Hyperspectral Images Classification and Dimensionality Reduction using spectral interaction and SVM classifier

Over the past decades, the hyperspectral remote sensing technology development has attracted growing interest among scientists in various domains. The rich and detailed spectral information provided by the hyperspectral sensors has improved the monitoring and detection capabilities of the earth surface substances. However, the high dimensionality of the hyperspectral images (HSI) is one of the main challenges for the analysis of the collected data. The existence of noisy, redundant and irrelevant bands increases the computational complexity, induce the Hughes phenomenon and decrease the target's classification accuracy. Hence, the dimensionality reduction is an essential step to face the dimensionality challenges. In this paper, we propose a novel filter approach based on the maximization of the spectral interaction measure and the support vector machines for dimensionality reduction and classification of the HSI. The proposed Max Relevance Max Synergy (MRMS) algorithm evaluates the relevance of every band through the combination of spectral synergy, redundancy and relevance measures. Our objective is to select the optimal subset of synergistic bands providing accurate classification of the supervised scene materials. Experimental results have been performed using three different hyperspectral datasets: "Indiana Pine", "Pavia University" and "Salinas" provided by the "NASA-AVIRIS" and the "ROSIS" spectrometers. Furthermore, a comparison with the state of the art band selection methods has been carried out in order to demonstrate the robustness and efficiency of the proposed approach. Keywords: Hyperspectral images, remote sensing, dimensionality reduction, classification, synergic, correlation, spectral interaction information, mutual infor

A novel information gain-based approach for classification and dimensionality reduction of hyperspectral images

Recently, the hyperspectral sensors have improved our ability to monitor the earth surface with high spectral resolution. However, the high dimensionality of spectral data brings challenges for the image processing. Consequently, the dimensionality reduction is a necessary step in order to reduce the computational complexity and increase the classification accuracy. In this paper, we propose a new filter approach based on information gain for dimensionality reduction and classification of hyperspectral images. A special strategy based on hyperspectral bands selection is adopted to pick the most informative bands and discard the irrelevant and noisy ones. The algorithm evaluates the relevancy of the bands based on the information gain function with the support vector machine classifier. The proposed method is compared using two benchmark hyperspectral datasets (Indiana, Pavia) with three competing methods. The comparison results showed that the information gain filter approach outperforms the other methods on the tested datasets and could significantly reduce the computation cost while improving the classification accuracy. Keywords: Hyperspectral images; dimensionality reduction; information gain; classification accuracy. Keywords: Hyperspectral images; dimensionality reduction; information gain; classification accuracy

Heart Sound Signals Segmentation and Multiclass Classification

The heart is the organ that pumps blood with oxygen and nutrients into all body organs by a rhythmic cycle overlapping between contraction and dilatation. This is done by producing an audible sound which we can hear using a stethoscope. Many are the causes affecting the normal function of this most vital organ. In this respect, the heart sound classification has become one of the diagnostic tools that allow the discrimination between patients and healthy people; this diagnosis is less painful, less costly and less time consuming. In this paper, we present a classification algorithm based on the extraction of 20 features from segmented phonocardiogram “PCG” signals. We applied four types of machine learning classifiers that are k- Near Neighbor “KNN”, Support Vector Machine “SVM”, Tree, and Naïve Bayes “NB” so as to train old features and predict the new entry. To make our results measurable, we have chosen the PASCAL Classifying Heart Sounds challenge, which is a rich database and is conducive to classifying the PCGs into four classes for dataset A and three classes for dataset B. The main finding is about 3.06 total precision of the dataset A and 2.37 of the dataset B.</em

Classification of cardiovascular diseases using dysphonia measurement in speech

Cardiovascular disease is the leading cause of death worldwide. The diagnosis is made by non-invasive methods, but it is far from being comfortable, rapid, and accessible to everyone. Speech analysis is an emerging non-invasive diagnostic tool, and a lot of researches have shown that it is efficient in speech recognition and in detecting Parkinson's disease, so can it be effective for differentiating between patients with cardiovascular disease and healthy people? This present work answers the question posed, by collecting a database of 75 people, 35 of whom suffering from cardiovascular diseases, and 40 are healthy. We took from each one three vocal recordings of sustained vowels (aaaaa…, ooooo… .. and iiiiiiii… ..). By measuring dysphonia in speech, we were able to extract 26 features, with which we will train three types of classifiers: the k-near-neighbor, the support vectors machine classifier, and the naive Bayes classifier. The methods were tested for accuracy and stability, and we obtained 81% accuracy as the best result using the k-near-neighbor classifier

Heart Sound Signals Segmentation and Multiclass Classification

The heart is the organ that pumps blood with oxygen and nutrients into all body organs by a rhythmic cycle overlapping between contraction and dilatation. This is done by producing an audible sound which we can hear using a stethoscope. Many are the causes affecting the normal function of this most vital organ. In this respect, the heart sound classification has become one of the diagnostic tools that allow the discrimination between patients and healthy people; this diagnosis is less painful, less costly and less time consuming. In this paper, we present a classification algorithm based on the extraction of 20 features from segmented phonocardiogram “PCG” signals. We applied four types of machine learning classifiers that are k- Near Neighbor “KNN”, Support Vector Machine “SVM”, Tree, and Naïve Bayes “NB” so as to train old features and predict the new entry. To make our results measurable, we have chosen the PASCAL Classifying Heart Sounds challenge, which is a rich database and is conducive to classifying the PCGs into four classes for dataset A and three classes for dataset B. The main finding is about 3.06 total precision of the dataset A and 2.37 of the dataset B

Heart Sounds Classification for a Medical Diagnostic Assistance

In order to develop the assessment of phonocardiogram “PCG” signal for discrimination between two of people classes – individuals with heart disease and healthy one- we have adopted the database provided by "The PhysioNet/Computing in Cardilogy Challenge 2016", which contains records of heart sounds 'PCG '. This database is chosen in order to compare and validate our results with those already published. We subsequently extracted 20 features from each provided record. For classification, we used the Generalized Linear Model (GLM), and the Support Vector Machines (SVMs) with its different types of kernels (i.e.; Linear, polynomial and MLP). The best classification accuracy obtained was 88.25%, using the SVM classifier with an MLP kernel

Heart Sounds Classification for a Medical Diagnostic Assistance

In order to develop the assessment of phonocardiogram “PCG” signal for discrimination between two of people classes – individuals with heart disease and healthy one- we have adopted the database provided by "The PhysioNet/Computing in Cardilogy Challenge 2016", which contains records of heart sounds 'PCG '. This database is chosen in order to compare and validate our results with those already published. We subsequently extracted 20 features from each provided record. For classification, we used the Generalized Linear Model (GLM), and the Support Vector Machines (SVMs) with its different types of kernels (i.e.; Linear, polynomial and MLP). The best classification accuracy obtained was 88.25%, using the SVM classifier with an MLP kernel

Heart Sounds Classification for a Medical Diagnostic Assistance

In order to develop the assessment of phonocardiogram “PCG” signal for discrimination between two of people classes – individuals with heart disease and healthy one- we have adopted the database provided by "The PhysioNet/Computing in Cardilogy Challenge 2016", which contains records of heart sounds 'PCG '. This database is chosen in order to compare and validate our results with those already published. We subsequently extracted 20 features from each provided record. For classification, we used the Generalized Linear Model (GLM), and the Support Vector Machines (SVMs) with its different types of kernels (i.e.; Linear, polynomial and MLP). The best classification accuracy obtained was 88.25%, using the SVM classifier with an MLP kernel.</span

Discrimination between patients with CVDs and healthy people by voiceprint using the MFCC and pitch

Heart diseases cause many deaths around the world every year, and his death rate makes the leader of the killer diseases. But early diagnosis can be helpful to decrease those several deaths and save lives. To ensure good diagnose, people must pass a series of clinical examinations and analyses, which make the diagnostic operation expensive and not accessible for everyone. Speech analysis comes as a strong tool which can resolve the task and give back a new way to discriminate between healthy people and person with cardiovascular diseases. Our latest paper treated this task but using a dysphonia measurement to differentiate between people with cardiovascular disease and the healthy one, and we were able to reach 81.5% in prediction accuracy. This time we choose to change the method to increase the accuracy by extracting the voiceprint using 13 Mel-Frequency Cepstral Coefficients and the pitch, extracted from the people's voices provided from a database which contain 75 subjects (35 has cardiovascular diseases, 40 are healthy), three records of sustained vowels (aaaaa…, ooooo… .. and iiiiiiii….) has been collected from each one. We used the k-near-neighbor classifier to train a model and to classify the test entities. We were able to outperform the previous results, reaching 95.55% of prediction accuracy

Characterization of Neutron Reaction Rates in Different Irradiation Channels of the CNESTEN’s TRIGA Mark II Research Reactor

International audienceThe National Center for Energy, Sciences and Nuclear Techniques (CNESTEN)'s Training Research and Isotope Production General Atomics (TRIGA) Mark II is a pool-type light water moderated and cooled research reactor operating at a maximum steady state thermal power of 2 MW. The reactor was designed to be used as a training facility for reactor operators, neutron activation analysis, isotopes production, and for implementing different reactor physics experiments. This article deals with the numerical and experimental characterization of reaction rates (RRs) in different irradiation channels inside the CNESTEN's TRIGA Mark II research reactor, located in Rabat/Morocco. The main objective of this study is to validate the calculated neutron RRs against the measured ones and to prove that the new TRIPOLI-4 model of the reactor is capable to reproduce the measured quantities. Therefore, the measurements were carried out using the neutron activation technique and gamma spectrometry measurements. Preliminary simulations were performed with TRIPOLI-4 transport Monte Carlo code to establish the experimental design and set up for activation foils experiments. The selected set of foils with known characteristics were irradiated, at different power levels, inside the irradiation facilities of the TRIGA reactor. The resulting activities were evaluated via γ spectrometry measurements. Normalized calculated and measured RRs were compared, and a good agreement was shown for most nuclides, which indicates that the new detailed TRIPOLI-4 model of the TRIGA reactor can accurately predict the relative experimental RRs values. Further work is ongoing to analyze absolute RR values, as well as to carry measurements in other irradiation channels