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[null,"1.","Record Nr.",null,"UNISALENTO991002814629707536"]
[null,null,"Autore",null,"Department of State, United States of America"]
[null,null,"Titolo",null,"Energy diplomacy and global issues / Department of State, United States of America ; editor Susan K. Holly ; general editor David S. Patterson"]
[null,null,"Pubbl/distr/stampa",null,"Washington : United States Government Printing Office, 1999"]
[null,null,"ISBN",null,"016050077X"]
[null,null,"Descrizione fisica",null,"xxx, 606 p. ; 24 cm."]
[null,null,"Collana",null,"Foreign relations of the United States, 1964-1968 ; 34"]
[null,null,"Soggetti",null,"Stati Uniti d'America - Relazioni estere"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNINA9911034577503321"]
[null,null,"Autore",null,"Sandhu Jasminder Kaur"]
[null,null,"Titolo",null,"Applied Computer Vision Through Artificial Intelligence"]
[null,null,"Pubbl/distr/stampa",null,"Newark : , : John Wiley & Sons, Incorporated, , 2025"]
[{"$nulls":4},"©2026"]
[null,null,"ISBN",null,"1-394-27262-6"]
[{"$nulls":4},"1-394-27261-8"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (501 pages)"]
[null,null,"Disciplina",null,"006.3/7"]
[null,null,"Soggetti",null,"Computer vision"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 An Overview of Medical Diagnostics through Artificial Intelligence-Powered Histopathological Imaging and Video Analysis -- 1.1 Introduction -- 1.1.1 A Focus on Digital Image and Video Analysis -- 1.1.2 Overview of Research Article -- 1.1.2.1 Comparison Between Different Techniques/Comparative Analysis Among the Techniques Available -- 1.1.2.2 Overview of Data Preprocessing and Meta-Heuristic Algorithms -- 1.1.3 The Organizational of the Research Article -- 1.2 Background -- 1.2.1 Difficulties with Feature Selection -- 1.3 Preliminaries -- 1.3.1 Selection of Features (FS) -- 1.3.2 Classification -- 1.3.2.1 Support Vector Machine -- 1.3.2.2 Naïve Bayes -- 1.3.2.3 ANN -- 1.3.3 Meta-Heuristic Algorithms in FS -- 1.3.3.1 Genetic Algorithm -- 1.3.3.2 Cuckoo Search Optimization -- 1.3.3.3 BAT Algorithm -- 1.3.3.4 Grey Wolf Optimizer -- 1.3.3.5 Harris Hawk Optimization -- 1.3.3.6 Transition from Exploration to Exploitation -- 1.4 Experimental Results -- 1.4.1 Challenges in the Application of a Metaheuristic Algorithm for Classification and Prediction of Medical Disease -- 1.4.2 Summary of the Review -- 1.5 Conclusion -- References -- Chapter 2 Generative Adversarial Networks: Theory and Application in Synthesis -- 2.1 Introduction -- 2.2 Ideologies of GAN -- 2.3 Architecture of GAN -- 2.4 Applications of GAN -- 2.4.1 Image Processing and Computer Vision -- 2.4.2 Healthcare and Medical Imaging -- 2.4.3 Natural Language Processing (NLP) -- 2.4.4 Video and Animation -- 2.4.5 Gaming and Entertainment -- 2.4.6 Cybersecurity and Anomaly "]
[{"$nulls":4},"Detection -- 2.4.7 Fashion and Retail -- 2.4.8 Art and Creativity -- 2.5 Conclusion -- References -- Chapter 3 From Pixels to Predictions: Deep Learning for Glaucoma Detection -- 3.1 Introduction -- 3.1.1 Glaucoma."]
[{"$nulls":4},"3.1.2 Detection of Glaucoma -- 3.1.3 Deep Learning -- 3.1.4 Glaucoma Detection Using Deep Learning -- 3.2 Literature Review -- 3.2.1 Glaucoma Classification -- 3.2.2 Glaucoma Detecting -- 3.3 Problem Statement -- 3.4 Hybrid Approach for Glaucoma Detection -- 3.5 Result and Discussion -- 3.5.1 Confusion Matrix has been Obtained During Testing that is Shown Below for 4 Models -- 3.6 Conclusion -- 3.7 Future Scope -- References -- Chapter 4 Advancements in Computer Vision for Object Detection and Recognition using DenseNet Deep Learning Model -- 4.1 Introduction -- 4.2 Literature Survey -- 4.2.1 Application of Principles -- 4.3 Proposed System -- 4.4 Results and Discussion -- 4.5 Conclusion -- References -- Chapter 5 Deep Learning-Based Detection of Cyber Extortion -- 5.1 Introduction -- 5.2 Related Works -- 5.3 Existing System -- 5.4 Proposed System -- 5.5 System Architecture -- 5.6 Methodology -- 5.6.1 Data Collection and Preprocessing -- 5.6.2 Feature Extraction -- 5.6.3 Voice Processing -- 5.6.4 Model Architecture -- 5.6.4.1 Text Vectorization Layer -- 5.6.4.2 Embedding Layer -- 5.6.4.3 Bidirectional LSTM Layer -- 5.6.4.4 Dense Layers -- 5.6.4.5 Dropout Regularization -- 5.6.5 Evaluation -- 5.6.5.1 Precision -- 5.6.5.2 Recall -- 5.6.5.3 F1 Score -- 5.6.5.4 Accuracy -- 5.7 Results and Discussion -- 5.8 Conclusion -- 5.9 Future Work -- References -- Chapter 6 GANs Unleashed: From Theory to Synthetic Realities -- 6.1 Introduction -- 6.2 Related Works -- 6.2.1 Accurate Representation of the Density -- 6.2.2 Classification/Regression -- 6.2.3 Computer Algorithms for Image Synthesis -- 6.2.4 Computer Algorithms Synthesize Pictures -- 6.3 Limitations that are Enforced by GAN -- 6.4 Conclusion -- References -- Chapter 7 RFID and Computer Vision-Enhanced Automotive Authentication Verification System -- 7.1 Introduction -- 7.2 Literature Survey."]
[{"$nulls":4},"7.3 Proposed System -- 7.4 Working -- 7.5 Block Diagram -- 7.6 Hardware Components -- 7.7 Result -- 7.8 Conclusion -- Bibliography -- Chapter 8 Synergizing Ensemble Learning Techniques for Robust Emotion Detection using EEG Signals -- 8.1 Introduction -- 8.1.1 Overview of EEG-Based Emotion Detection -- 8.1.2 Motivation for Using Ensemble Learning -- 8.2 Ensemble Learning Techniques -- 8.2.1 Random Forest Classifier -- 8.2.2 AdaBoost Classifier -- 8.2.3 Gradient Boosting Classifier -- 8.2.4 CatBoost Classifier -- 8.2.5 XGBoost Classifier -- 8.2.6 Extra Trees Classifier -- 8.3 Methodology -- 8.3.1 Data Collection and Preprocessing -- 8.3.2 Implementation Details -- 8.4 Experimental Results -- 8.4.1 Impact of Different Ensemble Techniques on Emotion Detection Accuracy -- 8.4.2 Robustness and Reliability -- 8.5 Discussion -- 8.5.1 Advantages of Ensemble Methods in EEG Emotion Detection -- 8.5.2 Future Directions -- 8.6 Conclusion -- Chapter 9 Understanding the Unseen: Explainability in Deep Learning for Computer Vision -- 9.1 Introduction -- 9.1.1 An Overview of the Success of Deep Learning in Computer Vision -- 9.1.2 The Importance of Interpretability and Explainability -- 9.2 The Need for Interpretation in Computer Vision -- 9.3 Understanding Interpretability in Deep Learning -- 9.4 Visualization Techniques -- 9.5 Maps of the Headland -- 9.6 Model Simplification -- 9.7 Meaning of Function -- 9.8 Feature Importance -- 9.9 Methods Based on Prototypes -- 9.10 Challenges and Future Directions -- 9.11 Conclusion -- 9.12 Future Vision -- References -- Chapter 10 Prefatory Study on Landslide Susceptibility Modeling Based on Binary Random Forest Classifier -- 10.1 Introduction -- 10.2 Materials and Methodology -- 10.2.1 Region "]
[{"$nulls":4},"of Study -- 10.2.2 Preparation of Dataset -- 10.2.3 Random Forest -- 10.2.4 Evaluation of Landslide Susceptibility Model."]
[{"$nulls":4},"10.3 Result Analysis -- 10.3.1 10-Fold Cross-Validation -- 10.3.2 Feature Selection -- 10.3.3 LSM by Binary RF Model -- 10.4 Conclusion -- References -- Chapter 11 Improving Digital Interactions using Augmented Reality and Computer Vision -- 11.1 Introduction -- 11.2 Literature Survey -- 11.3 Methodology -- 11.4 Results -- 11.5 Conclusion and Future Scope -- References -- Chapter 12 The Evolutionary Dynamics of Machine Learning and Deep Learning Architectures in Computer Vision -- 12.1 Introduction to Computer Vision and Its Evolution -- 12.2 Foundations of Machine Learning in Computer Vision -- 12.3 Rise of Deep Learning in Computer Vision -- 12.4 Key Architectures and Techniques in Deep Learning for Computer Vision -- 12.5 CNN Architectures -- 12.5.1 Inception -- 12.5.2 ResNet (Residual Network) -- 12.5.3 DenseNet -- 12.6 Transfer Learning and Fine-Tuning -- 12.7 Object Detection, Image Segmentation, and Image Classification -- 12.7.1 Visual Geometry Group (VGG) -- 12.7.2 MobileNet -- 12.7.3 Transfer Learning and Fine-Tuning -- 12.7.4 Mask R-CNN -- 12.7.5 DeepLab -- 12.7.6 EfficientNet -- 12.8 Evolution of Image Processing Models -- 12.8.1 Progression of Deep Learning (DL) Architectures -- 12.8.2 Recent Advancements in Computer Vision Research -- 12.8.3 Integration of Multimodal Learning -- 12.8.4 Continual Learning and Lifelong Adaptation -- 12.8.5 Ethical Considerations and Responsible AI -- 12.8.6 Robustness and Adversarial Defense -- 12.8.7 Interpretability and Explainability -- 12.8.8 Domain-Specific Adaptation and Transfer Learning -- 12.8.9 Human-Centric Vision Systems -- 12.9 Challenges and Future Directions -- 12.9.1 Challenges -- 12.9.1.1 Interpretability -- 12.9.1.2 Robustness -- 12.9.1.3 Scalability -- 12.9.1.4 Interpretability -- 12.9.1.5 Robustness -- 12.9.1.6 Scalability -- 12.9.1.7 Interpretability -- 12.9.1.8 Robustness."]
[{"$nulls":4},"12.9.1.9 Scalability -- 12.9.2 Future Directions -- 12.9.2.1 Multimodal Learning -- 12.9.2.2 Self-Supervised Learning -- 12.9.2.3 Incorporating Domain Knowledge -- 12.9.2.4 Multimodal Learning -- 12.9.2.5 Self-Supervised Learning -- 12.9.2.6 Incorporating Domain Knowledge -- 12.9.2.7 Multimodal Learning -- 12.9.2.8 Self-Supervised Learning -- 12.9.2.9 Incorporating Domain Knowledge -- 12.10 Applications and Impacts -- 12.10.1 Autonomous Driving -- 12.10.2 Medical Imaging -- 12.10.3 Surveillance and Security -- 12.10.4 Societal Impacts -- 12.10.5 Retail and E-Commerce -- 12.10.6 Agriculture -- 12.10.7 Art and Creative Industries -- 12.10.8 Accessibility -- 12.10.9 Environmental Monitoring -- 12.10.10 Industrial Quality Control -- 12.10.11 Augmented Reality (AR) and Virtual Reality (VR) -- 12.10.12 Smart Cities -- 12.10.13 Education -- 12.10.14 Humanitarian Aid and Disaster Response -- 12.11 Conclusion -- References -- Chapter 13 Real-World Applications: Transforming Industries with Computer Vision -- 13.1 Introduction -- 13.1.1 Definition and Brief History of Computer Vision -- 13.1.2 Importance of Computer Vision in Modern Industries -- 13.1.3 Purpose and Structure of the Paper -- 13.2 Healthcare -- 13.2.1 Medical Imaging Analysis -- 13.2.1.1 Use in Early Disease Detection (e.g., Cancer, Diabetic Retinopathy) -- 13.2.1.2 Case Studies and Statistics on Improved Diagnostic Accuracy -- 13.2.2 Robotic Surgery -- 13.2.2.1 Enhancements in Precision and Patient Outcomes -- 13.2.3 Patient Monitoring -- 13.2.3.1 Continuous Monitoring Systems and their Benefits -- 13.3 Manufacturing -- 13.3.1 Quality Control and Defect Detection -- 13.3.1.1 Automated Visual Inspection Systems -- 13.3.1.2 Case Studies on Efficiency and Waste Reduction -- 13.3.2 Predictive "]
[{"$nulls":4},"Maintenance -- 13.3.2.1 Early Detection of Machinery Issues."]
[{"$nulls":4},"13.3.2.2 Impact on Reducing Downtime and Extending Machinery Lifespan."]
[null,null,null,"Sommario/riassunto","Master the cutting-edge field of computer vision and artificial intelligence with this accessible guide to the applications of machine learning and deep learning for real-world solutions in robotics, healthcare, and autonomous systems."]