標(biāo)題: Titlebook: Artificial Intelligence for Neuroscience and Emotional Systems; 10th International W José Manuel Ferrández Vicente,Mikel Val Calvo,Hojj Con [打印本頁(yè)] 作者: Levelheaded 時(shí)間: 2025-3-21 16:33
書(shū)目名稱Artificial Intelligence for Neuroscience and Emotional Systems影響因子(影響力)
書(shū)目名稱Artificial Intelligence for Neuroscience and Emotional Systems影響因子(影響力)學(xué)科排名
書(shū)目名稱Artificial Intelligence for Neuroscience and Emotional Systems網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Artificial Intelligence for Neuroscience and Emotional Systems網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Artificial Intelligence for Neuroscience and Emotional Systems被引頻次
書(shū)目名稱Artificial Intelligence for Neuroscience and Emotional Systems被引頻次學(xué)科排名
書(shū)目名稱Artificial Intelligence for Neuroscience and Emotional Systems年度引用
書(shū)目名稱Artificial Intelligence for Neuroscience and Emotional Systems年度引用學(xué)科排名
書(shū)目名稱Artificial Intelligence for Neuroscience and Emotional Systems讀者反饋
書(shū)目名稱Artificial Intelligence for Neuroscience and Emotional Systems讀者反饋學(xué)科排名
作者: chisel 時(shí)間: 2025-3-21 21:06
Visualizing Brain Synchronization: An Explainable Representation of?Phase-Amplitude Coupling various cognitive processes and constitutes the basis of communication between populations of neurons. Cross-frequency coupling (CFC) refers to techniques directed to study the interactions between oscillations at different frequencies, providing a more comprehensive view of neural dynamics than tr作者: Crater 時(shí)間: 2025-3-22 03:52
Enhancing Neuronal Coupling Estimation by?NIRS/EEG Integrationr diagnosis. In this context, multimodal neuroimaging approaches, based on the neurovascular coupling phenomenon, exploit their individual strengths to provide complementary information on the neural activity of the brain cortex. This work proposes a novel method for combining electroencephalography作者: BOOR 時(shí)間: 2025-3-22 04:40
Causal Mechanisms of?Dyslexia via?Connectogram Modeling of?Phase Synchronyion flow between different brain regions. Connectograms are graphical representations that map the connectivity between neural nodes or EEG channels through lines and arrows of varying thickness and directionality. Here, inter-channel phase connectivity patterns were analyzed by computing Granger ca作者: Mundane 時(shí)間: 2025-3-22 08:49
Explainable Exploration of?the?Interplay Between HRV Features and?EEG Local Connectivity Patterns instem on the heart. It can provide insights into the balance between sympathetic and parasympathetic activity. The relationship between autonomic nervous system function, specifically parasympathetic activity, and certain learning disorders, including dyslexia, is currently under study. In this paper作者: candle 時(shí)間: 2025-3-22 16:30
Enhancing Intensity Differences in?EEG Cross-Frequency Coupling Maps for?Dyslexia DetectionFrequency Coupling (CFC) maps derived from EEG signals for dyslexia detection. Our approach addresses the challenge of subtle intensity differences in CFC maps, which can hinder the accurate identification of dyslexia-related patterns..Through visual inspection and quantitative analysis, we demonstr作者: 格子架 時(shí)間: 2025-3-22 20:02
Improving Prediction of?Mortality in?ICU via?Fusion of?SelectKBest with?SMOTE Method and?Extra Tree chanism based on Artificial Intelligence (AI), utilizing technology to explore hidden relations between data and assessment in medical contexts. Hence, predicting the mortality of Intensive Care Unit (ICU) patients is a vital yet challenging task with significant implications for clinical decision-m作者: 現(xiàn)代 時(shí)間: 2025-3-22 23:59 作者: 影響 時(shí)間: 2025-3-23 03:43 作者: theta-waves 時(shí)間: 2025-3-23 08:22
Enhancing Interpretability in?Machine Learning: A Focus on?Genetic Network Programming, Its Variants processes. However, the interpretability of solutions generated by these algorithms remains a significant challenge, as these models do not inherently prioritize explainability. This lack of interpretability hampers the analysis of decision-making rationales. One potential remedy to this issue is t作者: 金盤是高原 時(shí)間: 2025-3-23 11:45
Enhancing Coronary Artery Disease Classification Using Optimized MLP Based on?Genetic Algorithm diseases (CVDs) impose considerable morbidity and mortality rates and entail considerable financial strain on global healthcare infrastructures. According to the report of the World Health (WHO) Organization, the mortality rate of heart disease will increase to 23 million cases by 2030. In healthca作者: 言外之意 時(shí)間: 2025-3-23 17:54
Extracting Heart Rate Variability from?NIRS Signals for?an?Explainable Detection of?Learning Disordeecause they evaluate the variables from patients’ screening evaluation and disentangle the information that they contain. In this study, we propose a novel method for detecting developmental dyslexia by extracting heart signals from NIRS. Features in terms of different domains based on heart rate va作者: 使苦惱 時(shí)間: 2025-3-23 19:33 作者: 裙帶關(guān)系 時(shí)間: 2025-3-24 01:45 作者: 磨碎 時(shí)間: 2025-3-24 03:46
Diagnosis of?Schizophrenia in?EEG Signals Using dDTF Effective Connectivity and?New PreTrained CNN ative deficits. Electroencephalography (EEG) recordings are pivotal in SZ diagnosis, necessitating the expertise of specialist doctors and psychologists. However, the analysis of EEG signals is labor-intensive and susceptible to human error. This study introduces a deep learning (DL) pipeline for the作者: obeisance 時(shí)間: 2025-3-24 07:30 作者: 驚呼 時(shí)間: 2025-3-24 13:24 作者: 寬宏大量 時(shí)間: 2025-3-24 16:10
PDBIGDATA: A New Database for?Parkinsonism Research Focused on?Large Models Despite its importance, obtaining comprehensive imaging datasets remains challenging. In response, we introduce a new database comprising brain images from Parkinson’s patients and healthy controls, addressing the scarcity of such resources in the field. The database currently houses around 3000 su作者: 用樹(shù)皮 時(shí)間: 2025-3-24 19:20 作者: infringe 時(shí)間: 2025-3-24 23:18 作者: 廚師 時(shí)間: 2025-3-25 05:47 作者: 協(xié)定 時(shí)間: 2025-3-25 11:09
A Cross-Modality Latent Representation for?the?Prediction of?Clinical Symptomatology in?Parkinson’s gy scales such as UPDRS (R2?=?0.545), at the same time that provides tools for interpreting the results and the common latent distribution for both clinical data and neuroimaging, paving the way for interpretable machine learning tools in neurodegeneration.作者: ARK 時(shí)間: 2025-3-25 11:52 作者: immunity 時(shí)間: 2025-3-25 16:30 作者: Habituate 時(shí)間: 2025-3-25 22:16 作者: CESS 時(shí)間: 2025-3-26 00:24 作者: CANE 時(shí)間: 2025-3-26 05:10
Faktoren des verl?sslichen Handelnsignificant improvements in the significance of CFC map pixels, particularly in the Alpha-Beta coupling band, post-transformation. This enhancement in discriminative power was further supported by the reduction in entropy and the identification of texture feature changes through Gray-Level Co-occurrence Matrix (GLCM) analysis.作者: Vulnerable 時(shí)間: 2025-3-26 11:35
https://doi.org/10.1007/978-3-8350-5436-3retable solutions. This study provides a concise overview of GNP, exploring its modifications and applications to demonstrate its utility in addressing the interpretability challenge in machine learning algorithms.作者: Hemoptysis 時(shí)間: 2025-3-26 13:24
https://doi.org/10.1007/978-3-8350-5436-3to delineate neuroanatomical disparities between Parkinson’s patients and controls. Our findings not only underscore the potential of this database in advancing Parkinson’s research but also highlight its significance in facilitating the translation of findings into clinical practice, ultimately enhancing patient care and outcomes.作者: 控制 時(shí)間: 2025-3-26 20:04
Visualizing Brain Synchronization: An Explainable Representation of?Phase-Amplitude Coupling patterns in an explainable way, allowing to visualize them over time and to easily identify functional brain areas activated during a task development from the Phase-Amplitude Coupling (PAC) point of view.作者: lambaste 時(shí)間: 2025-3-27 00:24
Enhancing Intensity Differences in?EEG Cross-Frequency Coupling Maps for?Dyslexia Detectionignificant improvements in the significance of CFC map pixels, particularly in the Alpha-Beta coupling band, post-transformation. This enhancement in discriminative power was further supported by the reduction in entropy and the identification of texture feature changes through Gray-Level Co-occurrence Matrix (GLCM) analysis.作者: AVID 時(shí)間: 2025-3-27 04:10 作者: PALMY 時(shí)間: 2025-3-27 06:29 作者: 身心疲憊 時(shí)間: 2025-3-27 12:51 作者: Allure 時(shí)間: 2025-3-27 16:39 作者: voluble 時(shí)間: 2025-3-27 20:04 作者: 障礙物 時(shí)間: 2025-3-27 23:39
Explainable Exploration of?the?Interplay Between HRV Features and?EEG Local Connectivity Patterns int most contribute to different HRV features, with a focus on parasympathetic activity. Our findings suggest that HRV features related to stress can explain differential activations in the auditory cortex (Brodmann areas 39 and 40) during auditory stimulation in dyslexic children.作者: CALL 時(shí)間: 2025-3-28 02:40 作者: famine 時(shí)間: 2025-3-28 09:34
Zero-Shot Ensemble of?Language Models for?Fine-Grain Mental-Health Topic Classificationmodels with Zero-Shot approaches achieved an accuracy (ACC) of 43.29%, weighted-F1 (W-F1) of 41.32% and Macro-F1 (M-F1) of 31.79% in the 28 topics of Counsel-Chat; and 35.57% of ACC, 39.66% W-F1 and 28.12% of M-F1 in the 39 topics of 7Cups dataset. The error analysis reveals that models have difficu作者: myriad 時(shí)間: 2025-3-28 12:31
Extracting Heart Rate Variability from?NIRS Signals for?an?Explainable Detection of?Learning Disordeing to an area under the ROC curve of 0.79. The explanatory nature of our framework, based on Shapley Additive Explanations (SHAP), yields a deeper understanding of the evaluated phenomenon, revealing the presence of behavioral variables highly correlated with the model’s features. These findings de作者: 焦慮 時(shí)間: 2025-3-28 18:30 作者: Shuttle 時(shí)間: 2025-3-28 21:28
Early Diagnosis of?Schizophrenia in?EEG Signals Using One Dimensional Transformer Modeler architecture, incorporating various activation functions, is applied to extract features from the preprocessed EEG signals. In the architecture’s final layer, the Softmax activation function is utilized for classifying the data. The performance of the proposed model is assessed using a K-Fold cro作者: 寒冷 時(shí)間: 2025-3-29 01:09
Diagnosis of?Schizophrenia in?EEG Signals Using dDTF Effective Connectivity and?New PreTrained CNN aWT), and effective connectivity matrices are derived using the directed Directed Transfer Function (dDTF) technique. Following this, state-of-the-art pretrained DL models based on CNNs and transformers are applied to extract features and classify the 2D dDTF images obtained from different EEG sub-ba作者: 植物學(xué) 時(shí)間: 2025-3-29 06:41
A Survey on?EEG Phase Amplitude Coupling to?Speech Rhythm for?the?Prediction of?Dyslexiaights Ratio PAC. Analysis of the classification model reveal differences in the entrainment at regions typically associated to language, paving the way for an accurate and interpretable DLX diagnosis methodology.作者: emission 時(shí)間: 2025-3-29 11:06 作者: 抱負(fù) 時(shí)間: 2025-3-29 12:36
Artificial Intelligence for Neuroscience and Emotional Systems10th International W作者: Aggressive 時(shí)間: 2025-3-29 17:52
José Manuel Ferrández Vicente,Mikel Val Calvo,Hojj作者: FAWN 時(shí)間: 2025-3-29 20:03
,Alltag: Blaupause für die Angst,ing the Particle Swarm Optimization (PSO) algorithm, we achieved a significant enhancement in the classification rate, elevating it from 96% to an impressive 100%. These compelling results underscore the efficacy of our proposed methodology and illuminate the promising potential of smartphone-based 作者: 發(fā)起 時(shí)間: 2025-3-30 02:33
https://doi.org/10.1007/978-3-658-43298-0ty inferred from simultaneously recorded fNIRS signals. Thus, the resulting image sequences preserve spatial and temporal information of the communication and interaction between different neural processes and provide discriminative information that enables differentiation between controls and dysle作者: 經(jīng)典 時(shí)間: 2025-3-30 07:51 作者: 憤怒歷史 時(shí)間: 2025-3-30 10:37 作者: amorphous 時(shí)間: 2025-3-30 14:27 作者: AVOID 時(shí)間: 2025-3-30 17:53 作者: 步兵 時(shí)間: 2025-3-30 22:30 作者: placebo 時(shí)間: 2025-3-31 01:03 作者: 表兩個(gè) 時(shí)間: 2025-3-31 09:04 作者: 尖叫 時(shí)間: 2025-3-31 11:45
Faktoren des verl?sslichen HandelnsWT), and effective connectivity matrices are derived using the directed Directed Transfer Function (dDTF) technique. Following this, state-of-the-art pretrained DL models based on CNNs and transformers are applied to extract features and classify the 2D dDTF images obtained from different EEG sub-ba作者: propose 時(shí)間: 2025-3-31 17:22
Verl?sslichkeitsorientierte Forschungenights Ratio PAC. Analysis of the classification model reveal differences in the entrainment at regions typically associated to language, paving the way for an accurate and interpretable DLX diagnosis methodology.作者: 萬(wàn)花筒 時(shí)間: 2025-3-31 20:43 作者: alleviate 時(shí)間: 2025-3-31 22:11
0302-9743 aches; social and civil engineering through human AI translations; smart renewable energies: advancing AI algorithms in the renewable energy industry; bioinspired applications..978-3-031-61139-1978-3-031-61140-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
