A Web based System for Cricket Talent Identification, Enhancement and Selection (C-TIES)

Ahamad, Gulfam; Naqvi, S. Kazim; Beg, M. M. Sufyan; Ahmed, Tanvir

doi:10.1016/j.procs.2015.08.426

Cited by 13 publications

(5 citation statements)

References 8 publications

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“…Ahamad et al [31] proposed an ordered weighted averaging aggregation (OWA) operator based cricket TiD model. This model is the first cricket TiD of its kind [14].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal feature selection for cricket talent identification

2023

IJATEE

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Cricket talent identification (TiD) is a methodical process that aims to find the young athletes possessing a potential to excel in the cricket sport at an early age. The sports scientists have identified a set of twenty-eight parameters that determine the cricket TiD. In order to realize the objective of computational efficiency by reducing the feature set, we perform an optimal feature selection for cricket TiD using nine different feature selection techniques Viz. mutual information, information gain ratio, correlation, chi square, univariate root mean square error (RMSE), receiver operating characteristic (ROC) with decision tree classifier, reliefF, boruta and oneR. The individual results obtained from the feature selection techniques are provided along with the individual ranking. We aggregate the results using two different rank aggregation techniques namely average ranking aggregation and majority vote ranking aggregation. The aggregation resultsshow a significant agreement between the two schemes. Fourteen out of twenty-eight features are selected using a threshold of 0.52-the value selected on recommendation of four different domain experts. 71.4% of the selected features are sport-centric and only 28.6% of the selected features are from the cognitive ability category. To the best of our knowledge, this is first such attempt to identify the talent in cricket using this methodology.

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“…Ahamad et al [31] proposed an ordered weighted averaging aggregation (OWA) operator based cricket TiD model. This model is the first cricket TiD of its kind [14].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal feature selection for cricket talent identification

2023

IJATEE

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“…a viz. modern state of art machine learning [13], [25,26] Ordered Weighted Averaging Aggregation (OWA) Operator [18], Fuzzy Linguistic Quantifier [19], and many more for the talent identification in different sports. Among the various sports, Cricket is a highly attractive game among people.…”

Section: Literature Reviewmentioning

confidence: 99%

SVMCTI: Support Vector Machine-based Cricket Talent Identification Model

Reyaz

Ahamad

Khan

et al. 2023

Preprint

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Recognizing sports talent is one of the intensively discussed topics in this day and age. Cricket is a sport of keen interest and has fascinated researchers all over the world to ponder and work in this domain. In this era of technological competence, incorporating technology in cricket talent identification is an incumbent task. Also, early-age talent identification is considered to be more beneficial as instead of wasting time and other resources on random performers, we can limit the training and spending on the talented performers that could yield better results. In this article, a machine learning-based approach is proposed for Cricket Talent Identification using SVM (RBF kernel) to classify a dataset of performers into talented performers (possessing cricketing talent) and non-talented performers (not possessing cricketing talent). The dataset has been collected from early-age performers taking into consideration the benefits of talent identification at early ages. The data have been gathered concerning various physical/motor, anthropometric, and cognitive abilities. Using the feature selection technique, the best-contributing parameters were determined and supplied to the model. After performing experimentation, the results were evaluated based on various evaluation metrics like Accuracy, precision, and f1-Score. we achieved an accuracy score of 96.42%, a precision of 0.94, and an f1_score of 0.96. The results obtained have been cross-validated using the 10-cross validation technique.

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“…Authors in Refs. and have used OWA to test the player skills for cricket match. OWA has also been used in multi‐criteria decision making…”

Section: Preliminarymentioning

confidence: 99%

An OWA-Based Ranking Approach for University Books Recommendation

Sohail

Siddiqui

Ali

2017

Int. J. Intell. Syst.

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Generally the book recommendation approaches are personalized in nature, that is, they utilize the users’ purchasing behavior to recommend them the book similar to their preferences. The main problem with the personalized recommendation is its knowledge requirement about users’ past preferences. As a result, these techniques fail in producing appropriate recommendation for a new user whose preferences are not known. The personalized recommendation also needs extra space to store the users’ preferences. In this paper, a framework to recommend books to university students for their studies is presented. In order to answer which books are to be included in the syllabus, a specialized way of recommendation, where recommendations from experts of the subjects at different universities are considered, is presented. We have suggested a ranked recommendation approach for books, which employ Ordered Weighted Aggregation (OWA), a fuzzy‐based aggregation, to aggregate the several ranking of the top universities. On the one hand, it does not need user prior preferences, and on the other hand, it eases the complexities of personalized recommendation to huge number of users and replaces it with a single ranked recommendation. The experimental results are compared with the existing positional aggregation algorithm that demonstrates significant improvement in the results with respect to various performance metrics.

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A Web based System for Cricket Talent Identification, Enhancement and Selection (C-TIES)

Cited by 13 publications

References 8 publications

Optimal feature selection for cricket talent identification

Optimal feature selection for cricket talent identification

SVMCTI: Support Vector Machine-based Cricket Talent Identification Model

An OWA-Based Ranking Approach for University Books Recommendation

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