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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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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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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Koopman-Driven Grip Force Prediction Through EMG Sensing by Tomislav Bazina, Ervin Kamenar, Maria Fonoberova, Igor Mezic

“…The algorithm executes exceptionally fast, processing, estimating, and predicting a 0.5-second sEMG signal batch in just ~30 ms, facilitating real-time implementation.…”
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Real-time monitoring to predict depressive symptoms: study protocol by Yu-Rim Lee, Jong-Sun Lee

“…Passive data will be collected through sensors on the wearable-device, while EMA data will be collected four times a day through a smartphone app. A machine learning algorithm and multilevel model will be used to construct a predictive model for depressive symptoms using the collected data.DiscussionThis study explores the potential of wearable devices and smartphones to improve the understanding and treatment of depression in young adults. …”
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Conformal prediction quantifies wearable cuffless blood pressure with certainty by Zhan Shen, Tapabrata Chakraborti, Christopher R. S. Banerji, Xiaorong Ding

“…The model uncertainty was then calibrated using conformal prediction to obtain CIs with guaranteed reference values coverage. …”
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A Fusion Model for Predicting the Vibration Trends of Hydropower Units by Dong Liu, Youchun Pi, Zhengyang Tang, Hongpeng Hua, Xiaopeng Wang

“…To enable timely monitoring of unit performance, it is critical to investigate the trends in vibration signals, to enhance the accuracy and reliability of vibration trend prediction models. This paper proposes a fusion model for the vibration signal trend prediction of hydropower units based on the waveform extension method empirical mode decomposition (W-EMD) and long short-term memory neural network (LSTMNN). …”
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Predicting pathologic ≥N2 disease in women with breast cancer by Kerollos Nashat Wanis, Wenli Dong, Yu Shen, Funda Meric-Bernstam, Taiwo Adesoye, Henry M. Kuerer, Abigail S. Caudle, Nina Tamirisa, Sarah M. DeSnyder, Susie X. Sun, Isabelle Bedrosian, Puneet Singh, Solange E. Cox, Kelly K. Hunt, Rosa F. Hwang

“…Using data from a single institution on women with cN0 invasive breast cancer who were treated with upfront surgery, had 1-3 positive SLNs, and underwent completion ALND, we used gradient boosted trees (XGBoost) to develop a model for predicting ≥pN2 disease using clinicopathologic variables. …”
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Deep learning approach for survival prediction for patients with synovial sarcoma by Ilkyu Han, June Hyuk Kim, Heeseol Park, Han-Soo Kim, Sung Wook Seo

“…We developed a novel deep-learning-based prediction algorithm for survival rates of synovial sarcoma patients. …”
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