Prognostic prediction for inflammatory breast cancer patients using random survival forest modeling by Yiwei Jia, Chaofan Li, Cong Feng, Shiyu Sun, Yifan Cai, Peizhuo Yao, Xinyu Wei, Zeyao Feng, Yanbin Liu, Wei Lv, Huizi Wu, Fei Wu, Lu Zhang, Shuqun Zhang, Xingcong Ma

“…Random survival forest (RSF) algorithm was adopted to construct an accurate prognostic prediction model for IBC patients. …”
Get full text

Interpretable model based on MRI radiomics to predict the expression of Ki-67 in breast cancer by Li Zhang, Qinglin Du, Mengyi Shen, Xin He, Dingyi Zhang, Xiaohua Huang

“…Based on clinical-imaging features and DCE-MRI radiomics, the interpretable machine learning model can accurately predict the expression of Ki-67 in BC. Combining the SHAP algorithm with the model improves its interpretability, which may assist clinicians in formulating more accurate treatment strategies.…”
Get full text

Predicting Trip Duration and Distance in Bike-Sharing Systems Using Dynamic Time Warping by Ahmed Ali, Ahmad Salah, Mahmoud Bekhit, Ahmed Fathalla

“…While existing literature primarily focuses on predicting the number of rentals and returns per station, this study addresses the complementary aspect of predicting the trip duration and distance of the trip. …”
Get full text

Prognostic prediction of gastric cancer based on H&E findings and machine learning pathomics by Guoda Han, Xu Liu, Tian Gao, Lei Zhang, Xiaoling Zhang, Xiaonan Wei, Yecheng Lin, Bohong Yin

“…Aim: In this research, we aimed to develop a model for the accurate prediction of gastric cancer based on H&E findings combined with machine learning pathomics. …”
Get full text

Research on the prediction model of UV spectral water quality parameters based on INFO-LSSVM by Li Chao, Li Wen, Luo Xueke, Yu Zhuofan, Han Yakai, Yu Facheng

“…The common nitrate nitrogen (NO3-N) and nitrite nitrogen (NO2-N) in water quality testing as the solution to be measured, the UV-visible absorption spectral data filtering, spectral data integration, the establishment of INFO-LSSVM nonlinear prediction model; comparison of GA-LSSVM, PSO-LSSVM and LSSVM algorithm models, the results show that the INFO-LSSVM prediction model is effective, and pro- vides a good solution for water quality testing. …”
Get full text

DIFFERENTIAL DIAGNOSIS OF PROGENIC FORMS OF BITE AND ITS IMPORTANCE IN PREDICTING THE RESULTS OF ORTHODONTIC TREATMENT by P.S. Flis, K.V. Storozhenko

“…Conclusions • Differential diagnosis of progenic forms of bite according to our developed algorithm allows making diagnose more objectively, choosing a rational method of orthodontic treatment and predicting its result…”
Get full text

Wind Power Prediction Method Based on Long Short-term Memory Neural Network by Xiangjun LI, Gejian XU

“…Wind power generation process has strong randomness, which leads to low accuracy of wind power prediction. In view of the above phenomenon, a wind power generation power prediction method based on deep learning algorithm was proposed. …”
Get full text

An Improved Fast Prediction Method for Full-Space Bistatic Acoustic Scattering of Underwater Vehicles by Ruichong Gu, Zilong Peng, Yaqiang Xue, Cong Xu, Changxiong Chen

“…To reduce the data input required for predicting the scattering field, the monostatic to bistatic equivalence theorem is incorporated into the algorithm. …”
Get full text

Risk Prediction in Patients With Metabolic Dysfunction–Associated Steatohepatitis Using Natural Language Processing by Jordan Guillot, Christopher Y.K. Williams, Shadera Azzam, Balu Bhasuran, Gail Fernandes, Boshu Ru, Joe Yang, Xiao Zhang, R. Ravi Shankar, Jin Ge, Vivek A. Rudrapatna

Get full text

Prediction of Changes in the Tax Burden of Land Plots with the Use of Multivariate Statistical Analysis Methods by Krzysztof Dmytrów, Sebastian Gnat

“…The main finding is that these approaches can be used in the prediction of changes in the tax burden of land plots.…”
Get full text

Real-time ocean wave prediction in time domain with autoregression and echo state networks by Karoline Holand, Henrik Kalisch

“…It provides valuable insights into the trade-offs between accuracy and practicality in the real-time implementation of predictive models for wave elevation, which are needed in wave energy converters to optimise the control algorithm.…”
Get full text

Feature selection method for software defect number prediction based on maximum information coefficient by Guoqing LIU, Xingqi WANG, Dan WEI, Jinglong FANG, Yanli SHAO

“…The traditional feature selection method only considers the linear correlation between variables and ignores the nonlinear correlation, so it is difficult to select effective feature subsets to build the effective model to predict the number of faults in software modules.Considering the linear and nonlinear relationship, a feature selection method based on maximum information coefficient (MIC) was proposed.The proposed method separated the redundancy analysis and correlation analysis into two phases.In the previous phase, the cluster algorithm, which was based on the correlation between features, was used to divide the redundant features into the same cluster.In the later phase, the features in each cluster were sorted in descending order according to the correlation between features and the number of software defects, and then the top features were selected to form the feature subset.The experimental results show that the proposed method can improve the prediction performance of software defect number prediction model by effectively removing redundant and irrelevant features.…”
Get full text

Feature selection method for software defect number prediction based on maximum information coefficient by Guoqing LIU, Xingqi WANG, Dan WEI, Jinglong FANG, Yanli SHAO

“…The traditional feature selection method only considers the linear correlation between variables and ignores the nonlinear correlation, so it is difficult to select effective feature subsets to build the effective model to predict the number of faults in software modules.Considering the linear and nonlinear relationship, a feature selection method based on maximum information coefficient (MIC) was proposed.The proposed method separated the redundancy analysis and correlation analysis into two phases.In the previous phase, the cluster algorithm, which was based on the correlation between features, was used to divide the redundant features into the same cluster.In the later phase, the features in each cluster were sorted in descending order according to the correlation between features and the number of software defects, and then the top features were selected to form the feature subset.The experimental results show that the proposed method can improve the prediction performance of software defect number prediction model by effectively removing redundant and irrelevant features.…”
Get full text

Transient Stability Prediction of Power Systems Based on Deep Residual Network and Data Augmentation by Yanzhen ZHOU, Xianyu ZHA, Jian LAN, Qinglai GUO, Hongbin SUN, Feng XUE, Shengming WANG

“…In traditional data-driven power system transient stability assessment methods, the impact of noise in the collected data and the information missing problems are rarely considered for the transient stability prediction. To deal with these problems, this paper presents a method for transient stability prediction based on data augmentation and deep residual network (ResNet). …”
Get full text

Study on prediction model of nitrogen oxide concentration in reprocessing plant based on random forest by Xuankun Wei, Yan Xu, Xiaomeng Li, Gengxin Fan, Xuening Cheng, Tiantian Yu, Baihua Jiang

“…In situations where operating conditions fluctuate, the response capability of the treatment system exhibits a lag, resulting in a rapid short-term increase in NOx concentration during final emissions. To predict the trend of NOx concentration changes in the reprocessing process and enhance the response capability of the NOx treatment system, a NOx concentration prediction model was developed using the Random Forest algorithm, based on data collected from actual operations. …”
Get full text

Obesity Prediction Using Synthetic Minority Oversampling Technique for Numeric and Continous and XGBoost Approaches by Tiara Azahra Wika Putri, Umu Sa’adah, Ummu Habibah

“…This study investigates the effect of using SMOTE-NC on the XGBoost algorithm in predicting obesity. The main objective of this research is to determine the effect of implementing SMOTE-NC and also the features that are most influential in the prediction process. …”
Get full text

Research on global ship cargo capacity prediction based on multi-source heterogeneous data by Shuhang Chen, Zhihuan Wang, Zhihuan Wang, Tianye Lu, Jiayang Zhu, Chunchang Zhang, Chunchang Zhang, Xiangming Zeng, Xiangming Zeng, Jiayi Wang, Zandi Shang, Zandi Shang

“…Results demonstrate that the K-nearest neighbors (KNN) algorithm achieves 88% predictive accuracy on validation data—a 19-percentage-point improvement over conventional methods (69%). …”
Get full text

GARMT: Grouping-Based Association Rule Mining to Predict Future Tables in Database Queries by Peixiong He, Libo Sun, Xian Gao, Yi Zhou, Xiao Qin

Get full text

Prediction of Moisture Content in Kiwi (Actinidia deliciosa) Dried Using Machine Learning Approaches by Halil Nusret BULUS, Soner CELEN

“… Predicting product drying kinetics is crucial for achieving optimal drying processes without compromising product quality. …”
Get full text

Multi-feature fusion-based consumer perceived risk prediction and its interpretability study. by Lin Qi, Yunjie Xie, Qianqian Zhang, Jian Zhang, Yanhong Ma

“…Using the PCA-K-medoids-XGBoost algorithm, we developed a predictive model for perceived risk. …”
Get full text