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In this paper, we suggest an accessible and effective approach to the urban road state estimation. The main technical contribution of the proposed method is a novel feature extraction on the basis of the multi-resolution, along with support vector machine for classification. Experimental tests have been carried out to validate our proposed approach which can estimate road state in the sample of a...
Anti-Malware industry faces the challenge of evaluating huge amount of data for potential malicious contents. This is due to the fact that hackers introduce polymorphism to the existing malicious groups/classes. Effective feature extraction and classification of malware data is necessary to tackle such issues. In this paper, we visualize viruses in an image as they capture minor changes while retaining...
In this paper, we firstly introduce a new combined approach to enhance the performance of classification in network traffic. The proposed combination mainly focuses on taking advantages of two classification algorithms, Support Vector Machine (SVM), Self Organizing Map (SOM). We utilize both advantages that SVM takes a little time to produce outputs with a high accuracy, SOM makes a reliable prediction...
For the steel cord conveyor belt many types of defect signal classification is studied and the classification method based on the support vector machine optimized by particle swarm algorithm considering shrinkage factor(PSO-SF-SVM) is proposed. The PSO-SF-SVM method was carried out to achieve the defect classification for steel cord conveyor belt. The result shows the PSO-SF-SVM method has high classification...
In order to solve the bottleneck of tedious and time-consuming manual labeling in singing voice detection, in this paper we integrate the active learning mechanism into the conventional SVM-based supervised learning algorithm. By selecting most informative unlabeled samples and asking for human annotation, active learning substantially reduces the number of training samples to be labeled and meanwhile...
Bird plays an important role in the ecosystem, so the avian survey is getting more and more attention. Sometimes it is difficult for the bird watcher to identify its species without visual observation, using the hearing instead would be a direct but obscure alternative. In this case, an avian call identification system would interest the bird watcher and facilitate the biological research. Developing...
Accurate modeling of Electroencephalography (EEG) signals is an important problem in clinical diagnosis of brain diseases. The method using support vectors machine (SVM) based on the structure risk minimization provides us an effective way of learning machine. But solving the quadratic programming problem for training SVM becomes a bottle-neck of using SVM because of the long time of SVM training...
Along with the development of social network, more and more people know the world by reading news. The problem about what kind of emotion is inspired when people read news is very worthy of discussion. This paper will mix Deep Belief Networks (DBN) model and Support Vector Machine (SVM) to a hybrid neural network model by using the Contrast Divergence (CD) algorithm to estimate the weights when training...
The attorney's office in Brazil, receive daily a lot of notifications. These notifications must be manually analyzed by procurators to determine what kind of document should they prepare to respond. This situation causes in many cases notifications are not answered in time causing these prescribed. All this has motivated the development of this work whose main objective is the development of a computational...
Handwritten character recognition systems suffers from different training and testing sets distributions. In this paper, we propose a two-step domain adaptive multiple kernel learning algorithm, which learns a kernel function based on several kernels in the first step, and trains a target classifier by applying the learned kernel in the second step. Our method can be employed both in semi-supervised...
This paper studies support vector machine (SVM) for the parametric identification of ship coupled heave and pitch motions with real oceanic conditions. The simulation results are based on a mathematical model of a ship model in the simulated marine environment. By analyzing the identification precision of the equation parameters of coupled heave-pitch motions, this paper presents the proper numbers...
In this paper, the effect of different hyperspectral images feature extraction techniques in ANN and SVM classifiers is investigated. While a high accuracy and efficiency for HLFE method was shown in ANN classifier in the literature, in this study it is shown that using the RBF kernel in SVM provides increased accuracy for PCA and KPCA meanwhile poor classification accuracy is achieved for HLFE. Therefore...
The advances of network technology and mobile communication technology are making eHealth possible. In eHealth systems, physiological data and relevant context-aware data are acquired continuously and in real time. At the same time, such large-scale data results in huge challenges in the aspect of real-time big data processing since eHealth data appears in the form of data stream. Therefore, we propose...
Tweet data on Twitter as microblogging can be processed to be an important and useful information. We propose opinion mining with Support Vector Machine (SVM) algorithm to classify tweet opinion data which is a huge data. This opinion mining will be used to get insight of public opinion about State Islamic University of Sunan Gunung Djati Bandung which is one of large university in Indonesia. We have...
The main objective of the survey is to Study the emerging classifiers like Random Forest (RF) and Particle Swarm Optimization (PSO) and its application to Satellite Imageries to achieve enhanced and highly accurate Land Cover Classification Model. RF is an ensemble type voting based machine learning algorithm. RF algorithm considers single pixels for Classification instead of sets of pixels as in...
This paper presents an application of machine learning approach for automatic terrain classification suitable for optimal wireless sensor network performance in on-demand deployment. The work entails practical terrain image processing using supervised SVM kernel algorithm moving from gray scale level to color and covering every aspect of a typical terrain image. This paper showcases the integral part...
Research on the influence of specific hydrological environment change on current velocity and water exchange has a long history in oceanography; however, the majority of previous work has been based on traditional ocean models such as POM and FVCOM. This paper presents a stable joint method that combines a support vector machine with a hydrological model to predict current velocity in different hydrological...
The efficient condition assessment of road networks is crucial to prevent pavement distresses which can cause a spectrum of detrimental effects. The need for automation of the underlying process is originated from the costly, time-consuming and dangerous current methods. Presented herein is the automation of the patch detection process, which is essential for pavement surface evaluation and rating...
In the framework of a competitive commercial world, having accurate energy forecasting tools becomes a Key Performance Indicator (KPI) to the building owners. Energy forecasting plays a crucial role for any building when it undergoes the retrofitting works in order to maximize the benefits and utilities. This paper provides accurate and efficient energy forecasting tool based on Support Vector Machine...
This paper proposed about comparison of the animal recognition processed on the real world objects and the modeled 3D objects. The object classification consists of the three main steps - feature extraction, training classifier and image evaluation. Feature description is based on locale visual descriptors like SIFT or SURF. Support Vector Machine (SVM) in combination with the bags of visual keypoints...
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