國立虎尾科技大學 |

Handbook of research on automated feature engineering and advanced applications in data science

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Handbook of research on automated feature engineering and advanced applications in data science/ Mrutyunjaya Panda and Harekrishna Misra, editors.
其他作者:	Panda, Mrutyunjaya.
出版者:	Hershey, Pennsylvania :IGI Global, : 2021.,
面頁冊數:	1 online resource (xxviii, 392 p.)
標題:	Data mining. -
電子資源:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-7998-6659-6
ISBN:	9781799866619 (ebk.)

Handbook of research on automated feature engineering and advanced applications in data science
Handbook of research on automated feature engineering and advanced applications in data science[electronic resource] /Mrutyunjaya Panda and Harekrishna Misra, editors. - Hershey, Pennsylvania :IGI Global,2021. - 1 online resource (xxviii, 392 p.)

Includes bibliographical references and index.

Chapter 1. Feature engineering for various data types in data science -- Chapter 2. Feature selection techniques in high dimensional data with machine learning and deep learning -- Chapter 3. Hybrid attributes technique filter for the tracking of crowd behavior -- Chapter 4. Useful features for computer-aided diagnosis systems for melanoma detection using dermoscopic images -- Chapter 5. Development of rainfall prediction models using machine learning approaches for different agro-climatic zones -- Chapter 6. Multi-feature fusion and machine learning: a model for early detection of freezing of gait events in patients with Parkinson's disease -- Chapter 7. Developing brain tumor detection model using deep feature extraction via transfer learning -- Chapter 8. Feature engineering for structural health monitoring (SHM): a damage characterization review -- Chapter 9. Speech enhancement using neuro-fuzzy classifier -- Chapter 10. Applications of feature engineering techniques for text data -- Chapter 11. Deep learning for feature engineering-based improved weather prediction: a predictive modeling -- Chapter 12. Computationally efficient and effective machine learning model using time series data in different prediction problems -- Chapter 13. Machine learning and convolution neural network approaches to plant leaf recognition -- Chapter 14. Reciprocation of Indian States on trade relation -- Chapter 15. Performance evaluation of machine learning techniques for customer churn prediction in telecommunication sector -- Chapter 16. Efficient software reliability prediction with evolutionary virtual data position exploration -- Chapter 17. Secure chaotic image encryption based on multi-point row-column-crossover operation -- Chapter 18. Machine automation making cyber-policy violator more resilient: a proportionate study.

"This edited book will start with an introduction to feature engineering and then move onto recent concepts, methods and applications with the use of various data types that includes : text, image, streaming data, social network data, financial data, biomedical data, bioinformatics etc. to help readers gain insight into how features can be extracted and transformed from raw data"--

ISBN: 9781799866619 (ebk.)Subjects--Topical Terms:

528622
Data mining.

LC Class. No.: QA76.9.D343 / H36 2021

Dewey Class. No.: 006.3/12

Handbook of research on automated feature engineering and advanced applications in data science
LDR:03205nam a2200265 a 4500 001 1041435
003 IGIG
005 20211027164803.0
006 m o d
007 cr cn
008 211215s2021 pau fob 001 0 eng d
020 $a 9781799866619 (ebk.)
020 $a 9781799866596 (hbk.)
020 $a 9781799866602 (pbk.)
035 $a (OCoLC)1226612054
035 $a 1101012306
040 $a CaBNVSL $b eng $c CaBNVSL $d CaBNVSL
050 0 0 $a QA76.9.D343 $b H36 2021
082 0 0 $a 006.3/12 $2 23
245 0 0 $a Handbook of research on automated feature engineering and advanced applications in data science $h [electronic resource] / $c Mrutyunjaya Panda and Harekrishna Misra, editors.
260 $a Hershey, Pennsylvania : $b IGI Global, $c 2021.
300 $a 1 online resource (xxviii, 392 p.)
504 $a Includes bibliographical references and index.
505 0 $a Chapter 1. Feature engineering for various data types in data science -- Chapter 2. Feature selection techniques in high dimensional data with machine learning and deep learning -- Chapter 3. Hybrid attributes technique filter for the tracking of crowd behavior -- Chapter 4. Useful features for computer-aided diagnosis systems for melanoma detection using dermoscopic images -- Chapter 5. Development of rainfall prediction models using machine learning approaches for different agro-climatic zones -- Chapter 6. Multi-feature fusion and machine learning: a model for early detection of freezing of gait events in patients with Parkinson's disease -- Chapter 7. Developing brain tumor detection model using deep feature extraction via transfer learning -- Chapter 8. Feature engineering for structural health monitoring (SHM): a damage characterization review -- Chapter 9. Speech enhancement using neuro-fuzzy classifier -- Chapter 10. Applications of feature engineering techniques for text data -- Chapter 11. Deep learning for feature engineering-based improved weather prediction: a predictive modeling -- Chapter 12. Computationally efficient and effective machine learning model using time series data in different prediction problems -- Chapter 13. Machine learning and convolution neural network approaches to plant leaf recognition -- Chapter 14. Reciprocation of Indian States on trade relation -- Chapter 15. Performance evaluation of machine learning techniques for customer churn prediction in telecommunication sector -- Chapter 16. Efficient software reliability prediction with evolutionary virtual data position exploration -- Chapter 17. Secure chaotic image encryption based on multi-point row-column-crossover operation -- Chapter 18. Machine automation making cyber-policy violator more resilient: a proportionate study.
520 3 $a "This edited book will start with an introduction to feature engineering and then move onto recent concepts, methods and applications with the use of various data types that includes : text, image, streaming data, social network data, financial data, biomedical data, bioinformatics etc. to help readers gain insight into how features can be extracted and transformed from raw data"-- $c Provided by publisher.
650 0 $a Data mining. $3 528622
650 0 $a Big data $x Industrial applications. $3 1225250
650 0 $a Automatic data collection systems. $3 599534
650 0 $a Automatic classification. $3 787833
700 1 $a Panda, Mrutyunjaya. $3 1024948
700 1 $a Misra, H. K. $q (Harekrishna) $3 1340945
856 4 0 $u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-7998-6659-6