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This paper proposes support vector machine (SVM) based fuzzy filter for restoration of corrupted images from impulse noise in color image domain. In this proposed filter, filtering technique is performed using channel by channel operation on the corrupted pixel (R, G, and B channel separately). In this work, the system has been trained with the optimal feature set. During the testing phase, pixels...
In Face recognition, a combination of neural network (NN), known as an ensemble of neural network, often outperforms individual ones. This paper is aiming to present a support vector machines (SVM)-ensemble-based efficient face recognition system. The training samples are randomly chosen by means of bootstrap technique to train the different SVM independently. These SVM's are combined together to...
The death of the patients is an important event in the intensive care unit (ICU), mortality risk prediction thus offers much information for clinical decision making. However, Patient ICU mortality prediction faces challenges in many aspects, such as high dimensionality, imbalance distribution. In this paper, we modified the cost-sensitive principal component analysis (CSPCA), which is denoted by...
Sensors in industrial systems fault frequently leading to serious consequences regarding cost and safety. The authors propose support vector machine-based classifier with diverse time- and frequency-domain feature models to detect and classify these faults. Three different kernels, i.e., linear, polynomial, and radial-basis function, are employed separately to examine classifier's performance in each...
The present status of heart sound recognition is introduced in the paper. In order to improve the performance of heart sound recognition, a new model based on SVM is proposed. Firstly, the wavelet transform is used to reduce the noise of the heart sound, and then MFCC feature is extracted from heart sound. On this basis, the Support Vector Machine is used to build the classification model. In the...
At present, shallow characteristics are usually utilized to represent the distributed features of text for Chinese spam classification, causing the problem of inexact text vector representation and low classification performance. A novel Chinese spam classification method based on weighted distributed feature is proposed by combining the features of TF-IDF weighted algorithm with the distributed text-based...
In order to solve the problem of lacking shear wave velocity information in oil and gas field, based on conventional logging data, a support vector machine(SVM) model is used to map the relationship between shear wave velocity and natural gamma, acoustic time difference and resistivity of shale, and then a machine learning method for shear wave velocity prediction is proposed. The model was trained...
In this paper, a system to aid the visually impaired by providing contextual information of the surroundings using 360° view camera combined with deep learning is proposed. The system uses a 360° view camera with a mobile device to capture surrounding scene information and provide contextual information to the user in the form of audio. The scene information from the spherical camera feed is classified...
Keyword extraction is an automated process that collects a set of terms, illustrating an overview of the document. The term is defined how the keyword identifies the core information of a particular document. Analyzing huge number of documents to find out the relevant information, keyword extraction will be the key approach. This approach will help us to understand the depth of it even before we read...
Resistor detection are mostly suffered from compact size and multiple interferences of environment. In this paper, a method for resistor detection was proposed, which combined selective search (SS), convolutional neural networks (CNNs) and support vector machine (SVM). Using improved selective search method to reduce the time of generating candidate regions; taking advantages of independent features...
This paper analyzes the research status and development trend of heart sound identification at home and abroad. Concerning the dynamic characteristics and timedomain characteristics of heart sound signals, a new improved identification algorithm is proposed. It put the MFCC and shortterm energy together as new combined parameters. In order to further verify the validity of the parameters, an identification...
Support vector machine (SVM) algorithm received much attention in the research of voiceprint recognition, especially for small sample datasets. However, with the increase of recognition number and speech features number, the rate of model training and recognition is significantly reduced. In order to solve the problem, a new weighted clustering algorithm is proposed, which use “one to one” SVM model...
When we use binary tree support vector machine (SVM) to work the multi-classification problems out, we always find that the structure of the binary tree has a large chance and it has a great influence on the classification efficiency of the classification model. To solve this problem, according to the idea of separating the most widely distributed class first, an improved binary tree SVM multiple...
Composites are widely used in aviation, aerospace and other fields because of their high specific strength, high specific stiffness and easy molding. However, in the process of using the concentrated stress, heavy shocks may form different degrees of damage. Especially, the internal delamination will reduce the stability and safety of the structure. Based on the analysis of damage location and damage...
With the rapid development of society and technology, home service robot is becoming cheaper and smarter. Facing with the difficulties of aging and shortage of labor, we can use home service robot (HSR) as a good companion and servant. However, the security and reliability problems have become bottlenecks in this field. It is meaningful to do researches on fault diagnosis of HSR. Due to its excellent...
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,...
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...
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...
For better resolving the safety risk early warning of the apron effectively, the attribute reduction algorithm based on Rough Set is used to simplify the set as the warning index set of the apron is too large. The improved Particle Swarm Optimization (PSO) algorithm is used to optimize the parameters of Support Vector Machine. Combined with the Rough Set and SVM which is optimized by the improved...
Individualized blood transfusion management would benefit from the ability to prospectively identify patients at risk of complications of blood transfusion, and target them for closer monitoring or intervention. This study presents a simple and efficient multi-task learning method for predicting multiple surgical outcomes based on the weighted least squares support vector machine. To accelerate the...
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