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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HBO-LSTM: Optimized long short term memory with heap-based optimizer for wind power forecasting

Energy Conversion and Management, Год журнала: 2022, Номер 268, С. 116022 - 116022

Опубликована: Июль 27, 2022

Язык: Английский

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The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction

Applied Soft Computing, Год журнала: 2022, Номер 131, С. 109739 - 109739

Опубликована: Окт. 28, 2022

Язык: Английский

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Prediction of the standardized precipitation index based on the long short-term memory and empirical mode decomposition-extreme learning machine models: The Case of Sakarya, Türkiye

Physics and Chemistry of the Earth Parts A/B/C, Год журнала: 2023, Номер 131, С. 103418 - 103418

Опубликована: Май 18, 2023

Язык: Английский

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Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives

Wiley Interdisciplinary Reviews Climate Change, Год журнала: 2024, Номер unknown

Опубликована: Сен. 3, 2024

Abstract Extreme events such as heat waves and cold spells, droughts, heavy rain, storms are particularly challenging to predict accurately due their rarity chaotic nature, because of model limitations. However, recent studies have shown that there might be systemic predictability is not being leveraged, whose exploitation could meet the need for reliable predictions aggregated extreme weather measures on timescales from weeks decades ahead. Recently, numerous been devoted use artificial intelligence (AI) study make climate predictions. AI techniques great potential improve prediction uncover links large‐scale local drivers. Machine deep learning explored enhance prediction, while causal discovery explainable tested our understanding processes underlying predictability. Hybrid combining AI, which can reveal unknown spatiotemporal connections data, with models provide theoretical foundation interpretability physical world, improving skills extremes climate‐relevant possible. challenges persist in various aspects, including data curation, uncertainty, generalizability, reproducibility methods, workflows. This review aims at overviewing achievements subseasonal decadal timescale. A few best practices identified increase trust these novel techniques, future perspectives envisaged further scientific development. article categorized under: Climate Models Modeling > Knowledge Generation The Social Status Change Science Decision Making

Язык: Английский

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Load based dynamic channel allocation model to enhance the performance of device-to-device communication in WPAN

Wireless Networks, Год журнала: 2024, Номер 30(4), С. 2477 - 2509

Опубликована: Фев. 22, 2024

Abstract The modern communication network has advanced to such an extent that it is now possible for devices within a wireless personal area (WPAN) communicate among themselves directly. However, the limited shared radio resources of WPAN lead numerous issues, as cross-layer interference and data collisions, which wind up affecting quality communication. A load based dynamic channel allocation (LB-DCA) model been proposed enhance performance device-to-device in WPAN. This uses several control schemes collaboration with estimation balancing mechanisms allocate manage efficiently. objective this achieve high throughput, low energy consumption. implemented are on distributed coordination cell-splitting approach. These utilized estimate usage number active nodes network. done by using new efficiency formula. Further, takes into account hops factor values. obtained 98.58% CSI, 95.86% MCC, 96.35% delta-P, 97.96% FMI, 99.83% BMI, 21.52% enhanced spectrum efficiency, 16.38% scalability, 18.79% signal quality, 18.64% power 18.89% efficiency.

Язык: Английский

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Drought Impact, Vulnerability, Risk Assessment, Management and Mitigation under Climate Change: A Comprehensive Review

KSCE Journal of Civil Engineering, Год журнала: 2025, Номер 29(1), С. 100120 - 100120

Опубликована: Янв. 1, 2025

Язык: Английский

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Framework for Predicting and Modeling Stock Market Prices Based on Deep Learning Algorithms

Electronics, Год журнала: 2022, Номер 11(19), С. 3149 - 3149

Опубликована: Сен. 30, 2022

The creation of trustworthy models the equities market enables investors to make better-informed choices. A trading model may lessen risks that are connected with investing and it possible for traders choose companies offer highest dividends. However, due high degree correlation between stock prices, analysis is made more difficult by batch processing approaches. prediction has entered a technologically advanced era advent technological marvels such as global digitization. For this reason, artificial intelligence have become very important continuous increase in capitalization. novelty proposed study development robustness time series based on deep leaning forecasting future values marketing. primary purpose was develop an intelligent framework capability predicting direction which prices will move financial inputs. Among cutting-edge technologies, backbone many different predict markets. In particular, learning strategies been effective at behavior. article, we propose long short-term memory (LSTM) hybrid convolutional neural network (CNN-LSTM) LSTM closing Tesla, Inc. Apple, These predictions were using data collected over past two years. mean squared error (MSE), root (RMSE), normalization (NRMSE), Pearson’s (R) measures used computation findings models. Between models, CNN-LSTM scored slightly better (Tesla: R-squared = 98.37%; Apple: 99.48%). showed superior performance compared single existing systems prices.

Язык: Английский

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Forecasting weekly reference evapotranspiration using Auto Encoder Decoder Bidirectional LSTM model hybridized with a Boruta-CatBoost input optimizer

Computers and Electronics in Agriculture, Год журнала: 2022, Номер 198, С. 107121 - 107121

Опубликована: Июнь 9, 2022

Язык: Английский

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Multi-step daily forecasting of reference evapotranspiration for different climates of India: A modern multivariate complementary technique reinforced with ridge regression feature selection

Agricultural Water Management, Год журнала: 2022, Номер 272, С. 107812 - 107812

Опубликована: Июль 30, 2022

Язык: Английский

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