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DDC 338.102854678
I 69


    Internet of Things and machine learning in agriculture : : technological impacts and challenges / / edited by Vishal Jain, Jyotir Moy Chatterjee, Abhishek Kumar, Pramod Singh Rathore. - Berlin ; ; Boston : : Walter de Gruyter GmbH,, 2021. - 1 online resource (xvi, 410 pages) : : il. - (De Gruyter frontiers in computational intelligence ; ; volume 8). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/50820268-35AC-4432-8B7B-39A469CF486C. - ISBN 9783110691276 (electronic bk. ;. - ISBN 3110691272 (electronic bk. ;. - ISBN 9783110691283 (electronic bk. ;. - ISBN 3110691280 (electronic bk. ;
Print version of record.
Параллельные издания: Print version: : Chatterjee, Jyotir Moy Internet of Things and machine learning in agriculture. - Berlin/Boston : Walter de Gruyter GmbH,c2021

~РУБ DDC 338.102854678

Рубрики: Agriculture--Data processing.

   Internet of things.


   Artificial intelligence--Agricultural applications.


   Machine learning.


   Agriculture--Informatique.


   Internet des objets.


   Intelligence artificielle--Applications agricoles.


   Apprentissage automatique.


   COMPUTERS / Information Technology.


   Agriculture--Data processing.


   Internet of things.


   Electronic books.


Аннотация: Agriculture is one of the most fundamental human activities. As the farming capacity has expanded, the usage of resources such as land, fertilizer, and water has grown exponentially, and environmental pressures from modern farming techniques have stressed natural landscapes. Still, by some estimates, worldwide food production needs to increase to keep up with global food demand. 'Machine Learning and the Internet of Things' can play a promising role in the Agricultural industry, and help to increase food production while respecting the environment. This book explains how these technologies can be applied, offering many case studies developed in the research world.

Доп.точки доступа:
Jain, Vishal, (1983-) \editor.\
Chatterjee, Jyotir Moy, \editor.\
Kumar, Abhishek, \editor.\
Rathore, Pramod Singh, (1988-) \editor.\

Internet of Things and machine learning in agriculture : [Электронный ресурс] : technological impacts and challenges / / edited by Vishal Jain, Jyotir Moy Chatterjee, Abhishek Kumar, Pramod Singh Rathore., 2021. - 1 online resource (xvi, 410 pages) : с.

1.

Internet of Things and machine learning in agriculture : [Электронный ресурс] : technological impacts and challenges / / edited by Vishal Jain, Jyotir Moy Chatterjee, Abhishek Kumar, Pramod Singh Rathore., 2021. - 1 online resource (xvi, 410 pages) : с.


DDC 338.102854678
I 69


    Internet of Things and machine learning in agriculture : : technological impacts and challenges / / edited by Vishal Jain, Jyotir Moy Chatterjee, Abhishek Kumar, Pramod Singh Rathore. - Berlin ; ; Boston : : Walter de Gruyter GmbH,, 2021. - 1 online resource (xvi, 410 pages) : : il. - (De Gruyter frontiers in computational intelligence ; ; volume 8). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/50820268-35AC-4432-8B7B-39A469CF486C. - ISBN 9783110691276 (electronic bk. ;. - ISBN 3110691272 (electronic bk. ;. - ISBN 9783110691283 (electronic bk. ;. - ISBN 3110691280 (electronic bk. ;
Print version of record.
Параллельные издания: Print version: : Chatterjee, Jyotir Moy Internet of Things and machine learning in agriculture. - Berlin/Boston : Walter de Gruyter GmbH,c2021

~РУБ DDC 338.102854678

Рубрики: Agriculture--Data processing.

   Internet of things.


   Artificial intelligence--Agricultural applications.


   Machine learning.


   Agriculture--Informatique.


   Internet des objets.


   Intelligence artificielle--Applications agricoles.


   Apprentissage automatique.


   COMPUTERS / Information Technology.


   Agriculture--Data processing.


   Internet of things.


   Electronic books.


Аннотация: Agriculture is one of the most fundamental human activities. As the farming capacity has expanded, the usage of resources such as land, fertilizer, and water has grown exponentially, and environmental pressures from modern farming techniques have stressed natural landscapes. Still, by some estimates, worldwide food production needs to increase to keep up with global food demand. 'Machine Learning and the Internet of Things' can play a promising role in the Agricultural industry, and help to increase food production while respecting the environment. This book explains how these technologies can be applied, offering many case studies developed in the research world.

Доп.точки доступа:
Jain, Vishal, (1983-) \editor.\
Chatterjee, Jyotir Moy, \editor.\
Kumar, Abhishek, \editor.\
Rathore, Pramod Singh, (1988-) \editor.\

Sosnovshchenko, Alexander,. Machine learning with Swift : [Электронный ресурс] : artificial intelligence for iOS / / Alexander Sosnovshchenko., 2018. - 1 online resource (1 volume) : с.

2.

Sosnovshchenko, Alexander,. Machine learning with Swift : [Электронный ресурс] : artificial intelligence for iOS / / Alexander Sosnovshchenko., 2018. - 1 online resource (1 volume) : с.

DDC 332.0285631
K 51

Klaas, Jannes,.
    Machine learning for finance : : the practical guide to using data-driven algorithms in banking, insurance, and investments / / Jannes Klaas. - Birmingham : : Packt Publishing, Limited,, 2019. - 1 online resource (457 pages). - (Expert insight). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/D8285CEE-5285-4F93-9C81-31FBA9712F32. - ISBN 1789134692. - ISBN 9781789134698 (electronic bk.)
Print version record.
Параллельные издания: Print version: : Klaas, Jannes. Machine Learning for Finance : The Practical Guide to Using Data-Driven Algorithms in Banking, Insurance, and Investments. - Birmingham : Packt Publishing, Limited, ©2019. - ISBN 9781789136364
    Содержание:
Cover; Copyright; Mapt upsell; Contributors; Table of Contents; Preface; Chapter 1: Neural Networks and Gradient-Based Optimization; Our journey in this book; What is machine learning?; Supervised learning; Unsupervised learning; Reinforcement learning; The unreasonable effectiveness of data; All models are wrong; Setting up your workspace; Using Kaggle kernels; Running notebooks locally; Installing TensorFlow; Installing Keras; Using data locally; Using the AWS deep learning AMI; Approximating functions; A forward pass; A logistic regressor; Python version of our logistic regressor
Optimizing model parametersMeasuring model loss; Gradient descent; Backpropagation; Parameter updates; Putting it all together; A deeper network; A brief introduction to Keras; Importing Keras; A two-layer model in Keras; Stacking layers; Compiling the model; Training the model; Keras and TensorFlow; Tensors and the computational graph; Exercises; Summary; Chapter 2: Applying Machine Learning to Structured Data; The data; Heuristic, feature-based, and E2E models; The machine learning software stack; The heuristic approach; Making predictions using the heuristic model; The F1 score
Evaluating with a confusion matrixThe feature engineering approach; A feature from intuition -- fraudsters don't sleep; Expert insight -- transfer, then cash out; Statistical quirks -- errors in balances; Preparing the data for the Keras library; One-hot encoding; Entity embeddings; Tokenizing categories; Creating input models; Training the model; Creating predictive models with Keras; Extracting the target; Creating a test set; Creating a validation set; Oversampling the training data; Building the model; Creating a simple baseline; Building more complex models
A brief primer on tree-based methodsA simple decision tree; A random forest; XGBoost; E2E modeling; Exercises; Summary; Chapter 3: Utilizing Computer Vision; Convolutional Neural Networks; Filters on MNIST; Adding a second filter; Filters on color images; The building blocks of ConvNets in Keras; Conv2D; Kernel size; Stride size; Padding; Input shape; Simplified Conv2D notation; ReLU activation; MaxPooling2D; Flatten; Dense; Training MNIST; The model; Loading the data; Compiling and training; More bells and whistles for our neural network; Momentum; The Adam optimizer; Regularization
L2 regularizationL1 regularization; Regularization in Keras; Dropout; Batchnorm; Working with big image datasets; Working with pretrained models; Modifying VGG-16; Random image augmentation; Augmentation with ImageDataGenerator; The modularity tradeoff; Computer vision beyond classification; Facial recognition; Bounding box prediction; Exercises; Summary; Chapter 4: Understanding Time Series; Visualization and preparation in pandas; Aggregate global feature statistics; Examining the sample time series; Different kinds of stationarity; Why stationarity matters; Making a time series stationary; When to ignore stationarity issues.

~РУБ DDC 332.0285631

Рубрики: Finance--Data processing.

   Finance--Mathematical models.


   Machine learning.


   Finances--Informatique.


   Finances--Modèles mathématiques.


   Apprentissage automatique.


   Finance--Data processing.


   Finance--Mathematical models.


   Machine learning.


Аннотация: Machine Learning for Finance shows you how to build machine learning models for use in financial services organizations. It shows you how to work with all the key machine learning models, from simple regression to advanced neural networks. You will use machine learning to automate manual tasks, address systematic bias, and find new insights ...

Klaas, Jannes,. Machine learning for finance : [Электронный ресурс] : the practical guide to using data-driven algorithms in banking, insurance, and investments / / Jannes Klaas., 2019. - 1 online resource (457 pages) с. (Введено оглавление)

3.

Klaas, Jannes,. Machine learning for finance : [Электронный ресурс] : the practical guide to using data-driven algorithms in banking, insurance, and investments / / Jannes Klaas., 2019. - 1 online resource (457 pages) с. (Введено оглавление)


DDC 332.0285631
K 51

Klaas, Jannes,.
    Machine learning for finance : : the practical guide to using data-driven algorithms in banking, insurance, and investments / / Jannes Klaas. - Birmingham : : Packt Publishing, Limited,, 2019. - 1 online resource (457 pages). - (Expert insight). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/D8285CEE-5285-4F93-9C81-31FBA9712F32. - ISBN 1789134692. - ISBN 9781789134698 (electronic bk.)
Print version record.
Параллельные издания: Print version: : Klaas, Jannes. Machine Learning for Finance : The Practical Guide to Using Data-Driven Algorithms in Banking, Insurance, and Investments. - Birmingham : Packt Publishing, Limited, ©2019. - ISBN 9781789136364
    Содержание:
Cover; Copyright; Mapt upsell; Contributors; Table of Contents; Preface; Chapter 1: Neural Networks and Gradient-Based Optimization; Our journey in this book; What is machine learning?; Supervised learning; Unsupervised learning; Reinforcement learning; The unreasonable effectiveness of data; All models are wrong; Setting up your workspace; Using Kaggle kernels; Running notebooks locally; Installing TensorFlow; Installing Keras; Using data locally; Using the AWS deep learning AMI; Approximating functions; A forward pass; A logistic regressor; Python version of our logistic regressor
Optimizing model parametersMeasuring model loss; Gradient descent; Backpropagation; Parameter updates; Putting it all together; A deeper network; A brief introduction to Keras; Importing Keras; A two-layer model in Keras; Stacking layers; Compiling the model; Training the model; Keras and TensorFlow; Tensors and the computational graph; Exercises; Summary; Chapter 2: Applying Machine Learning to Structured Data; The data; Heuristic, feature-based, and E2E models; The machine learning software stack; The heuristic approach; Making predictions using the heuristic model; The F1 score
Evaluating with a confusion matrixThe feature engineering approach; A feature from intuition -- fraudsters don't sleep; Expert insight -- transfer, then cash out; Statistical quirks -- errors in balances; Preparing the data for the Keras library; One-hot encoding; Entity embeddings; Tokenizing categories; Creating input models; Training the model; Creating predictive models with Keras; Extracting the target; Creating a test set; Creating a validation set; Oversampling the training data; Building the model; Creating a simple baseline; Building more complex models
A brief primer on tree-based methodsA simple decision tree; A random forest; XGBoost; E2E modeling; Exercises; Summary; Chapter 3: Utilizing Computer Vision; Convolutional Neural Networks; Filters on MNIST; Adding a second filter; Filters on color images; The building blocks of ConvNets in Keras; Conv2D; Kernel size; Stride size; Padding; Input shape; Simplified Conv2D notation; ReLU activation; MaxPooling2D; Flatten; Dense; Training MNIST; The model; Loading the data; Compiling and training; More bells and whistles for our neural network; Momentum; The Adam optimizer; Regularization
L2 regularizationL1 regularization; Regularization in Keras; Dropout; Batchnorm; Working with big image datasets; Working with pretrained models; Modifying VGG-16; Random image augmentation; Augmentation with ImageDataGenerator; The modularity tradeoff; Computer vision beyond classification; Facial recognition; Bounding box prediction; Exercises; Summary; Chapter 4: Understanding Time Series; Visualization and preparation in pandas; Aggregate global feature statistics; Examining the sample time series; Different kinds of stationarity; Why stationarity matters; Making a time series stationary; When to ignore stationarity issues.

~РУБ DDC 332.0285631

Рубрики: Finance--Data processing.

   Finance--Mathematical models.


   Machine learning.


   Finances--Informatique.


   Finances--Modèles mathématiques.


   Apprentissage automatique.


   Finance--Data processing.


   Finance--Mathematical models.


   Machine learning.


Аннотация: Machine Learning for Finance shows you how to build machine learning models for use in financial services organizations. It shows you how to work with all the key machine learning models, from simple regression to advanced neural networks. You will use machine learning to automate manual tasks, address systematic bias, and find new insights ...

DDC 006.3/1
A 67


    Applications of computational science in artificial intelligence / / Anand Nayyar, Sandeep Kumar, Akshat Agrawal. - Hershey, PA : : Engineering Science Reference, an imprint of IGI Global,, [2022]. - 1 online resource (xvi, 284 pages) : : il. - (Advances in computational intelligence and robotics (ACIR) book series). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/958D599F-0325-4D09-BAFB-89818EB66639. - ISBN 9781799890140 (electronic book). - ISBN 1799890147 (electronic book). - ISBN 9781799890157 (electronic bk.). - ISBN 1799890155 (electronic bk.)
Description based on online resource; title from digital title page (viewed on June 08, 2022).
Параллельные издания: Print version: : Applications of computational science in artificial intelligence. - Hershey, PA : Engineering Science Reference, an imprint of IGI Global, [2022]. - ISBN 9781799890126
    Содержание:
Chapter 1. Using natural language processing techniques to assess the attitudes of nursing students during the COVID-19 pandemic -- Chapter 2. Power pell sequences, some periodic relations of these sequences, and a cryptographic application with power pell sequences -- Chapter 3. Robust diagnostic system for COVID-19 based on chest radiology images -- Chapter 4. Neural architecture search network for the diagnosis of COVID from the radiographic images -- Chapter 5. Autonomous vehicle tracking based on non-linear model predictive control approach -- Chapter 6. Medical data analysis using feature extraction and classification based on machine learning and metaheuristic optimization algorithm -- Chapter 7. Robust image matching for information systems using randomly uniform distributed SURF features -- Chapter 8. Prediction of movie success using sentimental analysis and data mining -- Chapter 9. Machine learning for risk analysis -- Chapter 10. Machine learning-based categorization of COVID-19 patients -- Chapter 11. Designing a hybrid approach for web recommendation using annotation.

~РУБ DDC 006.3/1

Рубрики: Machine learning.

   Artificial intelligence.


   Apprentissage automatique.


   Intelligence artificielle.


   artificial intelligence.


   Artificial intelligence.


   Machine learning.


Аннотация: "This book delivers technological solutions to improvise smart technologies architecture, healthcare, and environment sustainability covering diverse aspects regarding: Computational solutions, computation framework, smart prediction, healthcare solutions using computational informatics and many more"--

Доп.точки доступа:
Nayyar, Anand, \editor.\
Kumar, Sandeep, (1983-) \editor.\
Agrawal, Akshat, (1986-) \editor.\

Applications of computational science in artificial intelligence / [Электронный ресурс] / Anand Nayyar, Sandeep Kumar, Akshat Agrawal., [2022]. - 1 online resource (xvi, 284 pages) : с. (Введено оглавление)

4.

Applications of computational science in artificial intelligence / [Электронный ресурс] / Anand Nayyar, Sandeep Kumar, Akshat Agrawal., [2022]. - 1 online resource (xvi, 284 pages) : с. (Введено оглавление)


DDC 006.3/1
A 67


    Applications of computational science in artificial intelligence / / Anand Nayyar, Sandeep Kumar, Akshat Agrawal. - Hershey, PA : : Engineering Science Reference, an imprint of IGI Global,, [2022]. - 1 online resource (xvi, 284 pages) : : il. - (Advances in computational intelligence and robotics (ACIR) book series). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/958D599F-0325-4D09-BAFB-89818EB66639. - ISBN 9781799890140 (electronic book). - ISBN 1799890147 (electronic book). - ISBN 9781799890157 (electronic bk.). - ISBN 1799890155 (electronic bk.)
Description based on online resource; title from digital title page (viewed on June 08, 2022).
Параллельные издания: Print version: : Applications of computational science in artificial intelligence. - Hershey, PA : Engineering Science Reference, an imprint of IGI Global, [2022]. - ISBN 9781799890126
    Содержание:
Chapter 1. Using natural language processing techniques to assess the attitudes of nursing students during the COVID-19 pandemic -- Chapter 2. Power pell sequences, some periodic relations of these sequences, and a cryptographic application with power pell sequences -- Chapter 3. Robust diagnostic system for COVID-19 based on chest radiology images -- Chapter 4. Neural architecture search network for the diagnosis of COVID from the radiographic images -- Chapter 5. Autonomous vehicle tracking based on non-linear model predictive control approach -- Chapter 6. Medical data analysis using feature extraction and classification based on machine learning and metaheuristic optimization algorithm -- Chapter 7. Robust image matching for information systems using randomly uniform distributed SURF features -- Chapter 8. Prediction of movie success using sentimental analysis and data mining -- Chapter 9. Machine learning for risk analysis -- Chapter 10. Machine learning-based categorization of COVID-19 patients -- Chapter 11. Designing a hybrid approach for web recommendation using annotation.

~РУБ DDC 006.3/1

Рубрики: Machine learning.

   Artificial intelligence.


   Apprentissage automatique.


   Intelligence artificielle.


   artificial intelligence.


   Artificial intelligence.


   Machine learning.


Аннотация: "This book delivers technological solutions to improvise smart technologies architecture, healthcare, and environment sustainability covering diverse aspects regarding: Computational solutions, computation framework, smart prediction, healthcare solutions using computational informatics and many more"--

Доп.точки доступа:
Nayyar, Anand, \editor.\
Kumar, Sandeep, (1983-) \editor.\
Agrawal, Akshat, (1986-) \editor.\

DDC 610.285
D 30


    Deep neural networks for multimodal imaging and biomedical applications / / Annamalai Suresh, R. Udendran, S. Vimal. - 4018/978-1-7998-3591-2. - Hershey, PA : : Medical Information Science Reference, an imprint of IGI Global,, [2020]. - 1 online resource (xvi, 294 pages) : : il ( час. мин.), 4018/978-1-7998-3591-2. - (Advances in bioinformatics and biomedical engineering (ABBE) book series). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/36D920A4-1DD0-4BE6-889A-FE7B2FAC7F16. - ISBN 9781799835929 (electronic book). - ISBN 1799835928 (electronic book). - ISBN 1799835936. - ISBN 9781799835936 (electronic bk.)
"Premier Reference Source" -- taken from front cover. Description based on online resource; title from digital title page (viewed on August 06, 2020).
Параллельные издания: Print version: : Deep neural networks for multimodal imaging and biomedical applications. - Hershey, PA : Medical Information Science Reference, [2020]. - ISBN 9781799835912
    Содержание:
The Pivotal Role of Edge Computing With Machine Learning And It's Impact On Healthcare / Muthukumari S.M., George Dharma Prakash Raj -- Exploring Internet of Things and Artificial Intelligence for smart Healthcare solutions / G. Yamini Yamini -- A Comparative Study of Popular CNN Topologies Used For Imagenet Classification / Hmidi Alaeddine, Malek Jihene -- Advancements in Techniques of Biomedical Image Analysis / Rajitha B. -- Demystification of Deep Learning-driven Medical Image Processing and its impact on future Biomedical Applications / Udendhran Mudaliyar, M. Bala Murugan, Suresh Annamalai -- Transforming Biomedical Applications through Smart Sensing and Artificial Intelligence / Harini T.J., Suresh V., Carmel M. -- Use of eggshell as partial replacement for sand in concrete used in biomedical applications / Sebastin S., Murali Ram Kumar S.M. -- Deep Learning Models for Semantic Multi modal Medical Image Segmentation / V.R.S. Mani.

~РУБ DDC 610.285

Рубрики: Machine learning.

   Computational intelligence.


   Artificial intelligence--Medical applications.


   Deep Learning


   Multimodal Imaging--methods


   Image Interpretation, Computer-Assisted--methods


   Biomedical Technology--methods


   Apprentissage automatique.


   Intelligence informatique.


   Intelligence artificielle en médecine.


   Apprentissage profond.


   Artificial intelligence--Medical applications


   Computational intelligence


   Machine learning


Аннотация: "This book provides research exploring the theoretical and practical aspects of emerging data computing methods and imaging techniques within healthcare and biomedicine. The publication provides a complete set of information in a single module starting from developing deep neural networks to predicting disease by employing multi-modal imaging"--

Доп.точки доступа:
Suresh, Annamalai, (1977-) \editor.\
Udendran, R., (1992-) \editor.\
Vimal, S., (1984-) \editor.\

Deep neural networks for multimodal imaging and biomedical applications / [Электронный ресурс] / Annamalai Suresh, R. Udendran, S. Vimal., [2020]. - 1 online resource (xvi, 294 pages) : с. (Введено оглавление)

5.

Deep neural networks for multimodal imaging and biomedical applications / [Электронный ресурс] / Annamalai Suresh, R. Udendran, S. Vimal., [2020]. - 1 online resource (xvi, 294 pages) : с. (Введено оглавление)


DDC 610.285
D 30


    Deep neural networks for multimodal imaging and biomedical applications / / Annamalai Suresh, R. Udendran, S. Vimal. - 4018/978-1-7998-3591-2. - Hershey, PA : : Medical Information Science Reference, an imprint of IGI Global,, [2020]. - 1 online resource (xvi, 294 pages) : : il ( час. мин.), 4018/978-1-7998-3591-2. - (Advances in bioinformatics and biomedical engineering (ABBE) book series). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/36D920A4-1DD0-4BE6-889A-FE7B2FAC7F16. - ISBN 9781799835929 (electronic book). - ISBN 1799835928 (electronic book). - ISBN 1799835936. - ISBN 9781799835936 (electronic bk.)
"Premier Reference Source" -- taken from front cover. Description based on online resource; title from digital title page (viewed on August 06, 2020).
Параллельные издания: Print version: : Deep neural networks for multimodal imaging and biomedical applications. - Hershey, PA : Medical Information Science Reference, [2020]. - ISBN 9781799835912
    Содержание:
The Pivotal Role of Edge Computing With Machine Learning And It's Impact On Healthcare / Muthukumari S.M., George Dharma Prakash Raj -- Exploring Internet of Things and Artificial Intelligence for smart Healthcare solutions / G. Yamini Yamini -- A Comparative Study of Popular CNN Topologies Used For Imagenet Classification / Hmidi Alaeddine, Malek Jihene -- Advancements in Techniques of Biomedical Image Analysis / Rajitha B. -- Demystification of Deep Learning-driven Medical Image Processing and its impact on future Biomedical Applications / Udendhran Mudaliyar, M. Bala Murugan, Suresh Annamalai -- Transforming Biomedical Applications through Smart Sensing and Artificial Intelligence / Harini T.J., Suresh V., Carmel M. -- Use of eggshell as partial replacement for sand in concrete used in biomedical applications / Sebastin S., Murali Ram Kumar S.M. -- Deep Learning Models for Semantic Multi modal Medical Image Segmentation / V.R.S. Mani.

~РУБ DDC 610.285

Рубрики: Machine learning.

   Computational intelligence.


   Artificial intelligence--Medical applications.


   Deep Learning


   Multimodal Imaging--methods


   Image Interpretation, Computer-Assisted--methods


   Biomedical Technology--methods


   Apprentissage automatique.


   Intelligence informatique.


   Intelligence artificielle en médecine.


   Apprentissage profond.


   Artificial intelligence--Medical applications


   Computational intelligence


   Machine learning


Аннотация: "This book provides research exploring the theoretical and practical aspects of emerging data computing methods and imaging techniques within healthcare and biomedicine. The publication provides a complete set of information in a single module starting from developing deep neural networks to predicting disease by employing multi-modal imaging"--

Доп.точки доступа:
Suresh, Annamalai, (1977-) \editor.\
Udendran, R., (1992-) \editor.\
Vimal, S., (1984-) \editor.\

Page 1, Results: 5

 

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