CubicPat: Investigations on the Mental Performance and Stress Detection Using EEG Signals

Diagnostics, Journal Year: 2025, Volume and Issue: 15(3), P. 363 - 363

Published: Feb. 4, 2025

Background\Objectives: Solving the secrets of brain is a significant challenge for researchers. This work aims to contribute this area by presenting new explainable feature engineering (XFE) architecture designed obtain results related stress and mental performance using electroencephalography (EEG) signals. Materials Methods: Two EEG datasets were collected detect stress. To achieve classification results, XFE model was developed, incorporating novel extraction function called Cubic Pattern (CubicPat), which generates three-dimensional vector coding channels. Classification obtained cumulative weighted iterative neighborhood component analysis (CWINCA) selector t-algorithm-based k-nearest neighbors (tkNN) classifier. Additionally, generated CWINCA Directed Lobish (DLob). Results: The CubicPat-based demonstrated both interpretability. Using 10-fold cross-validation (CV) leave-one-subject-out (LOSO) CV, introduced CubicPat-driven achieved over 95% 75% accuracies, respectively, datasets. Conclusions: interpretable deploying DLob statistical analysis.

Language: Английский

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Glaucoma diagnosis in the era of deep learning: A survey

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 256, P. 124888 - 124888

Published: Aug. 1, 2024

Glaucoma, a leading cause of irreversible blindness worldwide, poses significant diagnostic challenges due to its reliance on subjective evaluation. Recent advances in computer vision and deep learning have demonstrated the potential for automated assessment. This paper provides comprehensive survey studies AI-based glaucoma diagnosis using fundus, optical coherence tomography, visual field images, with focus learning-based methods. We searched Web Science, PubMed, IEEE Xplore, Google Scholar, applying specific selection criteria identify relevant published from 2017 2023. Our analysis structured overview architectural paradigms, including convolutional neural networks, autoencoders, attention generative adversarial geometric models. Additionally, we discuss approaches extracting informative features, such as structural, statistical, hybrid techniques. Furthermore, outline key research future directions, emphasizing need larger, more diverse datasets, strategies early disease detection, multi-modal data integration, model explainability, clinical translation. is expected be useful Artificial Intelligence (AI) researchers seeking translate into practice ophthalmologists aiming improve workflows latest AI outcomes.

Language: Английский

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Low-Light Liquid Content Detection in Transparent Containers: A Benchmark

Published: Jan. 1, 2025

Language: Английский

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Detection of concealed object using terahertz images: A comprehensive review

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 148, P. 110432 - 110432

Published: March 10, 2025

Language: Английский

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Signal Super Prediction and Rock Burst Precursor Recognition Framework Based on Guided Diffusion Model with Transformer

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(6), P. 3264 - 3264

Published: March 17, 2025

Implementing precise and advanced early warning systems for rock bursts is a crucial approach to maintaining safety during coal mining operations. At present, FEMR data play key role in monitoring providing warnings bursts. Nevertheless, conventional are associated with certain limitations, such as short time low accuracy of warning. To enhance the timeliness bolster mines, novel model has been developed. In this paper, we present framework predicting signal deep future recognizing burst precursor. The involves two models, guided diffusion transformer super prediction an auxiliary was applied Buertai database, which recognized having risk. results demonstrate that can predict 360 h (15 days) using only 12 known signal. If duration compressed by adjusting CWT window length, it becomes possible over longer spans. Additionally, achieved maximum recognition 98.07%, realizes disaster. These characteristics make our attractive

Language: Английский

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Enriching the metadata of map images: a deep learning approach with geographic information systems-based data augmentation

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 181 - 203

Published: Jan. 1, 2025

Language: Английский

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Dctcnet: Sequency discrete cosine transform convolution network for visual recognition

Neural Networks, Journal Year: 2025, Volume and Issue: 185, P. 107143 - 107143

Published: Jan. 18, 2025

Language: Английский

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Explainable variable-weight multi-modal based deep learning framework for catheter malposition detection

Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 103170 - 103170

Published: April 1, 2025

Language: Английский

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Deep Learning-Driven Analysis of Petrophysical Dynamics in Pay Zone Quality and Reservoir Characterization

Natural Resources Research, Journal Year: 2025, Volume and Issue: unknown

Published: April 13, 2025

Language: Английский

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DCUFormer: Enhancing pavement crack segmentation in complex environments with dual-cross/upsampling attention

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: unknown, P. 125891 - 125891

Published: Nov. 1, 2024

Language: Английский

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