The framework is tested on a dataset of 16S genes and its performances, in terms of accuracy and F1 score, are compared to...doi:10.1016/B978-0-12-809633-8.20462-7Urso, AlfonsoStatsoft "Data mining - prediction methods", workshop materials, Krakow (2017)...
Reza Allahyari Soeini and Keyvan Vahidy Rodpysh: "Evaluations of Data Mining Methods in Order to Provide the Optimum Method for Customer Churn Prediction: Case Study Insurance Industry."2012 International Conference on Information and Computer Applications (ICICA 2012)IPCSI vol...
Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine ...
Predicting the crop is done by the farmer’s experience based on thefactors like soil types, climatic condition, seasons and weather, rainfall and irrigation facilities.Methods: Data mining techniques is the better choice for predicting the crop. Different Data Mining techniques are used and ...
His research interests include data mining, mobile data management and bioinformatics. His current work focuses on managing and querying genomic pathways, and efficient access methods for genomic sequences. Dimitrios Katsaros has received a B.Sc. and a Ph.D. degree in Informatics from the Aristotle...
Deep learning (DL) is a new discipline of computer science that extracts patterns from past data and makes accurate predictions using feature embedding methods. DL has been used successfully in a variety of fields, including stock price forecasting, personality recognition, disease prediction, text ca...
The present study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. The Cleveland Heart disease dataset with sixteen feature selection techniques in three categories of filter, wrapper,
Wafer fabrication is considered the most complex and costly challenge in the semiconductors industry. Cycle Time (CT), which denotes flow time, is one of its key performance measures. This work develops CT prediction models by applying Machine Learning (ML) and Data Mining (DM) methods. The mo...
diction, whose typical data mining methods consist of deci- sion tree classifier, Bayesian classifier, and neural networks classifier. Bayesian classifier is based on the hypothesis of class independency that is hard to meet in reality, and neu- ...
including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for "wide" data (p bigger than n), including multiple testing and fal...