An intelligent missing data imputation techniques: A review
The incomplete dataset is an unescapable problem in data preprocessing that primarily
machine learning algorithms could not employ to train the model. Various data imputation …
machine learning algorithms could not employ to train the model. Various data imputation …
A novel global solar exposure forecasting model based on air temperature: Designing a new multi-processing ensemble deep learning paradigm
The total quantity of solar energy falling on a horizontal plane surface is the global solar
exposure (GSE, ie, total solar energy). Precise forecasting of GSE is important in many fields …
exposure (GSE, ie, total solar energy). Precise forecasting of GSE is important in many fields …
A novel combined model for heat load prediction in district heating systems
Y Wang, Z Li, J Liu, Y Zhao, S Sun - Applied Thermal Engineering, 2023 - Elsevier
Accurate heat load prediction is essential for improving the operational efficiency of district
heating systems (DHSs). Numerous heat load prediction models have been proposed to …
heating systems (DHSs). Numerous heat load prediction models have been proposed to …
The effect of using data pre-processing by imputations in handling missing values
AE Karrar - Indonesian Journal of Electrical Engineering and …, 2022 - section.iaesonline.com
The evolution of big data analytics through machine learning and artificial intelligence
techniques has caused organizations in a wide range of sectors including health …
techniques has caused organizations in a wide range of sectors including health …
Prediction of Water Quality Using SoftMax-ELM Optimized Using Adaptive Crow-Search Algorithm
Water is a predominant source in the survival and development of all human lives. On top of
all, predicting water quality is a significant one since water is essential in regulating our …
all, predicting water quality is a significant one since water is essential in regulating our …
A block padding approach in multidimensional dependency missing data
H Xu, Y Chen - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
Without high-quality data, there will be no high-quality data mining results. Lack of data
value is one of the problems often encountered in data analysis. The filling method of …
value is one of the problems often encountered in data analysis. The filling method of …
[HTML][HTML] Optimised multiple data partitions for cluster-wise imputation of missing values in gene expression data
It is commonly agreed that the quality of data analysis may be degraded by the presence of
missing data. In various domains such as bioinformatics, an effective tool is required for the …
missing data. In various domains such as bioinformatics, an effective tool is required for the …
Enhanced short-term flow prediction in power dispatching network using a transfer learning approach with GRU-XGBoost module ding
The power dispatching network forms the backbone of efforts to automate and modernize
power grid dispatching, rendering it an indispensable infrastructure element within the …
power grid dispatching, rendering it an indispensable infrastructure element within the …
Incomplete data classification via positive approximation based rough subspaces ensemble
Y Yan, M Yang, Z Zheng, H Ge, Y Zhang, Y Zhang - Big Data Research, 2024 - Elsevier
Classifying incomplete data using ensemble techniques is a prevalent method for
addressing missing values, where multiple classifiers are trained on diverse subsets of …
addressing missing values, where multiple classifiers are trained on diverse subsets of …
A Machine Learning Approach to Well-Being in Late Childhood and Early Adolescence: The Children's Worlds Data Case
Explaining what leads to higher or lower levels of subjective well-being (SWB) in childhood
and adolescence is one of the cornerstones within this field of studies, since it can lead to …
and adolescence is one of the cornerstones within this field of studies, since it can lead to …