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on1090130110 |
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OCoLC |
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20241006213017.0 |
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m o d |
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cr cnu|||unuuu |
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190319t20192020sz a ob 101 0 eng d |
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|a N$T
|b eng
|e rda
|e pn
|c N$T
|d N$T
|d EBLCP
|d GW5XE
|d UKMGB
|d OCLCF
|d UKAHL
|d OCLCQ
|d OCLCA
|d OCLCQ
|d ESU
|d OCLCO
|d OCLCQ
|d COM
|d OCLCO
|d OCLCQ
|d OCLCA
|d AUD
|d OCLCO
|d OCLCL
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|a GBB958001
|2 bnb
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|a 019312453
|2 Uk
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|a 9783030141189
|q (electronic bk.)
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|a 3030141187
|q (electronic bk.)
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|z 9783030141172
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|a (OCoLC)1090130110
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| 037 |
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|a com.springer.onix.9783030141189
|b Springer Nature
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4 |
|a Q325.5
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| 072 |
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|a COM
|x 000000
|2 bisacsh
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|a HCDD
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2 |
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|a AMLTA (Conference)
|d (2019 :
|c Cairo, Egypt)
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| 245 |
1 |
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|a The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019) /
|c editors, Aboul Ella Hassanien, Ahmad Taher Azar, Tarek Gaber, Roheet Bhatnagar and Mohamed F. Tolba.
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| 246 |
3 |
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|a AMLTA 2019
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| 264 |
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1 |
|a Cham, Switzerland :
|b Springer,
|c [2019]
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| 264 |
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4 |
|c ©2020
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| 300 |
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|a 1 online resource :
|b illustrations
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Advances in intelligent systems and computing ;
|v volume 921
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| 504 |
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|a Includes bibliographical references and index.
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| 588 |
0 |
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|a Online resource; title from PDF title page (EBSCO, viewed March 20, 2019).
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| 505 |
0 |
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|a Intro; Preface; Organization; Honorary Chair; General Chair; International Advisory Board; Program Chairs; Publicity Chairs; Technical Program Committee; Local Arrangement Chairs; Keynote Speakers and Tutorial; Multi-modal and Uncertain Optimization with Differential Evolution -- Some Recent Approaches and Future Challenges; Modern Algorithms in Control and Design of Complex Systems; Hands-on to TensorFlow (Tutorial on); Contents; Deep Learning Applications; A Deep Learning Approach for Tongue Diagnosis; Abstract; 1 Introduction; 2 Tongue Image Syndrome Classification Model Based on CNN
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| 505 |
8 |
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|a 2.1 Data Pre-processing Stage2.2 Deep Learning Stage; 3 Experiment; 3.1 Two-Class Experiment; 3.2 Three-Class Experiment; 4 Result; 5 Platform; 6 Discussion and Conclusion; Acknowledgements; References; Reduced 3-D Deep Learning Framework for Hyperspectral Image Classification; 1 Introduction; 2 Proposed Framework; 2.1 Hyperspectral Dimension Reduction; 2.2 3D Convolutional Neural Network; 3 Experimental Results; 3.1 Experimental Datasets; 3.2 Experimental Design; 3.3 Classification Results; 4 Conclusion; References
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| 505 |
8 |
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|a An Efficient Deep Convolutional Neural Network for Visual Image Classification1 Introduction; 2 Related Work, Contribution, and Organization of this Paper; 3 The Proposed Deep Model (VICNet); 4 Experimental Evaluations; 4.1 ImageNet Dogs; 4.2 Features Classification; 4.3 Experimental Evaluation Metrics; 5 Results and Discussion; References; Deep Learning for Predictive Analytics in Healthcare; Abstract; 1 Introduction; 2 Deep Learning Predictive Analytics Survey in Health Care; 3 Deep Learning Framework for Healthcare Predictions; 3.1 Deep Learning Models
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| 505 |
8 |
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|a 3.2 Framework of Deep Learning in Healthcare4 Deep Learning Tools; 5 Future Trends of Deep Learning in Healthcare Predictions; 6 Conclusion; References; Deep Layer CNN Architecture for Breast Cancer Histopathology Image Detection; Abstract; 1 Introduction; 2 The Proposed Deep Layer CNN Architecture for Breast Cancer Image Detection; 3 Performance Measure for Breast Cancer Image Detection; 4 Experimental Results; 5 Conclusion; Acknowledgement; References; Regression Task on Big Data with Convolutional Neural Network; 1 Introduction; 2 Methods and Data; 3 Experiments; 4 Result
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| 505 |
8 |
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|a 5 Conclusion and Future WorkReferences; The Regression of MNIST Dataset Based on Convolutional Neural Network; 1 Introduction; 2 Method; 2.1 Established Relationship Between Regression and MNIST Dataset; 2.2 Description of the CNN in Deep Learning; 2.3 Predicting the Target of MNIST by the CNN; 3 Experiments and Results; 3.1 Establishing the Optimal Network Architecture; 3.2 Results of Regression with Labels of Different Standard Deviation; 4 Conclusion; References; Swarm Intelligence and Applications; Intelligent Hybrid Approach for Feature Selection; Abstract; 1 Introduction
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| 520 |
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|a This book presents the peer-reviewed proceedings of the 4th International Conference on Advanced Machine Learning Technologies and Applications (AMLTA 2019), held in Cairo, Egypt, on March 28-30, 2019, and organized by the Scientific Research Group in Egypt (SRGE). The papers cover the latest research on machine learning, deep learning, biomedical engineering, control and chaotic systems, text mining, summarization and language identification, machine learning in image processing, renewable energy, cyber security, and intelligence swarms and optimization.
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| 650 |
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0 |
|a Machine learning
|v Congresses.
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| 650 |
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0 |
|a Artificial intelligence
|v Congresses.
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| 650 |
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7 |
|a COMPUTERS
|x General.
|2 bisacsh
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| 650 |
|
7 |
|a Artificial intelligence
|2 fast
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| 650 |
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7 |
|a Machine learning
|2 fast
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| 655 |
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0 |
|a Electronic books.
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| 655 |
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7 |
|a proceedings (reports)
|2 aat
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| 655 |
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7 |
|a Conference papers and proceedings
|2 fast
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| 655 |
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7 |
|a Conference papers and proceedings.
|2 lcgft
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| 655 |
|
7 |
|a Actes de congrès.
|2 rvmgf
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| 700 |
1 |
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|a Hassanien, Aboul Ella,
|e editor.
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| 700 |
1 |
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|a Azar, Ahmad Taher,
|e editor.
|
| 700 |
1 |
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|a Gaber, Tarek,
|d 1975-
|e editor.
|1 https://id.oclc.org/worldcat/entity/E39PCjyvy8wKbPmkM9w6fQJ3Bd
|
| 700 |
1 |
|
|a Bhatnagar, Roheet,
|e editor.
|
| 700 |
1 |
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|a Ṭulbah, Muḥammad Fahmī,
|d 1947-
|e editor.
|1 https://id.oclc.org/worldcat/entity/E39PCjtrR8xHHQgbdqQ87JgWQq
|
| 830 |
|
0 |
|a Advances in intelligent systems and computing ;
|v 921.
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-030-14118-9
|y Click for online access
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| 903 |
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|a SPRING-ROBOTICS2020
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| 994 |
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|a 92
|b HCD
|