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Machine learning for real world appl...

Sharma, D. K.

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Machine learning for real world applications

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning for real world applications/ edited by Dinesh K. Sharma, H. S. Hota, Aaron Rasheed Rababaah.
其他作者:	Sharma, D. K.
出版者:	Singapore :Springer Nature Singapore : : 2024.,
面頁冊數:	xxv, 301 p. :ill. (some col.), digital ;24 cm.
內容註:	1. Artificial Intelligence to Meet Sustainability and Strategic Goals: Evidence from India -- 2. Development of a Sustainable Smart City Measurement Framework: A Study of the City of Bandung, Indonesia -- 3. Drive Harmony: An AI-based safety assistant driving System -- 4. Effective Integration of Clustering and Classification or Regression Machine Learning Algorithm -- 5. Sentiment Analysis of Textual Reviews Using Deep Learning Techniques -- 6. Machine Learning Based Detection of Parkinson's Disease Using Voice and Handwriting Analysis -- 7. Machine Learning Algorithmic model for Pairs Trading -- 8. BT-CAP: Brain Tumor detection from MRI images using Capsule network with Template-based K-means clustering and Discrete Wavelet Transformation -- 9. Application of Neural Network based Techniques to Network Intrusion Detection -- 10. Novel Adaptation Algorithms and Schemes -- 11. A Hybrid Machine Learning Techniques for Intrusion Detection -- 12. Forecasting the Covid-19 End in India Using MachineLearning and Population Density Clustering -- 13. A Machine Learning based Analysis of Internet Addiction among Children and Adolescents during Covid-19 Lockdown -- 14. Neural Computing for the Pandemic's Biological Waste Management -- 15. Deep learning-based Speech recognition systems for Sylheti and Bishnupriya Manipuri languages -- 16. Classification of Diabetic Retinopathy Using Modified Convolutional Neural Network -- 17. Building Architectural Styles Classification Using Convolutional Neural Networks Models -- 18. Synthetic Data Generation for SAR Vehicular Signature Modeling and ATR Deep Learning Training -- 19. Automatic Target Recognition (ATR) in Multi-object SAR Images based on Simulated Data -- 20. Fruit detection using pre trained and customized model with Deep Learning approach.
Contained By:	Springer Nature eBook
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-981-97-1900-6
ISBN:	9789819719006

Machine learning for real world applications
Machine learning for real world applications[electronic resource] /edited by Dinesh K. Sharma, H. S. Hota, Aaron Rasheed Rababaah. - Singapore :Springer Nature Singapore :2024. - xxv, 301 p. :ill. (some col.), digital ;24 cm. - Transactions on computer systems and networks,2730-7492. - Transactions on computer systems and networks..

1. Artificial Intelligence to Meet Sustainability and Strategic Goals: Evidence from India -- 2. Development of a Sustainable Smart City Measurement Framework: A Study of the City of Bandung, Indonesia -- 3. Drive Harmony: An AI-based safety assistant driving System -- 4. Effective Integration of Clustering and Classification or Regression Machine Learning Algorithm -- 5. Sentiment Analysis of Textual Reviews Using Deep Learning Techniques -- 6. Machine Learning Based Detection of Parkinson's Disease Using Voice and Handwriting Analysis -- 7. Machine Learning Algorithmic model for Pairs Trading -- 8. BT-CAP: Brain Tumor detection from MRI images using Capsule network with Template-based K-means clustering and Discrete Wavelet Transformation -- 9. Application of Neural Network based Techniques to Network Intrusion Detection -- 10. Novel Adaptation Algorithms and Schemes -- 11. A Hybrid Machine Learning Techniques for Intrusion Detection -- 12. Forecasting the Covid-19 End in India Using MachineLearning and Population Density Clustering -- 13. A Machine Learning based Analysis of Internet Addiction among Children and Adolescents during Covid-19 Lockdown -- 14. Neural Computing for the Pandemic's Biological Waste Management -- 15. Deep learning-based Speech recognition systems for Sylheti and Bishnupriya Manipuri languages -- 16. Classification of Diabetic Retinopathy Using Modified Convolutional Neural Network -- 17. Building Architectural Styles Classification Using Convolutional Neural Networks Models -- 18. Synthetic Data Generation for SAR Vehicular Signature Modeling and ATR Deep Learning Training -- 19. Automatic Target Recognition (ATR) in Multi-object SAR Images based on Simulated Data -- 20. Fruit detection using pre trained and customized model with Deep Learning approach.

This book provides a comprehensive coverage of machine learning techniques ranging from fundamental to advanced. The content addresses topics within the scope of the book from the ground up, providing readers with a trustworthy source of theoretical and technical learning content. The book emphasizes not only the theoretical features but also their practical and implementation aspects in real-world applications. These applications are crucial because they provide comprehensive experimental work that supports the validity of the offered approaches as well as clear instructions on how to apply such models in comparable and distinct settings and contexts. Furthermore, the chapters shed light on the problems and possibilities that researchers might use to direct their future research efforts. The book is beneficial for undergraduate and postgraduate students, researchers, and industry personnel.

ISBN: 9789819719006

Standard No.: 10.1007/978-981-97-1900-6doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning for real world applications
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505 0 $a 1. Artificial Intelligence to Meet Sustainability and Strategic Goals: Evidence from India -- 2. Development of a Sustainable Smart City Measurement Framework: A Study of the City of Bandung, Indonesia -- 3. Drive Harmony: An AI-based safety assistant driving System -- 4. Effective Integration of Clustering and Classification or Regression Machine Learning Algorithm -- 5. Sentiment Analysis of Textual Reviews Using Deep Learning Techniques -- 6. Machine Learning Based Detection of Parkinson's Disease Using Voice and Handwriting Analysis -- 7. Machine Learning Algorithmic model for Pairs Trading -- 8. BT-CAP: Brain Tumor detection from MRI images using Capsule network with Template-based K-means clustering and Discrete Wavelet Transformation -- 9. Application of Neural Network based Techniques to Network Intrusion Detection -- 10. Novel Adaptation Algorithms and Schemes -- 11. A Hybrid Machine Learning Techniques for Intrusion Detection -- 12. Forecasting the Covid-19 End in India Using MachineLearning and Population Density Clustering -- 13. A Machine Learning based Analysis of Internet Addiction among Children and Adolescents during Covid-19 Lockdown -- 14. Neural Computing for the Pandemic's Biological Waste Management -- 15. Deep learning-based Speech recognition systems for Sylheti and Bishnupriya Manipuri languages -- 16. Classification of Diabetic Retinopathy Using Modified Convolutional Neural Network -- 17. Building Architectural Styles Classification Using Convolutional Neural Networks Models -- 18. Synthetic Data Generation for SAR Vehicular Signature Modeling and ATR Deep Learning Training -- 19. Automatic Target Recognition (ATR) in Multi-object SAR Images based on Simulated Data -- 20. Fruit detection using pre trained and customized model with Deep Learning approach.
520 $a This book provides a comprehensive coverage of machine learning techniques ranging from fundamental to advanced. The content addresses topics within the scope of the book from the ground up, providing readers with a trustworthy source of theoretical and technical learning content. The book emphasizes not only the theoretical features but also their practical and implementation aspects in real-world applications. These applications are crucial because they provide comprehensive experimental work that supports the validity of the offered approaches as well as clear instructions on how to apply such models in comparable and distinct settings and contexts. Furthermore, the chapters shed light on the problems and possibilities that researchers might use to direct their future research efforts. The book is beneficial for undergraduate and postgraduate students, researchers, and industry personnel.
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700 1 $a Hota, H. S. $3 3724728
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