Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison
The prediction of global solar radiation for the regions is of great importance in terms of
giving directions of solar energy conversion systems (design, modeling, and operation) …
giving directions of solar energy conversion systems (design, modeling, and operation) …
Transforming big data into smart data: An insight on the use of the k‐nearest neighbors algorithm to obtain quality data
The k‐nearest neighbors algorithm is characterized as a simple yet effective data mining
technique. The main drawback of this technique appears when massive amounts of data …
technique. The main drawback of this technique appears when massive amounts of data …
Prediction of daily global solar radiation and air temperature using six machine learning algorithms; a case of 27 European countries
MK Nematchoua, JA Orosa, M Afaifia - Ecological Informatics, 2022 - Elsevier
The prediction of global solar radiation in a region is of great importance as it provides
investors and politicians with more detailed knowledge about the solar resource of that …
investors and politicians with more detailed knowledge about the solar resource of that …
A precise and stable machine learning algorithm: eigenvalue classification (EigenClass)
U Erkan - Neural Computing and Applications, 2021 - Springer
In this study, a precise and efficient eigenvalue-based machine learning algorithm,
particularly denoted as Eigenvalue Classification (EigenClass) algorithm, has been …
particularly denoted as Eigenvalue Classification (EigenClass) algorithm, has been …
A survey on classifying big data with label noise
JM Johnson, TM Khoshgoftaar - ACM Journal of Data and Information …, 2022 - dl.acm.org
Class label noise is a critical component of data quality that directly inhibits the predictive
performance of machine learning algorithms. While many data-level and algorithm-level …
performance of machine learning algorithms. While many data-level and algorithm-level …
Vibration analysis of shaft misalignment using machine learning approach under variable load conditions
AM Umbrajkaar, A Krishnamoorthy… - Shock and …, 2020 - Wiley Online Library
The Industry 4.0 revolution is insisting strongly for use of machine learning‐based processes
and condition monitoring. In this paper, emphasis is given on machine learning‐based …
and condition monitoring. In this paper, emphasis is given on machine learning‐based …
Fast and Scalable Approaches to Accelerate the Fuzzy k-Nearest Neighbors Classifier for Big Data
One of the best-known and most effective methods in supervised classification is the k-
nearest neighbors algorithm (kNN). Several approaches have been proposed to improve its …
nearest neighbors algorithm (kNN). Several approaches have been proposed to improve its …
Fuzzy KNN method with adaptive nearest neighbors
Due to its strong performance in handling uncertain and ambiguous data, the fuzzy-nearest-
neighbor method (FKNN) has realized substantial success in a wide variety of applications …
neighbor method (FKNN) has realized substantial success in a wide variety of applications …
Distributed nearest neighbor classification for large-scale multi-label data on spark
Modern data is characterized by its ever-increasing volume and complexity, particularly
when data instances belong to many categories simultaneously. This learning paradigm is …
when data instances belong to many categories simultaneously. This learning paradigm is …
Genders prediction from indoor customer paths by Levenshtein-based fuzzy kNN
Companies have an advantage over the competitors if they can present customized offers to
customers. Demographic information of customers is critical for the companies to develop …
customers. Demographic information of customers is critical for the companies to develop …