Model Based Sparse Feature Extraction for Biomedical Signal Classification

This article focuses on model based sparse feature extraction of biomedical signals for classification problems, which stems from sparse representation in modern signal processing. In the presented work, a novel approach based on sparse principal component analysis (SPCA) is proposed to extract signal features. This method involves partitioning signals and utilizing SPCA to select only a limited number of signal segments in order to construct signal principal components during the training stage. For signal classification purposes, a set of regression models based on sparse principal components of the selected training signal segments is constructed. Within this approach, model residuals are estimated and used as signal features for classification. The applications of the proposed approach are demonstrated by using both the synthetic data and real EEG signals. The high classification accuracy results suggest that the proposed methods may be useful for automatic event detection using long-term observational signals. keywords: Sparse Principal Component Analysis, Sparse Feature Extraction, Signal Classification, Long-term Signal

Simulation of Particle Impact with a Wedge in Dilute Two-phase Flow

Dilute solid-fluid flow over a wedge in a stationary channel is numerically solved using one-way coupling between fluid and solid particles. The two-dimensional, steady, laminar carrierphaseflow is determined by Galerkin finite-element method using Newton's iteration for primitive variables, pressure, and velocity. Velocity is interpolated biquadratically and pressure is interpolated linearly. Parameter continuation is used to compute solutions for relatively large values of flow Reynolds number. Individual particles are tracked from specified inlet positionsby a fourth-order Runge-Kutta method applied to the equations of motion ofthe particle. Forces acting on the particle include drag, pressure, and inertia. Forces due to particle-particle interaction and Basset forces are neglected. Collisions with the wedge and the walls of the channel are modelled via assumed coefficients ofrestitution in both the normal and the tangential directions. The point of actual impact is determined by interpolation. Results are presented for various parameters, such as particle diameter, wedge angle, Reynolds number, particle density, etc

Dynamic Principal Component Analysis with Nonoverlapping Moving Window and Its Applications to Epileptic EEG Classification

Classification of electroencephalography (EEG) is the most useful diagnostic and monitoring procedure for epilepsy study. A reliable algorithm that can be easily implemented is the key to this procedure. In this paper a novel signal feature extraction method based on dynamic principal component analysis and nonoverlapping moving window is proposed. Along with this new technique, two detection methods based on extracted sparse features are applied to deal with signal classification. The obtained results demonstrated that our proposed methodologies are able to differentiate EEGs from controls and interictal for epilepsy diagnosis and to separate EEGs from interictal and ictal for seizure detection. Our approach yields high classification accuracy for both single-channel short-term EEGs and multichannel long-term EEGs. The classification performance of the method is also compared with other state-of-the-art techniques on the same datasets and the effect of signal variability on the presented methods is also studied

Análisis de sentimiento de los datos de twitter de COVID-19 utilizando modelos de aprendizaje profundo y aprendizaje máquina

En este artículo, aplicamos técnicas de aprendizaje automático para predecir el sentimiento de las personas que usan las redes sociales como Twitter durante el pico de COVID-19 en abril de 2021. Los datos contienen tweets recopilados en las fechas entre el 16 de abril de 2021 y el 26 de abril de 2021, donde el texto de los tweets se ha etiquetado mediante la formación de los modelos con un conjunto de datos ya etiquetado de tweets de virus de corona como positivo, negativo y neutro. El análisis del sentimiento se llevó a cabo mediante un modelo de aprendizaje profundo conocido como Representaciones de Codificadores Bidireccionales de Transformers (BERT) y varios modelos de aprendizaje automático para el análisis de texto y el rendimiento, que luego se compararon entre sí. Los modelos ML utilizados son Bayes ingenuas, regresión logística, bosque aleatorio, máquinas vectoriales de soporte, descenso de gradiente estocástico y aumento de gradiente extremo. La precisión de cada sentimiento se calculó por separado. La precisión de clasificación de todos los modelos de ML producidos fue de 66.4 %, 77.7 %, 74.5 %, 74.7 %, 78.6 % y 75.5 %, respectivamente y el modelo BERT produjo 84.2 %. Cada modelo clasificado de sentimiento tiene una precisión de alrededor o superior al 75 %, que es un valor bastante significativo en los algoritmos de minería de texto. Vemos que la mayoría de las personas que tuitean están adoptando un enfoque positivo y neutral./The novel coronavirus disease (COVID-19) is an ongoing pandemic with large global attention. However, spreading fake news on social media sites like Twitter is creating unnecessary anxiety and panic among people towards this disease. In this paper, we applied machine learning (ML) techniques to predict the sentiment of the people using social media such as Twitter during the COVID-19 peak in April 2021. The data contains tweets collected on the dates between 16 April 2021 and 26 April 2021 where the text of the tweets has been labelled by training the models with an already labelled dataset of corona virus tweets as positive, negative, and neutral. Sentiment analysis was conducted by a deep learning model known as Bidirectional Encoder Representations from Transformers (BERT) and various ML models for text analysis and performance which were then compared among each other. ML models used were Naïve Bayes, Logistic Regression, Random Forest, Support Vector Machines, Stochastic Gradient Descent and Extreme Gradient Boosting. Accuracy for every sentiment was separately calculated. The classification accuracies of all the ML models produced were 66.4%, 77.7%, 74.5%, 74.7%, 78.6%, and 75.5%, respectively and BERT model produced 84.2%. Each sentiment-classified model has accuracy around or above 75%, which is a quite significant value in text mining algorithms. We could infer that most people tweeting are taking positive and neutral approaches

Sentimental analysis of COVID-19 twitter data using deep learning and machine learning models

The novel coronavirus disease (COVID-19) is an ongoing pandemic with large global attention. However, spreading fake news on social media sites like Twitter is creating unnecessary anxiety and panic among people towards this disease. In this paper, we applied machine learning (ML) techniques to predict the sentiment of the people using social media such as Twitter during the COVID-19 peak in April 2021. The data contains tweets collected on the dates between 16 April 2021 and 26 April 2021 where the text of the tweets has been labelled by training the models with an already labelled dataset of corona virus tweets as positive, negative, and neutral. Sentiment analysis was conducted by a deep learning model known as Bidirectional Encoder Representations from Transformers (BERT) and various ML models for text analysis and performance which were then compared among each other. ML models used were Naïve Bayes, Logistic Regression, Random Forest, Support Vector Machines, Stochastic Gradient Descent and Extreme Gradient Boosting. Accuracy for every sentiment was separately calculated. The classification accuracies of all the ML models produced were 66.4%, 77.7%, 74.5%, 74.7%, 78.6%, and 75.5\%, respectively and BERT model produced 84.2%. Each sentiment-classified model has accuracy around or above 75%, which is a quite significant value in text mining algorithms. We could infer that most people tweeting are taking positive and neutral approaches

Suppression of Motion Artifacts in Optical Action Potential Records by Independent Component Analysis

Optical signals reflect electrical changes in the heart; however, the presence of motion artifact (MA) complicates the evaluation. Possibility of MA suppression by independent component analysis (ICA) method is presented in this article with an analysis of ICA efficiency and its limitations. Suppression of MA by ICA method was compared with results obtained by state-of-the-art signal processing method, the ratio method. Based on this comparison, the ICA was found as highly precise and useful method for motion artifact removal. ICA seems to be a promising tool for analysis of optical signals recorded from the heart surface

Farmakološko djelovanje izatina i njegovih derivata

Isatin is an endogenous compound identified in humans that possesses wide range of biological activities. Isatin has anxiogenic, sedative, anticonvulsant activity and acts as a potent antagonist on atrial natriuretic peptide receptors in vitro. A series of p-substituted isatin semicarbazones have shown anticonvulsant activity in MES, scPTZ and scSTY tests. Various isatin N-Mannich bases of isatin-3-thiosemicarbazones have shown antiviral and tuberculostatic activity. Methisazone is an effective compound against variola and vaccinia viruses. The N-dimethyl and morpholino derivative of 5-methyl isatin and trimethoprim exhibited an EC50 of more than 4.3 and 17.7 microgram mL-1 and 17.7 microgram mL-1, respectively. Isatin (3-o-nitrophenyl)hydrazone has shown activity against Walker carcinoma-256. Various substituted indolinones showed antitubercular activity against M. tuberculosis H37Rv with MIC ranging from 10-20 microgram mL-1. Isatin derivatives of Mannich bases had fibrinolytic, muscle relaxant, antiallergic, immunosuppressant, and antithrombotic activity. Isatin showed cardioinhibitory effect on frog heart, and hypotensive, respiratory depression and antidiuretic effects.Izatin je endogeni spoj prisutan u organizmu čovjeka koji posjeduje niz farmakoloških učinaka. Izatin djeluje kao antioksidans, sedativ i antikonvulziv. In vitro je snažni antagonist na receptorima za natrijeve ione u atriju. Serija p-supstituiranih semikarbazona izatina pokazala je antikonvulzivno djelovanje u MES, scPTZ i scSTY testovima, a N-Mannichove baze izatina i izatin-3-tiosemikarbazona virustatsko i tuberkulostatsko djelovanje. Metisazon je učinkovit protiv infekcija variola i vakcinia virusima. EC50 N-dimetil i morfolino derivata 5-metilizatina i trimetoprima veći je od 4,3, odnosno 17,7 g mL-1. Izatin (3-o-nitrofenilhidrazon) inhibira rast tumorskih stanica Walker-256, a supstituirani indolinoni su aktivni protiv M. tuberculosis H37Rv (MIC vrijednosti 1020 g mL-1. Mannichove baze izatina djeluju kao fibrinolitici, miorelaksansi, antihistaminici, imunosupresivi i antitrombotici. Izatin ima kardioinhibitorni učinak na srce žabe, a djeluje i kao hipotenziv, depresor respiracije i antidiuretik

Farmakološko djelovanje izatina i njegovih derivata

Isatin is an endogenous compound identified in humans that possesses wide range of biological activities. Isatin has anxiogenic, sedative, anticonvulsant activity and acts as a potent antagonist on atrial natriuretic peptide receptors in vitro. A series of p-substituted isatin semicarbazones have shown anticonvulsant activity in MES, scPTZ and scSTY tests. Various isatin N-Mannich bases of isatin-3-thiosemicarbazones have shown antiviral and tuberculostatic activity. Methisazone is an effective compound against variola and vaccinia viruses. The N-dimethyl and morpholino derivative of 5-methyl isatin and trimethoprim exhibited an EC50 of more than 4.3 and 17.7 microgram mL-1 and 17.7 microgram mL-1, respectively. Isatin (3-o-nitrophenyl)hydrazone has shown activity against Walker carcinoma-256. Various substituted indolinones showed antitubercular activity against M. tuberculosis H37Rv with MIC ranging from 10-20 microgram mL-1. Isatin derivatives of Mannich bases had fibrinolytic, muscle relaxant, antiallergic, immunosuppressant, and antithrombotic activity. Isatin showed cardioinhibitory effect on frog heart, and hypotensive, respiratory depression and antidiuretic effects.Izatin je endogeni spoj prisutan u organizmu čovjeka koji posjeduje niz farmakoloških učinaka. Izatin djeluje kao antioksidans, sedativ i antikonvulziv. In vitro je snažni antagonist na receptorima za natrijeve ione u atriju. Serija p-supstituiranih semikarbazona izatina pokazala je antikonvulzivno djelovanje u MES, scPTZ i scSTY testovima, a N-Mannichove baze izatina i izatin-3-tiosemikarbazona virustatsko i tuberkulostatsko djelovanje. Metisazon je učinkovit protiv infekcija variola i vakcinia virusima. EC50 N-dimetil i morfolino derivata 5-metilizatina i trimetoprima veći je od 4,3, odnosno 17,7 g mL-1. Izatin (3-o-nitrofenilhidrazon) inhibira rast tumorskih stanica Walker-256, a supstituirani indolinoni su aktivni protiv M. tuberculosis H37Rv (MIC vrijednosti 1020 g mL-1. Mannichove baze izatina djeluju kao fibrinolitici, miorelaksansi, antihistaminici, imunosupresivi i antitrombotici. Izatin ima kardioinhibitorni učinak na srce žabe, a djeluje i kao hipotenziv, depresor respiracije i antidiuretik