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 limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and 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 systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.

Nonetheless, research that integrates physical fitness and goal orientation as a relative performance and uses them as a criterion predictor in the selection of potential athletes are considerably limited. Thus, this study aims to examine the relative performance quality pattern of 223 male athletes that trains under Terengganu sports development program in relation to physical fitness and psychological components. MATERIALS AND METHODS Participants:The participants of the current study comprised of 223 male adolescent athletes (17.38 ± 1.92 years). The participants were recruited from the interstate championship with various types of sports from Terengganu state in Malaysia. These athletes represented their corresponding divisions in the competition and were reflected the best at the state level for the under 21 year's age groups. These age groups were chosen because of research suggesting that participation motives and achievement behavior changes around this age. 10 The measurements were carried out in the first three months of 2015. The coaches and the managers of the athletes were informed about the purpose of the research. Writing approval was obtained, and all the players signed consent forms. Procedure of Battery Tests: Anthropometric Test: Standard anthropometric testing was conducted which constitutes of height and weight with the subjects dressed in light clothing. Height was measured with a wallmounted wooden stadiometer to the nearest 0.5 cm. Body weight was evaluated with a standardized electronic digital scale to the nearest 0.1 kg. Body mass index (BMI) was measured as body mass in kilograms ABSTRACTObjective: This study investigates the relative performance quality pattern of athletes that trains under Terengganu sports development program based on physical fitness and psychological components. Methods: Relative performance data (223×7) were obtained from various types of sport, and its main tributaries were evaluated for physical fitness and TEOSQ instrument. Multivariate methods of hierarchical agglomerative cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), and principal factor analysis (PFA), were used to study the relative performance variations of the most significant performance quality variables and to determine the origin of relative performance components. Results: Three clusters of performance were shaped in view of HACA. Forward and backward stepwise DA discriminates six and five performance quality variables from the first seven variables. PCA and FA were used to identify the origin of each quality performance variables based on three clustered groups. Three PCs were obtained with 67% total variation for the highperformance group (HPG) region, three PCs with 72% and 64% total variances were obtained for the moderate-performance group (MPG) and low-performance group (LPG) regions, respectively. The general performance sources for the three groups are from cardiovascular and ego orientation sources. The differences between groups are from flexibi...

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.

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This study aims to identify the essential performance indicators in two level of soccer expertise. A total of 84 elite's soccer players and 100 novice players from eight soccer academies in Malaysia were enrolled and subjected to standard anthropometric, fitness, skills related performance testing and responded to the questionnaire in mastery and performance. Principal component analysis (PCA) was employed to determine the most indispensable variables pertinent to the requirement of the game in relation to the level of expertise of the players. The initial PCA shows seven components out of 26 as the most significant for both elite and novice soccer players with a considerable eigenvalue > 1. Moreover, the PCA after varimax rotation highlighted seven principles components (PCs) for elite and novice players respectively. Each of the seven components contained varifactors (VF) selected based on their higher factor loading and that distinguish the players on their expertise. The first PCs for elite's players revealed strong loading from sit and reach (0.780), vertical jump (0.635), VO 2max (0.637) and age (0.752). The second PCs revealed weight (0.639), biceps (0.859), triceps (0.769), subscapular (0.847), suprailiac (0.886) and middle upper arm circumference (0.776). The third PCs revealed 505 agility (0.618), 5m speed (0.712), 10m speed (0.858) and 20m speed (0.929). The forth PCs revealed task (-0.675) and short pass (0.789) and the last PCs revealed sit up (-0.702). For novice's players, the first PCs revealed vertical jump (0.624), weight (0.861), height (0.856), sitting height (0.632), middle upper arm circumference (0.673), calf circumference (0.790) and maturity (0.651). The second PCs revealed biceps (0.832), triceps (0.899), subscapular (0.816) and suprailiac (0.869). The third PCs revealed 5m speed (-0.847), 10m speed (-0.877), 20m speed (-0.785) and VO 2max (0.658). The forth PCs revealed task (0.694) and ego (0.747) and the last PCs revealed short pass (0.766).

This study aims to predict the potential pattern of soccer technical skill on Malaysia youth soccer players relative performance using multivariate analysis and artificial neural network techniques. 184 male youth soccer players were recruited in Malaysia soccer academy (average age = 15.2±2.0) underwent to, physical fitness test, anthropometric, maturity, motivation and the level of skill related soccer. Unsupervised pattern recognition of principal component analysis (PCA) was used to identify the most significant parameters in soccer for the current study and intelligent prediction of artificial neural network (ANN) was developed to determine its predictive ability for the soccer relative performance index (SRPI). The PCA has indicated sit up, agility, 5m speed, 10m speed, 20m speed, weight, height, sitting height, bicep, tricep, subscapular, suprailiac, calf circumference, maturity, task, ego, short pass, shooting right top corner and shooting left top corner are the most significant parameters in soccer. Meanwhile, the PCA-ANN showed better predictive ability in the determination of SRPI with fewer parameters such as R 2 and root mean square error (RMSE) values of 0.922 and 0.190, respectively. The current study indicated that only a few parameters are needed to improve and enhanced the performance of novice group. Nevertheless, the prediction method techniques for the present study show very high and strong ability in prediction of the player's performance. It has highlighted the possibility of defining the optimum number of parameters for the player's relative performance evaluation, which in turn will reduce the costs, energy and time of the measurement.

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