Prediction of SNR Based on SVR and Adaptive Transmission Power Method for Underwater Acoustic Communication by Jixing ZHENG, Yufan YUAN, Xiaoxiao ZHUO, Xuesong LU, Fengzhong QU, Yan WEI

“…The simulation results show that compared with the exponential smoothing and autoregressive integrated moving average model(ARIMA) methods, the SVR algorithm based on the linear kernel function has the best performance in predicting signal-to-noise ratios and the smallest prediction error on test data. …”
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Research into prediction and influential factors of circuit breaker closing time using BFGS-NN by Longcheng Dai, Jiaying Yu, Zhihui Huang, Hui Ni, Yifan Zhang, Junting Dou

“…On-site operational data were analyzed to build a circuit breaker action time database. The BFGS algorithm trained on these data generated a closing time prediction model, achieving rapid convergence and optimal fit during learning. …”
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Choice of machine learning models for predicting the development of psychological disorders in people with hypothireosis and hyperthireosis by Нурал Гулієв

“…The article solves the problem of choosing the best models for predicting the occurrence of psychological disorders in people with endocrinological problems. …”
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Research on subway settlement prediction based on the WTD-PSR combination and GSM-SVR model by Miren Rong, Chao Feng, Yinping Pang, Hailong Wang, Ying Yuan, Wensong Zhang, Lanxin Luo

“…Furthermore, Particle Swarm Optimization (PSO), Gray Wolf Optimization (GWO), Marine Predators Algorithm (MPA), and Whale Optimization Algorithm (WOA) are introduced to optimize the SVR model, and the prediction performance is compared with that of the Long Short-Term Memory (LSTM) model. …”
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Digital biomarkers for interstitial glucose prediction in healthy individuals using wearables and machine learning by Xinyu Huang, Franziska Schmelter, Christian Seitzer, Lars Martensen, Hans Otzen, Artur Piet, Oliver Witt, Torsten Schröder, Ulrich L. Günther, Lisa Marshall, Marcin Grzegorzek, Christian Sina

“…Using ML approaches, correlations between glycemic measures and sensor data were assessed to estimate the feasibility of accurately predicting personalized IG alterations in real-time. …”
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Collagen gene signature in the tumor microenvironment predicts survival and guides prognosis in bladder cancer by Yi Huang, Shuogui Fang, Weibin Xie, Yitong Zou, Hui Zhuo, Gang Shen, Hua Zhou, ChunPing Mao, Cong Lai, Jianqiu Kong, Xinxiang Fan

“…Results The nomogram, incorporating the P3H4, C1QTNF6, COLGALT1, COL4A1, COL14A1, RGCC, PPARG, SCX and age by utilizing least absolute shrinkage and selection operator Cox regression algorithm, exhibits the favorable predictive capability in the area under the receiver operator characteristic curve, the calibration curve and decision curve analysis. …”
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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. …”
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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.…”
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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. …”
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Carbon Quota Allocation Prediction for Power Grids Using PSO-Optimized Neural Networks by Yixin Xu, Yanli Sun, Yina Teng, Shanglai Liu, Shiyu Ji, Zhen Zou, Yang Yu

“…Results indicate that the PSO algorithm mitigates local optimization constraints of the standard BP algorithm; the prediction error of carbon emissions by the combined model is significantly smaller than that of the single model, while its identification accuracy reaches 99.46%. …”
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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. …”
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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. …”
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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…”
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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. …”
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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. …”
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Prediction and Verification of Stability Domain in the Milling Process of Titanium Alloy Thin walled Workpiece by YUE Caixu, XIE Na, LI Xiaochen, LI Hengshuai, LIU Zhibo

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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

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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.…”
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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.…”
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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.…”
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