A robust transfer learning approach for high-dimensional linear regression to support integration of multi-source gene expression data. by Lulu Pan, Qian Gao, Kecheng Wei, Yongfu Yu, Guoyou Qin, Tong Wang

“…Extensive simulation experiments as well as an application demonstrate that Trans-PtLR demonstrates robustness and better performance of estimation and prediction when heavy-tail and outliers exist compared to transfer learning for linear regression model with normal error distribution. …”
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Determination of Alzheimer’s Drugs in a Human Urine Sample by Different Chemometric Methods by Güzide Pekcan

“…In the PLS method, the standard error of prediction (SEC), the sum of the prediction residual errors (PRESS), the limit of quantitation (LOQ), and the limit of detection (LOD) values were 0.015, 0.0030, 0.067, 0.24, 0.018, 0.0042, 0.089, and 0.301 for donepezil and rivastigmine, respectively. …”
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WisdomModel: convert data into wisdom by Israa Mahmood, Hasanen Abdullah

“…Purpose – Traditional classification algorithms always have an incorrect prediction. As the misclassification rate increases, the usefulness of the learning model decreases. …”
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Indicator Selection for Topic Popularity Definition Based on AHP and Deep Learning Models by Yuling Hong, Qishan Zhang

“…The purpose of this article is to predict the topic popularity on the social network accurately. …”
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The Role of Cognitive Science and Big Data Technology in the Design of Business Information Management Systems by Yangfan Li

“…It is verified that the prediction model has good accuracy, robustness, and universality. …”
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Road Performance and Ice-Melting Characteristics of Steel Wool Asphalt Mixture by Yifeng Xia, Jie Jia, Qian Chen

“…The average absolute error of the melting ice prediction model is 0.016, so the melting ice effect can be well predicted by this model.…”
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A New Approach for Resolving Conflicts in Actionable Behavioral Rules by Peng Su, Dan Zhu, Daniel Zeng

“…To overcome this problem, we develop a new method that utilizes rule ranking procedure as the basis for selecting the rule with the highest utility prediction accuracy. More specifically, we propose an integrative measure, which combines the measures of the support and antecedent length, to evaluate the utility prediction accuracies of conflicting rules. …”
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Rapid Determination of Aesculin, Aesculetin and Fraxetin in Cortex Fraxini Extract Solutions Based on Ultraviolet Spectroscopy by Ming Yang, Jia-Lei Chen, Xiu-Feng Shi, Hui-Jue Niu

“…And the root-mean-square error of prediction (RMSEP) for aesculin, aesculet and fraxetin were 11.99, 3.02 and 1.59 μg/mL. …”
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Impact of a Diagnostic Pressure Equation Constraint on Tornadic Supercell Thunderstorm Forecasts Initialized Using 3DVAR Radar Data Assimilation by Guoqing Ge, Jidong Gao, Ming Xue

“…It is found that the experiments using DPEC generally predict higher low-level vertical vorticity than the experiments not using DPEC near the time of observed tornadoes. …”
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Research on Human Pose Capture Based on the Deep Learning Algorithm by Wenbin Xie, Xueyun Gu, Xiuming Li, Luqi Zheng, Guodong Li, Haisheng Lu, Huakang Li

“…Aiming at the application demand of using artificial intelligence technology to accurately analyze and predict the motion training posture, a motion posture analysis and prediction system based on deep learning is designed in this paper. …”
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Identification of Initial Crack and Fracture Development Monitoring under Uniaxial Compression of Coal with High Bump Proneness by Zuoqing Bi, Han Liang, Qianjia Hui

“…Therefore, the initial crack distribution of high burst proneness coal, its fracture development, and failure process under loading conditions are of great significance for the prediction of rock burst. In this study, high burst proneness coal is used to prepare experiment samples. …”
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Review of Data-driven Decision Support Systems and Methodologies for the Diagnosis of Casting Defects by A. Burzyńska

“…Particularly important are fully data-driven predictive approaches that enable the discovery of hidden factors influencing defects in castings and the prediction of the specific time of occurrence by analyzing historical or real-time measurement data. …”
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Recursive Neural Network-Based Market Demand Forecasting Algorithm for Calligraphy Practice Products by Yi Xue

“…The artificial intelligence neural network method realizes the nonlinear relationship between the input and output of sample data through the self-learning ability of each neuron and has a certain nonlinear mapping ability in prediction, which plays a great role in the market demand prediction of many commercial products. …”
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The role of CTGF and MFG-E8 in the prognosis assessment of SCAP: a study combining machine learning and nomogram analysis by Tingting Lin, Tingting Lin, Huimin Wan, Jie Ming, Yifei Liang, Linxin Ran, Jingjing Lu

“…Using the CatBoost model for prediction, it performed the best, with key predictive features including Oxygenation Index, cTnT, MFG-E8, Dyspnea, CTGF and PaCO2.ConclusionThis study has highlighted the critical role of clinical and biochemical markers such as CTGF and MFG-E8 in assessing the severity and prognosis of SCAP. …”
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Alcohol Relapse After Liver Transplantation: Risk Factors, Outcomes, and a Comparison of Risk Stratification Models by Karen Young, Yuval A. Patel, Benson Hoffman, Sarah Peskoe, Shein-Chung Chow, Karli Erhart, Jennifer Jackson, Stephanie Garbarino

“…Conclusion: In our cohort, heavy alcohol use before transplantation and legal issues did not predict relapse, which are common components of prediction scores. …”
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Machine learning validation of the AVAS classification compared to ultrasound mapping in a multicentre study by Katerina Lawrie, Petr Waldauf, Peter Balaz, Radoslav Bortel, Ricardo Lacerda, Emma Aitken, Krzysztof Letachowicz, Mario D’Oria, Vittorio Di Maso, Pavel Stasko, Antonio Gomes, Joana Fontainhas, Matej Pekar, Alena Srdelic, VAVASC Study Group, Stephen O’Neill

“…Demographics, risk factors, vessels parameters, types of predicted and created VA (pVA, cVA) were collected. …”
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Data-driven modeling of open circuit voltage hysteresis for LiFePO4 batteries with conditional generative adversarial network by Lisen Yan, Jun Peng, Zeyu Zhu, Heng Li, Zhiwu Huang, Dirk Uwe Sauer, Weihan Li

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Quantitative Influence Analysis of the Development Scale of Market Economy on the Level of Music Innovation by Yang Li, Qiuyi Zhang, Tianzhuo Gong

“…The error is reduced by 0.27%. For the prediction of innovative forms of music, the largest prediction error is only 2.93%, which is closely related to the variability of popular music. …”
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Impact assessment of wastewater on water quality using new quality indices and multiple linear regression by Florinela Pirvu, Iuliana Paun, Vasile Iancu, Nicoleta Vasilache, Daniel Rudaru, Marcela Niculescu, Anda Gabriela Tenea, Florentina Laura Chiriac

“…Additionally, statistical analysis was employed to develop a simple prediction model through multiple linear regression (MLR) to predict WWQI based on various wastewater quality parameters. …”
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Safeguards-related event detection in surveillance video using semi-supervised learning approach by Se-Hwan Park, Byung-Hee Won, Seong-Kyu Ahn

“…Our model incorporates a reconstruction module and a prediction module independently. The reconstruction module is trained to generate video frames within a sliding window, while the prediction module is trained to predict future motion feature based on the motion features within the video frames in a sliding window. …”
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