Isbn: 9786200480408 - image analysis in big data architecture using artificial intelligence: compression and analysis of biomedical image based on machine learning and orthogonal transforms with application (6 resultados)

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    • Idioma: Inglés

      Editorial: LAP LAMBERT Academic Publishing, 2019

      6200480400 / 9786200480408

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      Librería: moluna, Greven, Alemaniamoluna

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      EUR 50,66

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      Cantidad disponible: Más de 20 disponibles

      Condición: New.

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      EUR 106,74

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      Paperback. Condición: Brand New. 148 pages. 8.66x5.91x0.34 inches. In Stock.

    • Idioma: Inglés

      Editorial: LAP LAMBERT Academic Publishing Dez 2019, 2019

      6200480400 / 9786200480408

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      Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.

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      EUR 61,90

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      Cantidad disponible: 2 disponibles

      Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In the medical field, data is increasingly growing and traditional methods cannot manage them efficiently. In the computational biomedical, the continuous challenges are management, analysis, and storage of the biomedical data. Nowadays, big data technology plays a significant role in the management, organization, and analysis of the data using machine learning and artificial intelligence techniques. It becomes very important to develop methods and/or architectures based on big data technologies for complete processing of biomedical images data. In this thesis, we propose a complete and optimal workflow based on big data technology and optimal algorithms drawn from literature to manage biomedical images. Compression step within the proposed optimal workflow will be considered as a study case implementing big data analysis technology. The proposed workflow implements an image compression algorithm for biomedical images, which is based on three main steps, orthogonal transform, vector quantization using machine learning and entropy encoding. The proposed algorithm allows us to develop appropriate and efficient methods to leverage a large number of images into the proposed workflow. 148 pp. Englisch.

    • Idioma: Inglés

      Editorial: LAP LAMBERT Academic Publishing, 2019

      6200480400 / 9786200480408

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      Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH

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      Condición: Nuevo

      EUR 88,59

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      Cantidad disponible: 1 disponibles

      Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In the medical field, data is increasingly growing and traditional methods cannot manage them efficiently. In the computational biomedical, the continuous challenges are management, analysis, and storage of the biomedical data. Nowadays, big data technology plays a significant role in the management, organization, and analysis of the data using machine learning and artificial intelligence techniques. It becomes very important to develop methods and/or architectures based on big data technologies for complete processing of biomedical images data. In this thesis, we propose a complete and optimal workflow based on big data technology and optimal algorithms drawn from literature to manage biomedical images. Compression step within the proposed optimal workflow will be considered as a study case implementing big data analysis technology. The proposed workflow implements an image compression algorithm for biomedical images, which is based on three main steps, orthogonal transform, vector quantization using machine learning and entropy encoding. The proposed algorithm allows us to develop appropriate and efficient methods to leverage a large number of images into the proposed workflow.

    • Idioma: Inglés

      Editorial: LAP LAMBERT Academic Publishing Dez 2019, 2019

      6200480400 / 9786200480408

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      Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000

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      Condición: Nuevo

      EUR 61,90

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      Cantidad disponible: 1 disponibles

      Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In the medical field, data is increasingly growing and traditional methods cannot manage them efficiently. In the computational biomedical, the continuous challenges are management, analysis, and storage of the biomedical data. Nowadays, big data technology plays a significant role in the management, organization, and analysis of the data using machine learning and artificial intelligence techniques. It becomes very important to develop methods and/or architectures based on big data technologies for complete processing of biomedical images data. In this thesis, we propose a complete and optimal workflow based on big data technology and optimal algorithms drawn from literature to manage biomedical images. Compression step within the proposed optimal workflow will be considered as a study case implementing big data analysis technology. The proposed workflow implements an image compression algorithm for biomedical images, which is based on three main steps, orthogonal transform, vector quantization using machine learning and entropy encoding. The proposed algorithm allows us to develop appropriate and efficient methods to leverage a large number of images into the proposed workflow.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 148 pp. Englisch.

    • Idioma: Inglés

      Editorial: LAP LAMBERT Academic Publishing, 2019

      6200480400 / 9786200480408

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      Librería: preigu, Osnabrück, Alemaniapreigu

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      Condición: Nuevo

      EUR 52,60

      Envío por EUR 70,00 
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      Cantidad disponible: 5 disponibles

      Taschenbuch. Condición: Neu. Image Analysis in Big Data Architecture using Artificial Intelligence | Compression and Analysis of Biomedical Image Based on Machine Learning and Orthogonal Transforms with Application | Aurelle Tchagna Kouanou (u. a.) | Taschenbuch | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786200480408 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.