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Machine Learning has a wide array of applications in the healthcare domain and has been used extensively for analyzing data. Apnea of Prematurity is a breathing disorder commonly observed in preterm infants. This paper compares the usage of Support Vector Machines and Random Forests, which are supervised learning algorithms, to predict Apnea of Prematurity at the end of the first week of the child's...
Epilepsy detection using EEG signals is an important clinical practice to study the occurrence of seizures. There is a need to analyze huge volumes of EEG data for finding the epileptic seizures. The manual analysis of EEG records for identifying seizure manifestations is time-consuming and creates an immense workload for the physician. To reduce the EEG analysis time, an autonomous epilepsy detection...
Development of an automated system for identifying and classifying different diseases of the contaminated plants is an emerging research area in precision agriculture. Identification of the diseases is the key to prevent qualitative and quantitative loss of agricultural yields. Rice (Oryza Sative) is one of the essential crops in India and losses due to the diseases badly impact the economy. Manual...
In current years, the binate codify of facial features, being local binary patterns (LBP) and local ternary patterns (LTP) has grown into face recognition. Those confined facet descriptors subsidize a smooth and influential way for texture description. With this, we conclude an innovative process, LTP with Genetic Algorithm to extricate feature vector and segregated features through Support Vector...
During the natural language communication, meaning understanding is the complex task that humans learn from their childhood but to automate this process of meaning understanding for computers has great real world applications. Simple text processing tasks are not enough to uncover the meaning from given unstructured natural language text. Our current research focuses on the issues pertaining to the...
It is very difficult, if not impossible, to obtain a clean reference signal of a noisy speech recorded in a practical environment. As a result, intrusive methods that evaluate the quality of speech signal with the help of a clean reference signal has little value in real world applications. In this paper, we investigate the effectiveness of data-driven non-intrusive method for assessing quality of...
The Complexity-Entropy Causality Plane (CECP) is a representation space with two dimensions: normalized permutation entropy (Hs) and Jensen-Shannon complexity (Cjs). CECP has wide found applications in non-linear dynamic analysis to classify a given signal according to its randomness and complexity which is a motivation to investigate its application for machine fault diagnostics. In this work we...
This work seeks to improve upon the accuracy of birdsong analysis based species recognition. We intend to accomplish this by creating a more effective bird syllable segmentation algorithms (MIRS), Support Vector machine based classifiers are used to train the features of IRS and MIRS. The experimental results show the effectiveness of the proposed algorithm.
Vascular networks in infrared faces are created due to the blood flow under the skin. Variations in blood flow in the blood vessels cause temperature difference, which produces the vascular networks. This paper deals with binary classification of various infrared facial expressions using vascular network. The classification has been performed using Support Vector Machine classifier on five types of...
The brain is one of the vital organ of the body where it is the custodian of the involuntary and voluntary actions like walking, vision, memory. Now a days the most common brain disorders are Alzheimer's disease, Epilepsy (paralysis or stroke), tumors, brain tumors. Early diagnosis and proper treatment of brain tumors is required. The Computer Aided Diagnostic tools (CAD) can be used by the doctor...
Since many sky-survey observations were performed, as well as appreciable amount of data were obtained, study on large-scale evolution of our Universe has become a field of interest. In this work, we concentrate on the X-ray astronomical samples from NASA's Chandra observatory, and propose an approach to classify galaxy clusters (GCs) based on their central gas profiles' morphological features. Firstly,...
Public Speaking Anxiety (PSA) is one of the most universal subtypes of social anxiety, and the facial expression recognition of PSA is an immediate area of research focus. The experiment obtained the facial expression data of 18 postgraduates in their thesis defense for the master's degree. Then, valid data were selected by using self-evaluation of the subject and the average evaluation of 5 audience...
We propose a data-driven method for automatic deception detection in real-life trial data using visual and verbal cues. Using OpenFace with facial action unit recognition, we analyze the movement of facial features of the witness when posed with questions and the acoustic patterns using OpenSmile. We then perform a lexical analysis on the spoken words, emphasizing the use of pauses and utterance breaks,...
The personal identification from the features of personal face and voice is described in this study. The face area is detected from the picture including both the face and the complicated background by using Microsoft Kinect sensor. The personal voice is also recorded from Kinect microphone array, which is used for the personal identification. The features of the personal face are calculated from...
In this paper, we present a real-time face recognition system for home security service robot which can be applied to recognize the person's face in front and give a warning when the identity of the person is a stranger. Considering the complexity of the actual situation, there might be some errors causing by the following factors like the angle, the size, the environment and the illumination of the...
This article describes the detection of the characters of the license plate through of computer vision techniques: such as cascade of classifiers based in sobel algorithm, analysis of peaks and valleys, and support vector machines; the search for the region of the plate begins by detecting vehicles, then character segmentation and concludes with the recognition of these. The system was tested in different...
Feature extraction is playing a major role in bio signal processing. Feature identification and selection has two approaches. The common approach is engineering handcraft which is based on user experience and application area. While the other approach is feature learning that based on making the system identify and select the best features suit the application. The idea behind feature learning is...
Electrical load monitoring, by means of a smart meter, is getting more and more popular these days. Power demand information from smart meters is drawing attention among researchers, since it could be applied for power demand control. Providing attractive services with smart meters encourage electricity retailers to utilize demand side management, which could be a solution for energy-related problems...
The major challenge of inertial navigation system (INS) is the rapid navigation error drift when aiding sensors are unavailable. However, if the dynamics of land vehicle can be detected, these errors can be corrected or restrained. A method based on support vector machine (SVM) using the outputs of MIMU is proposed here to identify the dynamics of land vehicle. This method computes part of the time-domain...
Searching through and selecting data sets from large traffic databases with sensor information is often a cumbersome manual process. In this paper we present an idea that may dramatically fasten and streamline this process. The idea is to build a fast search index (COSI: COngestion Search engIne) based on meta data in combination with features from the traffic patterns along routes. Instead of ploughing...
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