A Review of Business Analytics, Machine Learning, and Generative Artificial Intelligence Research 2020–2025: Toward Responsible Artificial Intelligence
This review examines the evolving intersections of data analytics, machine learning, and artificial intelligence—terms that have been frequently conflated since 2016 during a period of increased hype and investment. Following recent reviews across areas such as open innovation, supply chain deep learning, strategic alliances, natural language processing, and big data streaming, we focus on the emerging field of Responsible Artificial Intelligence (AI). We apply descriptive analysis to identify trends, patterns, and gaps in the research through a review of academic literature from 2020 to 2025. Analysis reveals five distinct clusters of Responsible AI papers using five dimensions: fairness, cross-validity, transparency, accuracy–interpretability tradeoff, and drift detection. This review discusses patterns across the artificial intelligence literature and identifies future research opportunities with an emphasis on Responsible AI.
Maintenance management of stationary combustion engines in the agricultural sector remains largely manual, increasing the risk of unplanned downtime. This study developed a machine learning-based predictive model to anticipate failures within a 60-day horizon, enabling the transi…
Discovery of Association Rules is one of the most common Data Mining techniques. Contrast data mining is a focused data mining research area for discovering interesting contrast patterns that state the significant differences between datasets, i.e., frequent itemsets in one datas…
Rotating machinery plays a critical role in transmission systems, while the scarcity of fault samples and labeled data limits the performance of existing diagnostic methods under few-shot conditions. This paper proposes a few-shot fault diagnosis method for rotating machinery bas…
Timely identification of company financial risks is crucial for investors and regulators. However, existing studies overlook the class imbalance caused by the scarcity of high-risk samples, and the interpretability of deep models is insufficient, making it difficult to meet the p…
Automatic electrocardiogram (ECG) classification using deep learning is sensitive to data leakage, class imbalance and the way multi-segment decisions are aggregated at record level. This study presents a two-channel time–frequency early-fusion pipeline for classifying ECG record…
The classification of malicious network-traffic is critical to cybersecurity. However, to the best of our knowledge, no previous studies have performed a comparative analysis of supervised algorithms for classifying malicious traffic, specifically within the network environment o…