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

Information, Год журнала: 2024, Номер 15(12), С. 755 - 755

Опубликована: Ноя. 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.

Язык: Английский

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A Deep Learning Method of Credit Card Fraud Detection Based on Continuous-Coupled Neural Networks

Mathematics, Год журнала: 2025, Номер 13(5), С. 819 - 819

Опубликована: Фев. 28, 2025

With the widespread use of credit cards in online and offline transactions, card fraud has become a significant challenge financial sector. The rapid advancement payment technologies led to increasingly sophisticated techniques, necessitating more effective detection methods. While machine learning been extensively applied detection, application deep methods remains relatively limited. Inspired by brain-like computing, this work employs Continuous-Coupled Neural Network (CCNN) for detection. Unlike traditional neural networks, CCNN enhances representation complex temporal spatial patterns through continuous neuron activation dynamic coupling mechanisms. Using Kaggle Credit Card Fraud Detection (CCFD) dataset, we mitigate data imbalance via Synthetic Minority Oversampling Technique (SMOTE) transform sample feature vectors into matrices training. Experimental results show that our method achieves an accuracy 0.9998, precision 0.9996, recall 1.0000, F1-score surpassing models, which highlight CCNN’s potential enhance security efficiency industry.

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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Deep Convolutional Neural Networks in Medical Image Analysis: A Review

Information, Год журнала: 2025, Номер 16(3), С. 195 - 195

Опубликована: Март 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.

Язык: Английский

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Improvement of Bank Fraud Detection Through Synthetic Data Generation with Gaussian Noise

Technologies, Год журнала: 2025, Номер 13(4), С. 141 - 141

Опубликована: Апрель 4, 2025

Bank fraud detection faces critical challenges in imbalanced datasets, where fraudulent transactions are rare, severely impairing model generalization. This study proposes a Gaussian noise-based augmentation method to address class imbalance, contrasting it with SMOTE and ADASYN. By injecting controlled perturbations into the minority class, our approach mitigates overfitting risks inherent interpolation-based techniques. Five classifiers, including XGBoost convolutional neural network (CNN), were evaluated on augmented datasets. achieved superior performance noise-augmented data (accuracy: 0.999507, AUC: 0.999506), outperforming These results underscore noise’s efficacy enhancing accuracy, offering robust alternative conventional oversampling methods. Our findings emphasize pivotal role of strategies optimizing classifier for financial data.

Язык: Английский

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ChatGPT in Education: A Review of Ethical Challenges and Approaches to Enhancing Transparency and Privacy

Procedia Computer Science, Год журнала: 2025, Номер 254, С. 181 - 190

Опубликована: Янв. 1, 2025

Язык: Английский

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A survey of explainable artificial intelligence in healthcare: Concepts, applications, and challenges

Informatics in Medicine Unlocked, Год журнала: 2024, Номер unknown, С. 101587 - 101587

Опубликована: Окт. 1, 2024

Язык: Английский

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