Intelligent Stroke Disease Prediction Model Using Deep Learning Approaches by Chunhua Gao, Hui Wang

“…Further ablation experiments also show that the designed prediction model has certain robustness and can effectively predict stroke diseases.…”
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Deep Learning-Driven Predictive Modelling for Optimizing Stingless Beekeeping Yields by Noor Hafizah Khairul Anuar, Mohd Amri Md Yunus, Muhammad Ariff Baharudin, Sallehuddin Ibrahim, Shafishuhaza Sahlan

“…The dataset extracted from the 6th of January 2024 to the 5th of February 2024, at a 15-minute time interval comprising a total of 2577 data points was analyzed using various deep learning approaches for best RMSE performance. A single-layer LSTM model with 50 units produced the best RMSE performance of 0.039, representing that the beehive weight was accurately predicted. …”
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The PLANS model predicts recurrent strokes in patients with minor ischemic strokes by Zhi-Xin Huang, Haike Lu, Yi Lu, Yingyi Dai, Jianguo Lin, Zhenguo Liu

“…Abstract Minor ischemic stroke (MIS) patients face significant risks of recurrent strokes, necessitating reliable predictive tools. This single-center retrospective study developed and validated a novel model for predicting 1-year stroke recurrence in MIS patients, defined as those with National Institutes of Health Stroke Scale scores < 4 within seven days of symptom onset. …”
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Prediction of success of slings in female stress incontinence, statistical and AI modeling by Bassem S. Wadie, Ahmed Abdelrasheed, Mohammed Taha, Ahmed Abdelrahman, Bassam Mohamed, Alaa Saber, Ahmed Badawi

“…In this study, we tested a statistical regression model and an AI model for the prediction of the outcome of mid-urethral sling. …”
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Offset-Free Strategy by Double-Layered Linear Model Predictive Control by Tao Zou

“…In the real applications, the model predictive control (MPC) technology is separated into two layers, that is, a layer of conventional dynamic controller, based on which is an added layer of steady-state target calculation. …”
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Double-Weighted Bayesian Model Combination for Metabolomics Data Description and Prediction by Jacopo Troisi, Martina Lombardi, Alessio Trotta, Vera Abenante, Andrea Ingenito, Nicole Palmieri, Sean M. Richards, Steven J. K. Symes, Pierpaolo Cavallo

“…Background/Objectives: This study presents a novel double-weighted Bayesian Ensemble Machine Learning (DW-EML) model aimed at improving the classification and prediction of metabolomics data. …”
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Predictive modeling of flexible EHD pumps using Kolmogorov–Arnold Networks by Yanhong Peng, Yuxin Wang, Fangchao Hu, Miao He, Zebing Mao, Xia Huang, Jun Ding

“…We evaluated KAN on a dataset of flexible EHD pump parameters and compared its performance against RF, and MLP models. KAN achieved superior predictive accuracy, with Mean Squared Errors of 12.186 and 0.012 for pressure and flow rate predictions, respectively. …”
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Model of metabolism and gene expression predicts proteome allocation in Pseudomonas putida by Juan D. Tibocha-Bonilla, Vishant Gandhi, Chloe Lieng, Oriane Moyne, Rodrigo Santibáñez-Palominos, Karsten Zengler

“…Compared to a metabolic-only model, iPpu1676-ME significantly expands on gene expression, macromolecular assembly, and cofactor utilization, enabling accurate growth predictions without additional constraints. …”
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Prediction of the monthly river water level by using ensemble decomposition modeling by Chaitanya Baliram Pande, Lariyah Mohd Sidek, Bijay Halder, Okan Mert Katipoğlu, Jitendra Rajput, Fahad Alshehri, Rabin Chakrabortty, Subodh Chandra Pal, Norlida Mohd Dom, Miklas Scholz

“…Abstract The decomposition, artificial intelligence (AI) and machine learning (ML) modeling have been important role in hydrological and river basin related prediction and forecasting to help the flood management and sustainable water resources development. …”
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Construction of Discrimination Models in Prediction of Bankruptcy if Polish Non-Public Enterprises by Bernard Kokczyński

Subjects: “…bankruptcy prediction models…”
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Effective workflow from multimodal MRI data to model-based prediction by Kyesam Jung, Kevin J. Wischnewski, Simon B. Eickhoff, Oleksandr V. Popovych

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Construction of disability risk prediction model for the elderly based on machine learning by Jing Chen, Yifei Ren, Jie Ding, Qingqing Hu, Jiajia Xu, Jun Luo, Zhaowen Wu, Ting Chu

Subjects: “…Prediction model…”
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Prediction of Gas Solubility in Ionic Liquids Using the Cosmo-Sac Model by Jaschik Manfred, Piech Daniel, Warmuzinski Krzysztof, Jaschik Jolanta

“…The predictions of the COSMOSAC model for N2 and O2 in ILs differ from the pertinent experimental data. …”
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Benchmarking foundation cell models for post-perturbation RNA-seq prediction by Gerold Csendes, Gema Sanz, Kristóf Z. Szalay, Bence Szalai

“…While perturbation data is ideal for building such predictive models, its availability is considerably lower than baseline (non-perturbed) cellular data. …”
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Risk-Predictive Models for Adverse Events in Cardiac Surgery: A Review by Huan Luo

“…Risk prediction models are an important part of assessing operative mortality and postoperative complication rates in current cardiac surgery practice. …”
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Application predictive modelling of Penicillium roqueforti germination in environmental conditions in cake by Hassan Nakhchian, Farideh Tabatabaee Yazdi, Seyed Ali Mortazavi, Mohebat Mohebi

“…The results of analysis of variance (ANOVA) proved that environmental conditions affect germination significantly (P &lt; 0.05). Predictive modelling illustrated that the temperature did not affect germination significantly, while no germination was seen at aw = 0.65. …”
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Landslide Displacement Prediction Model Based on Time Series and CNN-GRU by FU Zhentao, LI Limin, WANG Lianxia, REN Ruibin, CUI Chengtao, FENG Qingqing

“…Landslide displacement prediction is an important basis for early landslide warning.This paper proposes a prediction model of landslide moving states based on time series and convolutional gated recurrent unit (CNN-GRU) to deal with the shortcomings of previous prediction models.Firstly,after employing wavelet analysis to determine the displacement of the trend term,the exponential smoothing method is adopted to decompose the cumulative displacement to obtain two displacement types of the trend term and the periodic term,and the trend term is fitted by a five-order polynomial.Then,the autocorrelation function is utilized to test the periodic displacement characteristics,and the gray correlation method is applied to determine the correlation degree between each factor and the periodic term.Meanwhile,the periodic term and the influencing factor are input into the CNN-GRU model for prediction,and finally the predicted cumulative displacement value is obtained by superposition.By taking the Baishui River landslide in the Three Gorges Reservoir area as an example,this paper selects the data from January 2004 to December 2012 for study,and the average absolute error percentage of the final prediction results is only 0.525%,with RMSE of 9.614 and R² of 0.993.Experimental results show that CNN-GRU has higher prediction accuracy.…”
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Presenting a prediction model for HELLP syndrome through data mining by Boshra Farajollahi, Mohammadjavad Sayadi, Mostafa Langarizadeh, Ladan Ajori

Subjects: “…Prediction model…”
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Simulation and prediction of rural population changes using agent-based modeling by Shanshan Huang, Yao Huang, Shitai Bao, Jianfang Wang, Siying Chen

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Comparative Study on Prediction Models for Crack Opening Degree in Concrete Dam by HUANG Song, WU Jie, FANG Zhanchao, CHU Huaping, WU Yan'gang, XUE Zilong, HE Linbo

“…The results show that three models for predicting crack opening degree are successfully established based on the crack opening degree dataset measured in 2022. …”
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