[PDF][PDF] Is deep learning on tabular data enough? An assessment
It is critical to select the model that best fits the situation while analyzing the data. Many
scholars on classification and regression issues have offered ensemble techniques on …
scholars on classification and regression issues have offered ensemble techniques on …
[PDF][PDF] Numerical and experimental investigation of meteorological data using adaptive linear M5 model tree for the prediction of rainfall
Real-time predictions are always important for accurate and systematic thinking in planning
future processes. The failure in the availability of current machine learning approaches is a …
future processes. The failure in the availability of current machine learning approaches is a …
[PDF][PDF] An adaptive gradient boosting model for the prediction of rainfall using ID3 as a base estimator
Accepted: 4 April 2022 While analyzing the data, it is crucial to choose the model that best
matches the circumstance. Many experts in the field of classification and regression have …
matches the circumstance. Many experts in the field of classification and regression have …
Addressing the algorithm selection problem through an attention-based meta-learner approach
E Díaz de León-Hicks, SE Conant-Pablos… - Applied Sciences, 2023 - mdpi.com
In the algorithm selection problem, where the task is to identify the most suitable solving
technique for a particular situation, most methods used as performance mapping …
technique for a particular situation, most methods used as performance mapping …
Big Data in Academia: A Proposed Framework for Improving Students Performance.
IR Banday, M Zaman, SMK Quadri… - Revue d' …, 2022 - search.ebscohost.com
The way people learn has radically changed as a result of information technology. As an
informal method of learning, fragmented learning has become a popular way to learn new …
informal method of learning, fragmented learning has become a popular way to learn new …
[PDF][PDF] Hard voting meta classifier for disease diagnosis using mean decrease in impurity for tree models
Artificial Intelligence (AI) is rapidly transforming our world. With time it has spread into many
business areas [1-3] and is originating in medical field too with the increase in the …
business areas [1-3] and is originating in medical field too with the increase in the …
[PDF][PDF] How M5 Model Trees (M5-MT) on continuous data are used in rainfall prediction: An experimental evaluation
Accepted: 4 June 2022 When using machine learning to predict a class with a continuous
numeric value, there are several issues. Only a few machine-learning approaches are …
numeric value, there are several issues. Only a few machine-learning approaches are …
Prognostic Impact of Metabolic Syndrome and Steatotic Liver Disease in Hepatocellular Carcinoma Using Machine Learning Techniques
S Gil-Rojas, M Suárez, P Martínez-Blanco, AM Torres… - Metabolites, 2024 - mdpi.com
Metabolic dysfunction-associated steatotic liver disease (MASLD) currently represents the
predominant cause of chronic liver disease and is closely linked to a significant increase in …
predominant cause of chronic liver disease and is closely linked to a significant increase in …
A super ensembled and traditional models for the prediction of rainfall: An experimental evaluation of DT versus DDT versus RF
The main purpose of the current study is to use three traditional and ensemble machine
learning approaches namely Decision tree (DT), Distributed Decision tree (DDT) and …
learning approaches namely Decision tree (DT), Distributed Decision tree (DDT) and …
[PDF][PDF] A scalable framework to analyze data from heterogeneous sources at different levels of granularity
There is an enormous amount of data present in many different formats, including databases
(MsSql, MySQL, etc.), data repositories (. txt, html, pdf, etc.), and MongoDB (NoSQL, etc.) …
(MsSql, MySQL, etc.), data repositories (. txt, html, pdf, etc.), and MongoDB (NoSQL, etc.) …