Displaying publications 161 - 180 of 2383 in total

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  1. Fulltext Agent-based simulation for reconstructing social structure by observing collective movements with special reference to single-file movement
    Koda H, Arai Z, Matsuda I
    PLoS One, 2020;15(12):e0243173.
    PMID: 33270712 DOI: 10.1371/journal.pone.0243173
    Understanding social organization is fundamental for the analysis of animal societies. In this study, animal single-file movement data-serialized order movements generated by simple bottom-up rules of collective movements-are informative and effective observations for the reconstruction of animal social structures using agent-based models. For simulation, artificial 2-dimensional spatial distributions were prepared with the simple assumption of clustered structures of a group. Animals in the group are either independent or dependent agents. Independent agents distribute spatially independently each one another, while dependent agents distribute depending on the distribution of independent agents. Artificial agent spatial distributions aim to represent clustered structures of agent locations-a coupling of "core" or "keystone" subjects and "subordinate" or "follower" subjects. Collective movements were simulated following two simple rules, 1) initiators of the movement are randomly chosen, and 2) the next moving agent is always the nearest neighbor of the last moving agents, generating "single-file movement" data. Finally, social networks were visualized, and clustered structures reconstructed using a recent major social network analysis (SNA) algorithm, the Louvain algorithm, for rapid unfolding of communities in large networks. Simulations revealed possible reconstruction of clustered social structures using relatively minor observations of single-file movement, suggesting possible application of single-file movement observations for SNA use in field investigations of wild animals.
  2. Fulltext Agro-climatic sensitivity analysis for sustainable crop diversification; the case of Proso millet (Panicum miliaceum L.)
    Wimalasiri EM, Ashfold MJ, Jahanshiri E, Walker S, Azam-Ali SN, Karunaratne AS
    PLoS One, 2023;18(3):e0283298.
    PMID: 36952502 DOI: 10.1371/journal.pone.0283298
    Current agricultural production depends on very limited species grown as monocultures that are highly vulnerable to climate change, presenting a threat to the sustainability of agri-food systems. However, many hundreds of neglected crop species have the potential to cater to the challenges of climate change by means of resilience to adverse climate conditions. Proso millet (Panicum miliaceum L.), one of the underutilised minor millets grown as a rainfed subsistence crop, was selected in this study as an exemplary climate-resilient crop. Using a previously calibrated version of the Agricultural Production Systems Simulator (APSIM), the sensitivity of the crop to changes in temperature and precipitation was studied using the protocol of the Coordinated Climate Crop Modelling Project (C3MP). The future (2040-2069) production was simulated using bias-corrected climate data from 20 general circulation models of the Coupled Model Intercomparison Project (CMIP5) under RCP4.5 and 8.5 scenarios. According to the C3MP analysis, we found a 1°C increment of temperature decreased the yield by 5-10% at zero rainfall change. However, Proso millet yields increased by 5% within a restricted climate change space of up to 2°C of warming with increased rainfall. Simulated future climate yields were lower than the simulated yields under the baseline climate of the 1980-2009 period (mean 1707 kg ha-1) under both RCP4.5 (-7.3%) and RCP8.5 (-16.6%) though these changes were not significantly (p > 0.05) different from the baseline yields. Proso millet is currently cultivated in limited areas of Sri Lanka, but our yield mapping shows the potential for expansion of the crop to new areas under both current and future climates. The results of the study, indicating minor impacts from projected climate change, reveal that Proso millet is an excellent candidate for low-input farming systems under changing climate. More generally, through this study, a framework that can be used to assess the climate sensitivity of underutilized crops was also developed.
  3. Fulltext Alarm Variables for Dengue Outbreaks: A Multi-Centre Study in Asia and Latin America
    Bowman LR, Tejeda GS, Coelho GE, Sulaiman LH, Gill BS, McCall PJ, et al.
    PLoS One, 2016;11(6):e0157971.
    PMID: 27348752 DOI: 10.1371/journal.pone.0157971
    BACKGROUND: Worldwide, dengue is an unrelenting economic and health burden. Dengue outbreaks have become increasingly common, which place great strain on health infrastructure and services. Early warning models could allow health systems and vector control programmes to respond more cost-effectively and efficiently.

    METHODOLOGY/PRINCIPAL FINDINGS: The Shewhart method and Endemic Channel were used to identify alarm variables that may predict dengue outbreaks. Five country datasets were compiled by epidemiological week over the years 2007-2013. These data were split between the years 2007-2011 (historic period) and 2012-2013 (evaluation period). Associations between alarm/ outbreak variables were analysed using logistic regression during the historic period while alarm and outbreak signals were captured during the evaluation period. These signals were combined to form alarm/ outbreak periods, where 2 signals were equal to 1 period. Alarm periods were quantified and used to predict subsequent outbreak periods. Across Mexico and Dominican Republic, an increase in probable cases predicted outbreaks of hospitalised cases with sensitivities and positive predictive values (PPV) of 93%/ 83% and 97%/ 86% respectively, at a lag of 1-12 weeks. An increase in mean temperature ably predicted outbreaks of hospitalised cases in Mexico and Brazil, with sensitivities and PPVs of 79%/ 73% and 81%/ 46% respectively, also at a lag of 1-12 weeks. Mean age was predictive of hospitalised cases at sensitivities and PPVs of 72%/ 74% and 96%/ 45% in Mexico and Malaysia respectively, at a lag of 4-16 weeks.

    CONCLUSIONS/SIGNIFICANCE: An increase in probable cases was predictive of outbreaks, while meteorological variables, particularly mean temperature, demonstrated predictive potential in some countries, but not all. While it is difficult to define uniform variables applicable in every country context, the use of probable cases and meteorological variables in tailored early warning systems could be used to highlight the occurrence of dengue outbreaks or indicate increased risk of dengue transmission.

  4. Fulltext Allometry and Scaling of the Intraocular Pressure and Aqueous Humour Flow Rate in Vertebrate Eyes
    Zouache MA, Eames I, Samsudin A
    PLoS One, 2016;11(3):e0151490.
    PMID: 26990431 DOI: 10.1371/journal.pone.0151490
    In vertebrates, intraocular pressure (IOP) is required to maintain the eye into a shape allowing it to function as an optical instrument. It is sustained by the balance between the production of aqueous humour by the ciliary body and the resistance to its outflow from the eye. Dysregulation of the IOP is often pathological to vision. High IOP may lead to glaucoma, which is in man the second most prevalent cause of blindness. Here, we examine the importance of the IOP and rate of formation of aqueous humour in the development of vertebrate eyes by performing allometric and scaling analyses of the forces acting on the eye during head movement and the energy demands of the cornea, and testing the predictions of the models against a list of measurements in vertebrates collated through a systematic review. We show that the IOP has a weak dependence on body mass, and that in order to maintain the focal length of the eye, it needs to be an order of magnitude greater than the pressure drop across the eye resulting from gravity or head movement. This constitutes an evolutionary constraint that is common to all vertebrates. In animals with cornea-based optics, this constraint also represents a condition to maintain visual acuity. Estimated IOPs were found to increase with the evolution of terrestrial animals. The rate of formation of aqueous humour was found to be adjusted to the metabolic requirements of the cornea, scaling as Vac(0.67), where Vac is the volume of the anterior chamber. The present work highlights an interdependence between IOP and aqueous flow rate crucial to ocular function that must be considered to understand the evolution of the dioptric apparatus. It should also be taken into consideration in the prevention and treatment of glaucoma.
  5. Fulltext Alpha-tomatine attenuation of in vivo growth of subcutaneous and orthotopic xenograft tumors of human prostate carcinoma PC-3 cells is accompanied by inactivation of nuclear factor-kappa B signaling
    Lee ST, Wong PF, He H, Hooper JD, Mustafa MR
    PLoS One, 2013;8(2):e57708.
    PMID: 23437404 DOI: 10.1371/journal.pone.0057708
    Nuclear factor-kappa B (NF-κB) plays a role in prostate cancer and agents that suppress its activation may inhibit development or progression of this malignancy. Alpha (α)-tomatine is the major saponin present in tomato (Lycopersicon esculentum) and we have previously reported that it suppresses tumor necrosis factor-alpha (TNF-α)-induced nuclear translocation of nuclear factor-kappa B (NF-κB) in androgen-independent prostate cancer PC-3 cells and also potently induces apoptosis of these cells. However, the precise mechanism by which α-tomatine suppresses NF-κB nuclear translocation is yet to be elucidated and the anti-tumor activity of this agent in vivo has not been examined.
  6. Fulltext Alpha-tomatine induces apoptosis and inhibits nuclear factor-kappa B activation on human prostatic adenocarcinoma PC-3 cells
    Lee ST, Wong PF, Cheah SC, Mustafa MR
    PLoS One, 2011;6(4):e18915.
    PMID: 21541327 DOI: 10.1371/journal.pone.0018915
    Alpha-tomatine (α-tomatine) is the major saponin in tomato (Lycopersicon esculentum). This study investigates the chemopreventive potential of α-tomatine on androgen-independent human prostatic adenocarcinoma PC-3 cells.
  7. Fulltext Altered Proteome of Burkholderia pseudomallei Colony Variants Induced by Exposure to Human Lung Epithelial Cells
    Al-Maleki AR, Mariappan V, Vellasamy KM, Tay ST, Vadivelu J
    PLoS One, 2015;10(5):e0127398.
    PMID: 25996927 DOI: 10.1371/journal.pone.0127398
    Burkholderia pseudomallei primary diagnostic cultures demonstrate colony morphology variation associated with expression of virulence and adaptation proteins. This study aims to examine the ability of B. pseudomallei colony variants (wild type [WT] and small colony variant [SCV]) to survive and replicate intracellularly in A549 cells and to identify the alterations in the protein expression of these variants, post-exposure to the A549 cells. Intracellular survival and cytotoxicity assays were performed followed by proteomics analysis using two-dimensional gel electrophoresis. B. pseudomallei SCV survive longer than the WT. During post-exposure, among 259 and 260 protein spots of SCV and WT, respectively, 19 were differentially expressed. Among SCV post-exposure up-regulated proteins, glyceraldehyde 3-phosphate dehydrogenase, fructose-bisphosphate aldolase (CbbA) and betaine aldehyde dehydrogenase were associated with adhesion and virulence. Among the down-regulated proteins, enolase (Eno) is implicated in adhesion and virulence. Additionally, post-exposure expression profiles of both variants were compared with pre-exposure. In WT pre- vs post-exposure, 36 proteins were differentially expressed. Of the up-regulated proteins, translocator protein, Eno, nucleoside diphosphate kinase (Ndk), ferritin Dps-family DNA binding protein and peptidyl-prolyl cis-trans isomerase B were implicated in invasion and virulence. In SCV pre- vs post-exposure, 27 proteins were differentially expressed. Among the up-regulated proteins, flagellin, Eno, CbbA, Ndk and phenylacetate-coenzyme A ligase have similarly been implicated in adhesion, invasion. Protein profiles differences post-exposure provide insights into association between morphotypic and phenotypic characteristics of colony variants, strengthening the role of B. pseudomallei morphotypes in pathogenesis of melioidosis.
  8. Fulltext Amino Acid and Secondary Metabolite Production in Embryogenic and Non-Embryogenic Callus of Fingerroot Ginger (Boesenbergia rotunda)
    Ng TL, Karim R, Tan YS, Teh HF, Danial AD, Ho LS, et al.
    PLoS One, 2016;11(6):e0156714.
    PMID: 27258536 DOI: 10.1371/journal.pone.0156714
    Interest in the medicinal properties of secondary metabolites of Boesenbergia rotunda (fingerroot ginger) has led to investigations into tissue culture of this plant. In this study, we profiled its primary and secondary metabolites, as well as hormones of embryogenic and non-embryogenic (dry and watery) callus and shoot base, Ultra Performance Liquid Chromatography-Mass Spectrometry together with histological characterization. Metabolite profiling showed relatively higher levels of glutamine, arginine and lysine in embryogenic callus than in dry and watery calli, while shoot base tissue showed an intermediate level of primary metabolites. For the five secondary metabolites analyzed (ie. panduratin, pinocembrin, pinostrobin, cardamonin and alpinetin), shoot base had the highest concentrations, followed by watery, dry and embryogenic calli. Furthermore, intracellular auxin levels were found to decrease from dry to watery calli, followed by shoot base and finally embryogenic calli. Our morphological observations showed the presence of fibrils on the cell surface of embryogenic callus while diphenylboric acid 2-aminoethylester staining indicated the presence of flavonoids in both dry and embryogenic calli. Periodic acid-Schiff staining showed that shoot base and dry and embryogenic calli contained starch reserves while none were found in watery callus. This study identified several primary metabolites that could be used as markers of embryogenic cells in B. rotunda, while secondary metabolite analysis indicated that biosynthesis pathways of these important metabolites may not be active in callus and embryogenic tissue.
  9. Fulltext An Application of Bayesian Approach in Modeling Risk of Death in an Intensive Care Unit
    Wong RS, Ismail NA
    PLoS One, 2016;11(3):e0151949.
    PMID: 27007413 DOI: 10.1371/journal.pone.0151949
    There are not many studies that attempt to model intensive care unit (ICU) risk of death in developing countries, especially in South East Asia. The aim of this study was to propose and describe application of a Bayesian approach in modeling in-ICU deaths in a Malaysian ICU.
  10. 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.
  11. Fulltext An Asian validation of the TIMI risk score for ST-segment elevation myocardial infarction
    Selvarajah S, Fong AY, Selvaraj G, Haniff J, Uiterwaal CS, Bots ML
    PLoS One, 2012;7(7):e40249.
    PMID: 22815733 DOI: 10.1371/journal.pone.0040249
    Risk stratification in ST-elevation myocardial infarction (STEMI) is important, such that the most resource intensive strategy is used to achieve the greatest clinical benefit. This is essential in developing countries with wide variation in health care facilities, scarce resources and increasing burden of cardiovascular diseases. This study sought to validate the Thrombolysis In Myocardial Infarction (TIMI) risk score for STEMI in a multi-ethnic developing country.
  12. Fulltext An Automated Summarization Assessment Algorithm for Identifying Summarizing Strategies
    Abdi A, Idris N, Alguliyev RM, Aliguliyev RM
    PLoS One, 2016;11(1):e0145809.
    PMID: 26735139 DOI: 10.1371/journal.pone.0145809
    Summarization is a process to select important information from a source text. Summarizing strategies are the core cognitive processes in summarization activity. Since summarization can be important as a tool to improve comprehension, it has attracted interest of teachers for teaching summary writing through direct instruction. To do this, they need to review and assess the students' summaries and these tasks are very time-consuming. Thus, a computer-assisted assessment can be used to help teachers to conduct this task more effectively.
  13. Fulltext An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines
    Mansourvar M, Shamshirband S, Raj RG, Gunalan R, Mazinani I
    PLoS One, 2015;10(9):e0138493.
    PMID: 26402795 DOI: 10.1371/journal.pone.0138493
    Assessing skeletal age is a subjective and tedious examination process. Hence, automated assessment methods have been developed to replace manual evaluation in medical applications. In this study, a new fully automated method based on content-based image retrieval and using extreme learning machines (ELM) is designed and adapted to assess skeletal maturity. The main novelty of this approach is it overcomes the segmentation problem as suffered by existing systems. The estimation results of ELM models are compared with those of genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results signify improvement in assessment accuracy over GP and ANN, while generalization capability is possible with the ELM approach. Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies. According to the experimental results, the new presented method has the capacity to learn many hundreds of times faster than traditional learning methods and it has sufficient overall performance in many aspects. It has conclusively been found that applying ELM is particularly promising as an alternative method for evaluating skeletal age.
  14. Fulltext An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification
    Siddiqui MF, Reza AW, Kanesan J
    PLoS One, 2015;10(8):e0135875.
    PMID: 26280918 DOI: 10.1371/journal.pone.0135875
    A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT), principal component analysis (PCA), and least squares support vector machine (LS-SVM) are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF) kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients' benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities from the individual subjects, therefore, it can be used as a significant tool in clinical practice.
  15. 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.
  16. 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.
  17. Fulltext An Efficient Soft Set-Based Approach for Conflict Analysis
    Sutoyo E, Mungad M, Hamid S, Herawan T
    PLoS One, 2016;11(2):e0148837.
    PMID: 26928627 DOI: 10.1371/journal.pone.0148837
    Conflict analysis has been used as an important tool in economic, business, governmental and political dispute, games, management negotiations, military operations and etc. There are many mathematical formal models have been proposed to handle conflict situations and one of the most popular is rough set theory. With the ability to handle vagueness from the conflict data set, rough set theory has been successfully used. However, computational time is still an issue when determining the certainty, coverage, and strength of conflict situations. In this paper, we present an alternative approach to handle conflict situations, based on some ideas using soft set theory. The novelty of the proposed approach is that, unlike in rough set theory that uses decision rules, it is based on the concept of co-occurrence of parameters in soft set theory. We illustrate the proposed approach by means of a tutorial example of voting analysis in conflict situations. Furthermore, we elaborate the proposed approach on real world dataset of political conflict in Indonesian Parliament. We show that, the proposed approach achieves lower computational time as compared to rough set theory of up to 3.9%.
  18. Fulltext An Eigenspace approach for detecting multiple space-time disease clusters: Application to measles hotspots detection in Khyber-Pakhtunkhwa, Pakistan
    Ullah S, Daud H, Dass SC, Fanaee-T H, Khalil A
    PLoS One, 2018;13(6):e0199176.
    PMID: 29920540 DOI: 10.1371/journal.pone.0199176
    Identifying the abnormally high-risk regions in a spatiotemporal space that contains an unexpected disease count is helpful to conduct surveillance and implement control strategies. The EigenSpot algorithm has been recently proposed for detecting space-time disease clusters of arbitrary shapes with no restriction on the distribution and quality of the data, and has shown some promising advantages over the state-of-the-art methods. However, the main problem with the EigenSpot method is that it cannot be adapted to detect more than one spatiotemporal hotspot. This is an important limitation, since, in reality, we may have multiple hotspots, sometimes at the same level of importance. We propose an extension of the EigenSpot algorithm, called Multi-EigenSpot that is able to handle multiple hotspots by iteratively removing previously detected hotspots and re-running the algorithm until no more hotspots are found. In addition, a visualization tool (heatmap) has been linked to the proposed algorithm to visualize multiple clusters with different colors. We evaluated the proposed method using the monthly data on measles cases in Khyber-Pakhtunkhwa, Pakistan (Jan 2016- Dec 2016), and the efficiency was compared with the state-of-the-art methods: EigenSpot and Space-time scan statistic (SaTScan). The results showed the effectiveness of the proposed method for detecting multiple clusters in a spatiotemporal space.
  19. Fulltext An Empirical Analysis of Rough Set Categorical Clustering Techniques
    Uddin J, Ghazali R, Deris MM
    PLoS One, 2017;12(1):e0164803.
    PMID: 28068344 DOI: 10.1371/journal.pone.0164803
    Clustering a set of objects into homogeneous groups is a fundamental operation in data mining. Recently, many attentions have been put on categorical data clustering, where data objects are made up of non-numerical attributes. For categorical data clustering the rough set based approaches such as Maximum Dependency Attribute (MDA) and Maximum Significance Attribute (MSA) has outperformed their predecessor approaches like Bi-Clustering (BC), Total Roughness (TR) and Min-Min Roughness(MMR). This paper presents the limitations and issues of MDA and MSA techniques on special type of data sets where both techniques fails to select or faces difficulty in selecting their best clustering attribute. Therefore, this analysis motivates the need to come up with better and more generalize rough set theory approach that can cope the issues with MDA and MSA. Hence, an alternative technique named Maximum Indiscernible Attribute (MIA) for clustering categorical data using rough set indiscernible relations is proposed. The novelty of the proposed approach is that, unlike other rough set theory techniques, it uses the domain knowledge of the data set. It is based on the concept of indiscernibility relation combined with a number of clusters. To show the significance of proposed approach, the effect of number of clusters on rough accuracy, purity and entropy are described in the form of propositions. Moreover, ten different data sets from previously utilized research cases and UCI repository are used for experiments. The results produced in tabular and graphical forms shows that the proposed MIA technique provides better performance in selecting the clustering attribute in terms of purity, entropy, iterations, time, accuracy and rough accuracy.
  20. Fulltext An Experience Oriented-Convergence Improved Gravitational Search Algorithm for Minimum Variance Distortionless Response Beamforming Optimum
    Darzi S, Tiong SK, Tariqul Islam M, Rezai Soleymanpour H, Kibria S
    PLoS One, 2016;11(7):e0156749.
    PMID: 27399904 DOI: 10.1371/journal.pone.0156749
    An experience oriented-convergence improved gravitational search algorithm (ECGSA) based on two new modifications, searching through the best experiments and using of a dynamic gravitational damping coefficient (α), is introduced in this paper. ECGSA saves its best fitness function evaluations and uses those as the agents' positions in searching process. In this way, the optimal found trajectories are retained and the search starts from these trajectories, which allow the algorithm to avoid the local optimums. Also, the agents can move faster in search space to obtain better exploration during the first stage of the searching process and they can converge rapidly to the optimal solution at the final stage of the search process by means of the proposed dynamic gravitational damping coefficient. The performance of ECGSA has been evaluated by applying it to eight standard benchmark functions along with six complicated composite test functions. It is also applied to adaptive beamforming problem as a practical issue to improve the weight vectors computed by minimum variance distortionless response (MVDR) beamforming technique. The results of implementation of the proposed algorithm are compared with some well-known heuristic methods and verified the proposed method in both reaching to optimal solutions and robustness.
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