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[null,"1.","Record Nr.",null,"UNISA990000291510203316"]
[null,null,"Autore",null,"PARMENTOLA, Catello"]
[null,null,"Titolo",null,"Il soggetto psicologico e l'oggetto della psicologia nel codice deontologico degli psicologi italiani / Catello Parmentola"]
[null,null,"Pubbl/distr/stampa",null,"Milano : Giuffrè, copyr. 2000"]
[null,null,"ISBN",null,"88-14-08317-7"]
[null,null,"Descrizione fisica",null,"XVII, 173 p. ; 23 cm"]
[null,null,"Collana",null,"Psicologia sociale e clinica"]
[null,null,"Disciplina",null,"174.915"]
[null,null,"Soggetti",null,"Psicologi - Morale professionale"]
[null,null,"Collocazione",null,"II.3. Coll.44/ 45(VI ps B Coll. 77/ 86)"]
[{"$nulls":4},"II.3. Coll.44/ 45a(VI ps B Coll. 77/ 86A)"]
[{"$nulls":4},"II.3. Coll.44/ 45b(VI ps B Coll. 77/ 86bis)"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNINA9910746971103321"]
[null,null,"Autore",null,"Rivera Gilberto"]
[null,null,"Titolo",null,"Innovations in Machine and Deep Learning : Case Studies and Applications / / edited by Gilberto Rivera, Alejandro Rosete, Bernabé Dorronsoro, Nelson Rangel-Valdez"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023"]
[null,null,"ISBN",null,"3-031-40688-5"]
[null,null,"Edizione",null,"[1st ed. 2023.]"]
[null,null,"Descrizione fisica",null,"1 online resource (506 pages)"]
[null,null,"Collana",null,"Studies in Big Data, , 2197-6511 ; ; 134"]
[null,null,"Disciplina",null,"620.00285"]
[null,null,"Soggetti",null,"Engineering - Data processing"]
[{"$nulls":4},"Computational intelligence"]
[{"$nulls":4},"Big data"]
[{"$nulls":4},"Data Engineering"]
[{"$nulls":4},"Computational Intelligence"]
[{"$nulls":4},"Big Data"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Contents -- Analytics-Oriented Applications -- Recursive Multi-step Time-Series Forecasting for Residual-Feedback Artificial Neural Networks: A Survey -- 1 Introduction -- 2 Residual-Feedback ANNs: A Systematic Review -- 2.1 Systematic Review Planning and Execution -- 2.2 Overview of the Systematic Review Findings -- 3 The Existing Recursive Multi-step Forecast Strategy Solution -- 4 Limitation -- 5 Conclusions and Future Works -- References -- Feature Selection: Traditional and Wrapping Techniques with Tabu Search -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Data Description -- 3.2 Entropy-Based Feature Selection -- 3.3 Feature Selection Using Principal Component Analysis -- 3.4 Correlation-Based Feature Selection -- 4 Tabu Search -- 4.1 Initial Solution -- 4.2 Neighborhood -- 4.3 Objective Function -- 4.4 Memory Structures -- 5 Results -- 6 Discussion -- 7 Conclusions and Future Work -- References -- Pattern Classification with Holographic Neural Networks: A New Tool for Feature Selection -- 1 Introduction -- 2 Holographic Neural Networks -- 2.1 Basic Theory -- 2.2 Learning and Prediction Methods -- 2.3 red  Explainability and Optimization of Holographic Models -- 3 Feature Selection with Holographic Neural Neworks -- 3.1 Previous Works -- 3.2 Pythagorean Membership Grades -- 4 Pattern "]
[{"$nulls":4},"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."]
[{"$nulls":4},"Speech Emotion Recognition Using Deep CNNs Trained on Log-Frequency Spectrograms -- 1 Introduction -- 2 Literature Survey -- 2.1 Motivation -- 2.2 Contributions -- 3 Proposed Methodology -- 3.1 Data Augmentation -- 3.2 Extraction of Log-Frequency Spectrograms -- 3.3 Motivation Behind Using Spectrograms -- 3.4 Log-Frequency Spectrogram Extraction -- 3.5 Understanding What a Spectrogram Conveys -- 4 The Deep Convolutional Neural Network -- 4.1 Architecture -- 4.2 Training -- 5 Observations -- 5.1 Dataset Used -- 5.2 Performance Metrics Used -- 5.3 Results Obtained -- 5.4 Comparison Study -- 6 Conclusion -- References -- Text Classifier of Sensationalist Headlines in Spanish Using BERT-Based Models -- 1 Introduction -- 2 Background -- 2.1 Sensationalism -- 2.2 BERT-Based Models -- 3 Related Work -- 4 Dataset and Methods -- 4.1 Data Gathering and Data Labeling -- 4.2 Data Analysis -- 4.3 Model Generation and Fine-Tuning -- 5 Results -- 6 Conclusion -- References -- Arabic Question-Answering System Based on Deep Learning Models -- 1 Introduction -- 2 Natural Language Processing (NLP) -- 2.1 Difficulties in NLP -- 2.2 Natural Language Processing Phases -- 3 Question Answer System -- 3.1 Usage Deep Learning Models in Questions Answering System -- 3.2 Different Questions Based on Bloom's Taxonomy -- 3.3 Question-Answering System Based on Types -- 3.4 Wh-Type Questions (What, Which, When, Who) -- 4 List-Based Questions -- 5 Yes/No Questions -- 6 Causal Questions [Why or How] -- 7 Hypothetical Questions -- 8 Complex Questions -- 8.1 Question Answering System Issues -- 9 Arabic Language Overview -- 9.1 Arabic Language Challenges -- 10 Related Work -- 11 Proposed Methodology -- 11.1 Recurrent Neural Networks (RNNs) -- 11.2 Long Short-Term Memory (LSTM) -- 11.3 Gated Recurrent Unit (GRU) -- 12 Prepare the Dataset -- 12.1 Collecting Data -- 13 Data Preprocessing."]
[{"$nulls":4},"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 "]
[{"$nulls":4},"and Interpretation of Deep Convolutional Features Using Self-organizing Maps -- 1 Introduction -- 2 Materials -- 2.1 Convolutional Neural Networks -- 2.2 Self-organizing Maps -- 3 Proposed Method -- 3.1 Stage A: Training of CNN -- 3.2 Stage B: Extraction of Features -- 3.3 Stage C: SOM Training -- 3.4 Stage D: Analysis and Interpretation -- 4 Application Example -- 4.1 Experimental Setup -- 4.2 Result Analysis -- 5 Conclusions -- References."]
[{"$nulls":4},"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."]
[{"$nulls":4},"Artificial Neural Network-Based Model to Characterize the Reverberation Time of a Neonatal Incubator -- 1 Introduction -- 2 Materials and Methods -- 2.1 Artificial Neural Networks Using the Levenberg-Marquardt Algorithm -- 3 Results -- 3.1 Data Analysis -- 3.2 Artificial Neural Network-Based Model Training -- 4 Conclusions -- References -- A Comparative Study of Machine Learning Methods to Predict COVID-19 -- 1 Introduction -- 2 Related Works -- 3 Background -- 3.1 Covid-19 -- 3.2 Machine Learning -- 4 Materials and Methods -- 4.1 Dataset Pre-processing -- 4.2 Machine Learning Models -- 5 Results and Discussions -- 6 Conclusions -- References -- Sustainability-Oriented Applications -- Multi-product Inventory Supply and Distribution Model with Non-linear CO2 Emission Model to Improve Economic and Environmental Aspects of Freight Transportation -- 1 Introduction -- 2 Literature Review and Contributions -- 3 Development of the Integrated Routing Model -- 3.1 Inventory Planning with Non-deterministic Demand and Multiple Products -- 3.2 Non-linear Emission for Heterogeneous Fleet -- 3.3 Association of Variables -- 4 Assessment of the Model -- 4.1 Numerical Data and Solving Method -- 4.2 Analysis of Results -- 5 Future Work -- 6 Statement -- References -- Convolutional Neural Networks for Planting System Detection of Olive Groves -- 1 Background -- 1.1 Evolution of Production Techniques in Olive Groves -- 1.2 Current Situation of Modern Olive Cultivation Systems -- 1.3 Application of Remote "]
[{"$nulls":4},"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."]
[{"$nulls":4},"2 Related Study."]
[null,null,null,"Sommario/riassunto","In recent years, significant progress has been made in achieving artificial intelligence (AI) with an impact on students, managers, scientists, health personnel, technical roles, investors, teachers, and leaders. This book presents numerous successful applications of AI in various contexts. The innovative implications covered fall under the general field of machine learning (ML), including deep learning, decision-making, forecasting, pattern recognition, information retrieval, and interpretable AI. Decision-makers and entrepreneurs will find numerous successful applications in health care, sustainability, risk management, human activity recognition, logistics, and Industry 4.0. This book is an essential resource for anyone interested in challenges, opportunities, and the latest developments and real-world applications of ML. Whether you are a student, researcher, practitioner, or simply curious about AI, this book provides valuable insights and inspiration for your work and learning."]