Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
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 |
1-394-23425-2
1-394-23427-9 1-394-23426-0 |
| 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-9910877827803321 |
Rathore Pramod Singh
|
||
| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Recent Advancements in Artificial Intelligence : Proceedings of ICRAAI 2023 / / edited by Richi Nayak, Namita Mittal, Manoj Kumar, Zdzislaw Polkowski, Ajay Khunteta
| Recent Advancements in Artificial Intelligence : Proceedings of ICRAAI 2023 / / edited by Richi Nayak, Namita Mittal, Manoj Kumar, Zdzislaw Polkowski, Ajay Khunteta |
| Autore | Nayak Richi |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (409 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
MittalNamita
KumarManoj PolkowskiZdzislaw KhuntetaAjay |
| Collana | Innovations in Sustainable Technologies and Computing |
| Soggetto topico |
Computational intelligence
Artificial intelligence Cloud Computing Internet of things Computer networks - Security measures Computational Intelligence Artificial Intelligence Internet of Things Mobile and Network Security |
| ISBN | 981-9711-11-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- About This Book -- Contents -- Editors and Contributors -- 1 A Survey on Computer Vision Methods and Approaches for the Detection of Humans in Video Surveillance Systems -- 1 Introduction -- 2 Database -- 3 Pre-Processing Techniques -- 4 Feature Extraction -- 5 Classification -- 6 Real Time Detection Systems -- 7 Human Detection Challenges -- 7.1 Image Plane Variations -- 7.2 Variation in Pose -- 7.3 Texture and Lighting Variation -- 7.4 Variation in Background -- 7.5 Variation in Shape -- 8 Conclusion -- References -- 2 UNFAZEDROADS: Pothole Management System -- 1 Introduction -- 2 Literature Survey -- 3 Methodology -- 3.1 Research Design -- 3.2 Data Collection -- 3.3 Data Analysis -- 4 Proposed System -- 5 Table of Analysis -- 6 Results and Discussions -- 7 Scope of Research -- 8 Future Scope -- 9 Conclusion -- References -- 3 DeepMint: Non-fungible Token Generation Using Deep Learning -- 1 Introduction -- 2 Literature Review -- 2.1 Review of Existing Systems -- 2.2 Limitations of Existing Systems -- 3 Analysis -- 4 Implementation -- 4.1 Design Details -- 4.2 Architecture and Methodology -- 5 Results -- 5.1 Performance Evaluation Parameters -- 5.2 Implementation Results -- 6 Conclusion -- References -- 4 Empowering Gestures: Composing Succinct Meaning Using Vision and Swin Transformers for Indian Sign Language -- 1 Introduction -- 2 Literature Survey -- 3 Overview of Techniques -- 3.1 Convolutional Neural Networks -- 3.2 Vision Transformers -- 3.3 Swin Transformer -- 3.4 ReLU Activation Function -- 3.5 SoftMax Activation Function -- 3.6 GELU Activation Function -- 4 Experimentation and Implementation -- 4.1 Data -- 4.2 Design Methodology -- 5 Results and Evaluation -- 6 Conclusion -- References.
5 An Accuracy of Identifying Recyclable Objects and the Number of Objects Identified from Municipal Waste Without Occlusion Using Computer Vision Techniques -- 1 Introduction -- 2 Related Work -- 3 Proposed Work -- 4 Implementation -- 5 Result and Discussion -- 6 Conclusion -- References -- 6 A Hybrid Approach for Summarizing Text and Image Data Using ResNet and BART -- 1 Introduction -- 2 Literature Survey -- 3 Methodology -- 4 Results and Dicussion -- 5 Conclusion -- References -- 7 Epileptic Seizure Recognition System Using Neural Networks and Support Vector Machine Models -- 1 Introduction -- 2 Dataset -- 2.1 UCI Dataset -- 2.2 CHB-MIT Dataset -- 3 Survey of Existing Systems -- 4 Proposed Methodology -- 4.1 Support Vector Machine Model -- 4.2 Neural Networks Model -- 5 Results and Discussions -- 5.1 UCI -- 5.2 CHB-MIT -- 6 Conclusion -- References -- 8 Intelligent Tutoring Systems for Multidisciplinary Education -- 1 Introduction -- 1.1 Digital Pedagogy -- 1.2 Intelligent Tutoring System -- 1.3 Methodology -- 2 Pedagogy for Multidisciplinary Digital Education -- 3 Comparison of Learning Algorithms -- 3.1 Computer Programming Learning Algorithm -- 3.2 Recommender Algorithm for Philosophy Education -- 4 Analysis and Discussion -- References -- 9 A Comprehensive Study of SOMs, iSOMs, and Hybrid SOMs for Complex Data -- 1 Introduction -- 2 Literature Review -- 2.1 Self-Organizing Maps (SOM) -- 3 Comparison of SOM, iSOM and Hybrid SOM -- 4 Conclusion and Future Work -- References -- 10 Enhancing Energy Efficiency in Smart Cities Through Robust Deep Learning Frameworks -- 1 Introduction -- 1.1 Significance of Deep Learning in Smart City Energy Management -- 1.2 Challenges in Smart City Energy Management -- 1.3 Objectives and Structure -- 2 Literature Review -- 2.1 Deep Learning for Smart City Energy Management. 2.2 Data-Driven Approaches and Integrative Reviews -- 2.3 Machine Learning-Assisted Approaches -- 2.4 Wearable Sensors and Real-Time Energy Management -- 2.5 Deep Learning in Smart Buildings -- 2.6 Sustainable Transportation in Smart Cities -- 2.7 Real-Time Energy Management in Smart Homes -- 2.8 Smart Grid and Machine Learning -- 2.9 Urban Energy Management in Smart Cities -- 3 Smart City Infrastructure and Energy Management -- 3.1 Smart City Ecosystem Overview -- 3.2 Urban Energy Consumption Dynamics -- 3.3 Deep Learning for Smart City Energy Management -- 3.4 Challenges in Smart City Energy Management -- 3.5 Wearable Sensors and Data Collection -- 3.6 Data Fusion and Feature Extraction -- 3.7 Real-Time Energy Management -- 3.8 Case Studies and Real-World Experiments -- 3.9 Performance Evaluation Metrics -- 4 Deep Learning Frameworks for Smart City Energy Efficiency -- 4.1 Introduction to Deep Learning Frameworks -- 4.2 Applications of Deep Learning in Smart City Energy Management -- 4.3 Strengths and Limitations of Deep Learning Frameworks -- 4.4 Challenges and Strategies in Real-World Deployment -- 4.5 Performance Evaluation Metrics for Energy Efficiency -- 4.6 Research Directions and Future Prospects -- 5 Robustness and Resilience of Deep Learning Models -- 5.1 Introduction to Robustness and Resilience -- 5.2 Challenges in Model Robustness -- 5.3 Adaptability to Disruptions -- 5.4 Strategies for Model Robustness -- 5.5 Resilience Testing and Benchmarking -- 5.6 Ethical Considerations -- 6 Result and Analysis -- 6.1 Overview of Deep Learning Model Performance -- 6.2 Methodologies and Benchmarking Criteria -- 6.3 Comparison Table: Key Metrics Across Research Papers -- 6.4 Interpretation of Key Findings -- 7 Discussion -- 7.1 Model Interpretability -- 7.2 Ethical Considerations -- 7.3 Data Privacy -- 8 Future Research Direction. 8.1 Enhancing Model Interpretability -- 8.2 Addressing Ethical Considerations -- 8.3 Advancements in Data Privacy Techniques -- 8.4 Improving Model Robustness and Resilience -- 8.5 Interdisciplinary Collaboration -- 8.6 Validation and Benchmarking Frameworks -- 8.7 Long-Term Sustainability and Scalability -- 9 Conclusion -- References -- 11 Transformative Potential of AI and Remote Sensing in Sustainable Groundwater Management -- 1 Introduction -- 2 Groundwater Research Landscape -- 2.1 Evolution of Groundwater Research -- 2.2 Challenges in Groundwater Research -- 2.3 Traditional Research Methods -- 2.4 Emerging Technologies -- 3 The Role of Digital Technologies in Groundwater Research -- 3.1 The Role of AI in Groundwater Research -- 3.2 Remote Sensing Techniques -- 4 AI with Remote Sensing (AI-RS) Technique -- 5 Digital Twin of Groundwater Using AI-Remote Sensing Technique -- 5.1 Reduced Environmental Impact -- 5.2 Efficient Resource Management -- 5.3 Cost Savings -- 5.4 Long-Term Sustainability -- 5.5 Community and Stakeholder Engagement -- 6 Case Studies -- 7 Challenges and Future Directions -- 7.1 Data Quality and Availability -- 7.2 Data Privacy and Ethical Concerns -- 7.3 Interdisciplinary and Global Collaboration -- 7.4 Algorithm Validation and Interpretability -- 7.5 Scaling up Sustainable Technologies -- 7.6 Climate Change Adaptation -- 8 Conclusion -- References -- 12 Impact on Ocean Acidification Along the Hawaii Coastline Using Learning Algorithm -- 1 Introduction -- 2 Proposed Algorithm -- 2.1 Tuning Process -- 2.2 Correlation Map of the Proposed Work -- 3 Results and Discussions -- 4 Conclusion -- References -- 13 Smart Crop Security System Using IoT -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Analysis and Discussion -- 4 Conclusion -- 4.1 Future Scope -- References. 14 Energy Efficient Fault Tolerance System for a Reliable IoT Environment -- 1 Introduction -- 2 Related Work -- 3 Software Defined FTM for IOT -- 4 System Design -- 5 Results and Discussion -- 6 Conclusions -- References -- 15 A Novel Authentication Approach Based on Level 2 Minutiae-based Feature Extraction Using Gabor Filter -- 1 Introduction -- 1.1 Features in Fingerprint -- 1.2 Fingerprint Databases and Its Classification -- 2 Related Work -- 3 Proposed Methodology for Ridge Detection and Level 2 Feature Extraction -- 4 Experimental Results -- 5 Conclusion and Future Work -- References -- 16 A Blockchain Based Electronic Health Record Management System with PoA Consensus -- 1 Introduction -- 2 Literature Review -- 3 Proposed Blockchain Architecture -- 4 Results and Discussion -- 4.1 Decentralized Data Management -- 4.2 Proof of Authority Consensus -- 4.3 Enhanced Scalability -- 4.4 Data Security -- 4.5 Lightweight Cryptography -- 5 Conclusion -- References -- 17 Voice-Based Classification of Parkinson's Disease Using Machine Learning: An Extensive Study -- 1 Introduction -- 2 Relevant Studies -- 3 Materials and Methods -- 3.1 Dataset -- 3.2 Methods -- 4 Experimental Results -- 4.1 Accuracy and Classification Reports -- 4.2 Confusion Matrix -- 4.3 Comparison with Relevant Studies -- 5 Conclusion -- References -- 18 A Brief Perusal of Image-based Diagnosis for COVID-19 Using Image Processing Perspective -- 1 First Section -- 2 Corona Virus Disease (COVID-19) -- 2.1 Symptoms of Coronavirus -- 3 Image-based Classification Method -- 3.1 Extraction of Features -- 3.2 Parallel Implementation -- 3.3 Manta Ray Foraging Optimization (MRFO) -- 3.4 Improved MRFO with Feature Selection Based on D.E -- 4 Appraisal of the Suggested Model -- 5 Motivation -- 6 Literature Review -- 6.1 Research Trend -- 6.2 Research Gap -- 7 Comparative Analysis -- 8 Conclusion. References. |
| Record Nr. | UNINA-9910855381503321 |
|
Nayak Richi
|
||
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||