Displaying publications 21 - 40 of 253 in total

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  1. Elucidating the effect of process parameters on the production of hydrogen-rich syngas by biomass and coal Co-gasification techniques: A multi-criteria modeling approach
    Bahadar A, Kanthasamy R, Sait HH, Zwawi M, Algarni M, Ayodele BV, et al.
    Chemosphere, 2022 Jan;287(Pt 1):132052.
    PMID: 34478965 DOI: 10.1016/j.chemosphere.2021.132052
    The thermochemical processes such as gasification and co-gasification of biomass and coal are promising route for producing hydrogen-rich syngas. However, the process is characterized with complex reactions that pose a tremendous challenge in terms of controlling the process variables. This challenge can be overcome using appropriate machine learning algorithm to model the nonlinear complex relationship between the predictors and the targeted response. Hence, this study aimed to employ various machine learning algorithms such as regression models, support vector machine regression (SVM), gaussian processing regression (GPR), and artificial neural networks (ANN) for modeling hydrogen-rich syngas production by gasification and co-gasification of biomass and coal. A total of 12 machine learning algorithms which comprises the regression models, SVM, GPR, and ANN were configured, trained using 124 datasets. The performances of the algorithms were evaluated using the coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). In all cases, the ANN algorithms offer superior performances and displayed robust predictions of the hydrogen-rich syngas from the co-gasification processes. The R2 of both the Levenberg-Marquardt- and Bayesian Regularization-trained ANN obtained from the prediction of the hydrogen-rich syngas was found to be within 0.857-0.998 with low prediction errors. The sensitivity analysis to determine the effect of the process parameters on the model output revealed that all the parameters showed a varying level of influence. In most of the processes, the gasification temperature was found to have the most significant influence on the model output.
    Matched MeSH terms: Bayes Theorem
  2. Fulltext On Bayesian approach to composite Pareto models
    Abdul Majid MH, Ibrahim K
    PLoS One, 2021;16(9):e0257762.
    PMID: 34555115 DOI: 10.1371/journal.pone.0257762
    In data modelling using the composite Pareto distribution, any observations above a particular threshold value are assumed to follow Pareto type distribution, whereas the rest of the observations are assumed to follow a different distribution. This paper proposes on the use of Bayesian approach to the composite Pareto models involving specification of the prior distribution on the proportion of data coming from the Pareto distribution, instead of assuming the prior distribution on the threshold, as often done in the literature. Based on a simulation study, it is found that the parameter estimates determined when using uniform prior on the proportion is less biased as compared to the point estimates determined when using uniform prior on the threshold. Applications on income data and finance are included for illustrative examples.
    Matched MeSH terms: Bayes Theorem
  3. Fulltext Bayesian brain in tinnitus: Computational modeling of three perceptual phenomena using a modified Hierarchical Gaussian Filter
    Hu S, Hall DA, Zubler F, Sznitman R, Anschuetz L, Caversaccio M, et al.
    Hear Res, 2021 10;410:108338.
    PMID: 34469780 DOI: 10.1016/j.heares.2021.108338
    Recently, Bayesian brain-based models emerged as a possible composite of existing theories, providing an universal explanation of tinnitus phenomena. Yet, the involvement of multiple synergistic mechanisms complicates the identification of behavioral and physiological evidence. To overcome this, an empirically tested computational model could support the evaluation of theoretical hypotheses by intrinsically encompassing different mechanisms. The aim of this work was to develop a generative computational tinnitus perception model based on the Bayesian brain concept. The behavioral responses of 46 tinnitus subjects who underwent ten consecutive residual inhibition assessments were used for model fitting. Our model was able to replicate the behavioral responses during residual inhibition in our cohort (median linear correlation coefficient of 0.79). Using the same model, we simulated two additional tinnitus phenomena: residual excitation and occurrence of tinnitus in non-tinnitus subjects after sensory deprivation. In the simulations, the trajectories of the model were consistent with previously obtained behavioral and physiological observations. Our work introduces generative computational modeling to the research field of tinnitus. It has the potential to quantitatively link experimental observations to theoretical hypotheses and to support the search for neural signatures of tinnitus by finding correlates between the latent variables of the model and measured physiological data.
    Matched MeSH terms: Bayes Theorem
  4. Fulltext Comparison of scoring functions on greedy search Bayesian network learning algorithms
    ChongYong, Chua, HongChoon, Ong
    Pertanika Journal of Science & Technology, 2017;25(3):719-734.
    MyJurnal
    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
  5. Multiclass tumor identification using combined texture and statistical features
    Latif G, Bashar A, Awang Iskandar DNF, Mohammad N, Brahim GB, Alghazo JM
    Med Biol Eng Comput, 2023 Jan;61(1):45-59.
    PMID: 36323980 DOI: 10.1007/s11517-022-02687-w
    Early detection and diagnosis of brain tumors are essential for early intervention and eventually successful treatment plans leading to either a full recovery or an increase in the patient lifespan. However, diagnosis of brain tumors is not an easy task since it requires highly skilled professionals, making this procedure both costly and time-consuming. The diagnosis process relying on MR images gets even harder in the presence of similar objects in terms of their density, size, and shape. No matter how skilled professionals are, their task is still prone to human error. The main aim of this work is to propose a system that can automatically classify and diagnose glioma brain tumors into one of the four tumor types: (1) necrosis, (2) edema, (3) enhancing, and (4) non-enhancing. In this paper, we propose a combined texture discrete wavelet transform (DWT) and statistical features based on the first- and second-order features for the accurate classification and diagnosis of multiclass glioma tumors. Four well-known classifiers, namely, support vector machines (SVM), random forest (RF), multilayer perceptron (MLP), and naïve Bayes (NB), are used for classification. The BraTS 2018 dataset is used for the experiments, and with the combined DWT and statistical features, the RF classifier achieved the highest average accuracy whether for separated modalities or combined modalities. The highest average accuracy of 89.59% and 90.28% for HGG and LGG, respectively, was reported in this paper. It has also been observed that the proposed method outperforms similar existing methods reported in the extant literature.
    Matched MeSH terms: Bayes Theorem
  6. Fulltext Comparison of classification algorithms for predicting autistic spectrum disorder using WEKA modeler
    Mohd Radzi SF, Hassan MS, Mohd Radzi MAH
    BMC Med Inform Decis Mak, 2022 Nov 24;22(1):306.
    PMID: 36434656 DOI: 10.1186/s12911-022-02050-x
    BACKGROUND: In healthcare area, big data, if integrated with machine learning, enables health practitioners to predict the result of a disorder or disease more accurately. In Autistic Spectrum Disorder (ASD), it is important to screen the patients to enable them to undergo proper treatments as early as possible. However, difficulties may arise in predicting ASD occurrences accurately, mainly caused by human errors. Data mining, if embedded into health screening practice, can help to overcome the difficulties. This study attempts to evaluate the performance of six best classifiers, taken from existing works, at analysing ASD screening training dataset.

    RESULT: We tested Naive Bayes, Logistic Regression, KNN, J48, Random Forest, SVM, and Deep Neural Network algorithms to ASD screening dataset and compared the classifiers' based on significant parameters; sensitivity, specificity, accuracy, receiver operating characteristic, area under the curve, and runtime, in predicting ASD occurrences. We also found that most of previous studies focused on classifying health-related dataset while ignoring the missing values which may contribute to significant impacts to the classification result which in turn may impact the life of the patients. Thus, we addressed the missing values by implementing imputation method where they are replaced with the mean of the available records found in the dataset.

    CONCLUSION: We found that J48 produced promising results as compared to other classifiers when tested in both circumstances, with and without missing values. Our findings also suggested that SVM does not necessarily perform well for small and simple datasets. The outcome is hoped to assist health practitioners in making accurate diagnosis of ASD occurrences in patients.

    Matched MeSH terms: Bayes Theorem
  7. Fulltext Characterization of the plastid genome of Cratoxylum species (Hypericaceae) and new insights into phylogenetic relationships
    Sudmoon R, Kaewdaungdee S, Tanee T, Siripiyasing P, Ameamsri U, Syazwan SA, et al.
    Sci Rep, 2022 Nov 05;12(1):18810.
    PMID: 36335203 DOI: 10.1038/s41598-022-23639-2
    To expand the genomic information of Hypericaceae, particularly on Cratoxylum, we characterized seven novel complete plastid genomes (plastomes) of five Cratoxylum and two of its allied taxa, including C. arborescens, C. formosum subsp. formosum, C. formosum subsp. pruniflorum, C. maingayi, C. sumatranum, Hypericum hookerianum, and Triadenum breviflorum. For Cratoxylum, the plastomes ranged from 156,962 to 157,792 bp in length. Genomic structure and gene contents were observed in the five plastomes, and were comprised of 128-129 genes, which includes 83-84 protein-coding (CDS), 37 tRNA, and eight rRNA genes. The plastomes of H. hookerianum and T. breviflorum were 138,260 bp and 167,693 bp, respectively. A total of 110 and 127 genes included 72 and 82 CDS, 34 and 37 tRNA, as well as four and eight rRNA genes. The reconstruction of the phylogenetic trees using maximum likelihood (ML) and Bayesian inference (BI) trees based on the concatenated CDS and internal transcribed spacer (ITS) sequences that were analyzed separately have revealed the same topology structure at genus level; Cratoxylum is monophyletic. However, C. formosum subsp. pruniflorum was not clustered together with its origin, raising doubt that it should be treated as a distinct species, C. pruniflorum based on molecular evidence that was supported by morphological descriptions.
    Matched MeSH terms: Bayes Theorem
  8. Fulltext Occupational Injury Risk Mitigation: Machine Learning Approach and Feature Optimization for Smart Workplace Surveillance
    Khairuddin MZF, Lu Hui P, Hasikin K, Abd Razak NA, Lai KW, Mohd Saudi AS, et al.
    Int J Environ Res Public Health, 2022 Oct 27;19(21).
    PMID: 36360843 DOI: 10.3390/ijerph192113962
    Forecasting the severity of occupational injuries shall be all industries' top priority. The use of machine learning is theoretically valuable to assist the predictive analysis, thus, this study attempts to propose a feature-optimized predictive model for anticipating occupational injury severity. A public database of 66,405 occupational injury records from OSHA is analyzed using five sets of machine learning models: Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, Decision Tree, and Random Forest. For model comparison, Random Forest outperformed other models with higher accuracy and F1-score. Therefore, it highlighted the potential of ensemble learning as a more accurate prediction model in the field of occupational injury. In constructing the model, this study also proposed the feature optimization technique that revealed the three most important features; 'nature of injury', 'type of event', and 'affected body part' in developing model. The accuracy of the Random Forest model was improved by 0.5% or 0.895 and 0.954 for the prediction of hospitalization and amputation, respectively by redeveloping and optimizing the model with hyperparameter tuning. The feature optimization is essential in providing insight knowledge to the Safety and Health Practitioners for future injury corrective and preventive strategies. This study has shown promising potential for smart workplace surveillance.
    Matched MeSH terms: Bayes Theorem
  9. Preferences in medications for patients seeking treatment for opioid use disorder: A conjoint analysis
    Muthulingam D, Hassett TC, Madden LM, Bromberg DJ, Fraenkel L, Altice FL
    J Subst Use Addict Treat, 2023 Nov;154:209138.
    PMID: 37544510 DOI: 10.1016/j.josat.2023.209138
    INTRODUCTION: The opioid epidemic continues to be a public health crisis that has worsened during the COVID-19 pandemic. Medications for opioid use disorder (MOUD) are the most effective way to reduce complications from opioid use disorder (OUD), but uptake is limited by both structural and individual factors. To inform strategies addressing individual factors, we evaluated patients' preferences and trade-offs in treatment decisions using conjoint analysis.

    METHOD: We developed a conjoint analysis survey evaluating patients' preferences for FDA-approved MOUDs. We recruited patients with OUD presenting to initiate treatment. This survey included five attributes: induction, location and route of administration, impact on mortality, side effects, and withdrawal symptoms with cessation. Participants performed 12 choice sets, each with two hypothetical profiles and a "none" option. We used Hierarchical Bayes to identify relative importance of each attribute and part-worth utility scores of levels, which we compared using chi-squared analysis. We used the STROBE checklist to guide our reporting of this cross-sectional observational study.

    RESULTS: Five-hundred and thirty participants completed the study. Location with route of administration was the most important attribute. Symptom relief during induction and withdrawal was a second priority. Mortality followed by side effects had lowest relative importance. Attribute levels with highest part-worth utilities showed patients preferred monthly pick-up from a pharmacy rather than daily supervised dosing; and oral medications more than injection/implants, despite the latter's infrequency.

    CONCLUSION: We measured treatment preferences among patients seeking to initiate OUD treatment to inform strategies to scale MOUD treatment uptake. Patients prioritize the route of administration in treatment preference-less frequent pick up, but also injections and implants were less preferred despite their convenience. Second, patients prioritize symptom relief during the induction and withdrawal procedures of medication. These transition periods influence the sustainability of treatment. Although health professionals prioritize mortality, it did not drive decision-making for patients. To our knowledge, this is the largest study on patients' preferences for MOUD among treatment-seeking people with OUD to date. Future analysis will evaluate patient preference heterogeneity to further target program planning, counseling, and decision aid development.

    Matched MeSH terms: Bayes Theorem
  10. Fulltext Improved accuracy and less fault prediction errors via modified sequential minimal optimization algorithm
    Asim Shahid M, Alam MM, Mohd Su'ud M
    PLoS One, 2023;18(4):e0284209.
    PMID: 37053173 DOI: 10.1371/journal.pone.0284209
    The benefits and opportunities offered by cloud computing are among the fastest-growing technologies in the computer industry. Additionally, it addresses the difficulties and issues that make more users more likely to accept and use the technology. The proposed research comprised of machine learning (ML) algorithms is Naïve Bayes (NB), Library Support Vector Machine (LibSVM), Multinomial Logistic Regression (MLR), Sequential Minimal Optimization (SMO), K Nearest Neighbor (KNN), and Random Forest (RF) to compare the classifier gives better results in accuracy and less fault prediction. In this research, the secondary data results (CPU-Mem Mono) give the highest percentage of accuracy and less fault prediction on the NB classifier in terms of 80/20 (77.01%), 70/30 (76.05%), and 5 folds cross-validation (74.88%), and (CPU-Mem Multi) in terms of 80/20 (89.72%), 70/30 (90.28%), and 5 folds cross-validation (92.83%). Furthermore, on (HDD Mono) the SMO classifier gives the highest percentage of accuracy and less fault prediction fault in terms of 80/20 (87.72%), 70/30 (89.41%), and 5 folds cross-validation (88.38%), and (HDD-Multi) in terms of 80/20 (93.64%), 70/30 (90.91%), and 5 folds cross-validation (88.20%). Whereas, primary data results found RF classifier gives the highest percentage of accuracy and less fault prediction in terms of 80/20 (97.14%), 70/30 (96.19%), and 5 folds cross-validation (95.85%) in the primary data results, but the algorithm complexity (0.17 seconds) is not good. In terms of 80/20 (95.71%), 70/30 (95.71%), and 5 folds cross-validation (95.71%), SMO has the second highest accuracy and less fault prediction, but the algorithm complexity is good (0.3 seconds). The difference in accuracy and less fault prediction between RF and SMO is only (.13%), and the difference in time complexity is (14 seconds). We have decided that we will modify SMO. Finally, the Modified Sequential Minimal Optimization (MSMO) Algorithm method has been proposed to get the highest accuracy & less fault prediction errors in terms of 80/20 (96.42%), 70/30 (96.42%), & 5 fold cross validation (96.50%).
    Matched MeSH terms: Bayes Theorem
  11. Fulltext Diabetes disease prediction system using HNB classifier based on discretization method
    Al-Hameli BA, Alsewari AA, Basurra SS, Bhogal J, Ali MAH
    J Integr Bioinform, 2023 Mar 01;20(1).
    PMID: 36810102 DOI: 10.1515/jib-2021-0037
    Diagnosing diabetes early is critical as it helps patients live with the disease in a healthy way - through healthy eating, taking appropriate medical doses, and making patients more vigilant in their movements/activities to avoid wounds that are difficult to heal for diabetic patients. Data mining techniques are typically used to detect diabetes with high confidence to avoid misdiagnoses with other chronic diseases whose symptoms are similar to diabetes. Hidden Naïve Bayes is one of the algorithms for classification, which works under a data-mining model based on the assumption of conditional independence of the traditional Naïve Bayes. The results from this research study, which was conducted on the Pima Indian Diabetes (PID) dataset collection, show that the prediction accuracy of the HNB classifier achieved 82%. As a result, the discretization method increases the performance and accuracy of the HNB classifier.
    Matched MeSH terms: Bayes Theorem
  12. Fulltext An Image Processing Approach for Detection of Prenatal Heart Disease
    Selvan S, Thangaraj SJJ, Samson Isaac J, Benil T, Muthulakshmi K, Almoallim HS, et al.
    Biomed Res Int, 2022;2022:2003184.
    PMID: 35958813 DOI: 10.1155/2022/2003184
    Prenatal heart disease, generally known as cardiac problems (CHDs), is a group of ailments that damage the heartbeat and has recently now become top deaths worldwide. It connects a plethora of cardiovascular diseases risks to the urgent in need of accurate, trustworthy, and effective approaches for early recognition. Data preprocessing is a common method for evaluating big quantities of information in the medical business. To help clinicians forecast heart problems, investigators utilize a range of data mining algorithms to examine enormous volumes of intricate medical information. The system is predicated on classification models such as NB, KNN, DT, and RF algorithms, so it includes a variety of cardiac disease-related variables. It takes do with an entire dataset from the medical research database of patients with heart disease. The set has 300 instances and 75 attributes. Considering their relevance in establishing the usefulness of alternate approaches, only 15 of the 75 criteria are examined. The purpose of this research is to predict whether or not a person will develop cardiovascular disease. According to the statistics, naïve Bayes classifier has the highest overall accuracy.
    Matched MeSH terms: Bayes Theorem
  13. Fulltext Detection of Iris Presentation Attacks Using Feature Fusion of Thepade's Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features
    Khade S, Gite S, Thepade SD, Pradhan B, Alamri A
    Sensors (Basel), 2021 Nov 08;21(21).
    PMID: 34770715 DOI: 10.3390/s21217408
    Iris biometric detection provides contactless authentication, preventing the spread of COVID-19-like contagious diseases. However, these systems are prone to spoofing attacks attempted with the help of contact lenses, replayed video, and print attacks, making them vulnerable and unsafe. This paper proposes the iris liveness detection (ILD) method to mitigate spoofing attacks, taking global-level features of Thepade's sorted block truncation coding (TSBTC) and local-level features of the gray-level co-occurrence matrix (GLCM) of the iris image. Thepade's SBTC extracts global color texture content as features, and GLCM extracts local fine-texture details. The fusion of global and local content presentation may help distinguish between live and non-live iris samples. The fusion of Thepade's SBTC with GLCM features is considered in experimental validations of the proposed method. The features are used to train nine assorted machine learning classifiers, including naïve Bayes (NB), decision tree (J48), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), and ensembles (SVM + RF + NB, SVM + RF + RT, RF + SVM + MLP, J48 + RF + MLP) for ILD. Accuracy, precision, recall, and F-measure are used to evaluate the performance of the projected ILD variants. The experimentation was carried out on four standard benchmark datasets, and our proposed model showed improved results with the feature fusion approach. The proposed fusion approach gave 99.68% accuracy using the RF + J48 + MLP ensemble of classifiers, immediately followed by the RF algorithm, which gave 95.57%. The better capability of iris liveness detection will improve human-computer interaction and security in the cyber-physical space by improving person validation.
    Matched MeSH terms: Bayes Theorem
  14. Fulltext Deep learning in public health: Comparative predictive models for COVID-19 case forecasting
    Tariq MU, Ismail SB
    PLoS One, 2024;19(3):e0294289.
    PMID: 38483948 DOI: 10.1371/journal.pone.0294289
    The COVID-19 pandemic has had a significant impact on both the United Arab Emirates (UAE) and Malaysia, emphasizing the importance of developing accurate and reliable forecasting mechanisms to guide public health responses and policies. In this study, we compared several cutting-edge deep learning models, including Long Short-Term Memory (LSTM), bidirectional LSTM, Convolutional Neural Networks (CNN), hybrid CNN-LSTM, Multilayer Perceptron's, and Recurrent Neural Networks (RNN), to project COVID-19 cases in the aforementioned regions. These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. After a thorough evaluation, the model architectures most suitable for the specific conditions in the UAE and Malaysia were identified. Our study contributes significantly to the ongoing efforts to combat the COVID-19 pandemic, providing crucial insights into the application of sophisticated deep learning algorithms for the precise and timely forecasting of COVID-19 cases. These insights hold substantial value for shaping public health strategies, enabling authorities to develop targeted and evidence-based interventions to manage the virus spread and its impact on the populations of the UAE and Malaysia. The study confirms the usefulness of deep learning methodologies in efficiently processing complex datasets and generating reliable projections, a skill of great importance in healthcare and professional settings.
    Matched MeSH terms: Bayes Theorem
  15. Fulltext COVID-19 fear and volatility index movements: empirical insights from ASEAN stock markets
    Sadiq M, Hsu CC, Zhang Y, Chien F
    Environ Sci Pollut Res Int, 2021 Dec;28(47):67167-67184.
    PMID: 34245412 DOI: 10.1007/s11356-021-15064-1
    This research aims to look into the effect of COVID-19 on emerging stock markets in seven of the Association of Southeast Asian Nations' (ASEAN-7) member countries from March 21, 2020 to April 31, 2020. This paper uses a ST-HAR-type Bayesian posterior model and it highlights the stock market of this ongoing crisis, such as, COVID-19 outbreak in all countries and related industries. The empirical results shown a clear evidence of a transition during COVID-19 crisis regime, also crisis intensity and timing differences. The most negatively impacted industries were health care and consumer services due to the Covid-19 drug-race and international travel restrictions. More so, study results estimated that only a small number of sectors are affected by COVID-19 fear including  health care, consumer services, utilities, and technology, significance at the 1%, 5%, and 10%, that measure current volatility's reliance on weekly and monthly variables. Secondly, it is found that there is almost no chance that the COVID-19 pandemic would positively affect the stock market performance in all the countries, mainly Indonesia and Singapore were the countries most affected. Thirdly, results shown that Thailand's stock market output has dropped by 15%. Results shows that COVID-19 fear causes an eventual reason of public attention towards stock market volatility. The study presented comprehensive way forwards to stabilize movement of ASEAN equity market's volatility index and guided the policy implications to key stakeholders that can better help to mitigate drastic impacts of COVID-19 fear on the performance of equity markets.
    Matched MeSH terms: Bayes Theorem
  16. Fulltext Robust identification of polyethylene terephthalate (PET) plastics through Bayesian decision
    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*
  17. Fulltext A "Roziah" by any other name: a simple Bayesian method for determining ethnicity from names
    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*
  18. Fulltext Naive Bayes-guided bat algorithm for feature selection
    Taha AM, Mustapha A, Chen SD
    ScientificWorldJournal, 2013;2013:325973.
    PMID: 24396295 DOI: 10.1155/2013/325973
    When the amount of data and information is said to double in every 20 months or so, feature selection has become highly important and beneficial. Further improvements in feature selection will positively affect a wide array of applications in fields such as pattern recognition, machine learning, or signal processing. Bio-inspired method called Bat Algorithm hybridized with a Naive Bayes classifier has been presented in this work. The performance of the proposed feature selection algorithm was investigated using twelve benchmark datasets from different domains and was compared to three other well-known feature selection algorithms. Discussion focused on four perspectives: number of features, classification accuracy, stability, and feature generalization. The results showed that BANB significantly outperformed other algorithms in selecting lower number of features, hence removing irrelevant, redundant, or noisy features while maintaining the classification accuracy. BANB is also proven to be more stable than other methods and is capable of producing more general feature subsets.
    Matched MeSH terms: Bayes Theorem*
  19. Fulltext Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms
    Nhu VH, Shirzadi A, Shahabi H, Singh SK, Al-Ansari N, Clague JJ, et al.
    Int J Environ Res Public Health, 2020 04 16;17(8).
    PMID: 32316191 DOI: 10.3390/ijerph17082749
    Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms-Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine-in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.
    Matched MeSH terms: Bayes Theorem*
  20. Fulltext Two Monthly Continuous Dynamic Model Based on Nash Bargaining Theory for Conflict Resolution in Reservoir System
    Homayounfar M, Zomorodian M, Martinez CJ, Lai SH
    PLoS One, 2015;10(12):e0143198.
    PMID: 26641095 DOI: 10.1371/journal.pone.0143198
    So far many optimization models based on Nash Bargaining Theory associated with reservoir operation have been developed. Most of them have aimed to provide practical and efficient solutions for water allocation in order to alleviate conflicts among water users. These models can be discussed from two viewpoints: (i) having a discrete nature; and (ii) working on an annual basis. Although discrete dynamic game models provide appropriate reservoir operator policies, their discretization of variables increases the run time and causes dimensionality problems. In this study, two monthly based non-discrete optimization models based on the Nash Bargaining Solution are developed for a reservoir system. In the first model, based on constrained state formulation, the first and second moments (mean and variance) of the state variable (water level in the reservoir) is calculated. Using moment equations as the constraint, the long-term utility of the reservoir manager and water users are optimized. The second model is a dynamic approach structured based on continuous state Markov decision models. The corresponding solution based on the collocation method is structured for a reservoir system. In this model, the reward function is defined based on the Nash Bargaining Solution. Indeed, it is used to yield equilibrium in every proper sub-game, thereby satisfying the Markov perfect equilibrium. Both approaches are applicable for water allocation in arid and semi-arid regions. A case study was carried out at the Zayandeh-Rud river basin located in central Iran to identify the effectiveness of the presented methods. The results are compared with the results of an annual form of dynamic game, a classical stochastic dynamic programming model (e.g. Bayesian Stochastic Dynamic Programming model, BSDP), and a discrete stochastic dynamic game model (PSDNG). By comparing the results of alternative methods, it is shown that both models are capable of tackling conflict issues in water allocation in situations of water scarcity properly. Also, comparing the annual dynamic game models, the presented models result in superior results in practice. Furthermore, unlike discrete dynamic game models, the presented models can significantly reduce the runtime thereby avoiding dimensionality problems.
    Matched MeSH terms: Bayes Theorem
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