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

Information, Journal Year: 2024, Volume and Issue: 15(9), P. 517 - 517

Published: Aug. 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.

Language: Английский

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A Survey of Decision Trees: Concepts, Algorithms, and Applications

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 86716 - 86727

Published: Jan. 1, 2024

Language: Английский

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

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 96893 - 96910

Published: Jan. 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.

Language: Английский

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

Published: Aug. 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.

Language: Английский

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

Information, Journal Year: 2024, Volume and Issue: 15(7), P. 394 - 394

Published: July 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.

Language: Английский

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A Comprehensive Review of Deep Learning: Architectures, Recent Advances, and Applications

Information, Journal Year: 2024, Volume and Issue: 15(12), P. 755 - 755

Published: Nov. 27, 2024

Deep learning (DL) has become a core component of modern artificial intelligence (AI), driving significant advancements across diverse fields by facilitating the analysis complex systems, from protein folding in biology to molecular discovery chemistry and particle interactions physics. However, field deep is constantly evolving, with recent innovations both architectures applications. Therefore, this paper provides comprehensive review DL advances, covering evolution applications foundational models like convolutional neural networks (CNNs) Recurrent Neural Networks (RNNs), as well such transformers, generative adversarial (GANs), capsule networks, graph (GNNs). Additionally, discusses novel training techniques, including self-supervised learning, federated reinforcement which further enhance capabilities models. By synthesizing developments identifying current challenges, insights into state art future directions research, offering valuable guidance for researchers industry experts.

Language: Английский

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A Hybrid Deep Learning Approach with Generative Adversarial Network for Credit Card Fraud Detection

Technologies, Journal Year: 2024, Volume and Issue: 12(10), P. 186 - 186

Published: Oct. 2, 2024

Credit card fraud detection is a critical challenge in the financial industry, with substantial economic implications. Conventional machine learning (ML) techniques often fail to adapt evolving patterns and underperform imbalanced datasets. This study proposes hybrid deep framework that integrates Generative Adversarial Networks (GANs) Recurrent Neural (RNNs) enhance capabilities. The GAN component generates realistic synthetic fraudulent transactions, addressing data imbalance enhancing training set. discriminator, implemented using various DL architectures, including Simple RNN, Long Short-Term Memory (LSTM) networks, Gated Units (GRUs), trained distinguish between real transactions further fine-tuned classify as or legitimate. Experimental results demonstrate significant improvements over traditional methods, GAN-GRU model achieving sensitivity of 0.992 specificity 1.000 on European credit dataset. work highlights potential GANs combined architectures provide more effective adaptable solution for detection.

Language: Английский

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Comparative Study of Lightweight CNN Architectures for Maize Leaf Disease Detection

Deleted Journal, Journal Year: 2025, Volume and Issue: 3(2), P. 4 - 13

Published: March 1, 2025

The study evaluates different compact Convolutional Neural Networks (CNNs) used to detect maize leaf diseases because they serve vital functions in precision agriculture. Testing involved evaluating the performance of five various models including VGG19, ResNet50, MobileNetV3, Custom MobileNetV3 and InceptionV3 for detection four disease types namely Blight, Common Rust, Gray Leaf Spot Healthy. analysis demonstrates that surpasses all competing through its 97.63% accuracy 96.68% rating as well 97.96% recall value. model showed complete ability which indicated exceptional efficiency spotting this condition. ResNet50 displayed good by effectively detecting Rust together with Healthy leaves. level was lower based on results observed model. demonstrate surpassed both VGG19 MobileNetV3. poorest resulted from Wheat Blight being confused one another. stands out best since it delivers reliable while maximizing thus making appropriate limited resource scenarios. contributes useful information helps optimize machine learning applicable agricultural field usage.

Language: Английский

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Deep Convolutional Neural Networks in Medical Image Analysis: A Review

Information, Journal Year: 2025, Volume and Issue: 16(3), P. 195 - 195

Published: March 3, 2025

Deep convolutional neural networks (CNNs) have revolutionized medical image analysis by enabling the automated learning of hierarchical features from complex imaging datasets. This review provides a focused CNN evolution and architectures as applied to analysis, highlighting their application performance in different fields, including oncology, neurology, cardiology, pulmonology, ophthalmology, dermatology, orthopedics. The paper also explores challenges specific outlines trends future research directions. aims serve valuable resource for researchers practitioners healthcare artificial intelligence.

Language: Английский

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Classification of maize leaf diseases with deep learning: Performance evaluation of the proposed model and use of explicable artificial intelligence

Chemometrics and Intelligent Laboratory Systems, Journal Year: 2025, Volume and Issue: 262, P. 105412 - 105412

Published: April 23, 2025

Language: Английский

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