Results 51 to 60 of about 10,214 (201)

Predicting Solar Photovoltaic Power Output in Saudi Arabia's Jazan Region: Performance Comparison of Machine Learning Models

Energy Science &Engineering, EarlyView.
Workflow of the PV power estimation and ML forecasting methodology. ABSTRACT Accurate prediction of solar panel energy output is vital for managing power systems effectively and maintaining a stable electrical grid. This is especially important in regions that rely heavily on renewable sources. This research provides a direct comparison of five machine
Abdoalateef Alzhrani, Abdullahi Abubakar Mas'ud, Mohd Khairul Amri Kamarudin, Firdaus Muhammad‐Sukki, Abdullahi Mohammad   +4 more
wiley   +1 more source

Environmental Control for Edible Fungi Cultivation Based on Temporal Information and Deep Learning

Food Bioengineering, EarlyView.
ABSTRACT Currently, there are still prevalent issues in greenhouse environmental regulation, such as response lag, low control accuracy, and difficulty in coping with sudden environmental disturbances. To achieve high‐precision and dynamic control of the edible fungi cultivation environment, this study proposes an edible fungi environmental control ...
Xiangyan Wang, Shuangcheng Huang, Qingbo Lu, Xinyi Guo   +3 more
wiley   +1 more source

Power load forecasting based on SMD-Informer-BiLSTM

Zhejiang dianli
To address the limitations of traditional methods—such as inadequate feature extraction and insufficient nonlinear processing capability in single deep learning models—this paper proposes a novel power load forecasting model integrating secondary modal ...
XU Shixin, LI Wenting, PENG Daogang, SHUI Jijun, LIU Jie, ZHONG Huaping   +5 more
doaj   +1 more source

Hybrid Temporal Autoencoder and Similarity Matching for Low Aggregation Level Long Time Series Forecasting

Journal of Forecasting, EarlyView.
ABSTRACT Deep learning‐based long time series forecasting (LTSF) has achieved high accuracy by effectively capturing the underlying trends, seasonality, and temporal dependencies within time series data. However, at the individual entity level, termed the low aggregation level (LAL), intermittency, irregularity, and data sparsity undermine the ...
Hanbyeol Park, Sunghyun Sim, Kikun Park, Yongjae Lee, Eunhee Park, Hyerim Bae   +5 more
wiley   +1 more source

Automated Analysis of Evidentiality in Russian Media Discourse: Experience with Neural Network Models

Научный диалог
This article addresses the challenge of analyzing evidentiality within media discourse by leveraging contemporary machine learning methods. The study’s relevance stems from the need to interpret implicit and context-dependent modal meanings that are ...
D. V. Kozlovsky
doaj   +1 more source

Artificial Intelligence for Blue Transformation: A Review of Predictive Modeling and Decision Support Systems in Sustainable Aquaculture

Sustainable Development, EarlyView.
ABSTRACT The FAO's “blue transformation” roadmap necessitates a fundamental shift towards precision aquaculture to meet global food security targets while minimizing environmental footprints. This review provides a comprehensive overview of how artificial intelligence (AI) and decision support systems (DSS) serve as pivotal enablers for the “better ...
Mustafa Öz, Enes Üstüner
wiley   +1 more source

Graph neural network‐based attack prediction for communication‐based train control systems

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract The Advanced Persistent Threats (APTs) have emerged as one of the key security challenges to industrial control systems. APTs are complex multi‐step attacks, and they are naturally diverse and complex. Therefore, it is important to comprehend the behaviour of APT attackers and anticipate the upcoming attack actions.
Junyi Zhao, Tao Tang, Bing Bu, Qichang Li   +3 more
wiley   +1 more source

Social media user behavior analysis and marketing strategy optimization integrating affective computing

Journal of Applied Science and Engineering
With the rapid development of social media, user-generated content (UGC) has become a valuable source for understanding user preferences and behaviors. However, traditional user behavior analysis methods mainly focus on behavioral data while ignoring the
Yanping Song
doaj   +1 more source

UAV‐based deep transfer learning to improve grain yield prediction in winter wheat across temporal and spatial variability

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Accurate prediction of grain yield (GY) remains a major challenge in plant breeding due to complex interactions between genotype, environment, and management (G × E × M) factors. Remote sensing data from unmanned aerial vehicles (UAVs) equipped with multispectral sensors have emerged as a pivotal resource for high‐throughput phenotyping.
Swas Kaushal, Mohammad Maruf Billah, Mandeep Singh, Dante Koupal, Pradeep Raj Sitaula, Jyotirmoy Halder, Jagdeep Sidhu, Maitiniyazi Maimaitijiang, Sunish K. Sehgal   +8 more
wiley   +1 more source

A prediction method for offshore wind power ramp events in typhoon weather conditions

Zhejiang dianli
Typhoon weather conditions induce severe fluctuations in offshore wind power generation， posing a significant threat to the secure and stable operation of power systems. Accurate prediction of wind power ramp events is therefore crucial for ensuring grid
WANG Enrong, SU Xiangjing, GONG Junhao, LIU Zhengyu, FU Yang   +4 more
doaj   +1 more source