Abstract Machine Learning projects acquire a large amount of data to make predictions on
the data. Deploying machine learning models is a difficult task as it is involved a lot factors …

Machine learning is a result of the availability and accessibility of a massive amount of data
collected via sensors and the internet. The concept of machine learning demonstrates and …

This research article introduces AIOptimizer, a prototype for a software performance
optimisation tool based on cost reduction. AIOptimizer uses a recommendation system …

When dealing with real business problems at a company, developing an artificial
intelligence system no longer only relies on the success of compiling a program. The …

The ML development lifecycle is the SDLC equivalent of Machine Learning. While the ML
code is at the core of a real-world ML production system, it frequently represents only 5% or …

Machine learning (ML) based approaches provide efficient solutions successfully applied to
different domains. In Software Engineering (SE) domain, ML is improving and automating …

Early-stage startups often have more ideas than resources and require targeted resource
deployment to ensure suitable product delivery to market. Early-stage Web3 startups have …

Due to the overuse of antibiotics, antibiotic resistance in bacteria has emerged as a
significant public health concern. Various methods can be employed to determine the …

The research of developing complex algorithms whose accuracy increases over time is
known as machine learning. To solve the problem of generating and dealing with large …

The AI era established significant challenges for software developers, especially those
working on Machine Learning (ML)-based software. This article presents the findings of a …