Sanjay sanamdikar (62 risultati)

Condizione: New.

Taschenbuch. Condizione: Neu. Sewage Treatment Using PLC | Advance Method for Sewage | Sanjay Sanamdikar | Taschenbuch | Englisch | LAP Lambert Academic Publishing | EAN 9783659161735 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

Taschenbuch. Condizione: Neu. Arrhythmia Analysis and Interpretation of ECG Signals | Arrhythmia Analysis using Super vector Regressions | Sanjay Sanamdikar (u. a.) | Taschenbuch | Englisch | 2023 | LAP LAMBERT Academic Publishing | EAN 9786206156567 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

Taschenbuch. Condizione: Neu. Arrhythmia Detection by using Generative Adversarial Network Method | Analysis and Interpretation of Arrhythmia | Sanjay Sanamdikar (u. a.) | Taschenbuch | Englisch | 2023 | LAP LAMBERT Academic Publishing | EAN 9786206739135 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

Condizione: New.

paperback. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Paperback. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Condizione: New.

Condizione: New.

Taschenbuch. Condizione: Neu. Analyse et interprétation des arythmies Signaux ECG | Analyse de l'arythmie à l'aide de régressions super vectorielles | Sanjay Sanamdikar (u. a.) | Taschenbuch | Französisch | 2023 | Editions Notre Savoir | EAN 9786205972380 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

Condizione: New.

Condizione: New.

Taschenbuch. Condizione: Neu. Análise e interpretação de arritmias Sinais de ECG | Análise de arritmias através de regressões super vectoriais | Sanjay Sanamdikar (u. a.) | Taschenbuch | Portugiesisch | 2023 | Edições Nosso Conhecimento | EAN 9786205972410 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.…

Taschenbuch. Condizione: Neu. Analisi delle aritmie e interpretazione dei segnali ECG | Analisi delle aritmie mediante regressioni super vettoriali | Sanjay Sanamdikar (u. a.) | Taschenbuch | Italienisch | 2023 | Edizioni Sapienza | EAN 9786205972403 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

Taschenbuch. Condizione: Neu. Analyse und Interpretation von Herzrhythmusstörungen EKG-Signale | Arrhythmie-Analyse mit Super-Vektor-Regressionen | Sanjay Sanamdikar (u. a.) | Taschenbuch | 108 S. | Deutsch | 2023 | Verlag Unser Wissen | EAN 9786205972311 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. …

Taschenbuch. Condizione: Neu. Deteção de arritmia utilizando o método das redes adversárias generativas | Análise e interpretação da arritmia | Sanjay Sanamdikar (u. a.) | Taschenbuch | Portugiesisch | 2023 | Edições Nosso Conhecimento | EAN 9786206959847 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.…

Taschenbuch. Condizione: Neu. Rilevamento delle aritmie con il metodo delle reti avversarie generative | Analisi e interpretazione dell'aritmia | Sanjay Sanamdikar (u. a.) | Taschenbuch | Italienisch | 2023 | Edizioni Sapienza | EAN 9786206959830 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.…

Taschenbuch. Condizione: Neu. Arrhythmieerkennung mithilfe der Generative Adversarial Network-Methode | Analyse und Interpretation von Arrhythmien | Sanjay Sanamdikar (u. a.) | Taschenbuch | 148 S. | Deutsch | 2023 | Verlag Unser Wissen | EAN 9786206959809 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. …

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In this book,Sewage is waste water carrying wastes removed from residences, institutions, commercial and industrial establishments, together with such groundwater, surface water, and storm water as may be present. Its chemical constituents and the bacteriological organisms that it contains. So there is great need to treat waste water. We can treat such water by various methods. The contaminants in waste water are removed by physical, chemical, and biological means. We can achieve desire results with the help of PLC. Using PLC we want to develop an automated continuous process system for treating the waste water. In this book we are discussing how we are going to develop this system, what the basic theme of this system is and how our ideas will be applicable to the theme. We have designed a control system particularly a batch process. The purpose of the book is to alleviate the severe impacts of uncontrolled sewage discharges into the environment. Proper design, selection, construction and management of the waste water treatment plants and upgrading, construction of waste water collection networks would mitigate such negative impacts.…

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The various classification algorithms can be used to classify extracted features from ECG signal. High performance of classification depends on how well the vectors of features can be separated in the feature space. Proposed architecture presents ECG based arrhythmia classification with more robust features and Regression based classifier. It proposes an effective automated classification of cardiac arrhythmia using MIT-BIH arrhythmia database and Local Clinical dataset. Proposed Method have trained the Incremental Support Vector Regression Classifier with 320 samples of different arrhythmias. Proposed Method has been tested and compared with the most common classifier such as Artificial Neural network, Support Vector Machine and Minimum Distance Classifier. From The confusion matrix it is clear that our proposed algorithm works well for multiple class recognition problem. Proposed Architecture uses both time and frequency domain features for classification purpose. Due to use of higher order statistic our classification problem becomes simpler than traditional morphological feature. Proposed algorithm delivered high performance even with smaller learning data. 104 pp. Englisch.…

Kartoniert / Broschiert. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Sanamdikar SanjayProf.S.T.Sanamdikar,Assistant Professor, Instrumentation & Control ,PDEA s,College of Engineering,Manjari,Pune,University Of Pune,India.Dr.K.R.Harne,Principal, Civil Engineering,PDEA s,College ofEngineering,Manjari,P.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The various classification algorithms can be used to classify extracted features from ECG signal. High performance of classification depends on how well the vectors of features can be separated in the feature space. Proposed architecture presents ECG based .…

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book explains how a deep generative adversarial network built on a large dataset may detect arrhythmias more accurately than physicians. Furthermore, feature extraction has traditionally been seen as an essential component of electrocardiogram arrhythmia classification The purpose of this research is to examine ECG arrhythmia classification using a deep dense generative adversarial network. The GAN architecture shown in this book can be taught to produce ECG signals that are comparable to real-world ECG signals. The results indicate that using a sequence-based strategy for all ECG-beat types substantially improves area under curve on our test set. Traditional architecture does not naturally address this structure, and therefore suffers from decreased performance when such a structure is informative. This book compares the proposed technique to kernel principle component analysis with incremental support vector regression, discrete wavelet transforms with incremental support vector regression and general sparse neural network. From obtained results, it is concluded that the proposed GAN technique is superior to these three methods with an overall accuracy of 97.44 percent. 164 pp. Englisch.…

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Los distintos algoritmos de clasificación pueden utilizarse para clasificar las características extraídas de la señal de ECG. El alto rendimiento de la clasificación depende de lo bien que puedan separarse los vectores de características en el espacio de características. La arquitectura propuesta presenta una clasificación de arritmias basada en ECG con características más robustas y un clasificador basado en regresión. Propone una clasificación automatizada eficaz de las arritmias cardiacas utilizando la base de datos de arritmias MIT-BIH y el conjunto de datos clínicos locales. El método propuesto ha entrenado el clasificador de regresión de vectores de soporte incremental con 320 muestras de diferentes arritmias. El método propuesto se ha probado y comparado con los clasificadores más comunes, como la red neuronal artificial, la máquina de vectores de soporte y el clasificador de distancia mínima. De la matriz de confusión se desprende claramente que nuestro algoritmo propuesto funciona bien para problemas de reconocimiento de clases múltiples. La arquitectura propuesta utiliza características de dominio de tiempo y frecuencia para la clasificación. Debido al uso de estadísticas de orden superior, nuestro problema de clasificación es más simple que las características morfológicas tradicionales. El algoritmo propuesto ofrece un alto rendimiento incluso con datos de aprendizaje más pequeños. 104 pp. Spanisch.…

Condizione: New. Print on Demand.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book explains how a deep generative adversarial network built on a large dataset may detect arrhythmias more accurately than physicians. Furthermore, feature extraction has traditionally been seen as an essential component of electrocardiogram arrhythm.…

Condizione: New. PRINT ON DEMAND.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book explains how a deep generative adversarial network built on a large dataset may detect arrhythmias more accurately than physicians. Furthermore, feature extraction has traditionally been seen as an essential component of electrocardiogram arrhythmia classification The purpose of this research is to examine ECG arrhythmia classification using a deep dense generative adversarial network. The GAN architecture shown in this book can be taught to produce ECG signals that are comparable to real-world ECG signals. The results indicate that using a sequence-based strategy for all ECG-beat types substantially improves area under curve on our test set. Traditional architecture does not naturally address this structure, and therefore suffers from decreased performance when such a structure is informative. This book compares the proposed technique to kernel principle component analysis with incremental support vector regression, discrete wavelet transforms with incremental support vector regression and general sparse neural network. From obtained results, it is concluded that the proposed GAN technique is superior to these three methods with an overall accuracy of 97.44 percent.…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The various classification algorithms can be used to classify extracted features from ECG signal. High performance of classification depends on how well the vectors of features can be separated in the feature space. Proposed architecture presents ECG based arrhythmia classification with more robust features and Regression based classifier. It proposes an effective automated classification of cardiac arrhythmia using MIT-BIH arrhythmia database and Local Clinical dataset. Proposed Method have trained the Incremental Support Vector Regression Classifier with 320 samples of different arrhythmias. Proposed Method has been tested and compared with the most common classifier such as Artificial Neural network, Support Vector Machine and Minimum Distance Classifier. From The confusion matrix it is clear that our proposed algorithm works well for multiple class recognition problem. Proposed Architecture uses both time and frequency domain features for classification purpose. Due to use of higher order statistic our classification problem becomes simpler than traditional morphological feature. Proposed algorithm delivered high performance even with smaller learning data.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 104 pp. Englisch.…

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Los distintos algoritmos de clasificación pueden utilizarse para clasificar las características extraídas de la señal de ECG. El alto rendimiento de la clasificación depende de lo bien que puedan separarse los vectores de características en el espacio de características. La arquitectura propuesta presenta una clasificación de arritmias basada en ECG con características más robustas y un clasificador basado en regresión. Propone una clasificación automatizada eficaz de las arritmias cardiacas utilizando la base de datos de arritmias MIT-BIH y el conjunto de datos clínicos locales. El método propuesto ha entrenado el clasificador de regresión de vectores de soporte incremental con 320 muestras de diferentes arritmias. El método propuesto se ha probado y comparado con los clasificadores más comunes, como la red neuronal artificial, la máquina de vectores de soporte y el clasificador de distancia mínima. De la matriz de confusión se desprende claramente que nuestro algoritmo propuesto funciona bien para problemas de reconocimiento de clases múltiples. La arquitectura propuesta utiliza características de dominio de tiempo y frecuencia para la clasificación. Debido al uso de estadísticas de orden superior, nuestro problema de clasificación es más simple que las características morfológicas tradicionales. El algoritmo propuesto ofrece un alto rendimiento incluso con datos de aprendizaje más pequeños.…