AIMC Topic: Wavelet Analysis

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An Advanced Self-Similarity Measure: Average of Level-Pairwise Hurst Exponent Estimates (ALPHEE).

IEEE transactions on bio-medical engineering
Many natural processes are characterized by complex patterns of self-similarity, where repetitive structures occur across different resolutions. The Hurst exponent is a key parameter used to quantify this self-similarity. While wavelet-based techniqu...

Exploring best-performing radiomic features with combined multilevel discrete wavelet decompositions for multiclass COVID-19 classification using chest X-ray images.

Computers in biology and medicine
Discrete wavelet transforms have been applied in many machine learning models for the analysis of COVID-19; however, little is known about the impact of combined multilevel wavelet decompositions for the disease identification. This study proposes a ...

WAND: Wavelet Analysis-Based Neural Decomposition of MRS Signals for Artifact Removal.

NMR in biomedicine
Accurate quantification of metabolites in magnetic resonance spectroscopy (MRS) is challenged by low signal-to-noise ratio (SNR), overlapping metabolites, and various artifacts. Particularly, unknown and unparameterized baseline effects obscure the q...

Discriminative Detection for Multiple Volatile Organic Compounds via Dynamic Temperature Modulation Based on Mixed Potential Gas Sensor.

ACS sensors
Gas sensors combined with artificial intelligence capable of distinguishing multiple odors hold great promise in volatile organic compounds (VOCs) discriminative detection. However, various issues such as large size, high expenses, and mutual interfe...

MIMI-ONET: Multi-Modal image augmentation via Butterfly Optimized neural network for Huntington DiseaseDetection.

Brain research
Huntington's disease (HD) is a chronic neurodegenerative ailment that affects cognitive decline, motor impairment, and psychiatric symptoms. However, the existing HD detection methods are struggle with limited annotated datasets that restricts their ...

Identification of sorghum variety using hyperspectral technology with squeeze-and-excitation convolutional neural network algorithms.

Analytical methods : advancing methods and applications
In this study, hyperspectral technology along with a combination of squeeze-and-excitation convolutional neural networks and competitive adaptive reweighted sampling (CARS-SECNNet) was developed to identify sorghum varieties. In addition, the support...

Optimizing functional brain network analysis by incorporating nonlinear factors and frequency band selection with machine learning models.

Medicine
The accurate assessment of the brain's functional network is seen as crucial for the understanding of complex relationships between different brain regions. Hidden information within different frequency bands, which is often overlooked by traditional...

Enhancing ECG disease detection accuracy through deep learning models and P-QRS-T waveform features.

PloS one
Cardiovascular diseases (CVDs) have surpassed cancer and become the major cause of death worldwide. An electrocardiogram (ECG) is a non-invasive and quicker method for diagnosing abnormal heart conditions. While research has extensively focused on EC...

Lightweight wavelet-CNN tea leaf disease detection.

PloS one
Tea diseases can significantly impact crop yield and quality, necessitating accurate and efficient recognition methods. This study presents WaveLiteNet, a lightweight model designed for tea disease recognition, addressing the challenge of inadequate ...

Smart Seizure Detection System: Machine Learning Based Model in Healthcare IoT.

Current aging science
BACKGROUND: Epilepsy, the tendency to have recurrent seizures, can have various causes, including brain tumors, genetics, stroke, brain injury, infections, and developmental disorders. Epileptic seizures are usually transient events. They normally le...