INTERPRETABLE PREDICTIVE MODEL OF NETWORK INTRUSION USING SEVERAL MACHINE LEARNING ALGORITHMS by Muhammad Ahsan, Arif Khoirul Anam, Erdi Julian, Andi Indra Jaya

“…Some machine learning methods are used such as are logistic regression, random forest XGBoost, and CatBoost. The best model is chosen from these models based on its accuracy level. …”
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RETRACTED: Comparative Review on Machine Learning-Based Predictive Modeling for Mechanical Characterization by Himabindu Modi, Raj Vijilius Helena, Dutt Amit, Chandra Pradeep Kumar, Sethi Vandana Arora, Mohammad Q.

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A Bitcoin price prediction model assuming oscillatory growth and lengthening cycles by Guizhou Wang, Kjell Hausken

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A New Self-Tuning Nonlinear Model Predictive Controller for Autonomous Vehicles by Yasin Abdolahi, Sajad Yousefi, Jafar Tavoosi

“…This article consists of acquiring vehicle dynamics, extended model predictive control, and optimization paradigm. …”
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Predictive Study of Tuberculosis Incidence by ARMA Model Combined with Air Pollution Variables by Yanling Zheng

“…In this paper, based on the data of TB incidence and air pollution variables (PM2.5, PM10, SO2, CO, NO2, O3) in Urumqi, the ARMA (1, (1, 3)) + model was established by time series ARMA model method, cross-correlation analysis, and principal component regression method, and its predictive performance was superior to that of the ARMA (1, (1, 3)) model based on TB historical data. …”
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AKI prediction model in acute aortic dissection surgery: nomogram development and validation by Rui Du, Lai Wang, Yan Wang, Zhitao Zhao, Dahong Zhang, Shanshan Zuo

“…ObjectivesThis multicenter study developed and internally validated a biomarker-enhanced risk prediction nomogram integrating hemodynamic parameters and novel urinary biomarkers to stratify postoperative acute kidney injury (AKI) risks in patients undergoing emergency surgical repair for acute Stanford Type A aortic dissection (ATAAD).MethodsA cohort of 1,277 patients from the China Aortic Dissection Alliance (CADA) registry was chronologically split into derivation (70%, n = 894) and validation (30%, n = 383) sets. …”
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A mixed modeling approach to predict the effect of environmental modification on species distributions. by Francesco Cozzoli, Menno Eelkema, Tjeerd J Bouma, Tom Ysebaert, Vincent Escaravage, Peter M J Herman

“…Sustainable development requires the ability to predict responses of species to anthropogenic pressures. …”
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EVALUATION OF THE MODEL PREDICTION TOXICITY (LD50) FOR SERIES OF 42 ORGANOPHOSPHORUS PESTICIDES by HANANE FIKRI, TAOUFIQ FECHTALI, MOHAMED MAMOUMI

“… Structure-Toxicity Relationships have been studied for a set of 42 organophosphorous pesticides (OPs) through multiple linear regression (MLR) and artificial neural networks (ANN). A model with three descriptors, including: total lipophilicity [log (P)], widths radicals R1 [(LR1)] and R2 [(LR2)] has achieved good results in phase Training and phase prediction of toxicity [log LD50 (lethal dose 50, Oral rat)]. …”
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An interpretable machine learning model for precise prediction of biomarkers for intermittent fasting pattern by Xiaoli Hu, Qingjun Xu, Xuan Ma, Lin Li, Yongning Wu, Feifei Sun

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A risk prediction model for gastric cancer based on endoscopic atrophy classification by Yadi Lan, Weijia Sun, Shen Zhong, Qianqian Xu, Yining Xue, Zhaoyu Liu, Lei Shi, Bing Han, Tianyu Zhai, Mingyue Liu, Yujing Sun, Hongwei Xu

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Load Prediction Based on Hybrid Model of VMD-mRMR-BPNN-LSSVM by Gang Zhang, Hongchi Liu, Pingli Li, Meng Li, Qiang He, Hailiang Chao, Jiangbin Zhang, Jinwang Hou

“…Finally, each component is input into the prediction model together with its feature set, in which back propagation neural network (BPNN) is used to predict high-frequency components, least square-support vector machine (LS-SVM) is used to predict intermediate and low frequency components, and BPNN is also used to integrate the prediction results to obtain the final load prediction value, and compare the prediction results of method in this paper with that of the prediction models such as autoregressive moving average model (ARMA), LS-SVM, BPNN, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and VMD. …”
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Optimizing malicious website prediction: An advanced XGBoost-based machine learning model by Hussain Sumaira, Zada Islam, Alazab Moutaz, Alfraihi Hessa, Aldhayan Manal, Ullah Inam, Khan Mohammad Asmat Ullah

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Modelling on Car-Sharing Serial Prediction Based on Machine Learning and Deep Learning by Nihad Brahimi, Huaping Zhang, Lin Dai, Jianzi Zhang

“…After comparing the obtained results using different metrics, we found that CNN-LSTM outperformed other methods to predict the future car usage. Meanwhile, the model using all the different feature categories results in the most precise prediction than any of the models using one feature category at a time…”
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Predictive modeling of visible-light azo-photoswitches’ properties using structural features by Said Byadi, P. K. Hashim, Pavel Sidorov

“…The predictions of absorption wavelengths for this set are highly accurate; on the other hand, the model for thermal half-life is less reliable, likely due to the modest size of the data set related to half-life of photoisomers, although consensus modeling approach allows to improve the predictivity. …”
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Heartbeat information prediction based on transformer model using millimetre‐wave radar by Bojun Hu, Biao Jin, Hao Xue, Zhenkai Zhang, Zhaoyang Xu, Xiaohua Zhu

“…This study proposes a heartbeat prediction method based on the transformer model using millimetre‐wave radar. …”
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Quantifying Suicide Risk in Prostate Cancer: A SEER-Based Predictive Model by Jiaxing Du, Fen Zhang, Weinan Zheng, Xue Lu, Huiyi Yu, Jian Zeng, Sujun Chen

“…Time-dependent ROC analysis indicated strong accuracy in predicting suicide risk. Calibration plots displayed high concordance between predicted probabilities and actual outcomes, Kaplan-Meier analysis confirmed the model’s significant discriminative ability among risk groups. …”
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Using Gradient Boosting Regression to Improve Ambient Solar Wind Model Predictions by R. L. Bailey, M. A. Reiss, C. N. Arge, C. Möstl, C. J. Henney, M. J. Owens, U. V. Amerstorfer, T. Amerstorfer, A. J. Weiss, J. Hinterreiter

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Design and Application of Offset-Free Model Predictive Control Disturbance Observation Method by Xue Wang, Baocang Ding, Xin Yang, Zhaohong Ye

“…Model predictive control (MPC) with its lower request to the mathematical model, excellent control performance, and convenience online calculation has developed into a very important subdiscipline with rich theory foundation and practical application. …”
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Prediction of China’s Silicon Wafer Price: A GA-PSO-BP Model by Jining Wang, Hui Chen, Lei Wang

“…The BP (Back-Propagation) neural network model (hereafter referred to as the BP model) often gets stuck in local optima when predicting China’s silicon wafer price, which hurts the accuracy of the forecasts. …”
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Intelligent Trajectory Planning for Autonomous Vehicles via Adaptive Model Predictive Control by Catarina Goncalves, M. do Rosario Calado, Nuno Pombo

Subjects: “…Adaptive model predictive control (AMPC)…”
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