Felimban, R. (2025) Financial Prediction Models in Banks: Combining Statistical Approaches and Machine Learning Algorithms.

Abstract: A time-space (TS) traffic diagram, which presents traffic states in time-space cells with color, is an important traffic analysis and visualization tool. Despite its importance for ...

School of Computing and Engineering, University of West, London, UK. In recent years, inflation has been a worrying factor for every country, which has become particularly high due to various ...

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression using JavaScript. Linear regression is the simplest machine learning technique to predict a single numeric value, ...

The goal of a machine learning regression problem is to predict a single numeric value. There are roughly a dozen different regression techniques such as basic linear regression, k-nearest neighbors ...

In 1999, Bill Gates’s book Business @ the Speed of Thought predicted technology like the internet, email, and desktop business programs would transform industries. Gates argued that these tools should ...

eSpeaks host Corey Noles sits down with Qualcomm's Craig Tellalian to explore a workplace computing transformation: the rise of AI-ready PCs. Matt Hillary, VP of Security and CISO at Drata, details ...

Abstract: The accurate prediction of rainfall is essential for numerous industries like agriculture, water management, and disaster planning. In this study, a straightforward approach involving linear ...

Autistic regression refers to a loss of previously acquired skills or a backtracking of developmental milestones. In young children, it may represent autism onset. In older children and adults, it may ...

This paper critically examines ‘kitchen sink regression’, a practice characterised by the manual or automated selection of variables for a multivariable regression model based on p values or ...

When analyzing data through regression models, evaluating the goodness of fit is an essential step in determining how well the model represents the underlying data. One key measure used in this ...