Displaying publications 1 - 20 of 65 in total

Abstract:
Sort:
  1. Visualizing disaster attitudes resulting from terrorist activities
    Khalid HM, Helander MG, Hood NA
    Appl Ergon, 2013 Sep;44(5):671-9.
    PMID: 22944486 DOI: 10.1016/j.apergo.2012.06.005
    The purpose of this study was to analyze people's attitudes to disasters by investigating how people feel, behave and think during disasters. We focused on disasters induced by humans, such as terrorist attacks. Two types of textual information were collected - from Internet blogs and from research papers. The analysis enabled forecasting of attitudes for the design of proactive disaster advisory scheme. Text was analyzed using a text mining tool, Leximancer. The outcome of this analysis revealed core themes and concepts in the text concerning people's attitudes. The themes and concepts were sorted into three broad categories: Affect, Behaviour, and Cognition (ABC), and the data was visualized in semantic maps. The maps reveal several knowledge pathways of ABC for developing attitudinal ontologies, which describe the relations between affect, behaviour and cognition, and the sequence in which they develop. Clearly, terrorist attacks induced trauma and people became highly vulnerable.
    Matched MeSH terms: Data Mining
  2. Fulltext In-vitro diagnosis of single and poly microbial species targeted for diabetic foot infection using e-nose technology
    Yusuf N, Zakaria A, Omar MI, Shakaff AY, Masnan MJ, Kamarudin LM, et al.
    BMC Bioinformatics, 2015;16:158.
    PMID: 25971258 DOI: 10.1186/s12859-015-0601-5
    Effective management of patients with diabetic foot infection is a crucial concern. A delay in prescribing appropriate antimicrobial agent can lead to amputation or life threatening complications. Thus, this electronic nose (e-nose) technique will provide a diagnostic tool that will allow for rapid and accurate identification of a pathogen.
    Matched MeSH terms: Data Mining
  3. Fulltext Unsupervised Network Analysis of the Plastic Supraoptic Nucleus Transcriptome Predicts Caprin2 Regulatory Interactions
    Loh SY, Jahans-Price T, Greenwood MP, Greenwood M, Hoe SZ, Konopacka A, et al.
    eNeuro, 2017 12 21;4(6).
    PMID: 29279858 DOI: 10.1523/ENEURO.0243-17.2017
    The supraoptic nucleus (SON) is a group of neurons in the hypothalamus responsible for the synthesis and secretion of the peptide hormones vasopressin and oxytocin. Following physiological cues, such as dehydration, salt-loading and lactation, the SON undergoes a function related plasticity that we have previously described in the rat at the transcriptome level. Using the unsupervised graphical lasso (Glasso) algorithm, we reconstructed a putative network from 500 plastic SON genes in which genes are the nodes and the edges are the inferred interactions. The most active nodal gene identified within the network was Caprin2. Caprin2 encodes an RNA-binding protein that we have previously shown to be vital for the functioning of osmoregulatory neuroendocrine neurons in the SON of the rat hypothalamus. To test the validity of the Glasso network, we either overexpressed or knocked down Caprin2 transcripts in differentiated rat pheochromocytoma PC12 cells and showed that these manipulations had significant opposite effects on the levels of putative target mRNAs. These studies suggest that the predicative power of the Glasso algorithm within an in vivo system is accurate, and identifies biological targets that may be important to the functional plasticity of the SON.
    Matched MeSH terms: Data Mining
  4. Fulltext A phylogenomic approach to bacterial subspecies classification: proof of concept in Mycobacterium abscessus
    Tan JL, Khang TF, Ngeow YF, Choo SW
    BMC Genomics, 2013;14:879.
    PMID: 24330254 DOI: 10.1186/1471-2164-14-879
    Mycobacterium abscessus is a rapidly growing mycobacterium that is often associated with human infections. The taxonomy of this species has undergone several revisions and is still being debated. In this study, we sequenced the genomes of 12 M. abscessus strains and used phylogenomic analysis to perform subspecies classification.
    Matched MeSH terms: Data Mining
  5. Fulltext Application of an Effective Statistical Technique for an Accurate and Powerful Mining of Quantitative Trait Loci for Rice Aroma Trait
    Golestan Hashemi FS, Rafii MY, Ismail MR, Mohamed MT, Rahim HA, Latif MA, et al.
    PLoS One, 2015;10(6):e0129069.
    PMID: 26061689 DOI: 10.1371/journal.pone.0129069
    When a phenotype of interest is associated with an external/internal covariate, covariate inclusion in quantitative trait loci (QTL) analyses can diminish residual variation and subsequently enhance the ability of QTL detection. In the in vitro synthesis of 2-acetyl-1-pyrroline (2AP), the main fragrance compound in rice, the thermal processing during the Maillard-type reaction between proline and carbohydrate reduction produces a roasted, popcorn-like aroma. Hence, for the first time, we included the proline amino acid, an important precursor of 2AP, as a covariate in our QTL mapping analyses to precisely explore the genetic factors affecting natural variation for rice scent. Consequently, two QTLs were traced on chromosomes 4 and 8. They explained from 20% to 49% of the total aroma phenotypic variance. Additionally, by saturating the interval harboring the major QTL using gene-based primers, a putative allele of fgr (major genetic determinant of fragrance) was mapped in the QTL on the 8th chromosome in the interval RM223-SCU015RM (1.63 cM). These loci supported previous studies of different accessions. Such QTLs can be widely used by breeders in crop improvement programs and for further fine mapping. Moreover, no previous studies and findings were found on simultaneous assessment of the relationship among 2AP, proline and fragrance QTLs. Therefore, our findings can help further our understanding of the metabolomic and genetic basis of 2AP biosynthesis in aromatic rice.
    Matched MeSH terms: Data Mining
  6. Current advance methods for the identification of blast resistance genes in rice
    Tanweer FA, Rafii MY, Sijam K, Rahim HA, Ahmed F, Latif MA
    C. R. Biol., 2015 May;338(5):321-34.
    PMID: 25843222 DOI: 10.1016/j.crvi.2015.03.001
    Rice blast caused by Magnaporthe oryzae is one of the most devastating diseases of rice around the world and crop losses due to blast are considerably high. Many blast resistant rice varieties have been developed by classical plant breeding and adopted by farmers in various rice-growing countries. However, the variability in the pathogenicity of the blast fungus according to environment made blast disease a major concern for farmers, which remains a threat to the rice industry. With the utilization of molecular techniques, plant breeders have improved rice production systems and minimized yield losses. In this article, we have summarized the current advanced molecular techniques used for controlling blast disease. With the advent of new technologies like marker-assisted selection, molecular mapping, map-based cloning, marker-assisted backcrossing and allele mining, breeders have identified more than 100 Pi loci and 350 QTL in rice genome responsible for blast disease. These Pi genes and QTLs can be introgressed into a blast-susceptible cultivar through marker-assisted backcross breeding. These molecular techniques provide timesaving, environment friendly and labour-cost-saving ways to control blast disease. The knowledge of host-plant interactions in the frame of blast disease will lead to develop resistant varieties in the future.
    Matched MeSH terms: Data Mining
  7. Fulltext Pooling and expanding registries of familial hypercholesterolaemia to assess gaps in care and improve disease management and outcomes: Rationale and design of the global EAS Familial Hypercholesterolaemia Studies Collaboration
    EAS Familial Hypercholesterolaemia Studies Collaboration, Vallejo-Vaz AJ, Akram A, Kondapally Seshasai SR, Cole D, Watts GF, et al.
    Atheroscler Suppl, 2016 Dec;22:1-32.
    PMID: 27939304 DOI: 10.1016/j.atherosclerosissup.2016.10.001
    The potential for global collaborations to better inform public health policy regarding major non-communicable diseases has been successfully demonstrated by several large-scale international consortia. However, the true public health impact of familial hypercholesterolaemia (FH), a common genetic disorder associated with premature cardiovascular disease, is yet to be reliably ascertained using similar approaches. The European Atherosclerosis Society FH Studies Collaboration (EAS FHSC) is a new initiative of international stakeholders which will help establish a global FH registry to generate large-scale, robust data on the burden of FH worldwide.
    Matched MeSH terms: Data Mining
  8. IAPSO-AIRS: A novel improved machine learning-based system for wart disease treatment
    Abdar M, Wijayaningrum VN, Hussain S, Alizadehsani R, Plawiak P, Acharya UR, et al.
    J Med Syst, 2019 Jun 07;43(7):220.
    PMID: 31175462 DOI: 10.1007/s10916-019-1343-0
    Wart disease (WD) is a skin illness on the human body which is caused by the human papillomavirus (HPV). This study mainly concentrates on common and plantar warts. There are various treatment methods for this disease, including the popular immunotherapy and cryotherapy methods. Manual evaluation of the WD treatment response is challenging. Furthermore, traditional machine learning methods are not robust enough in WD classification as they cannot deal effectively with small number of attributes. This study proposes a new evolutionary-based computer-aided diagnosis (CAD) system using machine learning to classify the WD treatment response. The main architecture of our CAD system is based on the combination of improved adaptive particle swarm optimization (IAPSO) algorithm and artificial immune recognition system (AIRS). The cross-validation protocol was applied to test our machine learning-based classification system, including five different partition protocols (K2, K3, K4, K5 and K10). Our database consisted of 180 records taken from immunotherapy and cryotherapy databases. The best results were obtained using the K10 protocol that provided the precision, recall, F-measure and accuracy values of 0.8908, 0.8943, 0.8916 and 90%, respectively. Our IAPSO system showed the reliability of 98.68%. It was implemented in Java, while integrated development environment (IDE) was implemented using NetBeans. Our encouraging results suggest that the proposed IAPSO-AIRS system can be employed for the WD management in clinical environment.
    Matched MeSH terms: Data Mining
  9. Machine learning-based coronary artery disease diagnosis: A comprehensive review
    Alizadehsani R, Abdar M, Roshanzamir M, Khosravi A, Kebria PM, Khozeimeh F, et al.
    Comput Biol Med, 2019 08;111:103346.
    PMID: 31288140 DOI: 10.1016/j.compbiomed.2019.103346
    Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and often leads to a heart attack. It annually causes millions of deaths and billions of dollars in financial losses worldwide. Angiography, which is invasive and risky, is the standard procedure for diagnosing CAD. Alternatively, machine learning (ML) techniques have been widely used in the literature as fast, affordable, and noninvasive approaches for CAD detection. The results that have been published on ML-based CAD diagnosis differ substantially in terms of the analyzed datasets, sample sizes, features, location of data collection, performance metrics, and applied ML techniques. Due to these fundamental differences, achievements in the literature cannot be generalized. This paper conducts a comprehensive and multifaceted review of all relevant studies that were published between 1992 and 2019 for ML-based CAD diagnosis. The impacts of various factors, such as dataset characteristics (geographical location, sample size, features, and the stenosis of each coronary artery) and applied ML techniques (feature selection, performance metrics, and method) are investigated in detail. Finally, the important challenges and shortcomings of ML-based CAD diagnosis are discussed.
    Matched MeSH terms: Data Mining
  10. 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: Data Mining
  11. Fulltext First comprehensive in silico analysis of the functional and structural consequences of SNPs in human GalNAc-T1 gene
    Mohamoud HS, Hussain MR, El-Harouni AA, Shaik NA, Qasmi ZU, Merican AF, et al.
    Comput Math Methods Med, 2014;2014:904052.
    PMID: 24723968 DOI: 10.1155/2014/904052
    GalNAc-T1, a key candidate of GalNac-transferases genes family that is involved in mucin-type O-linked glycosylation pathway, is expressed in most biological tissues and cell types. Despite the reported association of GalNAc-T1 gene mutations with human disease susceptibility, the comprehensive computational analysis of coding, noncoding and regulatory SNPs, and their functional impacts on protein level, still remains unknown. Therefore, sequence- and structure-based computational tools were employed to screen the entire listed coding SNPs of GalNAc-T1 gene in order to identify and characterize them. Our concordant in silico analysis by SIFT, PolyPhen-2, PANTHER-cSNP, and SNPeffect tools, identified the potential nsSNPs (S143P, G258V, and Y414D variants) from 18 nsSNPs of GalNAc-T1. Additionally, 2 regulatory SNPs (rs72964406 and #x26; rs34304568) were also identified in GalNAc-T1 by using FastSNP tool. Using multiple computational approaches, we have systematically classified the functional mutations in regulatory and coding regions that can modify expression and function of GalNAc-T1 enzyme. These genetic variants can further assist in better understanding the wide range of disease susceptibility associated with the mucin-based cell signalling and pathogenic binding, and may help to develop novel therapeutic elements for associated diseases.
    Matched MeSH terms: Data Mining/methods
  12. Development of ESTs and data mining of pineapple EST-SSRs
    Ong WD, Voo CL, Kumar SV
    Mol Biol Rep, 2012 May;39(5):5889-96.
    PMID: 22207174 DOI: 10.1007/s11033-011-1400-3
    Improving the quality of the non-climacteric fruit, pineapple, is possible with information on the expression of genes that occur during the process of fruit ripening. This can be made known though the generation of partial mRNA transcript sequences known as expressed sequence tags (ESTs). ESTs are useful not only for gene discovery but also function as a resource for the identification of molecular markers, such as simple sequence repeats (SSRs). This paper reports on firstly, the construction of a normalized library of the mature green pineapple fruit and secondly, the mining of EST-SSRs markers using the newly obtained pineapple ESTs as well as publically available pineapple ESTs deposited in GenBank. Sequencing of the clones from the EST library resulted in 282 good sequences. Assembly of sequences generated 168 unique transcripts (UTs) consisting of 34 contigs and 134 singletons with an average length of ≈500 bp. Annotation of the UTs categorized the known proteins transcripts into the three ontologies as: molecular function (34.88%), biological process (38.43%), and cellular component (26.69%). Approximately 7% (416) of the pineapple ESTs contained SSRs with an abundance of trinucleotide SSRs (48.3%) being identified. This was followed by dinucleotide and tetranucleotide SSRs with frequency of 46 and 57%, respectively. From these EST-containing SSRs, 355 (85.3%) matched to known proteins while 133 contained flanking regions for primer design. Both the ESTs were sequenced and the mined EST-SSRs will be useful in the understanding of non-climacteric ripening and the screening of biomarkers linked to fruit quality traits.
    Matched MeSH terms: Data Mining*
  13. Characterization of spatial patterns in river water quality using chemometric pattern recognition techniques
    Gazzaz NM, Yusoff MK, Ramli MF, Aris AZ, Juahir H
    Mar Pollut Bull, 2012 Apr;64(4):688-98.
    PMID: 22330076 DOI: 10.1016/j.marpolbul.2012.01.032
    This study employed three chemometric data mining techniques (factor analysis (FA), cluster analysis (CA), and discriminant analysis (DA)) to identify the latent structure of a water quality (WQ) dataset pertaining to Kinta River (Malaysia) and to classify eight WQ monitoring stations along the river into groups of similar WQ characteristics. FA identified the WQ parameters responsible for variations in Kinta River's WQ and accentuated the roles of weathering and surface runoff in determining the river's WQ. CA grouped the monitoring locations into a cluster of low levels of water pollution (the two uppermost monitoring stations) and another of relatively high levels of river pollution (the mid-, and down-stream stations). DA confirmed these clusters and produced a discriminant function which can predict the cluster membership of new and/or unknown samples. These chemometric techniques highlight the potential for reasonably reducing the number of WQVs and monitoring stations for long-term monitoring purposes.
    Matched MeSH terms: Data Mining
  14. Fulltext Risk prediction analysis for classifying type 2 diabetes occurrence using local dataset
    M. Hafiz Fazren Abd Rahman, Wan Wardatul Amani Wan Salim, M. Firdaus Abd-Wahab
    Biological and Natural Resources Engineering Journal, 2020;3(1):48-61.
    MyJurnal
    The steep rise of cases pertaining to Diabetes Mellitus (DM) condition among global population has encouraged extensive researches on DM, which led to exhaustive accumulation of data related to DM. In this case, data mining and machine learning applications prove to be a powerful tool in transforming data into meaningful deductions. Several machine learning tools have shown great promise in diabetes classification. However, challenges remain in obtaining an accurate model suitable for real world application. Most disease risk-prediction modelling are found to be specific to a local population. Moreover, real-world data are likely to be complex, incomplete and unorganized, thus, convoluting efforts to develop models around it. This research aims to develop a robust prediction model for classification of type 2 diabetes mellitus (T2DM), with the interest of a Malaysian population, using three different machine learning algorithms; Decision Tree, Support Vector Machine and Naïve Bayes. Data pre-processing methods are utilised to the raw data to improve model performance. This study uses datasets obtained from the IIUM Medical Centre for classification and modelling. Ultimately, the performance of each model is validated, evaluated and compared based on several statistical metrics that measures accuracy, precision, sensitivity and efficiency. This study shows that the random forest model provides the best overall prediction performance in terms of accuracy (0.87), sensitivity (0.9), specificity (0.8), precision (0.9), F1-score (0.9) and AUC value (0.93) (Normal).
    Matched MeSH terms: Data Mining
  15. Fulltext YouTube Video Comments on Healthy Eating: Descriptive and Predictive Analysis
    Teng S, Khong KW, Pahlevan Sharif S, Ahmed A
    JMIR Public Health Surveill, 2020 10 01;6(4):e19618.
    PMID: 33001036 DOI: 10.2196/19618
    BACKGROUND: Poor nutrition and food selection lead to health issues such as obesity, cardiovascular disease, diabetes, and cancer. This study of YouTube comments aims to uncover patterns of food choices and the factors driving them, in addition to exploring the sentiments of healthy eating in networked communities.

    OBJECTIVE: The objectives of the study are to explore the determinants, motives, and barriers to healthy eating behaviors in online communities and provide insight into YouTube video commenters' perceptions and sentiments of healthy eating through text mining techniques.

    METHODS: This paper applied text mining techniques to identify and categorize meaningful healthy eating determinants. These determinants were then incorporated into hypothetically defined constructs that reflect their thematic and sentimental nature in order to test our proposed model using a variance-based structural equation modeling procedure.

    RESULTS: With a dataset of 4654 comments extracted from YouTube videos in the context of Malaysia, we apply a text mining method to analyze the perceptions and behavior of healthy eating. There were 10 clusters identified with regard to food ingredients, food price, food choice, food portion, well-being, cooking, and culture in the concept of healthy eating. The structural equation modeling results show that clusters are positively associated with healthy eating with all P values less than .001, indicating a statistical significance of the study results. People hold complex and multifaceted beliefs about healthy eating in the context of YouTube videos. Fruits and vegetables are the epitome of healthy foods. Despite having a favorable perception of healthy eating, people may not purchase commonly recognized healthy food if it has a premium price. People associate healthy eating with weight concerns. Food taste, variety, and availability are identified as reasons why Malaysians cannot act on eating healthily.

    CONCLUSIONS: This study offers significant value to the existing literature of health-related studies by investigating the rich and diverse social media data gleaned from YouTube. This research integrated text mining analytics with predictive modeling techniques to identify thematic constructs and analyze the sentiments of healthy eating.

    Matched MeSH terms: Data Mining
  16. Fulltext Data Mining and Statistical Approaches in Debris-Flow Susceptibility Modelling Using Airborne LiDAR Data
    Lay US, Pradhan B, Yusoff ZBM, Abdallah AFB, Aryal J, Park HJ
    Sensors (Basel), 2019 Aug 07;19(16).
    PMID: 31394777 DOI: 10.3390/s19163451
    Cameron Highland is a popular tourist hub in the mountainous area of Peninsular Malaysia. Most communities in this area suffer frequent incidence of debris flow, especially during monsoon seasons. Despite the loss of lives and properties recorded annually from debris flow, most studies in the region concentrate on landslides and flood susceptibilities. In this study, debris-flow susceptibility prediction was carried out using two data mining techniques; Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) models. The existing inventory of debris-flow events (640 points) were selected for training 70% (448) and validation 30% (192). Twelve conditioning factors namely; elevation, plan-curvature, slope angle, total curvature, slope aspect, Stream Transport Index (STI), profile curvature, roughness index, Stream Catchment Area (SCA), Stream Power Index (SPI), Topographic Wetness Index (TWI) and Topographic Position Index (TPI) were selected from Light Detection and Ranging (LiDAR)-derived Digital Elevation Model (DEM) data. Multi-collinearity was checked using Information Factor, Cramer's V, and Gini Index to identify the relative importance of conditioning factors. The susceptibility models were produced and categorized into five classes; not-susceptible, low, moderate, high and very-high classes. Models performances were evaluated using success and prediction rates where the area under the curve (AUC) showed a higher performance of MARS (93% and 83%) over SVR (76% and 72%). The result of this study will be important in contingency hazards and risks management plans to reduce the loss of lives and properties in the area.
    Matched MeSH terms: Data Mining
  17. Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models
    Azareh A, Rahmati O, Rafiei-Sardooi E, Sankey JB, Lee S, Shahabi H, et al.
    Sci Total Environ, 2019 Mar 10;655:684-696.
    PMID: 30476849 DOI: 10.1016/j.scitotenv.2018.11.235
    Gully erosion susceptibility mapping is a fundamental tool for land-use planning aimed at mitigating land degradation. However, the capabilities of some state-of-the-art data-mining models for developing accurate maps of gully erosion susceptibility have not yet been fully investigated. This study assessed and compared the performance of two different types of data-mining models for accurately mapping gully erosion susceptibility at a regional scale in Chavar, Ilam, Iran. The two methods evaluated were: Certainty Factor (CF), a bivariate statistical model; and Maximum Entropy (ME), an advanced machine learning model. Several geographic and environmental factors that can contribute to gully erosion were considered as predictor variables of gully erosion susceptibility. Based on an existing differential GPS survey inventory of gully erosion, a total of 63 eroded gullies were spatially randomly split in a 70:30 ratio for use in model calibration and validation, respectively. Accuracy assessments completed with the receiver operating characteristic curve method showed that the ME-based regional gully susceptibility map has an area under the curve (AUC) value of 88.6% whereas the CF-based map has an AUC of 81.8%. According to jackknife tests that were used to investigate the relative importance of predictor variables, aspect, distance to river, lithology and land use are the most influential factors for the spatial distribution of gully erosion susceptibility in this region of Iran. The gully erosion susceptibility maps produced in this study could be useful tools for land managers and engineers tasked with road development, urbanization and other future development.
    Matched MeSH terms: Data Mining
  18. Enhancing nitrate and strontium concentration prediction in groundwater by using new data mining algorithm
    Bui DT, Khosravi K, Karimi M, Busico G, Khozani ZS, Nguyen H, et al.
    Sci Total Environ, 2020 May 01;715:136836.
    PMID: 32007881 DOI: 10.1016/j.scitotenv.2020.136836
    Groundwater resources constitute the main source of clean fresh water for domestic use and it is essential for food production in the agricultural sector. Groundwater has a vital role for water supply in the Campanian Plain in Italy and hence a future sustainability of the resource is essential for the region. In the current paper novel data mining algorithms including Gaussian Process (GP) were used in a large groundwater quality database to predict nitrate (contaminant) and strontium (potential future increasing) concentrations in groundwater. The results were compared with M5P, random forest (RF) and random tree (RT) algorithms as a benchmark to test the robustness of the modeling process. The dataset includes 246 groundwater quality samples originating from different wells, municipals and agricultural. It was divided for the modeling process into two subgroups by using the 10-fold cross validation technique including 173 samples for model building (training dataset) and 73 samples for model validation (testing dataset). Different water quality variables including T, pH, EC, HCO3-, F-, Cl-, SO42-, Na+, K+, Mg2+, and Ca2+ have been used as an input to the models. At first stage, different input combinations have been constructed based on correlation coefficient and thus the optimal combination was chosen for the modeling phase. Different quantitative criteria alongside with visual comparison approach have been used for evaluating the modeling capability. Results revealed that to obtain reliable results also variables with low correlation should be considered as an input to the models together with those variables showing high correlation coefficients. According to the model evaluation criteria, GP algorithm outperforms all the other models in predicting both nitrate and strontium concentrations followed by RF, M5P and RT, respectively. Result also revealed that model's structure together with the accuracy and structure of the data can have a relevant impact on the model's results.
    Matched MeSH terms: Data Mining
  19. The Extended Polydimensional Immunome Characterization (EPIC) web-based reference and discovery tool for cytometry data
    Yeo JG, Wasser M, Kumar P, Pan L, Poh SL, Ally F, et al.
    Nat Biotechnol, 2020 06;38(6):679-684.
    PMID: 32440006 DOI: 10.1038/s41587-020-0532-1
    Matched MeSH terms: Data Mining
  20. Improved GART neural network model for pattern classification and rule extraction with application to power systems
    Yap KS, Lim CP, Au MT
    IEEE Trans Neural Netw, 2011 Dec;22(12):2310-23.
    PMID: 22067292 DOI: 10.1109/TNN.2011.2173502
    Generalized adaptive resonance theory (GART) is a neural network model that is capable of online learning and is effective in tackling pattern classification tasks. In this paper, we propose an improved GART model (IGART), and demonstrate its applicability to power systems. IGART enhances the dynamics of GART in several aspects, which include the use of the Laplacian likelihood function, a new vigilance function, a new match-tracking mechanism, an ordering algorithm for determining the sequence of training data, and a rule extraction capability to elicit if-then rules from the network. To assess the effectiveness of IGART and to compare its performances with those from other methods, three datasets that are related to power systems are employed. The experimental results demonstrate the usefulness of IGART with the rule extraction capability in undertaking classification problems in power systems engineering.
    Matched MeSH terms: Data Mining/methods*
Related Terms
Filters
Contact Us

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links