Subspecialty Training: Time for Change

2017 ◽  
Vol 41 (4) ◽  
pp. 558-560 ◽  
Author(s):  
Richard Balon
Keyword(s):  
Subspecialty Training ◽  
Training Time

scholarly journals Trends in UK endoscopy training in the BSG trainees’ national survey and strategic planning for the future

2017 ◽  
Vol 9 (3) ◽  
pp. 200-207 ◽  
Author(s):  
Sujata Biswas ◽  
Laith Alrubaiy ◽  
Louise China ◽  
Melanie Lockett ◽  
Antony Ellis ◽  
...  
Keyword(s):  
British Society ◽  
Endoscopic Management ◽  
Advisory Group ◽  
Subspecialty Training ◽  
Training Experience ◽  
Training Time ◽  
Endoscopy Training ◽  
Therapeutic Modalities ◽  
Training Programmes ◽  
The Uk

BackgroundImprovements in the structure of endoscopy training programmes resulting in certification from the Joint Advisory Group in Gastrointestinal Endoscopy have been acknowledged to improve training experience and contribute to enhanced colonoscopy performance.ObjectivesThe 2016 British Society of Gastroenterology trainees’ survey of endoscopy training explored the delivery of endoscopy training - access to lists; level of supervision and trainee’s progression through diagnostic, core therapy and subspecialty training. In addition, the barriers to endoscopy training progress and utility of training tools were examined.MethodsA web-based survey (Survey Monkey) was sent to all higher specialty gastroenterology trainees.ResultsThere were some improvements in relation to earlier surveys; 85% of trainees were satisfied with the level of supervision of their training. But there were ongoing problems; 12.5% of trainees had no access to a regular training list, and 53% of final year trainees had yet to achieve full certification in colonoscopy. 9% of final year trainees did not feel confident in endoscopic management of upper GI bleeds.ConclusionsThe survey findings provide a challenge to those agencies tasked with supporting endoscopy training in the UK. Acknowledging the findings of the survey, the paper provides a strategic response with reference to increased service pressures, reduced overall training time in specialty training programmes and the requirement to support general medical and surgical on-call commitments. It describes the steps required to improve training on the ground: delivering additional training tools and learning resources, and introducing certification standards for therapeutic modalities in parallel with goals for improving the quality of endoscopy in the UK.

Computer-managed instruction: Development and evaluation of student skill modules to reduce training time.

1979 ◽  
Author(s):  
Barbara L. McCombs ◽  
Jacqueline L. Dobrovolny ◽  
Wilson A. Judd
Keyword(s):  
Training Time ◽  
Student Skill

Bridging the gap between reduced training time and maintained operational demands

2004 ◽  
Author(s):  
N. G. Hans Jander ◽  
T. A. Jonathan Borgvall
Keyword(s):  
Training Time

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

SVM-based global vision system of sports competition and action recognition

2020 ◽  
pp. 1-12
Author(s):  
Changxin Sun ◽  
Di Ma
Keyword(s):  
Target Recognition ◽  
Vision System ◽  
Task Assignment ◽  
Recognition Algorithm ◽  
Control Factor ◽  
Training Time ◽  
Adaboost Algorithm ◽  
Advantages And Disadvantages ◽  
Time Control ◽  
Correlation Degree

In the research of intelligent sports vision systems, the stability and accuracy of vision system target recognition, the reasonable effectiveness of task assignment, and the advantages and disadvantages of path planning are the key factors for the vision system to successfully perform tasks. Aiming at the problem of target recognition errors caused by uneven brightness and mutations in sports competition, a dynamic template mechanism is proposed. In the target recognition algorithm, the correlation degree of data feature changes is fully considered, and the time control factor is introduced when using SVM for classification,At the same time, this study uses an unsupervised clustering method to design a classification strategy to achieve rapid target discrimination when the environmental brightness changes, which improves the accuracy of recognition. In addition, the Adaboost algorithm is selected as the machine learning method, and the algorithm is optimized from the aspects of fast feature selection and double threshold decision, which effectively improves the training time of the classifier. Finally, for complex human poses and partially occluded human targets, this paper proposes to express the entire human body through multiple parts. The experimental results show that this method can be used to detect sports players with multiple poses and partial occlusions in complex backgrounds and provides an effective technical means for detecting sports competition action characteristics in complex backgrounds.

On-line testing as a new method of student knowledge evaluation

2018 ◽  
Vol 22 (2) ◽  
pp. 353-356
Author(s):  
V.P. Kovalchuk ◽  
I.M. Kovalenko ◽  
S.V. Kovalenko ◽  
V.M. Burkot ◽  
V.O. Kovalenko
Keyword(s):  
Information Technologies ◽  
English Language ◽  
Educational Process ◽  
Personality Testing ◽  
Online Testing ◽  
Training Time ◽  
Internet Resources ◽  
New Information ◽  
On Line ◽  
Use Of The Internet

Innovations change the world in all spheres of life, and education is not an exception. Modern requirements of time put us new challenges that require the use of new information technologies at all stages of the educational process in higher education institutions, in particular the use of the Internet. In addition, it has been noted that Internet resources increase motivation and contribute to the formation of a fully developed personality. Testing and evaluating students' knowledge and abilities is an integral part of the credit-module system. One of the forms of evaluation of the initial level of knowledge, consolidation and improvement of assimilation of information is testing. It should be noted that in a number of countries, testing has shifted traditional forms of control — oral and written exams and interviews. However, in Ukraine, educators remain adherents of a combination of testing and classical analysis of material. It allows the most efficient distribution of the training time of a practical class, 100% control of the knowledge and the effectiveness of mastering the material of all the students of the academic group. Technical progress stimulates the search for new variants and possibilities of testing, its various variations. One of the options that can help solve this problem was a smartphone. In order to facilitate the work of the teacher at the Department of Microbiology, an online testing system with the use of smartphones was introduced. Online testing is conducted among students with Ukrainian and English language training. With the Google Forms platform, the teacher creates a form which contains the student's records and tests. Students directly from the teacher get a link to fill out an online form directly at the lesson. For testing, a database containing standard KROK-1 licensed test tasks is used. The form can contain any number of test tasks that are in arbitrary order, as well as a changed order of distractors, which makes it impossible to write off. At the same time, all students are in the same conditions: all write one option. After submitting the form, the student receives a notification that his response is recorded. Re-linking is not possible. In turn, the teacher receives a message on the result of the test in the table — the ratio of correct answers to the total number of questions, as well as options for their answers. First and foremost, questions are displayed on which students gave the largest number of incorrect answers. This allows the topic to be considered in the process of discussion of the most difficult tasks from the students perspective, and in the future it will allow more efficiently to create forms for on-line tests and to focus on these issues.

scholarly journals Evaluation of Mixed Deep Neural Networks for Reverberant Speech Enhancement

Biomimetics ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 1 ◽  
Author(s):  
Michelle Gutiérrez-Muñoz ◽  
Astryd González-Salazar ◽  
Marvin Coto-Jiménez
Keyword(s):  
Neural Networks ◽  
Short Term Memory ◽  
Computational Cost ◽  
Real Life ◽  
Fixed Number ◽  
Training Procedure ◽  
Statistical Validation ◽  
Significant Drop ◽  
Training Time ◽  
Important Solution

Speech signals are degraded in real-life environments, as a product of background noise or other factors. The processing of such signals for voice recognition and voice analysis systems presents important challenges. One of the conditions that make adverse quality difficult to handle in those systems is reverberation, produced by sound wave reflections that travel from the source to the microphone in multiple directions. To enhance signals in such adverse conditions, several deep learning-based methods have been proposed and proven to be effective. Recently, recurrent neural networks, especially those with long short-term memory (LSTM), have presented surprising results in tasks related to time-dependent processing of signals, such as speech. One of the most challenging aspects of LSTM networks is the high computational cost of the training procedure, which has limited extended experimentation in several cases. In this work, we present a proposal to evaluate the hybrid models of neural networks to learn different reverberation conditions without any previous information. The results show that some combinations of LSTM and perceptron layers produce good results in comparison to those from pure LSTM networks, given a fixed number of layers. The evaluation was made based on quality measurements of the signal’s spectrum, the training time of the networks, and statistical validation of results. In total, 120 artificial neural networks of eight different types were trained and compared. The results help to affirm the fact that hybrid networks represent an important solution for speech signal enhancement, given that reduction in training time is on the order of 30%, in processes that can normally take several days or weeks, depending on the amount of data. The results also present advantages in efficiency, but without a significant drop in quality.

scholarly journals An image denoising method based on BP neural network optimized by improved whale optimization algorithm

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chunzhi Wang ◽  
Min Li ◽  
Ruoxi Wang ◽  
Han Yu ◽  
Shuping Wang
Keyword(s):  
Neural Network ◽  
Image Denoising ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Weight Coefficient ◽  
Whale Optimization Algorithm ◽  
Denoising Method ◽  
Training Time ◽  
Whale Optimization ◽  
City Construction

AbstractAs an important part of smart city construction, traffic image denoising has been studied widely. Image denoising technique can enhance the performance of segmentation and recognition model and improve the accuracy of segmentation and recognition results. However, due to the different types of noise and the degree of noise pollution, the traditional image denoising methods generally have some problems, such as blurred edges and details, loss of image information. This paper presents an image denoising method based on BP neural network optimized by improved whale optimization algorithm. Firstly, the nonlinear convergence factor and adaptive weight coefficient are introduced into the algorithm to improve the optimization ability and convergence characteristics of the standard whale optimization algorithm. Then, the improved whale optimization algorithm is used to optimize the initial weight and threshold value of BP neural network to overcome the dependence in the construction process, and shorten the training time of the neural network. Finally, the optimized BP neural network is applied to benchmark image denoising and traffic image denoising. The experimental results show that compared with the traditional denoising methods such as Median filtering, Neighborhood average filtering and Wiener filtering, the proposed method has better performance in peak signal-to-noise ratio.

scholarly journals CNN Training Using 3D Virtual Models for Assisted Assembly with Mixed Reality and Collaborative Robots

2021 ◽  
Vol 11 (9) ◽  
pp. 4269
Author(s):  
Kamil Židek ◽  
Ján Piteľ ◽  
Michal Balog ◽  
Alexander Hošovský ◽  
Vratislav Hladký ◽  
...  
Keyword(s):  
Mixed Reality ◽  
Current Trend ◽  
Robot Vision ◽  
Processing Unit ◽  
Single Shot ◽  
Vision Systems ◽  
Collaborative Robots ◽  
Training Time ◽  
Position And Orientation ◽  
Collaborative Robot

The assisted assembly of customized products supported by collaborative robots combined with mixed reality devices is the current trend in the Industry 4.0 concept. This article introduces an experimental work cell with the implementation of the assisted assembly process for customized cam switches as a case study. The research is aimed to design a methodology for this complex task with full digitalization and transformation data to digital twin models from all vision systems. Recognition of position and orientation of assembled parts during manual assembly are marked and checked by convolutional neural network (CNN) model. Training of CNN was based on a new approach using virtual training samples with single shot detection and instance segmentation. The trained CNN model was transferred to an embedded artificial processing unit with a high-resolution camera sensor. The embedded device redistributes data with parts detected position and orientation into mixed reality devices and collaborative robot. This approach to assisted assembly using mixed reality, collaborative robot, vision systems, and CNN models can significantly decrease assembly and training time in real production.

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