Results 91 to 100 of about 35,156 (275)

DESIGN OF SMART TOURISM SYSTEMS TO FORECAST FOREIGN TOURIST ARRIVAL RATE USING DEEP LEARNING TECHNIQUES [PDF]

Proceedings on Engineering Sciences
India's tourism potential is vast, driven by its rich history, diverse ecology, and extensive natural beauty. The country offers various niche tourism experiences, including cruises, adventure, medical, wellness, sports, MICE, eco-tourism, film, rural ...
Ratna Kanth Gudala , Ravi Kumar Suggala , Venkata Nagaraju Datla , Sri Krishna Adusumalli   +3 more
doaj   +1 more source

Bayesian Optimization for Stock Price Prediction Using LSTM, GRU, Hybrid LSTM-GRU, and Hybrid GRU-LSTM [PDF]

Stocks have high price fluctuations, which include high risks and high potential returns for investors. This high potential return has attracted significant interest from investors.
Kharisudin, Iqbal, Utami, Mira Dwi
core   +2 more sources

Daily Residential Natural Gas Demand Forecasting Using Machine Learning Regression: Comparative Evaluation With a Case Study in Qazvin Province, Iran

Energy Science &Engineering, EarlyView.
This graphical abstract summarizes the proposed framework for improving short‐term residential natural gas consumption forecasting by integrating a novel socioeconomic indicator, the subscription growth ratio (SGR), with conventional meteorological variables.
Ali Pirzad, Mostafa Khanzadi
wiley   +1 more source

Adversarial perturbation and bidirectional attention mechanism for few-shot English text classification

Journal of Applied Science and Engineering
In few-shot text classification, how well the query and support sets are encoded largely decides the final accuracy. Yet, most prior methods overlook the pairwise correspondences between them and treat all features as equally important, neglecting the ...
Xianghua Wu
doaj   +1 more source

Decentralized Federated Learning for Wind Turbine Bearing Prognostics Under Data Scarcity and Statistical Heterogeneity

Energy Science &Engineering, EarlyView.
This paper proposes a decentralized peer‐to‐peer federated learning framework for wind turbine bearing remaining useful life prediction, introducing a virtual client paradigm in which statistical health indicators serve as independent feature‐level clients—enabling privacy‐preserving collaborative prognostics from a single physical asset under ...
Jihene Sidhom, Lotfi Saidi, Eric Bechhoefer   +2 more
wiley   +1 more source

Hourly Prediction of Irradiance and Temperature Using Recurrent Neural Networks and Gaussian Process Models

Tecnura
This research applies artificial intelligence techniques to predict physical variables such as irradiance and temperature, addressing the challenge of time series nonlinearity. The main objective is to compare the predictive performance of LSTM, GRU, and
Mónica Yolanda Moreno Revelo, Juan Bernardo Gómez Mendoza, Javier Revelo Fuelagán   +2 more
doaj   +1 more source

Tree‐Boost–Guided CNN–BiLSTM–Transformer for Solar Irradiance Forecasting: Cross‐Regional Evidence for Sustainable Energy Planning

Energy Science &Engineering, EarlyView.
This graphical abstract illustrates a reproducible pipeline that combines gradient‐boosting‐based feature selection with a CNN–BiLSTM–Transformer model to forecast solar irradiance across multi‐site satellite and ground datasets, delivering robust, high‐accuracy predictions that support sustainable grid planning and reliable PV integration.
Muhammad Farhan Hanif, Usama Hassan, Javid Hussain, Irfan Ali Khan, Yuldasheva Gulora, Syed Masood Arif Bukhari   +5 more
wiley   +1 more source

Model Hybird Fuzzy Logic dan Deep Learning untuk Prediksi Harga Saham

Edumatic
Stock price prediction is a major challenge in the financial sector due to nonlinear factors and data uncertainty. This study aims to develop a predictive model by integrating fuzzy logic into deep learning algorithms to improve accuracy and robustness ...
Asep Muhidin, Elkin Rilvani, Candra Naya
doaj   +1 more source

Fenómenos eléctricos imprevistos y como protegerse contra ellos [PDF]

, 2010
Publicaciones Icesi No. 38 – Enero/Marzo 1991.
Gru Uchitel, Jaime
core  

Graph Neural Network‐Based Prediction of Building Energy Consumption

Energy Science &Engineering, EarlyView.
A graph neural network that encodes a multi‐zone building as a graph accurately predicts hourly cooling and heating loads across three distinct climates, outperforming Random Forest and XGBoost baselines and serving as a fast surrogate to EnergyPlus simulations for scalable building energy management.
Ali Maboudi Reveshti, Jamal Dabbagh, Jhila Nasiri Reveshti, Karim Farajeyan Bonab, Farid Hosseini Mansoub   +4 more
wiley   +1 more source