An Analytical Method for Evaluating the Performance of the URA MAWASHI GERI Skill Using Time Series and Artificial Intelligence Techniques
Issue:
Volume 6, Issue 2, December 2022
Pages:
31-35
Received:
3 September 2022
Accepted:
20 September 2022
Published:
29 November 2022
DOI:
10.11648/j.ajai.20220602.11
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Abstract: Artificial intelligence has changed the way we consume and analyze sports. The role of artificial intelligence in improving decision-making and prediction in sports is expanding, due to the specific changes in the performance of athletes, a performance prediction model based on a machine learning algorithm has been proposed. The current research state of athlete performance modelling and prediction is analyze and the current athlete performance prediction model is found. The shortcomings of the model are analyze. The reason for the low prediction accuracy of the model was analyze. Then chaos theory is used to process the historical data of the athletes, within the range of data used in this study is the average data from the total reaction time ((RT, movement time (MT), response time) through the skill performance of the skill of URA MAWASHI GERI and about By using the electronic device to measure the reaction speed and approved by the Ministry of Higher Education and the Patent Office No. (217050754), and it was collected and used through a sample of (210) karate players who are registered on the database of the Egyptian Karate Federation, classified (age, height, Weight, training age). ata collected for time series analysis were categorized by year and time (RT, MT, reaction time), and organized using Microsoft Excel Office 365 and IBM And SPSS 20.0 was used, and the results came to determine the correct training status of the player's condition and how to legalize the player's training loads and work the training programs for the player throughout the training season and over the training periods during the training season or a For the training course, how to plan and implement the training content and evaluate the training periods, the conclusions came by standing on the skill level and forming predictions by making scientific predictions based on time-stamped historical data. It includes building predictive models through time series and artificial intelligence techniques.
Abstract: Artificial intelligence has changed the way we consume and analyze sports. The role of artificial intelligence in improving decision-making and prediction in sports is expanding, due to the specific changes in the performance of athletes, a performance prediction model based on a machine learning algorithm has been proposed. The current research st...
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Dominance Pruning in Machine Learning for Solving Financial Trading and Real-Time Multimedia Applications
Issue:
Volume 6, Issue 2, December 2022
Pages:
36-47
Received:
25 November 2022
Accepted:
26 December 2022
Published:
6 February 2023
Abstract: This paper presents the design of dominance relations to reduce the space traversed in machine learning for solving two applications in financial trading and real-time multimedia. A machine-learning algorithm designed for an application with a huge search space will need to perform an efficient traversal of the space during learning. It will be more effective if it employs a powerful pruning mechanism to eliminate suboptimal candidates before using them in the learning algorithm. In our approach, we present dominance relations for pruning subspaces with suboptimal kernels that are otherwise evaluated in learning, where kernels represent the statistical quality, average density, or probability of solutions in a subspace. Specifically, when one subspace dominates another by a dominance relation, we can prune the latter and guarantee without searching both that the kernel of the latter cannot be better than that of the first. As a result, a significant portion of the search space will be pruned by those non- dominated subspaces during learning. In the financial trading application studied, we use mean reversion as our strategy for learning the set of promising stocks and Pareto-optimality as our dominance relation to reduce the space evaluated in learning. In the multimedia application, we propose a dominance relation using an axiom from our past work to approximate the subspace of perceptual qualities within an error threshold. The pruning mechanism allows the learning of the mapping from controls to perceptual qualities while eliminating the evaluation of all those mappings that are within the error thresholds. In both cases, we can harness the complexity of machine learning by reducing the candidate space evaluated.
Abstract: This paper presents the design of dominance relations to reduce the space traversed in machine learning for solving two applications in financial trading and real-time multimedia. A machine-learning algorithm designed for an application with a huge search space will need to perform an efficient traversal of the space during learning. It will be mor...
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