Uncertainty-guided learning with scaled prediction errors in the basal ganglia. by Moritz Möller, Sanjay Manohar, Rafal Bogacz

“…Our results span across the levels of implementation, algorithm, and computation, and might have important implications for understanding the dopaminergic prediction error signal and its relation to adaptive and effective learning.…”
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Monthly Runoff Prediction Based on STL-CEEMDAN-LSTM Model by WANG Hai, SHEN Yanqing, QI Shansheng, PAN Hongzhong, HUO Jianzhen, WANG Zhance

“…The results show that the STL-CEEMDAN-LSTM prediction model has a good simulation effect. The Nash Sutcliffe efficiency (NSE), root mean square error (RMSE), and R² in the model prediction period are 0.813, 239.02, and 0.810, respectively, with the prediction accuracy better than the single model and the primary decomposition model. …”
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Explainable illicit drug abuse prediction using hematological differences by Aijun Chen, Yinchu Shen, Yu Xu, Jinhui Cai, Bo Ye, Jiaxue Sun, Jinze Du, Deshenyue Kong

“…Abstract This study aimed to develop a reliable and explainable predictive model for illicit drug use (IDU). The model uses a machine learning (ML) algorithm to predict IDU using hematological differences between illicit drug users (IDUr) and non-users (n-IDUr). …”
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Prediction of Aerosol Particle Size Distribution Based on Neural Network by Yali Ren, Jiandong Mao, Hu Zhao, Chunyan Zhou, Xin Gong, Zhimin Rao, Qiang Wang, Yi Zhang

“…To avoid solving such an integral equation, the BP neural network prediction model was established. In the model, the aerosol optical depth obtained by sun photometer CE-318 and kernel functions obtained by Mie scattering theory were used as the inputs of the neural network, particle size distributions collected by the aerodynamic particle sizer APS 3321 were used as the output, and the Levenberg–Marquardt algorithm with the fastest descending speed was adopted to train the model. …”
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A novel trajectory similarity–based approach for location prediction by Zelei Liu, Liang Hu, Chunyi Wu, Yan Ding, Jia Zhao

“…Location prediction impacts a wide range of research areas in mobile environment. …”
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Prediction of Lithium-Ion Battery Health Using GRU-BPP by Sahar Qaadan, Aiman Alshare, Alexander Popp, Benedikt Schmuelling

“…Accurate prediction of lithium-ion batteries’ (LIBs) state-of-health (SOH) is crucial for the safety and maintenance of LIB-powered systems. …”
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Enhancing Heart Disease Prediction with Federated Learning and Blockchain Integration by Yazan Otoum, Chaosheng Hu, Eyad Haj Said, Amiya Nayak

“…This paper introduces a novel approach for heart disease prediction using the TabNet model, which combines the strengths of tree-based models and deep neural networks. …”
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Application of artificial neural networks in the prediction of sugarcane juice Pol by Anderson P. Coelho, João V. T. Bettiol, Alexandre B. Dalri, João A. Fischer Filho, Rogério T. de Faria, Luiz F. Palaretti

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Data Transfer Schemes in Rotorcraft Fluid-Structure Interaction Predictions by Young H. You, Deokhwan Na, Sung N. Jung

“…The reason of the discrepancy is identified and discussed illustrating CFD-/CSD-coupled aeromechanics predictions.…”
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Applying binary mixed model to predict knee osteoarthritis pain. by Helal El-Zaatari, Liubov Arbeeva, Amanda E Nelson

“…Specifically, we utilized data from the baseline visit of the Osteoarthritis Initiative (OAI) and applied the Binary Mixed Models (BiMM) algorithm to predict two binary dependent variables. 1) presence of knee pain, stiffness or aching in the past 12 months and 2) presence of knee pain indicated by a KOOS pain score > 85. …”
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THE METHOD FOR PREDICTING THE TYPE OF SCAR TISSUE IN THE TREATMENT OF BURN WOUNDS by Yu. V. Yurova, E. V. Zinoviev, K. M. Krylov

“…Based on the results of the study, we developed the diagnostic algorithm for predicting the development of various types of scar tissue. …”
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Federated-Learning-Based Strategy for Enhancing Orbit Prediction of Satellites by Jiayi Tang, Wenxin Li, Qinchen Zhao, Hongmei Chi

“…Each satellite uses a Convolutional Neural Network (CNN) model to extract features from historical prediction error data. The server optimizes the global model through the Federated Averaging algorithm, learning more orbital patterns and enhancing accuracy. …”
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An accurate model to predict drilling fluid density at wellbore conditions by Mohammad Ali Ahmadi, Seyed Reza Shadizadeh, Kalpit Shah, Alireza Bahadori

“…In this regard, a couple of particle swarm optimization (PSO) and artificial neural network (ANN) was utilized to suggest a high-performance model for predicting the drilling fluid density. Moreover, two competitive machine learning models including fuzzy inference system (FIS) model and a hybrid of genetic algorithm (GA) and FIS (called GA-FIS) method were employed. …”
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Building Fire Location Predictions Based on FDS and Hybrid Modelling by Yanxi Cao, Hongyan Ma, Shun Wang, Yingda Zhang

“…Combining convolutional neural networks (CNNs) and support vector machines (SVMs) for prediction, the fire-source location prediction model with temperature, smoke, and CO concentration as feature quantities was constructed, and the hyperparameters affecting the model accuracy and generalisation were optimised by the Crested Porcupine Optimizer (CPO) algorithm. …”
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Feature fusion with attributed deepwalk for protein–protein interaction prediction by Mei-Yuan Cao, Suhaila Zainudin, Kauthar Mohd Daud

“…The weighted fusion approach effectively combines different aspects of protein data while reducing noise and redundancy, offering an improved technique for computational PPI prediction.…”
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Machine learning-based fatigue lifetime prediction of structural steels by Konstantinos Arvanitis, Pantelis Nikolakopoulos, Dimitrios Pavlou, Mina Farmanbar

“…Through preprocessing and feature selection, four techniques are explored: Polynomial Regression, Support Vector Regression (SVR), XGB Regression and Artificial Neural Network (ANN), aiming to identify the most effective algorithm. The implementation of these methodologies for fatigue lifetime prediction yields substantial outcomes. …”
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GRADED CRITERIA OF DIAGNOSTICS AND PREDICTION OF CENTRAL SEROUS CHORIORETINOPATHY OUTCOME by A. G. Shchuko, A. N. Zlobina, V. V. Bukina, A. A. Shchuko

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Machine learning-enabled prediction of bone metastasis in esophageal cancer by Liqiang Liu, Wanshi Duan, Tao She, Shouzheng Ma, Haihui Wang, Jiakuan Chen

“…This study aimed to develop a machine learning algorithm to predict the risk of bone metastasis in esophageal cancer patients, thereby supporting clinical decision-making support.MethodsClinical and pathological data of esophageal cancer patients were obtained from the SEER database of the U.S. …”
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Predicting Young’s Modulus of Aggregated Carbon Nanotube Reinforced Polymer by Roham Rafiee, Vahid Firouzbakht

“…Prediction of mechanical properties of carbon nanotube-based composite is one of the important issues which should be addressed reasonably. …”
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An Ensemble Learning Model for Short-Term Passenger Flow Prediction by Xiangping Wang, Lei Huang, Haifeng Huang, Baoyu Li, Ziyang Xia, Jing Li

“…The goal is to use the integrated model to accurately predict the short-term passenger flow of urban public transportation, using Multivariable Linear Regression (MLR), K-Nearest Neighbor (KNN), eXtreme Gradient Boosting (XGBoost), and Gated Recurrent Unit (GRU) as the four seed models, and then use regression algorithm to integrate the model and predict the passenger flow, station boarding and landing, and cross-sectional passenger flow data of the typical representative line 428 in the “Huitian Area” of Beijing from January 1, 2020, to May 31, 2020. …”
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