Taxi Demand Prediction Based on a Combination Forecasting Model in Hotspots by Zhizhen Liu, Hong Chen, Yan Li, Qi Zhang

“…In this study, we detected hotspots and proposed three methods to predict the taxi demand in hotspots. Next, we compared the predictive effect of the random forest model (RFM), ridge regression model (RRM), and combination forecasting model (CFM). …”
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Modeling the Impact of Climate Change on Soil Health Using Predictive Analytics by Alsalami Zaid, Mandapati A. H. A. Hussein, Sundari Venkata Rama

“…To answer the problem of soil degradation under climate stress, this research develops and evaluates predictive models capable of predicting soil health indicators. …”
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SOIL MOISTURE PREDICTION MODEL IN PEATLAND USING RANDOM FOREST REGRESSOR by Helda Yunita Taihuttu, Imas Sukaesih Sitanggang, Lailan Syaufina

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Development and Validation of an Incident Hypertension Risk Prediction Model for Young Adults by Jaejin An, Heidi Fischer, Liang Ni, Mengying Xia, Soon Kyu Choi, Kerresa L. Morrissette, Rong Wei, Kristi Reynolds, Paul Muntner, Lisandro D. Colantonio, Andrew E. Moran, Brandon K. Bellows, Monika M. Safford, Norrina B. Allen, Vanessa Xanthakis, Carmen R. Isasi, Linda C. Gallo, Krista M. Perreira, Yiyi Zhang

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A morphometric model to predict the sex of virginia rails (Rallus limicola) by Auriel M.V. Fournier, Mark C. Sheildcastle, Anthony C. Fries, Joseph K. Bump

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A human behavior-based model for respiratory infectious diseases prediction by Zhengwen Ma, Min Zhu, Chen Zhi, Huaguo Zhang, Minye Li, Nan Zhang, Hui Ma, Hui Ma

“…ObjectivesThe research aims to develop a human behavior-based model to predict respiratory infectious diseases.MethodsThis research employs semi-supervised machine learning techniques in conjunction with an RGB-depth camera to collect micro-level data. …”
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A New Prediction Model of Annular Pressure Buildup for Offshore Wells by Renjun Xie, Laibin Zhang

“…Results indicate that the error of annulus pressure buildup predicted by the multi-string mechanical model proposed in this paper that considers the deformation of the casing sealing section is approximately 13% lower than the one that does not consider this factor. …”
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Establishment of Hyperspectral Prediction Model of Water Content in Anshan-Type Magnetite by Xiaoxiao XIE, Yang BAI, Jiuling ZHANG, Yuna JIA

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Risk prediction and effect evaluation of complicated appendicitis based on XGBoost modeling by Sunmeng Chen, Jianfu Xia, Beibei Xu, Yi Huang, Miaomiao Teng, Juyi Pan

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Soft voting ensemble model to improve Parkinson’s disease prediction with SMOTE by Jumanto Unjung, Rofik Rofik, Endang Sugiharti, Alamsyah Alamsyah, Riza Arifudin, Budi Prasetiyo, Much Aziz Muslim

“…This study demonstrates that implementing the soft-voting ensemble-SMOTE method can enhance the model's predictive accuracy.…”
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Prediction Model of Late Fetal Growth Restriction with Machine Learning Algorithms by Seon Ui Lee, Sae Kyung Choi, Yun Sung Jo, Jeong Ha Wie, Jae Eun Shin, Yeon Hee Kim, Kicheol Kil, Hyun Sun Ko

“…Background: This study aimed to develop a clinical model to predict late-onset fetal growth restriction (FGR). …”
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Research on Credit Default Prediction Model Based on TabNet-Stacking by Shijie Wang, Xueyong Zhang

“…With the development of financial technology, the traditional experience-based and single-network credit default prediction model can no longer meet the current needs. …”
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Research on Annual Runoff Prediction Based on EMD-LSTM-ANFIS Model by HU Shunqiang, CUI Dongwen

“…To improve the accuracy of runoff prediction,this paper proposes a runoff prediction model based on the combination of empirical mode decomposition (EMD),long short-term memory (LSTM) neural network,and adaptive neuro-fuzzy inference system (ANFIS),decomposes the original runoff sequence into multiple regular component sequences through EMD,and reconstructs the phase space of each component sequence by the autocorrelation function method (AFM) and the false nearest neighbor method (FNN) to determine the input and output vectors,establishes the EMD-LSTM-ANFIS prediction model,and constructs the EMD-LSTM,EMD-ANFIS,LSTM,ANFIS as comparison models,as well as predicts and compares the annual runoff of the Longtan Station in Yunnan Province by the five models.The results show that the average relative error of the EMD-LSTM-ANFIS model for the annual runoff prediction is 3.18%,which is reduced by 55.0%、65.2%、68.1%、78.4% compared with the EMD-LSTM,EMD-ANFIS,LSTM,and ANFIS models respectively,with higher prediction accuracy and stronger generalization ability.Therefore,the EMD-LSTM-ANFIS model is feasible and reliable for runoff prediction.…”
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VOLATILITY ANALYSIS AND INFLATION PREDICTION IN PANGKALPINANG USING ARCH GARCH MODEL by Desy Yuliana Dalimunthe, Elyas Kustiawan, Khadijah -, Niken Halim, Helen Suhendra

“…This data was obtained through publications from the Central Statistics Agency of Bangka Beliltung Islands Province. The ARCH model is used to handle heteroscedasticity in data, while the GARCH model is a development of the ARCH model and serves as a generalization of the volatility model. …”
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Model based multi-loop predictive control scheme for multivariable processes by Arun Ramaveerapathiran, Muniraj Rathinam, Karuppiah Natarajan, Muthiah Athi, Patil Mounica

“…Abstract Industry applications of multi loop controller approaches are common because of their robustness, minimal tuning parameter, and straightforward design. A model based multi-loop predictive control method is presented. …”
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Development of a predictive model for metachronous liver metastasis in gastric cancer by Siyuan Wang, Siyuan Wang, Gaozan Zheng, Fengsu Wu, Ye Tian, Xinyu Qiao, Xinyu Dou, Hanjun Dan, Hanjun Dan, Guangming Ren, Liaoran Niu, Pengfei Wang, Lili Duan, Yumao Yang, Jianyong Zheng, Fan Feng

“…This study aims to identify the risk factors for MLM through clinical pathological parameters and develop a predictive model for MLM in gastric cancer.MethodsA retrospective analysis of 1248 gastric cancer patients who underwent radical surgery between December 2016 and December 2020 was conducted. …”
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Retracted: Advanced Computing Approach for Modeling and Prediction COVID-19 Pandemic by Applied Bionics and Biomechanics

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Predictive Risk Model for the Development of Encephalopathy in Patients with Nutritional Pancreatic Necrosis by V. S Mikhin, N. Sh. Burchuladze, A. S. Popov, M. I. Turovets, I. V. Mikhin, A. V. Kitaeva

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Analysis and Prediction of Energy Consumption Data, Using Data Mining Software by KAZOLIS Dimitrios, FANTIDIS Jacob, FOTAKIS Christos Dionyshs, TRAMANTZAS Kostantinos, TSIANTOS Vassilios

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Reliable QoE Prediction in IMVCAs Using an LMM-Based Agent by Michael Sidorov, Tamir Berger, Jonathan Sterenson, Raz Birman, Ofer Hadar

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