Conference paper Learning to rank learning curves Conference paper Data Readiness Report
The quality may be determined using k-fold cross validation and/or latent semantic indexing. In response to determining that the training data set has a satisfactory quality, the computing device then analyzes the training data set using machine learning to train a machine learning-based detection ...
Suri, B.bSpringer IndiaP. Singh and B. Suri, Quality assessment of data using statistical and machine learning methods. L. C.Jain, H. S.Behera, J. K.Mandal and D. P.Mohapatra (eds.), Computational Intelligence in Data Mining, 2 (2014), pp. 89-97....
In the paradigm of machine learning, software quality prediction can be cast as a classification or concept learning problem. In this paper we provide a general framework for applying machine learning approaches for assessment and prediction of software quality in large software organizations. Using ...
- Both public and private sector institutions may use these technologies for regulatory compliance, surveillance, data quality assessment, and fraud detection. With the FSB FinTech framework,1 our analysis reveals a number of potential benefits and risks for financial stability that should be monitored...
Current electrocardiogram (ECG) signal quality assessment studies have aimed to provide a two-level classification: clean or noisy. However, clinical usage demands more specific noise level classification for varying applications. This work outlines a five-level ECG signal quality classification algorithm....
The importance of these two dimensions in data quality was well recognized even before the ML era, and the related methodologies were classified as the ones helping “selection, customization, and application of data quality assessment and improvement techniques” [13]. Currently, the data quality ...
Machine learning is revolutionizing the insurance industry by enhancing risk assessment, underwriting decisions and fraud detection. It also helps improve customer experience and boost profitability. By analyzing vast amounts of data, ML algorithms can evaluate risks more accurately, so insurers can tailor...
Machine learning (ML) has been used in life cycle assessment (LCA) to estimate the values of environmental impact characterization factors and to conduct sensitivity analyses. ML has even been used to develop surrogate LCAs, which have enabled prediction of future products' full life cycle environme...
Machine learning (ML) is a rapidly advancing field with increasing utility in health care. We conducted a systematic review and critical appraisal of ML applications in vascular surgery. MEDLINE, Embase, and Cochrane CENTRAL were searched from inception