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In sports, the essence of a complete technical action is a complete information structure pattern and the athlete’s judgment of the action is actually the identification of the movement information structure pattern. Action recognition refers to the ability of the human brain to distinguish a perceived action from other actions and obtain predictiv...
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... Event detection technology refers to a set of tools and methods aimed at identifying and analyzing significant occurrences or patterns within a given data stream or system [1]. This technology often relies on advanced algorithms and machine learning techniques to sift through vast amounts of data in real-time, pinpointing events of interest and flagging them for further analysis or action [2]. One common application of event detection technology is in the realm of security and surveillance. ...
This paper presents an investigation into the application of advanced techniques, including deep learning classification and FCM centroid segmentation, for automated event detection and analysis in sports videos. Through a series of experiments and analyses, we demonstrate the effectiveness of deep learning models in accurately categorizing events within sports footage, alongside the insights provided by FCM segmentation into event occurrences at the frame level. This paper investigates the application of advanced techniques, such as deep learning classification and FCM centroid segmentation, for automated event detection in sports videos. Through experiments, we achieved an average accuracy of 93.2% using deep learning classification and identified key events with probabilities ranging from 0.01 to 0.90 using FCM segmentation.
... Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: ...
... The main objective is to maximize both the similarity among the data belonging to the same cluster and the dissimilarity of the data belonging to the different clusters [1]. Because the clustering algorithms do not need actual cluster labels, they are used in many areas such as data mining [2,3], machine learning [4][5][6][7][8], bioinformatics [9,10], pattern recognition [11][12][13], and streaming data mining [14][15][16]. ...
Clustering is a technique for statistical data analysis and is widely used in many areas where class labels are not available. Major problems related to clustering algorithms are handling high-dimensional, imbalanced, and/or varying-density datasets, detecting outliers, and defining arbitrary-shaped clusters. In this study, we proposed a novel clustering algorithm named as MCMSTClustering (Defining Non-Spherical Clusters by using Minimum Spanning Tree over KD-Tree-based Micro-Clusters) to overcome mentioned issues simultaneously. Our algorithm consists of three parts. The first part is defining micro-clusters using the KD-Tree data structure with range search. The second part is constructing macro-clusters by using minimum spanning tree (MST) on defined micro-clusters, and the final part is regulating defined clusters to increase the accuracy of the algorithm. To state the efficiency of our algorithm, we performed some experimental studies on some state-of-the-art algorithms. The findings were presented in detail with tables and graphs. The success of the proposed algorithm using various performance evaluation criteria was confirmed. According to the experimental studies, MCMSTClustering outperformed competitor algorithms in aspects of clustering quality in acceptable run-time. Besides, the obtained results showed that the novel algorithm can be applied effectively in solving many different clustering problems in the literature.
... Clustering approaches are unsupervised learning techniques that separate data into groups called clusters according to the similarities and dissimilarities among the data [1,2]. e DBSCAN [3], kmeans [4], BIRCH [5], Spectral Clustering [6], Agglomerative Clustering [7], HDBSCAN [8], A nity Propagation [9], and OPTICS [10] are some examples of them, and they are used in many elds such as pattern recognition [11][12][13], machine learning [14][15][16], data mining [17,18], web mining [1,19], bioinformatics [20,21], and streaming data mining [22,23]. On the other hand, measuring the performance of any proposed clustering approach is also an important issue because each algorithm has its special point of view, and the results of each clustering technique vary. ...
... Let C i and C j be two clusters and have N i and N j data points, respectively. e intracluster distance set of cluster C i will be a set as given equation (13). Moreover, the intercluster distance set is measured based on the distances of data pairs of clusters C i and C j . ...
The cluster evaluation process is of great importance in areas of machine learning and data mining. Evaluating the clustering quality of clusters shows how much any proposed approach or algorithm is competent. Nevertheless, evaluating the quality of any cluster is still an issue. Although many cluster validity indices have been proposed, there is a need for new approaches that can measure the clustering quality more accurately because most of the existing approaches measure the cluster quality correctly when the shape of the cluster is spherical. However, very few clusters in the real world are spherical. erefore, a new Validity Index for Arbitrary-Shaped Clusters based on the kernel density estimation (the VIASCKDE Index) to overcome the mentioned issue was proposed in the study. In the VIASCKDE Index, we used separation and compactness of each data to support arbitrary-shaped clusters and utilized the kernel density estimation (KDE) to give more weight to the denser areas in the clusters to support cluster compactness. To evaluate the performance of our approach, we compared it to the state-of-the-art cluster validity indices. Experimental results have demonstrated that the VIASCKDE Index outperforms the compared indices.
BACKGROUND: Sports have been a fundamental component of any culture and legacy for centuries. Athletes are widely regarded as a source of national pride, and their physical well-being is deemed to be of paramount significance. The attainment of optimal performance and injury prevention in athletes is contingent upon physical fitness. Technology integration has implemented Cyber-Physical Systems (CPS) to augment the athletic training milieu. OBJECTIVE: The present study introduces an approach for assessing athlete physical fitness in training environments: the Internet of Things (IoT) and CPS-based Physical Fitness Evaluation Method (IoT-CPS-PFEM). METHODS: The IoT-CPS-PFEM employs a range of IoT-connected sensors and devices to observe and assess the physical fitness of athletes. The proposed methodology gathers information on diverse fitness parameters, including heart rate, body temperature, and oxygen saturation. It employs machine learning algorithms to scrutinize and furnish feedback on the athlete’s physical fitness status. RESULTS: The simulation findings illustrate the efficacy of the proposed IoT-CPS-PFEM in identifying the physical fitness levels of athletes, with an average precision of 93%. The method under consideration aims to tackle the existing obstacles of conventional physical fitness assessment techniques, including imprecisions, time lags, and manual data-gathering requirements. The approach of IoT-CPS-PFEM provides the benefits of real-time monitoring, precision, and automation, thereby enhancing an athlete’s physical fitness and overall performance to a considerable extent. CONCLUSION: The research findings suggest that the implementation of IoT-CPS-PFEM can significantly impact the physical fitness of athletes and enhance the performance of the Indian sports industry in global competitions.
