A Distributed Model Predictive Control for Multiple Mobile Robots with the Model Uncertainty by Wei Shang, Hanzong Zhu, Yurong Pan, Xiuhong Li, Daode Zhang

“…In this paper, a distributed predictive control with the model uncertainty which uses the data-driven strategy and robust theory (data-driven RDMPC) is proposed for the formation control of multiple mobile robots. …”
Get full text

Model Reduction and Chattering Mitigation in Multi-Model Predictive Control for Quadrotor UAVs by Ghulam E. Mustafa Abro, Sufyan Ali Memon, Saddaf Rubab, Kaznah Alshammari, Faheem Khan

Subjects: Get full text

A vision-language model for predicting potential distribution land of soybean double cropping by Bei Gao, Yuefeng Liu, Yanli Li, Hongmei Li, Meirong Li, Wenli He

Subjects: Get full text

Disease-Specific Risk Models for Predicting Dementia: An Umbrella Review by Eugene Yee Hing Tang, Jacob Brain, Serena Sabatini, Eduwin Pakpahan, Louise Robinson, Maha Alshahrani, Aliya Naheed, Mario Siervo, Blossom Christa Maree Stephan

“…However, while numerous models have been developed to predict dementia, they are often not tailored to disease-specific groups. …”
Get full text

Deep Learning Models for Predicting the Recurrence of Idiopathic Granulomatous Mastitis by Li L, Yang W, Jia H

Subjects: Get full text

Hybrid Deep Learning Models for Predicting Student Academic Performance by Kuburat Oyeranti Adefemi, Murimo Bethel Mutanga, Vikash Jugoo

“…This study aims to bridge this gap by proposing a deep learning model to predict student academic performance with greater accuracy. …”
Get full text

Machine learning models for predicting tibial intramedullary nail length by Sercan Capkin, Ali Ihsan Kilic, Hakan Cici, Mehmet Akdemir, Mert Kahraman Marasli

“…The correlation between shoe size and the dependent variable was weaker (r = 0.823), and the inclusion of shoe size in the model negatively impacted the prediction accuracy. …”
Get full text

“Revealing the future”: an ARIMA model analysis for predicting remittance inflows by Imran Khan, Darshita Fulara Gunwant

“…Purpose – The purpose of this research is to develop a predictive model that can estimate the volume of remittances channeled toward Yemen’s economic reconstruction efforts. …”
Get full text

Hyperparameter optimization of machine learning models for predicting actual evapotranspiration by Chalachew Muluken Liyew, Elvira Di Nardo, Stefano Ferraris, Rosa Meo

“…Consequently, AET data is limited, prompting the use of meteorological and soil features for prediction. This study develops and evaluates machine learning models for AET prediction based on two input combinations. …”
Get full text

Predicting Excavation-Induced Tunnel Response by Process-Based Modelling by Linlong Mu, Jianhong Lin, Zhenhao Shi, Xingyu Kang

“…To illustrate such a concept, this paper presents a simple process-based model consisting of artificial neural network (ANN) module, inverse modelling module, and mechanistic module. …”
Get full text

Machine learning models for predicting spatiotemporal dynamics of groundwater recharge by Azeddine Elhassouny

“…Open public remote sensing datasets were used to develop machine learning prediction models (Random Forest, XGBoost, Keras models, etc.) and time series forecasting models (LSTM, CNN, etc.) for predicting and forecasting groundwater sheet recharge, respectively. …”
Get full text

Predicting Crime Time Intervals Using Machine Learning Models by Arief Deswandi, Widi Hastomo

Get full text

Limitations of XGBoost in Predicting Material Parameters for Complex Constitutive Models by Prates Pedro, Mitreiro Dário, Andrade-Campos António

“…Machine learning models, particularly Extreme Gradient Boosting, have been explored for predicting material parameters in constitutive models that describe the plastic behaviour of metal sheets. …”
Get full text

A structure-based model for predicting serum albumin binding. by Katrina W Lexa, Elena Dolghih, Matthew P Jacobson

“…This model was successfully used to predict serum albumin binding in a large test set of therapeutics that had experimental binding data.…”
Get full text

Generalized Additive Model for Predicting ECBR of Stabilized Subgrades for Pavement by Alka Shah, Tejaskumar Thaker, Vipin Shukla

“…This study presents a Generalized Additive Model (GAM) to predict the effective CBR (ECBR) of soil subgrade stabilized with recycled plastic waste under repeated loading (RL) conditions. …”
Get full text

Predicting volatility of bitcoin returns with ARCH, GARCH and EGARCH models by Hakan Yıldırım, Festus Victor Bekun

“…In this study we seek to identify the best fit model that can predict the volatility of return of Bitcoin, which is in high demand as an investment tool in recent times. …”
Get full text

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.…”
Get full text

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. …”
Get full text

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.…”
Get full text

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. …”
Get full text