Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications

Information, Год журнала: 2024, Номер 15(9), С. 517 - 517

Опубликована: Авг. 25, 2024

Recurrent neural networks (RNNs) have significantly advanced the field of machine learning (ML) by enabling effective processing sequential data. This paper provides a comprehensive review RNNs and their applications, highlighting advancements in architectures, such as long short-term memory (LSTM) networks, gated recurrent units (GRUs), bidirectional LSTM (BiLSTM), echo state (ESNs), peephole LSTM, stacked LSTM. The study examines application to different domains, including natural language (NLP), speech recognition, time series forecasting, autonomous vehicles, anomaly detection. Additionally, discusses recent innovations, integration attention mechanisms development hybrid models that combine with convolutional (CNNs) transformer architectures. aims provide ML researchers practitioners overview current future directions RNN research.

Язык: Английский

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Deep Learning for Credit Card Fraud Detection: A Review of Algorithms, Challenges, and Solutions

IEEE Access, Год журнала: 2024, Номер 12, С. 96893 - 96910

Опубликована: Янв. 1, 2024

Deep learning (DL), a branch of machine (ML), is the core technology in today's technological advancements and innovations. learning-based approaches are state-of-the-art methods used to analyse detect complex patterns large datasets, such as credit card transactions. However, most fraud models literature based on traditional ML algorithms, recently, there has been rise applications deep techniques. This study reviews recent DL-based presents concise description performance comparison widely DL techniques, including convolutional neural network (CNN), simple recurrent (RNN), long short-term memory (LSTM), gated unit (GRU). Additionally, an attempt made discuss suitable metrics, common challenges encountered when training using architectures potential solutions, which lacking previous studies would benefit researchers practitioners. Meanwhile, experimental results analysis real-world dataset indicate robustness detection.

Язык: Английский

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Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications

Опубликована: Авг. 12, 2024

Recurrent Neural Networks (RNNs) have significantly advanced the field of machine learning by enabling effective processing sequential data. This paper provides a comprehensive review RNNs and their applications, highlighting advancements in architectures such as Long Short-Term Memory (LSTM) networks, Gated Units (GRUs), Bidirectional LSTM (BiLSTM), stacked LSTM. The study examines application different domains, including natural language (NLP), speech recognition, financial time series forecasting, bioinformatics, autonomous vehicles, anomaly detection. Additionally, discusses recent innovations, integration attention mechanisms development hybrid models that combine with convolutional neural networks (CNNs) transformer architectures. aims to provide researchers practitioners overview current state future directions RNN research.

Язык: Английский

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Supervised machine learning in drug discovery and development: Algorithms, applications, challenges, and prospects

Machine Learning with Applications, Год журнала: 2024, Номер 17, С. 100576 - 100576

Опубликована: Июль 24, 2024

Язык: Английский

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Optimized Ensemble Learning Approach with Explainable AI for Improved Heart Disease Prediction

Information, Год журнала: 2024, Номер 15(7), С. 394 - 394

Опубликована: Июль 8, 2024

Recent advances in machine learning (ML) have shown great promise detecting heart disease. However, to ensure the clinical adoption of ML models, they must not only be generalizable and robust but also transparent explainable. Therefore, this research introduces an approach that integrates robustness ensemble algorithms with precision Bayesian optimization for hyperparameter tuning interpretability offered by Shapley additive explanations (SHAP). The classifiers considered include adaptive boosting (AdaBoost), random forest, extreme gradient (XGBoost). experimental results on Cleveland Framingham datasets demonstrate optimized XGBoost model achieved highest performance, specificity sensitivity values 0.971 0.989 dataset 0.921 0.975 dataset, respectively.

Язык: Английский

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Patterns among factors associated with myocardial infarction: chi-squared automatic interaction detection tree and binary logit model

BMC Public Health, Год журнала: 2025, Номер 25(1)

Опубликована: Янв. 24, 2025

Although mortality from myocardial infarction (MI) has declined worldwide due to advancements in emergency medical care and evidence-based pharmacological treatments, MI remains a significant contributor global cardiovascular morbidity. This study aims examine the risk factors associated with individuals who have experienced an Türkiye. Microdata obtained Türkiye Health Survey conducted by Turkish Statistical Institute 2019 were used this study. Binary logistic regression, Chi-Square, CHAID analyses identify affecting MI. The analysis identified several increased likelihood of MI, including hyperlipidemia, hypertension, diabetes, chronic disease status, male gender, older age, single marital lower education level, unemployment. Marginal effects revealed that elevated hyperlipidemia levels probability 4.6%, while presence or depression further heightened risk. Additionally, diseases lasting longer than six months found higher In contrast, such as being female, having education, married, employed, engaging moderate physical activity, alcohol consumption reduced To prevent emphasis should be placed on enhancing general health literacy. There focus increasing preventive public practices improve variables related healthy lifestyle behaviours, hyperlipidemia.

Язык: Английский

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Machine Learning Methods in Clinical Flow Cytometry

Cancers, Год журнала: 2025, Номер 17(3), С. 483 - 483

Опубликована: Фев. 1, 2025

This review will explore the integration of machine learning (ML) techniques to enhance analysis increasingly complex and voluminous flow cytometry data, as traditional manual methods are insufficient for handling this data. We attempt provide a comprehensive introduction ML in cytometry, detailing transition from gating computational emphasizing importance data quality. Key discussed, including supervised like logistic regression, support vector machines, neural networks, which rely on labeled classify disease states. Unsupervised methods, such k-means clustering, FlowSOM, UMAP, t-SNE, highlighted their ability identify novel cell populations without predefined labels. also delve into newer semi-supervised weakly leverage partial labeling improve model performance. Practical aspects implementing clinical settings addressed, regulatory considerations, preprocessing, training, validation, generalizability, we underscore collaborative effort required among pathologists, scientists, laboratory professionals ensure robust development deployment. Finally, show transformative potential uncovering new biological insights through advanced techniques.

Язык: Английский

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Predicting and Mapping of Soil Organic Matter with Machine Learning in the Black Soil Region of the Southern Northeast Plain of China

Agronomy, Год журнала: 2025, Номер 15(3), С. 533 - 533

Опубликована: Фев. 22, 2025

The estimation of soil organic matter (SOM) content is essential for understanding the chemical, physical, and biological functions soil. It also an important attribute reflecting quality black In this study, machine learning algorithms support vector (SVM), neural network (NN), decision tree (DT), random forest (RF), extreme gradient boosting (GBM), generalized linear model (GLM) were used to study accurate prediction SOM in Tieling County, City, Liaoning Province, China. models trained by using 1554 surface samples 19 auxiliary variables. Recursive feature elimination was as a selection method identify effective results showed that Normalized Difference Vegetation Index (NDVI) elevation key Based on 10-fold cross-validation, RF had highest accuracy. terms accuracy, coefficient determination 0.77, root mean square error 2.85. average 20.15 g/kg. spatial distribution shows higher concentrated east west, while lower found middle. cultivated land than land.

Язык: Английский

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Deep Learning in Finance: A Survey of Applications and Techniques

AI, Год журнала: 2024, Номер 5(4), С. 2066 - 2091

Опубликована: Окт. 28, 2024

Machine learning (ML) has transformed the financial industry by enabling advanced applications such as credit scoring, fraud detection, and market forecasting. At core of this transformation is deep (DL), a subset ML that robust in processing analyzing complex large datasets. This paper provides comprehensive overview key models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Deep Belief (DBNs), Transformers, Generative Adversarial (GANs), Reinforcement Learning (Deep RL). Beyond summarizing their mathematical foundations processes, study offers new insights into how these models are applied real-world contexts, highlighting specific advantages limitations tasks algorithmic trading, risk management, portfolio optimization. It also examines recent advances emerging trends alongside critical challenges data quality, model interpretability, computational complexity. These can guide future research directions toward developing more efficient, robust, explainable address evolving needs sector.

Язык: Английский

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Machine Learning in Solid‐State Hydrogen Storage Materials: Challenges and Perspectives

Advanced Materials, Год журнала: 2024, Номер unknown

Опубликована: Дек. 20, 2024

Abstract Machine learning (ML) has emerged as a pioneering tool in advancing the research application of high‐performance solid‐state hydrogen storage materials (HSMs). This review summarizes state‐of‐the‐art ML resolving crucial issues such low capacity and unfavorable de‐/hydrogenation cycling conditions. First, datasets, feature descriptors, prevalent models tailored for HSMs are described. Specific examples include successful titanium‐based, rare‐earth‐based, solid solution, magnesium‐based, complex HSMs, showcasing its role exploiting composition–structure–property relationships designing novel specific applications. One representative works is single‐phase Ti‐based HSM with superior cost‐effective comprehensive properties, to fuel cell feeding system at ambient temperature pressure through high‐throughput composition‐performance scanning. More importantly, this also identifies critically analyzes key challenges faced by domain, including poor data quality availability, balance between model interpretability accuracy, together feasible countermeasures suggested ameliorate these problems. In summary, work outlines roadmap enhancing ML's utilization research, promoting more efficient sustainable energy solutions.

Язык: Английский

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