Biomedical engineering Application of Machine Learning and Functional Data Analysis in Classification and Clustering of Functional Near Infrared Spectroscopy Signal in Response to Noxious Stimuli DREXEL UNIVERS
Machine learning (ML) is a subset of Artificial Intelligence (AI) that involves the development of algorithms and statistical models enabling computers to perform tasks without explicit instructions. By utilizing patterns and inference derived from data, ML algorithms can improve their performance over t...
Quick and accurate medical diagnoses are crucial for the successful treatment of diseases. Using machine learning algorithms and based on laboratory blood test results, we have built two models to predict a haematologic disease. One predictive model used
transformation (FFT), Fuzzy logic, and Park's vector analysis. The latest development in artificial intelligence (AI) is 'transfer learning', which can detect failure patterns of different devices. This technique can be used instead of MCSA to find localized anomalies and has been suggested in p...
Therefore, we believe that a new theory and methodology based on clustering techniques, in combination with proxy models, must be developed to reduce computational costs and reliably solve real-world sequential decision-making problems. Author contributions Amine wrote the paper and contributed to ...
Ambigavathi M, Sridharan D (2020) Analysis of clustering algorithms in machine learning for healthcare data. In: International conference on advances in computing and data sciences, Springer, Singapore, pp 117–128 Anand S, Padmanabham P, Govardhan A, Kulkarni RH (2018) An extensive review on...
An extreme value prediction method based on clustering algorithm. Reliability Engineering and System Safety, 2022, 222: 108442. DOI:10.1016/j.ress.2022.108442 114. Zhang, W., He, Y., Li, P. et al. Graph regression for pressure peak prediction in fracturing processes. Journal of Petroleum ...
Finally, the application Clustering is rarely the main object of the studies but plays a minor role in the Consumption sector. However, clustering is often conducted as a pre-processing step within the studies. Moving on to the analysis of the approach category, Fig. 6 shows a shift in the...
In this study, KPCA was used for unsupervised clustering (grouping) of the dataset with no class information in a data-driven manner. The grouping is key for this analytical procedure, but KPCA cannot calculate importance of variables directly due to an inner product computation process. To overc...
One major challenge in this context is the extraction of movement patterns emerging from the observed data, considering trajectories that share similar movement modes (see Fig. 1 (right)). This issue can be restated from a machine learning point of view as a large-scale clustering task involving...