some initial results that are based on acoustic signals emitted by different litter causing material when contacted by human. When a person interacts with objects made with specific material, characteristic sounds are produced as a result of the interactions...
Many computer vision tasks require efficient evaluation of Support Vector Machine (SVM) classifiers on large image databases. Our goal is to efficiently evaluate SVM classifiers on a large number of images. We propose a novel Error Space Encoding (ESE) scheme for SVM evaluation which utilizes large number of classifiers already evaluated on the similar data set. We model this problem as an encoding...
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