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The paper deals with the problem of stability during the solving of pattern recognition tasks from the point of view of transformation groups. It shows the possibility to avoid the necessity of regularization by using the geometric equaffine Lorentz transformation, exploiting as example the alpha-procedure.
Snoring affects the sleep quality of the snorer itself and its social circle. Some types of snoring are related to sleep apnea, which leads to sleepiness during the day and to several health risks. Thus automatic detection of the different types of snoring may lead to more specific diagnosis and consequent treatment. In this work, we propose to use a reduced set of speech related features that includes...
To improve the accuracy of surface defect detection, an approach of defect inspection based on visual saliency map and Support Vector Machine(SVM) is proposed. Monochrome fabric defect images are taken as examples in this paper. By analyzing the visual saliency maps of these images, the global associated value and the background associated value are extracted as the two features. After being normalized,...
Evaluating aesthetic value of digital photographs is a challenging task, mainly due to numerous factors that need to be taken into account and subjective manner of this process. In this paper, we propose to approach this problem using deep convolutional neural networks. Using a dataset of over 1.7 million photos collected from Flickr, we train and evaluate a deep learning model whose goal is to classify...
Unlike Support Vector Machine (SVM), Kernel Minimum Classification Error (KMCE) training frees kernels from training samples and jointly optimizes weights and kernel locations. Focusing on this feature of KMCE training, we propose a new method for developing compact (small scale but highly accurate) kernel classifiers by applying KMCE training to support vectors (SVs) that are selected (based on the...
Image classification domain has been an area which has attracted a lot of researchers over past years. Many classification methodologies for spatial image datasets has been developed. Artificial intelligence based approaches are getting popular now a days for getting the image classification task done in more efficient and correct way. The prime goal is to develop a classification mechanism which...
In this paper, we present a proposed algorithm to classify brain MRI as tumor-free or tumor present. For computing difference between normal and abnormal MR images, a set of features is calculated. The number of features of the original feature set is reduced by rough set based K-means algorithm and classification of the dataset into tumor-free or tumor-present category has been done by support vector...
Individuals utilize online networking sites like Facebook and Twitter to express their interests, opinions or reviews. The users used English language as their medium for communication in earlier days. Despite the fact that content can be written in Unicode characters now, people find it easier to communicate by mixing two or more languages together or lean toward writing their native language in...
In consideration of the harm to society, hiding narcotics in human bodies should be investigate strictly. While the automatic detection method is absent nowadays, and the inspection rate by human eyes is low. So we introduce a new method based on directional fractal dimension texture feature extraction and support vector machine(SVM) to classify the inspection x-ray images. Using this method, the...
Due to the high spectral resolution and the similarity of some spectrums between different classes, hyperspectral image classification turns out to be an important but challenging task. Researches show the powerful ability of deep learning for hyperspectral image classification. However, the lack of training samples makes it difficult to extract discriminative features and achieve performance as expected...
Network security has become a very important issue and attracted a lot of study and practice. To detect or prevent network attacks, a network intrusion detection (NID) system may be equipped with machine learning algorithms to achieve better accuracy and faster detection speed. One of the major advantages of applying machine learning to network intrusion detection is that we don't need expert knowledge...
This study presents a novel end-to-end architecture that learns hierarchical representations from raw EEG data using fully convolutional deep neural networks for the task of neonatal seizure detection. The deep neural network acts as both feature extractor and classifier, allowing for end-to-end optimization of the seizure detector. The designed system is evaluated on a large dataset of continuous...
Software Defined Networking (SDN) is a new promising networking concept which has a centralized control over the network and separates the data and control planes. This new approach provides abstraction of lower-level functionality and allows the network administrators to initialize, control, change, and manage network behavior programmatically. The centralized control, being the major advantage of...
Every organism emits energy around it which comprises UV-radiation, EM-radiation, infrared and thermal radiation. This energy around human body represents health condition of the subject under study. These energy fields are called as aura of the body under consideration. Several types of equipments are there to capture such energy. Kirlian camera captures the distribution of energy radiation around...
Ransomware is one type of malware that covertly installs and executes a cryptovirology attack on a victims computer to demand a ransom payment for restoration of the infected resources. This kind of malware has been growing largely in recent days and causes tens of millions of dollars losses to consumers. In this paper, we evaluate shallow and deep networks for the detection and classification of...
The electroencephalography (EEG) data records vast amounts of human cerebral activity yet is still reviewed primarily by human readers. Most of the times, the data is contaminated with non-cerebral originated signals, called artifacts, which could be very difficult to visually detect and, undiscovered, could damage the neural information analysis. The purpose of our work is to detect the artifacts...
This paper develops a hybrid electricity price-forecasting framework to improve the accuracy of prediction. A novel clustering method is proposed that uses a modified game theoretic self-organizing map (GTSOM) and neural gas (NG) along with competitive Hebbian Learning (CHL) to provide a better vector quantization (VQ). To resolve the deficiency of the original SOM, five strategies are proposed to...
Gender classification play a significant role in recognition performance. For the purpose of visual surveillance, gender is considered as an important factor. In this paper a hybrid approach is proposed by fusing Gait Energy Image (GEI) with spatio temporal parameters for the gender classification. The dataset used is CASIA B which comprises of 118 subjects (89 males and 29 females). The proposed...
Coreference resolution plays a significant role in natural language processing systems. It is the method of figuring out all the noun phrases that refer back to the identical real world entity. Several researches have been done in noun phrase coreference resolution by using certain machine learning techniques. Our paper proposes a machine learning approach using support vector machines (SVM) towards...
This paper presents a new discriminative learning framework to associate the relationship between the objects and the words in an image and perform template matching scheme for complex association patterns. The problem is first formulated as a bipartite graph matching problem. Thereafter, structural support vector machine (SVM) is employed to obtain the optimal compatibility function to encode the...
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