Using Speech Signal for Emotion Recognition Using Hybrid Features with SVM Classifier

Emotion recognition is a hot topic that has received a lot of attention and study,owing to its significance in a variety of fields, including applications needing human-computer interaction (HCI). Extracting features related to the emotional state of speech remains one of the important research challenges.This study investigated the approach of the core idea behind feature extraction is the residual signal of the prediction procedure is the difference between the original and the prediction .hence the visibility of using sets of extracting features from speech single when the statistical of local features were used to achieve high detection accuracy for seven emotions. The proposed approach is based on the fact that local features can provide efficient representations suitable for pattern recognition. Publicly available speech datasets like the Berlin dataset are tested using a support vector machine (SVM) classifier. The hybrid features were trained separately. The results indicated that some features were terrible. Some were very encouraging, reaching 99.4%. In this article, the SVM classifier test results with the same tested hybrid features that published in a previous article  will be presented, also a comparison between  some related works  and the proposed technique  in speech emotion recognition techniques

Open Educational Resources Textbook List

Discipline specific OER textbook list for departments at SHU, compiled by Zach Claybaugh and Chelsea Stone

Desarrollo del pensamiento variacional y computacional a través del lenguaje Python en Educación Básica Secundaria en Colombia

No aplicaEl documento presenta los resultados de trabajo de grado en la modalidad de Monografía, bajo la asesoría del profesor Víctor Manuel Mendoza y bajo la línea de investigación “Argumentación, pedagogía y aprendizaje”. Igualmente, se basó en la metodología de revisión, compilación y análisis de masas documentales, a partir de textos académicos, documentos de investigación, tesis de maestrías y artículos de revistas, producidos en el continente americano. El documento presenta una serie de referentes sobre autores, investigadores y académicos que han desarrollado los temas alrededor de lo que ha sido el desarrollo del pensamiento variacional y computacional a través del lenguaje Python en Educación Básica Secundaria en Colombia. La descripción del tema es una respuesta argumentada a la pregunta: ¿El desarrollo del pensamiento computacional es una herramienta didáctica que garantiza el desarrollo del pensamiento variacional y el aprendizaje significativo de las matemáticas?No aplic

Open Educational Resource 2017 Textbook List

This is an updated, discipline specific OER textbook list for departments at Sacred Heart University, compiled by Zach Claybaugh and Chelsea Stone

The fatigue life of steel specimens under axial, torsional and combined axial-torsional loading

Předkládaná diplomová práce se zabývá stanovením únavové životnosti při cyklickém víceosém zatěžování. Nejprve je v práci popsán únavový proces a způsob určování únavové životnosti při jednoosém a víceosém namáhání. Cílem praktické části je posouzení přesnosti predikce únavové životnosti válcových vzorků z oceli 1.2210, experimentálně namáhaných synchronní symetrickou kombinací axiální síly a torzního momentu, pomocí vybraných kritérií. K tomu je zapotřebí nejprve stanovit únavovou pevnost při prostém axiálním a prostém torzním namáhání.The master’s thesis deals with the fatigue life prediction under multiaxial cyclic loading. First, the fatigue process is discussed and the methods for fatigue life prediction under uniaxial and multiaxial loading are described. In the practical part, the accuracy of selected criteria is assessed based on experimental data obtained on cylindrical samples made of 1.2210 steel that were tested under synchronous symmetric axial-torsion loading. Application of criteria requires to determine the fatigue strength under pure axial and torsional loading.

Think Stats: Probability and Statistics for Programmers

Think Stats is an introduction to Probability and Statistics for Python programmers. Think Stats emphasizes simple techniques you can use to explore real data sets and answer interesting questions. The book presents a case study using data from the National Institutes of Health. Readers are encouraged to work on a project with real datasets. If you have basic skills in Python/ you can use them to learn concepts in probability and statistics. Think Stats is based on a Python library for probability distributions (PMFs and CDFs). Many of the exercises use short programs to run experiments and help readers develop understanding.https://mds.marshall.edu/oa-textbooks/1233/thumbnail.jp

Statistical Comparison of Different Machine-Learning Approaches for Malaria Parasites Detection in Microscopic Images

A malária é um grave problema de saúde pública em todo o mundo, particularmente nos países em desenvolvimento (cerca de 80% dos casos ocorrem em África), pondo em risco a vida de milhões de pessoas.Causada por 4 espécies diferentes de parasitas, cada um com diferentes estágios de evolução, a aproximação do diagnóstico correto sem acesso a equipamentos caros é complexo. Nos últimos anos, a investigação tem-se centrado na aceleração e redução dos custos deste diagnóstico, recorrendo à classificação de imagens microscópicas, com base em processos de Machine Learning.Ainda assim, persiste na maioria das abordagens a ausência constante de comparação estatística sistemática na literatura que suporta uma técnica ou recurso particular.Assim, os objetivos desta dissertação são: (i) projetar e executar uma comparação estatística de diferentes abordagens de ML para a detecção de tais parasitas em imagens microscópicas, (ii) identificar quais características são mais relevantes para a previsão, (iii) identificar quais modelos E as técnicas obtêm os melhores resultados, equilibrando a precisão e o recall, e (iv) investigando a aplicabilidade das técnicas de aprendizagem profunda e de conjunto.A conclusão bem-sucedida desta dissertação capacitará os países em desenvolvimento com ferramentas de diagnóstico mais rápidas, mais baratas e mais precisas, melhorando diretamente a vida e a saúde de populações.Malaria is a severe public health problem across the world, particularly in developing countries (≈80% of the cases occur in Africa), putting at special risk the most unprotected groups of society: children and pregnant women. Since it can be caused by 4 different species of parasites, each having different stages of evolution, approaching the right diagnosis without access to costly equipment is complex. Ergo, research has focused on speeding up and lowering the costs of its diagnosis, by resorting to automatic machine classification of microscopic images. Still, most approaches rely on simplistic, single-model classifiers, with a constant absence of a systematic statistical comparison in the literature that supports a particular technique or feature.Hence, this dissertation presents: (i) design and execute a statistical comparison of different ML approaches for the detection of such parasites in microscopic images, (ii) identify which features are more relevant for prediction, and (iii) identify which models and techniques achieve the best results, balancing precision and recall.Given the stated problem, and before approaching it, it was performed an initial statistical analysis to the dataset, to discover its proportions and to detect highly correlated features.After knowing the data, it was developed a framework that (i) optimizes the values of the considered classification and feature selection algorithms, (ii) computes a statistical comparison of different machine learning approaches to the same dataset, using different metrics on the cross validation, where there were used different metrics to measure the performance value variation and evaluate which one is consistent with the data, and, finally, (iii) performs a statistical hypothesis test,to guarantee that the data model with the best performance is distinct from all the others considered in this study. As result, one can verify an improvement over the established baseline, by using a Fdr feature selection method followed by a Ada Boosting classifier with 350 estimators