The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019)

The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019) / editors, Aboul Ella Hassanien, Ahmad Taher Azar, Tarek Gaber, Roheet Bhatnagar and Mohamed F. Tolba.

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 resear...

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Bibliographic Details
Corporate Author: AMLTA (Conference) Cairo, Egypt)
Other Authors: Hassanien, Aboul Ella (Editor), Azar, Ahmad Taher (Editor), Gaber, Tarek, 1975- (Editor), Bhatnagar, Roheet (Editor), Ṭulbah, Muḥammad Fahmī, 1947- (Editor)
Format: eBook
Language:English
Published: Cham, Switzerland : Springer, [2019]
Series:Advances in intelligent systems and computing ; 921.
Subjects:
Machine learning > Congresses.
Artificial intelligence > Congresses.
COMPUTERS > General.
Artificial intelligence
Machine learning
Electronic books.
proceedings (reports)
Conference papers and proceedings
Conference papers and proceedings.
Actes de congrès.
Online Access:Click for online access
Table of Contents:
  • 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
  • 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
  • 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
  • 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
  • 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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