A comprehensive survey on imputation of missing data in internet of things
The Internet of Things (IoT) is enabled by the latest developments in smart sensors,
communication technologies, and Internet protocols with broad applications. Collecting data …
communication technologies, and Internet protocols with broad applications. Collecting data …
A union of deep learning and swarm-based optimization for 3D human action recognition
Abstract Human Action Recognition (HAR) is a popular area of research in computer vision
due to its wide range of applications such as surveillance, health care, and gaming, etc …
due to its wide range of applications such as surveillance, health care, and gaming, etc …
A systematic review on bat algorithm: Theoretical foundation, variants, and applications
T Agarwal, V Kumar - Archives of Computational Methods in Engineering, 2022 - Springer
Bat algorithm (BA) is a population-based metaheuristic algorithm inspired by echolocation
behavior of bat. After the development of BA in 2010, it becomes the attention of researchers …
behavior of bat. After the development of BA in 2010, it becomes the attention of researchers …
A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset
T Liu, W Fan, C Wu - Artificial intelligence in medicine, 2019 - Elsevier
Abstract Background and Objective Cerebral stroke has become a significant global public
health issue in recent years. The ideal solution to this concern is to prevent in advance by …
health issue in recent years. The ideal solution to this concern is to prevent in advance by …
Effect of missing data imputation on deep learning prediction performance for vesicoureteral reflux and recurrent urinary tract infection clinical study
Missing observations are always a challenging problem that we have to deal with in
diseases that require follow‐up. In hospital records for vesicoureteral reflux (VUR) and …
diseases that require follow‐up. In hospital records for vesicoureteral reflux (VUR) and …
Estimation of missing LiDAR data for accurate AGV localization
This article evaluates several machine learning methods to substitute the missing light
detection and ranging data for better spatial localization of industrial automated guided …
detection and ranging data for better spatial localization of industrial automated guided …
Advancing post-earthquake structural evaluations via sequential regression-based predictive mean matching for enhanced forecasting in the context of missing data
H Luo, SG Paal - Advanced Engineering Informatics, 2021 - Elsevier
After an earthquake, every damaged building needs to be properly evaluated in order to
determine its capacity to withstand aftershocks as well as to assess safety for occupants to …
determine its capacity to withstand aftershocks as well as to assess safety for occupants to …
The use of synthetic data to train ai models: Opportunities and risks for sustainable development
T Marwala, E Fournier-Tombs, S Stinckwich - arXiv preprint arXiv …, 2023 - arxiv.org
In the current data driven era, synthetic data, artificially generated data that resembles the
characteristics of real world data without containing actual personal information, is gaining …
characteristics of real world data without containing actual personal information, is gaining …
Smoothing target encoding and class center-based firefly algorithm for handling missing values in categorical variable
One of the most common causes of incompleteness is missing data, which occurs when no
data value for the variables in observation is stored. An adaptive approach model …
data value for the variables in observation is stored. An adaptive approach model …
Few-Shot traffic prediction based on transferring prior knowledge from local network
Short-term traffic prediction has been widely studied in the community of Intelligent Transport
Systems for decades. Despite the advances in machine learning-based prediction …
Systems for decades. Despite the advances in machine learning-based prediction …