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[null,"1.","Record Nr.",null,"UNISA996601560703316"]
[null,null,"Autore",null,"Ferrández Vicente José Manuel"]
[null,null,"Titolo",null,"Artificial Intelligence for Neuroscience and Emotional Systems : 10th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2024, Olhâo, Portugal, June 4-7, 2024, Proceedings, Part I"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer, , 2024"]
[{"$nulls":4},"©2024"]
[null,null,"ISBN",null,"3-031-61140-3"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (560 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science Series ; ; v.14674"]
[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 -- Organization -- Contents - Part I -- Contents - Part II -- Machine Learning in Neuroscience -- Morning Anxiety Detection Through Smartphone-Based Photoplethysmography Signals Analysis Using Machine Learning Methods -- 1 Introduction -- 2 Methods -- 2.1 Data Collection -- 2.2 Pulse Wave Extraction -- 2.3 Feature Extraction -- 2.4 Stress Classification -- 2.5 Fisher's Discriminant Ratio -- 3 Results and Discussion -- 4 Conclusion -- References -- Visualizing Brain Synchronization: An Explainable Representation of Phase-Amplitude Coupling -- 1 Introduction -- 2 Materials and Methods -- 2.1 The LEEDUCA Dataset -- 2.2 Data Preprocessing -- 2.3 Phase-Amplitude Coupling -- 2.4 Visualization of Local PAC Patterns -- 3 Consistency of Temporal Patterns -- 4 Results and Discussion -- 5 Conclusions -- References -- Enhancing Neuronal Coupling Estimation by NIRS/EEG Integration -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset and Preprocessing -- 2.2 CFS Image Sequences -- 2.3 fNIRS Functional Activation -- 2.4 Classification -- 3 Results -- 4 Conclusions and Future Work -- References -- Causal Mechanisms of Dyslexia via Connectogram Modeling of Phase Synchrony -- 1 Introduction -- 2 Material and Methods -- 2.1 Data Acquisition -- 2.2 Preprocessing -- 2.3 Hilbert Transform -- 2.4 Granger Causality -- 2.5 Feature Selection -- 2.6 Connectograms -- 2.7 Machine Learning Classification -- 3 Results -- 4 Conclusions -- References -- Explainable Exploration of the Interplay Between HRV Features and EEG Local Connectivity Patterns in Dyslexia -- 1 "]
[{"$nulls":4},"Introduction -- 2 Materials and Methods -- 2.1 Database -- 2.2 Cross Frequency Coupling with ISPC -- 2.3 Heart Rate Variability Descriptors -- 2.4 Explainable Regression Experiments -- 3 Results -- 4 Discussion -- 5 Conclusion -- References."]
[{"$nulls":4},"Enhancing Intensity Differences in EEG Cross-Frequency Coupling Maps for Dyslexia Detection -- 1 Introduction -- 2 Materials and Methods -- 2.1 Database -- 2.2 Generation of CFC Maps from EEG Signals -- 2.3 Enhancing Differences in CFS Maps Through Histogram Transformation -- 2.4 Quantification of the Improvement -- 3 Results -- 4 Discussion and Conclusions -- References -- Improving Prediction of Mortality in ICU via Fusion of SelectKBest with SMOTE Method and Extra Tree Classifier -- 1 Introduction -- 2 Literature Review -- 3 Materials and Methods -- 3.1 Dataset Used -- 3.2 Preprocessing -- 3.3 AutoML -- 4 Result and Discussion -- 5 Conclusion -- References -- A Cross-Modality Latent Representation for the Prediction of Clinical Symptomatology in Parkinson's Disease -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset and Preprocessing -- 2.2 Multi-modal Joint Latent Variable Model -- 2.3 Evaluation -- 3 Results and Discussion -- 4 Conclusions -- References -- Zero-Shot Ensemble of Language Models for Fine-Grain Mental-Health Topic Classification -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 4 Experiments and Results -- 5 Conclusions -- References -- Enhancing Interpretability in Machine Learning: A Focus on Genetic Network Programming, Its Variants, and Applications -- 1 Introduction -- 2 The Most Important Versions of GNP -- 2.1 Some Specific Applications of GNP Algorithm -- 3 Conclusion and Future Works -- References -- Enhancing Coronary Artery Disease Classification Using Optimized MLP Based on Genetic Algorithm -- 1 Introduction -- 2 Related Works -- 3 Materials and Methods -- 3.1 Dataset -- 3.2 Data Preprocessing -- 3.3 The Important Process of This Study -- 4 Results and Discussion -- 5 Conclusion -- References -- Extracting Heart Rate Variability from NIRS Signals for an Explainable Detection of Learning Disorders."]
[{"$nulls":4},"1 Introduction -- 2 Material and Methods -- 2.1 Participants -- 2.2 NIRS Acquisition -- 2.3 Extraction of Heart Signal from NIRS -- 2.4 Preprocessing -- 2.5 Classification and Explainability -- 3 Results -- 4 Conclusions and Future Work -- References -- Diagnosis of Parkinson Disease from EEG Signals Using a CNN-LSTM Model and Explainable AI -- 1 Introduction -- 2 Proposed Method -- 2.1 Dataset -- 2.2 Preprocessing -- 2.3 Deep Learning Model -- 3 Statistical Metrics -- 4 Experiment Results -- 5 Discussion, Conclusion, and Future Works -- References -- Early Diagnosis of Schizophrenia in EEG Signals Using One Dimensional Transformer Model -- 1 Introduction -- 2 Proposed Method -- 2.1 Dataset -- 2.2 Preprocessing -- 2.3 Feature Extraction Based on Transformer -- 2.4 Classification -- 3 Statistical Metrics -- 4 Experiment Results -- 5 Discussion, Conclusion, and Future Works -- References -- Diagnosis of Schizophrenia in EEG Signals Using dDTF Effective Connectivity and New PreTrained CNN and Transformer Models -- 1 Introduction -- 2 Proposed Method -- 2.1 Dataset -- 2.2 Preprocessing -- 2.3 Feature Extraction and Classification -- 3 Experiment Results -- 4 Discussion, Conclusion, and Future Works -- References -- A Survey on EEG Phase Amplitude Coupling to Speech Rhythm for the Prediction of Dyslexia -- 1 Introduction -- 2 Methods -- 2.1 Data Acquisition and Preprocessing -- 2.2 Cross-Frequency Coupling -- 2.3 Feature Aggregation and Classification -- 2.4 Evaluation and Interpretability -- 3 Results and Discussion -- 3.1 Best Performing Stimuli and PAC Measures -- 3.2 Effect of Classification Hyperparameters -- 3.3 Assymetric Differences Between DLX and CTL -- 4 Conclusions -- References -- Comprehensive Evaluation of Stroke "]
[{"$nulls":4},"Rehabilitation Dynamics: Integrating Brain-Computer Interface with Robotized Orthesic Hand and Longitudinal EEG Changes."]
[{"$nulls":4},"1 Introduction -- 2 Material and Methods -- 2.1 Participants -- 2.2 Description of the Experimentation -- 2.3 EEG Data Analysis -- 3 Results -- 3.1 Time Frequency -- 3.2 Topographic Maps -- 3.3 Power Bands -- 4 Discussion, Study Limitations and Future Work -- 5 Conclusion -- References -- PDBIGDATA: A New Database for Parkinsonism Research Focused on Large Models -- 1 Introduction -- 2 Database Description -- 3 Experiments and Results -- 4 Conclusions -- References -- A Comparative Study of Deep Learning Approaches for Cognitive Impairment Diagnosis Based on the Clock-Drawing Test -- 1 Introduction -- 2 Materials and Methods -- 2.1 CDT Databases -- 2.2 Image Preprocessing -- 2.3 Convolutional Neural Network -- 2.4 Attentive Pairwise Interaction Network -- 3 Experiments and Results -- 4 Discussion -- 5 Conclusion -- References -- Artificial Intelligence in Neurophysiology -- Prediction of Burst Suppression Occurrence Under General Anaesthesia Using Pre-operative EEG Signals -- 1 Introduction -- 2 Methods -- 2.1 Data -- 2.2 Pre-processing -- 2.3 Training -- 2.4 Models Evaluation -- 2.5 Generating Explanations with SHAP -- 3 Experiments -- 4 Results and Discussion -- 4.1 Models Evaluation -- 4.2 Explainability Analysis -- 4.3 Limitations and Future Work -- 5 Conclusion -- References -- Advances in Denoising Spikes Waveforms for Electrophysiological Recordings -- 1 Introduction -- 2 Methods -- 2.1 Acquisition System and Dataset -- 2.2 Neural Signal Denoising -- 2.3 Spike Sorting -- 2.4 Implementation Details -- 2.5 Software Testing -- 3 Results -- 3.1 Denoising and Spike Extraction Quality Measurement -- 4 Discussion -- 5 Conclusions -- References -- Analysis of Anxiety Caused by Fasting in Obesity Patients Using EEG Signals -- 1 Introduction -- 2 Materials and Methods -- 2.1 Subjects -- 2.2 Procedure -- 2.3 Data Processing -- 3 Results and Discussion."]
[{"$nulls":4},"4 Conclusions -- References -- Evolution of EEG Fractal Dimension Along a Sequential Finger Movement Task -- 1 Introduction: Fractal Dimension and its Application on EEG Signals Analysis -- 2 Materials and Methods -- 2.1 Dataset and Motor Task -- 2.2 EEG Signals Preprocessing -- 2.3 EMG Signals Preprocessing -- 2.4 Katz Fractal Dimension -- 2.5 KFD Time Windows -- 3 Results -- 4 Conclusion -- References -- Neuromotor and Cognitive Disorders -- Stress Classification Model Using Speech: An Ambulatory Protocol-Based Database Study -- 1 Introduction -- 2 Methods -- 2.1 Database -- 2.2 Feature Extraction -- 2.3 Supervised Classification -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Exploring Spatial Cognition: Comparative Analysis of Agent-Based Models in Dynamic and Static Environments -- 1 Introduction -- 2 Methodology -- 2.1 EvoJAX -- 2.2 Materials and Method -- 2.3 Results -- 3 Conclusions and Future Directions -- References -- Machine Learning for Personality Type Classification on Textual Data -- 1 Introduction -- 2 Background -- 3 Materials and Methods -- 3.1 Data Collection: -- 3.2 Feature Extraction -- 3.3 Target Classes -- 3.4 Classification Models -- 3.5 Performance Evaluation Metrics -- 4 Results -- 5 Conclusions -- References -- Grad-CAM Applied to the Detection of Instruments Used in Facial Presentation Attacks -- 1 Introduction -- 2 Related Work and Background -- 2.1 PAD Techniques -- 2.2 XAI Techniques -- 3 Methods and Materials -- 3.1 Design and Implementation -- 3.2 eXpainable Artificial Intelligence Grad-CAM -- 3.3 Procediment -- 3.4 Statistical Analysis -- 3.5 Database -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Comparison of an Accelerated Garble Embedding Methodology for Privacy Preserving in Biomedical Data Analytics -- 1 Introduction -- 1.1 State of the Art -- 2 Theoretical "]
[{"$nulls":4},"Framework."]
[{"$nulls":4},"2.1 Information Comparison."]
[null,"2.","Record Nr.",null,"UNINA9910576877003321"]
[null,null,"Autore",null,"Romero-González Roberto"]
[null,null,"Titolo",null,"Detection of Residual Pesticides in Foods"]
[null,null,"Pubbl/distr/stampa",null,"Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022"]
[null,null,"Descrizione fisica",null,"1 online resource (166 p.)"]
[null,null,"Soggetti",null,"Public health and preventive medicine"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Sommario/riassunto","Pesticides are used worldwide, and despite the fact that organic farming is increasing, they are still widely applied in different countries with pesticide regulations and monitoring programs. This Special Issue will cover the topic of the presence of pesticide residues in food, paying special attention to the use of modern analytical techniques based on chromatographic techniques coupled to mass spectrometry. Generic extraction methods are needed to allow the detection of pesticides with different physic-chemical properties, as well as different families of contaminants can be extracted simultaneously. Nevertheless, pluriresidue methods are still necessary for \"orphan\" compounds that cannot be included in multiresidue methods. Thus, in addition to targeted pesticides, the scope of this analysis can be increased and nowadays, metabolites are also detected, evaluating the degradation of pesticide residues in different food matrices."]