Wonbyung Lee, Jang Hyun Kim^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5393-5412, 2025, DOI:10.32604/cmc.2025.065413 - 19 May 2025

Abstract Predicting player performance in sports is a critical challenge with significant implications for team success, fan engagement, and financial outcomes. Although, in Major League Baseball (MLB), statistical methodologies such as sabermetrics have been widely used, the dynamic nature of sports makes accurate performance prediction a difficult task. Enhanced forecasts can provide immense value to team managers by aiding strategic player contract and acquisition decisions. This study addresses this challenge by employing the temporal fusion transformer (TFT), an advanced and cutting-edge deep learning model for complex data, to predict pitchers’ earned run average (ERA), a key More >

Yuliang Zhang^1,2,*, Zezhou Yang¹, Lianghuai Tong^3,*, Yanjuan Zhao⁴, Xiaoqi Jia⁵, Anda Han⁶

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.4, pp. 891-938, 2025, DOI:10.32604/fdmp.2024.059903 - 06 May 2025

Abstract This paper investigates the start-up and shutdown phases of a five-bladed closed-impeller centrifugal pump through experimental analysis, capturing the temporal evolution of its hydraulic performances. The study also predicts the transient characteristics of the pump under non-rated operating conditions to assess the accuracy of various machine learning methods in forecasting its instantaneous performance. Results indicate that the pump’s transient behavior in power-frequency mode markedly differs from that in frequency-conversion mode. Specifically, the power-frequency mode achieves steady-state values faster and exhibits smaller fluctuations before stabilization compared to the other mode. During the start-up phase, as… More >

Long Huang^1,2,3,*, Junjia Zou³, Baoqing Liu¹, Zhijiang Jin^1,2, Jinyuan Qian¹

Frontiers in Heat and Mass Transfer, Vol.22, No.6, pp. 1613-1643, 2024, DOI:10.32604/fhmt.2024.058231 - 19 December 2024

Abstract This study explores the effectiveness of machine learning models in predicting the air-side performance of microchannel heat exchangers. The data were generated by experimentally validated Computational Fluid Dynamics (CFD) simulations of air-to-water microchannel heat exchangers. A distinctive aspect of this research is the comparative analysis of four diverse machine learning algorithms: Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forest (RF), and Gaussian Process Regression (GPR). These models are adeptly applied to predict air-side heat transfer performance with high precision, with ANN and GPR exhibiting notably superior accuracy. Additionally, this research further delves into… More >

Mohammad Javad Shayegan^*, Rosa Akhtari

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1251-1272, 2024, DOI:10.32604/csse.2024.052587 - 13 September 2024

Abstract After the spread of COVID-19, e-learning systems have become crucial tools in educational systems worldwide, spanning all levels of education. This widespread use of e-learning platforms has resulted in the accumulation of vast amounts of valuable data, making it an attractive resource for predicting student performance. In this study, we aimed to predict student performance based on the analysis of data collected from the OULAD and Deeds datasets. The stacking method was employed for modeling in this research. The proposed model utilized weak learners, including nearest neighbor, decision tree, random forest, enhanced gradient, simple Bayes, More >

Dongyang Ou, Yongjian Ren, Congfeng Jiang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2043-2067, 2024, DOI:10.32604/cmes.2023.029987 - 29 January 2024

Abstract Cloud service providers generally co-locate online services and batch jobs onto the same computer cluster, where the resources can be pooled in order to maximize data center resource utilization. Due to resource competition between batch jobs and online services, co-location frequently impairs the performance of online services. This study presents a quality of service (QoS) prediction-based scheduling model (QPSM) for co-located workloads. The performance prediction of QPSM consists of two parts: the prediction of an online service’s QoS anomaly based on XGBoost and the prediction of the completion time of an offline batch job based… More >

Xinzheng Wang^1,2,*, Bing Guo¹, Yan Shen³

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1183-1206, 2024, DOI:10.32604/cmes.2023.029552 - 17 November 2023

Abstract Predicting students’ academic achievements is an essential issue in education, which can benefit many stakeholders, for instance, students, teachers, managers, etc. Compared with online courses such as MOOCs, students’ academic-related data in the face-to-face physical teaching environment is usually sparsity, and the sample size is relatively small. It makes building models to predict students’ performance accurately in such an environment even more challenging. This paper proposes a Two-Way Neural Network (TWNN) model based on the bidirectional recurrent neural network and graph neural network to predict students’ next semester’s course performance using only their previous course More > Graphic Abstract

Yunru Wu¹, Xiangbo Liu¹, Haibo Huang^1,2,*, Yudong Wu¹, Weiping Ding^1,2, Mingliang Yang^1,2,*

Sound & Vibration, Vol.57, pp. 73-95, 2023, DOI:10.32604/sv.2023.044601 - 10 November 2023

Abstract Vehicle interior noise has emerged as a crucial assessment criterion for automotive NVH (Noise, Vibration, and Harshness). When analyzing the NVH performance of the vehicle body, the traditional SEA (Statistical Energy Analysis) simulation technology is usually limited by the accuracy of the material parameters obtained during the acoustic package modeling and the limitations of the application conditions. In order to effectively solve these shortcomings, based on the analysis of the vehicle noise transmission path, a multi-level objective decomposition architecture of the interior noise at the driver’s right ear is established. Combined with the data-driven method, More >

Ying Bai^1,*, Xiaoti Ren², Hong Li¹

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2999-3018, 2023, DOI:10.32604/iasc.2023.040124 - 11 September 2023

Abstract The combination of the Industrial Internet of Things (IIoT) and digital twin (DT) technology makes it possible for the DT model to realize the dynamic perception of equipment status and performance. However, conventional digital modeling is weak in the fusion and adjustment ability between virtual and real information. The performance prediction based on experience greatly reduces the inclusiveness and accuracy of the model. In this paper, a DT-IIoT optimization model is proposed to improve the real-time representation and prediction ability of the key equipment state. Firstly, a global real-time feedback and the dynamic adjustment mechanism… More >

Yuqin Wang^1,2,3,*, Luxiang Zhou³, Mengle Han¹, Lixiang Shen¹

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.9, pp. 2215-2228, 2023, DOI:10.32604/fdmp.2023.027188 - 16 May 2023

Abstract The main structural parameters of the IR100-80-100A type chemical centrifugal pump have been optimized by means of an orthogonal test approach. The centrifugal pump has been modeled using the CFturbo software, and 16 sets of orthogonal-test schemes have been defined on the basis of 4 parameters, namely, the blade number, blade outlet angle, impeller outlet diameter, and impeller outlet width. Such analysis has been used to determine the influence of each index parameter on the pump working efficiency and identify a set of optimal combinations of such parameters. The internal flow field in the centrifugal More > Graphic Abstract

Fawad Nasim^1,2,*, Muhammad Adnan Yousaf¹, Sohail Masood^1,2, Arfan Jaffar^1,2, Muhammad Rashid³

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2865-2877, 2023, DOI:10.32604/iasc.2023.034258 - 15 March 2023

Abstract Batsmen are the backbone of any cricket team and their selection is very critical to the team’s success. A good batsman not only scores run but also provides stability to the team’s innings. The most important factor in selecting a batsman is their ability to score runs. It is a generally accepted notion that the future performance of a batsman can be predicted by observing and analyzing their past record. This hypothesis is based on the fact that a player’s batting average is generally considered to be a good indicator of their future performance. We… More >