Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity DOI Creative Commons
Ahmad Almadhor, Gabriel Avelino Sampedro, Mideth Abisado

и другие.

Sensors, Год журнала: 2023, Номер 23(15), С. 6664 - 6664

Опубликована: Июль 25, 2023

Contemporary advancements in wearable equipment have generated interest continuously observing stress utilizing various physiological indicators. Early detection can improve healthcare by lessening the negative effects of chronic stress. Machine learning (ML) methodologies been modified for to monitor user health situations sufficient information. Nevertheless, more data are needed make applying Artificial Intelligence (AI) medical field easier. This research aimed detect using a stacking model based on machine algorithms chest-based features from Wearable Stress and Affect Detection (WESAD) dataset. We converted this natural dataset into convenient format suggested performing visualization preprocessing RESP feature analysis Z-score, SelectKBest feature, Synthetic Minority Over-Sampling Technique (SMOTE), normalization. The efficiency proposed was estimated regarding accuracy, precision, recall, F1-score. experimental outcome illustrated efficacy technique, achieving 0.99% accuracy. results revealed that methodology performed better than traditional previous studies.

Язык: Английский

Integrating Brain-Inspired Computation with Big-Data Analytics for Advanced Diagnostics in Neuroradiology DOI Creative Commons

A. V. Senthil Kumar,

J Ramprasath,

V. Kalpana

и другие.

Neuroscience Informatics, Год журнала: 2025, Номер unknown, С. 100202 - 100202

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Current Applications of Artificial Intelligence in Psychiatry DOI
Nicholas A Kerna,

Adina Boulos,

Melany Abreu

и другие.

Scientia. Technology, science and society., Год журнала: 2025, Номер 2(4), С. 125 - 143

Опубликована: Апрель 1, 2025

The integration of artificial intelligence (AI) into psychiatric practice has accelerated rapidly, driven by advances in computational methods and the availability diverse data sources. present paper examines contemporary AI applications across diagnostic support, predictive analytics, therapeutic interventions, digital phenotyping, telepsychiatry integration, ethical, legal, social considerations. Foundations machine learning, deep natural language processing are delineated alongside relevant modalities, including structured clinical records, unstructured notes, multimodal signals. roles symptom detection, neuroimaging pattern recognition, biomarker discovery, differential diagnosis evaluated. Predictive models for suicide risk, relapse, treatment response reviewed, with attention to personalization algorithms. Therapeutic tools, such as conversational agents, virtual reality, gamified mobile applications, discussed. Passive monitoring techniques, workflows, clinician dashboards described. Ethical challenges, privacy, algorithmic bias, regulatory frameworks, considered. Implementation barriers adoption factors analyzed. Emerging trends, federated fusion, explainable AI, low-resource settings, explored. Implications patient outcomes, health systems, policy synthesized, concluding recommendations future research practice.

Язык: Английский

Процитировано

0
LEMate: An Early Prototype of an Artificial Intelligence-Powered Learner Engagement Detection System for Low-Resource Classrooms DOI
Mufti Mahmud, M. Shamim Kaiser, David J. Brown

и другие.

Lecture notes in networks and systems, Год журнала: 2025, Номер unknown, С. 417 - 432

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Advancing Healthcare: Intelligent Speech Technology for Transcription, Disease Diagnosis, and Interactive Control of Medical Equipment in Smart Hospitals DOI Creative Commons
Ahmed Elhadad, Safwat Hamad, Noha Elfiky

и другие.

AI, Год журнала: 2024, Номер 5(4), С. 2497 - 2517

Опубликована: Ноя. 26, 2024

Intelligent Speech Technology (IST) is revolutionizing healthcare by enhancing transcription accuracy, disease diagnosis, and medical equipment control in smart hospital environments. This study introduces an innovative approach employing federated learning with Multi-Layer Perceptron (MLP) Gated Recurrent Unit (GRU) neural networks to improve IST performance. Leveraging the “Medical Speech, Transcription, Intent” dataset from Kaggle, comprising a variety of speech recordings corresponding symptom labels, noise reduction was applied using Wiener filter audio quality. Feature extraction through MLP sequence classification GRU highlighted model’s robustness capacity for detailed understanding. The framework enabled collaborative model training across multiple sites, preserving patient privacy avoiding raw data exchange. distributed allowed learn diverse, real-world while ensuring compliance strict protection standards. Through rigorous five-fold cross-validation, proposed Fed MLP-GRU demonstrated accuracy 98.6%, consistently high sensitivity specificity, highlighting its reliable generalization test conditions. In real-time applications, effectively performed transcription, provided symptom-based diagnostic insights, facilitated hands-free equipment, reducing contamination risks workflow efficiency. These findings indicate that IST, powered networks, can significantly delivery, operational efficiency clinical settings. research underscores transformative potential advanced addressing pressing challenges modern setting stage future innovations intelligent technology.

Язык: Английский

Процитировано

2
Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity DOI Creative Commons
Ahmad Almadhor, Gabriel Avelino Sampedro, Mideth Abisado

и другие.

Sensors, Год журнала: 2023, Номер 23(15), С. 6664 - 6664

Опубликована: Июль 25, 2023

Contemporary advancements in wearable equipment have generated interest continuously observing stress utilizing various physiological indicators. Early detection can improve healthcare by lessening the negative effects of chronic stress. Machine learning (ML) methodologies been modified for to monitor user health situations sufficient information. Nevertheless, more data are needed make applying Artificial Intelligence (AI) medical field easier. This research aimed detect using a stacking model based on machine algorithms chest-based features from Wearable Stress and Affect Detection (WESAD) dataset. We converted this natural dataset into convenient format suggested performing visualization preprocessing RESP feature analysis Z-score, SelectKBest feature, Synthetic Minority Over-Sampling Technique (SMOTE), normalization. The efficiency proposed was estimated regarding accuracy, precision, recall, F1-score. experimental outcome illustrated efficacy technique, achieving 0.99% accuracy. results revealed that methodology performed better than traditional previous studies.

Язык: Английский

Процитировано

5