Results 41 to 50 of about 3,821,405 (172)

A State‐of‐the‐Art Comprehensive Review: Application of Artificial Intelligence in Predicting the Load‐Bearing Capacity of Piles

Engineering Reports, Volume 8, Issue 10, October 2026.
This graphical abstract provides a systematic bibliometric and thematic dissection of artificial intelligence applications for predicting pile load‐bearing capacity, synthesized from 135 rigorously selected studies published across the past three decades.
Mohammadreza Ahmadi Golsefidi, Mahzad Esmaeili‐Falak   +1 more
wiley   +1 more source

EffiSegNet: Advances in Automated Segmentation of Actinic Cheilitis and Lip Squamous Cell Carcinoma in Clinical Photographs

Journal of Oral Pathology &Medicine, Volume 55, Issue 9, Page 1073-1080, October 2026.
ABSTRACT Background Actinic cheilitis (AC) is a potentially malignant oral disorder linked to lip squamous cell carcinoma (LSCC). Clinical diagnosis is hindered by lesion heterogeneity. While AI tools have shown promise in oral lesion detection and diagnosis, image segmentation remains underexplored. This study developed and externally validated a deep
Vitor Simbalista Teixeira Soares, Anna Luíza Damaceno Araújo, Ivan José Correia‐Neto, Cristina Saldivia‐Siracusa, Raísa Sales de Sá, Hugo Abritta Reis, Ayrton da Costa Ganem Filho, André Carlos Ponce de Leon Ferreira de Carvalho, Thierry Urruty, Paulo Rogério Ferreti Bonan, Nathalia Rodrigues Gomes, Felipe Paiva Fonseca, Pablo Agustin Vargas, Alan Roger Santos‐Silva, Luiz Paulo Kowalski, Matheus Cardoso Moraes, Marcio Ajudarte Lopes   +16 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, Volume 14, Issue 8, Page 3656-3673, August 2026.
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

Research on Monthly Runoff Forecast in Dry Seasons Based on GEO-RVM Model

Renmin Zhujiang, 2022
To improve the accuracy of monthly runoff forecasts during dry seasons,this study proposes a forecasting method that combines the golden eagle optimization (GEO) algorithm and the relevance vector machine (RVM).On the basis of the runoff data of 67 a ...
ZHANG Yajie, CUI Dongwen
doaj  

Stability Analysis of Rock Slope Based on Improved Principal Component Analysis Model: Taking Fuwushan Slope as an Example

Geofluids, 2021
Aiming at the problems of low accuracy, low efficiency, and many parameters required in the current calculation of rock slope stability, a prediction model of rock slope stability is proposed, which combines principal component analysis (PCA) and ...
Lihua Huang, Liudan Mao, YiRong Zhu, YuLing Wang   +3 more
doaj   +1 more source

Research on application of ReliefF and improved RVM in water quality grade evaluation

Water Science and Technology, 2022
ReliefF algorithm was used to analyze the weight of each water quality evaluation factor, and then based on the Relevance Vector Machine (RVM), Particle Swarm Optimization (PSO) was used to optimize the kernel width factor and hyperparameters of RVM to ...
Yu-Lin Gong, Ming-Jia Hu, Hui-Fang Yang, Bo Han   +3 more
doaj   +1 more source

Localizing Root Causes of Incipient Failures of Distribution Oil‐Immersed Transformer Using a Distinctive Dynamic Multi‐Scale Attention CNN‐LSTM Algorithm via Composite Multiple Data Integration for Transformer Predictive Maintenance

Engineering Reports, Volume 8, Issue 7, July 2026.
The root cause localization framework in Figure 1 is an extension of anomaly detection where further insights on incipient transformer faults or failures are analyzed. The framework aims to provide a complete diagnostic solution to detected anomalies to ensure efficient transformer maintenance while minimizing downtimes and allowing for optimal use of ...
E. Tamakloe, B. Kommey, J. J. Kponyo, D. Opoku   +3 more
wiley   +1 more source

Probability prediction of true‐triaxial compressive strength of intact rocks based on the improved PSO‐RVM model

Deep Underground Science and Engineering, Volume 5, Issue 2, Page 396-409, June 2026.
In this work, we propose an improved particle swarm optimization (PSO) algorithm and develop an improved PSO‐relevance vector machine (RVM) model as a substitute for traditional true‐triaxial testing. The model's high prediction accuracy was validated through comparisons with two other machine learning methods and five three‐dimensional Hoek–Brown type
Qi Zhang, Maohui Wang, Ning Wang, Yixin Shen, Xuzhen He   +4 more
wiley   +1 more source

Transformer fault diagnosis based on chemical reaction optimization algorithm and relevance vector machine

MATEC Web of Conferences, 2017
Power transformer is one of the most important equipment in power system. In order to predict the potential fault of power transformer and identify the fault types correctly, we proposed a transformer fault intelligent diagnosis model based on chemical ...
Luo Wei, Peng Minfang, Wan Xun, Wang Shengquan, Liao Xiupu   +4 more
doaj   +1 more source

Wheat's war against stripe rust: Integrating host immunity, genomics and breeding for durable resistance

The Plant Genome, Volume 19, Issue 2, June 2026.
Abstract Wheat (Triticum aestivum L.), a foundation of global food security, faces persistent threats from stripe rust caused by Puccinia striiformis f. sp. tritici (Pst). The pathogen thrives in cool and humid environments and regularly causes epidemics that lead to severe yield losses.
Farkhandah Jan, Mukesh Rathore, Dinesh Kumar Saini, Ayushi Singh, Naeela Qureshi, Nikita Aggarwal, Mahpara Bashir, Mehnaz Shakeel, Vikas Gupta, Sundeep Kumar, Rajeev Kumar Varshney, Reyazul Rouf Mir   +11 more
wiley   +1 more source