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 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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Deep Learning in Finance: A Survey of Applications and Techniques

AI, Journal Year: 2024, Volume and Issue: 5(4), P. 2066 - 2091

Published: Oct. 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.

Language: Английский

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Alzheimer’s Disease: Exploring Pathophysiological Hypotheses and the Role of Machine Learning in Drug Discovery

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(3), P. 1004 - 1004

Published: Jan. 24, 2025

Alzheimer’s disease (AD) is a major neurodegenerative dementia, with its complex pathophysiology challenging current treatments. Recent advancements have shifted the focus from traditionally dominant amyloid hypothesis toward multifactorial understanding of disease. Emerging evidence suggests that while amyloid-beta (Aβ) accumulation central to AD, it may not be primary driver but rather part broader pathogenic process. Novel hypotheses been proposed, including role tau protein abnormalities, mitochondrial dysfunction, and chronic neuroinflammation. Additionally, gut–brain axis epigenetic modifications gained attention as potential contributors AD progression. The limitations existing therapies underscore need for innovative strategies. This study explores integration machine learning (ML) in drug discovery accelerate identification novel targets candidates. ML offers ability navigate AD’s complexity, enabling rapid analysis extensive datasets optimizing clinical trial design. synergy between these themes presents promising future more effective

Language: Английский

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Additive manufacturing for biomedical bone implants: Shaping the future of bones

Materials Science and Engineering R Reports, Journal Year: 2025, Volume and Issue: 163, P. 100931 - 100931

Published: Jan. 30, 2025

Language: Английский

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Machine Learning-Based Bioactivity Classification of Natural Products Using LC-MS/MS Metabolomics

Journal of Natural Products, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 7, 2025

The rediscovery of known drug classes represents a major challenge in natural products discovery. Compound inhibits the ability researchers to explore novel and wastes significant amounts time resources. This study introduces machine learning framework that can effectively characterize bioactivity by leveraging liquid chromatography tandem mass spectrometry untargeted metabolomics analysis. accelerates product discovery addressing dereplicating previously discovered bioactive compounds. Utilizing SIRIUS 5 software suite in-silico-generated fragmentation spectra, we have trained ML model capable predicting compound's class. approach enables rapid identification scaffolds from LC-MS/MS data, even without reference experimental spectra. was on diverse set molecular fingerprints generated classify compounds based their core pharmacophores. Our robustly classified 21 classes, achieving accuracies greater than 93% underscores potential combined with MFPs dereplicate pharmacophore, streamlining process expediting improved methods isolating antibacterial antifungal agents.

Language: Английский

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Winter wheat harvest detection via Sentinel-2 MSI images

International Journal of Remote Sensing, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19

Published: Feb. 6, 2025

Wheat is one of the most important staple crops globally. Timely mapping and monitoring wheat harvests are essential for efficiently scheduling large-scale harvesters, ensuring timely completion harvest, maintaining grain quality. Traditional manual survey methods obtaining harvest information neither highly accurate nor cost-effective do not meet needs agricultural management departments. This study introduces two novel indices detection: optical-band brightness index (OBHI) visible-band (VBHI). The research structured into three primary components: (1) Extraction planting areas, utilizing phenological features from multiple growth stages; (2) harvesting features, where proposed OBHI VBHI analysed using box plot method to identify characteristics croplands; (3) detection, employing OBHI, VBHI, a threshold determine status. key findings as follows: Combining with achieves highest accuracy in detecting Sentinel-2 MSI images; Integrating multispectral remote sensing imagery enables real-time progress. In area, commenced on 1 June 2023 (0.62%) was nearly complete by 13 (97.94%). this have potential assist departments improving efficiency supervision. However, further validation necessary generalizability applicability method.

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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Exploring Clustering Tools in Process Systems Engineering: Innovations, Applications, and Future Directions

Process Safety and Environmental Protection, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

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Artificial intelligence and its application in clinical microbiology

Expert Review of Anti-infective Therapy, Journal Year: 2025, Volume and Issue: unknown

Published: March 25, 2025

Traditional microbiological diagnostics face challenges in pathogen identification speed and antimicrobial resistance (AMR) evaluation. Artificial intelligence (AI) offers transformative solutions, necessitating a comprehensive review of its applications, advancements, integration clinical microbiology. This examines AI-driven methodologies, including machine learning (ML), deep (DL), convolutional neural networks (CNNs), for enhancing detection, AMR prediction, diagnostic imaging. Applications virology (e.g. COVID-19 RT-PCR optimization), parasitology malaria detection), bacteriology automated colony counting) are analyzed. A literature search was conducted using PubMed, Scopus, Web Science (2018-2024), prioritizing peer-reviewed studies on AI's accuracy, workflow efficiency, validation. AI significantly improves precision operational efficiency but requires robust validation to address data heterogeneity, model interpretability, ethical concerns. Future success hinges interdisciplinary collaboration develop standardized, equitable tools tailored global healthcare settings. Advancing explainable federated frameworks will be critical bridging current implementation gaps maximizing potential combating infectious diseases.

Language: Английский

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