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. …”
Get full text

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. …”
Get full text

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.…”
Get full text

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. …”
Get full text

GRADED CRITERIA OF DIAGNOSTICS AND PREDICTION OF CENTRAL SEROUS CHORIORETINOPATHY OUTCOME by A. G. Shchuko, A. N. Zlobina, V. V. Bukina, A. A. Shchuko

Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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.…”
Get full text

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. …”
Get full text

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. …”
Get full text

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). …”
Get full text

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. …”
Get full text

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. …”
Get full text

Prediction of Power System Ramping Demand Using Meteorological Features by Kuan Lu, Song Gao, Jun Li, Kang Chen, Chunhao Yu

“…This study focuses on predicting uncertain ramping demand influenced by meteorological factors. …”
Get full text

Spatial distribution prediction of pore pressure based on Mamba model by Xingye Liu, Xingye Liu, Bing Liu, Wenyue Wu, Qian Wang, Yuwei Liu

“…The model is a structured state-space model designed to process complex time-series data, and improve efficiency through parallel scan algorithm, making it suitable for large-scale three-dimensional data prediction. …”
Get full text

Clinical prediction model for MODY type diabetes mellitus in children by D. N. Laptev, E. A. Sechko, E. M. Romanenkova, I. A. Eremina, O. B. Bezlepkina, V. A. Peterkova, N. G. Mokrysheva

“…Based on clinical data, a feedforward neural network (NN) was implemented - a multilayer perceptron.MATERIALS AND METHODS: Development of the most effective algorithm for predicting MODY in children based on available clinical indicators of 1710 patients with diabetes under the age of 18 years using a multilayer feedforward neural network.RESULTS: The sample consisted of 1710 children under the age of 18 years with T1DM (78%) and MODY (22%) diabetes. …”
Get full text

Development and validation of machine learning models predicting hospitalizations of hypertensive patients over 12 months by A. E. Andreychenko, A. D. Ermak, D. V. Gavrilov, R. E. Novitsky, O. M. Drapkina, A. V. Gusev

“…To develop models for predicting hospitalizations of hypertensive (HTN) over 12 months using machine learning algorithms and to validate them using real-world practice data.Material and methods. …”
Get full text

Predicting equilibrium scour depth around non-circular bridge piers with shallow foundations using hybrid explainable machine learning methods by Nasrin Eini, Saeid Janizadeh, Sayed M. Bateni, Changhyun Jun, Essam Heggy, Marek Kirs

“…This study combines two metaheuristic optimization techniques—Siberian tiger optimization (STO) and brown-bear optimization algorithms (BOA)—with artificial neural networks (ANNs) to enhance deq prediction accuracy for both round- and sharp-nosed piers using both field and laboratory data. …”
Get full text

Cardiometabolic index predicts cardiovascular events in aging population: a machine learning-based risk prediction framework from a large-scale longitudinal study by Yuanxi Luo, Yuanxi Luo, Zhiyang Yin, Xin Li, Xin Li, Chong Sheng, Ping Zhang, Dongjin Wang, Dongjin Wang, Yunxing Xue

“…Following baseline characteristic comparisons and CVD incidence rate calculations, we implemented multiple Cox regression models to assess CMI’s cardiovascular risk prediction capabilities. For nomogram construction, we utilized an ensemble machine learning framework, combining Boruta algorithm-based feature selection with Random Forest (RF) and XGBoost analyses to determine key predictive parameters.ResultsThroughout the median follow-up duration of 84 months, we documented 1,500 incident CVD cases, comprising 1,148 cardiac events and 488 cerebrovascular events. …”
Get full text