Exploring rounD Dataset for Domain Generalization in Autonomous Vehicle Trajectory Prediction by Zikai Zhang

“…We develop a trajectory prediction framework inspired by Gated Recurrent Unit (GRU) networks and graph-based modules to effectively model vehicle interactions. …”
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User Evaluation of a Machine Learning-Based Student Performance Prediction Platform by Hoti Arbër H., Zenuni Xhemal, Hamiti Mentor, Ajdari Jaumin

“…While most of the work has been focused on prediction algorithms design and evaluations, little work has been done on user-centric evaluations.…”
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Application of Machine Learning Methods for Employee Turnover Prediction Based on Open Data by A. N. Kazinets

“…An analysis of existing approaches to predicting staff turnover is conducted, the need to use modern machine learning algorithms is substantiated. …”
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OSBPL3 modulates the immunosuppressive microenvironment and predicts therapeutic outcomes in pancreatic cancer by Qihui Sun, Xiaoqi Zhu, Qi Zou, Yang Chen, Tingting Wen, Tingting Jiang, Xiaojia Li, Fang Wei, Keping Xie, Jia Liu

“…We integrated multi-dataset analyses, single-cell transcriptomic data, and functional experiments to explore the role of OSBPL3 in pancreatic cancer. Results Our risk prediction model, developed using machine learning algorithms, demonstrated high predictive accuracy across multiple datasets. …”
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Integration of mitochondrial gene expression and immune landscape in acute kidney injury prediction by Xiaoping Xia, Renyang Liu, Xiaohui Jiang

“…The MRS model showed strong predictive performance. We found that XRCC3 significantly promoted the activities of HK-2 cells and improved the integrity of mitochondrial structure and function in vivo and in vitro.Conclusion The mitochondrial gene-based MRS model is a robust tool for predicting AKI. …”
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Quantitative Prediction of Protein Content in Corn Kernel Based on Near-Infrared Spectroscopy by Chenlong Fan, Ying Liu, Tao Cui, Mengmeng Qiao, Yang Yu, Weijun Xie, Yuping Huang

“…Experimental results indicated that the PLSR model, preprocessed with 1D + MSC, yielded the best performance, achieving a root mean square error of prediction (RMSEP) of 0.3 g/kg, a correlation coefficient (R_p) of 0.93, and a residual predictive deviation (RPD) of 3. …”
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General Framework of Reversible Watermarking Based on Asymmetric Histogram Shifting of Prediction Error by Sheng Chen, Xianyi Chen, Huijuan Fu

“…Different from the conventional algorithms using single-prediction scheme to create symmetric histogram, the proposed method employs a multi-prediction scheme, which calculates multiple prediction values for the pixels. …”
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An SMVQ compressed data hiding scheme based on multiple linear regression prediction by Hengxiao Chi, Chin-Chen Chang, Yanjun Liu

“…In this paper, we propose a side matching vector quantisation (SMVQ) data hiding scheme for image using multiple linear regression prediction. For each pixel block, the proposed scheme combines the multiple linear regression algorithm and the SMVQ algorithm, so that it can more accurately match the codeword or directly obtain the predicted value closer to the real pixel. …”
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Noise-robust linear prediction analysis of speech based on super-Gaussian excitation by Bin ZHOU, Xia ZOU, Xiong-wei ZHANG, Gai-hua ZHAO

“…To overcome the problem that the performance of the traditional linear prediction (LP) analysis of speech dete-riorates significantly in the presence of background noise,a novel algorithm for robust LP analysis of speech based on super-Gaussian excitation was proposed.The excitation noise of LP was modeled as a Student-t distribution,which was shown to be super-Gaussian.Then a novel probabilistic graphical model for robust LP analysis of speech was built by in-corporating the effect of additive noise explicitly.Furthermore,variational Bayesian inference was adopted to approxi-mate the intractable posterior distributions of the model parameters,based on which the LP coefficients of the noisy speech were estimated iteratively.The experimental results show that the developed algorithm performs well in terms of LP coefficients estimation of speech and is much more robust to ambient noise than several other algorithms.…”
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Machine learning-based ultrasound radiomics for predicting risk of recurrence in breast cancer by Wei Fan, Hao Cui, Xiaoxue Liu, Xudong Zhang, Xinran Fang, Junjia Wang, Zihao Qin, Xiuhua Yang, Jiawei Tian, Lei Zhang

“…PurposeTo develop a radiomics model based on ultrasound images for predicting risk of recurrence in breast cancer patients.MethodsIn this retrospective study, 420 patients with pathologically confirmed breast cancer were included, randomly divided into training (70%) and test (30%) sets, with an independent external validation cohort of 90 patients. …”
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Prediction of the volume of shallow landslides due to rainfall using data-driven models by J. Tuganishuri, C.-Y. Yune, G. Kim, S. W. Lee, M. D. Adhikari, S.-G. Yum

“…The objectives of this research are to construct a model using advanced data-driven algorithms (i.e., ordinary least squares or linear regression (OLS), random forest (RF), support vector machine (SVM), extreme gradient boosting (EGB), generalized linear model (GLM), decision tree (DT), deep neural network (DNN), <span class="inline-formula"><i>k</i></span>-nearest-neighbor (KNN), and ridge regression (RR) algorithms) for the prediction of the volume of landslides due to rainfall, considering geological, geomorphological, and environmental conditions. …”
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Prediction of postoperative vault after implantable collamer lens implantation with deep learning by Dong-Qing Yuan, Fu-Nan Tang, Ying Wang, Hui Zhang, Wei-Wei Zhang, Liu-Wei Gu, Qing-Huai Liu

“…AIM: To predict the post-operative vault and the suitable size of the implantable collamer lens (ICL) by comparing the performance of multiple artificial intelligence (AI) algorithms. …”
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Personalized Human Thermal Sensation Prediction Based on Bayesian-Optimized Random Forest by Hao Yang, Maoyu Ran

“…More accurate personalized thermal sensation prediction models were then constructed using various machine learning algorithms, followed by a comparative analysis of their performance. …”
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Onboard Interference Prediction for the Cognitive Medium Access in the LEO Satellite Uplink Transmission by Zhuochen Xie, Huijie Liu, Xuwen Liang

“…With MCB, we can achieve the effective long-term interference prediction to meet the special requirements of the LEO satellite. …”
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Prediction of customer engagement behaviour response to marketing posts based on machine learning by Yonghui Dai, Tao Wang

“…In order to better understand customer behaviours in the social media marketing context, we draw on the Stimulus-Organism-Response theory, and conceptualise and characterise marketing posts from six dimensions to get various features as stimuli, which induce or activate customers’ cognitive and affective states to varying levels, and ultimately lead to different behaviour responses. Machine learning algorithms are applied to the customer engagement behaviour choice prediction when facing marketing posts. …”
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Prediction of Thermal and Optical Properties of Oxyfluoride Glasses Based on Interpretable Machine Learning by Yuhao Xie, Xiangfu Wang

“…Based on the components of glasses, four algorithms, namely K-Nearest Neighbor, Random Forest, Support Vector Machine, and eXtreme Gradient Boosting, were used to construct an optimal machine learning model to predict the thermal and optical properties of oxyfluoride glass, namely glass transition temperature, density, Abbe number, liquidus temperature, thermal expansion coefficient, and refractive index. …”
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A Critical Comparison of Machine Learning Classifiers to Predict Match Outcomes in the NFL by Beal Ryan, Norman Timothy J., Ramchurn Sarvapali D.

“…In this paper, we critically evaluate the performance of nine machine learning classification techniques when applied to the match outcome prediction problem presented by American Football. Specifically, we implement and test nine techniques using real-world datasets of 1280 games over 5 seasons from the National Football League (NFL). …”
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Early Prediction of Stroke Risk Using Machine Learning Approaches and Imbalanced Data by Hassan Qassim

“… Classifying medical datasets using machine learning algorithms could help physicians to provide accurate diagnosing and suitable treatment. …”
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Prediction of HIV status based on socio-behavioural characteristics in East and Southern Africa. by Erol Orel, Rachel Esra, Janne Estill, Amaury Thiabaud, Stéphane Marchand-Maillet, Aziza Merzouki, Olivia Keiser

“…<h4>Methods</h4>We analysed the most recent Demographic and Health Survey from these 10 countries to predict individual's HIV status using four different algorithms (a penalized logistic regression, a generalized additive model, a support vector machine, and a gradient boosting trees). …”
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Multispectral Sensors and Machine Learning as Modern Tools for Nutrient Content Prediction in Soil by Rafael Felippe Ratke, Paulo Roberto Nunes Viana, Larissa Pereira Ribeiro Teodoro, Fábio Henrique Rojo Baio, Paulo Eduardo Teodoro, Dthenifer Cordeiro Santana, Carlos Eduardo da Silva Santos, Alan Mario Zuffo, Jorge González Aguilera

“…This soil analysis technique requires more refined correlation models for accurate prediction.…”
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