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Applications of Biotribology in Biomedical Systems / / edited by Abhishek Kumar, Avinash Kumar, Ashwani Kumar
| Applications of Biotribology in Biomedical Systems / / edited by Abhishek Kumar, Avinash Kumar, Ashwani Kumar |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (462 pages) |
| Disciplina | 610.28 |
| Altri autori (Persone) |
KumarAbhishek
KumarAvinash KumarAshwani <1989-> |
| Soggetto topico |
Biomedical engineering
Tribology Corrosion and anti-corrosives Coatings Biomedical Engineering and Bioengineering Corrosion |
| ISBN | 9783031583278 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction to Biotribology: A Science of Surface Interaction -- Characterization of Hydrogel Properties in the Advancement of Biotribology -- Recent Advancements in Developing Nano-biosensors for Treating Inflammatory Diseases of Human: A Comprehensive Overview -- Biological Smart Materials for Cancer Treatment -- Tribological Measurements of Human Skin -- Tribological Hurdles in Biomedical Manufacturing: A Comprehensive Examination -- Navigating the Landscape: Cutting-edge Biomedical Manufacturing Techniques -- Animal Tribology -- Medical Devices Tribology -- Composites for Drug Eluting Devices: Emerging Biomedical Applications -- Biological Smart Biomaterials: Materials for Biomedical Applications -- Bioresorbable Composite for Orthopaedics and Drug Delivery Applications -- Wear and Friction Mechanism Study in Knee and Hip Rehabilitation: A Comprehensive Review -- Challenges and Perspective of Manufacturing Techniques in Biomedical Applications. |
| Record Nr. | UNINA-9910869175703321 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bio-Inspired Optimization for Medical Data Mining
| Bio-Inspired Optimization for Medical Data Mining |
| Autore | Srivastava Sumit |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (334 pages) |
| Disciplina | 610.285 |
| Altri autori (Persone) |
AnandAbhineet
KumarAbhishek SainiBhavna RathorePramod Singh |
| Soggetto topico |
Medical informatics - Data processing
Data mining |
| ISBN |
1-394-21421-9
1-394-21420-0 |
| 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 Bioinspired Algorithms: Opportunities and Challenges -- 1.1 Introduction -- 1.1.1 Definition and Significance of Bioinspired Algorithms -- 1.1.2 Overview of the Chapter -- 1.2 Bioinspired Principles and Algorithms -- 1.2.1 Evolutionary Algorithms -- 1.2.2 Swarm Intelligence Algorithms -- 1.2.3 Artificial Neural Networks -- 1.2.4 Other Bioinspired Algorithms -- 1.3 Opportunities of Bioinspired Algorithms -- 1.3.1 Solving Complex Optimization Problems -- 1.3.2 Robustness in Dealing With Uncertainty and Noise -- 1.3.3 Parallel and Distributed Computing -- 1.3.4 Application Areas and Success Stories -- 1.4 Challenges of Bioinspired Algorithms -- 1.4.1 Parameter Tuning and Algorithm Configuration -- 1.4.2 Lack of Theoretical Analysis and Understanding -- 1.4.3 Risk of Premature Convergence -- 1.4.4 Computational Cost for Large-Scale Problems -- 1.4.5 Ethical Considerations and Limitations -- 1.5 Prominent Bioinspired Algorithms -- 1.5.1 Genetic Algorithms -- 1.5.2 Particle Swarm Optimization -- 1.5.3 Ant Colony Optimization -- 1.5.4 Artificial Neural Networks -- 1.6 Applications of Bioinspired Algorithms -- 1.6.1 Optimization Problems -- 1.6.2 Pattern Recognition and Machine Learning -- 1.6.3 Swarm Robotics -- 1.6.4 Other Domains -- 1.7 Future Research Directions -- 1.7.1 Improving Efficiency and Scalability -- 1.7.2 Enhancing Interpretability and Explainability -- 1.7.3 Integration With Other Computational Techniques -- 1.7.4 Addressing Ethical Concerns -- 1.8 Conclusion -- 1.8.1 Summary of Key Points -- 1.8.2 Implications and Future Prospects of Bioinspired Algorithms -- References -- Chapter 2 Evaluation of Phytochemical Screening and In Vitro Antiurolithiatic Activity of Myristica fragrans by Titrimetry Method Using Machine Learning -- 2.1 Introduction.
2.2 Methodology -- 2.2.1 Collection of Plant Material -- 2.2.2 Qualitative Analysis of Phytochemicals -- 2.2.3 Study of In Vitro Antiurolithiatic Activity Using Titrimetry Method -- 2.2.3.1 Preparation of Calcium Oxalate -- 2.2.3.2 Preparation of Semipermeable Membrane From Eggs -- 2.2.3.3 In Vitro Antiurolithiatic Test Using Titrimetry Method -- 2.3 Result and Discussion -- 2.3.1 In Vitro Antiurolithiatic Activity Test -- 2.3.2 Analysis of Dissolved Calcium Oxalate -- 2.4 Conclusion -- References -- Chapter 3 Parkinson's Disease Detection Using Voice and Speech- Systematic Literature Review -- 3.1 Introduction -- 3.2 Research Questions -- 3.3 Method -- 3.3.1 Search Strategy -- 3.3.2 Inclusion Criteria -- 3.3.3 Subprocesses Involved in PD Detection Process -- 3.3.4 Data Sets -- 3.3.4.1 Parkinson's Data Set-UCI Machine Learning Dataset -- 3.3.4.2 PC-GITA Dataset -- 3.3.4.3 mPower Dataset -- 3.3.4.4 Mobile Device Voice Recordings (MDVR-KCL) Dataset -- 3.3.4.5 Italian Parkinson's Voice and Speech (IPVS) Dataset -- 3.3.4.6 Parkinson Speech Dataset With Multiple Types of Sound Recordings Dataset -- 3.3.4.7 Parkinson's Telemonitoring Dataset -- 3.4 Algorithms -- 3.5 Features -- 3.5.1 Acoustic Features -- 3.5.1.1 Jitter (Local, Absolute) -- 3.5.1.2 Jitter (Local) -- 3.5.1.3 Jitter (rap) -- 3.5.1.4 Jitter (ppq5) -- 3.5.1.5 Shimmer (Local) -- 3.5.1.6 Shimmer (local, dB) -- 3.5.1.7 Shimmer (apq3) -- 3.5.1.8 Shimmer (apq5) -- 3.5.2 Spectogram-Based Methods -- 3.5.2.1 MFCC -- 3.6 Conclusion -- References -- Chapter 4 Tumor Detection and Classification -- 4.1 Introduction -- 4.2 Methods Used for Detection of Tumors -- 4.3 Methods Used for Classification of Tumours -- 4.3.1 Segmentation -- 4.3.2 Region Growing Method -- 4.3.3 Seeded Region Growing Method -- 4.3.4 Unseeded Region Growing Method -- 4.3.5 .-Connected Method -- 4.3.6 Threshold Based Method. 4.3.7 K-Means Method -- 4.3.8 Watershed Method -- 4.3.9 Comparison of Different Segmentation Techniques Based on the Advantages and Disadvantages -- 4.3.10 Comparison of Different Segmentation Techniques Based on Accuracy -- 4.3.11 Comparison of Region Based and Threshold Based Segmentation Techniques Based on Different Parameters -- 4.4 Machine Learning -- 4.4.1 Supervised Learning -- 4.4.2 Unsupervised Learning -- 4.4.3 Reinforcement Learning -- 4.4.4 K-Nearest Neighbour (KNN) -- 4.4.5 Support Vector Machine (SVM) -- 4.4.6 Random Forest -- 4.5 Deep Learning (DL) -- 4.5.1 Convolutional Neural Networks (CNN) -- 4.5.1.1 Convolution Layer -- 4.5.1.2 Pooling Layer -- 4.5.1.3 Architecture of CNN -- 4.5.1.4 Comparison of Different Variations of CNN Techniques -- 4.5.2 Long Short-Term Memory (LSTM) -- 4.5.3 Artificial Neural Network (ANN) -- 4.5.4 Accuracy of Different Models Discussed Above -- 4.5.5 Accuracy of Other Different Techniques Being Used -- 4.6 Performance Metrics -- 4.6.1 Accuracy -- 4.6.2 Precision -- 4.6.3 Recall -- 4.6.4 Specificity -- 4.6.5 F1-Measure -- 4.7 Method Wise Trend of Using Techniques for Detection of Brain Tumor -- 4.8 Conclusion -- References -- Chapter 5 Advancements in Tumor Detection and Classification -- 5.1 Introduction -- 5.2 Imaging Techniques Used in Tumor Detection and Classification -- 5.2.1 X-Ray -- 5.2.2 CT Scan -- 5.2.3 MRI -- 5.2.4 Ultrasound -- 5.3 Molecular Biology Techniques -- 5.3.1 PCR -- 5.3.2 FISH -- 5.3.3 Next-Generation Sequencing -- 5.3.4 Western Blotting -- 5.4 Machine Learning and Artificial Intelligence -- 5.5 Tumor Classification -- 5.5.1 TNM Staging System -- 5.5.2 Histological Grading -- 5.5.3 Molecular Subtyping -- 5.6 Challenges and Future Directions -- References -- Chapter 6 Classification of Brain Tumor Using Machine Learning Techniques: A Comparative Study -- 6.1 Introduction. 6.2 Related Work -- 6.3 Datasets -- 6.4 Experimental Setup -- 6.5 Results and Discussion -- 6.5.1 Evaluation Metrics -- 6.6 Conclusion -- 6.6.1 Significance of the Study -- References -- Chapter 7 Exploring the Potential of Dingo Optimizer: A Promising New Metaheuristic Approach -- 7.1 Introduction -- 7.2 Architecture of Dingo Optimizer -- 7.3 Initialization Process -- 7.3.1 Population Size -- 7.3.2 Dingo Population Initialization -- 7.3.3 Fitness Assessment -- 7.3.4 Best Dingo -- 7.3.5 Recordkeeping -- 7.4 Iteration Phase -- 7.6 Other Optimization Techniques -- 7.7 Conclusion -- References -- Chapter 8 Bioinspired Genetic Algorithm in Medical Applications -- 8.1 Introduction -- 8.2 The Genetic Algorithm -- 8.3 Radiology -- 8.4 Oncology -- 8.5 Endocrinology -- 8.6 Obstetrics and Gynecology -- 8.7 Pediatrics -- 8.8 Surgery -- 8.9 Infectious Diseases -- 8.10 Radiotherapy -- 8.11 Rehabilitation Medicine -- 8.12 Neurology -- 8.13 Health Care Management -- 8.14 Conclusion -- References -- Chapter 9 Artificial Immune System Algorithms for Optimizing Nanoparticle Design in Targeted Drug Delivery -- 9.1 Introduction -- 9.2 Artificial Immune Cells -- 9.3 The Artificial Immune System Architecture -- References -- Chapter 10 Diabetic Retinopathy Detection by Retinal Blood Vessel Segmentation and Classification Using Ensemble Model -- 10.1 Introduction -- 10.2 Literature Review -- 10.3 Proposed System -- 10.4 Conclusion and Future Scope -- References -- Chapter 11 Diabetes Prognosis Model Using Various Machine Learning Techniques -- 11.1 Introduction -- 11.1.1 Disease Identification -- 11.1.2 Data, Information, and Knowledge -- 11.1.3 Knowledge Discovery in Databases -- 11.1.4 Predictive Analytics -- 11.1.5 Supervised Learning and Machine Learning -- 11.1.6 Predictive Models -- 11.1.7 Data Validation and Cleaning -- 11.1.8 Discretization. 11.2 Literature Review -- 11.2.1 Neural Networks -- 11.2.2 Trees -- 11.2.3 K-Nearest Neighbors -- 11.3 Proposed Model -- 11.3.1 Predictive Models in Health -- 11.4 Experimental Results and Discussion -- 11.4.1 Prediction of Diabetes with Artificial Neural Networks Supervised Learning Algorithms -- 11.4.2 Improving the Prediction Ratio of Diabetes Diagnoses Using Fuzzy Logic and Neural Networks -- 11.4.3 ARIC: Type 2 Diabetes Risk Predictive Model -- 11.4.4 Evaluation of Neural Network Algorithms for Prediction Models of Type 2 Diabetes -- 11.4.5 Reliable and Objective Recommendation System for the Diagnosis of Chronic Diseases -- 11.5 Conclusion -- References -- Chapter 12 Diagnosis of Neurological Disease Using Bioinspired Algorithms -- 12.1 Introduction -- 12.1.1 Neurological Diseases -- 12.1.2 Introduction to Bioinspired Algorithms -- 12.1.3 Types of Bioinspired Algorithms Commonly Used in Healthcare -- 12.1.4 Advantages and Limitations of Bioinspired Algorithms -- 12.1.5 Limitations -- 12.1.6 Applications of Bioinspired Algorithms in Healthcare -- 12.1.7 Benefits of Bioinspired Algorithms in Healthcare Over Traditional Approaches -- 12.2 Neurological Disease Diagnosis -- 12.2.1 Bioinspired Algorithms for Neurological Disease Diagnosis -- 12.2.2 Neural Networks in Neurological Disease Diagnosis -- 12.2.2.1 How NNs Can Be Trained Using Bioinspired Optimization Techniques -- 12.2.3 Other Bioinspired Algorithms in Neurological Disease Diagnosis -- 12.3 Challenges and Future Directions -- 12.4 Conclusion -- References -- Chapter 13 Optimizing Artificial Neural-Network Using Genetic Algorithm -- 13.1 Introduction -- 13.1.1 ANN -- 13.1.2 Genetic Algorithm -- 13.2 Methodology -- 13.2.1 Mathematical Working -- 13.3 Brief Study on Existing Implementations -- 13.3.1 Using Different Types of ANNs -- 13.3.2 Using MLPs. 13.4 Comparative Study on Different Implementations. |
| Record Nr. | UNINA-9910877425703321 |
Srivastava Sumit
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| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Blockchain Security in Cloud Computing [[electronic resource] /] / edited by K.M. Baalamurugan, S. Rakesh Kumar, Abhishek Kumar, Vishal Kumar, Sanjeevikumar Padmanaban
| Blockchain Security in Cloud Computing [[electronic resource] /] / edited by K.M. Baalamurugan, S. Rakesh Kumar, Abhishek Kumar, Vishal Kumar, Sanjeevikumar Padmanaban |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (XIII, 317 p. 111 illus., 90 illus. in color.) |
| Disciplina | 621.382 |
| Collana | EAI/Springer Innovations in Communication and Computing |
| Soggetto topico |
Electrical engineering
Computational intelligence Computer security Communications Engineering, Networks Computational Intelligence Systems and Data Security Privacy |
| Soggetto genere / forma | Electronic books. |
| ISBN | 3-030-70501-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Cloud Security -- Block Chain -- Block Chain Cloud Paradigm -- Block Chain Security -- Blockchain for Cloud -- Block chain-based cloud data storage security -- Clustering using Blockchain for cloud -- Cloud Assisted Secure Health System using blockchain -- Next Generation AI&ML using Blockchain -- Cloud Key Management for Secure Connection -- Computational Efficiency of Blockchain on cloud paradigm -- Conclusion. |
| Record Nr. | UNINA-9910497110003321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Blockchain, Artificial Intelligence, and the Internet of Things : Possibilities and Opportunities
| Blockchain, Artificial Intelligence, and the Internet of Things : Possibilities and Opportunities |
| Autore | Raj Pethuru |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing AG, , 2021 |
| Descrizione fisica | 1 online resource (218 pages) |
| Altri autori (Persone) |
DubeyAshutosh Kumar
KumarAbhishek RathorePramod Singh |
| Collana | EAI/Springer Innovations in Communication and Computing Ser. |
| Soggetto genere / forma | Electronic books. |
| ISBN |
9783030776374
9783030776367 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910510565503321 |
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Raj Pethuru
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| Cham : , : Springer International Publishing AG, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Blockchain, artificial intelligence, and the Internet of things : possibilities and opportunities / / Pethuru Raj [and three others] editors
| Blockchain, artificial intelligence, and the Internet of things : possibilities and opportunities / / Pethuru Raj [and three others] editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (218 pages) |
| Disciplina | 004.678 |
| Collana | EAI/Springer Innovations in Communication and Computing |
| Soggetto topico |
Internet of things
Blockchains (Databases) |
| ISBN | 3-030-77637-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910523909503321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Computational Intelligence : Theory and Applications
| Computational Intelligence : Theory and Applications |
| Autore | Kumar T. Ananth |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (407 pages) |
| Altri autori (Persone) |
JulieE. Golden
BurugaddaVenkata Raghuveer KumarAbhishek KumarPuneet |
| ISBN |
1-394-21425-1
1-394-21423-5 1-394-21424-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910898094303321 |
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Kumar T. Ananth
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| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Deep Learning and Its Applications Using Python
| Deep Learning and Its Applications Using Python |
| Autore | Basha Niha Kamal |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (255 pages) |
| Altri autori (Persone) |
Bhatia KhanSurbhi
KumarAbhishek MashatArwa |
| ISBN |
1-394-16779-2
1-394-16778-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910830198303321 |
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Basha Niha Kamal
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| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Deep Learning and Its Applications Using Python
| Deep Learning and Its Applications Using Python |
| Autore | Basha Niha Kamal |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (255 pages) |
| Disciplina | 006.3/1 |
| Altri autori (Persone) |
Bhatia KhanSurbhi
KumarAbhishek MashatArwa |
| Soggetto topico |
Deep learning (Machine learning)
Python (Computer program language) |
| ISBN |
9781394167791
1394167792 9781394167784 1394167784 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910876901403321 |
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Basha Niha Kamal
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| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| 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 |
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 |
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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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Intelligent Green Technologies for Sustainable Smart Cities
| Intelligent Green Technologies for Sustainable Smart Cities |
| Autore | Tripathi Suman Lata |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2022 |
| Descrizione fisica | 1 online resource (369 pages) |
| Altri autori (Persone) |
GanguliSouvik
KumarAbhishek MagradzeTengiz |
| Collana | Advances in Cyber Security Ser. |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-81609-2
1-119-81610-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910590099503321 |
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Tripathi Suman Lata
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| Newark : , : John Wiley & Sons, Incorporated, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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