Results 141 to 150 of about 8,972,433 (325)

Evaluating the use of diagnostic CT with flattening filter free beams for palliative radiotherapy: Dosimetric impact of scanner calibration variability

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Purpose Palliative radiotherapy comprises a significant portion of the radiation treatment workload. Volumetric‐modulated arc therapy (VMAT) improves dose conformity and, in conjunction with flattening filter free (FFF) delivery, can decrease treatment times, both of which are desirable in a population with a high probability of retreatment ...
Madeleine L. Van de Kleut, Lesley A. Buckley, Elsayed S. M. Ali   +2 more
wiley   +1 more source

A Unified Analytical Framework for Trustable Machine Learning and Automation Running with Blockchain [PDF]

arXiv, 2019
Traditional machine learning algorithms use data from databases that are mutable, and therefore the data cannot be fully trusted. Also, the machine learning process is difficult to automate. This paper proposes building a trustable machine learning system by using blockchain technology, which can store data in a permanent and immutable way. In addition,
arxiv  

Private Machine Learning via Randomised Response [PDF]

arXiv, 2020
We introduce a general learning framework for private machine learning based on randomised response. Our assumption is that all actors are potentially adversarial and as such we trust only to release a single noisy version of an individual's datapoint.
arxiv  

Human papillomavirus (HPV) prediction for oropharyngeal cancer based on CT by using off‐the‐shelf features: A dual‐dataset study

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Background This study aims to develop a novel predictive model for determining human papillomavirus (HPV) presence in oropharyngeal cancer using computed tomography (CT). Current image‐based HPV prediction methods are hindered by high computational demands or suboptimal performance.
Junhua Chen, Yanyan Cheng, Lijun Chen, Banghua Yang   +3 more
wiley   +1 more source

MLBench: How Good Are Machine Learning Clouds for Binary Classification Tasks on Structured Data? [PDF]

arXiv, 2017
We conduct an empirical study of machine learning functionalities provided by major cloud service providers, which we call machine learning clouds. Machine learning clouds hold the promise of hiding all the sophistication of running large-scale machine learning: Instead of specifying how to run a machine learning task, users only specify what machine ...
arxiv  

A Machine Learning-Oriented Survey on Tiny Machine Learning

IEEE Access
The emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of Artificial Intelligence by promoting the joint design of resource-constrained IoT hardware devices and their learning-based software architectures. TinyML carries an essential role within the fourth and fifth industrial revolutions in helping societies, economies,
Capogrosso, Luigi, Cunico, Federico, Cheng, Dong Seon, Fummi, Franco, Cristani, Marco   +4 more
openaire   +4 more sources

Facilitating 1.5T MR‐Linac adoption: Workflow strategies and practical tips

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Background MR‐guided radiotherapy (MRgRT) offers new opportunities but also introduces workflow complexities requiring dedicated optimization. Implementing magnetic resonance linear accelerator (MR‐Linac) technology comes with challenges such as prolonged treatment times and workflow integration issues.
Madeline Michel, Zelda Paquier, Christelle Bouchart, Akos Gulyban, Nicolas Jullian, Dirk Van Gestel, Sara Poeta, Nick Reynaert, Philippe Martinive, Robbe Van Den Begin   +9 more
wiley   +1 more source

Fuzzy-Based Image Contrast Enhancement for Wind Turbine Detection: A Case Study Using Visual Geometry Group Model 19, Xception, and Support Vector Machines

Machines
Traditionally, condition monitoring of wind turbines has been performed manually by certified rope teams. This method of inspection can be dangerous for the personnel involved, and the resulting downtime can be expensive.
Zachary Ward, Jordan Miller, Jeremiah Engel, Mohammad A. S. Masoum, Mohammad Shekaramiz, Abdennour Seibi   +5 more
doaj   +1 more source

GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data

Genome Biology, 2020
Most methods for inferring gene-gene interactions from expression data focus on intracellular interactions. The availability of high-throughput spatial expression data opens the door to methods that can infer such interactions both within and between ...
Ye Yuan, Ziv Bar-Joseph
doaj   +1 more source

Machine Learning in Photonics

(Presentation given at 9th International workshop on FIBER OPTICS in ACCESS NETWORK (FOAN2021), Ljubljana, Slovenia, 26-27 October 2021, 2021
The Machine learning toolbox brings significant advantages to optics. Machine learning is effective in learning complex mappings Improve measurements accuracy Design/optimization of optical components (amplifiers, photonic chips, etc.) Enhance communication over the fiber optic ...
openaire   +2 more sources