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Applied Computer Vision Through Artificial Intelligence
| Applied Computer Vision Through Artificial Intelligence |
| Autore | Sandhu Jasminder Kaur |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (501 pages) |
| Altri autori (Persone) |
KumarAbhishek
SahuRakesh AhujaSachin |
| ISBN |
1-394-27262-6
1-394-27261-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 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.
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. 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 of Study -- 10.2.2 Preparation of Dataset -- 10.2.3 Random Forest -- 10.2.4 Evaluation of Landslide Susceptibility Model. 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. 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 Maintenance -- 13.3.2.1 Early Detection of Machinery Issues. 13.3.2.2 Impact on Reducing Downtime and Extending Machinery Lifespan. |
| Record Nr. | UNINA-9911034577503321 |
Sandhu Jasminder Kaur
|
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| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Blockchain in the Tourism Industry: A New Era of Secure and Transparent Travel Solutions / / edited by Abhishek Kumar, António Abreu, Priya Batta, Sachin Ahuja, Pramod Singh Rathore
| Blockchain in the Tourism Industry: A New Era of Secure and Transparent Travel Solutions / / edited by Abhishek Kumar, António Abreu, Priya Batta, Sachin Ahuja, Pramod Singh Rathore |
| Autore | Kumar Abhishek |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (357 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
AbreuAntónio
BattaPriya AhujaSachin RathorePramod Singh |
| Collana | Information Systems Engineering and Management |
| Soggetto topico |
Computational intelligence
Engineering - Data processing Quantum computers Computational Intelligence Data Engineering Quantum Computing |
| ISBN |
9783031953415
9783031953408 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Blockchain in Tourism Opening the Door to Safe, Transparent, and Decentralized Travel Solutions -- Blockchain Fundamentals and Its Applications in the Tourism Industry -- Blockchain in Hospitality and Accommodation -- Decentralized Tourism The Transformative Role of Blockchain in the Travel Industry -- Blockchain Powered Airlines: Enhancing Security, Transparency, and Efficiency in Aviation -- Blockchain Applications in Aviation Securing Transactions, Streamlining Operations, and Improving Passenger Experience -- Enhancing Supply Chain Transparency and Efficiency in Tourism through Blockchain Technology -- Clear Travel Networks: Improving Logistics Efficiency via Blockchain -- Distributed Voyages Blockchain’s Safeguard for Traveler Authentication -- Blockchain in Tourism Enhancing Security, Reducing Fraud, and Revolutionizing Customer Engagement -- Cross Border Collaboration in the New Digital Era with Blockchain Integration -- Blockchain for Digital Tourism in Northeast India Understanding Tourist Priorities Using Best Worst Scaling -- Emerging Trends and Innovation of Blockchain in Tourism. |
| Record Nr. | UNINA-9911018751903321 |
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Kumar Abhishek
|
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Deep Learning Techniques for Automation and Industrial Applications
| Deep Learning Techniques for Automation and Industrial Applications |
| Autore | Rathore Pramod Singh |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (280 pages) |
| Disciplina | 670.42/7028 |
| Altri autori (Persone) |
AhujaSachin
BurriSrinivasa Rao KhuntetaAjay BaliyanAnupam KumarAbhishek |
| Soggetto topico | Automation |
| ISBN |
9781394234257
1394234252 9781394234271 1394234279 9781394234264 1394234260 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Text Extraction from Images Using Tesseract -- 1.1 Introduction -- 1.1.1 Areas -- 1.1.2 Why Text Extraction? -- 1.1.3 Applications of OCR -- 1.2 Literature Review -- 1.3 Development Areas -- 1.3.1 React JavaScript (JS) -- 1.3.2 Flask -- 1.4 Existing System -- 1.5 Enhancing Text Extraction Using OCR Tesseract -- 1.6 Unified Modeling Language (UML) Diagram -- 1.6.1 Use Case Diagram -- 1.6.2 Model Architecture -- 1.6.3 Pseudocode -- 1.7 System Requirements -- 1.7.1 Software Requirements -- 1.7.2 Hardware Requirements -- 1.8 Testing -- 1.9 Result -- 1.10 Future Scope -- 1.11 Conclusion -- References -- Chapter 2 Chili Leaf Classification Using Deep Learning Techniques -- 2.1 Introduction -- 2.2 Objectives -- 2.3 Literature Survey -- 2.4 About the Dataset -- 2.5 Methodology -- 2.6 Result -- 2.7 Conclusion and Future Work -- References -- Chapter 3 Fruit Leaf Classification Using Transfer Learning Techniques -- 3.1 Introduction -- 3.2 Literature Review -- 3.3 Methodology -- 3.3.1 Image Preprocessing -- 3.3.2 Data Augmentation -- 3.3.3 Deep Learning Models -- 3.3.4 Accuracy Chart -- 3.3.5 Accuracy and Loss Graph -- 3.4 Conclusion and Future Work -- References -- Chapter 4 Classification of University of California (UC), Merced Land-Use Dataset Remote Sensing Images Using Pre-Trained Deep Learning Models -- 4.1 Introduction -- 4.2 Motivation and Contribution -- 4.2.1 Related Work -- 4.3 Methodology -- 4.3.1 Pre-Trained Models -- 4.3.2 Dataset -- 4.3.3 Training Processes -- 4.4 Experiments and Results -- 4.4.1 VGG Family -- 4.4.2 ResNet Family -- 4.4.2.1 ResNet101 -- 4.4.2.2 ResNet152 -- 4.4.3 MobileNet Family -- 4.4.4 Inception Family -- 4.4.5 Xception Family -- 4.4.6 DenseNet Family -- 4.4.7 NasNet Family -- 4.4.8 EfficientNet Family -- 4.4.9 ResNet Version 2.
4.5 Conclusion -- References -- Chapter 5 Sarcastic and Phony Contents Detection in Social Media Hindi Tweets -- 5.1 Introduction -- 5.1.1 Sarcasm in Social Media Hindi Tweets -- 5.2 Literature Review -- 5.2.1 Literature Review of Sarcasm Detection Based on Data Analysis Without Machine Learning Algorithms -- 5.2.1.1 Other Related Works without Machine Learning Algorithms for Sarcasm Detection -- 5.2.2 Literature Review of Sarcasm Detection with Machine Learning Algorithms and Based on Manual Feature Engineering Approach -- 5.3 Research Gap -- 5.4 Objective -- 5.5 Proposed Methodology -- 5.6 Expected Outcomes -- References -- Chapter 6 Removal of Haze from Synthetic and Real Scenes Using Deep Learning and Other AI Techniques -- 6.1 Introduction -- 6.2 Formation of a Haze Model -- 6.3 Different Techniques of Single-Image Dehazing -- 6.3.1 Contrast Enhancement -- 6.3.2 Dark Channel Prior -- 6.3.3 Color Attenuation Prior -- 6.3.4 Fusion Techniques -- 6.3.5 Deep Learning -- 6.4 Results and Discussions -- 6.5 Output for Synthetic Scenes -- 6.6 Output for Real Scenes -- 6.7 Conclusions -- References -- Chapter 7 HOG and Haar Feature Extraction-Based Security System for Face Detection and Counting -- 7.1 Introduction -- 7.1.1 Need for a Better Security System -- 7.2 Literature Survey -- 7.3 Proposed Work -- 7.3.1 Tools Used -- 7.3.2 Algorithm of the Proposed System -- 7.3.2.1 HOG-Based Individual Counting -- 7.3.2.2 Haar-Based Individual Counting -- 7.3.2.3 Combination of HOG and Haar -- 7.3.2.4 AdaBoost Learning Technique -- 7.3.2.5 KLT Tracker -- 7.4 Experiments and Results -- 7.5 Conclusion and Scope of Future Work -- References -- Chapter 8 A Comparative Analysis of Different CNN Models for Spatial Domain Steganalysis -- 8.1 Introduction -- 8.2 General Framework -- 8.2.1 Dataset -- 8.2.2 Deep Learning CNN Models -- 8.2.2.1 XuNet. 8.2.2.2 Pretrained Networks -- 8.3 Experimental Results and Analysis -- 8.4 Conclusion and Discussion -- Acknowledgments -- References -- Chapter 9 Making Invisible Bluewater Visible Using Machine and Deep Learning Techniques-A Review -- 9.1 Introduction -- 9.1.1 Why is It Difficult to Measure Subsurface Groundwater? -- 9.1.2 What are High Level Tasks Involved in Groundwater Measurement? -- 9.2 Determination of Groundwater Potential (GWP) Parameters -- 9.2.1 Groundwater Potential (GWP) Parameters -- 9.2.2 Analysis of the Key GWP Parameters -- 9.3 GWP Determination: Methods and Techniques -- 9.4 GWP Output: Applications -- 9.5 GWP Research Gaps: Future Research Areas -- 9.6 Conclusion -- References -- Chapter 10 Fruit Leaf Classification Using Transfer Learning for Automation and Industrial Applications -- 10.1 Introduction -- 10.1.1 Overview of Fruit Leaf Classification and Its Relevance in Automation and Industrial Applications -- 10.1.2 Challenges of Building a Classification Model from Scratch -- 10.1.3 Introduction to Transfer Learning as a Solution -- 10.1.4 Overview of Popular Pre-Trained Models -- 10.1.4.1 Visual Geometry Group -- 10.1.4.2 Residual Network -- 10.1.4.3 Inception -- 10.2 Data Collection and Preprocessing -- 10.2.1 Importance of Data Collection and Preprocessing -- 10.2.2 Data Augmentation in Fruit Leaf Classification -- 10.2.3 Normalization and Resizing in Fruit Leaf Classification -- 10.3 Loading a Pre-Trained Model for Fruit Leaf Classification Using Transfer Learning -- 10.3.1 Code Examples for Implementing Transfer Learning Using TensorFlow -- 10.4 Training and Evaluation -- 10.4.1 Explanation of Training and Evaluation Process -- 10.4.2 Metrics for Measuring Model Performance -- 10.5 Applications in Automation and Industry -- 10.5.1 Benefits of Using Transfer Learning in Automation and Industrial Settings. 10.5.2 Case Studies of Fruit Leaf Classification in Industry Using Transfer Learning -- 10.6 Conclusion -- 10.7 Future Work -- References -- Chapter 11 Green AI: Carbon-Footprint Decoupling System -- 11.1 Introduction -- 11.2 CO2 Emissions in Sectors -- 11.3 Heating and Cooking Emissions -- 11.4 Automobile Systems Emission -- 11.5 Power Systems Emission -- 11.5.1 Map -- 11.6 Total CO2 Emission -- 11.6.1 Relationship Between Tables -- 11.6.2 Group by Clause -- 11.6.3 Offshore Wind Storage Integration Method -- 11.6.4 Offshore Floating Wind and Power Generation Technology (OFWPP) -- 11.6.5 Wind Power Plant for Storage Mixing -- 11.6.6 The Effect on the Environment when Using Battery Storage -- 11.7 Green AI With a Control Strategy of Carbon Emission -- 11.8 Green Software -- 11.9 Conclusion -- 11.10 Future Scope and Limitation -- References -- Chapter 12 Review of State-of-Art Techniques for Political Polarization from Social Media Network -- 12.1 Introduction -- 12.1.1 Social Media -- 12.2 Political Polarization -- 12.2.1 Identification of the Parties -- 12.2.2 Definition of Political Ideology -- 12.2.3 Voting Conduct (Definition) -- 12.2.4 Definition of Policy Positions -- 12.2.5 Definition of Affective Polarization -- 12.2.6 Identifiability of Parties (Definition) -- 12.2.7 Definition of Political Ideology -- 12.2.8 Definition of Voting Behavior -- 12.2.9 Policy Positions (Definition) -- 12.2.10 Party Sorting -- 12.2.11 Affective Polarization (Definition) -- 12.3 State-of-the-Art Techniques -- 12.3.1 Word Embedding (WE) -- 12.3.2 Customary Models -- 12.3.3 Models of Deep Neural Networks (DNN) -- 12.3.4 Single and Hybrid ML Techniques -- 12.3.4.1 Single Methods -- 12.3.4.2 Hybrid Approaches -- 12.3.5 Multitask Learning (V) -- 12.3.5.1 Learning-Related Problem -- 12.3.5.2 Multi-Task Learning MTL -- 12.3.5.3 Architectures for Multiple Tasks. 12.3.5.4 Two MTL Deep Learning Methods -- 12.3.6 Techniques for Deep Learning -- 12.4 Literature Survey -- 12.5 Conclusion -- References -- Chapter 13 Collaborative Design and Case Analysis of Mobile Shopping Apps: A Deep Learning Approach -- 13.1 Introduction -- 13.1.1 Basic Rules in Shopping App Interaction Design -- 13.1.1.1 User-Centered Design Rules -- 13.1.2 Visual Interface Consistency -- 13.2 Personalized Interaction Design Framework for Mobile Shopping -- 13.2.1 Modelized Interaction Information Framework -- 13.2.2 Interactive Design Path Analysis -- 13.2.3 Optimization Design in the Page System -- 13.3 Case Analysis -- 13.4 Conclusions -- References -- Chapter 14 Exploring the Potential of Machine Learning and Deep Learning for COVID-19 Detection -- 14.1 Introduction -- 14.2 Supervised Learning Techniques -- 14.3 Unsupervised Learning Techniques -- 14.4 Deep Learning Techniques -- 14.5 Reinforcement Learning Techniques -- 14.6 Comparison of Machine Learning and Deep Learning Techniques -- 14.7 Challenges and Limitations -- 14.8 Conclusion and Future Directions -- References -- Index -- EULA. |
| Record Nr. | UNINA-9911020223803321 |
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Rathore Pramod Singh
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| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Integrating Neurocomputing with Artificial Intelligence
| Integrating Neurocomputing with Artificial Intelligence |
| Autore | Kumar Abhishek |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (337 pages) |
| Disciplina | 006.3/2 |
| Altri autori (Persone) |
Singh RathorePramod
AhujaSachin LilhoreUmesh Kumar |
| Soggetto topico | Neural networks (Computer science) |
| ISBN |
1-394-33571-7
1-394-33570-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9911020335403321 |
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Kumar Abhishek
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| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Targeted Chemotherapy with Personalized Immunotherapy : An AI Approach
| Targeted Chemotherapy with Personalized Immunotherapy : An AI Approach |
| Autore | Kumar Abhishek |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (536 pages) |
| Disciplina | 615.58 |
| Altri autori (Persone) |
DasPrasenjit
RathorePramod Singh AhujaSachin SharmaChetan |
| ISBN |
1-394-27061-5
1-394-27060-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9911027076503321 |
|
Kumar Abhishek
|
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| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||