“Revealing the future”: an ARIMA model analysis for predicting remittance inflows by Imran Khan, Darshita Fulara Gunwant

“…Purpose – The purpose of this research is to develop a predictive model that can estimate the volume of remittances channeled toward Yemen’s economic reconstruction efforts. …”
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Application of Thermodynamic Models to Predict the Solubility of Biologically Active Substances by K. S. Stepanov, G. N. Turmanidze, V. V. Sorokin, A. D. Sakharov

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Research on link prediction model based on hierarchical attention mechanism by Xiaojuan ZHAO, Yan JIA, Aiping LI, Kai CHEN

“…In order to solve the problem that the existing graph attention mechanism tends to cause attention distribution to certain relations with high frequency when performing link prediction related tasks, a new link prediction model based on hierarchical attention mechanism was proposed.In the link prediction task, a hierarchical attention mechanism was designed to give different attention to the relationships of different relationship types connected to a given entity in the knowledge graph according to the relationship in the prediction task.While the characteristics of multi-hop neighbor entities were pay attention to, the relationship characteristics was pay more attention to find the relationship type that matches the target relationship.Through comparison experiments with the mainstream models on multiple benchmark data sets, the results show that the performance of the model is better than the mainstream models and has good robustness.…”
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An Intelligent Deep Learning Model for CO2 Adsorption Prediction by Hanan Ahmed Hosni Mahmoud, Nada Ali Hakami, Alaaeldin M. Hafez

“…Three tree machine learning models, namely, gradient decision model (GDM), scalable boosting tree model (SBT), and gradient variant decision tree model (GVD), were fused. …”
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Connectome-based prediction of functional impairment in experimental stroke models. by Oliver Schmitt, Peter Eipert, Yonggang Wang, Atsushi Kanoke, Gratianne Rabiller, Jialing Liu

“…Dynamic modeling with the weighted bilateral connectome detected changes in signal propagation in the remote hippocampus in all 3 stroke types, predicting the extent of hippocampal hypoactivation and impairment in spatial learning and memory function. …”
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A donor heart scoring model to predict transplant outcomes by E. A. Tenchurina, M. G. Minina

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Research on Yield Prediction Model Driven by Mechanism and Data Fusion by Xin Meng, Xingyu Liu, Hancong Duan, Ze Hu, Min Wang

“…Then, a Global–Local Branch Prediction Model is designed to enhance both long-term trend estimation and local feature capture in a production time series. …”
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Development of an Early-Warning Model for Predicting the Capecitabine-Induced Diarrhea by Liu Z, Qiu S, Xu Y, Wang X, Sun J, Cui L, Ye L, Liang Z, Gao S, Chen W, Wang Z

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Numerical Permeation Models to Predict the Annulus Composition of Flexible Pipes by João Marcos B. Vieira, José Renato M. de Sousa

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Human genotype-to-phenotype predictions: Boosting accuracy with nonlinear models. by Aleksandr Medvedev, Satyarth Mishra Sharma, Evgenii Tsatsorin, Elena Nabieva, Dmitry Yarotsky

“…First, we ask if state-of-the-art nonlinear predictive models, such as boosted decision trees, can be more efficient for phenotype prediction than conventional linear models. …”
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Traffic Flow Prediction Based on Fractional Seasonal Grey Model by SHEN Qinqin; ZHANG Zhijie; QI Xucun; YUE Xinyi

“…The numerical results show that the average absolute percentage of the new model has a fitting error of 8. 1260% and a prediction error of 7. 6216%, which are much better than those of the seasonal rolling GM(1, 1)model, fractional GM(1, 1) model and seasonal discrete GM(1, 1) model.…”
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A Stock Prediction Model Based on Cart and Boosting Algorithm by WANG Yu, CHEN De-yun, TANG Yuan-xin

Subjects: “…stock prediction model…”
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A novel model for malaria prediction based on ensemble algorithms. by Mengyang Wang, Hui Wang, Jiao Wang, Hongwei Liu, Rui Lu, Tongqing Duan, Xiaowen Gong, Siyuan Feng, Yuanyuan Liu, Zhuang Cui, Changping Li, Jun Ma

“…<h4>Background and objective</h4>Most previous studies adopted single traditional time series models to predict incidences of malaria. A single model cannot effectively capture all the properties of the data structure. …”
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Prediction of Landslide Displacement Based on EMD-TAR Combined Model by CHEN Xi, GAO Yaping, TU Rui

Subjects: “…prediction of landslide displacement…”
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Hyperparameter optimization of machine learning models for predicting actual evapotranspiration by Chalachew Muluken Liyew, Elvira Di Nardo, Stefano Ferraris, Rosa Meo

“…Consequently, AET data is limited, prompting the use of meteorological and soil features for prediction. This study develops and evaluates machine learning models for AET prediction based on two input combinations. …”
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Predicting Excavation-Induced Tunnel Response by Process-Based Modelling by Linlong Mu, Jianhong Lin, Zhenhao Shi, Xingyu Kang

“…To illustrate such a concept, this paper presents a simple process-based model consisting of artificial neural network (ANN) module, inverse modelling module, and mechanistic module. …”
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Flight delay stacking ensemble prediction model for severe weather by SUN Yue, DING Jianli

“…Weather factors, as the primary factors affecting flight delays, have an important impact on flight delay prediction. Confronting the severe weather, multi-classification prediction of flight delay duration was made, and a Stacking-based integrated flight delay prediction model was proposed for the problems of low prediction accuracy and poor stability of traditional single model. …”
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Machine learning models for predicting spatiotemporal dynamics of groundwater recharge by Azeddine Elhassouny

“…Open public remote sensing datasets were used to develop machine learning prediction models (Random Forest, XGBoost, Keras models, etc.) and time series forecasting models (LSTM, CNN, etc.) for predicting and forecasting groundwater sheet recharge, respectively. …”
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Air Quality Index Prediction Using DNN-Markov Modeling by Roba Zayed, Maysam Abbod

“…DNN-Markov modeling techniques are used to predict air quality, based on environmental conditions at peak hours. …”
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A study on the effectiveness of machine learning models for hepatitis prediction by Popy Khatun, Shafeel Umam, Rubaiya Binte Razzak, Iffat Binta Shamsuddin, Nahid Salma

“…We employed one traditional predictive model, logistic regression, alongside six machine learning models: support vector machine (SVM), K-nearest neighbors (KNN), artificial neural network (ANN), random forest (RF), AdaBoost, and XGBoost. …”
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