IRCM‐Caps: An X‐ray image detection method for COVID‐19

The Clinical Respiratory Journal, Год журнала: 2023, Номер 17(5), С. 364 - 373

Опубликована: Март 15, 2023

Abstract Objective COVID‐19 is ravaging the world, but traditional reverse transcription‐polymerase reaction (RT‐PCR) tests are time‐consuming and have a high false‐negative rate lack of medical equipment. Therefore, lung imaging screening methods proposed to diagnose due its fast test speed. Currently, commonly used convolutional neural network (CNN) model requires large number datasets, accuracy basic capsule for multiple classification limital. For this reason, paper proposes novel based on CNN CapsNet. Methods The integrates And attention mechanism module multi‐branch lightweight applied enhance performance. Use contrast adaptive histogram equalization (CLAHE) algorithm preprocess image contrast. preprocessed images input into training, ReLU was as activation function adjust parameters achieve optimal. Result dataset includes 1200 X‐ray (400 COVID‐19, 400 viral pneumonia, normal), we replace VGG16, InceptionV3, Xception, Inception‐Resnet‐v2, ResNet50, DenseNet121, MoblieNetV2 integrate with Compared CapsNet, improves 6.96%, 7.83%, 9.37%, 10.47%, 10.38% in accuracy, area under curve (AUC), recall, F1 scores, respectively. In binary experiment, compared AUC, recall rate, score were increased by 5.33%, 5.34%, 2.88%, 8.00%, 5.56%, Conclusion embedded advantages has good effect small dataset.

Язык: Английский

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A neural learning approach for simultaneous object detection and grasp detection in cluttered scenes

Frontiers in Computational Neuroscience, Год журнала: 2023, Номер 17

Опубликована: Фев. 20, 2023

Object detection and grasp are essential for unmanned systems working in cluttered real-world environments. Detecting configurations each object the scene would enable reasoning manipulations. However, finding relationships between objects is still a challenging problem. To achieve this, we propose novel neural learning approach, namely SOGD, to predict best configuration detected from an RGB-D image. The background first filtered out via 3D-plane-based approach. Then two separate branches designed detect candidates, respectively. relationship proposals candidates learned by additional alignment module. A series of experiments conducted on public datasets (Cornell Grasp Dataset Jacquard Dataset) results demonstrate superior performance our SOGD against SOTA methods predicting reasonable "from scene."

Язык: Английский

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Hybrid Nature-Inspired Based Oversampling and Feature Selection Approach for Imbalance Data Streams Classification

Опубликована: Фев. 8, 2023

A big data stream is described using 5 $\mathrm{V}prime$s (Volume, Variety, Velocity, Variability, and Veracity). These characteristics impose various challenges. In many cases, streams are unbalanced, making traditional mining approaches impossible to employ. The standard not suitable for imbalanced achieving analytical efficiency because they require periodic analyses, but requires real-time analytics. Additionally, the induction model must be re-run rebuilt each time add up most recent data. Mining these unique streams, on other hand, one of intriguing research areas. Deep learning (DL) algorithms were developed increase classification performance issues requiring large sets with varying types characteristics. Feature selection (F.S.) a critical stage in any application. F.S. entails eliminating superfluous redundant characteristics, resulting prediction that more efficient, interpretable, fast. While complete solutions available F.S., managing massive need instantaneous processing challenging by its own nature. This work provides hybrid metaheuristic strategy FFSMOTE oversampling Honey Bee algorithm feature selection. Hybridization aims improve processes combing advantage both algorithms. Furthermore, Ensemble classifiers used classification.

Язык: Английский

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Characterizing the Internal Structure of Chinese Steamed Bread during Storage for Quality Evaluation Using X-ray Computer Tomography

Sensors, Год журнала: 2023, Номер 23(21), С. 8804 - 8804

Опубликована: Окт. 29, 2023

Chinese steamed bread (CSB) is a traditional food of the nation, and preservation its quality freshness during storage very important for industrial production. Therefore, it necessary to study characteristics CSB. Non-destructive CT technology was utilized characterize visualize microstructure CSB storage, also further changes. Two-dimensional three-dimensional images CSBs were obtained through X-ray scanning 3D reconstruction. Morphological parameters acquired based on image using processing methods. Additionally, commonly used physicochemical indexes (hardness, flexibility, moisture content) evaluation analyzed. Moreover, correlation analysis conducted morphological CSBs. The results showed that negatively correlated with content (Pearson coefficient range-0.86~-0.97) positively hardness range-0.87~0.99). indicate inspiring capability in CSB, providing potential analytical method detection production

Язык: Английский

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A computational fractional order model for optimal control of wearable healthcare monitoring devices for maternal health

Healthcare Analytics, Год журнала: 2024, Номер 5, С. 100308 - 100308

Опубликована: Фев. 11, 2024

The post-COVID-19 landscape has propelled the global telemedicine sector to a projected valuation of USD 91.2 billion by 2022, with remarkable compounded annual growth rate (CAGR) 18.6% from 2023 2030. This paper introduces an analytical wearable healthcare monitoring device (WHMD) designed for timely detection and seamless transmission crucial health vitals telemedical cloud agents. fractional order modeling approach is employed delineate efficacy WHMD in pregnancy-related contexts. Caputo calculus framework harnessed articulate device's capturing communicating vital data medical experts precisely at layer. Our formulation establishes model's positivity, existence, uniqueness, substantiating its mathematical validity. investigation encompasses two pivotal equilibrium points: disease-free accounting disease presence, both interconnected WHMD. explores impact integrating during pregnancy cycles. Analytical findings show that basic reproduction number remains below unity, showing mitigating complications. Furthermore, multi-stage differential transform method (FMSDTM) facilitates optimal control scenarios involving utilisation among pregnant patients. proposed exhibits robustness conclusively elucidates dynamic potential bolstering maternal throughout pregnancy. significantly contributes evolving research, underlining critical role WHMDs safeguarding well-being risks edge reconfigurable architectures.

Язык: Английский

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A comprehensive survey on Covid-19 disease diagnosis: Datasets, deep learning approaches and challenges

AIP conference proceedings, Год журнала: 2024, Номер 3104, С. 040001 - 040001

Опубликована: Янв. 1, 2024

Millions of people were affected by the global health disaster brought on coronavirus (Covid-19) pandemic in December 2019, severely impacting international economy. Deep learning (DL) methods successfully analyzed and detected infectious areas radiological images. This research analyses Covid-19 open-source datasets Learning methodologies develops a categorization based diagnostic approaches at most using X-ray CT imaging. Coronavirus diagnosis image region level analysis is systematically divided into classification, segmentation, multi-stage procedures. Furthermore, discussion significant obstacles potential future directions included.

Язык: Английский

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An explainable decision model based on extended belief-rule-based systems to predict admission to the intensive care unit during COVID-19 breakout

Applied Soft Computing, Год журнала: 2023, Номер 149, С. 110961 - 110961

Опубликована: Окт. 19, 2023

Язык: Английский

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Enhancing IoT Networking Through Predictive Big Data Processing

Опубликована: Март 15, 2024

the net of things (IoT) promises to revolutionize way humans interact with their surroundings. Notwithstanding this promise, deployment IoT networks is hindered by using several challenges, particularly related communication, scalability, and electricity performance. This paper proposes a technique address those demanding situations through predictive big statistics processing blended use new network technology. In particular, prediction fashions, system studying, analytics algorithms will permit investigate past present facts in order forecast destiny traits. Additionally, latest existing technologies, which include extremely-low strength mesh networks, 5G, electricity-conscious routing protocols help improve overall performance networks. conclusion, gives method decorate characteristic as more effective, efficient, reliable communique for all devices.

Язык: Английский

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Anterior Cruciate Ligament Tear Detection Based on T-Distribution Slice Attention Framework with Penalty Weight Loss Optimisation

Bioengineering, Год журнала: 2024, Номер 11(9), С. 880 - 880

Опубликована: Авг. 30, 2024

Anterior cruciate ligament (ACL) plays an important role in stabilising the knee joint, prevents excessive anterior translation of tibia, and provides rotational stability. ACL injuries commonly occur as a result rapid deceleration, sudden change direction, or direct impact to during sports activities. Although several deep learning techniques have recently been applied detection tears, challenges such effective slice filtering nuanced relationship between varying tear grades still remain underexplored. This study used advanced model that integrated T-distribution-based attention mechanism with penalty weight loss function improve performance for tears. A T-distribution module was effectively utilised develop robust system model. By incorporating class relationships substituting conventional cross-entropy function, classification accuracy our is markedly increased. The combination shows significant improvements diagnostic across six different backbone networks. In particular, VGG-Slice-Weight provided area score 0.9590 under receiver operating characteristic curve (AUC). framework this offers tool supports better injury clinical diagnosis practice.

Язык: Английский

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Estimating nosocomial infection and its outcomes in hospital patients in England with a diagnosis of COVID-19 using machine learning

International Journal of Data Science and Analytics, Год журнала: 2023, Номер unknown

Опубликована: Июль 5, 2023

Язык: Английский

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