AI Article Synopsis
Article Abstract
Coronavirus disease so called as COVID-19 is an infectious disease and its spread takes place due to human interaction by their pathogen materials during coughing and sneezing. COVID-19 is basically a respiratory disease as evidence proved that a large number of infected people died due to short breathing. Most widely and uncontrollably spreading unknown viral genome infecting people worldwide was announced to be 2019-2020 by WHO on January 30, 2020. Based on the seriousness of its spread and unavailability of vaccination or any form of treatment, it was an immediate health-emergency of concern of international-level. The paper analyses effects of this virus in countries, such as India and United States on day-to-day basis because of their greater variability. In this study, various performance measures, such as root mean square error (RMSE), mean absolute error (MAE), coefficient of determination , mean absolute standard error (MASE) and mean absolute percentage error (MAPE) which characterize models' performances. value has been achieved to be closest to 1, i.e., 0.999 from Wavelet Neuronal Network Fuzzified Inferences' Layered Multivariate Adaptive Regression Spline (WNNFIL-MARS) for both the countries' data. It is important to capture the essence of this pandemic affecting millions of the population daily ever since its spread began from January, 2020.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8932471 | PMC |
| http://dx.doi.org/10.1140/epjs/s11734-022-00531-8 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Temporal logic inference for interpretable fault diagnosis of bearings via sparse and structured neural attention.
ISA Trans
January 2025
State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China. Electronic address:
This paper addresses the critical challenge of interpretability in machine learning methods for machine fault diagnosis by introducing a novel ad hoc interpretable neural network structure called Sparse Temporal Logic Network (STLN). STLN conceptualizes network neurons as logical propositions and constructs formal connections between them using specified logical operators, which can be articulated and understood as a formal language called Weighted Signal Temporal Logic. The network includes a basic word network using wavelet kernels to extract intelligible features, a transformer encoder with sparse and structured neural attention to locate informative signal segments relevant to decision-making, and a logic network to synthesize a coherent language for fault explanation.
View Article and Find Full Text PDFArtificial intelligence models using F-wave responses predict amyotrophic lateral sclerosis.
Brain
January 2025
Department of Neurology, Mayo Clinic, Rochester, MN, USA.
Nerve conduction F-wave studies contain critical information about subclinical motor dysfunction which may be used to diagnose patients with amyotrophic lateral sclerosis (ALS). However, F-wave responses are highly variable in morphology, making waveform interpretation challenging. Artificial Intelligence techniques can extract time-frequency features to provide new insights into ALS diagnosis and prognosis.
View Article and Find Full Text PDFOptimal time-frequency localized wavelet filters for identification of Alzheimer's disease from EEG signals.
Cogn Neurodyn
December 2025
Department of Electrical and Electronics Engineering, Jazan, 45142 Jazan Saudi Arabia.
Alzheimer's disease (AD) is a chronic disability that occurs due to the loss of neurons. The traditional methods to detect AD involve questionnaires and expensive neuro-imaging tests, which are time-consuming, subjective, and inconvenient to the target population. To overcome these limitations, Electroencephalogram (EEG) based methods have been developed to classify AD patients from normal controlled (NC) and mild cognitive impairment (MCI) subjects.
View Article and Find Full Text PDFElectroencephalogram criticality in cognitive impairment: a monitoring biomarker?
Cogn Neurodyn
December 2024
Laboratory of Physiology, Department of Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
Unlabelled: Critical states present scale-free dynamics, optimizing neuronal complexity and serving as a potential biomarker in cognitively impaired patients. We explored electroencephalogram (EEG) criticality in amnesic Mild Cognitive Impairment patients with clinical improvement in working memory, verbal memory, verbal fluency and overall executive functions after the completion of a 6-month prospective memory training. We compared "before" and "after" stationary resting-state EEG records of right-handed MCI patients (n = 17; 11 females), using the method of critical fluctuations and Haar wavelet analysis.
View Article and Find Full Text PDFA new class of chaotic attractors using different activation functions in neuron with multi dendrites.
Cogn Neurodyn
December 2024
University Larbi Tebessi, Tebessa, Algeria.
This paper introduces a novel class of chaotic attractors by lever- aging different activation functions within neurons possessing multiple dendrites. We propose a comprehensive framework where the activation functions in neurons are varied, allowing for diverse behaviors such as amplification, fluctuation, and folding of scrolls within the resulting chaotic attractors. By employing wavelet functions and other model-specific activation functions, we demonstrate the capability to modify scroll characteristics, including size and direction.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!