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Big data management in sensing : applications in AI and IoT / / editors : Renny Fernandez, Terrance Frederick Fernandez
| Big data management in sensing : applications in AI and IoT / / editors : Renny Fernandez, Terrance Frederick Fernandez |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Gistrup, Denmark : , : River Publishers, , [2021] |
| Descrizione fisica | 1 online resource (288 pages) |
| Disciplina | 005.7 |
| Collana | River Publishers series in biomedical engineering |
| Soggetto topico |
Big data - Industrial applications
Big data Machine learning - Industrial applications |
| ISBN |
1-000-79743-0
1-00-333735-X 1-003-33735-X 1-000-79427-X 1-5231-4445-9 87-7022-414-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910795113103321 |
| Gistrup, Denmark : , : River Publishers, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Big data management in sensing : applications in AI and IoT / / editors : Renny Fernandez, Terrance Frederick Fernandez
| Big data management in sensing : applications in AI and IoT / / editors : Renny Fernandez, Terrance Frederick Fernandez |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Gistrup, Denmark : , : River Publishers, , [2021] |
| Descrizione fisica | 1 online resource (288 pages) |
| Disciplina | 005.7 |
| Collana | River Publishers series in biomedical engineering |
| Soggetto topico |
Big data - Industrial applications
Big data Machine learning - Industrial applications |
| ISBN |
1-000-79743-0
1-00-333735-X 1-003-33735-X 1-000-79427-X 1-5231-4445-9 87-7022-414-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910826413803321 |
| Gistrup, Denmark : , : River Publishers, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Intelligent computing theories and application . Part II : 18th International Conference, ICIC 2022, Xi'an, China, August 7-11, 2022, proceedings / / editors, De-Shuang Huang [and five others]
| Intelligent computing theories and application . Part II : 18th International Conference, ICIC 2022, Xi'an, China, August 7-11, 2022, proceedings / / editors, De-Shuang Huang [and five others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (843 pages) |
| Disciplina | 006.3 |
| Collana | Lecture notes in computer science |
| Soggetto topico |
Machine learning - Industrial applications
Computational intelligence Biomedical engineering - Data processing |
| ISBN | 3-031-13829-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents - Part II -- Biomedical Data Modeling and Mining -- A Comparison Study of Predicting lncRNA-Protein Interactions via Representative Network Embedding Methods -- 1 Introduction -- 2 Materials and Methods -- 2.1 Datasets -- 2.2 Survey of Network Embedding Methods -- 2.3 LncRNA-Protein Interactions Prediction -- 3 Results and Discussion -- 4 Conclusion -- References -- GATSDCD: Prediction of circRNA-Disease Associations Based on Singular Value Decomposition and Graph Attention Network -- 1 Introduction -- 2 Materials and Methods -- 2.1 Datasets -- 2.2 Feature Representation -- 2.3 Singular Value Decomposition for Feature Noise Reduction -- 2.4 Graph Attention Network Embedding Features -- 2.5 Neural Network for Prediction -- 2.6 Evaluation Criteria -- 3 Experiments and Results -- 3.1 GATSDCD Performance -- 3.2 Impact of Parameters -- 3.3 Ablation Study -- 3.4 Performance Comparison with Other Methods -- 3.5 Case Study -- 4 Conclusion -- References -- Anti-breast Cancer Drug Design and ADMET Prediction of ERa Antagonists Based on QSAR Study -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Dataset and Data Processing -- 3.2 Hierarchical Clustering -- 3.3 Model Building -- 3.4 Multiple Stepwise Regression -- 3.5 Fisher Discrimination -- 4 Experimental Results -- 4.1 MLP Results -- 4.2 Results of Stepwise Regression -- 4.3 Optimization of Candidate Compounds Based on Fisher Discriminant -- 5 Conclusion -- References -- Real-Time Optimal Scheduling of Large-Scale Electric Vehicles Based on Non-cooperative Game -- 1 Introduction -- 2 Mathematical Models of New Energy Microgrid and Electric Vehicle Charging and Discharging Behavior -- 2.1 The Price Function of Selling Electricity of New Energy Microgrid -- 2.2 Modeling of Electric Vehicle Charging and Discharging Behavior -- 3 Optimization Objective.
4 Decentralized Electric Vehicle Control Method Based on Non-cooperative Game -- 4.1 Non-cooperative Game Model -- 4.2 Broadcast Programming for Strategy Solving -- 5 Experimental Results -- 5.1 Evaluation Index -- 5.2 Experimental Results -- 6 Conclusion -- References -- TBC-Unet: U-net with Three-Branch Convolution for Gliomas MRI Segmentation -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 TBC Module -- 3.2 Loss Function -- 4 Experiments and Results -- 4.1 Dataset -- 4.2 Metrics for Evaluation -- 4.3 Experiment Detail -- 4.4 Ablation Study -- 4.5 Results -- 5 Conclusion -- References -- Drug-Target Interaction Prediction Based on Graph Neural Network and Recommendation System -- 1 Introduction -- 2 Materials and Methods -- 2.1 Datasets -- 2.2 Attribute Representation -- 2.3 Graph Convolutional Network -- 2.4 Neural Factorization Machine -- 2.5 Architecture -- 3 Result and Discussion -- 3.1 Evaluation Criteria -- 3.2 Performance Evaluation of GCNNFM Using 5-Fold Cross-Validation -- 3.3 Compared GCNNFM with Different Machine Learning Algorithms -- 3.4 Compared GCNNFM with Existing State-of-the-Art Prediction Methods -- 4 Conclusions -- References -- NSAP: A Neighborhood Subgraph Aggregation Method for Drug-Disease Association Prediction -- 1 Introduction -- 2 Dataset -- 3 Method -- 3.1 Neighborhood Graph Extraction -- 3.2 Metagraph and Contextual Graph Extraction -- 3.3 Metagraph and Contextual Graph Aggregation -- 3.4 Link Prediction -- 4 Experiment -- 4.1 Comparison Methods -- 4.2 Comparison of Results -- 4.3 Parameter Sensitivity Analysis -- 5 Conclusion -- References -- Comprehensive Evaluation of BERT Model for DNA-Language for Prediction of DNA Sequence Binding Specificities in Fine-Tuning Phase -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Model Architectures -- 2.3 Training and Fine-Tuning. 3 Results and Analysis -- 3.1 Relatively Small Learning Rate Leads to Better Performance -- 3.2 DNABERT with Different k Value of k-mer Embedding Achieves Similar Performances -- 3.3 DNABERT Achieves Outstanding Performance Overall -- 4 Conclusion -- References -- Identification and Evaluation of Key Biomarkers of Acute Myocardial Infarction by Machine Learning -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data Collection -- 2.2 DEG Screening -- 2.3 GO, KEGG, DO and GSEA Enrichment Analysis -- 2.4 Screening and Identification of Gene Prediction Model for Early Diagnosis -- 2.5 The Immune Cell Infiltration Analysis -- 3 Results -- 3.1 Preprocessing and Analysis of AMI-Related Differentially Expressed Genes -- 3.2 GO, KEGG, DO and GSEA Enrichment Analysis of Differential Genes -- 3.3 Screening and Identification of Gene Prediction Model for Early Diagnosis -- 3.4 Immune Infiltration Analyses -- 4 Discussion -- References -- Glioblastoma Subtyping by Immuogenomics -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data Collection -- 2.2 Cluster Analysis -- 2.3 Evaluation of Tumor Components -- 2.4 GO, KEGG Pathway and GSEA Analysis -- 2.5 Statistical Methods -- 3 Results -- 3.1 Clinical Information of Patients in the Cancer Genome Atlas Dataset -- 3.2 Immune Typing and Immune Scoring -- 3.3 Correlation Between Immune Typing and Human Leukocyte Antigen, Smoking and Some Immune Genes -- 3.4 Distribution and Gene Enrichment Analysis of Tumor-Infiltrating Immune Cells in Immunophenotyping -- 4 Discussion -- References -- Functional Analysis of Molecular Subtypes with Deep Similarity Learning Model Based on Multi-omics Data -- 1 Introduction -- 2 Methodology -- 2.1 Dataset Collection and Processing -- 2.2 The Proposed Workflow -- 2.3 Performance Evaluation Metrics -- 3 Experimental Results -- 3.1 Performance Validation. 3.2 Clinical Characteristics Analysis of Ovarian Subtypes -- 3.3 Biological Function Analysis of Breast Molecular Subtypes -- 4 Conclusion and Discussion -- References -- Predicting Drug-Disease Associations by Self-topological Generalized Matrix Factorization with Neighborhood Constraints -- 1 Introduction -- 2 Related Work -- 3 Materials and Methods -- 3.1 Materials and Preprocessing -- 3.2 Weighted Similarity Data Fusion -- 3.3 NSGMF for DDAs Prediction -- 4 Experiments -- 4.1 Ablation Studies -- 4.2 Comparison with State-of-the-Art DDAs Prediction Methods -- 4.3 Case Studies -- 5 Conclusion -- References -- Intelligent Computing in Computational Biology -- iEnhancer-BERT: A Novel Transfer Learning Architecture Based on DNA-Language Model for Identifying Enhancers and Their Strength -- 1 Introduction -- 2 Materials and Methods -- 2.1 Benchmark Datasets -- 2.2 Methods -- 2.3 Two-Stage Identification Framework -- 2.4 Baseline Method -- 2.5 Performance Evaluation Metrics -- 3 Experimental Results -- 3.1 Different k-mer Pre-training Models -- 3.2 Effect of Pre-training on Model Performance -- 3.3 Effect of Different Fine-Tuning Methods -- 3.4 Performance Comparison with Existing Methods -- 4 Discussion and Conclusion -- References -- GCNMFCDA: A Method Based on Graph Convolutional Network and Matrix Factorization for Predicting circRNA-Disease Associations -- 1 Introduction -- 2 Materials and Methods -- 2.1 Known CircRNA-Disease Association -- 2.2 Disease Semantic Similarity Network -- 2.3 CircRNA Functional Similarity Network -- 2.4 Gaussian Interaction Profile Kernel Similarity for CircRNA and Disease -- 2.5 Combine Multiple Similarity (CircRNA and Disease) -- 2.6 Feature Extraction Based on Graph Convolution Networks -- 2.7 CircRNA-disease Association Prediction and Loss Function -- 3 Results and Discussion -- 3.1 Experimental Setup. 3.2 Performance Analysis -- 3.3 Compared with Other Methods -- 3.4 Parameters Setting -- 3.5 Case Studies -- 4 Conclusions -- References -- Prediction of MiRNA-Disease Association Based on Higher-Order Graph Convolutional Networks -- 1 Introduction -- 2 Material and Methods -- 2.1 Human MiRNA-disease Associations Database -- 2.2 MiRNA Functional Similarity -- 2.3 Disease Semantic Similarity -- 2.4 Gaussian Interaction Profile Kernel Similarity for MiRNAs and Diseases -- 2.5 Integrated Similarity for MiRNAs and Diseases -- 2.6 MIXHOPMDA -- 3 Results -- 3.1 Experiment Settings -- 3.2 Performance Evaluation -- 3.3 Effect of Number of Projection Dimensions -- 3.4 Effect of Number of Layers -- 3.5 Effect of Number of the Value of P -- 3.6 Comparison with Other Latest Methods -- 4 Case Studies -- 5 Conclusion -- References -- SCDF: A Novel Single-Cell Classification Method Based on Dimension-Reduced Data Fusion -- 1 Introduction -- 2 Materials and Methods -- 2.1 Datasets -- 2.2 Normalization -- 2.3 Determining the Optimal Number of Low-Dimensional Components -- 2.4 Concatenation -- 2.5 Classification Using Fused Data -- 3 Result -- 3.1 The Optimal Number of Low-Dimensional Components -- 3.2 The Accuracy of Classification with SCDF -- 4 Conclusion -- References -- Research on the Potential Mechanism of Rhizoma Drynariae in the Treatment of Periodontitis Based on Network Pharmacology -- 1 Introduction -- 2 Material and Method -- 2.1 Screening of the Active Ingredients of Rhizoma Drynariae and Corresponding Targets -- 2.2 Periodontitis Related Targets Retrieval -- 2.3 Common Targets of Rhizoma Drynariae and Periodontitis -- 2.4 Network of Rhizoma Drynariae Active Ingredient and Periodontal Disease Target -- 2.5 Protein-Protein Interaction (PPI) Network -- 2.6 GO and KEGG Pathway Analysis -- 3 Results. 3.1 Active Compounds and Corresponding Targets in Rhizoma Drynariae. |
| Record Nr. | UNISA-996485668503316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Interpretability for Industry 4.0 : statistical and machine learning approaches / / Antonio Lepore, Biagio Palumbo, Jean-Michel Poggi, editors
| Interpretability for Industry 4.0 : statistical and machine learning approaches / / Antonio Lepore, Biagio Palumbo, Jean-Michel Poggi, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (130 pages) : illustrations |
| Disciplina | 658.4038028563 |
| Soggetto topico |
Industry 4.0
Machine learning - Industrial applications Industry 4.0 - Statistical methods Aprenentatge automàtic Aplicacions industrials |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-12402-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- 1 Different Views of Interpretability -- 1.1 Introduction -- 1.2 Interpretability: In Praise of Transparent Models -- 1.2.1 What Happened? -- 1.2.2 What Will Happen? -- 1.2.3 What Shall be Done to Make It Happen? -- 1.2.4 Patterns and Models -- 1.3 Generalizability and Interpretability with Industry 4.0 Implications -- 1.3.1 Introduction to Interpretable AI -- 1.3.2 A Wide Angle Perspective of Generalizability -- 1.3.3 Statistical Generalizability -- 1.4 Connections Between Interpretability in Machine Learning and Sensitivity Analysis of Model Outputs -- 1.4.1 Machine Learning and Uncertainty Quantification -- 1.4.2 Basics on Sensitivity Analysis and Its Main Settings -- 1.4.3 A Brief Taxonomy of Interpretability in Machine Learning -- 1.4.4 A Review of Sensitivity Analysis Powered Interpretability Methods -- References -- 2 Model Interpretability, Explainability and Trust for Manufacturing 4.0 -- 2.1 Manufacturing 4.0: Driving Trends for Data Mining -- 2.1.1 Process Monitoring in Manufacturing 4.0 -- 2.1.2 Design of Experiments in Manufacturing 4.0 -- 2.1.3 Increasing Trust in AI Models for Manufacturing 4.0: Interpretability, Explainability and Robustness -- 2.2 Additive Manufacturing as a Paradigmatic Example of Manufacturing 4.0 -- 2.3 Increase Trust in Additive Manufacturing: Robust Functional Analysis of Variance in Video-Image Analysis -- 2.3.1 The RoFANOVA Approach -- 2.3.2 An Additive Manufacturing Application -- References -- 3 Interpretability via Random Forests -- 3.1 Introduction -- 3.2 Interpretable Rule-Based Models -- 3.2.1 Literature Review -- 3.2.1.1 Definitions and Origins of Rule Models -- 3.2.1.2 Decision Trees -- 3.2.1.3 Tree-Based Rule Learning -- 3.2.1.4 Modern Rule Learning -- 3.2.2 SIRUS: Stable and Interpretable RUle Set -- 3.2.2.1 SIRUS Algorithm -- 3.2.2.2 Theoretical Analysis.
3.2.2.3 Experiments -- 3.2.3 Discussion -- 3.3 Post-Processing of Black-Box Algorithms via Variable Importance -- 3.3.1 Literature Review -- 3.3.1.1 Model-Specific Variable Importance -- 3.3.1.2 Global Sensitivity Analysis -- 3.3.1.3 Local Interpretability -- 3.3.2 Sobol-MDA -- 3.3.2.1 Sobol-MDA Algorithm -- 3.3.2.2 Sobol-MDA Properties -- 3.3.2.3 Experiments -- 3.3.3 SHAFF: SHApley eFfects Estimates via Random Forests -- 3.3.3.1 SHAFF Algorithm -- 3.3.3.2 SHAFF Consistency -- 3.3.3.3 Experiments -- 3.3.4 Discussion -- References -- 4 Interpretability in Generalized Additive Models -- 4.1 GAMs: A Basic Framework for Flexible Interpretable Regression -- 4.1.1 Flexibility Can Be Important -- 4.1.2 Making the Model Computable -- 4.1.3 Estimation and Inference -- 4.1.4 Checking, Effective Degrees of Freedom and Model Selection -- 4.1.5 GAM Computation with mgcv in R -- 4.1.6 Smooths of Several Predictors -- 4.1.7 Further Interpretable Structure -- 4.2 From GAM to GAMLSS: Interpretability for Model Building -- 4.2.1 GAMLSS Modelling of UK Aggregate Electricity Demand -- 4.2.1.1 Data Overview and Pre-processing -- 4.2.1.2 Interactive GAMLSS Model Building -- 4.3 From GAMs to Aggregations of Experts, Are We Still Interpretable? -- 4.3.1 Online Forecasting with Online Aggregation of Experts -- 4.3.2 Visualizing the Black Boxes -- References. |
| Record Nr. | UNISA-996495169103316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Konvolutionäre neuronale Netze in der industriellen Bildverarbeitung und Robotik / / Norbert Mitschke
| Konvolutionäre neuronale Netze in der industriellen Bildverarbeitung und Robotik / / Norbert Mitschke |
| Autore | Mitschke Norbert |
| Pubbl/distr/stampa | Karlsruhe : , : KIT Scientific Publishing, , 2022 |
| Descrizione fisica | 1 online resource (212 p.) |
| Disciplina | 621.399 |
| Collana | Forschungsberichte aus der Industriellen Informationstechnik |
| Soggetto topico |
Image processing - Industrial applications
Robotics - Industrial applications Machine learning - Industrial applications |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | ger |
| Record Nr. | UNINA-9910805676903321 |
Mitschke Norbert
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| Karlsruhe : , : KIT Scientific Publishing, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine learning and artificial intelligence with industrial applications : from big data to small data / / Diego Carou, Antonio Sartal, and J. Paulo Davim, editors
| Machine learning and artificial intelligence with industrial applications : from big data to small data / / Diego Carou, Antonio Sartal, and J. Paulo Davim, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer International Publishing, , [2022] |
| Descrizione fisica | 1 online resource (216 pages) |
| Disciplina | 006.3 |
| Collana | Management and Industrial Engineering |
| Soggetto topico |
Artificial intelligence - Industrial applications
Machine learning - Industrial applications |
| ISBN | 3-030-91006-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910552744003321 |
| Cham, Switzerland : , : Springer International Publishing, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine learning and data mining in materials science / / edited by Norbert Huber, Surya R. Kalidindi, Benjamin Klusemann and Christian Johannes Cyro
| Machine learning and data mining in materials science / / edited by Norbert Huber, Surya R. Kalidindi, Benjamin Klusemann and Christian Johannes Cyro |
| Pubbl/distr/stampa | Lausanne : , : Frontiers Media SA, , 2020 |
| Descrizione fisica | 1 online resource : illustrations |
| Soggetto topico |
Machine learning - Industrial applications
Data mining Materials science - Data processing |
| ISBN |
9782889636518
2889636518 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557221003321 |
| Lausanne : , : Frontiers Media SA, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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 |
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Prakash Kolla Bhanu
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| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine Learning for Sustainable Energy Solutions
| Machine Learning for Sustainable Energy Solutions |
| Autore | Said Zafar |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2026 |
| Descrizione fisica | 1 online resource (307 pages) |
| Soggetto topico |
Alternative fuels - Data processing
Biomass energy - Data processing Motor fuels - Data processing Energy storage - Materials - Data processing Thermofluid systems - Materials - Data processing Diesel motor exhaust gas - Environmental aspects - Data processing Machine learning - Industrial applications |
| ISBN |
1-394-26743-6
1-394-26742-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- List of Contributors -- Preface -- Chapter 1 Green Energy-Led Sustainable Development: Barriers and Opportunities -- 1.1 Introduction -- 1.2 The Current Landscape of Green Energy -- 1.2.1 Green Energy Types and Technologies -- 1.2.2 Global Green Energy Usage Statistics -- 1.3 Barriers to Green Energy Implementation -- 1.3.1 Economic and Financial Challenges -- 1.3.1.1 High Initial Costs -- 1.3.1.2 Investment Risks -- 1.3.2 Regulatory and Policy Frameworks -- 1.3.3 Social Acceptance and Cultural Factors -- 1.3.4 Technological Barriers -- 1.4 ML and AI in Green Energy -- 1.4.1 Technological Assessment and Optimization -- 1.4.2 Predictive Net-Zero Initiative -- 1.4.3 Enhancing Energy Storage Systems -- 1.4.4 Energy Demand and Supply Forecasting -- 1.4.5 Setting Ambitious Goal -- 1.4.6 Activate Support and Financial Investment -- 1.5 Challenges in the Integration of ML and AI in Renewable Energy -- 1.6 Directive in ML and AI Improvement Toward Its.Application -- 1.6.1 Workforce Capacity Increase -- 1.6.2 Large-Scale Project Implementation -- 1.6.3 Public Awareness -- 1.6.4 Continuous Progress Monitoring and Strategies Adjustment -- 1.7 Conclusion -- References -- Chapter 2 Machine Learning-Driven Valorization of Organic Waste for Sustainable Bio-Hydrogen Production -- 2.1 Introduction -- 2.1.1 Objectives -- 2.2 Literature Review -- 2.3 Proposed Method -- 2.4 Results and Discussion -- 2.4.1 Bio-Hydrogen Production Efficiency Analysis -- 2.4.2 Performance Analysis -- 2.4.3 Adaptability Analysis -- 2.5 Conclusion -- Author Contributions -- Acknowledgment -- Data Availability Statement -- Funding Statement -- Conflict of Interest -- References -- Chapter 3 Application of Neural Networks for Model Prediction of Combustion and Emissions in Diesel Engines -- 3.1 Introduction.
3.2 Artificial Neural Networks -- 3.2.1 Types of Artificial Neural Networks -- 3.3 AI and ANN in Internal Combustion Engines -- 3.4 ANN in Diesel Engines -- 3.4.1 ANN for Different Fuel Properties -- 3.4.2 ANN for Diesel Engine Performance -- 3.4.3 ANN for Diesel Engines Using Biodiesel Blends -- 3.4.4 ANN for Gaseous Fuels -- 3.4.4.1 MISO Model Studies -- 3.4.4.2 MIMO Model Studies -- 3.4.4.3 Comparative Studies -- 3.4.5 ANN for HCCI Engines -- 3.5 Conclusions -- References -- Chapter 4 Enhanced Energy Storage with Hybrid Nanoparticles and Machine Learning for Energy Sustainability -- 4.1 Introduction -- 4.2 Materials and Methods -- 4.3 Result and Discussion -- 4.4 Conclusion -- Author Contributions -- Acknowledgment -- Data Availability Statement -- Funding -- Conflict of Interest -- References -- Chapter 5 Model Prediction of Biomass Gasification Using Support Vector Machines -- 5.1 Introduction and Literature Survey -- 5.2 Materials and Methods -- 5.3 Results and Discussion -- 5.4 Conclusion -- References -- Chapter 6 Role of Machine Learning Techniques in Modeling and Optimization of Biomass Gasification Parameters in a Downdraft Gasifier -- 6.1 Introduction -- 6.2 Biomass Gasification -- 6.2.1 Gasification Process -- 6.2.2 Gasification Parameters -- 6.2.2.1 Biomass Characterization -- 6.2.2.2 Equivalence Ratio -- 6.2.2.3 Gasification Temperature -- 6.2.2.4 Biomass Consumption Rate -- 6.2.2.5 Cold Gas Efficiency (CGE) -- 6.2.2.6 Importance of Various Gasifying Agents in the Gasification Process -- 6.2.2.7 Effect of the Gasification Parameters on the Producer Gas -- 6.3 Machine Learning Techniques in Biomass Gasification -- 6.3.1 Gaussian Process Regression -- 6.3.2 Support Vector Machines -- 6.3.3 Artificial Neural Network -- 6.3.4 Decision Trees -- 6.4 Model Performance Metrics -- 6.5 Application of the ML Model in Biomass Gasification. 6.6 Challenges and Prospects -- 6.7 Conclusion -- References -- Chapter 7 Response Surface Methodology-Based Multiattribute Optimization of a Hydrogen-Powered Dual-Fuel Engines -- 7.1 Introduction -- 7.2 Materials and Methods -- 7.2.1 Test Engine Setup and Fuel -- 7.2.2 Analysis of Variance -- 7.2.3 Response Surface Methodology -- 7.3 Results and Discussion -- 7.3.1 Correlation Analysis -- 7.3.2 Analysis of Variance -- 7.3.3 Surface Diagrams and Predictions -- 7.3.4 Parametric Optimization -- 7.4 Conclusion -- References -- Chapter 8 Addition of Nanoparticles to Biodiesel-Diesel Blends to Improve Engine Efficiency and Reduce Tailpipe Emission -- 8.1 Introduction -- 8.2 Background and Performance of Biodiesel Blends in Engine Efficiency -- 8.2.1 Properties of the Biodiesel -- 8.2.2 Performance -- 8.2.3 Performance of Biodiesel Blends in Emission Characteristics -- 8.3 Mechanisms of Nanoparticles in Combustion Improvement -- 8.4 Biodiesel-Diesel Blends Nanoparticle Method -- 8.4.1 Limitations -- 8.4.2 Future Work -- 8.5 Conclusion -- Author Contributions -- Statement of Interest -- Acknowledgment -- References -- Chapter 9 Hybrid Nanoparticles to Improve Solar-Based Energy Storage -- 9.1 Introduction -- 9.2 Thermal Energy Storage Systems -- 9.2.1 Sensible Heat Storage -- 9.2.2 Latent Heat Storage (LHS) -- 9.2.2.1 Phase Change Material (PCM) -- 9.2.3 Thermochemical Energy Storage -- 9.3 Solar Energy Storage Systems -- 9.3.1 TES for Solar Energy Storage Systems -- 9.3.2 Latent Heat TES in Solar Energy Storage Systems -- 9.4 Role of Nanotechnology in Solar Energy Storage -- 9.4.1 Types of Nanoparticles -- 9.4.2 Nanoparticles in Thermal Energy Storage -- 9.4.2.1 Inorganic-Based Nanomaterials -- 9.4.2.2 Carbon-Based Nanomaterials -- 9.4.2.3 Hybrid Nanomaterials -- 9.5 Applications of Nanoparticles in Solar Energy Storage -- 9.5.1 Solar Collectors. 9.5.2 Solar Thermal Energy Conversion -- 9.5.3 Solar Photovoltaic System -- 9.5.4 Solar Heater -- 9.5.5 Solar Desalination -- 9.5.6 Other Applications -- 9.6 Conclusions and Future Recommendations -- References -- Chapter 10 Application of Artificial Intelligence to Model-Predict the Thermo-physical Property of Hybrid Nanofluids -- 10.1 Introduction -- 10.2 Materials and Methods -- 10.2.1 Synthesis -- 10.2.2 Machine Learning -- 10.2.2.1 Linear Regression -- 10.2.2.2 Tweedie Regression -- 10.2.2.3 Huber Regression -- 10.2.2.4 Extreme Gradient Boosting -- 10.3 Results and Discussion -- 10.3.1 Data Analysis and Correlation -- 10.3.2 Linear Regression Model -- 10.3.3 Huber Regression Model -- 10.3.4 Tweedie Regression Model -- 10.3.5 XGBoost Model -- 10.3.6 Model Comparison -- 10.4 Conclusion -- References -- Chapter 11 Optimization of Nanofluids for Heat Exchangers: Dealing with Sedimentation and Pump Losses -- 11.1 Introduction -- 11.2 Sedimentation -- 11.3 Pump Losses -- 11.4 Thermo-Economic Aspect of the Nanofluids -- 11.5 Conclusion -- References -- Chapter 12 Clean Combustion with Biogas and Nano-Biodiesel in CI Engines -- 12.1 Introduction -- 12.2 Materials and Methods -- 12.2.1 Engine Specifications -- 12.2.2 Experimental Design -- 12.2.3 Fuel Properties -- 12.3 Modeling and Optimization -- 12.3.1 RSM Modeling -- 12.3.2 ANN Modeling -- 12.3.3 Optimization of RSM and ANN model -- 12.4 Results and Discussion -- 12.4.1 RSM Model Analysis -- 12.4.2 ANN Model Analysis -- 12.4.3 Optimization of RSM and ANN Model -- 12.5 Conclusions -- References -- Chapter 13 A Differentiation of Energy Storage Methods -- 13.1 Introduction -- 13.1.1 Conventional Energy Storage -- 13.1.2 Mechanical Energy Storage -- 13.1.3 Electrical Energy Storage -- 13.1.4 Electrochemical Energy Storage -- 13.1.5 Thermal Energy Storage. 13.1.6 Characteristics of Thermal Energy Storage -- 13.1.7 Sensible Heat Storage -- 13.1.8 Aquifer Thermal Energy Storage -- 13.1.9 Hot Water Energy Storage -- 13.1.10 Cavern Energy Storage -- 13.1.11 Gravel Energy Storage -- 13.1.12 Molten Salt Energy Storage -- 13.1.13 Borehole Energy Storage -- 13.1.14 Packed-Bed Energy Storage -- 13.1.15 Latent Heat Storage -- 13.1.15.1 Latent Heat Energy Storage by Phase Change Material -- 13.1.15.2 Encapsulation of PCM -- 13.1.15.3 Latent Heat Energy Storage by Salt Hydrates -- 13.1.16 Thermochemical Energy Storage -- 13.2 Artificial Intelligence (AI) -- 13.2.1 AI in Energy Sector -- 13.2.1.1 Artificial Neural Network (ANN) -- 13.2.1.2 Fuzzy Logic (FL) -- 13.2.1.3 Adaptive Neuro Fuzzy Inference System (ANFIS) -- 13.2.1.4 Particle Swarm Optimization (PSO) -- 13.2.1.5 Support Vector Machine (SVM) -- 13.2.1.6 Implementation of AI in Energy Storage -- 13.3 Conclusion -- References -- Chapter 14 Application of IoT and Machine Learning to Improve Biogas Production Through Anaerobic Digestion -- 14.1 Introduction -- 14.2 Biogas Production -- 14.3 Techniques for Biogas Production Enhancement -- 14.4 Literature Review -- 14.5 Implementation of Mathematical Techniques for.Biogas Production Enhancement -- 14.6 Conclusion -- References -- Index -- EULA. |
| Record Nr. | UNINA-9911048920103321 |
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Said Zafar
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| Newark : , : John Wiley & Sons, Incorporated, , 2026 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine learning tools for chemical engineering : methodologies and applications / / Francisco Javier López-Flores, Rogelio Ochoa-Barragán, Alma Yunuen Raya-Tapia, César Ramírez-Márquez, José Maria Ponce-Ortega
| Machine learning tools for chemical engineering : methodologies and applications / / Francisco Javier López-Flores, Rogelio Ochoa-Barragán, Alma Yunuen Raya-Tapia, César Ramírez-Márquez, José Maria Ponce-Ortega |
| Autore | López-Flores Francisco Javier |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Elsevier Science [Imprint] |
| Descrizione fisica | 1 online resource (630 pages) |
| Disciplina | 660.0285631 |
| Soggetto topico |
Chemical engineering - Data processing
Machine learning - Industrial applications Génie chimique - Informatique Apprentissage automatique - Applications industrielles |
| ISBN | 0-443-29059-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9911044026503321 |
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López-Flores Francisco Javier
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| Elsevier Science [Imprint] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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