Search results for "Training time"
showing 8 items of 8 documents
PolyACO+: a multi-level polygon-based ant colony optimisation classifier
2017
Ant Colony Optimisation for classification has mostly been limited to rule based approaches where artificial ants walk on datasets in order to extract rules from the trends in the data, and hybrid approaches which attempt to boost the performance of existing classifiers through guided feature reductions or parameter optimisations. A recent notable example that is distinct from the mainstream approaches is PolyACO, which is a proof of concept polygon-based classifier that resorts to ant colony optimisation as a technique to create multi-edged polygons as class separators. Despite possessing some promise, PolyACO has some significant limitations, most notably, the fact of supporting classific…
Dual Careers of Athletes During COVID-19 Lockdown
2021
This study aimed to investigate the student-athletes' capability to face the academic, sport, and social challenges during the coronavirus disease 2019 (COVID-19) lockdown and to disclose novel aspects of dual careers. A 32-item online survey encompassing demographic characteristics, sport and university engagement, support and dual-career benefits, physical activity, sitting time, and the time deemed necessary to recover the previous level of performance was developed. Four hundred sixty-seven student-athletes (males: 57%, females: 43%) from 11 countries, competing in 49 different sports (individual: 63.4%, team: 36.6%) at regional (17.5%), national (43.3%), and international (39.2%) level…
Neuromuscular adaptations to combined strength and endurance training: order and time-of-day
2017
AbstractThe present study examined the effects of 24 weeks of morning vs. evening same-session combined strength (S) and endurance (E) training on neuromuscular and endurance performance. Fifty-one men were assigned to the morning (m) or evening (e) training group, where S preceded E or vice versa (SEm, ESm, SEe and ESe), or to the control group. Isometric force, voluntary activation, EMG and peak wattage during the maximal cycling test were measured. Training time did not significantly affect the adaptations. Therefore, data are presented for SEm+e (SEm+SEe) and ESm+e (ESm+ESe). In the morning, no order specific gains were observed in neuromuscular performance. In the evening, the changes …
Low back pain and other overuse injuries in a group of Japanese triathletes.
1996
OBJECTIVE: To document the incidence of low back pain and other overuse injuries in a group of triathletes, and to investigate any associations with various physical and triathlon related factors. METHODS: By means of a questionnaire, the physical characteristics, training habits, and the incidences of overuse injuries of 92 Japanese triathletes (70 males, 22 females) were documented. Student's t and chi 2 tests were used to determine the significance of any associations with injury incidence, as well as differences between subjects experiencing or not experiencing low back pain in the previous year. RESULTS: Low back pain was experienced by 32% of subjects in the previous year. The majorit…
Neural network prediction in a system for optimizing simulations
2002
Neural networks have been widely used for both prediction and classification. Back-propagation is commonly used for training neural networks, although the limitations associated with this technique are well documented. Global search techniques such as simulated annealing, genetic algorithms and tabu search have also been used for this purpose. The developers of these training methods, however, have focused on accuracy rather than training speed in order to assess the merit of new proposals. While speed is not important in settings where training can be done off-line, the situation changes when the neural network must be trained and used on-line. This is the situation when a neural network i…
Deep Convolutional Neural Networks for Fire Detection in Images
2017
Detecting fire in images using image processing and computer vision techniques has gained a lot of attention from researchers during the past few years. Indeed, with sufficient accuracy, such systems may outperform traditional fire detection equipment. One of the most promising techniques used in this area is Convolutional Neural Networks (CNNs). However, the previous research on fire detection with CNNs has only been evaluated on balanced datasets, which may give misleading information on real-world performance, where fire is a rare event. Actually, as demonstrated in this paper, it turns out that a traditional CNN performs relatively poorly when evaluated on the more realistically balance…
Real-time feedback systems for cardiopulmonary resuscitation training: time for a paradigm shift.
2018
Among the new tools to improve the quality of cardiopulmonary resuscitation (CPR), real-time feedback systems (FS) have been largely studied during the last decade (1). These systems permit the real-time analysis of CPR.
ELM Regularized Method for Classification Problems
2016
Extreme Learning Machine (ELM) is a recently proposed algorithm, efficient and fast for learning the parameters of single layer neural structures. One of the main problems of this algorithm is to choose the optimal architecture for a given problem solution. To solve this limitation several solutions have been proposed in the literature, including the regularization of the structure. However, to the best of our knowledge, there are no works where such adjustment is applied to classification problems in the presence of a non-linearity in the output; all published works tackle modelling or regression problems. Our proposal has been applied to a series of standard databases for the evaluation o…