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Data Analytics and Computational Intelligence: Novel Models, Algorithms and Applications / / edited by Gilberto Rivera, Laura Cruz-Reyes, Bernabé Dorronsoro, Alejandro Rosete
| Data Analytics and Computational Intelligence: Novel Models, Algorithms and Applications / / edited by Gilberto Rivera, Laura Cruz-Reyes, Bernabé Dorronsoro, Alejandro Rosete |
| Autore | Rivera Gilberto |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (597 pages) |
| Disciplina | 006.312 |
| Altri autori (Persone) |
Cruz-ReyesLaura
DorronsoroBernabé RoseteAlejandro |
| Collana | Studies in Big Data |
| Soggetto topico |
Engineering - Data processing
Computational intelligence Big data Data Engineering Computational Intelligence Big Data |
| ISBN | 3-031-38325-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Cluster Analysis using k-means in school dropout -- Topic modeling based on OWA aggregation to improve the semantic focusing on relevant information extraction problems -- An Affiliated Approach to Data Validation: The US 2020 Governor’s County Election -- Acquisition, Processing and Visualization of Meteorological Data in Real-Time using Apache Flink -- Topological Data Analysis for the evolution of student grades before, during and after the COVID-19 pandemic -- Redescending M-Estimators analysis on the Intuitionistic Fuzzy clustering algorithm for skin lesion delimitation -- Big Data Platform as a Service for Anomaly Detection -- An overview of model-driven and data-driven forecasting methods for smart transportation -- Data augmentation techniques for facial image generation: a brief literature review -- A review on machine learning-aided multi-omics data integration techniques for healthcare -- Learning of Conversational Systems based on Linguistic Data Summarization applications in BIM environments -- Fuzzified case-based reasoning blockchain framework for predictive maintenance in Industry 4.0 -- Machine learning for identifying atomic species from optical emission spectra generated by an atmospheric pressure non-thermal plasma -- Agent-based simulation: various scenarios -- Multi-Hop Ridesharing ∈ NPC -- A content-based group recommender system using feature weighting and virtual users aggregation -- Performance Evaluation of AquaFeL-PSO Informative Path Planner under Different Contamination Profiles -- Adapting Swarm Intelligence to a Fixed Wing Unmanned Combat Aerial Vehicle Platform -- Cellular Processing Algorithm for Time Dependent-Traveling Salesman Problem -- Portfolio Optimization using Reinforcement Learning and Hierarchical Risk Parity Approach -- Reducing recursion costs in last-mile delivery routes with failed deliveries -- Intelligent Decision-Making Dashboard for CNC Milling Machines in Industrial Equipment: A Comparative Analysis of MOORA and TOPSIS Methods. |
| Record Nr. | UNINA-9910744505203321 |
Rivera Gilberto
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Innovations in Machine and Deep Learning : Case Studies and Applications / / edited by Gilberto Rivera, Alejandro Rosete, Bernabé Dorronsoro, Nelson Rangel-Valdez
| Innovations in Machine and Deep Learning : Case Studies and Applications / / edited by Gilberto Rivera, Alejandro Rosete, Bernabé Dorronsoro, Nelson Rangel-Valdez |
| Autore | Rivera Gilberto |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (506 pages) |
| Disciplina | 620.00285 |
| Altri autori (Persone) |
RoseteAlejandro
DorronsoroBernabé Rangel-ValdezNelson |
| Collana | Studies in Big Data |
| Soggetto topico |
Engineering - Data processing
Computational intelligence Big data Data Engineering Computational Intelligence Big Data |
| ISBN | 3-031-40688-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Analytics-Oriented Applications -- Recursive Multi-step Time-Series Forecasting for Residual-Feedback Artificial Neural Networks: A Survey -- 1 Introduction -- 2 Residual-Feedback ANNs: A Systematic Review -- 2.1 Systematic Review Planning and Execution -- 2.2 Overview of the Systematic Review Findings -- 3 The Existing Recursive Multi-step Forecast Strategy Solution -- 4 Limitation -- 5 Conclusions and Future Works -- References -- Feature Selection: Traditional and Wrapping Techniques with Tabu Search -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Data Description -- 3.2 Entropy-Based Feature Selection -- 3.3 Feature Selection Using Principal Component Analysis -- 3.4 Correlation-Based Feature Selection -- 4 Tabu Search -- 4.1 Initial Solution -- 4.2 Neighborhood -- 4.3 Objective Function -- 4.4 Memory Structures -- 5 Results -- 6 Discussion -- 7 Conclusions and Future Work -- References -- Pattern Classification with Holographic Neural Networks: A New Tool for Feature Selection -- 1 Introduction -- 2 Holographic Neural Networks -- 2.1 Basic Theory -- 2.2 Learning and Prediction Methods -- 2.3 red Explainability and Optimization of Holographic Models -- 3 Feature Selection with Holographic Neural Neworks -- 3.1 Previous Works -- 3.2 Pythagorean Membership Grades -- 4 Pattern Classification -- 4.1 Iris Dataset -- 4.2 red NIPS Feature Selection Challenge -- 5 red Conclusions and Future Works -- References -- Reusability Analysis of K-Nearest Neighbors Variants for Classification Models -- 1 Introduction -- 2 The K-Nearest Neighbors Algorithm -- 3 The Parameter K -- 4 Closeness Metrics -- 5 Analysis of KNN Variants -- 5.1 Heuristics for Class Assignment -- 5.2 Reduction of Dataset Records -- 5.3 Estimation of Dataset Variables -- 5.4 Discussion -- 6 Conclusions -- References.
Speech Emotion Recognition Using Deep CNNs Trained on Log-Frequency Spectrograms -- 1 Introduction -- 2 Literature Survey -- 2.1 Motivation -- 2.2 Contributions -- 3 Proposed Methodology -- 3.1 Data Augmentation -- 3.2 Extraction of Log-Frequency Spectrograms -- 3.3 Motivation Behind Using Spectrograms -- 3.4 Log-Frequency Spectrogram Extraction -- 3.5 Understanding What a Spectrogram Conveys -- 4 The Deep Convolutional Neural Network -- 4.1 Architecture -- 4.2 Training -- 5 Observations -- 5.1 Dataset Used -- 5.2 Performance Metrics Used -- 5.3 Results Obtained -- 5.4 Comparison Study -- 6 Conclusion -- References -- Text Classifier of Sensationalist Headlines in Spanish Using BERT-Based Models -- 1 Introduction -- 2 Background -- 2.1 Sensationalism -- 2.2 BERT-Based Models -- 3 Related Work -- 4 Dataset and Methods -- 4.1 Data Gathering and Data Labeling -- 4.2 Data Analysis -- 4.3 Model Generation and Fine-Tuning -- 5 Results -- 6 Conclusion -- References -- Arabic Question-Answering System Based on Deep Learning Models -- 1 Introduction -- 2 Natural Language Processing (NLP) -- 2.1 Difficulties in NLP -- 2.2 Natural Language Processing Phases -- 3 Question Answer System -- 3.1 Usage Deep Learning Models in Questions Answering System -- 3.2 Different Questions Based on Bloom's Taxonomy -- 3.3 Question-Answering System Based on Types -- 3.4 Wh-Type Questions (What, Which, When, Who) -- 4 List-Based Questions -- 5 Yes/No Questions -- 6 Causal Questions [Why or How] -- 7 Hypothetical Questions -- 8 Complex Questions -- 8.1 Question Answering System Issues -- 9 Arabic Language Overview -- 9.1 Arabic Language Challenges -- 10 Related Work -- 11 Proposed Methodology -- 11.1 Recurrent Neural Networks (RNNs) -- 11.2 Long Short-Term Memory (LSTM) -- 11.3 Gated Recurrent Unit (GRU) -- 12 Prepare the Dataset -- 12.1 Collecting Data -- 13 Data Preprocessing. 14 Results and Discussion -- 15 Conclusion and Future Work -- References -- Healthcare-Oriented Applications -- Machine and Deep Learning Algorithms for ADHD Detection: A Review -- 1 Introduction -- 2 Research Methodology -- 3 Related Work -- 3.1 Machine Learning Approaches -- 3.2 Deep Learning Approaches -- 4 Approaches for ADHD Detection Using AI Algorithms -- 4.1 Machine Learning-Based Approaches -- 4.2 Deep Learning-Based Approaches -- 5 Datasets for ADHD Detection -- 5.1 Hyperaktiv -- 5.2 Working Memory and Reward in Children with and Without ADHD -- 5.3 Working Memory and Reward in Adults -- 5.4 Eeg Data for ADHD -- 6 Machine Learning and Deep Learning Classifiers for ADHD Detection -- 7 Trends and Challenges -- 7.1 New Types of Sensors or Biosensors -- 7.2 Multi-Modal Detection and/or Diagnosis of ADHD -- 7.3 The Use of Biomarkers as Variables for Diagnosis -- 7.4 Interpretability -- 7.5 Building of Standardized and Accurate Public Datasets -- 7.6 Different Classification Techniques -- 8 Conclusion -- References -- Mosquito on Human Skin Classification Using Deep Learning -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Dataset Description -- 3.2 Deep Convolutional Neural Networks and Transfer Learning -- 3.3 Hyperparameter Tuning -- 3.4 Proposed Workflow -- 4 Experiments and Results -- 5 Conclusion and Future Work -- References -- Analysis and Interpretation of Deep Convolutional Features Using Self-organizing Maps -- 1 Introduction -- 2 Materials -- 2.1 Convolutional Neural Networks -- 2.2 Self-organizing Maps -- 3 Proposed Method -- 3.1 Stage A: Training of CNN -- 3.2 Stage B: Extraction of Features -- 3.3 Stage C: SOM Training -- 3.4 Stage D: Analysis and Interpretation -- 4 Application Example -- 4.1 Experimental Setup -- 4.2 Result Analysis -- 5 Conclusions -- References. A Hybrid Deep Learning-Based Approach for Human Activity Recognition Using Wearable Sensors -- 1 Introduction -- 2 Literature Analysis -- 3 OPPORTUNITY Dataset -- 4 MHEALTH Dataset -- 5 HARTH Dataset -- 6 Materials and Methods -- 6.1 Some Preliminaries -- 6.2 Basic Architecture of CNN -- 7 Long-Short Term Memory (LSTM) -- 7.1 Working Principle of LSTM -- 8 Proposed Model Architecture -- 9 Dataset Description -- 9.1 MHEALTH Dataset -- 9.2 OPPORTUNITY Dataset -- 9.3 HARTH Dataset -- 10 Experimental Results -- 10.1 Evaluation Metrics Used -- 10.2 Results Analysis on MHEALTH Dataset -- 10.3 Results Analysis on OPPORTUNITY Dataset -- 10.4 Results Analysis on HARTH Dataset -- 10.5 Result Summary and Comparison -- 11 Conclusion and Future Works -- References -- Predirol: Predicting Cholesterol Saturation Levels Using Big Data, Logistic Regression, and Dissipative Particle Dynamics Simulation -- 1 Introduction -- 2 Related Works -- 2.1 Models for the Simulation of Fluids -- 2.2 Data Mining Application for Prevention of Cardiovascular Diseases -- 2.3 Comparative Analysis -- 3 PREDIROL Architecture -- 3.1 Big Data Model -- 3.2 Cholesterol Saturation Level Prediction Module -- 3.3 Cholesterol Levels Simulation Module with Dissipative Particle Dynamics -- 4 Case Study: Prediction of Cholesterol Levels of a Hospital Patients -- 5 Conclusions and Future Work -- References -- Convolutional Neural Network-Based Cancer Detection Using Histopathologic Images -- 1 Introduction -- 2 Image Processing Techniques -- 2.1 Statistical-Based Algorithms -- 2.2 Learning-Based Algorithms -- 2.3 Hyper-Parameters of CNN -- 2.4 Evaluation Metrics -- 2.5 Implementation -- 3 Stage 3: CNN Algorithm Training -- 3.1 Model Training Phase -- 3.2 Model Optimization Phase -- 4 Conclusion -- References. Artificial Neural Network-Based Model to Characterize the Reverberation Time of a Neonatal Incubator -- 1 Introduction -- 2 Materials and Methods -- 2.1 Artificial Neural Networks Using the Levenberg-Marquardt Algorithm -- 3 Results -- 3.1 Data Analysis -- 3.2 Artificial Neural Network-Based Model Training -- 4 Conclusions -- References -- A Comparative Study of Machine Learning Methods to Predict COVID-19 -- 1 Introduction -- 2 Related Works -- 3 Background -- 3.1 Covid-19 -- 3.2 Machine Learning -- 4 Materials and Methods -- 4.1 Dataset Pre-processing -- 4.2 Machine Learning Models -- 5 Results and Discussions -- 6 Conclusions -- References -- Sustainability-Oriented Applications -- Multi-product Inventory Supply and Distribution Model with Non-linear CO2 Emission Model to Improve Economic and Environmental Aspects of Freight Transportation -- 1 Introduction -- 2 Literature Review and Contributions -- 3 Development of the Integrated Routing Model -- 3.1 Inventory Planning with Non-deterministic Demand and Multiple Products -- 3.2 Non-linear Emission for Heterogeneous Fleet -- 3.3 Association of Variables -- 4 Assessment of the Model -- 4.1 Numerical Data and Solving Method -- 4.2 Analysis of Results -- 5 Future Work -- 6 Statement -- References -- Convolutional Neural Networks for Planting System Detection of Olive Groves -- 1 Background -- 1.1 Evolution of Production Techniques in Olive Groves -- 1.2 Current Situation of Modern Olive Cultivation Systems -- 1.3 Application of Remote Sensing Techniques for Image Analysis -- 1.4 Scope of the Present Chapter -- 2 Materials and Experimental Methods -- 2.1 Area of Study and Image Acquisition -- 2.2 Methodology -- 3 Results and Discussion -- 4 Conclusions and Future Lines -- References -- A Conceptual Model for Analysis of Plant Diseases Through EfficientNet: Towards Precision Farming -- 1 Introduction. 2 Related Study. |
| Record Nr. | UNINA-9910746971103321 |
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Rivera Gilberto
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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