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

---

Advancements and future outlook of Artificial Intelligence in energy and climate change modeling

Advances in Applied Energy, Journal Year: 2025, Volume and Issue: unknown, P. 100211 - 100211

Published: Jan. 1, 2025

Language: Английский

---

Improving daily reference evapotranspiration forecasts: Designing AI-enabled recurrent neural networks based long short-term memory

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 102995 - 102995

Published: Jan. 1, 2025

Language: Английский

---

An Ensemble Approach to Predict a Sustainable Energy Plan for London Households

Sustainability, Journal Year: 2025, Volume and Issue: 17(2), P. 500 - 500

Published: Jan. 10, 2025

The energy sector plays a vital role in driving environmental and social advancements. Accurately predicting demand across various time frames offers numerous benefits, such as facilitating sustainable transition planning of resources. This research focuses on consumption using three individual models: Prophet, eXtreme Gradient Boosting (XGBoost), long short-term memory (LSTM). Additionally, it proposes an ensemble model that combines the predictions from all to enhance overall accuracy. approach aims leverage strengths each for better prediction performance. We examine accuracy Mean Absolute Error (MAE), Percentage (MAPE), Root Square (RMSE) through means resource allocation. investigates use real data smart meters gathered 5567 London residences part UK Power Networks-led Low Carbon project Datastore. performance was recorded follows: 62.96% Prophet model, 70.37% LSTM, 66.66% XGBoost. In contrast, proposed which XGBoost, achieved impressive 81.48%, surpassing models. findings this study indicate enhances efficiency supports towards future. Consequently, can accurately forecast maximum loads distribution networks households. addition, work contributes improvement load forecasting networks, guide higher authorities developing plans.

Language: Английский

---

Hybrid LSTM-PSO optimization techniques for enhancing wind power bidding efficiency in electricity markets

Ain Shams Engineering Journal, Journal Year: 2025, Volume and Issue: 16(2), P. 103285 - 103285

Published: Feb. 1, 2025

Language: Английский

---

AI-enabled Computational Intelligence Approach to Neurodevelopmental Disorders Detection Using rs-fMRI Data

Computers & Electrical Engineering, Journal Year: 2025, Volume and Issue: 123, P. 110117 - 110117

Published: Feb. 3, 2025

Language: Английский

---

Comparative Analysis of Long Short-Term Memory and Gated Recurrent Unit Models for Chicken Egg Fertility Classification Using Deep Learning

Published: Feb. 20, 2025

Language: Английский

---

Spatial Downscaling of Satellite-Based Soil Moisture Products Using Machine Learning Techniques: A Review

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(12), P. 2067 - 2067

Published: June 7, 2024

Soil moisture (SM) is a key variable driving hydrologic, climatic, and ecological processes. Although it highly variable, both spatially temporally, there limited data availability to inform about SM conditions at adequate spatial temporal scales over large regions. Satellite retrievals, especially L-band microwave remote sensing, has emerged as feasible solution offer continuous global-scale information. However, the coarse resolution of these retrievals poses uncertainties in many regional- local-scale applications which require high amount details. Numerous studies have been conducted develop downscaling algorithms enhance coarse-resolution satellite-derived datasets. Machine Learning (ML)-based models gained prominence recently due their ability capture non-linear, complex relationships between its factors, such vegetation, surface temperature, topography, climatic conditions. This review paper presents comprehensive ML-based approaches used downscaling. The usage classical, ensemble, neural nets, deep learning methods downscale products comparison multiple are detailed this paper. Insights into significance ancillary variables for model accuracy improvements made also discussed. Overall, provides useful insights future on developing reliable, high-spatial-resolution datasets using algorithms.

Language: Английский

---

A novel deep CNN model with entropy coded sine cosine for corn disease classification

Journal of King Saud University - Computer and Information Sciences, Journal Year: 2024, Volume and Issue: 36(7), P. 102126 - 102126

Published: July 14, 2024

Corn diseases significantly impact crop yields, posing a major challenge to agricultural productivity. Early and accurate detection of these is crucial for effective management mitigation. Existing methods, mostly relying on analyzing corn leaves, often lack the precision identify classify wide range under varying conditions. This study introduces novel approach detecting using image processing deep learning techniques, aiming enhance accuracy through pre-processing, improved feature extraction selection, classification algorithms. A new Convolutional Neural Network (CNN) model named TreeNet, with 35 layers 38 connections, proposed. TreeNet pre-trained Plant Village imaging dataset. For YCbCr color space utilized improve representation contrast. Feature performed two models, Darknet-53, DenseNet-201, features fused serial-based fusion method. The Entropy-coded Sine Cosine Algorithm applied optimizing set classification. selected are used train Support Vector Machine (SVM) K-Nearest Neighbor (KNN) classifiers, extensive experiments conducted both 5-fold 10-fold cross-validation, sizes ranging from 200 1150. proposed method achieves accuracy, precision, recall, F1-score 99.8%, 99%, 100%, respectively, surpassing existing benchmarks. integration Darknet-53 along robust pre-processing improves disease detection, highlighting potential advanced CNN architectures in agriculture.

Language: Английский

---

Explainable deep learning approach for advanced persistent threats (APTs) detection in cybersecurity: a review

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(11)

Published: Sept. 18, 2024

Abstract In recent years, Advanced Persistent Threat (APT) attacks on network systems have increased through sophisticated fraud tactics. Traditional Intrusion Detection Systems (IDSs) suffer from low detection accuracy, high false-positive rates, and difficulty identifying unknown such as remote-to-local (R2L) user-to-root (U2R) attacks. This paper addresses these challenges by providing a foundational discussion of APTs the limitations existing methods. It then pivots to explore novel integration deep learning techniques Explainable Artificial Intelligence (XAI) improve APT detection. aims fill gaps in current research thorough analysis how XAI methods, Shapley Additive Explanations (SHAP) Local Interpretable Model-agnostic (LIME), can make black-box models more transparent interpretable. The objective is demonstrate necessity explainability propose solutions that enhance trustworthiness effectiveness models. offers critical approaches, highlights their strengths limitations, identifies open issues require further research. also suggests future directions combat evolving threats, paving way for effective reliable cybersecurity solutions. Overall, this emphasizes importance enhancing performance systems.

Language: Английский

---