The design of consumer behavior prediction and optimization model by integrating DQN and LSTM. by Na Liu, Dajiang Hu

“…The framework initially employs a Transformer network to process consumer behavioral data using a multi-headed attention mechanism, It then integrates DQN to optimize the model culminating in an enhanced prediction layer that refines consumer interest analysis. …”
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THE STRENGTH MODELING OF BOGIE WELDED FRAME AND ITS INFLUENCE ON LIFE PREDICTION by LIU Xu, ZHANG KaiLin, ZHOU ChunPing, LIU ChangLin

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Construction of a prediction model for sarcopenic obesity based on machine learning by Mengru Xu, Mengru Xu, Jia Liu, Jia Liu, Song Hu, Song Hu, Tongxiao Luan, Tongxiao Luan, Yuting Duan, Yuting Duan, Aohua Wang, Aohua Wang, Ziwei Cui, Ziwei Cui, Jing Zhou, Yongjun Mao, Yongjun Mao

“…Among these models, the RF model exhibited the best average performance in the training set, with an AUC value of 0.839.ConclusionWe constructed a predictive model based on the results of the RF model, combining four clinical predictors—BMI, Barthel Index score, grip strength, and calf circumference—to reliably predict SO.…”
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Performance Analysis of an Optimized ANN Model to Predict the Stability of Smart Grid by Ayushi Chahal, Preeti Gulia, Nasib Singh Gill, Jyotir Moy Chatterjee

“…This study concludes that the deep learning predictive model ANN optimized with Adam optimizer provides better results than other predictive models. …”
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A virtual scalable model of the Hepatic Lobule for acetaminophen hepatotoxicity prediction by Stelian Camara Dit Pinto, Jalal Cherkaoui, Debarshi Ghosh, Valentine Cazaubon, Kenza E. Benzeroual, Steven M. Levine, Mohammed Cherkaoui, Gagan K. Sood, Sharmila Anandasabapathy, Sadhna Dhingra, John M. Vierling, Nicolas R. Gallo

“…Abstract Addressing drug-induced liver injury is crucial in drug development, often causing Phase III trial failures and market withdrawals. Traditional animal models fail to predict human liver toxicity accurately. …”
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Periodformer: An Energy Consumption Prediction Model Based on Decomposition of Time Series by CHEN Bowen, DENG Jian, ZHU Qianliu

Subjects: “…train energy consumption prediction…”
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Thermodynamic Modeling and Phase Prediction for Binary System Dinitrogen Monoxide and Propane by Alireza Bozorgian, Behrouz Raei

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Application of Improved EEMD-NNBR Coupling Model in Annual Runoff Prediction by ZHENG Fangfang, WANG Wensheng, ZHANG Lanting

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Improving model-free prediction of chaotic dynamics by purifying the incomplete input by Hongfang Tan, Lufa Shi, Shengjun Wang, Shi-Xian Qu

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Research on a complaint prediction model utilizing joint neural networks by Xiaoliang MA, Ying LIU, Jie GAO

“…By conducting in-depth exploration on the key factors affecting repeat complaints of telecom operators, this study aimed to improve service quality and construct a risk prediction model.Based on the operator’s customer service data, the study employed Logistic regression, BP neural network, and their combined modeling methods.The Logistic regression model identified five major influencing factors, predicting the probability of repeat complaints with an accuracy of 80.0%.The BP neural network selected 81 influencing factors, achieving a prediction accuracy of 90.6%.On this basis, a combined model was constructed with an accuracy rate of up to 92.8%.After practical application in a provincial telecom operator, the repeat complaint rate decreased by 3.2%, demonstrating a significant impact.Strong support is provided for improving the service quality of telecom operators and reducing repeat complaints, which is of great significance for the development of the telecom industry in China.…”
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Hybrid approaches enhance hydrological model usability for local streamflow prediction by Yiheng Du, Ilias G. Pechlivanidis

“…Abstract Hydrological models are essential for predicting water flux dynamics, including extremes, and managing water resources, yet traditional process-based large-scale models often struggle with accuracy and process understanding due to their inability to represent complex, non-linear hydrometeorological processes, limiting their effectiveness in local conditions. …”
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3D modeling to predict vascular involvement in resectable pancreatic adenocarcinoma by Sguinzi R, Vidal J, Poroes F, Bartolucci DA, Litchinko A, Gossin E, Fingerhut A, Toso C, Buhler L, Egger B

“…The pre-operative computed tomography (CT) images were processed by segmentation with 3D reconstruction and printed as 3D models. Two radiologists specialized in pancreatic imaging and two pancreatic surgeons blindly and independently analyzed the pre-operative CT scans and 3D models using a defined checklist. …”
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Prediction and Monitoring Model of Concrete Dam Deformation Based on WOA-RFR by FENG Yu, WU Yunxing, GU Wenjing, PANG Qiong, GU Yanchang, CHEN Siyu

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Influencing factors of cross screening rate and its intelligent prediction model by Lala ZHAO, Feng XU, Chenlong DUAN, Chenhao GUO, Wei WANG, Haishen JIANG, Jinpeng QIAO

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Effects of Test Conditions on APA Rutting and Prediction Modeling for Asphalt Mixtures by Hui Wang, Haoqi Tan, Tian Qu, Jiupeng Zhang

“…The proposed indoor APA rutting prediction model has good prediction accuracy, and the correlation coefficient between the predicted and the measured rutting depths is 96.3%.…”
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Artificial Intelligence model to predict resistances in Gram-negative bloodstream infections by Cecilia Bonazzetti, Ettore Rocchi, Alice Toschi, Nicolas Riccardo Derus, Claudia Sala, Renato Pascale, Matteo Rinaldi, Caterina Campoli, Zeno Adrien Igor Pasquini, Beatrice Tazza, Armando Amicucci, Milo Gatti, Simone Ambretti, Pierluigi Viale, Gastone Castellani, Maddalena Giannella

“…Abstract Artificial intelligence (AI) models are promising tools for predicting antimicrobial susceptibility in gram-negative bloodstream infections (GN-BSI). …”
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An interpretable and adaptable data-driven model for performance prediction in thermal plants by G. Prokhorskii, M. Preißinger, S. Rudra, E. Eder

“…To safely operate complex industrial systems such as thermal power plants, establishing reliable monitoring tools is paramount for better understanding the underlying processes. Data-driven models are a useful aid for monitoring and control of thermal power plants, but they require an effective feature selection to allow for an accurate, computationally efficient, and interpretable model. …”
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Machine learning model for random forest acute oral toxicity prediction by A.M. Elsayad, M.M. Zeghid, K.A. Elsayad, A.N. Khan, ِA.K.M. Baareh, A. Sadiq, S.A. Mukhtar, H.F. Ali, S. Abd El-kader

“…A surrogate decision tree developed from random forests predictions reached an area under the curve of 0.929.CONCLUSION: Random forest models effectively predicted acute oral toxicity, particularly when addressing class imbalance through cost-sensitive learning and resampling. leveraging explainable artificial intelligence techniques, including permutation feature importance, surrogate decision tree analysis and local interpretable model-agnostic explanations, this study identified key molecular descriptors driving toxicity. …”
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Development of a Hybridized Model for Predicting the Life Span of Power Transformers by O.O. Olaluwoye, A.A. Okubanjo, O.K. Oyetola, P.O. Alao, O.G. Olasunkanmi

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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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