Machine learning (ML) is a new-age thriving technology, which facilitates computers to read and interpret from the previously present data automatically. It makes use of multiple algorithms to build models, mathematical in nature, and then makes predictions for the new data using the past data...

The future is unknown and uncertain, but there are ways to predict future events and reap the rewards safely. One such opportunity is the application of machine learning and artificial intelligence for stock market prediction. The stock market is turbulent, yet using artificial intelligence to...

The valuation of house prices is drawing noteworthy attention due to worldwide financial and real estate crises in the last decade. Therefore, there is an immediate need to design more effective predictive systems of house prices. Indeed, investors, creditors, and governments are all interested...

The generation of huge data with high velocity creates alteration in the distribution of the stream data, which is defined as the concept of drifts. The concept drifts negatively influence the classification accuracy and the stability of the data streams. Numerous machine learning-based models...

Methods based on artificial neural network (ANN) or deep neural network (DNN) applications have been proposed to predict job cycle time effectively. However, the predicting mechanism of ANNs (or DNNs) is often difficult to understand and communicate. This problem has hindered their acceptability...

Technology is pivotal in the rapid growth of services and intensifying the quality of life. Recent technology, like the Internet of Things (IoT), demonstrates an impressive performance in fast-forward development. Intrusion Detection System (IDS) is used as a lifeline to prevent attacks by...

Sentiment analysis is crucial in understanding and analyzing public opinions, feedback, and social media data. In this study, we propose a modified Bayesian Boosting algorithm with weight-guided optimal feature selection for sentiment analysis. The goal is to improve the accuracy and efficiency...

This study proposes two types of bivariate Poisson extended exponential distributions: the basic bivariate Poisson extended exponential distribution and the Sarmanov-based bivariate Poisson extended exponential distribution. The two bivariate Poisson extended exponential distributions are then...

Some unusual combinations of predictor values in multivariate regression often influence tampering with the output, and filtering those observations becomes the trickiest and most challenging task. This concern is prevalent and predominant in ecological domains, especially in soil samples, as...