Results 61 to 70 of about 261,268 (307)

AI‐Driven Optimization of a Hybrid PV–Wind–BESS Microgrid for a Rural Educational Institution in Developing Countries

Energy Science &Engineering, EarlyView.
An AI‐driven CNN–LSTM forecasting framework is integrated with HOMER Pro to optimally design a grid‐connected PV–wind–BESS microgrid for a rural school in Bangladesh, achieving 91.7% renewable penetration, low energy cost (0.0397 USD/kWh), and an 81.5% reduction in CO2 emissions. ABSTRACT Hybrid renewable microgrid planning in HOMER Pro often relies on
Robiul Khan, Md. Wajed Ali, Md. Feroz Ali, Md. Shafiul Alam, Mohammad Ali, Imil Hamda Imran   +5 more
wiley   +1 more source

Detecting Language Anxiety in Indonesian Students: Deep Learning and Traditional Classification Methods for English Learning Anxiety

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Mastery of the English language represents a fundamental determinant of professional achievement, particularly for individuals seeking to develop their careers and participate in international contexts.
Kemas Muslim Lhaksmana, Muhammad Sya’bani Falif, Iis Kurnia Nurhayati, Muhammad Yudhi Rezaldi, Esa Prakasa, Rickman Roedavan   +5 more
doaj   +1 more source

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

Real‐Time Data‐Driven Fault Diagnosis of Photovoltaic Arrays Using an Edge‐Server Machine‐Learning Framework

Energy Science &Engineering, EarlyView.
A real‐time, data‐driven framework detects and classifies photovoltaic array faults using edge sensing and server‐side machine learning. Ensemble tree models achieve near‐perfect accuracy with low latency, enabling practical, low‐cost deployment for reliable PV monitoring and intelligent maintenance.
Premkumar Manoharan, Sowmya Ravichandran, Deepak Kumar Sinha, Chandran Ramakrishnan, Omar Almomani   +4 more
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

Feasibility of Wind‐Powered Green Hydrogen Production via a Hybrid Graph Neural Network‐Transformer Forecasting Model

Energy Science &Engineering, EarlyView.
ABSTRACT Accurate long‐term wind speed forecasting is pivotal for the strategic planning of renewable energy infrastructure, particularly for assessing the techno‐economic feasibility of wind‐powered green hydrogen facilities. However, capturing the complex spatiotemporal dependencies in climate data remains a significant challenge. This study proposes
Iman Baghaei, Mojtaba Mirhosseini, Alireza Zahedi   +2 more
wiley   +1 more source

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

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

Modeling and sensorless tension control of SMA actuator using GRU

Nihon Kikai Gakkai ronbunshu, 2020
This paper proposes a method for sensorless tension control of shape memory alloy(SMA) actuators. Sensorless control has advantages in cost and size. Gated recurrent unit(GRU) was adopted to model complex characteristics of the SMA actuators.
Soki ITO, Hiroyuki HARADA
doaj   +1 more source

Second observation of Common Crane Grus grus in Senegal

Bulletin of the African Bird Club, 2013
(Uploaded by Plazi from the Biodiversity Heritage Library) No abstract provided.
Volker Salewski, Troels Eske Ortvad, Kasper Thorup   +2 more
openaire   +2 more sources