Displaying publications 41 - 60 of 1459 in total

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  1. The identification of high potential archers based on fitness and motor ability variables: A Support Vector Machine approach
    Taha Z, Musa RM, P P Abdul Majeed A, Alim MM, Abdullah MR
    Hum Mov Sci, 2018 Feb;57:184-193.
    PMID: 29248809 DOI: 10.1016/j.humov.2017.12.008
    Support Vector Machine (SVM) has been shown to be an effective learning algorithm for classification and prediction. However, the application of SVM for prediction and classification in specific sport has rarely been used to quantify/discriminate low and high-performance athletes. The present study classified and predicted high and low-potential archers from a set of fitness and motor ability variables trained on different SVMs kernel algorithms. 50 youth archers with the mean age and standard deviation of 17.0 ± 0.6 years drawn from various archery programmes completed a six arrows shooting score test. Standard fitness and ability measurements namely hand grip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle strength were also recorded. Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the performance variables tested. SVM models with linear, quadratic, cubic, fine RBF, medium RBF, as well as the coarse RBF kernel functions, were trained based on the measured performance variables. The HACA clustered the archers into high-potential archers (HPA) and low-potential archers (LPA), respectively. The linear, quadratic, cubic, as well as the medium RBF kernel functions models, demonstrated reasonably excellent classification accuracy of 97.5% and 2.5% error rate for the prediction of the HPA and the LPA. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from a combination of the selected few measured fitness and motor ability performance variables examined which would consequently save cost, time and effort during talent identification programme.
    Matched MeSH terms: Algorithms
  2. Modelling complex features from histone modification signatures using genetic algorithm for the prediction of enhancer region
    Lee NK, Fong PK, Abdullah MT
    Biomed Mater Eng, 2014;24(6):3807-14.
    PMID: 25227097 DOI: 10.3233/BME-141210
    Using Genetic Algorithm, this paper presents a modelling method to generate novel logical-based features from DNA sequences enriched with H3K4mel histone signatures. Current histone signature is mostly represented using k-mers content features incapable of representing all the possible complex interactions of various DNA segments. The main contributions are, among others: (a) demonstrating that there are complex interactions among sequence segments in the histone regions; (b) developing a parse tree representation of the logical complex features. The proposed novel feature is compared to the k-mers content features using datasets from the mouse (mm9) genome. Evaluation results show that the new feature improves the prediction performance as shown by f-measure for all datasets tested. Also, it is discovered that tree-based features generated from a single chromosome can be generalized to predict histone marks in other chromosomes not used in the training. These findings have a great impact on feature design considerations for histone signatures as well as other classifier design features.
    Matched MeSH terms: Algorithms*
  3. Fulltext Optimizing supercritical antisolvent process parameters to minimize the particle size of paracetamol nanoencapsulated in L-polylactide
    Kalani M, Yunus R, Abdullah N
    Int J Nanomedicine, 2011;6:1101-5.
    PMID: 21698077 DOI: 10.2147/IJN.S18979
    The aim of this study was to optimize the different process parameters including pressure, temperature, and polymer concentration, to produce fine small spherical particles with a narrow particle size distribution using a supercritical antisolvent method for drug encapsulation. The interaction between different process parameters was also investigated.
    Matched MeSH terms: Algorithms
  4. Fulltext Detecting opinion spams through supervised boosting approach
    Hazim M, Anuar NB, Ab Razak MF, Abdullah NA
    PLoS One, 2018;13(6):e0198884.
    PMID: 29889897 DOI: 10.1371/journal.pone.0198884
    Product reviews are the individual's opinions, judgement or belief about a certain product or service provided by certain companies. Such reviews serve as guides for these companies to plan and monitor their business ventures in terms of increasing productivity or enhancing their product/service qualities. Product reviews can also increase business profits by convincing future customers about the products which they have interest in. In the mobile application marketplace such as Google Playstore, reviews and star ratings are used as indicators of the application quality. However, among all these reviews, hereby also known as opinions, spams also exist, to disrupt the online business balance. Previous studies used the time series and neural network approach (which require a lot of computational power) to detect these opinion spams. However, the detection performance can be restricted in terms of accuracy because the approach focusses on basic, discrete and document level features only thereby, projecting little statistical relationships. Aiming to improve the detection of opinion spams in mobile application marketplace, this study proposes using statistical based features that are modelled through the supervised boosting approach such as the Extreme Gradient Boost (XGBoost) and the Generalized Boosted Regression Model (GBM) to evaluate two multilingual datasets (i.e. English and Malay language). From the evaluation done, it was found that the XGBoost is most suitable for detecting opinion spams in the English dataset while the GBM Gaussian is most suitable for the Malay dataset. The comparative analysis also indicates that the implementation of the proposed statistical based features had achieved a detection accuracy rate of 87.43 per cent on the English dataset and 86.13 per cent on the Malay dataset.
    Matched MeSH terms: Algorithms*
  5. Thermodynamic heuristics with case-based reasoning: combined insights for RNA pseudoknot secondary structure
    Al-Khatib RM, Rashid NA, Abdullah R
    J Biomol Struct Dyn, 2011 Aug;29(1):1-26.
    PMID: 21696223
    The secondary structure of RNA pseudoknots has been extensively inferred and scrutinized by computational approaches. Experimental methods for determining RNA structure are time consuming and tedious; therefore, predictive computational approaches are required. Predicting the most accurate and energy-stable pseudoknot RNA secondary structure has been proven to be an NP-hard problem. In this paper, a new RNA folding approach, termed MSeeker, is presented; it includes KnotSeeker (a heuristic method) and Mfold (a thermodynamic algorithm). The global optimization of this thermodynamic heuristic approach was further enhanced by using a case-based reasoning technique as a local optimization method. MSeeker is a proposed algorithm for predicting RNA pseudoknot structure from individual sequences, especially long ones. This research demonstrates that MSeeker improves the sensitivity and specificity of existing RNA pseudoknot structure predictions. The performance and structural results from this proposed method were evaluated against seven other state-of-the-art pseudoknot prediction methods. The MSeeker method had better sensitivity than the DotKnot, FlexStem, HotKnots, pknotsRG, ILM, NUPACK and pknotsRE methods, with 79% of the predicted pseudoknot base-pairs being correct.
    Matched MeSH terms: Algorithms
  6. Fulltext Sequential Insertion Heuristic with Adaptive Bee Colony Optimisation Algorithm for Vehicle Routing Problem with Time Windows
    Jawarneh S, Abdullah S
    PLoS One, 2015;10(7):e0130224.
    PMID: 26132158 DOI: 10.1371/journal.pone.0130224
    This paper presents a bee colony optimisation (BCO) algorithm to tackle the vehicle routing problem with time window (VRPTW). The VRPTW involves recovering an ideal set of routes for a fleet of vehicles serving a defined number of customers. The BCO algorithm is a population-based algorithm that mimics the social communication patterns of honeybees in solving problems. The performance of the BCO algorithm is dependent on its parameters, so the online (self-adaptive) parameter tuning strategy is used to improve its effectiveness and robustness. Compared with the basic BCO, the adaptive BCO performs better. Diversification is crucial to the performance of the population-based algorithm, but the initial population in the BCO algorithm is generated using a greedy heuristic, which has insufficient diversification. Therefore the ways in which the sequential insertion heuristic (SIH) for the initial population drives the population toward improved solutions are examined. Experimental comparisons indicate that the proposed adaptive BCO-SIH algorithm works well across all instances and is able to obtain 11 best results in comparison with the best-known results in the literature when tested on Solomon's 56 VRPTW 100 customer instances. Also, a statistical test shows that there is a significant difference between the results.
    Matched MeSH terms: Algorithms
  7. Fulltext Kidney-inspired algorithm with reduced functionality treatment for classification and time series prediction
    Jaddi NS, Abdullah S
    PLoS One, 2019;14(1):e0208308.
    PMID: 30608936 DOI: 10.1371/journal.pone.0208308
    Optimization of an artificial neural network model through the use of optimization algorithms is the common method employed to search for an optimum solution for a broad variety of real-world problems. One such optimization algorithm is the kidney-inspired algorithm (KA) which has recently been proposed in the literature. The algorithm mimics the four processes performed by the kidneys: filtration, reabsorption, secretion, and excretion. However, a human with reduced kidney function needs to undergo additional treatment to improve kidney performance. In the medical field, the glomerular filtration rate (GFR) test is used to check the health of kidneys. The test estimates the amount of blood that passes through the glomeruli each minute. In this paper, we mimic this kidney function test and the GFR result is used to select a suitable step to add to the basic KA process. This novel imitation is designed for both minimization and maximization problems. In the proposed method, depends on GFR test result which is less than 15 or falls between 15 and 60 or is more than 60 a particular action is performed. These additional processes are applied as required with the aim of improving exploration of the search space and increasing the likelihood of the KA finding the optimum solution. The proposed method is tested on test functions and its results are compared with those of the basic KA. Its performance on benchmark classification and time series prediction problems is also examined and compared with that of other available methods in the literature. In addition, the proposed method is applied to a real-world water quality prediction problem. The statistical analysis of all these applications showed that the proposed method had a ability to improve the optimization outcome.
    Matched MeSH terms: Algorithms*
  8. Improvement of production of citric acid from oil palm empty fruit bunches: optimization of media by statistical experimental designs
    Bari MN, Alam MZ, Muyibi SA, Jamal P, Abdullah-Al-Mamun
    Bioresour Technol, 2009 Jun;100(12):3113-20.
    PMID: 19231166 DOI: 10.1016/j.biortech.2009.01.005
    A sequential optimization based on statistical design and one-factor-at-a-time (OFAT) method was employed to optimize the media constituents for the improvement of citric acid production from oil palm empty fruit bunches (EFB) through solid state bioconversion using Aspergillus niger IBO-103MNB. The results obtained from the Plackett-Burman design indicated that the co-substrate (sucrose), stimulator (methanol) and minerals (Zn, Cu, Mn and Mg) were found to be the major factors for further optimization. Based on the OFAT method, the selected medium constituents and inoculum concentration were optimized by the central composite design (CCD) under the response surface methodology (RSM). The statistical analysis showed that the optimum media containing 6.4% (w/w) of sucrose, 9% (v/w) of minerals and 15.5% (v/w) of inoculum gave the maximum production of citric acid (337.94 g/kg of dry EFB). The analysis showed that sucrose (p<0.0011) and mineral solution (p<0.0061) were more significant compared to inoculum concentration (p<0.0127) for the citric acid production.
    Matched MeSH terms: Algorithms*
  9. Fulltext An Efficient Next Hop Selection Algorithm for Multi-Hop Body Area Networks
    Ayatollahitafti V, Ngadi MA, Mohamad Sharif JB, Abdullahi M
    PLoS One, 2016;11(1):e0146464.
    PMID: 26771586 DOI: 10.1371/journal.pone.0146464
    Body Area Networks (BANs) consist of various sensors which gather patient's vital signs and deliver them to doctors. One of the most significant challenges faced, is the design of an energy-efficient next hop selection algorithm to satisfy Quality of Service (QoS) requirements for different healthcare applications. In this paper, a novel efficient next hop selection algorithm is proposed in multi-hop BANs. This algorithm uses the minimum hop count and a link cost function jointly in each node to choose the best next hop node. The link cost function includes the residual energy, free buffer size, and the link reliability of the neighboring nodes, which is used to balance the energy consumption and to satisfy QoS requirements in terms of end to end delay and reliability. Extensive simulation experiments were performed to evaluate the efficiency of the proposed algorithm using the NS-2 simulator. Simulation results show that our proposed algorithm provides significant improvement in terms of energy consumption, number of packets forwarded, end to end delay and packet delivery ratio compared to the existing routing protocol.
    Matched MeSH terms: Algorithms*
  10. Fulltext Evaluation and selection of open-source EMR software packages based on integrated AHP and TOPSIS
    Zaidan AA, Zaidan BB, Al-Haiqi A, Kiah ML, Hussain M, Abdulnabi M
    J Biomed Inform, 2015 Feb;53:390-404.
    PMID: 25483886 DOI: 10.1016/j.jbi.2014.11.012
    Evaluating and selecting software packages that meet the requirements of an organization are difficult aspects of software engineering process. Selecting the wrong open-source EMR software package can be costly and may adversely affect business processes and functioning of the organization. This study aims to evaluate and select open-source EMR software packages based on multi-criteria decision-making. A hands-on study was performed and a set of open-source EMR software packages were implemented locally on separate virtual machines to examine the systems more closely. Several measures as evaluation basis were specified, and the systems were selected based a set of metric outcomes using Integrated Analytic Hierarchy Process (AHP) and TOPSIS. The experimental results showed that GNUmed and OpenEMR software can provide better basis on ranking score records than other open-source EMR software packages.
    Matched MeSH terms: Algorithms
  11. Fulltext A Novel Hybrid Harris Hawk-Arithmetic Optimization Algorithm for Industrial Wireless Mesh Networks
    Devan PAM, Ibrahim R, Omar M, Bingi K, Abdulrab H
    Sensors (Basel), 2023 Jul 07;23(13).
    PMID: 37448072 DOI: 10.3390/s23136224
    A novel hybrid Harris Hawk-Arithmetic Optimization Algorithm (HHAOA) for optimizing the Industrial Wireless Mesh Networks (WMNs) and real-time pressure process control was proposed in this research article. The proposed algorithm uses inspiration from Harris Hawk Optimization and the Arithmetic Optimization Algorithm to improve position relocation problems, premature convergence, and the poor accuracy the existing techniques face. The HHAOA algorithm was evaluated on various benchmark functions and compared with other optimization algorithms, namely Arithmetic Optimization Algorithm, Moth Flame Optimization, Sine Cosine Algorithm, Grey Wolf Optimization, and Harris Hawk Optimization. The proposed algorithm was also applied to a real-world industrial wireless mesh network simulation and experimentation on the real-time pressure process control system. All the results demonstrate that the HHAOA algorithm outperforms different algorithms regarding mean, standard deviation, convergence speed, accuracy, and robustness and improves client router connectivity and network congestion with a 31.7% reduction in Wireless Mesh Network routers. In the real-time pressure process, the HHAOA optimized Fractional-order Predictive PI (FOPPI) Controller produced a robust and smoother control signal leading to minimal peak overshoot and an average of a 53.244% faster settling. Based on the results, the algorithm enhanced the efficiency and reliability of industrial wireless networks and real-time pressure process control systems, which are critical for industrial automation and control applications.
    Matched MeSH terms: Algorithms*
  12. Fulltext A Sequential Handwriting Recognition Model Based on a Dynamically Configurable CRNN
    Al-Saffar A, Awang S, Al-Saiagh W, Al-Khaleefa AS, Abed SA
    Sensors (Basel), 2021 Nov 02;21(21).
    PMID: 34770612 DOI: 10.3390/s21217306
    Handwriting recognition refers to recognizing a handwritten input that includes character(s) or digit(s) based on an image. Because most applications of handwriting recognition in real life contain sequential text in various languages, there is a need to develop a dynamic handwriting recognition system. Inspired by the neuroevolutionary technique, this paper proposes a Dynamically Configurable Convolutional Recurrent Neural Network (DC-CRNN) for the handwriting recognition sequence modeling task. The proposed DC-CRNN is based on the Salp Swarm Optimization Algorithm (SSA), which generates the optimal structure and hyperparameters for Convolutional Recurrent Neural Networks (CRNNs). In addition, we investigate two types of encoding techniques used to translate the output of optimization to a CRNN recognizer. Finally, we proposed a novel hybridized SSA with Late Acceptance Hill-Climbing (LAHC) to improve the exploitation process. We conducted our experiments on two well-known datasets, IAM and IFN/ENIT, which include both the Arabic and English languages. The experimental results have shown that LAHC significantly improves the SSA search process. Therefore, the proposed DC-CRNN outperforms the handcrafted CRNN methods.
    Matched MeSH terms: Algorithms
  13. Fulltext An Artificial Neural Network Based Analysis of Factors Controlling Particle Size in a Virgin Coconut Oil-Based Nanoemulsion System Containing Copper Peptide
    Samson S, Basri M, Fard Masoumi HR, Abdul Malek E, Abedi Karjiban R
    PLoS One, 2016;11(7):e0157737.
    PMID: 27383135 DOI: 10.1371/journal.pone.0157737
    A predictive model of a virgin coconut oil (VCO) nanoemulsion system for the topical delivery of copper peptide (an anti-aging compound) was developed using an artificial neural network (ANN) to investigate the factors that influence particle size. Four independent variables including the amount of VCO, Tween 80: Pluronic F68 (T80:PF68), xanthan gum and water were the inputs whereas particle size was taken as the response for the trained network. Genetic algorithms (GA) were used to model the data which were divided into training sets, testing sets and validation sets. The model obtained indicated the high quality performance of the neural network and its capability to identify the critical composition factors for the VCO nanoemulsion. The main factor controlling the particle size was found out to be xanthan gum (28.56%) followed by T80:PF68 (26.9%), VCO (22.8%) and water (21.74%). The formulation containing copper peptide was then successfully prepared using optimum conditions and particle sizes of 120.7 nm were obtained. The final formulation exhibited a zeta potential lower than -25 mV and showed good physical stability towards centrifugation test, freeze-thaw cycle test and storage at temperature 25°C and 45°C.
    Matched MeSH terms: Algorithms
  14. Discriminatory ability of cervical vertebral maturation stages in predicting attainment of the legal age threshold of 14 years: A pilot study using lateral cephalograms
    Banda TR, Komuravelli AK, Balla SB, Korrai BR, Alluri K, Kondapaneni J, et al.
    Imaging Sci Dent, 2020 Sep;50(3):209-216.
    PMID: 33005578 DOI: 10.5624/isd.2020.50.3.209
    Purpose: In India, the age of 14 years is the legal age threshold for child labour. Therefore, in suspected instances of child labour, age assessment plays a crucial role in determining whether a violation of the law on the employment of children has occurred. The aim of this retrospective cross-sectional study was to assess the discriminatory ability of stages of cervical vertebral maturation (CVM) in predicting the legal age threshold of 14 years.

    Materials and Methods: Routinely taken lateral cephalograms from 408 subjects aged 10 to 18 years were evaluated retrospectively using the CVM stages described by Baccetti et al. Descriptive statistics, accuracy, sensitivity, specificity, positive and negative predictive values, and likelihood ratios were calculated for stages 2, 3, and 4 of CVM.

    Results: Real age increased as the CVM stage gradually increased. The results of 2×2 contingency tables showed that CVM stage 4 produced an accuracy of 71% and 73%, a false positive rate of 7% and 18%, and a post-test probability of 59% and 68% for boys and girls, respectively.

    Conclusion: Based on these findings, it can be concluded that the stages of CVM are of limited use for predicting the attainment of the legal age threshold of 14 years. Future studies should investigate whether combinations of skeletal and dental methods could achieve better accuracy and post-test probability.

    Matched MeSH terms: Algorithms
  15. A hybrid ART-GRNN online learning neural network with a epsilon -insensitive loss function
    Yap KS, Lim CP, Abidin IZ
    IEEE Trans Neural Netw, 2008 Sep;19(9):1641-6.
    PMID: 18779094 DOI: 10.1109/TNN.2008.2000992
    In this brief, a new neural network model called generalized adaptive resonance theory (GART) is introduced. GART is a hybrid model that comprises a modified Gaussian adaptive resonance theory (MGA) and the generalized regression neural network (GRNN). It is an enhanced version of the GRNN, which preserves the online learning properties of adaptive resonance theory (ART). A series of empirical studies to assess the effectiveness of GART in classification, regression, and time series prediction tasks is conducted. The results demonstrate that GART is able to produce good performances as compared with those of other methods, including the online sequential extreme learning machine (OSELM) and sequential learning radial basis function (RBF) neural network models.
    Matched MeSH terms: Algorithms*
  16. Fulltext Evaluation of risk factors for prolonged invasive mechanical ventilation in Paediatric Intensive Care Unit (PICU)
    Ismail, I., Yap, B.W., Abidin, A.S.Z.
    Pertanika Journal of Science & Technology, 2017;25(3):811-824.
    MyJurnal
    Prolonged mechanical ventilation (PMV) is associated with increase in mortality and resource utilisation as well as hospitalisation costs. This study evaluates the risk factors of PMV. A retrospective study was conducted involving 890 paediatric patients comprising 237 neonates, 306 infants, 223 of pre-school age and 124 who are of school going age. The data mining decision trees algorithms and logistic regression was employed to develop predictive models for each age category. The independent variables were classified into four categories, that is, demographic data, admission factors, medical factors and score factors. The dependent variable is the duration of ventilation where it is categorized 0 denoting non-PMV and 1 denoting PMV. The performances of three decision tree models (CHAID, CART and C5.0) and logistic regression were compared to determine the best model. The results indicated that the decision tree outperformed the logistic regression model for all age categories, given its good accuracy rate for testing dataset. Decision trees results identified length of stay and inotropes as significant risk factors in all age categories. PRISM 12 hours and principal diagnosis were identified as significant risk factors for infants.
    Matched MeSH terms: Algorithms
  17. A novel nonlinear time-varying sigmoid transfer function in binary whale optimization algorithm for descriptors selection in drug classification
    Mohd Yusof N, Muda AK, Pratama SF, Abraham A
    Mol Divers, 2023 Feb;27(1):71-80.
    PMID: 35254585 DOI: 10.1007/s11030-022-10410-y
    In computational chemistry, the high-dimensional molecular descriptors contribute to the curse of dimensionality issue. Binary whale optimization algorithm (BWOA) is a recently proposed metaheuristic optimization algorithm that has been efficiently applied in feature selection. The main contribution of this paper is a new version of the nonlinear time-varying Sigmoid transfer function to improve the exploitation and exploration activities in the standard whale optimization algorithm (WOA). A new BWOA algorithm, namely BWOA-3, is introduced to solve the descriptors selection problem, which becomes the second contribution. To validate BWOA-3 performance, a high-dimensional drug dataset is employed. The proficiency of the proposed BWOA-3 and the comparative optimization algorithms are measured based on convergence speed, the length of the selected feature subset, and classification performance (accuracy, specificity, sensitivity, and f-measure). In addition, statistical significance tests are also conducted using the Friedman test and Wilcoxon signed-rank test. The comparative optimization algorithms include two BWOA variants, binary bat algorithm (BBA), binary gray wolf algorithm (BGWOA), and binary manta-ray foraging algorithm (BMRFO). As the final contribution, from all experiments, this study has successfully revealed the superiority of BWOA-3 in solving the descriptors selection problem and improving the Amphetamine-type Stimulants (ATS) drug classification performance.
    Matched MeSH terms: Algorithms*
  18. Fulltext The performance of expectation maximization (EM) Algorithm in Gaussian Mixed Models (GMM)
    Mohd. Izhan Mohd. Yusoff, Mohd. Rizam Abu Bakar, Abu Hassan Shaari Mohd. Nor
    Pertanika Journal of Science & Technology, 2009;17(2):-.
    MyJurnal
    Expectation Maximization (EM) algorithm has experienced a significant increase in terms of usage in many fields of study. In this paper, the performance of the said algorithm in finding the Maximum Likelihood for the Gaussian Mixed Models (GMM), a probabilistic model normally used in fraud detection and recognizing a person’s voice in speech recognition field, is shown and discussed. At the end of the paper, some suggestions for future research works will also be given.
    Matched MeSH terms: Algorithms
  19. Effects of different implant-abutment connections on micromotion and stress distribution: prediction of microgap formation
    Saidin S, Abdul Kadir MR, Sulaiman E, Abu Kasim NH
    J Dent, 2012 Jun;40(6):467-74.
    PMID: 22366313 DOI: 10.1016/j.jdent.2012.02.009
    The aim of this study was to analyse micromotion and stress distribution at the connections of implants and four types of abutments: internal hexagonal, internal octagonal, internal conical and trilobe.
    Matched MeSH terms: Algorithms
  20. Comparison of signal-to-noise, blank determination, and linear regression methods for the estimation of detection and quantification limits for volatile organic compounds by gas chromatography
    Sanagi MM, Ling SL, Nasir Z, Hermawan D, Ibrahim WA, Abu Naim A
    J AOAC Int, 2010 2 20;92(6):1833-8.
    PMID: 20166602
    LOD and LOQ are two important performance characteristics in method validation. This work compares three methods based on the International Conference on Harmonization and EURACHEM guidelines, namely, signal-to-noise, blank determination, and linear regression, to estimate the LOD and LOQ for volatile organic compounds (VOCs) by experimental methodology using GC. Five VOCs, toluene, ethylbenzene, isopropylbenzene, n-propylbenzene, and styrene, were chosen for the experimental study. The results indicated that the estimated LODs and LOQs were not equivalent and could vary by a factor of 5 to 6 for the different methods. It is, therefore, essential to have a clearly described procedure for estimating the LOD and LOQ during method validation to allow interlaboratory comparisons.
    Matched MeSH terms: Algorithms
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