Personalised regional modelling predicts tau progression in the human brain. by Pavanjit Chaggar, Jacob W Vogel, Alexa Pichet Binette, Travis B Thompson, Olof Strandberg, Niklas Mattsson-Carlgren, Linda Karlsson, Erik Stomrud, Saad Jbabdi, Stefano Magon, Gregory Klein, with the Alzheimer’s Disease Neuroimaging Initiative, Oskar Hansson, Alain Goriely

“…Further, we demonstrate that the model can predict accurately subject-specific longitudinal tau accumulation at the regional level, potentially providing a new clinical tool to monitor and classify patient disease progression.…”
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A New Prediction Model of Dam Deformation and Successful Application by Shuangping Li, Bin Zhang, Meng Yang, Senlin Li, Zuqiang Liu

“…In most dam deformation monitoring practices, some single-point models do not consider the spatial correlation, and the traditional regression models do not consider the nonlinear relationship between the environmental quantity and the deformation quantity, resulting in poor prediction accuracy. …”
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Clinical validation of a model predicting the risk of preterm delivery. by Yohann Dabi, Sophie Nedellec, Claire Bonneau, Blandine Trouchard, Roman Rouzier, Alexandra Benachi

“…<h4>Objectives</h4>To validate a model predicting the risk of threatened preterm delivery and to establish the optimal threshold for this risk scoring system.…”
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Integrated Cox’s model for predicting survival time of glioblastoma multiforme by Zhibing Ai, Longti Li, Rui Fu, Jing-Min Lu, Jing-Dong He, Sen Li

“…Patients were separated into two groups by using 1 year as a cutoff, and a logistic regression model was used to figure out any variables that can predict whether the patient was able to live longer than 1 year. …”
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Statistical models for predicting the number of under-five mortality in Nepal. by Madhav Kumar Bhusal, Shankar Prasad Khanal

“…<h4>Objective</h4>This study aimed to develop a suitable statistical model using the associated factors to predict the number of under-five mortality a mother in Nepal encountered throughout her lifetime.…”
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An Analytical Model for the Prediction of Emptying Processes in Single Water Pipelines by Carlos R. Payares Guevara, Alberto Patiño-Vanegas, Enrique Pereira-Batista, Oscar E. Coronado-Hernández, Vicente S. Fuertes-Miquel

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Signature Prediction Modeling in Cytolethal Distending Toxin-Producing Strains by Maryam Javadi, Mana Oloomi, Saeid Bouzari

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LLM-driven semantic explanations for soil moisture prediction models by Bamory Ahmed Toru Koné, Khouloud Boukadi, Rima Grati, Emna Ben Abdallah, Massimo Mecella

“…We propose a framework that leverages large language models (LLMs) to generate textual explanations based on a proposed irrigation and soil moisture ontology, thus making the model's predictions more understandable to farmers. …”
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Research on COP Prediction Model of Chiller Based on PSO-SVR by Zhou Xuan, Cai Panpan, Lian Sizhen, Yan Junwei

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A New Hybrid Model for Underwater Acoustic Signal Prediction by Guohui Li, Wanni Chang, Hong Yang

“…The experimental results show that the proposed new hybrid model can provide enhanced accuracy with the reduction of prediction error compared with other existing prediction methods.…”
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Prediction of Vapor-Liquid Equilibria Using CEOS /GE Models

“…To predict VLE data in multicomponent symmetric and asymmetric mixtures such as systems that contain light gases (nitrogen, carbon dioxide, etc.) and heavy hydrocarbons, the SRK equation of state has been combined with excess Gibbs energy models. …”
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Development of time to event prediction models using federated learning by Rasmus Rask Kragh Jørgensen, Jonas Faartoft Jensen, Tarec El-Galaly, Martin Bøgsted, Rasmus Froberg Brøndum, Mikkel Runason Simonsen, Lasse Hjort Jakobsen

“…Alternatively, federated learning (FL) can be utilized to train models based on data located at multiple sites. Method We propose two methods for training time-to-event prediction models based on distributed data, relying on FL algorithms, for time-to-event prediction models. …”
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Integrated Machine Learning Models for Bakery Product Defect Prediction by Nataliia Zaiets, Nataliia Lutska, Lidiia Vlasenko

Subjects: “…defect prediction…”
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Predicting cell morphological responses to perturbations using generative modeling by Alessandro Palma, Fabian J. Theis, Mohammad Lotfollahi

“…To tackle these shortcomings, we present IMage Perturbation Autoencoder (IMPA), a generative style-transfer model predicting morphological changes of perturbations across genetic and chemical interventions. …”
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Satellite-Based Validation of Contrail Prediction Models for Sustainable Aviation by Baddireddi Sree Chandana, Pelleti Nandieswar Reddy, Rithvika Alapati, Sai Aswath Reddy, Radha Doraisamy, Uma Sankari

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A panel data model to predict airline passenger volume by Xiaoting Wang, Junyu Cai, Junyan Wang

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Groundwater quality parameters prediction based on data-driven models by Mohammed Falah Allawi, Yasir Al-Ani, Arkan Dhari Jalal, Zainab Malik Ismael, Mohsen Sherif, Ahmed El-Shafie

“…According to the evaluation results, adding more input variables can sometimes increase the efficacy of the proposed models with regard to prediction accuracy. Moreover, the findings show that the PNN model provides a promising performance in predicting the groundwater’s water quality (WQ) matrices, showing superior performance compared to the RBFNN model.…”
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Dynamic Modeling for Prediction of Amino Acid Requirements in Broiler Diets by Guangju Wang, Xin Zhao, Mengjie Xu, Zhenwu Huang, Jinghai Feng, Minhong Zhang

“…This study developed a dynamic model to predict standardized ileal digestible amino acid requirements throughout broiler growth using a factorial approach and the comparative slaughter technique, considering maintenance, growth, and gender factors. …”
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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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Modeling of Complex Life Cycle Prediction Based on Cell Division by Fucheng Zhang, Yahui Wang, De Zhang

“…At present, the life prediction methods of the equipment are equipment life prediction based on condition monitoring, combined forecasting model, and driven data. …”
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