Custom Loss Functions in XGBoost Algorithm for Enhanced Critical Error Mitigation in Drill-Wear Analysis of Melamine-Faced Chipboard DOI Creative Commons
Michał Bukowski, Jarosław Kurek, Bartosz Świderski

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(4), P. 1092 - 1092

Published: Feb. 7, 2024

The advancement of machine learning in industrial applications has necessitated the development tailored solutions to address specific challenges, particularly multi-class classification tasks. This study delves into customization loss functions within eXtreme Gradient Boosting (XGBoost) algorithm, which is a critical step enhancing algorithm’s performance for applications. Our research motivated by need precision and efficiency domain, where implications misclassification can be substantial. We focus on drill-wear analysis melamine-faced chipboard, common material furniture production, demonstrate impact custom functions. paper explores several variants Weighted Softmax Loss Functions, including Edge Penalty Adaptive Loss, challenges class imbalance heightened importance accurately classifying edge classes. findings reveal that these significantly reduce errors without compromising overall accuracy model. not only contributes field providing nuanced approach function but also underscores context-specific adaptations algorithms. results showcase potential balancing efficiency, ensuring reliable effective settings.

Language: Английский

Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features DOI

Zeni Zhao,

Sining Yun,

Lingyun Jia

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 121, P. 105982 - 105982

Published: Feb. 22, 2023

Language: Английский

Citations

163
Deep learning in hydrology and water resources disciplines: concepts, methods, applications, and research directions DOI Creative Commons
Kumar Puran Tripathy, Ashok K. Mishra

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 628, P. 130458 - 130458

Published: Nov. 15, 2023

Language: Английский

Citations

95
A hybrid ensemble-based automated deep learning approach to generate 3D geo-models and uncertainty analysis DOI Creative Commons
Abbas Abbaszadeh Shahri, Chunling Shan,

Stefan Larsson

et al.

Engineering With Computers, Journal Year: 2023, Volume and Issue: 40(3), P. 1501 - 1516

Published: Aug. 8, 2023

Abstract There is an increasing interest in creating high-resolution 3D subsurface geo-models using multisource retrieved data, i.e., borehole, geophysical techniques, geological maps, and rock properties, for emergency managements. However, dedicating meaningful, thus interpretable views from such integrated heterogeneous data requires developing a new methodology convenient post-modeling analyses. To this end, the current paper hybrid ensemble-based automated deep learning approach modeling of bedrock proposed. The uncertainty then was quantified novel ensemble randomly deactivating process implanted on jointed weight database. applicability capturing optimum topology validated by geo-model laser-scanned bedrock-level Sweden. In comparison with intelligent quantile regression traditional geostatistical interpolation algorithms, proposed showed higher accuracy visualizing post-analyzing model. Due to use multi-source presented here subsequently created model can be representative reconcile geoengineering applications.

Language: Английский

Citations

53
Meta-transfer learning-based method for multi-fault analysis and assessment in power system DOI
Lingfeng Zheng, Yuhong Zhu, Yongzhi Zhou

et al.

Applied Intelligence, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 10, 2024

Language: Английский

Citations

21
A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering DOI Creative Commons
Elaheh Yaghoubi, Elnaz Yaghoubi, Ahmed A. Khamees

et al.

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(21), P. 12655 - 12699

Published: May 13, 2024

Abstract Artificial neural networks (ANN), machine learning (ML), deep (DL), and ensemble (EL) are four outstanding approaches that enable algorithms to extract information from data make predictions or decisions autonomously without the need for direct instructions. ANN, ML, DL, EL models have found extensive application in predicting geotechnical geoenvironmental parameters. This research aims provide a comprehensive assessment of applications addressing forecasting within field related engineering, including soil mechanics, foundation rock environmental geotechnics, transportation geotechnics. Previous studies not collectively examined all algorithms—ANN, EL—and explored their advantages disadvantages engineering. categorize address this gap existing literature systematically. An dataset relevant was gathered Web Science subjected an analysis based on approach, primary focus objectives, year publication, geographical distribution, results. Additionally, study included co-occurrence keyword covered techniques, systematic reviews, review articles data, sourced Scopus database through Elsevier Journal, were then visualized using VOS Viewer further examination. The results demonstrated ANN is widely utilized despite proven potential methods engineering due real-world laboratory civil engineers often encounter. However, when it comes behavior scenarios, techniques outperform three other methods. discussed here assist understanding benefits geo area. enables practitioners select most suitable creating certainty resilient ecosystem.

Language: Английский

Citations

20
A multi-scale analysis method with multi-feature selection for house prices forecasting DOI
Jin Shao, Lean Yu, Nengmin Zeng

et al.

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 112779 - 112779

Published: Jan. 1, 2025

Language: Английский

Citations

2
Survey on deep learning based computer vision for sonar imagery DOI Creative Commons
Yannik Steiniger, Dieter Kraus, Tobias Meisen

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2022, Volume and Issue: 114, P. 105157 - 105157

Published: July 8, 2022

Research on the automatic analysis of sonar images has focused classical, i.e. non deep learning based, approaches for a long time. Over past 15 years, however, application in this research field constantly grown. This paper gives broad overview and current involving feature extraction, classification, detection segmentation sidescan synthetic aperture imagery. Most been directed towards investigation convolutional neural networks (CNN) extraction classification tasks, with result that even small CNNs up to four layers outperform conventional methods. The purpose work is twofold. On one hand, due quick development it serves as an introduction researchers, either just starting their specific or working classical methods helps them learn about recent achievements. other our main goal guide further by identifying gaps bridge. We propose leverage combining available data into open source dataset well carrying out comparative studies developed

Language: Английский

Citations

62
Design of concrete incorporating microencapsulated phase change materials for clean energy: A ternary machine learning approach based on generative adversarial networks DOI
Afshin Marani, Lei Zhang, Moncef L. Nehdi

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2022, Volume and Issue: 118, P. 105652 - 105652

Published: Nov. 30, 2022

Language: Английский

Citations

41
Detecting and recognizing driver distraction through various data modality using machine learning: A review, recent advances, simplified framework and open challenges (2014–2021) DOI
Hong Vin Koay, Joon Huang Chuah, Chee‐Onn Chow

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2022, Volume and Issue: 115, P. 105309 - 105309

Published: Aug. 13, 2022

Language: Английский

Citations

39
Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review DOI Open Access
Mojtaba Zaresefat, Reza Derakhshani

Water, Journal Year: 2023, Volume and Issue: 15(9), P. 1750 - 1750

Published: May 2, 2023

Developing precise soft computing methods for groundwater management, which includes quality and quantity, is crucial improving water resources planning management. In the past 20 years, significant progress has been made in management using hybrid machine learning (ML) models as artificial intelligence (AI). Although various review articles have reported advances this field, existing literature must cover ML. This article aims to understand current state-of-the-art ML used achievements domain. It most cited employed from 2009 2022. summarises reviewed papers, highlighting their strengths weaknesses, performance criteria employed, highly identified. worth noting that accuracy was significantly enhanced, resulting a substantial improvement demonstrating robust outcome. Additionally, outlines recommendations future research directions enhance of including prediction related knowledge.

Language: Английский

Citations

33