Development of a comprehensive model to predict stock prices in the stock market with an interpretive structural modeling approach by Shiva Rezaeyan, Mohammad Taleghani, Azita Sherej Sharifi

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Modeling and control of automatic voltage regulation for a hydropower plant using advanced model predictive control by Ebunle Akupan Rene, Willy Stephen Tounsi Fokui

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Morphological Model for Erosion Prediction of India’s Largest Braided River Using MIKE 21C Model by Kuldeep Pareta

“…The approach leverages predictive models to predict morphological changes over a 2–3 years timeframe, contributing to improved river management.…”
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Large language models' knowledge of children's memory and suggestibility: Evaluating model predictions of prior experimental results by Pekka Santtila, Yongjie Sun, Kristjan Kask, Liisa Järvilehto, Jiaxing Xiu

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To What Extent Does Discounting ‘Hot’ Climate Models Improve the Predictive Skill of Climate Model Ensembles? by Abigail McDonnell, Adam Michael Bauer, Cristian Proistosescu

“…The Intergovernmental Panel on Climate Change's (IPCC) Assessment Report Six (AR6) took a step toward ending so‐called ‘model democracy’ by discounting climate models that are too warm over the historical period (i.e., models that ‘run hot’) when making projections of global temperature change. …”
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Temporal trends and predictive modeling of air pollutants in Delhi: a comparative study of artificial intelligence models by Omer A. Alawi, Haslinda Mohamed Kamar, Ali Alsuwaiyan, Zaher Mundher Yaseen

“…The modelling results revealed that Bi-LSTM model had the best predictability performance for forecasting CO with (R2 = 0.979), NO with (R2 = 0.961), NO2 with (R2 = 0.956), SO2 with (R2 = 0.955), PM10 with (R2 = 0.9751) and NH3 with (R2 = 0.971). …”
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A robust multi-model framework for groundwater level prediction: The BFSA-MVMD-GRU-RVM model by Akram Seifi, Sharareh Pourebrahim, Mohammad Ehteram, Hanieh Shabanian

“…Finally, the extracted data is fed into the RVM model to predict groundwater levels one month ahead in Iran's Bastam Plain. …”
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Generalization of a fast, predictive model for tokamak scenario design in ASTRA and comparison to a RAPTOR model by Raphael Schramm, Alexander Bock, Emiliano Fable, Jörg Stober, Simon van Mulders, Marc Maraschek, Rainer Fischer, Andreas Burckhart, Philip Schneider, Olivier Sauter, Hartmut Zohm, the ASDEX Upgrade Team, JET Contributors

“…We have previously presented an analytical predictive model to aid in the AT scenario design, optimized for a single scenario (Schramm et al 2024 Nucl. …”
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A hybrid workflow connecting a network and an agent-based model for predictive pedestrian movement modelling by Anita Ullrich, Franziska Hunger, Ioanna Stavroulaki, Adam Bilock, Klas Jareteg, Yury Tarakanov, Alexander Gösta, Alexander Gösta, Johannes Quist, Meta Berghauser Pont, Fredrik Edelvik

“…This study outlines a comprehensive workflow tailored for urban planners. It proposes a hybrid model that integrates an agent-based model, which simulates the micro-scale movement of pedestrians in outdoor urban environments, with a network model, which predicts the aggregated pedestrian flows on a macro-scale. …”
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Joint Decision-Making Model Based on Consensus Modeling Technology for the Prediction of Drug-Induced Liver Injury by Yukun Wang, Xuebo Chen

“…Compared with the main published studies, our method exhibits certain advantage in data size, transparency, and standardization of the modeling process and accuracy and credibility of prediction results. …”
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Construction and application of optimized model for mine water inflow prediction based on neural network and ARIMA model by Xiaoyu Gong, Bo Li, Yu Yang, MengHua Li, Tao Li, Beibei Zhang, Lulin Zheng, Hongfei Duan, Pu Liu, Xin Hu, Xin Xiang, Xinju Zhou

Subjects: “…Mine influx prediction…”
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Risk prediction models for short-term mortality in ICU stroke patients: a systematic review and meta-analysis by Jiali Zhang, Yijie Fu, Yan Liu, TianHeng Liu, Yue Deng, LiFei Dai, Tianmin Zhu, Hui Li

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Risk prediction models for ovarian hyperstimulation syndrome: a systematic review and meta-analysis by Jinghui Liu, Fangli Liu, Wenqi Xu, Luwen Zhang, Qingmi Tian, Libaihe Du, Siyuan Li, Mingyang Zhang

“…We conducted a meta-analysis of predictors from the developed models using Stata 15.0 software. Results A total of 16 studies were included, comprising 29 OHSS risk prediction models. …”
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A Comparative Study of Drug Prediction Models using KNN, SVM, and Random Forest by Susi Eva Maria Purba

Subjects: “…drug classification, machine learning, k-nearest neighbors (knn), support vector machine (svm), random forest, predictive modeling…”
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Machine-learning prediction models for any blood component transfusion in hospitalized dengue patients by Md. Shahid Ansari, Dinesh Jain, Sandeep Budhiraja

“…The XGBoost model presented significantly better predictive performance (area under the curve: 0.793; 95 % confidence interval: 0.699–0.795) than the other models. …”
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Optimized deep learning models for stress-based stroke prediction from EEG signals by Sivasankaran Pichandi, Gomathy Balasubramanian, Venkatesh Chakrapani, J. Samuel Manoharan

“…The effectiveness of the proposed model is validated through experiments conducted with the benched mark DEAP dataset, demonstrating its robust performance in both stress and stroke prediction.…”
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Diagnosis models to predict peripheral arterial disease: a systematic review and meta analysis by Xiaoyan Quan, Huarong Xiong, Xiaoyu Liu, Pan Song, Dan Wang, Qin Chen, Xiaoli Hu, Meihong Shi

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Cervical cancer prediction using machine learning models based on routine blood analysis by Jie Su, Hui Lu, Ruihuan Zhang, Na Cui, Chao Chen, Qin Si, Biao Song

“…This study aimed to develop an interpretable model for predicting CC risk using routine blood data. …”
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Prediction of the pharmaceutical solubility in water and organic solvents via different soft computing models by A. Yousefi, k. movagharnejad

“…Molecular weight, melting point, temperature and the number of each functional group in the pharmaceutical compound and organic solvents were selected as the input variables of these three different neural network models. The neural network predictions were compared with the experimental data and the SVR-PSO model with the Average Absolute Relative Deviation equal to 0.0166 for the solubility in water and 0.0707 for solubility in organic compounds was selected as the most accurate model.…”
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Combining diffusion and transformer models for enhanced promoter synthesis and strength prediction in deep learning by Xin Lei, Xing Wang, Guanlin Chen, Ce Liang, Quhuan Li, Huaiguang Jiang, Wei Xiong

“…Our prediction results were more accurate than those obtained with a convolutional neural network model. …”
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