Identification of high-performance volleyball players from anthropometric variables and psychological readiness: A machine-learning approach

2021 ◽  
pp. 175433712110454
Author(s):  
Rabiu Muazu Musa ◽  
Anwar P.P. Abdul Majeed ◽  
Muhammad Zuhaili Suhaimi ◽  
Mohamad Razali Abdullah ◽  
Mohd Azraai Mohd Razman ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Clustering Algorithm ◽  
Team Sports ◽  
Minority Class ◽  
Analysis Technique ◽  
Volleyball Players ◽  
Low Performance ◽  
High Level ◽  
High Degree

Modern indoor volleyball has evolved into a high-level strength sport and is seen as one of the most popular open-skilled team sports. The nature of the sport as an open-based skill requires players to have a high degree of both psychological skill and physical ability to cope with the sport’s externally and internally induced pace. The purposes of this study were to examine the essential basic anthropometric variables, as well as competition and practice psychological readiness, that could provide a performance edge and identify high and low-performance players based on the parameters. The anthropometric variables of height, weight, and age were assessed, while the test for performance strategies instrument was used to evaluate competition and practice psychological readiness skills of the players. The players’ performances were analyzed in real-time during a volleyball tournament. The Louvain clustering algorithm was used to determine the performance class of the players with reference to the variables evaluated. A total of 45 players were ascertained as high-performance volleyball players (HVP), while 20 players were deemed as low-performance volleyball players (LVP) via the clustering analysis technique. The logistic regression classifier was used to classify the performance of the players. Nonetheless, owing to the skewed representation between the HVP and LVP during the training of the model, the Synthetic Minority Oversampling TEchnique (SMOTE) was employed to artificially increase the minority class dataset to avoid the overfitting notion upon classification. It was shown from the study that, through the machine learning pipeline developed, an excellent identification of the HVP and LVP could be attained. The findings could be invaluable to coaches and other relevant stakeholders in team preparation and the selection of high-performance players in volleyball.

scholarly journals A categorization tool for fabric systems used in firefighters' clothing based on their thermal protective and thermo-physiological comfort performances

2018 ◽  
Vol 89 (16) ◽  
pp. 3244-3259 ◽  
Author(s):  
Sumit Mandal ◽  
Simon Annaheim ◽  
Andre Capt ◽  
Jemma Greve ◽  
Martin Camenzind ◽  
...  
Keyword(s):  
Performance Index ◽  
Protective Clothing ◽  
High Performance ◽  
Clustering Algorithm ◽  
Health And Safety ◽  
Thermal Protective Performance ◽  
Thermal Protective Clothing ◽  
Low Performance ◽  
Fabric System ◽  
Protective Performance

Fabric systems used in firefighters' thermal protective clothing should offer optimal thermal protective and thermo-physiological comfort performances. However, fabric systems that have very high thermal protective performance have very low thermo-physiological comfort performance. As these performances are inversely related, a categorization tool based on these two performances can help to find the best balance between them. Thus, this study is aimed at developing a tool for categorizing fabric systems used in protective clothing. For this, a set of commercially available fabric systems were evaluated and categorized. The thermal protective and thermo-physiological comfort performances were measured by standard tests and indexed into a normalized scale between 0 (low performance) and 1 (high performance). The indices dataset was first divided into three clusters by using the k-means algorithm. Here, each cluster had a centroid representing a typical Thermal Protective Performance Index (TPPI) value and a typical Thermo-physiological Comfort Performance Index (TCPI) value. By using the ISO 11612:2015 and EN 469:2014 guidelines related to the TPPI requirements, the clustered fabric systems were divided into two groups: Group 1 (high thermal protective performance-based fabric systems) and Group 2 (low thermal protective performance-based fabric systems). The fabric systems in each of these TPPI groups were further categorized based on the typical TCPI values obtained from the k-means clustering algorithm. In this study, these categorized fabric systems showed either high or low thermal protective performance with low, medium, or high thermo-physiological comfort performance. Finally, a tool for using these categorized fabric systems was prepared and presented graphically. The allocations of the fabric systems within the categorization tool have been verified based on their properties (e.g., thermal resistance, weight, evaporative resistance) and construction parameters (e.g., woven, nonwoven, layers), which significantly affect the performance. In this way, we identified key characteristics among the categorized fabric systems which can be used to upgrade or develop high-performance fabric systems. Overall, the categorization tool developed in this study could help clothing manufacturers or textile engineers select and/or develop appropriate fabric systems with maximum thermal protective performance and thermo-physiological comfort performance. Thermal protective clothing manufactured using this type of newly developed fabric system could provide better occupational health and safety for firefighters.

scholarly journals Class Imbalanced Fault Diagnosis via Combining K-Means Clustering Algorithm with Generative Adversarial Networks

2021 ◽  
Vol 25 (3) ◽  
pp. 346-355
Author(s):  
Huifang Li ◽  
◽  
Rui Fan ◽  
Qisong Shi ◽  
Zijian Du
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Diagnostic Accuracy ◽  
Clustering Algorithm ◽  
Generative Adversarial Networks ◽  
Similar Distribution ◽  
Generative Adversarial Network ◽  
Minority Class ◽  
Adversarial Network ◽  
Original Dataset

Recent advancements in machine learning and communication technologies have enabled new approaches to automated fault diagnosis and detection in industrial systems. Given wide variation in occurrence frequencies of different classes of faults, the class distribution of real-world industrial fault data is usually imbalanced. However, most prior machine learning-based classification methods do not take this imbalance into consideration, and thus tend to be biased toward recognizing the majority classes and result in poor accuracy for minority ones. To solve such problems, we propose a k-means clustering generative adversarial network (KM-GAN)-based fault diagnosis approach able to reduce imbalance in fault data and improve diagnostic accuracy for minority classes. First, we design a new k-means clustering algorithm and GAN-based oversampling method to generate diverse minority-class samples obeying the similar distribution to the original minority data. The k-means clustering algorithm is adopted to divide minority-class samples into k clusters, while a GAN is applied to learn the data distribution of the resulting clusters and generate a given number of minority-class samples as a supplement to the original dataset. Then, we construct a deep neural network (DNN) and deep belief network (DBN)-based heterogeneous ensemble model as a fault classifier to improve generalization, in which DNN and DBN models are trained separately on the resulting dataset, and then the outputs from both are averaged as the final diagnostic result. A series of comparative experiments are conducted to verify the effectiveness of our proposed method, and the experimental results show that our method can improve diagnostic accuracy for minority-class samples.

Higher-level geophysical modelling

2021 ◽  
Author(s):  
Roman Nuterman ◽  
Dion Häfner ◽  
Markus Jochum
Keyword(s):  
Machine Learning ◽  
Programming Languages ◽  
High Performance ◽  
Ocean Model ◽  
User Friendliness ◽  
Model Code ◽  
Building Models ◽  
Fortran Implementation ◽  
High Level ◽  
New Generation

<p>Until recently, our pure Python, primitive equation ocean model Veros <br>has been about 1.5x slower than a corresponding Fortran implementation. <br>But thanks to a thriving scientific and machine learning library <br>ecosystem, tremendous speed-ups on GPU, and to a lesser degree CPU, are <br>within reach. Leveraging Google's JAX library, we find that our Python <br>model code can reach a 2-5 times higher energy efficiency on GPU <br>compared to a traditional Fortran model.</p><p>Therefore, we propose a new generation of geophysical models: One that <br>combines high-level abstractions and user friendliness on one hand, and <br>that leverages modern developments in high-performance computing and <br>machine learning research on the other hand.</p><p>We discuss what there is to gain from building models in high-level <br>programming languages, what we have achieved in Veros, and where we see <br>the modelling community heading in the future.</p>

scholarly journals Jumping Flying Distance and Jump Performance of Elite Male Volleyball Players at FIVB Volleyball Men’s World Championship

2020 ◽  
Vol 10 (6) ◽  
pp. 2045
Author(s):  
Damian Pawlik ◽  
Adam Kawczyński ◽  
Jan Chmura ◽  
Krzysztof Maćkała ◽  
Marcin Kutrzyński ◽  
...  
Keyword(s):  
The Other ◽  
Jump Height ◽  
Jump Performance ◽  
World Championship ◽  
Volleyball Player ◽  
Volleyball Players ◽  
High Level ◽  
High Degree

We investigated different specific jumping performances of high-level male volleyball players. The aim of this study was to assess covered jumping distance, jump height, and number of jumps performed at certain positions by volleyball players competing at the 2014 FIVB Volleyball Men’s World Championship in Poland. A total of 140 male volleyball players from national teams participated in the study. The analysis was performed for jumping flying distance (JFD), jump serve height (SJH), attack jump height (AJH), block jump height (BJH), and quantity of jumps (JC). The analysis of JFD of attack jumps showed that the middles covered a shorter distance than the other players. When analyzing the block jump lengths, distance during jump performance covered by the receivers (R1) was shorter than that of the opposites. Analysis of SJH by volleyball players at various positions showed statistically significant differences (P < 0.05) among the middles (M1, M2), receivers (R1, R2), and opposites (O). Statistically significant differences (P < 0.05) in BJH were found between the middles and the rest of the players. The results of the experiment show a high degree of reliability for jump height during serve and attack, jumping flying distance covered during an attack, and number of block jumps. The strongest relationship was seen between jump components, which predominantly depend only on a volleyball player performing a specific action (e.g., jump serve or attack jump).

Factors Affecting the Choice of Medical Tourism Destination: A Case Study of Medical Tourists from the Arab Region in Turkey

2019 ◽  
Vol 21 (4) ◽  
pp. 465-475
Author(s):  
M. Said Yıldız ◽  
M. Mahmud Khan
Keyword(s):  
High Performance ◽  
Medical Tourism ◽  
Arab World ◽  
Healthcare Providers ◽  
Arab Region ◽  
Factors Affecting ◽  
Low Performance ◽  
Performance Area ◽  
High Degree

The main purpose of this study is to identify the aspects that are considered relatively more important by medical tourists from the Arab world as well as Turkish healthcare providers’ ability to perform these tasks and functions well. Additionally, patients from the Arab world were asked for their reasons for seeking care abroad and the factors affecting their choice of destination (Turkey). Each of the parameters’ importance–performance scores that were collected from medical tourists through a structured questionnaire were analysed by their positions on the importance–performance analysis (IPA) graph. The IPA demonstrated that eight out of 17 aspects of medical services were considered very important by patients and were delivered with high degree of performance by a facility. Seven of the parameters were in ‘low priority—Quadrant 3’, which means that providers performed relatively poorly in these areas and patients did not consider these as important as well. Only one of the parameters was in low importance–high performance area and one other was in high importance–low performance area. The attributes of medical tourism patients found by the analysis may help Turkish healthcare providers to identify the aspects that should be strengthened to improve customer satisfaction.

scholarly journals An ensemble learning approach to digital corona virus preliminary screening from cough sounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Emad A. Mohammed ◽  
Mohammad Keyhani ◽  
Amir Sanati-Nezhad ◽  
S. Hossein Hejazi ◽  
Behrouz H. Far
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Original Data ◽  
Variable Number ◽  
Operating Characteristics ◽  
Minority Class ◽  
Screening Model ◽  
Sound Files ◽  
Cough Sound ◽  
Sound Data

AbstractThis work develops a robust classifier for a COVID-19 pre-screening model from crowdsourced cough sound data. The crowdsourced cough recordings contain a variable number of coughs, with some input sound files more informative than the others. Accurate detection of COVID-19 from the sound datasets requires overcoming two main challenges (i) the variable number of coughs in each recording and (ii) the low number of COVID-positive cases compared to healthy coughs in the data. We use two open datasets of crowdsourced cough recordings and segment each cough recording into non-overlapping coughs. The segmentation enriches the original data without oversampling by splitting the original cough sound files into non-overlapping segments. Splitting the sound files enables us to increase the samples of the minority class (COVID-19) without changing the feature distribution of the COVID-19 samples resulted from applying oversampling techniques. Each cough sound segment is transformed into six image representations for further analyses. We conduct extensive experiments with shallow machine learning, Convolutional Neural Network (CNN), and pre-trained CNN models. The results of our models were compared to other recently published papers that apply machine learning to cough sound data for COVID-19 detection. Our method demonstrated a high performance using an ensemble model on the testing dataset with area under receiver operating characteristics curve = 0.77, precision = 0.80, recall = 0.71, F1 measure = 0.75, and Kappa = 0.53. The results show an improvement in the prediction accuracy of our COVID-19 pre-screening model compared to the other models.

scholarly journals NEURODYNAMIC FUNCTIONS HIGH-LEVEL ATHLETES GAME QUALIFICATIONS SPORTS

2020 ◽  
Vol 1 ◽  
pp. 50-55
Author(s):  
Vladimir Lizogub ◽  
◽  
Vitaliy Pustovalov ◽  
Viktoriya Suprunovych ◽  
Nataliya Grebinyuk ◽  
...  
Keyword(s):  
Key Words ◽  
Team Sports ◽  
Complex Reaction ◽  
Highly Qualified ◽  
Basketball Players ◽  
Volleyball Players ◽  
High Level ◽  
Team Types

The article considers questions concerning features of neurodynamic functions of high level qualification sportsmen in various game sports. Different variants of display of neurodynamic functions in highly qualified game sports athletes have been established. Volleyball players were characterized by probably higher indicators of simple sensorimotor and complex reaction of choice, as well as individual-typological properties of the balance of neurotic processes, compared with similar results of football and basketball players. Neurodynamic functions of highly qualified athletes in team types determine the level of players’ sportsmanship, as well as can become a criterial characteristics of the success of their playing activities. Key words: neurodynamic functions, sensorimotor reactions, individual typological peculiarities of neurotic processes, team sports.

scholarly journals A real-time FPGA-based cluster finding algorithm for LHCb silicon pixel detector

2021 ◽  
Vol 251 ◽  
pp. 04016
Author(s):  
Giovanni Bassi ◽  
Luca Giambastiani ◽  
Federico Lazzari ◽  
Michael J. Morello ◽  
Tommaso Pajero ◽  
...  
Keyword(s):  
Real Time ◽  
Heterogeneous Computing ◽  
Clustering Algorithm ◽  
General Purpose ◽  
Pixel Detector ◽  
Promising Alternative ◽  
Level Trigger ◽  
High Level ◽  
High Degree ◽  
Algorithm Implementation

Starting from the next LHC run, the upgraded LHCb High Level Trigger will process events at the full LHC collision rate (averaging 30 MHz). This challenging goal, tackled using a large and heterogeneous computing farm, can be eased addressing lowest-level, more repetitive tasks at the earliest stages of the data acquisition chain. FPGA devices are very well-suited to perform with a high degree of parallelism and efficiency certain computations, that would be significantly demanding if performed on general-purpose architectures. A particularly time-demanding task is the cluster-finding process, due to the 2D pixel geometry of the new LHCb pixel detector. We describe here a custom highly parallel FPGA-based clustering algorithm and its firmware implementation. The algorithm implementation has shown excellent reconstruction quality during qualification tests, while requiring a modest amount of hardware resources. Therefore it can run in the LHCb FPGA readout cards in real time, during data taking at 30 MHz, representing a promising alternative solution to more common CPU-based algorithms.

scholarly journals [ANT]: A Machine Learning Approach for Building Performance Simulation: Methods and Development

2019 ◽  
Vol 3 (1) ◽  
pp. 205
Author(s):  
Mahmoud M. Abdelrahman ◽  
Ahmed Mohamed Yousef Toutou
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
General Purpose ◽  
Ease Of Use ◽  
Building Performance ◽  
Performance Simulation ◽  
Building Performance Simulation ◽  
Wide Range ◽  
Performance Development ◽  
High Level

In this paper, we represent an approach for combining machine learning (ML) techniques with building performance simulation by introducing four methods in which ML could be effectively involved in this field i.e. Classification, Regression, Clustering and Model selection . Rhino-3d-Grasshopper SDK was used to develop a new plugin for involving machine learning in design process using Python programming language and making use of scikit-learn module, that is, a python module which provides a general purpose high level language to nonspecialist user by integration of wide range supervised and unsupervised learning algorithms with high performance, ease of use and well documented features. ANT plugin provides a method to make use of these modules inside Rhino\Grasshopper to be handy to designers. This tool is open source and is released under BSD simplified license. This approach represents promising results regarding making use of data in automating building performance development and could be widely applied. Future studies include providing parallel computation facility using PyOpenCL module as well as computer vision integration using scikit-image.

scholarly journals Application of data mining to grading indian industries on the basis of financial ratios

2017 ◽  
Vol 7 (1) ◽  
pp. 92-103
Author(s):  
MANIMANNAN G ◽  
LAKSHMI PRIYA R ◽  
ANISH PREETHI V
Keyword(s):  
Data Mining ◽  
Factor Analysis ◽  
High Performance ◽  
Clustering Algorithm ◽  
Financial Ratios ◽  
Public And Private ◽  
Structural Patterns ◽  
Five Factors ◽  
Low Performance ◽  
Indian Industries

An attempt is made to introduce a new method of rating the top ranking industriess on the basis of certain financial ratios. It is well known that the financial ratios are being used as a yardstick by researchers for many purposes. About 500 industries from public and private sectors were considered for each year from 2007 to 2012, which were ranked according to their net sales. Twenty financial ratios were carefully chosen out of numerous ratios that could give different notion of the objectives and have significant meaning in the literature. The unique feature of this study is the application of factor, k-mean clustering and discriminant analyses as data mining tools to exploit the hidden structure present in the data for each of the study periods. Initially, factor analysis is used to uncover the patterns underlying financial ratios. The scores from extracted factors were used to find initial groups by k-mean clustering algorithm. A few outlierindustries, which could not be classified to any of the larger groups, were discarded as some of the ratios possessed higher values. The clusters thus obtained formed the basis for the further analyses as they inherited the structural patterns found by the factor analysis. The cluster analysis was followed by iterative discriminant procedure with original ratios until cent percent classification was achieved. Finally, the groups were identified as industries belonging to Grade A, Grade B and Grade C in that order, which exhibit the behavior of High performance, Moderate performance and Low performance. From the present study it was observed that a little over 90% of the total variations of the data were explained by the first five factors for each year. These five factors revealed the underlying structural patterns among the twenty ratios that were initially considered in the analysis. Also only three clusters could be meaningfully formed for each of theperiods. It is also interesting to note that the clusters could be arranged by magnitude of their mean vectors on selected ratios, thus permitting the groups to be identified on the basis of their performance.

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