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Bot Assistants can be an efficient and low-cost solution to Patient Care. One important aspect of Assistant Bots is successful Communication and Socialization with the patient. A new Conditional Entropy Retrieval Based model is proposed and also an Attitude Modeling based on Popitz Powers. The algorithm successfully retrieves the suitable answer with a high success rate in the patient-Bot Assistant...
Classification is part of various applications and it is an important problem that represents active research topic. Support vector machine is one of the widely used and very powerful classifier. The accuracy of support vector machine highly depends on learning parameters. Optimal parameters can be efficiently determined by using swarm intelligence algorithms. In this paper, we proposed recent elephant...
The remaining useful life (RUL) prediction of bearings has emerged as a critical technique for providing failure warnings in advance, reducing costly unscheduled maintenance and enhancing the reliability of bearings. Recently, a fusion prognostics method combining exponential model and relevance vector machine (RVM) has been proposed and applied to the RUL prediction of bearings. This fusion prognostics...
The Timber Health Monitoring System, which enables constant monitoring of wooden buildings by artificial intelligence based analysis of the signals of a piezoelectric sensor attached to a piece of timber, is proposed. Basic verification was carried out by modeling timber damage and performing vibration tests. Analysis of the obtained waveform data using the k-nearest neighbor (k-NN) method and a support...
In this paper, we study the potential of the new satellite Sentinel-2 (S2) images to identify tree species in temperate forests. Fourteen tree species are classified from eleven S2 images acquired from winter 2015 to autumn 2016 with 2181 reference pixels. Two datasets are compared: (1) the 4-bands dataset including the 10-m VNIR images only and (2) the 10-bands dataset including the red-edge and...
Wind speed forecasting has drawn a lot of research interests around the globe as it plays a key role in wind power plant operation. Accurate wind speed forecasting is vital for the integration of wind energy conversion system into existing electric power grids. The important factor of wind speed forecast is the choice of accurate prediction algorithm. Support Vector Machine Regression Model (SVM-R),...
The paper proposes a classification model for human behavioral patterns recognition in which the decisions are provided based on several Support Vector Machines classifiers within a multi-level decision structure. SVMs are suitable for applications in which the input data feature spaces are very large, involving many features. The human behavior recognition is a relevant example of such application...
In the information age, sentiment classification of internet topics is of great significance. This paper proposes a microblog sentiment classification approach with parallel support vector machine (SVM). The proposed method integrates the features of microblog with preprocessing to ensure the data suitable for sentiment classification. After the preprocessing process, Apache Spark parallel SVM is...
According to privatization and deregulation of power system, accurate electric load forecasting has come into prominence recently. The new energy market and the smart grid paradigm ask for both better demand side management policies and for more reliable forecasts from single end-users, up to system scale. However, it is complex to predict the electric demand owing to the influencing factors such...
Yield estimation is becoming a challenging task for circuits that are replicated in millions of instances on a large design (High Replication Circuits, HRC) such as SRAMs and flip flops. This is because a rare event in a circuit cell may have a large impact on the system yield. To achieve high yield in HRC, the failure probability of the individual cell is requested to be very small. Thus the number...
Support Vector Machines (SVM) belong to a class of supervised machine learning algorithms with applications inclassification and regression analysis. SVM training is modeled as a convex optimization problem that is computationally tedious and has large memory requirements. Specifically, it is a quadratic programming problem which scales rapidly with the training set size rather than the dimensionality...
Users of electronic devices, e.g., laptop, smartphone, etc. have characteristic behaviors while surfing the Web. Profiling this behavior can help identify the person using a given device. In this paper, we introduce a technique to profile users based on their web transactions. We compute several features extracted from a sequence of web transactions and use them with one-class classification techniques...
Support vector machine (SVM) has good generalization performance and is suitable for solving small sample classification problems, so it is often used in the fault diagnosis for aero engine gas path. In this paper, the traditional genetic algorithm and the idea of simulated annealing are combined to optimize the parameters of SVM, and a fault diagnosis method of aero engine gas path based on parameter...
In this work, a simple method for separation between normal and abnormal heart sounds (Phonocardiogram) is presented. Mel-Frequency Cepstral Coefficients (MFCC) are extracted from two different datasets of heartbeats. Several Classifiers, such as, Support Vectors Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Classification Tree (CT) and discriminative analysis (DA), are used. Simulation...
A linear SVM scales linearly with the size of a dataset, and hence is very desirable as a classifier for large datasets. However, it is not able to classify a dataset having a nonlinear decision boundary between the classes unless the dataset has been transformed by some mapping function so that the decision boundary becomes linear or it is a good approximation to a linear boundary. Often these mapping...
This paper discusses the application of least squares support vector machine (LS-SVM) in image inpainting. The data with strong correlation with the damaged area are selected to train the LS-SVM model, and then predict the damaged parts with the obtained model. In order to make full use of the correlation in the image, this paper employs the additive high order kernel function to improve the prediction...
Accurate prediction of the traffic state can help to solve the problem of urban traffic congestion, providing guiding advices for people's travel and traffic regulation. In this paper, we propose a novel short-term traffic flow prediction algorithm, which is based on Multi-kernel Support Vector Machine (MSVM) and Adaptive Particle Swarm Optimization (APSO). Firstly, we explore both the nonlinear and...
Recently, functional network connectivity (FNC) is widely applied to detect the functional organization of brain network in the field of psychiatric illnesses analysis. Conventional FNC analyses assume that temporal stationarity and ignore the topology information among multiple brain regions. By the graph encoding of static FNC and dynamic FNC, in this paper we performed a graph approach combining...
This paper presents a grid search approach to optimize the kernel's parameters for the support vector machines classifier. The most encountered three kernels are considered: linear, radial basis, and sigmoid. We show that the optimization of parameters improves the recognition performance for audio signals classification, especially in the case of sigmoid kernel. The behavior of the model is very...
Support Vector Data Description (SVDD) is a machine learning technique used for single class classification and outlier detection. SVDD formulation with kernel function provides a flexible boundary around data. The value of kernel function parameters affects the nature of data boundary. For example, it is observed that with Gaussian kernel, as the value of kernel bandwidth is lowered, the data boundary...
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