Displaying publications 21 - 40 of 1459 in total

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  1. Fulltext Master-Leader-Slave Cuckoo Search with Parameter Control for ANN Optimization and Its Real-World Application to Water Quality Prediction
    Jaddi NS, Abdullah S, Abdul Malek M
    PLoS One, 2017;12(1):e0170372.
    PMID: 28125609 DOI: 10.1371/journal.pone.0170372
    Artificial neural networks (ANNs) have been employed to solve a broad variety of tasks. The selection of an ANN model with appropriate weights is important in achieving accurate results. This paper presents an optimization strategy for ANN model selection based on the cuckoo search (CS) algorithm, which is rooted in the obligate brood parasitic actions of some cuckoo species. In order to enhance the convergence ability of basic CS, some modifications are proposed. The fraction Pa of the n nests replaced by new nests is a fixed parameter in basic CS. As the selection of Pa is a challenging issue and has a direct effect on exploration and therefore on convergence ability, in this work the Pa is set to a maximum value at initialization to achieve more exploration in early iterations and it is decreased during the search to achieve more exploitation in later iterations until it reaches the minimum value in the final iteration. In addition, a novel master-leader-slave multi-population strategy is used where the slaves employ the best fitness function among all slaves, which is selected by the leader under a certain condition. This fitness function is used for subsequent Lévy flights. In each iteration a copy of the best solution of each slave is migrated to the master and then the best solution is found by the master. The method is tested on benchmark classification and time series prediction problems and the statistical analysis proves the ability of the method. This method is also applied to a real-world water quality prediction problem with promising results.
    Matched MeSH terms: Algorithms
  2. Lensed optical fiber sensors for on-line measurement of flow
    Ibrahim S, Green RG, Dutton K, Abdul Rahim R
    ISA Trans, 2002 Jan;41(1):13-8.
    PMID: 12014798
    This paper describes a system using lensed optical fiber sensors that are arranged in the form of two orthogonal projections. The sensors are placed around a process vessel for upstream and downstream measurements. The purpose of the system is for on-line monitoring of particles and droplets being conveyed by a fluid. The lenses were constructed using a custom heating fixture. The fixture enables the lenses to be constructed with similar radii resulting in identical characteristics with minimum differences in transmitted intensity and emission angle. By collimating radiation from two halogen bulbs, radiation can be obtained by the sensors with radiation intensity related to the nature of the media. Each sensor interrogates a finite section of the measurement section. Each sensor provides a view. Parallel sensors provide a projection. Signal processing is carried out on the measured data in the time and frequency domains to investigate the latent information present in the flow signals.
    Matched MeSH terms: Algorithms
  3. Application of artificial neural network for yield prediction of lipase-catalyzed synthesis of dioctyl adipate
    Abdul Rahman MB, Chaibakhsh N, Basri M, Salleh AB, Abdul Rahman RN
    Appl Biochem Biotechnol, 2009 Sep;158(3):722-35.
    PMID: 19132557 DOI: 10.1007/s12010-008-8465-z
    In this study, an artificial neural network (ANN) trained by backpropagation algorithm, Levenberg-Marquadart, was applied to predict the yield of enzymatic synthesis of dioctyl adipate. Immobilized Candida antarctica lipase B was used as a biocatalyst for the reaction. Temperature, time, amount of enzyme, and substrate molar ratio were the four input variables. After evaluating various ANN configurations, the best network was composed of seven hidden nodes using a hyperbolic tangent sigmoid transfer function. The correlation coefficient (R2) and mean absolute error (MAE) values between the actual and predicted responses were determined as 0.9998 and 0.0966 for training set and 0.9241 and 1.9439 for validating dataset. A simulation test with a testing dataset showed that the MAE was low and R2 was close to 1. These results imply the good generalization of the developed model and its capability to predict the reaction yield. Comparison of the performance of radial basis network with the developed models showed that radial basis function was more accurate but its performance was poor when tested with unseen data. In further part of the study, the feedforward backpropagation model was used for prediction of the ester yield within the given range of the main parameters.
    Matched MeSH terms: Algorithms
  4. Skin colour detection based on an adaptive multi thresholding technique
    Ahmed M. Mbarib, Mohammad Hamiruce Marhaban, Abdul Rahman Ramli
    Pertanika Journal of Science & Technology, 2007;15(2):103-111.
    MyJurnal
    Skin colour is an important visual cue for face detection, face recogmtlon, hand segmentation for gesture analysis and filtering of objectionable images. In this paper, the adaptive skin color detection model is proposed, based on two bivariate normal distribution models of the skin chromatic subspace, and on image segmentation using an automatic and adaptive multi-thresholding technique. Experimental results on images presenting a wide range of variations in lighting condition and background demonstrate the efficiency of the proposed skin-segmentation algorithm.
    Matched MeSH terms: Algorithms
  5. Multiobjective optimization to reconstruct biological networks
    Naef A, Abdullah R, Abdul Rashid N
    Biosystems, 2018 Sep 17;174:22-36.
    PMID: 30236951 DOI: 10.1016/j.biosystems.2018.09.003
    Automated methods for reconstructing biological networks are becoming increasingly important in computational systems biology. Public databases containing information on biological processes for hundreds of organisms are assisting in the inference of such networks. This paper proposes a multiobjective genetic algorithm method to reconstruct networks related to metabolism and protein interaction. Such a method utilizes structural properties of scale-free networks and known biological information about individual genes and proteins to reconstruct metabolic networks represented as enzyme graph and protein interaction networks. We test our method on four commonly-used protein networks in yeast. Two are networks related to the metabolism of the yeast: KEGG and BioCyc. The other two datasets are networks from protein-protein interaction: Krogan and BioGrid. Experimental results show that the proposed method is capable of reconstructing biological networks by combining different omics data and structural characteristics of scale-free networks. However, the proposed method to reconstruct the network is time-consuming because several evaluations must be performed. We parallelized this method on GPU to overcome this limitation by parallelizing the objective functions of the presented method. The parallel method shows a significant reduction in the execution time over the GPU card which yields a 492-fold speedup.
    Matched MeSH terms: Algorithms
  6. Fulltext Adaptive Neuro-Fuzzy Determination of the Effect of Experimental Parameters on Vehicle Agent Speed Relative to Vehicle Intruder
    Shamshirband S, Banjanovic-Mehmedovic L, Bosankic I, Kasapovic S, Abdul Wahab AW
    PLoS One, 2016;11(5):e0155697.
    PMID: 27219539 DOI: 10.1371/journal.pone.0155697
    Intelligent Transportation Systems rely on understanding, predicting and affecting the interactions between vehicles. The goal of this paper is to choose a small subset from the larger set so that the resulting regression model is simple, yet have good predictive ability for Vehicle agent speed relative to Vehicle intruder. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data resulting from these measurements. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the prediction of agent speed relative to intruder. This process includes several ways to discover a subset of the total set of recorded parameters, showing good predictive capability. The ANFIS network was used to perform a variable search. Then, it was used to determine how 9 parameters (Intruder Front sensors active (boolean), Intruder Rear sensors active (boolean), Agent Front sensors active (boolean), Agent Rear sensors active (boolean), RSSI signal intensity/strength (integer), Elapsed time (in seconds), Distance between Agent and Intruder (m), Angle of Agent relative to Intruder (angle between vehicles °), Altitude difference between Agent and Intruder (m)) influence prediction of agent speed relative to intruder. The results indicated that distance between Vehicle agent and Vehicle intruder (m) and angle of Vehicle agent relative to Vehicle Intruder (angle between vehicles °) is the most influential parameters to Vehicle agent speed relative to Vehicle intruder.
    Matched MeSH terms: Algorithms
  7. Optimizing neural network algorithms for submerged membrane bioreactor: A comparative study of OVAT and RSM hyperparameter optimization techniques
    Ibrahim S, Abdul Wahab N
    Water Sci Technol, 2024 Apr;89(7):1701-1724.
    PMID: 38619898 DOI: 10.2166/wst.2024.099
    Hyperparameter tuning is an important process to maximize the performance of any neural network model. This present study proposed the factorial design of experiment for screening and response surface methodology to optimize the hyperparameter of two artificial neural network algorithms. Feed-forward neural network (FFNN) and radial basis function neural network (RBFNN) are applied to predict the permeate flux of palm oil mill effluent. Permeate pump and transmembrane pressure of the submerge membrane bioreactor system are the input variables. Six hyperparameters of the FFNN model including four numerical factors (neuron numbers, learning rate, momentum, and epoch numbers) and two categorical factors (training and activation function) are used in hyperparameter optimization. RBFNN includes two numerical factors such as a number of neurons and spreads. The conventional method (one-variable-at-a-time) is compared in terms of optimization processing time and the accuracy of the model. The result indicates that the optimal hyperparameters obtained by the proposed approach produce good accuracy with a smaller generalization error. The simulation results show an improvement of more than 65% of training performance, with less repetition and processing time. This proposed methodology can be utilized for any type of neural network application to find the optimum levels of different parameters.
    Matched MeSH terms: Algorithms*
  8. New modified algorithm: θ-turbulent flow of water-based optimization
    Naderipour A, Davoudkhani IF, Abdul-Malek Z
    Environ Sci Pollut Res Int, 2023 Jun;30(28):71726-71740.
    PMID: 34472027 DOI: 10.1007/s11356-021-16072-x
    The reactive power control of a power system is discussed under two types of variables: continuous variables (e.g., generator bus voltages) and discrete variables (e.g., transformer taps and the size of switched shunt capacitors). This paper proposes a novel and powerful algorithm, named turbulent flow of water-based optimization (TFWO) as well as a new improved version of this algorithm, called θ-TFWO, for optimal reactive power distribution (ORPD) to reduce losses. The proposed method is applied to two large-scale IEEE 57-bus systems. Furthermore, to demonstrate the competitive performance of the suggested algorithm, its performance was compared to that of several other algorithms, including biogeography-based optimization (BBO), social spider algorithm (SSA), and optics inspired optimization (OIO), in terms of solving the ORPD problem. The results confirmed the robustness and effectiveness of the proposed method as a powerful optimizer applicable to optimal reactive power distribution in power systems.
    Matched MeSH terms: Algorithms*
  9. Fulltext Emergency traffic adaptive MAC protocol for wireless body area networks based on prioritization
    Masud F, Abdullah AH, Abdul-Salaam G
    PLoS One, 2019;14(12):e0225518.
    PMID: 31790457 DOI: 10.1371/journal.pone.0225518
    This paper proposes an emergency Traffic Adaptive MAC (eTA-MAC) protocol for WBANs based on Prioritization. The main advantage of the protocol is to provide traffic ranking through a Traffic Class Prioritization-based slotted-Carrier Sense Multiple Access/Collision Avoidance (TCP-CSMA/CA) scheme. The emergency traffic is handled through Emergency Traffic Class Provisioning-based slotted-CSMA/CA (ETCP-CSMA/CA) scheme. The emergency-based traffic adaptivity is provided through Emergency-based Traffic Adaptive slotted-CSMA/CA (ETA-CSMA/CA) scheme. The TCP-CSMA/CA scheme assigns a distinct, minimized and prioritized backoff period range to each traffic class in every backoff during channel access in Contention Access Period (CAP). The ETCP-CSMA/CA scheme delivers the sporadic emergency traffic that occurs at a single or multiple BMSN(s) instantaneously, with minimum delay and packet loss. It does this while being aware of normal traffic in the CAP. Then, the ETA-CSMA/CA scheme creates a balance between throughput and energy in the sporadic emergency situation with energy preservation of normal traffic BMSNs. The proposed protocol is evaluated using NS-2 simulator. The results indicate that the proposed protocol is better than the existing Medium Access Control (MAC) protocols by 86% decrease in packet delivery delay, 61% increase in throughput, and a 76% decrease in energy consumption.
    Matched MeSH terms: Algorithms
  10. Fulltext An Efficacious Multi-Objective Fuzzy Linear Programming Approach for Optimal Power Flow Considering Distributed Generation
    Warid W, Hizam H, Mariun N, Abdul-Wahab NI
    PLoS One, 2016;11(3):e0149589.
    PMID: 26954783 DOI: 10.1371/journal.pone.0149589
    This paper proposes a new formulation for the multi-objective optimal power flow (MOOPF) problem for meshed power networks considering distributed generation. An efficacious multi-objective fuzzy linear programming optimization (MFLP) algorithm is proposed to solve the aforementioned problem with and without considering the distributed generation (DG) effect. A variant combination of objectives is considered for simultaneous optimization, including power loss, voltage stability, and shunt capacitors MVAR reserve. Fuzzy membership functions for these objectives are designed with extreme targets, whereas the inequality constraints are treated as hard constraints. The multi-objective fuzzy optimal power flow (OPF) formulation was converted into a crisp OPF in a successive linear programming (SLP) framework and solved using an efficient interior point method (IPM). To test the efficacy of the proposed approach, simulations are performed on the IEEE 30-busand IEEE 118-bus test systems. The MFLP optimization is solved for several optimization cases. The obtained results are compared with those presented in the literature. A unique solution with a high satisfaction for the assigned targets is gained. Results demonstrate the effectiveness of the proposed MFLP technique in terms of solution optimality and rapid convergence. Moreover, the results indicate that using the optimal DG location with the MFLP algorithm provides the solution with the highest quality.
    Matched MeSH terms: Algorithms
  11. Fulltext A newton cooperative genetic algorithm method for in silico optimization of metabolic pathway production
    Ismail MA, Deris S, Mohamad MS, Abdullah A
    PLoS One, 2015;10(5):e0126199.
    PMID: 25961295 DOI: 10.1371/journal.pone.0126199
    This paper presents an in silico optimization method of metabolic pathway production. The metabolic pathway can be represented by a mathematical model known as the generalized mass action model, which leads to a complex nonlinear equations system. The optimization process becomes difficult when steady state and the constraints of the components in the metabolic pathway are involved. To deal with this situation, this paper presents an in silico optimization method, namely the Newton Cooperative Genetic Algorithm (NCGA). The NCGA used Newton method in dealing with the metabolic pathway, and then integrated genetic algorithm and cooperative co-evolutionary algorithm. The proposed method was experimentally applied on the benchmark metabolic pathways, and the results showed that the NCGA achieved better results compared to the existing methods.
    Matched MeSH terms: Algorithms*
  12. Fulltext Distinctive accuracy measurement of binary descriptors in mobile augmented reality
    Tan SY, Arshad H, Abdullah A
    PLoS One, 2019;14(1):e0207191.
    PMID: 30605474 DOI: 10.1371/journal.pone.0207191
    Mobile Augmented Reality (MAR) requires a descriptor that is robust to changes in viewing conditions in real time application. Many different descriptors had been proposed in the literature for example floating-point descriptors (SIFT and SURF) and binary descriptors (BRIEF, ORB, BRISK and FREAK). According to literature, floating-point descriptors are not suitable for real-time application because its operating speed does not satisfy real-time constraints. Binary descriptors have been developed with compact sizes and lower computation requirements. However, it is unclear which binary descriptors are more appropriate for MAR. Hence, a distinctive and efficient accuracy measurement of four state-of-the-art binary descriptors, namely, BRIEF, ORB, BRISK and FREAK were performed using the Mikolajczyk dataset and ALOI dataset to identify the most appropriate descriptor for MAR in terms of computation time and robustness to brightness, scale and rotation changes. The obtained results showed that FREAK is the most appropriate descriptor for MAR application as it able to produce an application that are efficient (shortest computation time) and robust towards scale, rotation and brightness changes.
    Matched MeSH terms: Algorithms*
  13. Enhancement of seminal stains using background correction algorithm with colour filters
    Lee WC, Khoo BE, Abdullah AFL
    Forensic Sci Int, 2016 06;263:1-9.
    PMID: 27061146 DOI: 10.1016/j.forsciint.2016.03.046
    Evidence in crime scenes available in the form of biological stains which cannot be visualized during naked eye examination can be detected by imaging their fluorescence using a combination of excitation lights and suitable filters. These combinations selectively allow the passage of fluorescence light emitted from the targeted stains. However, interference from the fluorescence generated by many of the surface materials bearing the stains often renders it difficult to visualize the stains during forensic photography. This report describes the use of background correction algorithm (BCA) to enhance the visibility of seminal stain, a biological evidence that fluoresces. While earlier reports described the use of narrow band-pass filters for other fluorescing evidences, here, we utilize BCA to enhance images captured using commonly available colour filters, yellow, orange and red. Mean-based contrast adjustment was incorporated into BCA to adjust the background brightness for achieving similarity of images' background appearance, a crucial step for ensuring success while implementing BCA. Experiment results demonstrated the effectiveness of our proposed colour filters' approach using the improved BCA in enhancing the visibility of seminal stains in varying dilutions on selected surfaces.
    Matched MeSH terms: Algorithms
  14. A robust background correction algorithm for forensic bloodstain imaging using mean-based contrast adjustment
    Lee WC, Khoo BE, Abdullah AFL
    Sci Justice, 2016 May;56(3):201-209.
    PMID: 27162018 DOI: 10.1016/j.scijus.2016.01.001
    Background correction algorithm (BCA) is useful in enhancing the visibility of images captured in crime scenes especially those of untreated bloodstains. Successful implementation of BCA requires all the images to have similar brightness which often proves a problem when using automatic exposure setting in a camera. This paper presents an improved background correction algorithm (BCA) that applies mean-based contrast adjustment as a pre-correction step to adjust the mean brightness of images to be similar before implementing BCA. The proposed modification, namely mean-based adaptive BCA (mABCA) was tested on various image samples captured under different illuminations such as 385 nm, 415 nm and 458 nm. We also evaluated mABCA of two wavelengths (415 nm and 458 nm) and three wavelengths (415 nm, 380 nm and 458 nm) in enhancing untreated bloodstains on different surfaces. The proposed mABCA is found to be more robust in processing images captured in different brightness and thus overcomes the main issue faced in the original BCA.
    Matched MeSH terms: Algorithms*
  15. Fulltext Delaunay triangulation as a new coverage measurement method in wireless sensor network
    Chizari H, Hosseini M, Poston T, Razak SA, Abdullah AH
    Sensors (Basel), 2011;11(3):3163-76.
    PMID: 22163792 DOI: 10.3390/s110303163
    Sensing and communication coverage are among the most important trade-offs in Wireless Sensor Network (WSN) design. A minimum bound of sensing coverage is vital in scheduling, target tracking and redeployment phases, as well as providing communication coverage. Some methods measure the coverage as a percentage value, but detailed information has been missing. Two scenarios with equal coverage percentage may not have the same Quality of Coverage (QoC). In this paper, we propose a new coverage measurement method using Delaunay Triangulation (DT). This can provide the value for all coverage measurement tools. Moreover, it categorizes sensors as 'fat', 'healthy' or 'thin' to show the dense, optimal and scattered areas. It can also yield the largest empty area of sensors in the field. Simulation results show that the proposed DT method can achieve accurate coverage information, and provides many tools to compare QoC between different scenarios.
    Matched MeSH terms: Algorithms*
  16. Data-Centric Routing for Intra Wireless Body Sensor Networks
    Bangash JI, Khan AW, Abdullah AH
    J Med Syst, 2015 Sep;39(9):91.
    PMID: 26242749 DOI: 10.1007/s10916-015-0268-5
    A significant proportion of the worldwide population is of the elderly people living with chronic diseases that result in high health-care cost. To provide continuous health monitoring with minimal health-care cost, Wireless Body Sensor Networks (WBSNs) has been recently emerged as a promising technology. Depending on nature of sensory data, WBSNs might require a high level of Quality of Service (QoS) both in terms of delay and reliability during data reporting phase. In this paper, we propose a data-centric routing for intra WBSNs that adapts the routing strategy in accordance with the nature of data, temperature rise issue of the implanted bio-medical sensors due to electromagnetic wave absorption, and high and dynamic path loss caused by postural movement of human body and in-body wireless communication. We consider the network models both with and without relay nodes in our simulations. Due to the multi-facet routing strategy, the proposed data-centric routing achieves better performance in terms of delay, reliability, temperature rise, and energy consumption when compared with other state-of-the-art.
    Matched MeSH terms: Algorithms
  17. A proposed hierarchical fuzzy inference system for the diagnosis of arthritic diseases
    Lim CK, Yew KM, Ng KH, Abdullah BJ
    Australas Phys Eng Sci Med, 2002 Sep;25(3):144-50.
    PMID: 12416592 DOI: 10.1007/BF03178776
    Development of computer-based medical inference systems is always confronted with some difficulties. In this paper, difficulties of designing an inference system for the diagnosis of arthritic diseases are described, including variations of disease manifestations under various situations and conditions. Furthermore, the need for a huge knowledge base would result in low efficiency of the inference system. We proposed a hierarchical model of the fuzzy inference system as a possible solution. With such a model, the diagnostic process is divided into two levels. The first level of the diagnosis reduces the scope of diagnosis to be processed by the second level. This will reduce the amount of input and mapping for the whole diagnostic process. Fuzzy relational theory is the core of this system and it is used in both levels to improve the accuracy.
    Matched MeSH terms: Algorithms*
  18. Modelling of methylene blue adsorption using peroxidase immobilized functionalized Buckypaper/polyvinyl alcohol membrane via ant colony optimization
    Jun LY, Karri RR, Mubarak NM, Yon LS, Bing CH, Khalid M, et al.
    Environ Pollut, 2020 Apr;259:113940.
    PMID: 31931415 DOI: 10.1016/j.envpol.2020.113940
    Jicama peroxidase (JP) was covalently immobilized onto functionalized multi-walled carbon nanotube (MWCNT) Buckypaper/Polyvinyl alcohol (BP/PVA) membrane and employed for degradation of methylene blue dye. The parameters of the isotherm and kinetic models are estimating using ant colony optimization (ACO), which do not meddle the non-linearity form of the respective models. The proposed inverse modelling through ACO optimization was implemented, and the parameters were evaluated to minimize the non-linear error functions. The adsorption of MB dye onto JP-immobilized BP/PVA membrane follows Freundlich isotherm model (R2 = 0.99) and the pseudo 1st order or 2nd kinetic model (R2 = 0.980 & 0.968 respectively). The model predictions from the parameters estimated by ACO resulted values close the experimental values, thus inferring that this approach captured the inherent characteristics of MB adsorption. Moreover, the thermodynamic studies indicated that the adsorption was favourable, spontaneous, and exothermic in nature. The comprehensive structural analyses have confirmed the successful binding of peroxidase onto BP/PVA membrane, as well as the effective MB dye removal using immobilized JP membrane. Compared to BP/PVA membrane, the reusability test revealed that JP-immobilized BP/PVA membrane has better dye removal performances as it can retain 64% of its dye removal efficiency even after eight consecutive cycles. Therefore, the experimental results along with modelling results demonstrated that JP-immobilized BP/PVA membrane is expected to bring notable impacts for the development of effective green and sustainable wastewater treatment technologies.
    Matched MeSH terms: Algorithms
  19. Removal of BCG artefact from concurrent fMRI-EEG recordings based on EMD and PCA
    Javed E, Faye I, Malik AS, Abdullah JM
    J Neurosci Methods, 2017 11 01;291:150-165.
    PMID: 28842191 DOI: 10.1016/j.jneumeth.2017.08.020
    BACKGROUND: Simultaneous electroencephalography (EEG) and functional magnetic resonance image (fMRI) acquisitions provide better insight into brain dynamics. Some artefacts due to simultaneous acquisition pose a threat to the quality of the data. One such problematic artefact is the ballistocardiogram (BCG) artefact.

    METHODS: We developed a hybrid algorithm that combines features of empirical mode decomposition (EMD) with principal component analysis (PCA) to reduce the BCG artefact. The algorithm does not require extra electrocardiogram (ECG) or electrooculogram (EOG) recordings to extract the BCG artefact.

    RESULTS: The method was tested with both simulated and real EEG data of 11 participants. From the simulated data, the similarity index between the extracted BCG and the simulated BCG showed the effectiveness of the proposed method in BCG removal. On the other hand, real data were recorded with two conditions, i.e. resting state (eyes closed dataset) and task influenced (event-related potentials (ERPs) dataset). Using qualitative (visual inspection) and quantitative (similarity index, improved normalized power spectrum (INPS) ratio, power spectrum, sample entropy (SE)) evaluation parameters, the assessment results showed that the proposed method can efficiently reduce the BCG artefact while preserving the neuronal signals.

    COMPARISON WITH EXISTING METHODS: Compared with conventional methods, namely, average artefact subtraction (AAS), optimal basis set (OBS) and combined independent component analysis and principal component analysis (ICA-PCA), the statistical analyses of the results showed that the proposed method has better performance, and the differences were significant for all quantitative parameters except for the power and sample entropy.

    CONCLUSIONS: The proposed method does not require any reference signal, prior information or assumption to extract the BCG artefact. It will be very useful in circumstances where the reference signal is not available.

    Matched MeSH terms: Algorithms*
  20. Fulltext A machine learning approach of predicting high potential archers by means of physical fitness indicators
    Muazu Musa R, P P Abdul Majeed A, Taha Z, Chang SW, Ab Nasir AF, Abdullah MR
    PLoS One, 2019;14(1):e0209638.
    PMID: 30605456 DOI: 10.1371/journal.pone.0209638
    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.
    Matched MeSH terms: Algorithms
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