Machine learning (ML) and deep learning (DL) analyze raw data to extract valuable insights
in specific phases. The rise of continuous practices in software projects emphasizes …

The need for AI systems to provide explanations for their behaviour is now widely
recognised as key to their adoption. In this article, we examine the problem of trustworthy AI …

MLOps is essential to streamline the machine learning (ML) development process, ensure
ML models stay operational, and provide users with the desired value. MLOps enhances the …

A prominent area of data analytics is “timeseries modeling” where it is possible to forecast
future values for the same variable using previous data. Numerous usage examples …

In this paper, we comprehensively report on an efficient approach for the integration of
artificial intelligence (AI) processing pipelines in camera traps for smart on-site wildlife …

Explanations in interactive machine-learning systems facilitate debugging and improving
prediction models. However, the effectiveness of various global model-centric and data …

Moroccan Darija, a dialect of Arabic, presents unique challenges for natural language
processing due to its lack of standardized orthographies, frequent code switching, and status …

Machine learning (ML) models in production pipelines are frequently retrained on the latest
partitions of large, continually-growing datasets. Due to engineering bugs, partitions in such …

The use of synthetic data is recognized as a crucial step in the development of neural
network-based Artificial Intelligence (AI) systems. While the methods for generating synthetic …

This research conducted a systematic review of the literature on machine learning (ML)-
based methods in the context of Continuous Integration (CI) over the past 22 years. The …