Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). (2020)
- Record Type:
- Book
- Title:
- Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). (2020)
- Main Title:
- Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020)
- Other Titles:
- AICV 2020
- Further Information:
- Note: Aboul-Ella Hassanien, Ahmad Taher Azar, Tarek Gaber, Diego Oliva, Fahmy M. Tolba, editors.
- Other Names:
- Azar, Ahmad Taher
Gaber, Tarek, 1975-
Oliva, Diego
Ṭulbah, Muḥammad Fahmī
Hassanien, Aboul Ella
International Conference on Artificial Intelligence and Computer Vision, 1st - Contents:
- Intro -- Preface -- Organization -- Honorary Chair -- International Advisory Board -- General Chair -- Conference Co-chairs -- Program Chairs -- Publicity Chairs -- Technical Program Committee -- Local Arrangement Committee -- Contents -- Artificial Intelligence -- Artificial Intelligence-Based Plant's Diseases Classification -- Abstract -- 1 Introduction -- 2 Preliminaries -- 2.1 Convolutions Neural Network -- 2.2 VGG16 Architecture -- 2.3 Gaussian Optimization Method -- 3 Materials and Methods -- 3.1 Plant's Image Dataset -- 3.2 The Proposed Plant's Diseases Classification Model 3.2.1 Preprocessing Phase -- 3.2.2 Classification and Evaluation Phase -- 3.3 Hyper Parameter Optimization for CNN Using Gaussian Process Phase -- 4 Experiments Results and Discussion -- 4.1 Experiment (I): Without Optimization -- 4.2 Experiment (II): Hyper_Parameter Optimization Using Gaussian Process -- 4.3 Experiment (III): Hyperparameters Optimization -- 5 Conclusion and Future Work -- References -- Machine Learning for Apple Fruit Diseases Classification System -- Abstract -- 1 Introduction -- 2 Literature Review -- 3 Apple Diseases Classification System -- 3.1 Preprocessing Phase 3.2 Apple Segmentation Phase -- 3.3 Feature Extraction Phase -- 3.3.1 Local Ternary Patterns (LTP) -- 3.3.2 Local Binary Pattern (LBP) -- 3.3.3 Histogram of Oriented Gradients (HOG) -- 3.3.4 Gray Level Co-occurrence Matrix (GLCM) -- 3.3.5 Color Coherence Vector (CCV) -- 3.4 Apple Classification Phase -- 4Intro -- Preface -- Organization -- Honorary Chair -- International Advisory Board -- General Chair -- Conference Co-chairs -- Program Chairs -- Publicity Chairs -- Technical Program Committee -- Local Arrangement Committee -- Contents -- Artificial Intelligence -- Artificial Intelligence-Based Plant's Diseases Classification -- Abstract -- 1 Introduction -- 2 Preliminaries -- 2.1 Convolutions Neural Network -- 2.2 VGG16 Architecture -- 2.3 Gaussian Optimization Method -- 3 Materials and Methods -- 3.1 Plant's Image Dataset -- 3.2 The Proposed Plant's Diseases Classification Model 3.2.1 Preprocessing Phase -- 3.2.2 Classification and Evaluation Phase -- 3.3 Hyper Parameter Optimization for CNN Using Gaussian Process Phase -- 4 Experiments Results and Discussion -- 4.1 Experiment (I): Without Optimization -- 4.2 Experiment (II): Hyper_Parameter Optimization Using Gaussian Process -- 4.3 Experiment (III): Hyperparameters Optimization -- 5 Conclusion and Future Work -- References -- Machine Learning for Apple Fruit Diseases Classification System -- Abstract -- 1 Introduction -- 2 Literature Review -- 3 Apple Diseases Classification System -- 3.1 Preprocessing Phase 3.2 Apple Segmentation Phase -- 3.3 Feature Extraction Phase -- 3.3.1 Local Ternary Patterns (LTP) -- 3.3.2 Local Binary Pattern (LBP) -- 3.3.3 Histogram of Oriented Gradients (HOG) -- 3.3.4 Gray Level Co-occurrence Matrix (GLCM) -- 3.3.5 Color Coherence Vector (CCV) -- 3.4 Apple Classification Phase -- 4 Experimental Results -- 5 Conclusions -- References -- Experimental Modeling of Hexapod Robot Using Artificial Intelligence -- 1 Introduction -- 2 Kinematic Model of Hexapod -- 2.1 Forward Kinematics Problem -- 2.2 Inverse Kinematics Problem -- 3 Neural Network Structure 4 Experimental Results and Discussion -- 5 Conclusion -- References -- A Systematic Review of the Factors Affecting the Artificial Intelligence Implementation in the Health Care Sector -- Abstract -- 1 Introduction -- 2 Background -- 2.1 Study Importance and Contribution -- 2.2 Problem Definition -- 2.3 The Aim of Research -- 3 Literature Review -- 3.1 Artificial Intelligence Projects in the Health Sector -- 3.2 The Technology Acceptance Model (TAM) -- 3.3 Linking Technology Acceptance Model (TAM) to Artificial Intelligence (AI) Projects -- 4 Research Methodology -- 5 Conclusion and Future Work Acknowledgment -- References -- Machine Learning and Deep Learning Techniques for Cybersecurity: A Review -- Abstract -- 1 Introduction -- 2 Literature Review -- 3 Classification of Machine Learning Algorithms to Cybersecurity -- 3.1 Classical Machine Learning Techniques -- 3.2 Deep Learning Techniques -- 4 Cybersecurity Datasets -- 4.1 KDD Cup 1999 Dataset -- 4.2 ISOT (Information Security and Object Technology) Dataset -- 4.3 HTTP CSIC 2010 Dataset -- 4.4 CTU-13 (Czech Technical University) Dataset -- 4.5 UNSW-NB15 Dataset -- 5 Conclusion and Future Work -- References … (more)
- Publisher Details:
- Cham : Springer
- Publication Date:
- 2020
- Extent:
- 1 online resource (880 pages)
- Subjects:
- 006.3
Artificial intelligence -- Congresses
Computer vision -- Congresses
Artificial intelligence
Computer vision
Electronic books
Conference papers and proceedings - Languages:
- English
- ISBNs:
- 9783030442897
3030442896 - Related ISBNs:
- 9783030442880
- Notes:
- Note: Print version record.
- Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
- Access Usage:
- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.508418
- Ingest File:
- 03_085.xml