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Machine learning for radiation oncology
Published in Jun Deng, Lei Xing, Big Data in Radiation Oncology, 2019
Machine learning tasks are typically classified into supervised learning and unsupervised learning as shown in Figure 4.1, where the former is intended to learn a general rule that maps inputs to outputs from known labels and the latter aims to find structure from inputs with no labels provided. Considering the desired output category of a machine learning system, machine learning can be categorized into classification, regression, clustering, and so forth. Machine learning is closely related to computational statistics, and mathematical optimization provides methods, theory, and application domains to it. Machine learning is sometimes conflated with data mining, where the former focuses on prediction based on known properties from the training data and the latter focuses on the discovery of unknown properties in the data. [3,4]
Smart Diabetes System Using CNN in Health Data Analytics
Published in S. Poonkuntran, Rajesh Kumar Dhanraj, Balamurugan Balusamy, Object Detection with Deep Learning Models, 2023
P. Ravikumaran, K. Vimala Devi, K. Valarmathi
After these steps have been completed, if the model appears to be performing satisfactorily, it can be deployed for its intended task. The model may be utilized to provide score data for predict the disease, for projections of Electronic Medical Record, to generate useful insight for decision making or research, or to automate tasks. Machine learning is closely related to computational statistics, a procedure which focuses in prediction through the use of computers. ML methods are implemented with optimization techniques, which deliver methods, theory and application domains to the field.
Application of machine learning and deep learning in cybersecurity
Published in Sabyasachi Pramanik, Anand Sharma, Surbhi Bhatia, Dac-Nhuong Le, An Interdisciplinary Approach to Modern Network Security, 2022
Dushyant Kaushik, Muskan Garg, Annu, Ankur Gupta, Sabyasachi Pramanik
The relationship between ML, DL, and AI is full of puzzles. Machine learning is an AI division and is intricately connected to computational statistics, which also focuses on the use of prediction-making computers. DL is a sub-field in the study of machine learning. Its motivation lies in the creation of a neural network that simulates the human brain for analytical learning. It mimics the human brain’s role in processing data such as picture, sounds, and texts.
An overview of the current progress, challenges, and prospects of human biomonitoring and exposome studies
Published in Journal of Toxicology and Environmental Health, Part B, 2019
Mariana Zuccherato Bocato, João Paulo Bianchi Ximenez, Christian Hoffmann, Fernando Barbosa
Machine learning is a basic procedure for performing most of the data mining processes. Derived from the artificial intelligence field, it encompasses methods of computational statistics, mathematical optimization, and computational fundamentals to shape algorithms and make them capable of making automatic discoveries of patterns and information in datasets that lead to decision-making (Bishop and Nasrabadi 2010). Data mining problems, which involve the analysis of databases (such as those contemplated in this research), are not solved by standard static algorithms and require that these algorithms evolve and adapt autonomously as the scenario surrounding the solution is modified, and in these cases, machine learning is especially useful.