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

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

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