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

Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer’s disease detection DOI Creative Commons
Vimbi Viswan,

Noushath Shaffi,

Mufti Mahmud

и другие.

Brain Informatics, Год журнала: 2024, Номер 11(1)

Опубликована: Апрель 5, 2024

Abstract Explainable artificial intelligence (XAI) has gained much interest in recent years for its ability to explain the complex decision-making process of machine learning (ML) and deep (DL) models. The Local Interpretable Model-agnostic Explanations (LIME) Shaply Additive exPlanation (SHAP) frameworks have grown as popular interpretive tools ML DL This article provides a systematic review application LIME SHAP interpreting detection Alzheimer’s disease (AD). Adhering PRISMA Kitchenham’s guidelines, we identified 23 relevant articles investigated these frameworks’ prospective capabilities, benefits, challenges depth. results emphasise XAI’s crucial role strengthening trustworthiness AI-based AD predictions. aims provide fundamental capabilities XAI enhancing fidelity within clinical decision support systems prognosis.

Язык: Английский

Процитировано

65
Federated Learning for the Healthcare Metaverse: Concepts, Applications, Challenges, and Future Directions DOI
Ali Kashif Bashir, Nancy Victor, Sweta Bhattacharya

и другие.

IEEE Internet of Things Journal, Год журнала: 2023, Номер 10(24), С. 21873 - 21891

Опубликована: Авг. 14, 2023

Recent technological advancements have considerably improved healthcare systems to provide various intelligent services, improving life quality. The Metaverse, often described as the next evolution of Internet, helps users interact with each other and environment, thus offering a seamless connection between virtual physical worlds. Additionally, by integrating emerging technologies, such artificial intelligence (AI), cloud edge computing, Internet Things (IoT), blockchain, semantic communications, can potentially transform many vertical domains in general sector (healthcare Metaverse) particular. Metaverse holds huge potential revolutionize development systems, presenting new opportunities for significant delivery, personalized experiences, medical education, collaborative research, so on. However, challenges are associated realization privacy, interoperability, data management, security. Federated learning (FL), branch AI, opens up enormous deal aforementioned exploiting computing resources available at distributed devices. This motivated us present survey on adopting FL Metaverse. Initially, we preliminaries IoT-based conventional healthcare, Furthermore, benefits discussed. Subsequently, discuss several applications FL-enabled including diagnosis, patient monitoring, infectious disease, drug discovery. Finally, highlight solutions toward realizing

Язык: Английский

Процитировано

62
Artificial intelligence in positive mental health: a narrative review DOI Creative Commons

Anoushka Thakkar,

Ankita Gupta, Avinash De Sousa

и другие.

Frontiers in Digital Health, Год журнала: 2024, Номер 6

Опубликована: Март 18, 2024

The paper reviews the entire spectrum of Artificial Intelligence (AI) in mental health and its positive role health. AI has a huge number promises to offer care this looks at multiple facets same. first defines scope area It then various like machine learning, supervised learning unsupervised other AI. psychiatric disorders neurodegenerative disorders, intellectual disability seizures are discussed along with awareness, diagnosis intervention disorders. emotional regulation impact schizophrenia, autism mood is also highlighted. article discusses limitations based approaches need for be culturally aware, structured flexible algorithms an awareness biases that can arise ethical issues may use visited.

Язык: Английский

Процитировано

60
Explainable Artificial Intelligence in Alzheimer’s Disease Classification: A Systematic Review DOI Creative Commons
Vimbi Viswan,

Noushath Shaffi,

Mufti Mahmud

и другие.

Cognitive Computation, Год журнала: 2023, Номер 16(1), С. 1 - 44

Опубликована: Ноя. 13, 2023

Abstract The unprecedented growth of computational capabilities in recent years has allowed Artificial Intelligence (AI) models to be developed for medical applications with remarkable results. However, a large number Computer Aided Diagnosis (CAD) methods powered by AI have limited acceptance and adoption the domain due typical blackbox nature these models. Therefore, facilitate among practitioners, models' predictions must explainable interpretable. emerging field (XAI) aims justify trustworthiness predictions. This work presents systematic review literature reporting Alzheimer's disease (AD) detection using XAI that were communicated during last decade. Research questions carefully formulated categorise into different conceptual approaches (e.g., Post-hoc, Ante-hoc, Model-Agnostic, Model-Specific, Global, Local etc.) frameworks (Local Interpretable Model-Agnostic Explanation or LIME, SHapley Additive exPlanations SHAP, Gradient-weighted Class Activation Mapping GradCAM, Layer-wise Relevance Propagation LRP, XAI. categorisation provides broad coverage interpretation spectrum from intrinsic Ante-hoc models) complex patterns Post-hoc taking local explanations global scope. Additionally, forms interpretations providing in-depth insight factors support clinical diagnosis AD are also discussed. Finally, limitations, needs open challenges research outlined possible prospects their usage detection.

Язык: Английский

Процитировано

53
Finger pinching and imagination classification: A fusion of CNN architectures for IoMT-enabled BCI applications DOI
Giuseppe Varone, Wadii Boulila, Maha Driss

и другие.

Information Fusion, Год журнала: 2023, Номер 101, С. 102006 - 102006

Опубликована: Сен. 6, 2023

Язык: Английский

Процитировано

29
A Cognitive Medical Decision Support System for IoT-Based Human-Computer Interface in Pervasive Computing Environment DOI
Haosong Gou,

Gaoyi Zhang,

Elias Paulino Medeiros

и другие.

Cognitive Computation, Год журнала: 2024, Номер 16(5), С. 2471 - 2486

Опубликована: Янв. 22, 2024

Язык: Английский

Процитировано

10
AI-Enhanced Neurophysiological Assessment DOI
Deepak Kumar, Punet Kumar,

Sushma Pal

и другие.

Advances in psychology, mental health, and behavioral studies (APMHBS) book series, Год журнала: 2025, Номер unknown, С. 33 - 64

Опубликована: Янв. 3, 2025

Advancements in artificial intelligence (AI) are revolutionizing neurophysiology, enhancing precision and efficiency assessing brain nervous system function. AI-driven neurophysiological assessment integrates machine learning, deep neural networks, advanced data analytics to process complex from electroencephalography, electromyography techniques. This technology enables earlier diagnosis of neurological disorders like epilepsy Alzheimer's by detecting subtle patterns that may be missed human analysis. AI also facilitates real-time monitoring predictive analytics, improving outcomes critical care neurorehabilitation. Challenges include ensuring quality, addressing ethical concerns, overcoming computational limits. The integration into neurophysiology offers a precise, scalable, accessible approach treating disorders. chapter discusses the methodologies, applications, future directions assessment, emphasizing its transformative impact clinical research fields.

Язык: Английский

Процитировано

1
Zebrafish models for studying cognitive enhancers DOI
Tatiana O. Kolesnikova, Konstantin A. Demin, Fabiano V. Costa

и другие.

Neuroscience & Biobehavioral Reviews, Год журнала: 2024, Номер 164, С. 105797 - 105797

Опубликована: Сен. 1, 2024

Язык: Английский

Процитировано

5
Machine learning assisted prediction of land surface temperature (LST) based on major air pollutants over the Annamayya District of India DOI Open Access
Jagadish Kumar Mogaraju

International Journal of Engineering and Geosciences, Год журнала: 2024, Номер 9(2), С. 233 - 246

Опубликована: Июль 28, 2024

Remote sensing (RS), Geographic information systems (GIS), and Machine learning can be integrated to predict land surface temperatures (LST) based on the data related carbon monoxide (CO), Formaldehyde (HCHO), Nitrogen dioxide (NO2), Sulphur (SO2), absorbing aerosol index (AAI), Aerosol optical depth (AOD). In this study, LST was predicted using machine classifiers, i.e., Extra trees classifier (ET), Logistic regressors (LR), Random Forests (RF). The accuracy of LR (0.89 or 89%) is higher than ET (82%) RF classifiers. Evaluation metrics for each are presented in form accuracy, Area under curve (AUC), Recall, Precision, F1 score, Kappa, MCC (Matthew’s correlation coefficient). Based relative performance ML it concluded that performed better. RS tools were used extract across spatial temporal scales (2019 2022). order evaluate model graphically, ROC (Receiver operating characteristic) curve, Confusion matrix, Validation Classification report, Feature importance plot, t- SNE (t-distributed stochastic neighbour embedding) plot used. On validation classifier, observed returned complexity due limited availability other factors yet studied post availability. Sentinel-5-P MODIS study.

Язык: Английский

Процитировано

5
Can sensors be used to measure the Arm Curl Test results? a systematic review DOI Creative Commons

Tomás D. Matos,

Daniel Vornicoglo,

Paulo Jorge Coelho

и другие.

Deleted Journal, Год журнала: 2024, Номер 6(2)

Опубликована: Янв. 31, 2024

Abstract There is growing interest in the automated measurement of physical fitness tests, such as Arm Curl Test, to enable more objective and accurate assessments. This review aimed systematically analyze types sensors technological methods used for Test their benefits different populations. The search consisted related possibilities measure results with scientific databases, including PubMed Central, IEEE Explore, Elsevier, Springer, MDPI, ACM, PMC, published from January 2010 October 2022. analysis included 30 studies 15 nations diverse populations analyzed. According data extraction, most prevalent were chronometers, accelerometers, stadiometers, dynamometers. In investigations, statistical predominated. study shows how sensor technologies can objectively Test. detected combined techniques enhance Applications may be improved even research on cutting-edge algorithms. evaluation offers insightful information about utilizing sensor-based automation Testing.

Язык: Английский

Процитировано

3