An intelligent model for predicting the behavior of soil conditions depending on external weather conditions by Antamoshkin Oleslav, Mikhalev Anton, Menshenin Andrey, Lukishin Alexander

“…This research integrates advanced machine learning models, including LSTM, Transformer, TCN, and XGBoost, to predict changes in road conditions based on meteorological and soil data. …”
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Developing new machine-learning intelligent models to predict the excavation-tunnel displacements by Abdollah Tabaroei, Muhand Jawad Jasim, Ali Mohammed Al-Araji, Amir Hossein Vakili

“…Finally in the third part, based on the simulation results two models developed for predict and validate the $${\delta }_{hrm}$$ , $${\delta }_{vm}$$ , $${\delta }_{htm}$$ and $${\delta }_{vtm}$$ values. …”
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Malicious traffic prediction model for ResNet based on Maple-IDS dataset. by Qingfeng Li, Boyu Wang, Xueyan Wen, Yuao Chen

“…However, the imbalance among various attack categories diminishes the accuracy of model predictions. To address this issue, we propose the Maple-IDS dataset as an innovative solution. …”
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Prediction of seepage flow through earthfill dams using machine learning models by Issam Rehamnia, Ahmed Mohammed Sami Al-Janabi, Saad Sh. Sammen, Binh Thai Pham, Indra Prakash

“…Moreover, including the periodicity factors improves prediction accuracy of the machine learning models.…”
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Automated Cardiac Disease Prediction Using Composite GAN and DeepLab Model by Sohail Jabbar, Umar Raza, Muhammad Asif Habib, Muhammad Farhan, Saqib Saeed

“…However, constraints like limited annotation and model generalization persist. We introduce GenDeep, a novel framework integrating an unsupervised Generative Adversarial Network (GAN) and DeepLab model for robust cardiac pathology classification from cine-MRI scans. …”
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NSPLformer: exploration of non-stationary progressively learning model for time series prediction by Sun Jiaxing, Li Yanhui, Zhao Yuying

Subjects: “…Times series prediction…”
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The Effect of Temperature on Intestinal Flora Imbalance Based on Time Series Prediction Model by Jing Zhu, Yafei Xue, Shuyuan Yang

“…Based on the sequence information of historical temperature change parameters, a temperature prediction device based on time series model is proposed. …”
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Machine learning models for predicting the risk of depressive symptoms in Chinese college students by Chengfu Yu, Xiangxuan Kong, Weijie Yu, Xingcan Ni, Jing Chen, Xiaoyan Liao

“…Given the limitations of traditional linear models in managing high-dimensional data, this study employed machine learning techniques to predict depressive symptoms.MethodData were collected from 1,635 Chinese college students and included 38 sociodemographic, psychological, and social variables. …”
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Prediction Model of Moisture Field Migration in Subgrade Soil under Rainfall Conditions by WANG Kangyu, YE Jiahuan, WANG Chengquan

“…Accurately characterizing the spatiotemporal distribution pattern of water content field and the prediction modeling of its moisture field migration mechanism is a key prerequisite for promoting slope stability evaluation methods and optimizing geological disaster prevention strategies. …”
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Comparative modeling approaches for predicting Olea and Quercus pollen seasons in Thessaloniki, Greece by S. Papadogiannaki, K. Karatzas, S. Kontos, A. Poupkou, D. Melas

“…Abstract In the Mediterranean region, Olive (Olea europaea L.) is a primary source of airborne allergenic pollen, while Quercus contribute substantial quantities of pollen grains to the atmosphere, posing significant challenges in predicting their Main Pollen Seasons (MPS). This study addresses these challenges through the application of various predictive methodologies, including Thermal Time (TT) models, which integrate chilling and heat requirements, along with Partial Least Squares Regression (PLS), and Temperature-Photoperiod (TP) models. …”
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TBM Net Advance Rate Prediction Model Based on Ridge Regression Analysis by SHI Jian, ZHANG Shilin, FAN Zuosong, KONG Desen

Subjects: “…tbm net advance rate prediction model…”
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Development of the treatment prediction model in the artificial intelligence in depression – medication enhancement study by David Benrimoh, Caitrin Armstrong, Joseph Mehltretter, Robert Fratila, Kelly Perlman, Sonia Israel, Adam Kapelner, Sagar V. Parikh, Jordan F. Karp, Katherine Heller, Gustavo Turecki

“…We predict probabilities of remission across multiple pharmacological treatments, validate model predictions, and examine them for biases. …”
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COMPARISON OF LEAST SQUARE SPLINE AND ARIMA MODELS FOR PREDICTING INDONESIA COMPOSITE INDEX by Any Tsalasatul Fitriyah, Nur Chamidah, Toha Saifudin

“…The parametric approach in this study uses the ARIMA model. ARIMA is widely used to predict time series data. …”
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Predicting suicidal behavior outcomes: an analysis of key factors and machine learning models by Mohammad Bazrafshan, Kourosh Sayehmiri

“…This study aimed to determine the critical risk factors associated with suicidal behavior mortality and identify an effective classification model for predicting suicidal behavior outcomes. …”
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Preoperative MR - based model for predicting prognosis in patients with intracranial extraventricular ependymoma by Liyan Li, Xueying Wang, Zeming Tan, Yipu Mao, Deyou Huang, Xiaoping Yi, Muliang Jiang, Bihong T. Chen

“…Objectives: To develop and validate a prediction model based on brain MRI features to predict disease-free survival (DFS) and overall survival (OS) for patients with intracranial extraventricular ependymoma (IEE). …”
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Multimodal fusion for athlete state prediction leveraging XLNet and deep generative models by Yafeng Feng, Yong Sun, Chengfang Hang

“…A multilayer perceptron and AdaBoost ensemble are employed for comprehensive feature fusion and state prediction. Experimental results show that our model achieves a significant improvement in classification accuracy, with a 12% increase in emotional state recognition compared to traditional models, and a 15% reduction in prediction error for physiological state estimation. …”
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Prediction of the burst pressure for defective pipelines using different semi-empirical models by S. Budhe, M.D. Banea, S. de Barros

“…The main aim of this work is to predict the theoretical burst pressure of defective pipelines using different semi-empirical models and compare them with the hydrostatic test results. …”
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PEMFC RUL Prediction for Non-Stationary Time Series Based on Crossformer Model by Ning Zhou, He Zeng, Zefei Zheng, Ke Wang, Jianxin Zhou

“…In this paper, we propose a PEMFC RUL prediction model based on the Crossformer for non-stationary time series (De-stationary-Crossformer). …”
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Hybrid Intelligence Model Based on Image Features for the Prediction of Flotation Concentrate Grade by YaLin Wang, XiaoFang Chen, XiaoLing Zhou, WeiHua Gui, Louis Caccetta, Honglei Xu

“…In flotation processes, concentrate grade is the key production index but is difficult to be measured online. The mechanism models reflect the basic tendency of concentrate grade changes but cannot provide adequate prediction precision. …”
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NECOP Propagation Experiment: Rain-Rate Distributions Observations and Prediction Model Comparisons by J. S. Ojo, S. E. Falodun

“…The empirical distribution functions were compared with cumulative distribution functions generated using four different rain-rate distribution models. It is found that although each of the models shows similar qualitative features at lower exceedance of time, the characteristic at higher time percentages shows quantitative difference from the experimental data except the improved version of Moupfouma model. …”
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