Human activity recognition (HAR) is an emerging research topic in pattern recognition, especially in computer vision. The main objective of human activity recognition is to automatically detect and analyze human activities from the information acquired from different sensors. Human activity prediction using big data remains a challengingly open problem. Several approaches have recently been developed in order to find practical ways to solve high dimensionality of data problems. The aim of this study is to attempt, using data mining techniques, to deal with HAR modeling involving a significant number of variables in order to identify relevant parameters from data and thus to maximize the classification accuracy while minimizing the number of features. The proposed framework has 1032 Ismail El Moudden et al. been tested on a publicly HAR available dataset and the results have been interpreted and discussed.
Global increases in the occurrence and frequency of flood have highlighted the need for resilience approaches to deal with future floods. The principal component analysis (PCA) has been used widely to understand the resilience of the urban system to floods. Based on feature extraction and dimensionality reduction, the PCA reduces datasets to representations consisting of principal components. Kernel PCA (KPCA) is the nonlinear form of PCA, which efficiently presents a complicated data in a lower dimensional space. In this work the KPCA techniques was applied to measure and map flood resilience across a local level. Therefore, it aims to improve the performance achieved by non-linear PCA application, compared to standard PCA. Twenty-one resilience indicators were gathered, including social, economic, physical, and natural components into a composite index (Flood resilience Index). The experimental results demonstrate the KPCA performance to get a better Flood Resilience Index, guiding q decision making to strengthen the flood resilience in our case of study of M’diq-Fnideq and martil municipalities in Northern of Morocco.
Hidden Markov Models (HMM) are used in a wide range of artifificial intelligence applications including speech recognition, computer vision, computational biology and fifinance. Estimating an HMM parameters is often addressed via the Baum-Welch algorithm (BWA), but this algorithm tends to convergence to local optimum of the model parameters. Therefore, optimizing HMM parameters remains a crucial and challenging work. In this paper, a Variable Neighborhood Search (VNS) combined with Baum-Welch algorithm (VNS-BWA) is proposed. The idea is to use VNS to escape from local minima, enable greater exploration of the search space, and enhance the learning capability of HMMs models. The proposed algorithm has entire advantage of combination of the search mechanism in VNS algorithm for training with no gradient information, and the BWA algorithm that utilizes this kind of knowledge. The performance of the proposed method is validated on a real dataset. The results show that the VNS-BWA has better performance fifinding the optimal parameters of HMM models, enhancing its learning capability and classifification performance.
Classification and statistical learning by hidden markov model has achieved remarkable progress in the past decade. They have been applied in many areas like speech recognition and handwriting recognition. However, learning by Hidden Markov Model (HMM) is still restricted to supervised problems. In this paper, we propose a new learning method 2484 Badreddine Benyacoub et al. based on HMM techniques estimations, to built a model for classification. The approach consists of evaluation of the probability to belonging in one group, given the observations by a linear classifier. Our developed algorithm is based on discrete states and discrete observations cases of HMM. Experimental results show that the new method has strong performance.
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