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This paper focuses on a classification of ISAR low resolution imaging of sea targets (cargo vessels) using the most popular classifiers. First, a method of the raw, passive data collection is described. Second, two feature vectors related to a size of the object (its width and length) are extracted, which then are applied for separating the objects into classes. Finally, the accuracy analysis of different...
Human machine interaction fieldhas potentialapplications in different domainssuch as medicine therapies for vulnerable persons. Thus, allowing the machine to identify and understand emotional states is one of the primordial stages for affective interactivity with Humans. Recent studies have proved that physiological signals contribute to recognize the emotion. In this paper, we aim to classify the...
In present days, the social media and networking act as one of the key platforms for sharing information and opinions. Many people share ideas, express their view points and opinions on various topic of their interest. Social media text has rich information about the companies, their products and various services offered by them. In this research we focus exploring the association of sentiments of...
In order to study the feature and extraction methods of series arc fault, the series arc fault experiments under different current conditions were carried out with the motor load and inverter respectively. A method of feature extraction based on improved singular value decomposition was proposed, and arc faults were distinguished by support vector machine (SVM). SVM was optimized by genetic algorithm...
This paper aims to develop an effective flower classification approach using the technology of feature extraction. With this regard, a fused descriptor based on Pyramid Histogram of Visual Words (PHOW) is used to extract the color, texture and contour information of flower image. Secondly, Dictionary Learning and Locality-constrained Linear Coding (LLC) are operated on PHOW feature and then images...
This paper presents an approach for gender recognition from single channel EEG signal. For this purpose, approximately 24 hour-long EEG data, obtained during daily routine activities including sleep, was used. First, cepstrum coefficients of EEG signals were obtained in the frequency domain to construct the features SET. Second, a machine learning step was performed using these features with Support...
Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease and can often lead to fibrosis, cirrhosis, cancer and complete liver failure. Liver biopsy is the current standard of care to quantify hepatic steatosis, but it comes with increased patient risk and only samples a small portion of the liver. Imaging approaches to assess NAFLD include proton density fat fraction...
The delays in the detection of fire in fire detection systems continue to be a life threatening problem for living things. Techniques based on image processing have been developed in order to remove this problem and minimize the detection period. This study also focused on the smoke image that appeared before the flame at the time of the fire. Smoke detection can provide earlier notification than...
In this study, binary sleep stage classification (sleep or awake state) was performed using single-channel EEG signal. A new frequency warping function is proposed for this purpose. This function provides a bending function that can proper orientation and depth of the EEG signal frequency content. In this way a generalized filter set of was designed. With the help of this filter set, cepstrum features...
Neonatal death can be prevented by early prediction of pre-term labor. During the last decade, uterine electrohysterography (EHG) signal has been considered as a noninvasive method for this aim. There is a wide range of researches which investigated EHG signals for diagnosis of pre-term labor. In this article, features have been extracted by Discrete Wavelet Transform (DWT) from EHG signals then Support...
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...
Experiment design has a key role in the functional magnetic resonance imaging (fMRI) data analyses. Block designs are suitable to localize functional areas but are not able to measure the transient changes in the brain activity. Event related design is a better approach and saves time and resources like single trial analyses. In this study, we explored the event related design with single, and multi...
In recent years convolutional neural network have obtained more popularity because of its progressive performance for different applications especially for object recognition. In neuroimaging, data varies from person to person and condition to condition so it is always a challenging job to model the brain data. Any analysis in neuroimaging is also dependent on the quality of data and currently, functional...
Peach is one of the most important commodities in the global fresh product market. With the development of people's living standard, consumers pay more attention to the internal quality of fruits than the appearance quality of fruits. The requirement of nondestructive analysis could be satisfied by near infrared (NIR) spectroscopy with appropriate data analysis methods. In this paper, we measured...
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...
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...
A diagnosis of a stadium of Plasmodium can help physicians in managing patients with malaria. The stadium of Plasmodium is divided into three stadiums, namely trophozoite, schizont, and gametocyte. Staging is based on the morphological characteristics of each stadium, including shape, size, texture, and color. We selected 15 features using a correlation-based feature selection method with a “best...
Improving health care in rural areas is a major concern today. Sleep apnea is a common sleep disorder which often goes undiagnosed and leads to serious problems like stroke, heart attacks etc. Conventional diagnosis of sleep apnea is done by continuous recording of physiological signals for 6 to 7 hrs during sleep and then manually marking the events. This process is expensive and not affordable to...
Systems developed to classify human activities to identify unintentional falls are highly demanding and play an important role in our daily life. Human falls are the main obstacle for elderly people to live independently and it is also a major health concern due to aging population. Different approaches are used to develop human fall detection systems for elderly and people with special needs. The...
Real datasets can have missing values for a different reasons such as in data that were not kept on file and data corruption. Climate forecasting has a highly relevant effect in agricultural fields and industries sectors. The process of predicting climate conditions is required for different areas of life sectors. Handling missing data is significant because a lot of machine learning algorithms performance...
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