Intelligent fault identification of hydraulic pump using deep adaptive normalized CNN and synchrosqueezed wavelet transform

Reliability Engineering & System Safety, Journal Year: 2022, Volume and Issue: 224, P. 108560 - 108560

Published: May 7, 2022

Language: Английский

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Machine learning applications for COVID-19 outbreak management

Neural Computing and Applications, Journal Year: 2022, Volume and Issue: 34(18), P. 15313 - 15348

Published: June 10, 2022

Language: Английский

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A lightweight CNN-based network on COVID-19 detection using X-ray and CT images

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 146, P. 105604 - 105604

Published: May 11, 2022

Language: Английский

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A Novel Data Augmentation-Based Brain Tumor Detection Using Convolutional Neural Network

Applied Sciences, Journal Year: 2022, Volume and Issue: 12(8), P. 3773 - 3773

Published: April 8, 2022

Brain tumor is a severe cancer and life-threatening disease. Thus, early detection crucial in the process of treatment. Recent progress field deep learning has contributed enormously to health industry medical diagnosis. Convolutional neural networks (CNNs) have been intensively used as approach detect brain tumors using MRI images. Due limited dataset, algorithms CNNs should be improved more efficient. one most known techniques improve model performance Data Augmentation. This paper presents detailed review various CNN architectures highlights characteristics particular models such ResNet, AlexNet, VGG. After that, we provide an efficient method for detecting magnetic resonance imaging (MRI) datasets based on data augmentation. Evaluation metrics values proposed solution prove that it succeeded being contribution previous studies terms both architectural design high success.

Language: Английский

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Deep learning techniques for detection and prediction of pandemic diseases: a systematic literature review

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(2), P. 5893 - 5927

Published: May 29, 2023

Abstract Deep learning (DL) is becoming a fast-growing field in the medical domain and it helps timely detection of any infectious disease (IDs) essential to management diseases prediction future occurrences. Many scientists scholars have implemented DL techniques for pandemics, IDs other healthcare-related purposes, these outcomes are with various limitations research gaps. For purpose achieving an accurate, efficient less complicated DL-based system therefore, this study carried out systematic literature review (SLR) on pandemics using techniques. The survey anchored by four objectives state-of-the-art forty-five papers seven hundred ninety retrieved from different scholarly databases was analyze evaluate trend application areas pandemics. This used tables graphs extracted related articles online repositories analysis showed that good tool pandemic prediction. Scopus Web Science given attention current because they contain suitable scientific findings subject area. Finally, presents forty-four (44) studies technique performances. challenges identified include low performance model due computational complexities, improper labeling absence high-quality dataset among others. suggests possible solutions such as development improved or reduction output layer architecture pandemic-prone considerations.

Language: Английский

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Segmentation-Based Classification Deep Learning Model Embedded with Explainable AI for COVID-19 Detection in Chest X-ray Scans

Diagnostics, Journal Year: 2022, Volume and Issue: 12(9), P. 2132 - 2132

Published: Sept. 2, 2022

Background and Motivation: COVID-19 has resulted in a massive loss of life during the last two years. The current imaging-based diagnostic methods for detection multiclass pneumonia-type chest X-rays are not so successful clinical practice due to high error rates. Our hypothesis states that if we can have segmentation-based classification rate <5%, typically adopted 510 (K) regulatory purposes, system be adapted settings. Method: This study proposes 16 types deep learning-based systems automatic, rapid, precise COVID-19. segmentation networks, namely UNet UNet+, along with eight models, VGG16, VGG19, Xception, InceptionV3, Densenet201, NASNetMobile, Resnet50, MobileNet, were applied select best-suited combination networks. Using cross-entropy function, performance was evaluated by Dice, Jaccard, area-under-the-curve (AUC), receiver operating characteristics (ROC) validated using Grad-CAM explainable AI framework. Results: best performing model UNet, which exhibited accuracy, loss, AUC 96.35%, 0.15%, 94.88%, 90.38%, 0.99 (p-value <0.0001), respectively. UNet+Xception, precision, recall, F1-score, 97.45%, 97.46%, 97.43%, 0.998 outperformed existing models. mean improvement UNet+Xception over all remaining studies 8.27%. Conclusion: is viable option as (error <5%) holds true thus adaptable practice.

Language: Английский

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Real-World Evidence—Current Developments and Perspectives

International Journal of Environmental Research and Public Health, Journal Year: 2022, Volume and Issue: 19(16), P. 10159 - 10159

Published: Aug. 16, 2022

Real-world evidence (RWE) is increasingly involved in the early benefit assessment of medicinal drugs. It expected that RWE will help to speed up approval processes comparable developments vaccine research during COVID-19 pandemic. Definitions are diverse, marking highly fluid status this field. So far, comprises information produced from data routinely collected on patient's health and/or delivery care various sources other than traditional clinical trials. These can include electronic records, claims, patient-generated including home-use settings, mobile devices, as well patient, product, and disease registries. The aim present update was review current guidelines, mainly U.S. Europe over last decade. has already been included procedures regulatory authorities, reflecting its actual acceptance growing importance evaluating accelerating new therapies. However, since still a transition process, number gaps field have explored, more guidance consented definition necessary increase implementation real-world data.

Language: Английский

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Computer vision classification of dry beans (Phaseolus vulgaris L.) based on deep transfer learning techniques

European Food Research and Technology, Journal Year: 2022, Volume and Issue: 248(11), P. 2707 - 2725

Published: Aug. 5, 2022

Language: Английский

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COVID-19 detection on chest X-ray images using Homomorphic Transformation and VGG inspired deep convolutional neural network

Journal of Applied Biomedicine, Journal Year: 2022, Volume and Issue: 43(1), P. 1 - 16

Published: Nov. 24, 2022

Language: Английский

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An augmented Snake Optimizer for diseases and COVID-19 diagnosis

Biomedical Signal Processing and Control, Journal Year: 2023, Volume and Issue: 84, P. 104718 - 104718

Published: Feb. 17, 2023

Language: Английский

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