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Data Science Handbook : A Practical Approach
| Data Science Handbook : A Practical Approach |
| Autore | Prakash Kolla Bhanu |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2022 |
| Descrizione fisica | 1 online resource (472 pages) |
| ISBN |
1-119-85801-1
1-119-85800-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Dedication -- Contents -- Acknowledgment -- Preface -- 1 Data Munging Basics -- 1 Introduction -- 1.1 Filtering and Selecting Data -- 1.2 Treating Missing Values -- 1.3 Removing Duplicatesduplicates -- 1.4 Concatenating and Transforming Data -- 1.5 Grouping and Data Aggregation -- References -- 2 Data Visualization -- 2.1 Creating Standard Plots (Line, Bar, Pie) -- 2.2 Defining Elements of a Plot -- 2.3 Plot Formatting Segment 3 Plot formatting -- 2.4 Creating Labels and Annotations -- 2.5 Creating Visualizations from Time Series Data -- 2.6 Constructing Histograms, Box Plots, and Scatter Plots -- References -- 3 Basic Math and Statistics -- 3.1 Linear Algebra -- 3.2 Calculus -- 3.2.1 Differential Calculus -- 3.2.2 Integral Calculus -- Statistics for Data Science -- 3.3 Inferential Statistics -- 3.3.1 Central Limit Theorem -- 3.3.2 Hypothesis Testing -- 3.3.3 ANOVA -- 3.3.4 Qualitative Data Analysis -- 3.4 Using NumPy to Perform Arithmetic Operations on Data -- 3.5 Generating Summary Statistics Using Pandas and Scipy -- 3.6 Summarizing Categorical Data Using Pandas -- 3.7 Starting with Parametric Methods in Pandas and Scipy -- 3.8 Delving Into Non-Parametric Methods Using Pandas and Scipy -- 3.9 Transforming Dataset Distributions -- References -- 4 Introduction to Machine Learning -- 4.1 Introduction to Machine Learning -- 4.2 Types of Machine Learning Algorithms -- 4.3 Explanatory Factor Analysis -- 4.4 Principal Component Analysis (PCA) -- References -- 5 Outlier Analysis -- 5.1 Extreme Value Analysis Using Univariate Methods -- 5.2 Multivariate Analysis for Outlier Detection -- 5.3 DBSCan Clustering to Identify Outliers -- References -- 6 Cluster Analysis -- 6.1 K-Means Algorithm -- 6.2 Hierarchial Methods -- 6.3 Instance-Based Learning w/k-Nearest Neighbor.
References -- 7 Network Analysis with NetworkX -- 7.1 Working with Graph Objects -- 7.2 Simulating a Social Network (ie -- Directed Network Analysis) -- 7.3 Analyzing a Social Network -- References -- 8 Basic Algorithmic Learning -- 8.1 Linear Regression -- 8.2 Logistic Regression -- 8.3 Naive Bayes Classifiers -- References -- 9 Web-Based Data Visualizations with Plotly -- 9.1 Collaborative Analytics -- 9.2 Basic Charts -- 9.3 Statistical Charts -- 9.4 Plotly Maps -- References -- 10 Web Scraping with Beautiful Soup -- 10.1 The BeautifulSoup Object -- 10.2 Exploring NavigableString Objects -- 10.3 Data Parsing -- 10.4 Web Scraping -- 10.5 Ensemble Models with Random Forests -- References -- 11 Covid19 Detection and Prediction -- Bibliography -- 12 Leaf Disease Detection -- Bibliography -- 13 Brain Tumor Detection with Data Science -- Bibliography -- 14 Color Detection with Python -- Bibliography -- 15 Detecting Parkinson's Disease -- Bibliography -- 16 Sentiment Analysis -- Bibliography -- 17 Road Lane Line Detection -- Bibliography -- 18 Fake News Detection -- Bibliography -- 19 Speech Emotion Recognition -- Bibliography -- 20 Gender and Age Detection with Data Science -- Bibliography -- 21 Diabetic Retinopathy -- Bibliography -- 22 Driver Drowsiness Detection in Python -- Bibliography -- 23 Chatbot Using Python -- Bibliography -- 24 Handwritten Digit Recognition Project -- Bibliography -- 25 Image Caption Generator Project in Python -- Bibliography -- 26 Credit Card Fraud Detection Project -- Bibliography -- 27 Movie Recommendation System -- Bibliography -- 28 Customer Segmentation -- Bibliography -- 29 Breast Cancer Classification -- Bibliography -- 30 Traffic Signs Recognition -- Bibliography -- EULA. |
| Record Nr. | UNINA-9910623986203321 |
Prakash Kolla Bhanu
|
||
| Newark : , : John Wiley & Sons, Incorporated, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data science handbook : a practical approach / / Kolla Bhanu Prakash
| Data science handbook : a practical approach / / Kolla Bhanu Prakash |
| Autore | Prakash Kolla Bhanu |
| Pubbl/distr/stampa | Hoboken, New Jersey ; ; Beverly, Massachusetts : , : Wiley : , : Scrivener Publishing, , [2022] |
| Descrizione fisica | 1 online resource (472 pages) |
| Disciplina | 005.7 |
| Collana | Next-generation computing and communication engineering series |
| Soggetto topico |
Big data
Data mining Information visualization |
| ISBN |
1-119-85801-1
1-119-85800-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Dedication -- Contents -- Acknowledgment -- Preface -- 1 Data Munging Basics -- 1 Introduction -- 1.1 Filtering and Selecting Data -- 1.2 Treating Missing Values -- 1.3 Removing Duplicatesduplicates -- 1.4 Concatenating and Transforming Data -- 1.5 Grouping and Data Aggregation -- References -- 2 Data Visualization -- 2.1 Creating Standard Plots (Line, Bar, Pie) -- 2.2 Defining Elements of a Plot -- 2.3 Plot Formatting Segment 3 Plot formatting -- 2.4 Creating Labels and Annotations -- 2.5 Creating Visualizations from Time Series Data -- 2.6 Constructing Histograms, Box Plots, and Scatter Plots -- References -- 3 Basic Math and Statistics -- 3.1 Linear Algebra -- 3.2 Calculus -- 3.2.1 Differential Calculus -- 3.2.2 Integral Calculus -- Statistics for Data Science -- 3.3 Inferential Statistics -- 3.3.1 Central Limit Theorem -- 3.3.2 Hypothesis Testing -- 3.3.3 ANOVA -- 3.3.4 Qualitative Data Analysis -- 3.4 Using NumPy to Perform Arithmetic Operations on Data -- 3.5 Generating Summary Statistics Using Pandas and Scipy -- 3.6 Summarizing Categorical Data Using Pandas -- 3.7 Starting with Parametric Methods in Pandas and Scipy -- 3.8 Delving Into Non-Parametric Methods Using Pandas and Scipy -- 3.9 Transforming Dataset Distributions -- References -- 4 Introduction to Machine Learning -- 4.1 Introduction to Machine Learning -- 4.2 Types of Machine Learning Algorithms -- 4.3 Explanatory Factor Analysis -- 4.4 Principal Component Analysis (PCA) -- References -- 5 Outlier Analysis -- 5.1 Extreme Value Analysis Using Univariate Methods -- 5.2 Multivariate Analysis for Outlier Detection -- 5.3 DBSCan Clustering to Identify Outliers -- References -- 6 Cluster Analysis -- 6.1 K-Means Algorithm -- 6.2 Hierarchial Methods -- 6.3 Instance-Based Learning w/k-Nearest Neighbor.
References -- 7 Network Analysis with NetworkX -- 7.1 Working with Graph Objects -- 7.2 Simulating a Social Network (ie -- Directed Network Analysis) -- 7.3 Analyzing a Social Network -- References -- 8 Basic Algorithmic Learning -- 8.1 Linear Regression -- 8.2 Logistic Regression -- 8.3 Naive Bayes Classifiers -- References -- 9 Web-Based Data Visualizations with Plotly -- 9.1 Collaborative Analytics -- 9.2 Basic Charts -- 9.3 Statistical Charts -- 9.4 Plotly Maps -- References -- 10 Web Scraping with Beautiful Soup -- 10.1 The BeautifulSoup Object -- 10.2 Exploring NavigableString Objects -- 10.3 Data Parsing -- 10.4 Web Scraping -- 10.5 Ensemble Models with Random Forests -- References -- 11 Covid19 Detection and Prediction -- Bibliography -- 12 Leaf Disease Detection -- Bibliography -- 13 Brain Tumor Detection with Data Science -- Bibliography -- 14 Color Detection with Python -- Bibliography -- 15 Detecting Parkinson's Disease -- Bibliography -- 16 Sentiment Analysis -- Bibliography -- 17 Road Lane Line Detection -- Bibliography -- 18 Fake News Detection -- Bibliography -- 19 Speech Emotion Recognition -- Bibliography -- 20 Gender and Age Detection with Data Science -- Bibliography -- 21 Diabetic Retinopathy -- Bibliography -- 22 Driver Drowsiness Detection in Python -- Bibliography -- 23 Chatbot Using Python -- Bibliography -- 24 Handwritten Digit Recognition Project -- Bibliography -- 25 Image Caption Generator Project in Python -- Bibliography -- 26 Credit Card Fraud Detection Project -- Bibliography -- 27 Movie Recommendation System -- Bibliography -- 28 Customer Segmentation -- Bibliography -- 29 Breast Cancer Classification -- Bibliography -- 30 Traffic Signs Recognition -- Bibliography -- EULA. |
| Record Nr. | UNINA-9910677188103321 |
|
Prakash Kolla Bhanu
|
||
| Hoboken, New Jersey ; ; Beverly, Massachusetts : , : Wiley : , : Scrivener Publishing, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning for Industrial Applications
| Machine Learning for Industrial Applications |
| Autore | Prakash Kolla Bhanu |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (341 pages) |
| Disciplina | 006.3/1 |
| Collana | Next-generation computing and communication engineering |
| Soggetto topico | Machine learning - Industrial applications |
| ISBN |
9781394268993
1394268998 9781394268986 139426898X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Series Page -- Title Page -- Copyright Page -- Dedication Page -- Contents -- Preface -- Chapter 1 Overview of Machine Learning -- 1.1 Introduction -- 1.2 Sorts of Machine Learning -- 1.3 Regulated Gaining Knowledge of Dog and Human -- 1.4 Solo Learning -- 1.5 Support Mastering -- 1.6 Bundles or Applications of Machine Learning -- 1.6.1 Photograph Reputation -- 1.6.2 Discourse Recognition -- 1.6.3 Traffic Prediction -- 1.6.4 Item Recommendations -- 1.6.5 Self-Using Vehicles -- 1.6.6 Electronic Mail Unsolicited Mail And Malware Filtering -- 1.6.7 Computerized Private Assistant -- 1.6.8 Online Fraud Detection -- 1.6.9 Securities Exchange Buying and Selling -- 1.6.10 Clinical Prognosis -- 1.6.11 Computerized Language Translation -- 1.6.12 Online Media Features -- 1.6.13 Feeling Evaluation -- 1.6.14 Robotizing Employee Get Right of Entry to Manipulate -- 1.6.15 Marine Flora and Fauna Protection -- 1.6.16 Anticipate Potential Coronary Heart Failure -- 1.6.17 Directing Healthcare Efficiency and Scientific Offerings -- 1.6.18 Transportation and Commuting (Uber) -- 1.6.19 Dynamic Pricing -- 1.6.19.1 How Does Uber Decide the Cost of Your Excursion? -- 1.6.20 Online Video Streaming (Netflix) -- 1.7 Challenges in Machine Learning -- 1.8 Limitations of Machine Learning -- 1.9 Projects in Machine Learning -- References -- Chapter 2 Machine Learning Building Blocks -- 2.1 Data Collection -- 2.1.1 Importing the Data from CSV Files -- 2.2 Data Preparation -- 2.2.1 Data Exploration -- 2.2.2 Data Pre-Processing -- 2.3 Data Wrangling -- 2.4 Data Analysis -- 2.5 Model Selection -- 2.6 Model Building -- 2.7 Model Evaluation -- 2.7.1 Classification Metrics -- 2.7.1.1 Accuracy -- 2.7.1.2 Precision -- 2.7.1.3 Recall -- 2.7.2 Regression Metrics -- 2.7.2.1 Mean Squared Error -- 2.7.2.2 Root Mean Squared Error -- 2.7.2.3 Mean Absolute Error -- 2.8 Deployment.
2.8.1 Machine Learning Projects -- 2.8.2 Spam Detection Using Machine Learning -- 2.8.3 Spam Detection for YouTube Comments Using Naïve Bayes Classifier -- 2.8.4 Fake News Detection -- 2.8.5 House Price Prediction -- 2.8.6 Gold Price Prediction -- Bibliography -- Chapter 3 Multilayer Perceptron (in Neural Networks) -- 3.1 Multilayer Perceptron for Digit Classification -- 3.1.1 Implementation of MLP using TensorFlow for Classifying Image Data -- 3.2 Training Multilayer Perceptron -- 3.3 Backpropagation -- References -- Chapter 4 Kernel Machines -- 4.1 Different Kernels and Their Applications -- 4.2 Some Other Kernel Functions -- 4.2.1 Gaussian Radial Basis Function (RBF) -- 4.2.2 Laplace RBF Kernel -- 4.2.3 Hyperbolic Tangent Kernel -- 4.2.4 Bessel Function of the First-Kind Kernel -- 4.2.5 ANOVA Radial Basis Kernel -- 4.2.6 Linear Splines Kernel in One Dimension -- 4.2.7 Exponential Kernel -- 4.2.8 Kernels in Support Vector Machine -- References -- Chapter 5 Linear and Rule-Based Models -- 5.1 Least Squares Methods -- 5.2 The Perceptron -- 5.2.1 Bias -- 5.2.2 Perceptron Weighted Sum -- 5.2.3 Activation Function -- 5.2.3.1 Types of Activation Functions -- 5.2.4 Perceptron Training -- 5.2.5 Online Learning -- 5.2.6 Perceptron Training Error -- 5.3 Support Vector Machines -- 5.4 Linearity with Kernel Methods -- References -- Chapter 6 Distance-Based Models -- 6.1 Introduction -- 6.1.1 Distance-Based Clustering -- 6.2 K-Means Algorithm -- 6.2.1 K-Means Algorithm Working Process -- 6.3 Elbow Method -- 6.4 K-Median -- 6.4.1 Algorithm -- 6.5 K-Medoids, PAM (Partitioning Around Medoids) -- 6.5.1 Advantages -- 6.5.2 Drawbacks -- 6.5.3 Algorithm -- 6.6 CLARA (Clustering Large Applications) -- 6.6.1 Advantages -- 6.6.2 Disadvantages -- 6.7 CLARANS (Clustering Large Applications Based on Randomized Search) -- 6.7.1 Advantages -- 6.7.2 Disadvantages. 6.7.3 Algorithm -- 6.8 Hierarchical Clustering -- 6.9 Agglomerative Nesting Hierarchical Clustering (AGNES) -- 6.10 DIANA -- References -- Chapter 7 Model Ensembles -- 7.1 Bagging -- 7.1.1 Advantages -- 7.1.2 Disadvantages -- 7.1.3 Bagging Workage -- 7.1.4 Algorithm -- 7.2 Boosting -- 7.2.1 Types of Boosting -- 7.2.2 Advantages -- 7.2.3 Disadvantages -- 7.2.4 Algorithm -- 7.3 Stacking -- 7.3.1 Architecture of Stacking -- 7.3.2 Stacking Ensemble Family -- References -- Chapter 8 Binary and Beyond Binary Classification -- 8.1 Binary Classification -- 8.2 Logistic Regression -- 8.3 Support Vector Machine -- 8.4 Estimating Class Probabilities -- 8.5 Confusion Matrix -- 8.6 Beyond Binary Classification -- 8.7 Multi-Class Classification -- 8.8 Multi-Label Classification -- Reference -- Chapter 9 Model Selection -- 9.1 Model Selection Considerations -- 9.1.1 What Do We Care Approximately When Choosing the Final Version? -- 9.2 Model Selection Strategies -- 9.3 Types of Model Selection -- 9.3.1 Methods of Re-Sampling -- 9.3.2 Random Separation -- 9.3.3 Time Divide -- 9.3.4 K-Fold Cross-Validation -- 9.3.5 Stratified K-Fold -- 9.3.6 Bootstrap -- 9.3.7 Possible Steps -- 9.3.8 Akaike Information Criterion (AIC) -- 9.3.9 Bayesian Information Criterion (BIC) -- 9.3.10 Minimum Definition Length (MDL) -- 9.3.11 Building Risk Reduction (SRM) -- 9.3.12 Excessive Installation (Overfitting) -- 9.4 The Principle of Parsimony -- 9.5 Examples of Model Selection Criterions -- 9.6 Other Popular Properties -- 9.7 Key Considerations -- 9.8 Model Validation -- 9.8.1 Why is Model Validation Important? -- 9.8.2 How to Validate the Model -- 9.8.3 What is a Model Validation Test? -- 9.8.4 Benefits of Modeling Validation -- 9.8.5 Model Validation Traps -- 9.8.6 Data Verification -- 9.8.7 Model Performance and Validation -- 9.9 Self-Driving Cars -- 9.10 K-Fold Cross Validation. 9.11 No One-Size-Fits-All Model Validation -- 9.12 Validation Strategies -- 9.13 K-Fold Cross-Validation -- 9.14 Capture Confirmation Using Hold-Out Validation -- 9.15 Comparison of Validation Strategy -- References -- Chapter 10 Support Vector Machines -- 10.1 History -- 10.2 Model -- 10.3 Kinds of Support Vector Machine -- 10.3.1 Straight SVM -- 10.3.2 Non-Direct SVM -- 10.3.3 Benefits of Help Vector Machines -- 10.3.4 The Negative Marks of Help Vector Machines -- 10.3.5 Applications -- 10.4 Hyperplane and Support Vectors Inside the SVM Set of Rules -- 10.4.1 Hyperplane -- 10.5 Support Vectors -- 10.6 SVM Kernel -- 10.7 How Can It Function? -- 10.7.1 See the Right Hyperplane (Circumstance 1) -- 10.7.2 See the Appropriate Hyperplane (Situation 2) -- 10.7.3 Distinguish the Right Hyper-Airplane (Situation 3) -- 10.7.4 Would We Have the Option to Organize Models (Circumstance 4)? -- 10.7.5 Track Down the Hyperplane to Isolate Into Guidelines (Situation 5) -- 10.8 SVM for Classification -- 10.9 SVM for Regression -- 10.10 Python Implementation of Support Vector Machine -- 10.10.1 Data Pre-Taking Care of Step -- 10.10.2 Fitting the SVM Classifier to the Readiness Set -- 10.10.2.1 Outcome -- 10.10.3 Anticipating the Investigated Set Final Product -- 10.10.3.1 Yield -- 10.10.4 Fostering the Disarray Lattice -- 10.10.5 Picturing the Preparation Set Outcome -- 10.10.5.1 Yield -- 10.10.6 Imagining the Investigated Set Outcome -- 10.10.6.1 Yield -- 10.10.7 Part or Kernel -- 10.10.8 Support Vector Machine (SVM) Code in Python -- 10.10.9 Intricacy of SVM -- References -- Chapter 11 Clustering -- 11.1 Example -- 11.2 Kinds of Clustering -- 11.2.1 Hard Clustering -- 11.2.2 Delicate Clustering -- 11.2.2.1 Dividing Clustering or Partitioning Clustering -- 11.2.2.2 Thickness Essentially Based Clustering or Density Fundamentally Based Clustering. 11.2.2.3 Transport Model-Based Clustering or Distribution Model-Based Clustering -- 11.2.2.4 Progressive Clustering or Hierarchical Clustering -- 11.2.2.5 Fluffy Clustering or Fuzzy Clustering -- 11.3 What are the Utilization of Clustering? -- 11.4 Models -- 11.5 Uses of Clustering -- 11.5.1 In Character of Most Tumor Cells -- 11.5.2 In Web Crawlers Like Google -- 11.5.3 Shopper Segmentation -- 11.5.4 In Biology -- 11.5.5 In Land Use -- 11.6 Bunching Algorithms or Clustering Algorithms -- 11.6.1 K-Means Clustering -- 11.6.2 Mean-Shift Clustering -- 11.6.3 Thickness or Density-Based Spatial Clustering of Application with Noise (DBSCAN) -- 11.6.4 Assumption Maximization Clustering Utilizing Gaussian Combination Models -- 11.6.5 Agglomerative Hierarchical Clustering -- 11.7 Instances of Clustering Algorithms -- 11.7.1 Library Setup -- 11.7.2 Grouping or Clustering Dataset -- 11.7.3 Fondness or Affinity Propagation -- 11.7.4 Agglomerative Clustering -- 11.7.5 BIRCH -- 11.7.6 DBSCAN -- 11.7.7 K-Means -- 11.7.8 Mini-Batch K-Means -- 11.7.9 Mean Shift -- 11.7.10 OPTICS -- 11.7.11 Unearthly or Spectral Clustering -- 11.7.12 Gaussian Mixture Model -- 11.8 Python Implementation of K-Means -- 11.8.1 Stacking the Data -- 11.8.2 Plotting the Information -- 11.8.3 Choosing the Component -- 11.8.4 Clustering -- 11.8.5 Clustering Results -- 11.8.6 WCSS and Elbow Technique -- 11.8.7 Uses of K-Mean Bunching -- 11.8.8 Benefits of K-Means -- 11.8.9 Bad Marks of K-MEAN -- References -- Chapter 12 Reinforcement Learning -- 12.1 Model -- 12.2 Terms Utilized in Reinforcement Learning -- 12.3 Key Elements of Reinforcement Learning -- 12.4 Instances of Reinforcement Learning -- 12.5 Advantages of Reinforcement Learning -- 12.6 Challenges with Reinforcement Learning -- 12.7 Sorts of Reinforcement -- 12.7.1 Positive -- 12.7.2 Negative. 12.8 What are the Useful Utilizations of Reinforcement Learning?. |
| Record Nr. | UNINA-9911019499103321 |
|
Prakash Kolla Bhanu
|
||
| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
New Frontiers in Materials Science : Interdisciplinary Approaches to Innovation and Technologies
| New Frontiers in Materials Science : Interdisciplinary Approaches to Innovation and Technologies |
| Autore | Prakash Kolla Bhanu |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (250 pages) |
| Disciplina | 620.11 |
| Altri autori (Persone) |
RanganayakuluS. V
RaoK. S. Jagannatha |
| ISBN |
1-394-31493-0
1-394-31492-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Series Page -- Title Page -- Copyright Page -- Dedication Page -- Contents -- Preface -- Acknowledgments -- Chapter 1 Nanomaterial Synthesis and Its Applications -- 1.1 Introduction -- 1.1.1 Nanomaterials Based in Metals -- 1.1.2 Nanomaterials for Metal and Nonmetals on Semiconductor -- 1.1.3 Micro and Nanocomposite Materials -- 1.2 Nanomaterials and Preparation of Metal Matrix Composite -- 1.3 Bio-Medical Applications of Nanomaterials -- 1.3.1 Advantage of Nanoscale Materials for Biomedical Applications -- 1.3.2 Types of Bio-Nanoparticles Used for the Production of Drugs -- 1.3.3 Biomedical Applications of Nanoscale Materials -- 1.3.3.1 Bio-Molecular Imaging with Nanoparticle Composite -- 1.3.3.2 Nanotherapies in the Field of Biomedical Medicine -- 1.3.3.3 Optical Nanosensors -- 1.4 Conclusion -- Bibliography -- Chapter 2 Advances in Meta-Materials: Engineering Light and Sound Waves for Next-Generation Technologies -- Introduction -- Literature Review -- Research Methodology -- Research Findings -- Proposed Conceptual Framework -- Suggestion -- Conclusion -- References -- Chapter 3 Advanced Intersection of Material and Medicine Revolutionizing Healthcare Outcomes -- 3.1 Overview of Nanotechnology -- 3.1.1 Nanotechnology's Impact on Material Sciences -- 3.1.1.1 Nanomaterials Synthesis -- 3.1.1.2 Enhanced Mechanical Properties -- 3.1.1.3 Improved Electrical and Thermal Conductivity -- 3.1.1.4 Surface Modification and Functionalization -- 3.1.1.5 Advanced Coatings and Films -- 3.1.1.6 Nanostructured Catalysts -- 3.1.1.7 Smart and Responsive Materials -- 3.2 Nanomaterials for Imaging and Diagnosis -- 3.3 Biomaterials and Bioactive Devices for Medical Devices -- 3.3.1 Types of Biomaterials and Bioactive Devices -- 3.3.1.1 Biodegradable Polymers -- 3.3.1.2 Beneficial Windows and Tiles -- 3.3.1.3 Intelligent Polymers.
3.3.1.4 Synthetic Polymers -- 3.4 Bioprinting of Living Tissues and Organs -- 3.4.1 Cell Selection and Seeding -- 3.4.2 Bioprinting Process -- 3.4.3 Post-Printing Processing -- 3.4.4 Tissue Maturation and Integration -- 3.4.5 Applications of Bioprinting -- 3.4.5.1 Bioprinted Organs -- 3.4.5.2 Individualized Medicine -- 3.4.5.3 Biological Studies and Development -- 3.5 Smart Fabrics for Health Monitoring -- 3.5.1 Sensor Integration -- 3.5.2 Data Collection and Transmission -- 3.5.3 Comfort and Wearability -- 3.5.4 Biometric Authentication -- 3.5.5 Applications -- 3.5.6 Remote Patient Monitoring -- 3.6 Brain-Computer Interfaces for Communication and Control -- 3.6.1 Neural Recording and Decoding -- 3.6.2 Assistive Communication -- 3.6.3 Motor Restoration -- 3.6.4 Neurorehabilitation -- 3.6.5 Cognitive Enhancement -- 3.6.6 Cellular Alignment and Guidance -- 3.6.7 Enhanced Cell Proliferation and Differentiation -- 3.6.8 Sensing and Monitoring -- 3.7 Micro and Nanoscale Carriers for Drug Transport -- 3.7.1 Improved Drug Solubility and Stability -- 3.7.2 Targeted Drug Delivery -- 3.7.3 Controlled Drug Release -- 3.7.4 Enhanced Cellular Uptake and Intracellular Delivery -- 3.7.5 Multifunctional Carriers -- 3.7.6 Minimized Side Effects and Toxicity -- 3.8 Biocompatible Surgical Instruments and Implants -- 3.8.1 Enhanced Biocompatibility -- 3.8.2 Reduced Risk of Infection -- 3.8.3 Customization and Personalization -- 3.8.4 Biodegradability and Restorability -- 3.8.5 Promotion of Tissue Integration and Regeneration -- 3.8.6 Compatibility with Imaging and Diagnostic Technologies -- 3.8.7 Innovations in Minimally Invasive Surgery -- 3.9 Conclusion and Future Directions -- References -- Chapter 4 Nanoscopic Marvels: Exploring Carbon Nanoparticles in Biomedicine -- 4.1 Introduction -- 4.2 Applications. 4.2.1 Biological Imaging, Pathology-Related Detection, and Diagnostics -- 4.2.2 Diagnostics Photoacoustic -- 4.2.3 Hem Sorbents for Adsorption, Hemofiltration, and Hemodialysis -- 4.2.4 Photodynamic Therapy -- 4.2.5 Vaccine Production -- 4.2.6 Therapy-Platform of Delivery and Intervention -- 4.3 Conclusions -- 4.4 Future Scope -- References -- Chapter 5 Harnessing the Power of Materials for Efficient Energy Storage and Conversion -- 5.1 Introduction to Energy Storage Materials -- 5.1.1 Overview of Energy Storage Systems -- 5.1.2 Importance of Material Selection -- 5.1.3 Current Trends and Innovations -- 5.1.3.1 Transition Beyond Lithium-Ion -- 5.1.3.2 Material Optimization and Discovery -- 5.1.3.3 Sustainability and Circular Economy -- 5.1.3.4 Expanding Applications -- 5.2 Electrochemical Energy Storage -- 5.2.1 Battery Technologies -- 5.2.1.1 Established Battery Technologies -- 5.2.1.2 Emerging Battery Technologies -- 5.2.2 Supercapacitors -- 5.2.3 Fuel Cells -- 5.3 Materials for Batteries -- 5.3.1 Cathode Materials -- 5.3.2 Anode Materials -- 5.3.2.1 Graphite -- 5.3.2.2 Advanced Anode Materials -- 5.3.3 Electrolytes and Separators -- 5.3.3.1 Electrolytes -- 5.3.3.2 Types of Electrolytes -- 5.3.4 Separators -- 5.3.4.1 Types of Separators -- 5.4 Photovoltaic Materials -- 5.5 Hydrogen Storage Materials -- 5.5.1 Metal Hydrides -- 5.5.2 Chemical Hydrides -- 5.5.3 Carbon-Based Materials -- 5.6 Challenges and Future Directions -- 5.7 Conclusion -- References -- Chapter 6 Biogenic AgNPs: Leaf-Mediated Green Synthesis, Analytical Spectroscopic Characterization, and Applications -- 6.1 Introduction -- 6.2 Synthesis, Characterization, and Applications of Biogenic AgNPs -- 6.3 Conclusions -- References -- Chapter 7 Material for Energy Storage and Conversion -- 7.1 Introduction -- 7.2 2D Materials in Energy Application -- 7.2.1 Materials Used in Energy Storage. 7.2.1.1 Supercapacitors -- 7.2.1.2 Battery -- 7.2.2 Materials Used in Energy Conversion -- 7.2.2.1 Oxygen Reduction Reaction (ORR) -- 7.2.2.2 Oxygen Evolution Reaction (OER) -- 7.2.2.3 Hydrogen Evolution Reaction (HER) -- 7.2.2.4 Carbon Dioxide Reduction Reaction (CRR) -- 7.2.2.5 Water Splitting -- 7.3 Summary -- References -- Chapter 8 Rare Earth Elements in Photonic Materials -- 8.1 Introduction -- 8.2 History of Rare Earth Elements -- 8.3 Trivalent Lanthanides Electronic Configuration -- 8.4 Energy Level Splitting of 4f States in Lanthanides -- 8.5 The Energy Levels of Lanthanides and Dieke Diagram -- 8.6 Physical and Chemical Properties of RE Elements -- 8.6.1 Basic Properties of Lanthanides -- 8.6.2 Lanthanide Contraction -- 8.6.3 Chemical Properties -- 8.6.4 Magnetism -- 8.7 Optical Properties -- 8.7.1 Luminescence -- 8.7.2 Phosphorescence -- 8.8 Applications of Rare Earth Materials -- 8.8.1 Phosphors in Photonics -- 8.8.2 Rare Earth Doped Glasses -- 8.8.3 Ceramics -- 8.8.4 Magnetic Materials -- References -- Chapter 9 Emerging Materials for Future Energy Storage and Energy Conversion Application -- 9.1 Importance of Energy Storage and Conversion in Modern Society -- 9.1.1 Challenges Posed by Fossil Fuels and the Need for Clean Energy Alternatives -- 9.1.2 Global Energy Demands and Renewable Energy Sources -- 9.1.3 Role of Energy Storage in Balancing Supply and Demand -- 9.2 Role of Materials in Energy Storage and Conversion -- 9.2.1 The Importance of Material Innovation in Energy Systems -- 9.2.2 Conversion of Various Energy Forms to Electrical Energy -- 9.3 Energy Storage Materials -- 9.3.1 Trends in Materials Research: Graphene, Perovskites, and Hydrogen Storage Materials -- 9.3.2 Overview of Different Storage Technologies -- 9.4 Batteries -- 9.4.1 Types of Batteries: Alkaline, Lead-Acid, Lithium-Ion, and Nickel-Metal Hydride. 9.4.1.1 Alkaline Batteries -- 9.4.1.2 Lead-Acid Batteries -- 9.4.1.3 Lithium-Ion Batteries -- 9.4.1.4 Nickel-Metal Hydride Batteries -- 9.4.2 Advantages and Limitations of Each Type -- 9.5 Supercapacitors -- 9.5.1 Structure and Function -- 9.5.2 Advantages Over Traditional Batteries -- 9.6 Fuel Cells -- 9.6.1 Types and Applications -- 9.6.2 Hydrogen Storage and Conversion -- 9.7 Energy Conversion Materials -- 9.7.1 Photovoltaic Systems -- 9.7.2 Thermoelectric Materials -- 9.7.3 Catalysts for Energy Conversion -- 9.8 Characterization Methods for Energy Materials -- 9.9 Conclusion -- References -- Chapter 10 Innovations in the Synthesis of Nanomaterials: Cutting- Edge Techniques Along with the Diverse Implementations of These Nanomaterials in Nanotechnology Methods -- 10.1 Introduction -- 10.2 Bottom-Up Method -- 10.3 Chemical Method -- 10.3.1 Sol-Gel Method -- 10.3.1.1 Applications and Advantages of the Sol-Gel Method -- 10.3.2 Spinning Method -- 10.3.2.1 Applications of the Spinning Method in Nanoparticle Synthesis -- 10.3.3 Template Method -- 10.3.3.1 Applications of Template Methods -- 10.3.4 Laser Pyrolysis -- 10.3.4.1 Applications of Laser Pyrolysis -- 10.3.5 Chemical Vapor Deposition Method -- 10.3.6 Hydrothermal Method -- 10.3.6.1 Benefits of Hydrothermal Synthesis -- 10.3.6.2 Uses -- 10.3.7 Reverse Micelle Method -- 10.4 Green Synthesis or Biological Method -- 10.4.1 From Roots -- 10.4.2 Flowers -- 10.4.3 Leaves -- 10.4.4 Bacteria -- 10.5 Top-Down Method -- 10.6 Physical and Chemical Methods -- 10.6.1 Thermal Decomposition -- 10.6.2 Mechanical Milling -- 10.6.2.1 Type of Ball Mills -- 10.6.3 Laser Ablation Method -- 10.6.4 Sputtering Method -- 10.6.5 The Arc-Discharge Method -- 10.6.6 Nanolithography Method -- 10.7 Conclusion and Future Scope -- References. Chapter 11 Emerging Trends and Future Developments in Smart Materials and Their Applications: A Comprehensive Review. |
| Record Nr. | UNINA-9911020326803321 |
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Prakash Kolla Bhanu
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| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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