Two new studies from the Department of Computational Biomedicine at Cedars-Sinai are advancing what we know about using machine learning and big data to improve health care and medical research ...

The graph below shows the total number of publications each year in Control Chart Pattern Recognition Using Neural Networks and Support Vector Machines.

There is a strong correlation between physical health and metal ion levels in biological fluids, making them an attractive analyte for noninvasive health monitoring. Given the pressing need for ...

This leading textbook provides a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as ...

ABSTRACT: This study presents a comparative analysis of machine learning models for threat detection in Internet of Things (IoT) devices using the CICIoT2023 dataset. We evaluate Logistic Regression, ...

ABSTRACT: In the field of machine learning, support vector machine (SVM) is popular for its powerful performance in classification tasks. However, this method could be adversely affected by data ...

In conclusion, the Embed-then-Regress method showcases the flexibility of string-based in-context regression for Bayesian Optimization across diverse problems, achieving results comparable to standard ...

Within the domain of pattern recognition, the automated identification of handwritten characters or symbols presents a complex handwriting recognition challenges. In this paper, a novel methodology is ...

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We present an interpretable machine learning approach to map patterns related to territorial protected and anthropogenic areas as proxies of naturalness and human influence using satellite imagery.