The study of machine learning has revealed that it can unleash new applications in a variety
of disciplines. Many limitations limit their expressiveness, and researchers are working to …

Over the years, there has been a considerable discussion regarding machine learning (ML)
techniques to forecast software faults. It can be challenging to choose a suitable machine …

Fraud detection is a critical task across various domains, requiring accurate identification of
fraudulent activities within vast arrays of transactional data. The significant challenges in …

Imbalanced data is a common problem in many areas, and it can have significant impacts on
the performance and generalizability of machine learning models. This is because the …

Malicious software detection is the most prominent process required by various industries to
avoid server failure. It is required to detect malicious software accurately to avoid time and …

The accurate prediction of software faults is crucial in guaranteeing the quality and reliability
of software systems. Recent research has highlighted the vast potential of various deep …

Optical module faults are among the most serious threats to Internet Data Centers (IDCs),
which are crucial to a company's data processing and information storage operations …

Software fault prediction aims to classify whether the module is defective or not-defective. In
software systems, there are some software metrics may contain irrelevant or redundant …

Software Defect Prediction (SDP) is a crucial and helpful method for upgrading software
reliability and quality. It enables more effective project management by predicting potential …

Software fault/defect prediction aids software engineers in identifying defective
constructions, including classes and modules early in software development life cycle. Deep …