Epileptic Seizures Detection Using Deep Learning Techniques: A Review

International Journal of Environmental Research and Public Health, Journal Year: 2021, Volume and Issue: 18(11), P. 5780 - 5780

Published: May 27, 2021

A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a areas, one its branches is deep learning (DL). Before the rise DL, conventional machine algorithms involving feature extraction were performed. This limited their performance ability those handcrafting features. However, in features classification are entirely automated. The advent these techniques many areas medicine, such as diagnosis has made significant advances. In this study, comprehensive overview works focused on automated seizure detection DL neuroimaging modalities presented. Various methods seizures automatically EEG MRI described. addition, rehabilitation systems developed for analyzed, summary provided. tools include cloud computing hardware required implementation algorithms. important challenges accurate with discussed. advantages limitations employing DL-based Finally, most promising models possible future delineated.

Language: Английский

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Application of artificial intelligence in wearable devices: Opportunities and challenges

Computer Methods and Programs in Biomedicine, Journal Year: 2021, Volume and Issue: 213, P. 106541 - 106541

Published: Nov. 17, 2021

Language: Английский

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Machine Learning (ML) in Medicine: Review, Applications, and Challenges

Mathematics, Journal Year: 2021, Volume and Issue: 9(22), P. 2970 - 2970

Published: Nov. 21, 2021

Today, artificial intelligence (AI) and machine learning (ML) have dramatically advanced in various industries, especially medicine. AI describes computational programs that mimic simulate human intelligence, for example, a person’s behavior solving problems or his ability learning. Furthermore, ML is subset of intelligence. It extracts patterns from raw data automatically. The purpose this paper to help researchers gain proper understanding its applications healthcare. In paper, we first present classification learning-based schemes According our proposed taxonomy, healthcare are categorized based on pre-processing methods (data cleaning methods, reduction methods), (unsupervised learning, supervised semi-supervised reinforcement learning), evaluation (simulation-based practical implementation-based real environment) (diagnosis, treatment). classification, review some studies presented We believe helps familiarize themselves with the newest research medicine, recognize their challenges limitations area, identify future directions.

Language: Английский

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The internet of medical things and artificial intelligence: trends, challenges, and opportunities

Journal of Applied Biomedicine, Journal Year: 2022, Volume and Issue: 42(3), P. 749 - 771

Published: June 2, 2022

Language: Английский

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Deep learning combined wind speed forecasting with hybrid time series decomposition and multi-objective parameter optimization

Applied Energy, Journal Year: 2022, Volume and Issue: 311, P. 118674 - 118674

Published: Feb. 12, 2022

Language: Английский

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Forecasting Cryptocurrency Prices Using LSTM, GRU, and Bi-Directional LSTM: A Deep Learning Approach

Fractal and Fractional, Journal Year: 2023, Volume and Issue: 7(2), P. 203 - 203

Published: Feb. 18, 2023

Highly accurate cryptocurrency price predictions are of paramount interest to investors and researchers. However, owing the nonlinearity market, it is difficult assess distinct nature time-series data, resulting in challenges generating appropriate predictions. Numerous studies have been conducted on prediction using different Deep Learning (DL) based algorithms. This study proposes three types Recurrent Neural Networks (RNNs): namely, Long Short-Term Memory (LSTM), Gated Unit (GRU), Bi-Directional LSTM (Bi-LSTM) for exchange rate major cryptocurrencies world, as measured by their market capitalization—Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC). The experimental results both Root Mean Squared Error (RMSE) Absolute Percentage (MAPE) show that Bi-LSTM performed better than GRU. Therefore, can be considered best algorithm. presented most compared GRU LSTM, with MAPE values 0.036, 0.041, 0.124 BTC, LTC, ETH, respectively. paper suggests models predicting prices beneficial traders. Additionally, future research should focus exploring other factors may influence prices, such social media trading volumes.

Language: Английский

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Solar Energy Production Forecasting Based on a Hybrid CNN-LSTM-Transformer Model

Mathematics, Journal Year: 2023, Volume and Issue: 11(3), P. 676 - 676

Published: Jan. 28, 2023

Green energy is very important for developing new cities with high consumption, in addition to helping environment preservation. Integrating solar into a grid challenging and requires precise forecasting of production. Recent advances Artificial Intelligence have been promising. Particularly, Deep Learning technologies achieved great results short-term time-series forecasting. Thus, it suitable use these techniques production In this work, combination Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, Transformer was used Besides, clustering technique applied the correlation analysis input data. Relevant features historical data were selected using self-organizing map. The hybrid CNN-LSTM-Transformer model Fingrid open dataset training evaluating proposed model. experimental demonstrated efficiency Compared existing models other combinations, such as LSTM-CNN, highest accuracy. show that can be trusted facilitates integration grids.

Language: Английский

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Evolution of artificial intelligence in healthcare: a 30-year bibliometric study

Frontiers in Medicine, Journal Year: 2025, Volume and Issue: 11

Published: Jan. 15, 2025

In recent years, the development of artificial intelligence (AI) technologies, including machine learning, deep and large language models, has significantly supported clinical work. Concurrently, integration with medical field garnered increasing attention from experts. This study undertakes a dynamic longitudinal bibliometric analysis AI publications within healthcare sector over past three decades to investigate current status trends fusion between medicine intelligence. Following search on Web Science, researchers retrieved all reviews original articles concerning in published January 1993 December 2023. The employed Bibliometrix, Biblioshiny, Microsoft Excel, incorporating bibliometrix R package for data mining analysis, visualized observed bibliometrics. A total 22,950 documents were collected this study. From 2023, there was discernible upward trajectory scientific output United States China emerged as primary contributors research, Harvard University leading publication volume among institutions. Notably, rapid expansion emerging topics such COVID-19 new drug discovery years is noteworthy. Furthermore, top five most cited papers 2023 pertinent theme ChatGPT. reveals sustained explosive growth trend technologies increasingly profound applications medicine. Additionally, research dynamically evolving advent technologies. Moving forward, concerted efforts bolster international collaboration enhance comprehension utilization are imperative fostering novel innovations healthcare.

Language: Английский

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A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models

Scientific Reports, Journal Year: 2022, Volume and Issue: 12(1)

Published: Feb. 14, 2022

This study aims to develop an assumption-free data-driven model accurately forecast COVID-19 spread. Towards this end, we firstly employed Bayesian optimization tune the Gaussian process regression (GPR) hyperparameters efficient GPR-based for forecasting recovered and confirmed cases in two highly impacted countries, India Brazil. However, machine learning models do not consider time dependency data series. Here, dynamic information has been taken into account alleviate limitation by introducing lagged measurements constructing investigated models. Additionally, assessed contribution of incorporated features prediction using Random Forest algorithm. Results reveal that significant improvement can be obtained proposed In addition, results highlighted superior performance GPR compared other (i.e., Support vector regression, Boosted trees, Bagged Decision tree, Forest, XGBoost) achieving averaged mean absolute percentage error around 0.1%. Finally, provided confidence level predicted based on showed predictions are within 95% interval. presents a promising shallow simple approach predicting

Language: Английский

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Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression

Cognitive Neurodynamics, Journal Year: 2022, Volume and Issue: 17(6), P. 1501 - 1523

Published: Nov. 12, 2022

Language: Английский

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