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6G Urban Innovation : AI and Digital Twin for Next-Gen Sustainable Cities
| 6G Urban Innovation : AI and Digital Twin for Next-Gen Sustainable Cities |
| Autore | Taneja Ashu |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (238 pages) |
| Disciplina | 621.38456 |
| Altri autori (Persone) |
KumarAbhishek
Vishnudas LimkarSuresh OuaissaMariya OuaissaMariyam |
| Collana | ISTE Invoiced Series |
| ISBN |
1-394-41133-2
1-394-41131-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9911028672003321 |
Taneja Ashu
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| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Analyses for Retaining walls, Slope Stability and Landslides : Select Proceedings of 8th ICRAGEE 2024 / / edited by Rajib Sarkar, B.K. Maheshwari, Abhishek Kumar
| Analyses for Retaining walls, Slope Stability and Landslides : Select Proceedings of 8th ICRAGEE 2024 / / edited by Rajib Sarkar, B.K. Maheshwari, Abhishek Kumar |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (XIV, 430 p. 254 illus., 218 illus. in color.) |
| Disciplina | 624.151 |
| Collana | Lecture Notes in Civil Engineering |
| Soggetto topico |
Geotechnical engineering
Rock mechanics Soil mechanics Engineering geology Geotechnical Engineering and Applied Earth Sciences Soil and Rock Mechanics Geoengineering |
| ISBN | 981-9616-83-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Field and laboratory testing of soils for the estimation of dynamic soil properties -- Chapter 2: Latest findings on liquefaction of soils -- Chapter 3: Seismic slope stability and landslides -- Chapter 4: Seismic design of retaining walls, marine structures, and dams -- Chapter 5. Seismic design of shallow and deep foundations -- Chapter 6: Soil-structure interaction under dynamic loading -- Chapter 7: Engineering seismology, strong ground motions -- Chapter 8: Ground response analyses and local site effects -- Chapter 9: Seismic hazard analyses: zonation, microzonation, risk assessment -- Chapter 10:Ground improvement techniques for reduction of seismic hazard -- Chapter 11: Role of building codes in reduction of seismic risk -- Chapter 12: Wave propagation, engineering vibrations -- Chapter 13: Vibration problems of high-speed railways -- Chapter 14: Vibration absorption/isolation applications -- Chapter 15:Performance of constructed facilities in extreme events/case histories ofgeotechnical earthquake engineering -- Chapter 16. Reconnaissance reports of recent damaging earthquakes -- Chapter 17: GIS and remote sensing, AI/ ML applications for geo- hazards -- Chapter 18: Sensors and satellite technology for disaster management.Chapter 19. Seismic risk management and economics -- Chapter 20. Community preparedness and pre-earthquake disaster management -- Chapter 21. Innovative geotechnical applications in earthquake disaster management -- Chapter 22. Earthquake engineering education -- Chapter 23. Review of seismic design codes. |
| Record Nr. | UNINA-9911007476503321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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 | ||
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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 |
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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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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 |
9781394214211
1394214219 9781394214204 1394214200 |
| 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-9911020077403321 |
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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 | ||
| ||
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) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
JulieE. Golden
BurugaddaVenkata Raghuveer KumarAbhishek KumarPuneet |
| Soggetto topico |
Computational intelligence
Artificial intelligence |
| ISBN |
9781394214259
1394214251 9781394214235 1394214235 9781394214242 1394214243 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Introduction -- Chapter 1 Computational Intelligence Theory: An Orientation Technique -- 1.1 Computational Intelligence -- 1.2 Application Fields for Computational Intelligence -- 1.2.1 Neural Networks -- 1.2.1.1 Classification -- 1.2.1.2 Clustering or Compression -- 1.2.1.3 Generation of Sequences or Patterns -- 1.2.1.4 Control Systems -- 1.2.1.5 Evolutionary Computation -- 1.2.2 Fuzzy Logic -- 1.2.2.1 Fuzzy Control Systems -- 1.2.2.2 Fuzzy Systems -- 1.2.2.3 Behavioral Motivations for Fuzzy Logic -- 1.3 Computational Intelligence Paradigms -- 1.3.1 Artificial Neural Networks -- 1.3.2 Evolutionary Computation (EC) -- 1.3.3 Optimization Method -- 1.3.3.1 Optimization -- 1.4 Architecture Assortment -- 1.4.1 Swarm Intelligence -- 1.4.2 Artificial Immune Systems -- 1.5 Myths About Computational Intelligence -- 1.6 Supervised Learning in Computational Intelligence -- 1.6.1 Performance Measures |
| Record Nr. | UNINA-9911019391703321 |
|
Kumar T. Ananth
|
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| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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 |
|
Basha Niha Kamal
|
||
| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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