Explore the vast range of titles available.

k-nearest neighbour (k-NN) has been shown to be an effective learning algorithm for classification and prediction. However, the application of k-NN for prediction and classification in specific sport is still in its infancy. The present study classified and predicted high and low potential archers from a set of physical fitness variables trained on a variation of k-NN algorithms and logistic regression. 50 youth archers with the mean age and standard deviation of (17.0 ± 0.56) years drawn from various archery programmes completed a one end archery shooting score test. Standard fitness measurements of the handgrip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle strength were conducted. Multiple linear regression was utilised to ascertain the significant variables that affect the shooting score. It was demonstrated from the analysis that core muscle strength and vertical jump were statistically significant. Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the significant variables identified. k-NN model variations, i.e., fine, medium, coarse, cosine, cubic and weighted functions as well as logistic regression, were trained based on the significant performance variables. The HACA clustered the archers into high potential archers (HPA) and low potential archers (LPA). The weighted k-NN outperformed all the tested models at itdemonstrated reasonably good classification on the evaluated indicators with an accuracy of 82.5 ± 4.75% for the prediction of the HPA and the LPA. Moreover, the performance of the classifiers was further investigated against fresh data, which also indicates the efficacy of the weighted k-NN model. These findings could be valuable to coaches and sports managers to recognise high potential archers from a combination of the selected few physical fitness performance indicators identified which would subsequently save cost, time and energy for a talent identification programme.

The identification and prediction of athletic talent are pivotal in the development of successful sporting careers. Traditional subjective assessment methods have proven unreliable due to their inherent subjectivity, prompting the rise of data-driven techniques favoured for their objectivity. This evolution in statistical analysis facilitates the extraction of pertinent athlete information, enabling the recognition of their potential for excellence in their respective sporting careers. In the current study, we applied a logistic regression-based machine learning pipeline (LR) to identify potential skateboarding athletes from a combination of fitness and motor skills performance variables. Forty-five skateboarders recruited from a variety of skateboarding parks were evaluated on various skateboarding tricks while their fitness and motor skills abilities that consist of stork stance test, dynamic balance, sit ups, plank test, standing broad jump, as well as vertical jump, were evaluated. The performances of the skateboarders were clustered and the LR model was developed to classify the classes of the skateboarders. The cluster analysis identified two groups of skateboarders: high and low potential skateboarders. The LR model achieved 90% of mean accuracy specifying excellent prediction of the skateboarder classes. Further sensitivity analysis revealed that static and dynamic balance, lower body strength, and endurance were the most important factors that contributed to the model’s performance. These factors are therefore essential for successful performance in skateboarding. The application of machine learning in talent prediction can greatly assist coaches and other relevant stakeholders in making informed decisions regarding athlete performance.

The present study aims to identify the essential technical and tactical performance indicators that could differentiate winning and losing performance in the Asian elite beach soccer competition. A set of 20 technical and tactical performance indicators namely; shot back-third, shot mid-third, shot front-third, pass back-third, pass mid-third, pass front-third, shot in box, shot outbox, chances created, interception, turnover, goals scored 1st period, goals scored 2nd period, goals scored 3rd period, goals scored extra time, tackling, fouls committed, complete save, incomplete save and passing error were observed during the beach soccer Asian Football Confederation tournament 2017 held in Malaysia. A total of 23 matches from 12 teams were notated using StatWatch application in real-time. Discriminant analysis (DA) of standard, backward as well stepwise modes were used to develop a model for the winning (WT) and losing team (LT) whilst Mann-Whitney U test was utilized to ascertain the differences between the WT and LT with respect to the performance indicators evaluated. The standard backward, forward and stepwise discriminates the WT and the LT with an excellent accuracy of 95.65%, 91.30% and 89.13%, respectively. The standard DA model discriminated the teams from seven performance indicators whilst both the backward and forward stepwise identified two performance indicators. The Mann-Whitney U test analysis indicated that the WT is statistically significant from the LT based on the performance indicators determined from the standard mode model of the DA. It was demonstrated that seven performance indicators namely; shot front-third, pass front-third, chances created, goals scores at the 1st period, goals scored at the 2nd period, goals scored at 3rd period were directly linked to a successful performance whilst the incomplete save by the keeper attribute towards the poor performance of the team. The present finding could serve useful to the coaches as well as performance analysts as a measure of profiling successful performance and enables team improvement with respect to the associated performance indicators.

Introduction: Effective weight loss programs should consider more than just total body weight reduction, incorporating other critical anthropometric and health-related markers. Objective: This study aimed to identify the key markers essential for a healthy and effective weight loss regimen in women. Methodology: A total of 143 women (mean age 39.32±8.60 years; BMI 30.27±5.94) from Malaysia participated. Various anthropometric and health-related markers were measured using standard procedures. Principal Component Analysis (PCA) was employed to extract the crucial markers for an effective weight loss program, while Multiple Regression Analysis (MLR) validated these variables. Results: A 3-factor solution proved effective, with Component 1 including arm, hip, bust, thigh, and waist circumferences; Component 2 comprising body fat, weight, and visceral fat; and Component 3 involving bone mass and basal metabolic rate. These components were renamed as anthropometric attributes, adiposity, and metabolic skeletal health, respectively. The MLR model, with these components as independent variables and weight as the dependent variable, yielded a significant regression (F (2163, 1) = 73.3, p < 0.0001, R² = 0.979), explaining 98% of the variability in weight. All variables significantly contributed to the model (p < 0.001). Discussion: The results highlight that effective weight loss goes beyond just reducing body weight. Key factors such as body measurements, fat levels, and metabolic health play a crucial role. The strong predictive ability of these factors (R² = 0.979, p < 0.001) suggests that weight loss programs should focus on overall body composition and health rather than just weight reduction for better, long-term outcomes. Conclusions: This study accentuates the need for a holistic approach to weight loss, considering a comprehensive evaluation beyond just physical symptoms.

Brain Computer-Interface (BCI) technology plays a considerable role in the control of rehabilitation or peripheral devices for stroke patients. This is particularly due to their inability to control such devices from their inherent physical limitations after such an attack. More often than not, the control of such devices exploits electroencephalogram (EEG) signals. Nonetheless, it is worth noting that the extraction of the features and the classification of the signals is non-trivial for a successful BCI system. The use of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features. However, the employment of such a method towards BCI applications, particularly in regard to EEG signals, are somewhat limited. The present study aims to evaluate the effectiveness of different TL models in extracting features for the classification of wink-based EEG signals. The extracted features are classified by means of fine-tuned Random Forest (RF) classifier. The raw EEG signals are transformed into a scalogram image via Continuous Wavelet Transform (CWT) before it was fed into the TL models, namely InceptionV3, Inception ResNetV2, Xception and MobileNet. The dataset was divided into training, validation, and test datasets, respectively, via a stratified ratio of 60:20:20. The hyperparameters of the RF models were optimised through the grid search approach, in which the five-fold cross-validation technique was adopted. The optimised RF classifier performance was compared with the conventional TL-based CNN classifier performance. It was demonstrated from the study that the best TL model identified is the Inception ResNetV2 along with an optimised RF pipeline, as it was able to yield a classification accuracy of 100% on both the training and validation dataset. Therefore, it could be established from the study that a comparable classification efficacy is attainable via the Inception ResNetV2 with an optimised RF pipeline. It is envisaged that the implementation of the proposed architecture to a BCI system would potentially facilitate post-stroke patients to lead a better life quality.

Background: Monitoring training load in youth athletes is essential for optimizing performance and reducing injury risk, yet limited research has examined how anthropometric characteristics influence load tolerance in badminton. This study investigated the association between training load measures and anthropometric profiles in competitive youth players. Methods: Fifty male and female athletes participated, with external workload captured via accelerometer sensors and anthropometric assessments conducted following standardized protocols. Louvain clustering was applied to classify players into different load groups, while multinomial logistic regression (MLR) identified key predictors of load classification. Results: Louvain clustering revealed three distinct load groups i.e., High Load (HL), Moderate Load (ML), and Low Load (LL) groups, reflecting natural patterns in external workload distribution. The MLR analysis demonstrated that height, weight, and leg length were significant predictors of load classification. Taller and heavier players were more likely to belong to the HL group, while longer leg length was positively associated with ML classification, potentially linked to stride mechanics and movement economy. Other circumferential measures (waist, hip, MUAC) showed minimal impact, and years of playing experience did not significantly predict load tolerance. Conclusion: These findings emphasize the value of combining network-based clustering with multivariate modeling to capture complex athlete load interactions. Practically, the results suggest that specific anthropometric traits particularly stature, body mass, and limb length, play an important role in shaping athletes’ ability to sustain training loads. Integrating individualized anthropometric assessment into load monitoring can support evidence-based coaching strategies that enhance performance and mitigate injury risk in developing badminton players. Antecedentes: El monitoreo de la carga de entrenamiento en atletas juveniles es esencial para optimizar el rendimiento y reducir el riesgo de lesiones; sin embargo, existe investigación limitada sobre cómo las características antropométricas influyen en la tolerancia a la carga en el bádminton. Este estudio investigó la asociación entre las medidas de carga de entrenamiento y los perfiles antropométricos en jugadores juveniles competitivos. Métodos: Participaron cincuenta atletas, hombres y mujeres, con la carga externa registrada mediante sensores acelerométricos y evaluaciones antropométricas realizadas bajo protocolos estandarizados. Se aplicó el algoritmo de clustering Louvain para clasificar a los jugadores en diferentes grupos de carga, mientras que la regresión logística multinomial (RLM) identificó los predictores clave de la clasificación de carga. Resultados: El clustering Louvain reveló tres grupos de carga distintos: Alta (HL), Moderada (ML) y Baja (LL), reflejando patrones naturales en la distribución de la carga externa. El análisis de RLM mostró que la estatura, el peso corporal y la longitud de pierna fueron predictores significativos de la clasificación. Los jugadores más altos y pesados tendieron a pertenecer al grupo HL, mientras que una mayor longitud de pierna se asoció positivamente con la clasificación ML, posiblemente vinculada a la mecánica de zancada y la economía del movimiento. Otras medidas circunferenciales (cintura, cadera, perímetro braquial medio) tuvieron un impacto mínimo, y los años de experiencia no predijeron significativamente la tolerancia a la carga. Conclusión: Estos hallazgos subrayan el valor de combinar técnicas de clustering basadas en redes con modelos multivariados para capturar interacciones complejas en la carga del atleta. En la práctica, los resultados sugieren que ciertos rasgos antropométricos, particularmente la estatura, la masa corporal y la longitud de las extremidades, desempeñan un papel importante en la capacidad de los atletas para sostener cargas de entrenamiento. La integración de evaluaciones antropométricas individualizadas en el monitoreo de carga puede respaldar estrategias de entrenamiento basadas en evidencia que potencien el rendimiento y reduzcan el riesgo de lesiones en jugadores juveniles de bádminton. Enquadramento: A monitorização da carga de treino em atletas juvenis é essencial para otimizar o desempenho e reduzir o risco de lesões; No entanto, existe uma investigação limitada sobre a forma como as características antropométricas influenciam a tolerância à carga no badminton. Este estúdio investigou a associação entre as medidas de carga de treino e os perfis antropométricos em jugadores juvenis de competição. Métodos: Participação de cinco atletas, homens e mulheres, com carga externa registada através de sensores acelerométricos e avaliações antropométricas realizadas sob protocolos padronizados. Se aplicou o algoritmo de clustering Louvain para classificar os jogadores em diferentes grupos de carga, enquanto a regressão logística multinomial (RLM) identificava os preditores chave da classificação de carga. Resultados: O cluster Louvain revelou três grupos de carga distintos: Alta (HL), Moderada (ML) e Baja (LL), refletindo padrões naturais na distribuição da carga externa. A análise de RLM mostrou que a estatura, o peso corporal e a longitude de piercing foram preditores significativos da classificação. Os jogadores mais altos e pesados ​​​​tendiam a pertencer ao grupo HL, enquanto uma grande longitude de pedra se associava positivamente à classificação ML, possivelmente ligada à mecânica de zancada e à economia de movimento. Outras medidas circunferenciais (cintura, cadeia, perímetro braquial médio) tiveram um impacto mínimo, e os anos de experiência não predizeram significativamente a tolerância à carga. Conclusão: Estes hallazgos subrayan o valor de combinar técnicas de clustering baseadas em redes com modelos multivariados para capturar interações complexas na carga do atleta. Na prática, os resultados sugerem que certos rasgos antropométricos, especialmente a estatura, a massa corporal e a longitude das extremidades, desempenham um papel importante na capacidade dos atletas para sustentar as cargas de treino. A integração de avaliações antropométricas individualizadas na monitorização da carga pode suportar estratégias de treino baseadas na evidência de que potencia o desempenho e reduz o risco de lesão em jogadores de badminton juvenis.

The role of firefighters has evolved from traditional tasks like rescuing cats from trees and extinguishing house fires to more complex land, sea, and air rescues. The increasing demands for public safety necessitate rigorous training and high fitness levels for firefighters to manage their daily tasks effectively. In this study, final assessments of fitness and anthropometric parameters were gathered from 746 Malaysian firefighter recruits. A k-means clustering algorithm was utilized to group the performance levels of the firefighters whilst a quadratic discriminant analysis model was employed to predict the grouping of firefighters based on these parameters. Feature importance analysis was used to identify the most significant parameters contributing to model performance. Concurrently, the Mann-Whitney test was used to determine the essential anthro-fitness parameters differentiating between the groups of firefighters. The k-means clustering identified two performance groups: excellent and average anthro-fitness readiness (EFR and AFR) groups. The model demonstrated a mean performance accuracy of 91% for training and 87% for independent tests. Feature importance analysis revealed that inclined pull-ups, standing broad jump, shuttle run, 2.4 km run, age, and sit-ups were the most significant parameters. The Mann-Whitney test showed that the EFR group outperformed the AFR group in all anthro-fitness parameters except for height, weight, and age, which showed no significant difference. This study highlights the critical role of specific fitness and anthropometric parameters in distinguishing high-performing firefighters. By identifying the most significant contributors to overall fitness, fire departments can better prepare their personnel to meet the increasing public safety demands. The high accuracy of the predictive model also suggests its potential application in ongoing firefighter assessments and training optimization.

Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not only limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of publications over the past two decades, further indicates the consistent improvements, as well as breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG) based BCI system is been deliberated. Secondly, a considerable number of popular BCI applications are reviewed in terms of its electrophysiological control signals, feature extraction, classification algorithms as well as the performance evaluation metrics. Finally, the challenges to the recent BCI system are discussed, and the possible solutions to mitigate the issues are recommended.

This study aims at classifying flat ground tricks, namely Ollie, Kickflip, Shove-it, Nollie and Frontside 180, through the identification of significant input image transformation on different transfer learning models with optimized Support Vector Machine (SVM) classifier. A total of six amateur skateboarders (20 ± 7 years of age with at least 5.0 years of experience) executed five tricks for each type of trick repeatedly on a customized ORY skateboard (IMU sensor fused) on a cemented ground. From the IMU data, a total of six raw signals extracted. A total of two input image type, namely raw data (RAW) and Continous Wavelet Transform (CWT), as well as six transfer learning models from three different families along with grid-searched optimized SVM, were investigated towards its efficacy in classifying the skateboarding tricks. It was shown from the study that RAW and CWT input images on MobileNet, MobileNetV2 and ResNet101 transfer learning models demonstrated the best test accuracy at 100% on the test dataset. Nonetheless, by evaluating the computational time amongst the best models, it was established that the CWT-MobileNet-Optimized SVM pipeline was found to be the best. It could be concluded that the proposed method is able to facilitate the judges as well as coaches in identifying skateboarding tricks execution.

The rice leaves related diseases often pose threats to the sustainable production of rice affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice leaf infection is crucial in facilitating healthy growth of the rice plants to ensure adequate supply and food security to the rapidly increasing population. Therefore, machine-driven disease diagnosis systems could mitigate the limitations of the conventional methods for leaf disease diagnosis techniques that is often time-consuming, inaccurate, and expensive. Nowadays, computer-assisted rice leaf disease diagnosis systems are becoming very popular. However, several limitations ranging from strong image backgrounds, vague symptoms’ edge, dissimilarity in the image capturing weather, lack of real field rice leaf image data, variation in symptoms from the same infection, multiple infections producing similar symptoms, and lack of efficient real-time system mar the efficacy of the system and its usage. To mitigate the aforesaid problems, a faster region-based convolutional neural network (Faster R-CNN) was employed for the real-time detection of rice leaf diseases in the present research. The Faster R-CNN algorithm introduces advanced RPN architecture that addresses the object location very precisely to generate candidate regions. The robustness of the Faster R-CNN model is enhanced by training the model with publicly available online and own real-field rice leaf datasets. The proposed deep-learning-based approach was observed to be effective in the automatic diagnosis of three discriminative rice leaf diseases including rice blast, brown spot, and hispa with an accuracy of 98.09%, 98.85%, and 99.17% respectively. Moreover, the model was able to identify a healthy rice leaf with an accuracy of 99.25%. The results obtained herein demonstrated that the Faster R-CNN model offers a high-performing rice leaf infection identification system that could diagnose the most common rice diseases more precisely in real-time.