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3rd EAI International Conference on Data and Information in Online Environments (DIONE)

作者： Somavilla, Eliane de Araujo, Gustavo Medeiros Univ Fed Santa Catarina PGCIN Florianopolis Brazil

ISBN: (纸本)9783031223235;9783031223242

This study focused on the survival analysis of patientswith heart failure whowere admitted to the Institute of Cardiology and Allied Hospital of Faisalabad-Pakistan during April and December 2015. All patients had left ventricular systolic dysfunction, belonging to classes III and IV of the classification carried out by the New York Heart Association. Several Machine Learning algorithms capable of analyzing data through regression and classification techniques were used to predict the mortality rate of future patients with similar problems. Characteristics such as age, ejection fraction, serum creatinine, serum sodium, anemia, platelets, creatinine phosphokinase, blood pressure, diabetes and smoking were considered as potential contributors to mortality. All characteristics were analyzed in order to identify the minimum set of information necessary for a quick and efficient diagnosis of heart failure.

关键词： Heart patients regression algorithms Classification algorithms Feature select

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Welding strength prediction in nuts to sheets joints: machine learning and ANFIS comparative analysis

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2025年第4期19卷 2477-2492页

作者： Albak, Bircan Erden, Caner Unal, Osman Akkas, Nuri Ozkan, Sinan Serdar Sakarya Univ Appl Sci Fac Technol Dept Mfg Engn TR-54050 Sakarya Turkiye Sakarya Univ Appl Sci Fac Technol Dept Comp Engn Sakarya Turkiye Sakarya Univ Appl Sci AI Res & Applicat Ctr Sakarya Turkiye Sakarya Univ Appl Sci Maritime Vocat High Sch TR-54050 Sakarya Turkiye Sakarya Univ Appl Sci Fac Technol Dept Mech Engn TR-54050 Sakarya Turkiye Sakarya Univ Appl Sci Fac Technol Dept Mechatron Engn TR-54050 Sakarya Turkiye

This study uses machine learning algorithms and the Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict welding strength in DD13 sheet metal joints with AISI 1010 nuts. The objective is to optimize industrial welding processes and improve quality control. The study investigates weld current, time, and hold time as critical input variables for joint integrity. The performance of different ML algorithms, including linear regression, random forest regression, ridge regression, Bayesian regression, K-Nearest Neighbors regression, decision tree regression, and ANFIS, are evaluated. Training and testing data consist of welding parameters and corresponding strength measurements. Performance metrics such as R2 score, mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE) are used to assess the predictive capabilities. Random forest regression is the most efficient algorithm, with a high R2 score of 0.992 and minimal errors. ANFIS also exhibits comparable performance, highlighting its efficacy in this context. These findings can be useful for optimizing welding parameters in industrial settings, potentially leading to improved quality control and weld strength, particularly in automotive applications. Using ML and ANFIS, industries can make informed decisions to optimize welding processes and ensure joint integrity, ultimately meeting the rigorous demands of demanding applications.

关键词： Welding strength prediction Nut sheet welding Machine learning regression algorithms Industrial applications ANFIS

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Seismic response prediction of asymmetric structures with SMA dampers using machine learning algorithms

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Asian Journal of Civil Engineering 2025年第6期26卷 2475-2497页

作者： Parghi, Anant Gohel, Jay Rastogi, Apurwa Yucel, Melda Avci-Karatas, Cigdem Mevada, Snehal Department of Civil Engineering Sardar Vallabhbhai National Institute of Technology Gujarat Surat 395007 India Department of Civil Engineering İstanbul Aydin University Küçükçekmece Istanbul 34295 Turkey Department of Transportation Engineering Faculty of Engineering Yalova University Yalova 77200 Turkey Structural Engineering Department B.V.M. Engineering College Gujarat Anand (Vallabh-Vidyanagar) India

The dynamic response of asymmetric structures to seismic forces is challenging due to mass, stiffness, and damping distribution irregularities. Shape memory alloy (SMA) dampers have successfully dealt with these issues because of their distinctive super elasticity and energy dissipation characteristics. In this work, we study regression algorithms’ effectiveness in predicting the seismic behavior of asymmetric structures installed with SMA dampers. A numerical simulation produces a comprehensive dataset of structural parameters consisting of the structure’s varying periods, frequency ratios, and eccentricity ratios. The critical responses of structures, including lateral and torsional displacement, lateral and torsional acceleration, and stiff and flexible edge damper forces, are predicted using machine learning (ML) techniques, artificial neural networks, decision trees, support vector machines, ensemble bagged trees, and Gaussian process regression. The model is validated using performance metrics such as mean absolute error and root mean square error, mean absolute percentage error, coefficient of determination, and Shapley Additive explanations values, ensuring that predictions are robust and consistent. The results revealed that regression methods accurately model the nonlinear dynamic behavior of SMA dampers in asymmetric structures, providing exact and computationally efficient predictions of seismic response. This predictive paradigm facilitates optimal damper configuration, minimizing the computational complexity of iterative design methods. The proposed research integrates advanced materials with ML methods to create seismically resilient structural systems. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Advanced materials Asymmetric structure Machine learning regression algorithms Seismic response prediction Shape memory alloys Structural eccentricity

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Experimental Study on 164 algorithms Available in Software Tools for Solving Standard Non-Linear regression Problems

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IEEE ACCESS 2019年 7卷 108916-108939页

作者： Jose Gacto, Maria Manuel Soto-Hidalgo, Jose Alcala-Fdez, Jesus Alcala, Rafael Univ Jaen Dept Comp Sci Jaen 23071 Spain Univ Cordoba Dept Elect & Comp Engn E-14071 Cordoba Spain Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain

In the specialized literature, researchers can find a large number of proposals for solving regression problems that come from different research areas. However, researchers tend to use only proposals from the area in which they are experts. This paper analyses the performance of a large number of the available regression algorithms from some of the most known and widely used software tools in order to help non-expert users from other areas to properly solve their own regression problems and to help specialized researchers developing well-founded future proposals by properly comparing and identifying algorithms that will enable them to focus on significant further developments. To sum up, we have analyzed 164 algorithms that come from 14 main different families available in 6 software tools (Neural Networks, Support Vector Machines, regression Trees, Rule-Based Methods, Stacking, Random Forests, Model trees, Generalized Linear Models, Nearest Neighbor methods, Partial Least Squares and Principal Component regression, Multivariate Adaptive regression Splines, Bagging, Boosting, and other methods) over 52 datasets. A new measure has also been proposed to show the goodness of each algorithm with respect to the others. Finally, a statistical analysis by non-parametric tests has been carried out over all the algorithms and on the best 30 algorithms, both with and without bagging. Results show that the algorithms from Random Forest, Model Tree and Support Vector Machine families get the best positions in the rankings obtained by the statistical tests when bagging is not considered. In addition, the use of bagging techniques significantly improves the performance of the algorithms without excessive increase in computational times.

关键词： Data mining supervised learning regression algorithms experimental study

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Machine learning approach for optimizing usability of healthcare websites

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Scientific Reports 2025年第1期15卷 1-24页

作者： Kaur, Amandeep Singh, Jaswinder Kaur, Satinder Dept. of Computer Science Guru Nanak Dev University Punjab Amritsar India Dept. of Computer Engineering and Technology Guru Nanak Dev University Punjab Amritsar India

In today’s digital era, hospital websites serve as crucial informational resources, providing patients with easy access to medical services. Ensuring the usability of these websites is essential, as it directly impacts users’ ability to navigate and retrieve vital medical information. Despite the recognized importance of website usability in the healthcare sector, there is a notable lack of empirical studies leveraging machine learning to assess this usability. This study aims to fill this gap by evaluating the user-friendliness of hospital websites using machine learning models, including Decision Trees, Random Forests, Ridge regression, and Support Vector regression. The dataset used in this analysis was generated by a custom-built automated tool developed by the author, which assessed the usability of 100 hospital websites. Among the models, Random Forest regression and Ridge regression demonstrated exceptional performance, achieving an accuracy rate of 98% and 87% respectively. Key metrics such as R-square, Mean Square Error (MSE), Mean Absolute Error (MAE), and Explained Variance Score (EVS) confirmed the model’s predictive accuracy. Cross-validation further highlighted the robustness of Ridge regression exhibiting low overfitting. The importance rankings consistently underscored the critical role of overall usability in predicting website performance. The study recommends expanding the dataset to include a broader range of healthcare websites, integrating user interaction data, and exploring advanced analytical techniques like deep learning to enhance future usability assessments. These advancements could lead to optimized website design and improved digital healthcare experiences for a diverse range of users. © The Author(s) 2025.

关键词： Healthcare Machine learning regression algorithms Usability Website quality

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Forward stage-wise ensemble regression algorithm to improve base regressors prediction ability: an empirical study

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EXPERT SYSTEMS 2014年第1期31卷 1-8页

作者： Ozcift, Akin Gaziantep Univ Comp Programming Div Gaziantep Vocat Sch Higher Educ Gaziantep Turkey

Analysis of scientific data requires accurate regressor algorithms to decrease prediction errors. Lots of machine learning algorithms, that is, neural networks, rule-based algorithms, regression trees and some kinds of lazy learners, are used to realize this need. In recent years, different ensemble regression strategies were improved to obtain enhanced predictors with lower forecasting errors. Ensemble algorithms combine good models that make errors in different parts of analyzed data. There are mainly two approaches in ensemble regression algorithm generation; boosting and bagging. The aim of this article is to evaluate a boosting-based ensemble approach, forward stage-wise additive modelling (FSAM), to improve some widely used base regressors’ prediction ability. We used 10 regression algorithms in four different types to make predictions on 10 diverse data from different scientific areas and we compared the experimental results in terms of correlation coefficient, mean absolute error, and root mean squared error metrics. Furthermore, we made use of scatter plots to demonstrate the effect of ensemble modelling on the prediction accuracies of evaluated algorithms. We empirically obtained that in general FSAM enhances the accuracies of base regressors or it at least maintains the base regressor performance.

关键词： forward stage-wise additive modelling boosting ensemble regression regression algorithms

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Online sparse Gaussian process regression using FITC and PITC approximations

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IFAC-PapersOnLine 2015年第28期48卷 703-708页

作者： Bijl, Hildo van Wingerden, Jan-Willem B. Schön, Thomas Verhaegen, Michel Delft Center for Systems and Control Delft University of Technology Netherlands Department of Information Technology Uppsala University Sweden

We provide a method which allows for online updating of sparse Gaussian Process (GP) regression algorithms for any set of inducing inputs. This method is derived both for the Fully Independent Training Conditional (FITC) and the Partially Independent Training Conditional (PITC) approximation, and it allows the inclusion of a new measurement point xn+1 in O(m2) time, with m denoting the size of the set of inducing inputs. Due to the online nature of the algorithms, it is possible to forget earlier measurement data, which means that also the memory space required is O(m2), both for FITC and PITC. We show that this method is able to efficiently apply GP regression to a large data set with accurate results. © 2015

关键词： regression analysis Gaussian distribution Gaussian noise (electronic) Large datasets Measurement data Measurement points Memory space regression algorithms Sparse Gaussian process

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Automatic Estimation of Web Bloggers' Age Using regression Models 17th

Automatic Estimation of Web Bloggers' Age Using Regression M...

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17th International Conference on Speech and Computer (SPECOM)

作者： Simaki, Vasiliki Aravantinou, Christina Mporas, Iosif Megalooikonomou, Vasileios Univ Patras Dept Comp Engn & Informat Multidimens Data Anal & Knowledge Management Lab Rion 26500 Greece

ISBN: (纸本)9783319231327;9783319231310

In this article, we address the problem of automatic age estimation of web users based on their posts. Most studies on age identification treat the issue as a classification problem. Instead of following an age category classification approach, we investigate the appropriateness of several regression algorithms on the task of age estimation. We evaluate a number of well-known and widely used machine learning algorithms for numerical estimation, in order to examine their appropriateness on this task. We used a set of 42 text features. The experimental results showed that the Bagging algorithm with RepTree base learner offered the best performance, achieving estimation of web users' age with mean absolute error equal to 5.44, while the root mean squared error is approximately 7.14.

关键词： Author's age estimation Text processing regression algorithms

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A regression-based machine learning approach for the prediction of lung function decline 12

A regression-based machine learning approach for the predict...

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12th IEEE International Scientific Technical Conference on Dependable Systems, Services and Technologies (DESSERT) - Dependable and Trustworthy AI, IoE, Big Data Analytics for a Safe and Secure World

作者： Poulou, Angeliki Poulos, Marios Panas, Maximilianos Ionian Univ Fac Informat Sci & Informat Lab Informat Technol Corfu Greece

ISBN: (纸本)9798350333046

Pulmonary fibrosis is a progressive disease of the lungs which usually gets worse over time. Once this disease damages the lungs, it cannot be cured totally, but early detection and proper diagnosis can help to keep the disease in control. The Kaggle competition entitled "OSIC Pulmonary Fibrosis Progression Predict lung function decline" ran from July to September 2020 with the goal of early detection of the disease. Our approach achieved a Laplace Log Likelihood score of -6.8590 which was within the bronze medal band. The Kaggle dataset contained CT scans and anonymized demographic and clinical data from multiple patient visits, such as spirometry forced vital capacity (FVC), for 176 unique patients. In our method we predict FVC and a confidence measure using a sigmoid equation. This equation is extracted via a novel transformation using only three of the given parameters. In this way we created a simple but accurate model for the prediction of lung function decline.

关键词： Machine Learning regression algorithms Prediction error Pulmonary Fibrosis Prognostic tool

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Optimal Layout Method for Roadside LiDAR and Camera

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IEEE ACCESS 2024年 12卷 26905-26918页

作者： Li, Yan Zhang, Han Cheng, Zhiheng Wang, Zijian Zhang, Ziyi Yao, Xinpeng Shandong Hispeed Construct Management Grp Co Ltd Jinan 250117 Peoples R China Shandong Key Lab Smart Transportat Preparat Jinan 250002 Peoples R China Shandong Univ Sch Qilu Transportat Jinan 250061 Peoples R China Shandong Hispeed Grp Co Ltd Jinan 250002 Peoples R China

With the widespread application of LiDAR and camera, the integration of LiDAR and camera has become an urgent issue. In this study, we proposed a optimal layout method for roadside LiDAR and camera. Firstly, the experimental design phase took into consideration various application scenarios, such as curved road sections and gradient road sections. Secondly, the video data and point cloud data collected from different experimental setups were subjected to object detection and recognition using YOLOv5s weights and PointPillars weights, respectively. These weights are applied to the video data under different layout schemes to output the mAP value of each scheme. By comparing the mAP values under different layout schemes, the optimal layout scheme for the road scene is determined. Thirdly, the four road scene parameters and six installation parameters from all scenarios were collected into a database. Furthermore, five machine learning algorithms were employed for optimal selection. Finally, the three regression algorithms with the highest accuracy are selected for the final prediction model based on different control groups. Through the field experiment, the results show that the optimized layout method can significantly reduce blind spot detection and vehicle occlusion problems for camera and LiDAR. The optimal deployment method can increase the Mean Average Precision (MAP) by over 4% through adjusting the installation parameters. The regression algorithm used can predict the optimal deployment scheme for roadside LiDAR and cameras in unknown scenarios with over 95% accuracy. This optimal deployment method improves the detection accuracy of roadside devices for road vehicles by changing the installation parameters and provides guidance for the future installation of roadside LiDAR and camera.

关键词： Optimal layout YOLOv5 OpenPCDet PointPillars mAP regression algorithms

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