A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications

Journal Of Big Data, Год журнала: 2023, Номер 10(1)

Опубликована: Апрель 14, 2023

Abstract Data scarcity is a major challenge when training deep learning (DL) models. DL demands large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate train frameworks. Usually, manual labeling needed provide labeled data, which typically involves human annotators with vast background knowledge. This annotation process costly, time-consuming, and error-prone. every framework fed by significant automatically learn representations. Ultimately, larger would generate better model its performance also application dependent. issue the main barrier for dismissing use DL. Having sufficient first step toward any successful trustworthy application. paper presents holistic survey on state-of-the-art techniques deal models overcome three challenges including small, imbalanced datasets, lack generalization. starts listing techniques. Next, types architectures are introduced. After that, solutions address listed, such as Transfer Learning (TL), Self-Supervised (SSL), Generative Adversarial Networks (GANs), Model Architecture (MA), Physics-Informed Neural Network (PINN), Deep Synthetic Minority Oversampling Technique (DeepSMOTE). Then, these were followed some related tips about acquisition prior purposes, well recommendations ensuring trustworthiness dataset. The ends list that suffer from scarcity, several alternatives proposed in order more each Electromagnetic Imaging (EMI), Civil Structural Health Monitoring, Medical imaging, Meteorology, Wireless Communications, Fluid Mechanics, Microelectromechanical system, Cybersecurity. To best authors’ knowledge, this review offers comprehensive overview strategies tackle

Язык: Английский

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Dynamic Levy Flight Chimp Optimization

Knowledge-Based Systems, Год журнала: 2021, Номер 235, С. 107625 - 107625

Опубликована: Окт. 22, 2021

Язык: Английский

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Improved deep convolutional neural networks using chimp optimization algorithm for Covid19 diagnosis from the X-ray images

Expert Systems with Applications, Год журнала: 2022, Номер 213, С. 119206 - 119206

Опубликована: Ноя. 4, 2022

Язык: Английский

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Hybrid CNN and XGBoost Model Tuned by Modified Arithmetic Optimization Algorithm for COVID-19 Early Diagnostics from X-ray Images

Electronics, Год журнала: 2022, Номер 11(22), С. 3798 - 3798

Опубликована: Ноя. 18, 2022

Developing countries have had numerous obstacles in diagnosing the COVID-19 worldwide pandemic since its emergence. One of most important ways to control spread this disease begins with early detection, which allows that isolation and treatment could perhaps be started. According recent results, chest X-ray scans provide information about onset infection, may evaluated so diagnosis can begin sooner. This is where artificial intelligence collides skilled clinicians’ diagnostic abilities. The suggested study’s goal make a contribution battling epidemic by using simple convolutional neural network (CNN) model construct an automated image analysis framework for recognizing afflicted data. To improve classification accuracy, fully connected layers CNN were replaced efficient extreme gradient boosting (XGBoost) classifier, used categorize extracted features layers. Additionally, hybrid version arithmetic optimization algorithm (AOA), also developed facilitate proposed research, tune XGBoost hyperparameters images. Reported experimental data showed approach outperforms other state-of-the-art methods, including cutting-edge metaheuristics algorithms, tested same framework. For validation purposes, balanced images dataset 12,000 observations, belonging normal, viral pneumonia classes, was used. method, tuned introduced AOA, superior performance, achieving accuracy approximately 99.39% weighted average precision, recall F1-score 0.993889, 0.993887 0.993887, respectively.

Язык: Английский

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Cov-Net: A computer-aided diagnosis method for recognizing COVID-19 from chest X-ray images via machine vision

Expert Systems with Applications, Год журнала: 2022, Номер 207, С. 118029 - 118029

Опубликована: Июль 5, 2022

Язык: Английский

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Brain tumor segmentation based on optimized convolutional neural network and improved chimp optimization algorithm

Computers in Biology and Medicine, Год журнала: 2023, Номер 168, С. 107723 - 107723

Опубликована: Ноя. 19, 2023

Язык: Английский

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An enhanced and efficient approach for feature selection for chronic human disease prediction: A breast cancer study

Heliyon, Год журнала: 2024, Номер 10(5), С. e26799 - e26799

Опубликована: Фев. 28, 2024

Computer-aided diagnosis (CAD) systems play a vital role in modern research by effectively minimizing both time and costs. These support healthcare professionals like radiologists their decision-making process efficiently detecting abnormalities as well offering accurate dependable information. heavily depend on the efficient selection of features to accurately categorize high-dimensional biological data. can subsequently assist related medical conditions. The task identifying patterns biomedical data be quite challenging due presence numerous irrelevant or redundant features. Therefore, it is crucial propose then utilize feature (FS) order eliminate these primary goal FS approaches improve accuracy classification eliminating that are less informative. phase plays critical attaining optimal results machine learning (ML)-driven CAD systems. effectiveness ML models significantly enhanced incorporating during training phase. This empirical study presents methodology for using technique. proposed approach incorporates three soft computing-based optimization algorithms, namely Teaching Learning-Based Optimization (TLBO), Elephant Herding (EHO), hybrid algorithm two. algorithms were previously employed; however, addressing issues predicting human diseases has not been investigated. following evaluation focuses categorization benign malignant tumours publicly available Wisconsin Diagnostic Breast Cancer (WDBC) benchmark dataset. five-fold cross-validation technique employed mitigate risk over-fitting. approach's proficiency determined based several metrics, including sensitivity, specificity, precision, accuracy, area under receiver-operating characteristic curve (AUC), F1-score. best value computed through suggested 97.96%. clinical decision system demonstrates highly favourable performance outcome, making valuable tool practitioners secondary opinion reducing overburden expert practitioners.

Язык: Английский

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AOAAO: The Hybrid Algorithm of Arithmetic Optimization Algorithm With Aquila Optimizer

IEEE Access, Год журнала: 2022, Номер 10, С. 10907 - 10933

Опубликована: Янв. 1, 2022

Many new algorithms have been proposed to solve the mathematical equations formulated describe real-world problems. But there still does not exist one algorithm that could problems all. And most of defects in some aspects, they need be improved application. In order find a more efficient optimization and inspired by better performance Arithmetic Optimization (AOA) Aquila Optimizer (AO), we hybridization them abbreviated AOAAO this paper. Considering Harris Hawk (HHO) algorithm, an energy parameter E was also introduced balance exploration exploitation procedures individuals swarms, furthermore, piecewise linear map decrease randomness parameter. Pseudo code presented, Simulation experiments were carried out on benchmark functions three classical engineering involved optimization. Nine popular well demonstrated included for comparison. Results confirmed would with faster convergence rate, higher accuracy.

Язык: Английский

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Optimization of constraint engineering problems using robust universal learning chimp optimization

Advanced Engineering Informatics, Год журнала: 2022, Номер 53, С. 101636 - 101636

Опубликована: Май 30, 2022

Язык: Английский

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Automated multi-class classification of lung diseases from CXR-images using pre-trained convolutional neural networks

Expert Systems with Applications, Год журнала: 2022, Номер 211, С. 118650 - 118650

Опубликована: Авг. 27, 2022

Язык: Английский

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