LITMIR.BIZ

      1  Cover

      2  Title Page

      3  Copyright

      4  Dedication

      5  Foreword

      6  Preface

      7  Acknowledgment

      8  Part I INTELLIGENT COMPUTING AND APPLICATIONS 1 Assessing Mental Workload Using Eye Tracking Technology and Deep Learning Models 1.1 Introduction 1.2 Data Acquisition Method 1.3 Feature Extraction 1.4 Deep Learning Models 1.5 Results 1.6 Discussion 1.7 Advantages and Disadvantages of the Study 1.8 Limitations of the Study 1.9 Conclusion References 2 Artificial Neural Networks in DNA Computing and Implementation of DNA Logic Gates 2.1 Introduction 2.2 Biological Neurons 2.3 Artificial Neural Networks 2.4 DNA Neural Networks 2.5 DNA Logic Gates 2.6 Advantages and Limitations 2.7 Conclusion Acknowledgment References 3 Intelligent Garment Detection Using Deep Learning 3.1 Introduction 3.2 Literature 3.3 Methodology 3.4 Experimental Results 3.5 Highlights 3.6 Conclusion and Future Works Acknowledgements References 4 Intelligent Computing on Complex Numbers for Cryptographic Applications 4.1 Introduction 4.2 Modular Arithmetic 4.3 Complex Plane 4.4 Matrix Algebra 4.5 Elliptic Curve Arithmetic 4.6 Cryptographic Applications 4.7 Conclusion References 5 Application of Machine Learning Framework for Next-Generation Wireless Networks: Challenges and Case Studies 5.1 Introduction 5.2 Machine/Deep Learning for Future Wireless Communication 5.3 Case Studies 5.4 Major Findings 5.5 Future Research Directions 5.6 Conclusion References 6 Designing of Routing Protocol for Crowd Associated Networks (CrANs) 6.1 Introduction 6.2 Background Study 6.3 CrANs 6.4 Simulation of MANET Network 6.5 Simulation of VANET Network 6.6 CrANs 6.7 Conclusion References 7 Application of Group Method of Data Handling–Based Neural Network (GMDH-NN) for Forecasting Permeate Flux (%) of Disc-Shaped Membrane 7.1 Introduction 7.2 Experimental Procedure 7.3 Methodology 7.4 Results and Discussions 7.5 Conclusions Acknowledgements References 8 Automated Extraction of Non-Functional Requirements From Text Files: A Supervised Learning Approach 8.1 Introduction Скачать книгу