Evaluating suitability of regression models in small data regimes using concrete with recycled copper tailings as a case study by Ahed Habib, Samer Barakat, Samir Dirar, Salah Al-Toubat, Zaid A. Al-Sadoon

“…The utilization of regression models for the prediction of construction material properties is well-established, yet their performance when applied to small datasets is still unclear. …”

Research on PMF Model Based on BP Neural Network Ensemble Learning Bagging and Fuzzy Clustering by Zhengjin Zhang, Guilin Huang, Yong Zhang, Siwei Wei, Baojin Shi, Jiabao Jiang, Baohua Liang

“…Firstly, the membership function of fuzzy clustering and the selection of cluster center are used to calculate the user-item rating matrix; secondly, BP neural network trains the user-item scoring matrix after clustering, further improving the accuracy of scoring prediction; finally, the bagging method in ensemble learning is introduced, which takes the number of user-item scores as the base learner, trains the base learner through BP neural network, and finally obtains the score prediction through the voting results, which improves the stability of the model. …”
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Measurement of Degree of Compaction of Fine-Grained Soil Subgrade Using Light Dynamic Penetrometer by Junhui Zhang, Yongsheng Yao, Jianlong Zheng, Xiangqun Huang, Tian Lan

“…In addition, the water content should be determined firstly while using the prediction equation which is proposed in this study. …”
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Research on Monitoring Topping Time of Cotton Based on AdaBoost+Decision Tree by Yibai Li, Guangqiao Cao, Chao Ji, Dong Liu, Jinlong Zhang, Liang Li, Cong Chen

“…AdaBoost + decision tree method is proposed as a fitting model, the fitting results show that the coefficient of determination (R2) between multispectrum and cotton plant height is 0.96, and the average prediction error (RMSEP) is 0.40 cm, the coefficient of determination (R2) between multispectrum information and the fruiting branches is 0.97, the prediction mean error (RMSEP) is 0.54, and the correlation determination (R2) with the flower buds is 0.84, and the prediction mean error is (RMSEP) 0.49. …”
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Application of Convolutional Neural Networks and Recurrent Neural Networks in Food Safety by Haohan Ding, Haoke Hou, Long Wang, Xiaohui Cui, Wei Yu, David I. Wilson

“…This paper also makes a comparative analysis in many aspects: Firstly, the advantages and disadvantages of traditional food safety detection and risk prediction methods are compared with deep learning technologies such as CNNs and RNNs. …”
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Construction and Analysis of Hepatocellular Carcinoma Prognostic Model Based on Random Forest by Yikai Wang, Le Ma, Pengjun Xue, Bianni Qin, Ting Wang, Bo Li, Lina Wu, Liyan Zhao, Xiongtao Liu

“…The purpose of this study was to study HCC-related genes, build a survival prognosis prediction model, and provide references for treatment and mechanism research. …”
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Accuracy and Uncertainty Analysis of PSBT Benchmark Exercises Using a Subchannel Code MATRA by Dae-Hyun Hwang, Seong-Jin Kim, Kyong-Won Seo, Hyuk Kwon

“…The DNBR prediction uncertainties for various DNB models were evaluated from a Monte-Carlo simulation for a selected steady-state condition.…”
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Application, opportunities, and challenges of digital technologies in the decarbonizing shipping industry: a bibliometric analysis by Guangnian Xiao, Lei Pan, Fengbo Lai

“…Concerning emission prediction, future research efforts could focus on integrating more diverse external data sources into emission prediction models, adopting emerging technology applications, such as ship-based carbon capture (SBCC), introducing blockchain into smart emission monitoring systems, etc. …”
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Investigating the Efficacy of Topologically Derived Time Series for Flare Forecasting. I. Data Set Preparation by Thomas Williams, Christopher B. Prior, David MacTaggart

“…This publicly available living data set will allow users to incorporate these data into their own flare prediction algorithms.…”
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Rapid and Nondestructive Determination of Egg Freshness Category and Marked Date of Lay using Spectral Fingerprint by Thomas O. S. Akowuah, Ernest Teye, Julius Hagan, Kwasi Nyandey

“…The potential of nondestructive prediction of egg freshness based on near-infrared (NIR) spectra fingerprints would be beneficial to quality control officers and consumers alike. …”
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Untargeted urine metabolomics reveals dynamic metabolic differences and key biomarkers across different stages of Alzheimer’s disease by Xiaoya Feng, Shenglan Zhao

“…Predictive models based on these metabolites demonstrated excellent classification and prediction capabilities.ConclusionThis study systematically analyzed the dynamic metabolic differences during the progression of AD and identified key metabolites and pathways as potential biomarkers for early prediction and intervention. …”
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The Relationship between Compressive Strength and Splitting Tensile Strength of high-Performance Fiber-Reinforced Cementitious Composites by Mahdieh Sabbaghian, Ali Kheyroddin

“…After testing 108 cylinder and cube specimens at 7 and 28 days, a mathematical exponential function between STS and CS was proposed with a prediction error of less than ±15%. In the second phase, in order to evaluate the effect of steel fiber volume fraction and age on the prediction equation, three distinct volume fractions of steel fibers were considered: 0%, 1%, and 2%. …”
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Experimental and numerical simulation of the attenuation effect of blast shock waves in tunnels at different altitudes by Changjiang Liu, Hujun Li, Zhen Wang, Yong He, Guokai Zhang, Mingyang Wang

“…The method can effectively predict the overpressure attenuation of blast wave in tunnel at various altitudes.…”
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Effect of moisture change on water retention behavior of unsaturated silty soil under loading–unloading conditions by Syed Kamran Hussain Shah, Adnan Anwar Malik, Shay Haq, Waqar Ahmad, Umair Ali, Rabia Chaudhry

“…To examine the impact on the void ratio, the experimental data have been compared with the selected model incorporating suction, void ratio, and degree of saturation for prediction of soil water retention behaviors. The model prediction deviates from the experimental data and shifts away from the model prediction indicating that the loading–unloading sequence coupled with the variation in moisture does affect the soil behavior.…”
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A Review on Automatic Mammographic Density and Parenchymal Segmentation by Wenda He, Arne Juette, Erika R. E. Denton, Arnau Oliver, Robert Martí, Reyer Zwiggelaar

“…There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). …”
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Bioimpedance as an alternative tool for subjective, visual scoring of a prevalent ham quality defect by Abie Sisay Mebre, Suliga Paweł, Egelandsdal Bjørg, Münch Daniel

“…., Py, pHu, and CIELAB L* a* b*, the multivariate regression model had a prediction error of 0.76 for the visual scores. This was close to the error describing the subjective bias of visual scoring, more specifically the prediction error between the two observers (0.85). …”
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Predviđanje razvoja povrtarstva u Republici Srpskoj (Forecasting of Vegetable Production in Republic of Srpska) by Beba Mutavdžic, Ljiljana Drinic, Nebojša Novković, Aleksandar Ostojić, Gordana Rokvic

“…On the basis of estimated model is derived predicting the values of the parameters observed in 2016. year. …”
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Law of conservation-guided neural network with gradient aggregation for improved energy efficiency optimization in industrial processes by Santi Bardeeniz, Chanin Panjapornpon, Moonyong Lee

“…Through evaluation of two large-scale case studies—vinyl chloride monomer and detergent powder production—the proposed model produced substantial improvements in prediction accuracy and model reliability, with a test prediction improvement of 12.2 % and 5.87 % over published methods. …”
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Sistem Object Tracking pada Quadcopter Menggunakan Segmentasi Citra dengan Deteksi Warna HSV dan Metode Regresi Linier Berbasis Raspberry Pi by Fahmi Erza, Hurriyatul Fitriyah, Eko Setiawan

“…The accuracy of pitch motion prediction is 84%, yaw motion prediction is 94%, and up/down motion prediction is 91.5%. …”
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Similarities and Differences between ICME-driven Shocks Observed by VEX (∼0.72 au) and WIND (∼1.0 au) by Mengsi Ruan, Pingbing Zuo, Zilu Zhou, Jiayun Wei, Xueshang Feng, Yi Wang, Qi Xu, Xiaojun Xu, Zhengyang Zou, Zhenning Shen

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