Displaying publications 1 - 20 of 203 in total

  1. Masuyama N, Loo CK, Wermter S
    Int J Neural Syst, 2019 Jun;29(5):1850052.
    PMID: 30764724 DOI: 10.1142/S0129065718500521
    This paper attempts to solve the typical problems of self-organizing growing network models, i.e. (a) an influence of the order of input data on the self-organizing ability, (b) an instability to high-dimensional data and an excessive sensitivity to noise, and (c) an expensive computational cost by integrating Kernel Bayes Rule (KBR) and Correntropy-Induced Metric (CIM) into Adaptive Resonance Theory (ART) framework. KBR performs a covariance-free Bayesian computation which is able to maintain a fast and stable computation. CIM is a generalized similarity measurement which can maintain a high-noise reduction ability even in a high-dimensional space. In addition, a Growing Neural Gas (GNG)-based topology construction process is integrated into the ART framework to enhance its self-organizing ability. The simulation experiments with synthetic and real-world datasets show that the proposed model has an outstanding stable self-organizing ability for various test environments.
    Matched MeSH terms: Bayes Theorem*
  2. Abdo A, Salim N
    ChemMedChem, 2009 Feb;4(2):210-8.
    PMID: 19072820 DOI: 10.1002/cmdc.200800290
    Many methods have been developed to capture the biological similarity between two compounds for use in drug discovery. A variety of similarity metrics have been introduced, the Tanimoto coefficient being the most prominent. Many of the approaches assume that molecular features or descriptors that do not relate to the biological activity carry the same weight as the important aspects in terms of biological similarity. Herein, a novel similarity searching approach using a Bayesian inference network is discussed. Similarity searching is regarded as an inference or evidential reasoning process in which the probability that a given compound has biological similarity with the query is estimated and used as evidence. Our experiments demonstrate that the similarity approach based on Bayesian inference networks is likely to outperform the Tanimoto similarity search and offer a promising alternative to existing similarity search approaches.
    Matched MeSH terms: Bayes Theorem*
  3. Salarzadeh Jenatabadi H, Moghavvemi S, Wan Mohamed Radzi CWJB, Babashamsi P, Arashi M
    PLoS One, 2017;12(9):e0182311.
    PMID: 28886019 DOI: 10.1371/journal.pone.0182311
    Learning is an intentional activity, with several factors affecting students' intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data) were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods' results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated.
    Matched MeSH terms: Bayes Theorem*
  4. Abdo A, Saeed F, Hamza H, Ahmed A, Salim N
    J Comput Aided Mol Des, 2012 Mar;26(3):279-87.
    PMID: 22249773 DOI: 10.1007/s10822-012-9543-4
    Query expansion is the process of reformulating an original query to improve retrieval performance in information retrieval systems. Relevance feedback is one of the most useful query modification techniques in information retrieval systems. In this paper, we introduce query expansion into ligand-based virtual screening (LBVS) using the relevance feedback technique. In this approach, a few high-ranking molecules of unknown activity are filtered from the outputs of a Bayesian inference network based on a single ligand molecule to form a set of ligand molecules. This set of ligand molecules is used to form a new ligand molecule. Simulated virtual screening experiments with the MDL Drug Data Report and maximum unbiased validation data sets show that the use of ligand expansion provides a very simple way of improving the LBVS, especially when the active molecules being sought have a high degree of structural heterogeneity. However, the effectiveness of the ligand expansion is slightly less when structurally-homogeneous sets of actives are being sought.
    Matched MeSH terms: Bayes Theorem
  5. 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.
    Matched MeSH terms: Bayes Theorem
  6. Ross IN, Abraham T
    Trans R Soc Trop Med Hyg, 1987;81(3):374-7.
    PMID: 3686631
    We used Bayes' theorem to calculate the probability of enteric fever in 260 patients presenting with undiagnosed fever, without recourse to blood or stool culture results. These individuals were divided into 110 patients with enteric fever (63 culture positive, 47 culture negative) and 150 patients with other causes of fever. Comparison of the frequencies of occurrence of 19 clinical and laboratory events, said to be helpful in the diagnosis of enteric fever, in the two groups revealed that only 8 events were significantly more frequent in enteric fever. These were: a positive Widal test at a screening dilution of 1:40; a peak temperature greater than = 39 degrees C; previous treatment for the fever; a white blood cell count less than 9 X 10(6)/litre; a polymorphonuclear leucocyte count less than 3.5 X 10(6)/litre; splenomegaly; fever duration greater than 7 d; and hepatomegaly. When the probability of enteric fever was determined prospectively in 110 patients, using only 6 of these discriminating events, the probability of patients with a positive prediction having enteric fever (diagnostic specificity) was 0.80 (95% confidence interval: 0.68 to 0.91) and the probability of those with a negative prediction not having enteric fever (diagnostic sensitivity) was 0.92 (0.85 to 0.99). Using all 19 events did not alter the diagnostic specificity or diagnostic sensitivity. This study shows that a small number of clinical and laboratory features can objectively discriminate enteric fever from other causes of fever in the majority of patients. Calculating the probability of enteric fever can aid in diagnosis, when culturing for salmonella is either unavailable or is negative.
    Matched MeSH terms: Bayes Theorem
  7. Yusoff, A.N., Mohamad, M., Hamid, K.A., Hamid, A.I.A., Manan, H.A., Hashim, M.H.
    ASM Science Journal, 2010;4(2):158-172.
    In this multiple-subject study, intrinsic couplings between the primary motor (M1) and supplementary motor areas (SMA) were investigated. Unilateral (UNIright and UNIleft) self-paced tapping of hand fingers were performed to activate M1 and SMA. The intrinsic couplings were analysed using statistical parametric mapping, dynamic causal modeling (DCM) and Bayesian model analysis. Brain activation observed for UNIright and UNIleft showed contralateral and ipsilateral involvement of M1 and SMA. Ten full connectivity models were constructed with right and left M1 and SMA as processing centres. DCM indicated that all subjects prefer M1 as the intrinsic input for UNIright and UNIleft as indicated by a large group Bayes factor (GBF). Positive evidence ratio (PER) that showed strong evidence of Model 3 and Model 6 against other models in at least 12 out of 16 subjects, supported GBF results. The GBF and PER results were later found to be consistent with that of BMS for group studies with high expected posterior probability and exceedance probability. It was concluded that during unilateral finger tapping, the contralateral M1 would act as the input centre which in turn triggered the propagation of signals to SMA in the same hemisphere and to M1 and SMA in the opposite hemisphere.
    Matched MeSH terms: Bayes Theorem
  8. Li LX, Abdul Rahman SS
    R Soc Open Sci, 2018 Jul;5(7):172108.
    PMID: 30109052 DOI: 10.1098/rsos.172108
    Students are characterized according to their own distinct learning styles. Discovering students' learning style is significant in the educational system in order to provide adaptivity. Past researches have proposed various approaches to detect the students' learning styles. Among all, the Bayesian network has emerged as a widely used method to automatically detect students' learning styles. On the other hand, tree augmented naive Bayesian network has the ability to improve the naive Bayesian network in terms of better classification accuracy. In this paper, we evaluate the performance of the tree augmented naive Bayesian in automatically detecting students' learning style in the online learning environment. The experimental results are promising as the tree augmented naive Bayes network is shown to achieve higher detection accuracy when compared to the Bayesian network.
    Matched MeSH terms: Bayes Theorem
  9. ChongYong, Chua, HongChoon, Ong
    Score-based structure learning algorithm is commonly used in learning the Bayesian Network. Other than searching strategy, scoring functions play a vital role in these algorithms. Many studies proposed various types of scoring functions with different characteristics. In this study, we compare the performances of five scoring functions: Bayesian Dirichlet equivalent-likelihood (BDe) score (equivalent sample size, ESS of 4 and 10), Akaike Information Criterion (AIC) score, Bayesian Information Criterion (BIC) score and K2 score. Instead of just comparing networks with different scores, we included different learning algorithms to study the relationship between score functions and greedy search learning algorithms. Structural hamming distance is used to measure the difference between networks obtained and the true network. The results are divided into two sections where the first section studies the differences between data with different number of variables and the second section studies the differences between data with different sample sizes. In general, the BIC score performs well and consistently for most data while the BDe score with an equivalent sample size of 4 performs better for data with bigger sample sizes.
    Matched MeSH terms: Bayes Theorem
  10. Grismer LL, Wood PLJ, Grismer JL, Quah ESH, Thy N, Phimmachak S, et al.
    Zootaxa, 2019 Jul 16;4638(2):zootaxa.4638.2.1.
    PMID: 31712473 DOI: 10.11646/zootaxa.4638.2.1
    An integrative taxonomic analysis of the Ptychozoon lionotum group across its range in Indochina and Sundaland recovers P. lionotum sensu lato Annandale, 1905 as paraphyletic with respect to P. popaense Grismer, Wood, Thura, Grismer, Brown, Stuart, 2018a and composed of four allopatric, genetically divergent, ND2 mitochondrial lineages. Multivariate and univariate analyses of continuous and discrete morphological and color pattern characters statistically and discretely diagnose each lineage from one another and together, with maximum likelihood and Bayesian inference analyses, provide the foundation for the recognition of each lineage as a new species-hypotheses corroborated with a Generalized Mixed Yule Coalescent species delimitation analysis. Ptychozoon cicakterbang sp. nov. ranges throughout Peninsular Malaysia to Pulau Natuna Besar, Indonesia; P. kabkaebin sp. nov. is endemic to northern and central Laos; and P. tokehos sp. nov. ranges from southern Thailand south of the Isthmus of Kra northward to Chiang Mai, fringing the Chao Phraya Basin and ranging southward through Cambodia to southern Vietnam. Ptychozoon lionotum sensu stricto ranges from northwestern Laos through southern Myanmar to eastern India. The phylogeographic structure within each species varies considerably with P. lionotum s.s. showing no genetic divergence across its 1,100 km range compared to P. cicakterbang sp. nov. showing upwards of 8.2% sequence divergence between syntopic individuals. Significant phylogeographic structure exists within P. tokehos sp. nov. and increased sampling throughout Thailand may require additional taxonomic changes within this species.
    Matched MeSH terms: Bayes Theorem
  11. Lokanathan Y, Adura Mohd-Adnan, Sheila Nathan
    Sains Malaysiana, 2016;45:1969-1979.
    Protein antigen-i parasit ikan C. irritans berpotensi tinggi digunakan sebagai calon dalam pembangunan vaksin komersial terhadap C. irritans. Walau bagaimanapun, kewujudan variasi pada antigen-i serotip C. irritans yang berbeza mempengaruhi tahap perlindungan yang bakal diberikan terhadap varians C. irritans yang berbeza apabila antigen-i digunakan sebagai vaksin. Kajian ini dijalankan untuk membandingkan jujukan pelbagai antigen-i pencilan C. irritans di Malaysia berbanding antigen-i pencilan C. irritans yang pernah dilaporkan. Perbandingan filogenetik dijalankan untuk meramalkan potensi protein tersebut dalam usaha membangunkan calon serodiagnostik dan pemvaksinan terhadap pencilan C. irritans yang berlainan. Penjajaran jujukan berbilang bagi jujukan asid amino antigen-i dilakukan dengan perisian CLUSTALX dan analisis filogenetik antigen-i dilakukan menggunakan kaedah parsimoni maksimum (MP) dan kaedah Bayes. Sembilan transkrip unik (TU) C. irritans yang mempunyai padanan signifikan dengan antigen-i di pangkalan data protein NCBI didapati mempunyai peratus kesamaan antara 41% hingga 71%. Kedua-dua pohon MP dan Bayesian yang dijana menunjukkan varians antigen-i cn56 and cn57 terkelompok bersama dalam satu kumpulan manakala varians antigen-i yang lain terbahagi kepada dua kumpulan berasingan dan pengkelompokan ini disokong oleh kehadiran asid amino yang terpulihara dalam kumpulan masing-masing. Kajian lanjutan boleh dilakukan untuk mengenal pasti varians antigen-i yang sesuai sebagai calon serodiagnosis dan juga dapat memberi perlindungan silang terhadap pelbagai pencilan C. irritans di serata dunia.
    Matched MeSH terms: Bayes Theorem
  12. Kamarulzaman Bin Ibrahim, Abdul Aziz Jermain
    Dengue is one of the main factors of mortality of inhabitants in the region of South East Asia. Malaysia is one of the countries which is facing a high incidence of dengue, particularly in the 70's and early 80's. The Ministry of Health has taken various measures in order to reduce the dengue epidemic. These include educating people about dengue and conducting research such as investigation of factors that influence the epidemic of dengue. In this study, a sequential Bayesian approach is applied to data of the proportion of death due to dengue over the period from 1982 to 1992. In the sequential Bayesian approach, the data for the year 1982 becomes the prior information for the 1983 data and so on. The data for the different periods are combined in a chronological manner until the final posterior distribution of the proportion of death due to dengue is obtained. It is found that the overall proportion is 0.59% and its standard deviation is 0.00002%.
    Denggi adalah satu daripada faktor utama kematian bagi penduduk di rantau Asia Tenggara. Malaysia pula merupakan satu daripada negara yang sedang mengalami kadar insiden denggi yang tinggi, khususnya dalam tahun 70-an dan pada awal 80-an. Kementerian Kesihatan telah mengambil pelbagai langkah untuk mengurangkan wabak denggi. Ini termasuk memberikan pendidikan tentang denggi dan membuat kajian tentang faktor-faktor yang mempengaruhinya. Dalam kajian ini kaedah Bayesan jujukan digunakan terhadap data perkadaran yang mati akibat denggi dalam tempoh 1982 hingga 1992. Dalam kaedah ini, data tahun 1982 digunakan sebagai maklumat prior untuk data tahun 1983 dan seterusnya. Data dari tahun yang berlainan digabungkan secara kronologi sehingga diperoleh taburan posterior yang terakhir bagi perkadaran yang mati akibat denggi. Didapati bahawa perkadaran keseluruhan ialah 0.59% dan sisihan piawainya 0.00002%.
    Matched MeSH terms: Bayes Theorem
  13. Haliza Abd. Rahman, Arifah Bahar, Norhayati Rosli, Madihah Md. Salleh
    Sains Malaysiana, 2012;41:1635-1642.
    Non-parametric modeling is a method which relies heavily on data and motivated by the smoothness properties in estimating a function which involves spline and non-spline approaches. Spline approach consists of regression spline and smoothing spline. Regression spline with Bayesian approach is considered in the first step of a two-step method in estimating the structural parameters for stochastic differential equation (SDE). The selection of knot and order of spline can be done heuristically based on the scatter plot. To overcome the subjective and tedious process of selecting the optimal knot and order of spline, an algorithm was proposed. A single optimal knot is selected out of all the points with exception of the first and the last data which gives the least value of Generalized Cross Validation (GCV) for each order of spline. The use is illustrated using observed data of opening share prices of Petronas Gas Bhd. The results showed that the Mean Square Errors (MSE) for stochastic model with parameters estimated using optimal knot for 1,000, 5,000 and 10,000 runs of Brownian motions are smaller than the SDE models with estimated parameters using knot selected heuristically. This verified the viability of the two-step method in the estimation of the drift and diffusion parameters of SDE with an improvement of a single knot selection.
    Matched MeSH terms: Bayes Theorem
  14. Kamarulzaman bin Ibrahim
    An integral art of the Bayesian approach which is not present in the classical approach is the prior distribution. Different researchers may have different level of prior knowledge regarding the parameter of interest before seeing the data. Sometimes different prior distributions can result in different decisions, as such investigations have to be careful in making the choice of the prior distribution. In this paper, we compare results from the Bayesian analyses based on three possible choices of the prior distributions, which are uniform prior, lognormal prior and an improper prior, in the evaluation of the effectiveness of mini-roundabouts. Data from five before and after studies into the effect of mini-roundabouts when replacing priority junctions are used. The effects of the different prior distributions are distinguishable from the analysis of an anamolous 'desk-drawer' study. The uniform and improper prior pull the estimated treatment effect away from one more than the lognormal prior. The results based on lognormal prior depict a less worst scenario of the ineffectiveness of mini-roundabouts and this may correspond to the deficiency in engineering design at only a few sites. Consequently, it is more appropriate to use the lognormal prior in the analysis of mini-roundabouts as a road safety measure.
    Satu ciri yang penting dalam kaedah Bayesian yang tidak ada dalam kaedah klasik ialah taburan prior. Sebelum melihat data, mungkin setiap penyelidik mempunyai tahap pengetahuan prior yang berbeza berkenaan sesuatu parameter yang ingin dikaji. Kadang-kala taburan prior yang berlainan boleh menghasilkan keputusan yang berlainan. Oleh itu, pengkaji perlu berhati-hati dalam memilih taburan prior. Dalam kertas-kerja ini, kami bandingkan keputusan dari analisis Bayesian berdasarkan tiga pilihan taburan prior yang menasabah iaitu prior seragam, prior lognormal dan prior tak wajar untuk menilai keberkesanan bulatan mini. Data dari kajian-kajian sebelum dan selepas terhadap kesan mengantikan persimpangan dengan bulatan mini digunakan. Kesan taburan prior yang berlainan dapat dibezakan berasaskan keputusan analisis terhadap satu kajian 'laci-meja' yang ganjil. Prior seragam dan prior fak wajar felah menyebabkan anggaran nilai kesan rawatan melebihi satu lebih dari prior lognormal. Keputusan berasaskan prior lognormal ini menunjukkan senario yang kurang teruk tentang kurang berkesannya bulatan mini dan mungkin ini boleh dikaitkan dengan rekabentuk kejuruteraan yang tidak baik di beberapa tempat sahaja. Dengan itu, prior lognormal adalah lebih sesuai digunakan untuk menilai bulatan mini sebagai langkah keselamatan jalanraya.
    Matched MeSH terms: Bayes Theorem
  15. Annazirin Eli, Mardhiyyah Shaffie, Wan Zawiah W
    Sains Malaysiana, 2012;41:1403-1410.
    Statistical modeling of extreme rainfall is essential since the results can often facilitate civil engineers and planners to estimate the ability of building structures to survive under the utmost extreme conditions. Data comprising of annual maximum series (AMS) of extreme rainfall in Alor Setar were fitted to Generalized Extreme Value (GEV) distribution using method of maximum likelihood (ML) and Bayesian Markov Chain Monte Carlo (MCMC) simulations. The weakness of ML method in handling small sample is hoped to be tackled by means of Bayesian MCMC simulations in this study. In order to obtain the posterior densities, non-informative and independent priors were employed. Performances of parameter estimations were verified by conducting several goodness-of-fit tests. The results showed that Bayesian MCMC method was slightly better than ML method in estimating GEV parameters.
    Matched MeSH terms: Bayes Theorem
  16. Nuzlinda Abdul Rahman, Abdul Aziz Jemain, Kamarulzaman Ibrahim, Ahmad Mahir Razali
    Kajian ini bertujuan untuk memetakan kes kemortalan bayi mengikut daerah di Semenanjung Malaysia bagi tahun 1991 hingga 2000. Penganggaran risiko relatif berdasarkan kaedah Bayes empirik telah digunakan dalam kajian ini. Tiga kaedah penganggaran parameter dihuraikan iaitu kaedah momen, kaedah kebolehjadian maksimum dan kaedah penganggaran gabungan momen dan kebolehjadian maksimum. Keteguhan anggaran parameter yang diperoleh diuji menggunakan kaedah Bootstrap. Hasil kajian mendapati jurang antara kawasan berisiko rendah dengan kawasan berisiko tinggi adalah lebih besar pada awal dekad 2000 berbanding pada awal dekad 1990-an walaupun pada dasarnya kadar mortaliti bayi secara keseluruhannya adalah semakin berkurangan pada peringkat nasional. Kawasan pantai timur Semenanjung Malaysia masih pada takuk yang sama iaitu masih berada dalam kategori berisiko tinggi sepanjang tempoh yang dikaji. Seterusnya, gambaran terdapatnya tompokan risiko juga turut terpapar dalam peta yang dihasilkan. Berdasarkan kaedah Bootstrap, parameter-parameter yang dianggarkan dalam kajian ini adalah teguh.
    Matched MeSH terms: Bayes Theorem
  17. Yavari Nejad F, Varathan KD
    BMC Med Inform Decis Mak, 2021 04 30;21(1):141.
    PMID: 33931058 DOI: 10.1186/s12911-021-01493-y
    BACKGROUND: Dengue fever is a widespread viral disease and one of the world's major pandemic vector-borne infections, causing serious hazard to humanity. The World Health Organisation (WHO) reported that the incidence of dengue fever has increased dramatically across the world in recent decades. WHO currently estimates an annual incidence of 50-100 million dengue infections worldwide. To date, no tested vaccine or treatment is available to stop or prevent dengue fever. Thus, the importance of predicting dengue outbreaks is significant. The current issue that should be addressed in dengue outbreak prediction is accuracy. A limited number of studies have conducted an in-depth analysis of climate factors in dengue outbreak prediction.

    METHODS: The most important climatic factors that contribute to dengue outbreaks were identified in the current work. Correlation analyses were performed in order to determine these factors and these factors were used as input parameters for machine learning models. Top five machine learning classification models (Bayes network (BN) models, support vector machine (SVM), RBF tree, decision table and naive Bayes) were chosen based on past research. The models were then tested and evaluated on the basis of 4-year data (January 2010 to December 2013) collected in Malaysia.

    RESULTS: This research has two major contributions. A new risk factor, called the TempeRain factor (TRF), was identified and used as an input parameter for the model of dengue outbreak prediction. Moreover, TRF was applied to demonstrate its strong impact on dengue outbreaks. Experimental results showed that the Bayes Network model with the new meteorological risk factor identified in this study increased accuracy to 92.35% for predicting dengue outbreaks.

    CONCLUSIONS: This research explored the factors used in dengue outbreak prediction systems. The major contribution of this study is identifying new significant factors that contribute to dengue outbreak prediction. From the evaluation result, we obtained a significant improvement in the accuracy of a machine learning model for dengue outbreak prediction.

    Matched MeSH terms: Bayes Theorem
  18. Aznan A, Gonzalez Viejo C, Pang A, Fuentes S
    Sensors (Basel), 2021 Sep 23;21(19).
    PMID: 34640673 DOI: 10.3390/s21196354
    Rice quality assessment is essential for meeting high-quality standards and consumer demands. However, challenges remain in developing cost-effective and rapid techniques to assess commercial rice grain quality traits. This paper presents the application of computer vision (CV) and machine learning (ML) to classify commercial rice samples based on dimensionless morphometric parameters and color parameters extracted using CV algorithms from digital images obtained from a smartphone camera. The artificial neural network (ANN) model was developed using nine morpho-colorimetric parameters to classify rice samples into 15 commercial rice types. Furthermore, the ANN models were deployed and evaluated on a different imaging system to simulate their practical applications under different conditions. Results showed that the best classification accuracy was obtained using the Bayesian Regularization (BR) algorithm of the ANN with ten hidden neurons at 91.6% (MSE = <0.01) and 88.5% (MSE = 0.01) for the training and testing stages, respectively, with an overall accuracy of 90.7% (Model 2). Deployment also showed high accuracy (93.9%) in the classification of the rice samples. The adoption by the industry of rapid, reliable, and accurate methods, such as those presented here, may allow the incorporation of different morpho-colorimetric traits in rice with consumer perception studies.
    Matched MeSH terms: Bayes Theorem
  19. Zulkifley MA, Mustafa MM, Hussain A, Mustapha A, Ramli S
    PLoS One, 2014;9(12):e114518.
    PMID: 25485630 DOI: 10.1371/journal.pone.0114518
    Recycling is one of the most efficient methods for environmental friendly waste management. Among municipal wastes, plastics are the most common material that can be easily recycled and polyethylene terephthalate (PET) is one of its major types. PET material is used in consumer goods packaging such as drinking bottles, toiletry containers, food packaging and many more. Usually, a recycling process is tailored to a specific material for optimal purification and decontamination to obtain high grade recyclable material. The quantity and quality of the sorting process are limited by the capacity of human workers that suffer from fatigue and boredom. Several automated sorting systems have been proposed in the literature that include using chemical, proximity and vision sensors. The main advantages of vision based sensors are its environmentally friendly approach, non-intrusive detection and capability of high throughput. However, the existing methods rely heavily on deterministic approaches that make them less accurate as the variations in PET plastic waste appearance are too high. We proposed a probabilistic approach of modeling the PET material by analyzing the reflection region and its surrounding. Three parameters are modeled by Gaussian and exponential distributions: color, size and distance of the reflection region. The final classification is made through a supervised training method of likelihood ratio test. The main novelty of the proposed method is the probabilistic approach in integrating various PET material signatures that are contaminated by stains under constant lighting changes. The system is evaluated by using four performance metrics: precision, recall, accuracy and error. Our system performed the best in all evaluation metrics compared to the benchmark methods. The system can be further improved by fusing all neighborhood information in decision making and by implementing the system in a graphics processing unit for faster processing speed.
    Matched MeSH terms: Bayes Theorem*
  20. Komahan K, Reidpath DD
    Am J Epidemiol, 2014 Aug 1;180(3):325-9.
    PMID: 24944286 DOI: 10.1093/aje/kwu129
    Correct identification of ethnicity is central to many epidemiologic analyses. Unfortunately, ethnicity data are often missing. Successful classification typically relies on large databases (n > 500,000 names) of known name-ethnicity associations. We propose an alternative naïve Bayesian strategy that uses substrings of full names. Name and ethnicity data for Malays, Indians, and Chinese were provided by a health and demographic surveillance site operating in Malaysia from 2011-2013. The data comprised a training data set (n = 10,104) and a test data set (n = 9,992). Names were spliced into contiguous 3-letter substrings, and these were used as the basis for the Bayesian analysis. Performance was evaluated on both data sets using Cohen's κ and measures of sensitivity and specificity. There was little difference between the classification performance in the training and test data (κ = 0.93 and 0.94, respectively). For the test data, the sensitivity values for the Malay, Indian, and Chinese names were 0.997, 0.855, and 0.932, respectively, and the specificity values were 0.907, 0.998, and 0.997, respectively. A naïve Bayesian strategy for the classification of ethnicity is promising. It performs at least as well as more sophisticated approaches. The possible application to smaller data sets is particularly appealing. Further research examining other substring lengths and other ethnic groups is warranted.
    Matched MeSH terms: Bayes Theorem*
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