Turbofan engine health status prediction with artificial neural network by Slawomir Szrama, Tomasz Lodygowski

“… The main purpose of this study is to present the concept of the aircraft turbofan engine health status prediction with artificial neural network augmentation process. …”
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Route Prediction Based Vehicular Mobility Management Scheme for VANET by DaeWon Lee, Yoon-Ho Kim, HwaMin Lee

“…Furthermore, we propose mobility management scheme based on route prediction in VANET. Handoffs with intrahighway mobility are managed locally and transparency is provided to CHs, while global mobility is managed with Mobile IPv6. …”
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Risk prediction and analysis of gallbladder polyps with deep neural network by Kerong Yuan, Xiaofeng Zhang, Qian Yang, Xuesong Deng, Zhe Deng, Xiangyun Liao, Weixin Si

“…This algorithm utilizes the aforementioned risk factors to predict the nature of gallbladder polyps. …”
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Probabilistic prediction of fatigue damage based on linear fracture mechanics by M. Krejsa, L. Koubova, J. Flodr, J. Protivinsky, Q. T. Nguyen

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Temporal Backtracking and Multistep Delay of Traffic Speed Series Prediction by Licheng Qu, Minghao Zhang, Zhaolu Li, Wei Li

“…With a real traffic data set, the coordinate descent algorithm was employed to search and determine the optimal backtracking length of traffic sequence, and multistep delay predictions were performed to demonstrate the relationship between delay steps and prediction accuracies. …”
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Prediction of success in assisted reproductive technology with the help of morphology of the testis by N. G. Kulchenko

“…Based on the study, a diagnostic algorithm of patients with male infertility is proposed, which allows to predict the success of ART taking into account morphological changes in the testicle.Conclusion. …”
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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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A Hybrid Learning Framework for Enhancing Bridge Damage Prediction by Amal Abdulbaqi Maryoosh, Saeid Pashazadeh, Pedram Salehpour

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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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Cognitive abilities predict naturalistic speech length in older adults by Patrick Neff, Burcu Demiray, Mike Martin, Christina Röcke

“…Audio data of 83 participants are analyzed with a machine learning speaker identification algorithm. Using Elastic Net regularized regression, results indicate that higher levels of working memory, cognitive speed, and semantic fluency predict own speech in everyday life. …”
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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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