Results 171 to 180 of about 1,612,060 (336)

Review of "Some Behavioural Factors Affecting the Training of Naive Users of an Interactive Computer System by Kennedy, T. C. S." Internatl. J. Man-Machine Studies 7, 6 (Nov. 1975), 817-834 [PDF]

, 1977
Theodore C. Willoughby
openalex   +1 more source

Improving dose delivery in non‐coplanar cranial SRS: Stereoscopic x‐ray‐guided mitigation of table walkout errors

Journal of Applied Clinical Medical Physics, EarlyView.
Purpose Linear accelerator (LINAC)‐based single‐isocenter multi‐target (SIMT) treatment has streamlined the cranial stereotactic radiosurgery (SRS) workflow. Though efficient, SIMT delivery adds additional challenges that should be considered, including increased sensitivity to rotational errors for off‐isocenter targets.
Yohan A. Walter, Philip F. Durham, Anne N. Hubbard, William E. Burrell, Hsinshun T. Wu   +4 more
wiley   +1 more source

Data‐driven performance metrics for neural network learning

International Journal of Adaptive Control and Signal Processing, EarlyView., 2023
Summary Effectiveness of data‐driven neural learning in terms of both local mimima trapping and convergence rate is addressed. Such issues are investigated in a case study involving the training of one‐hidden‐layer feedforward neural networks with the extended Kalman filter, which reduces the search for the optimal network parameters to a state ...
Angelo Alessandri, Mauro Gaggero, Marcello Sanguineti   +2 more
wiley   +1 more source

Computer End-User Training, Learning and Evaluation in the New South Africa: A Port Elizabeth Technikon Experience

, 1996
Annerie Retief
openalex   +1 more source

MI 2 MI: Training Dyad with Collaborative Brain-Computer Interface and Cooperative Motor Imagery Tasks for Better BCI Performance [PDF]

arXiv
Collaborative brain-computer interface (cBCI) that conduct motor imagery (MI) among multiple users has the potential not only to improve overall BCI performance by integrating information from multiple users, but also to leverage individuals' performance in decision-making or control.
arxiv  

Unsupervised non‐small cell lung cancer tumor segmentation using cycled generative adversarial network with similarity‐based discriminator

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Background Tumor segmentation is crucial for lung disease diagnosis and treatment. Most existing deep learning‐based automatic segmentation methods rely on manually annotated data for network training. Purpose This study aims to develop an unsupervised tumor segmentation network smic‐GAN by using a similarity‐driven generative adversarial ...
Chengyijue Fang, Xiaoyang Li, Yidong Yang   +2 more
wiley   +1 more source

‘Greater Benefits’ from Computer-based Auditory Training for CI Users

, 2021

openalex   +1 more source

AAPM Task Group No. 249.B—Essentials and guidelines for clinical medical physics residency training program

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract The establishment of guidelines and curriculum standards for medical physics residency training is a critical component of setting expectations and competencies for the profession. Since the last publication of these standards, residency training has become integrated into the eligibility criteria for most medical physics certification bodies.
Jonathon A. Nye, Joseph P. Dugas, William D. Erwin, David P. Gierga, Darryl G. Kaurin, Stephanie M. Leon, Ho‐Ling Anthony Liu, Zheng Feng Lu, Alphonso W. Magri, Lynn N. Rill, Leah K. Schubert, Beth A. Schueler, Irina Vergalasova, John Vetter, Brian D. Wichman, Amy Shu‐Jung Yu, Dandan Zheng   +16 more
wiley   +1 more source

Enunciate: An internet-accessible computer-aided pronunciation training system and related user evaluations [PDF]

, 2011
Ka-Wa Yuen, Wai-Kim Leung, Pengfei Liu, Ka Ho Wong, Xiaojun Qian, Wai-Kit Lo, Helen Meng   +6 more
openalex   +1 more source

Automated rapidplan model validation using Eclipse scripting API

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract RapidPlan offers efficiency gains and quality improvements in treatment planning. Prior to its use in the clinic, it requires an extensive validation procedure in which established clinical plans and those generated by the model are compared. The manual iterative nature of this process is resource intensive, as numerous iterations are required
Bradley Beeksma, Andrew Dipuglia, Joerg Lehmann   +2 more
wiley   +1 more source