Search
Now showing items 21-27 of 27
Human and machine: The impact of machine input on decision making under cognitive limitations
(Informs, 2023-03)
The rapid adoption of artificial intelligence (AI) technologies by many organizations has recently raised concerns that AI may eventually replace humans in certain tasks. In fact, when used in collaboration, machines can ...
Disentangling human trafficking types and the identification of pathways to forced labor and sex: an explainable analytics approach
(Springer, 2023-07)
Terms such as human trafficking and modern-day slavery are ephemeral but reflect manifestations of oppression, servitude, and captivity that perpetually have threatened the basic right of all humans. Operations research ...
Comparative study of credit risk evaluation for unbalanced datasets using deep learning classifiers
(IEEE, 2023)
Credit risk assessment deals with calculating the risk of a loan not being repaid. For this reason, a lot of research effort is directed at credit risk analysis. In this study, machine learning models such as Light ...
Advancing home healthcare through machine learning: Predicting service time for enhanced patient care
(IEEE, 2023)
Providing healthcare services at home is crucial for patients who require long-term care or face mobility or other health-related constraints that prevent them from traveling to healthcare facilities. Effective data analysis ...
Feature extraction for enhancing data-driven urban building energy models
(European Council on Computing in Construction (EC3), 2023)
Building energy demand assessment plays a crucial role in designing energy-efficient building stocks. However, most studies adopting a data-driven approach feel the deficiency of datasets with building-specific information ...
Incremental analysis of large-scale system logs for anomaly detection
(IEEE, 2019)
Anomalies during system execution can be detected by automated analysis of logs generated by the system. However, large scale systems can generate tens of millions of lines of logs within days. Centralized implementations ...
Evaluation of distributed machine learning algorithms for anomaly detection from large-scale system logs: a case study
(IEEE, 2018)
Anomaly detection is a valuable feature for detecting and diagnosing faults in large-scale, distributed systems. These systems usually provide tens of millions of lines of logs that can be exploited for this purpose. ...
Share this page