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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*
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*
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*
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*
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
Fulltext Robust observation detection for single object tracking: deterministic and probabilistic patch-based approaches

Zulkifley MA, Rawlinson D, Moran B

Sensors (Basel), 2012;12(11):15638-70.
PMID: 23202226 DOI: 10.3390/s121115638

In video analytics, robust observation detection is very important as the content of the videos varies a lot, especially for tracking implementation. Contrary to the image processing field, the problems of blurring, moderate deformation, low illumination surroundings, illumination change and homogenous texture are normally encountered in video analytics. Patch-Based Observation Detection (PBOD) is developed to improve detection robustness to complex scenes by fusing both feature- and template-based recognition methods. While we believe that feature-based detectors are more distinctive,however, for finding the matching between the frames are best achieved by a collection of points as in template-based detectors. Two methods of PBOD-the deterministic and probabilistic approaches-have been tested to find the best mode of detection. Both algorithms start by building comparison vectors at each detected points of interest. The vectors are matched to build candidate patches based on their respective coordination. For the deterministic method, patch matching is done in 2-level test where threshold-based position and size smoothing are applied to the patch with the highest correlation value. Forthe second approach, patch matching is done probabilistically by modelling the histograms of the patches by Poisson distributions for both RGB and HSV colour models. Then,maximum likelihood is applied for position smoothing while a Bayesian approach is appliedfor size smoothing. The result showed that probabilistic PBOD outperforms the deterministic approach with average distance error of 10.03% compared with 21.03%. This algorithm is best implemented as a complement to other simpler detection methods due to heavy processing requirement.

Matched MeSH terms: Bayes Theorem
Fulltext Functional specialisation and effective connectivity in cerebral motor cortices: an fMRI study on seven right handed female subjects

Ahmad Nazlim Yusoff, Mazlyfarina Mohamad, Aini Ismafairus Abd Hamid, Wan Ahmad Kamil Wan Abdullah, Mohd Harith Hashim, Nurul Zafirah Zulkifli

Malaysian Journal of Medicine and Health Sciences, 2010;6(2):71-92.
MyJurnal

Objective: This study investigates functional specialisation in, and effective connectivity between the
precentral gyrus (PCG) and supplementary motor area (SMA) in seven right handed female subjects.
Methods: Unimanual (UNIright and UNIleft) and bimanual (BIM) self-paced tapping of hand fingers were
performed by the subjects to activate PCG and SMA. Brain activations and effective connectivity were
analysed using statistical parametric mapping (SPM), dynamic causal modeling (DCM) and Bayesian
model selection (BMS) and were reported based on group fixed (FFX) and random (RFX) effects
analyses. Results: Group results showed that the observed brain activation for UNIright and UNIleft fulfill contralateral behavior of motor coordination with a larger activation area for UNIright. The activation for BIM occurs in both hemispheres with BIMright showing higher extent of activation as compared to BIMleft. Region of interest (ROI) analyses reveal that the number of activated voxel (NOV) and percentage of signal change (PSC) on average is higher in PCG than SMA for all tapping conditions. However, comparing between hemispheres for both UNI and BIM, higher PSC is observed in the right PCG and the left SMA. DCM and BMS results indicate that most subjects prefer PCG as the intrinsic input for UNIright and UNIleft. The input was later found to be bi-directionally connected to SMA for UNIright. The bi-directional model was then used for BIM in the left and right hemispheres. The model was in favour of six out of seven subjects. DCM results for BIM indicate the existence of interhemispheric connectivity between the right and left hemisphere PCG. Conclusion: The findings strongly support the existence of functional specialisation and integration i.e. effective connectivity in human brain during finger tapping and can be used as baselines in determining the probable motor coordination pathways and their connection strength in a population of subjects.

Matched MeSH terms: Bayes Theorem
Fulltext Population estimates of Bornean orang-utans using Bayesian analysis at the greater Batang Ai-Lanjak-Entimau landscape in Sarawak, Malaysia

Pandong J, Gumal M, Alen L, Sidu A, Ng S, Koh LP

Sci Rep, 2018 10 23;8(1):15672.
PMID: 30353034 DOI: 10.1038/s41598-018-33872-3

The integration of Bayesian analysis into existing great ape survey methods could be used to generate precise and reliable population estimates of Bornean orang-utans. We used the Marked Nest Count (MNC) method to count new orang-utan nests at seven previously undocumented study sites in Sarawak, Malaysia. Our survey teams marked new nests on the first survey and revisited the plots on two more occasions; after about 21 and 42 days respectively. We used the N-mixture models to integrate suitability, abundance and detection models which account for zero inflation and imperfect detection for the analysis. The result was a combined estimate of 355 orang-utans with the 95% highest density interval (HDI) of 135 to 602 individuals. We visually inspected the posterior distributions of our parameters and compared precisions between study sites. We subsequently assess the strength or reliability of the generated estimates using identifiability tests. Only three out of the seven estimates had <35% overlap to indicate strong reliability. We discussed the limitations and advantages of our study design, and made recommendations to improve the sampling scheme. Over the course of this research, two of the study sites were gazetted as extensions to the Lanjak-Entimau Wildlife Sanctuary for orang-utan conservation.

Matched MeSH terms: Bayes Theorem
Approximate bayesian estimates of weibull parameters with Lindley’s method

Chris Bambey Guure, Noor Akma Ibrahim

Sains Malaysiana, 2014;43:1433-1437.

One of the most important lifetime distributions that is used for modelling and analysing data in clinical, life sciences and engineering is the Weibull distribution. The main objective of this paper was to determine the best estimator for the two-parameter Weibull distribution. The methods under consideration are the frequentist maximum likelihood estimator, least square regression estimator and the Bayesian estimator by using two loss functions, which are squared error and linear exponential. Lindley approximation is used to obtain the Bayes estimates. Comparisons are made through simulation study to determine the performance of these methods. Based on the results obtained from this simulation study the Bayesian approach used in estimating the Weibull parameters under linear exponential loss function is found to be superior as compared to the conventional maximum likelihood and least squared methods.

Matched MeSH terms: Bayes Theorem
Fulltext Prevalence of Latent Tuberculosis Infection Among Non-US-Born Persons by Country of Birth-United States, 2012-2017

Collins JM, Stout JE, Ayers T, Hill AN, Katz DJ, Ho CS, et al.

Clin Infect Dis, 2021 11 02;73(9):e3468-e3475.
PMID: 33137172 DOI: 10.1093/cid/ciaa1662

BACKGROUND: Most tuberculosis (TB) disease in the United States (US) is attributed to reactivation of remotely acquired latent TB infection (LTBI) in non-US-born persons who were likely infected with Mycobacterium tuberculosis in their countries of birth. Information on LTBI prevalence by country of birth could help guide local providers and health departments to scale up the LTBI screening and preventive treatment needed to advance progress toward TB elimination.
METHODS: A total of 13 805 non-US-born persons at high risk of TB infection or progression to TB disease were screened for LTBI at 16 clinical sites located across the United States with a tuberculin skin test, QuantiFERON Gold In-Tube test, and T-SPOT.TB test. Bayesian latent class analysis was applied to test results to estimate LTBI prevalence and associated credible intervals (CrIs) for each country or world region of birth.
RESULTS: Among the study population, the estimated LTBI prevalence was 31% (95% CrI, 26%-35%). Country-of-birth-level LTBI prevalence estimates were highest for persons born in Haiti, Peru, Somalia, Ethiopia, Vietnam, and Bhutan, ranging from 42% to 55%. LTBI prevalence estimates were lowest for persons born in Colombia, Malaysia, and Thailand, ranging from 8% to 13%.
CONCLUSIONS: LTBI prevalence in persons born outside the US varies widely by country. These estimates can help target community outreach efforts to the highest-risk groups.

Matched MeSH terms: Bayes Theorem
Fulltext Forecasting COVID-19 Case Trends Using SARIMA Models during the Third Wave of COVID-19 in Malaysia

Tan CV, Singh S, Lai CH, Zamri ASSM, Dass SC, Aris TB, et al.

Int J Environ Res Public Health, 2022 Jan 28;19(3).
PMID: 35162523 DOI: 10.3390/ijerph19031504

With many countries experiencing a resurgence in COVID-19 cases, it is important to forecast disease trends to enable effective planning and implementation of control measures. This study aims to develop Seasonal Autoregressive Integrated Moving Average (SARIMA) models using 593 data points and smoothened case and covariate time-series data to generate a 28-day forecast of COVID-19 case trends during the third wave in Malaysia. SARIMA models were developed using COVID-19 case data sourced from the Ministry of Health Malaysia's official website. Model training and validation was conducted from 22 January 2020 to 5 September 2021 using daily COVID-19 case data. The SARIMA model with the lowest root mean square error (RMSE), mean absolute percentage error (MAE) and Bayesian information criterion (BIC) was selected to generate forecasts from 6 September to 3 October 2021. The best SARIMA model with a RMSE = 73.374, MAE = 39.716 and BIC = 8.656 showed a downward trend of COVID-19 cases during the forecast period, wherein the observed daily cases were within the forecast range. The majority (89%) of the difference between the forecasted and observed values was well within a deviation range of 25%. Based on this work, we conclude that SARIMA models developed in this paper using 593 data points and smoothened data and sensitive covariates can generate accurate forecast of COVID-19 case trends.

Matched MeSH terms: Bayes Theorem
Ecological surveillance of bat coronaviruses in Sarawak, Malaysian Borneo

Tan CS, Noni V, Sathiya Seelan JS, Denel A, Anwarali Khan FA

BMC Res Notes, 2021 Dec 20;14(1):461.
PMID: 34930456 DOI: 10.1186/s13104-021-05880-6

OBJECTIVE: Coronaviruses (CoVs) are natural commensals of bats. Two subgenera, namely Sarbecoviruses and Merbecoviruses have a high zoonotic potential and have been associated with three separate spillover events in the past 2 decades, making surveillance of bat-CoVs crucial for the prevention of the next epidemic. The study was aimed to elucidate the presence of coronavirus in fresh bat guano sampled from Wind Cave Nature Reserve (WCNR) in Sarawak, Malaysian Borneo. Samples collected were placed into viral transport medium, transported on ice within the collection day, and preserved at - 80 °C. Nucleic acid was extracted using the column method and screened using consensus PCR primers targeting the RNA-dependent RNA polymerase (RdRp) gene. Amplicons were sequenced bidirectionally using the Sanger method. Phylogenetic tree with maximum-likelihood bootstrap and Bayesian posterior probability were constructed.
RESULTS: CoV-RNA was detected in ten specimens (47.6%, n = 21). Six alphacoronavirus and four betacoronaviruses were identified. The bat-CoVs can be phylogenetically grouped into four novel clades which are closely related to Decacovirus-1 and Decacovirus-2, Sarbecovirus, and an unclassified CoV. CoVs lineages unique to the Island of Borneo were discovered in Sarawak, Malaysia, with one of them closely related to Sarbecovirus. All of them are distant from currently known human coronaviruses.

Matched MeSH terms: Bayes Theorem
Nitrate Classification Based on Optical Absorbance Data Using Machine Learning Algorithms for a Hydroponics System

Sulaiman R, Azeman NH, Abu Bakar MH, Ahmad Nazri NA, Masran AS, Ashrif A Bakar A

Appl Spectrosc, 2023 Feb;77(2):210-219.
PMID: 36348500 DOI: 10.1177/00037028221140924

Nutrient solution plays an essential role in providing macronutrients to hydroponic plants. Determining nitrogen in the form of nitrate is crucial, as either a deficient or excessive supply of nitrate ions may reduce the plant yield or lead to environmental pollution. This work aims to evaluate the performance of feature reduction techniques and conventional machine learning (ML) algorithms in determining nitrate concentration levels. Two features reduction techniques, linear discriminant analysis (LDA) and principal component analysis (PCA), and seven ML algorithms, for example, k-nearest neighbors (KNN), support vector machine, decision trees, naïve bayes, random forest (RF), gradient boosting, and extreme gradient boosting, were evaluated using a high-dimensional spectroscopic dataset containing measured nitrate-nitrite mixed solution absorbance data. Despite the limited and uneven number of samples per class, this study demonstrated that PCA outperformed LDA on the high-dimensional spectroscopic dataset. The classification accuracy of ML algorithms combined with PCA ranged from 92.7% to 99.8%, whereas the classification accuracy of ML algorithms combined with LDA ranged from 80.7% to 87.6%. The PCA with the RF algorithm exhibited the best performance with 99.8% accuracy.

Matched MeSH terms: Bayes Theorem
Occurrence of five distinct clades of mermithid nematodes (Nematoda: Mermithidae) infecting black fly larvae (Diptera: Simuliidae) in tropical streams in Malaysia

Lourdes EY, Low VL, Izwan-Anas N, Dawood MM, Sofian-Azirun M, Takaoka H, et al.

Parasitol Int, 2023 Jun;94:102733.
PMID: 36693472 DOI: 10.1016/j.parint.2023.102733

Mermithids are the most common parasites of black flies and are associated with host feminization and sterilization in infected hosts. However, information on the species / lineage of black fly mermithids in Southeast Asia, including Malaysia requires further elucidation. In this study, mermithids were obtained from black fly larvae collected from 138 freshwater stream sites across East and West Malaysia. A molecular approach based on nuclear-encoded 18S ribosomal RNA (18S rRNA) gene was used to identify the species identity / lineage of 77 nematodes successfully extracted and sequenced from the specimens collected. Maximum likelihood and neighbor-joining phylogenetic analyses demonstrated five distinct mermithid lineages. Four species delimitation analyses: automated simultaneous analysis phylogenetics (ASAP), maximum likelihood Poisson tree processes with Bayesian inferences (bPTP_ML), generalized mixed yule coalescent (GMYC) and single rate Poisson tree processes (PTP) were applied to delimit the species boundaries of mermithid lineages in this data set along with genetic distance analysis. Data analysis supports five distinct lineages or operational taxonomic units for mermithids in the present study, with two requiring further investigation as they may represent intraspecific variation or closely related taxa. One mermithid lineage was similar to that previously observed in Simulium nigrogilvum from Thailand. Co-infection with two mermithids of different lineages was observed in one larva of Simulium trangense. This study represents an important first step towards exploring other aspects of host - parasite interactions in black fly mermithids.

Matched MeSH terms: Bayes Theorem
Decadal stability in coral cover could mask hidden changes on reefs in the East Asian Seas

Chan YKS, Affendi YA, Ang PO, Baria-Rodriguez MV, Chen CA, Chui APY, et al.

Commun Biol, 2023 Jun 10;6(1):630.
PMID: 37301948 DOI: 10.1038/s42003-023-05000-z

Coral reefs in the Central Indo-Pacific region comprise some of the most diverse and yet threatened marine habitats. While reef monitoring has grown throughout the region in recent years, studies of coral reef benthic cover remain limited in spatial and temporal scales. Here, we analysed 24,365 reef surveys performed over 37 years at 1972 sites throughout East Asia by the Global Coral Reef Monitoring Network using Bayesian approaches. Our results show that overall coral cover at surveyed reefs has not declined as suggested in previous studies and compared to reef regions like the Caribbean. Concurrently, macroalgal cover has not increased, with no indications of phase shifts from coral to macroalgal dominance on reefs. Yet, models incorporating socio-economic and environmental variables reveal negative associations of coral cover with coastal urbanisation and sea surface temperature. The diversity of reef assemblages may have mitigated cover declines thus far, but climate change could threaten reef resilience. We recommend prioritisation of regionally coordinated, locally collaborative long-term studies for better contextualisation of monitoring data and analyses, which are essential for achieving reef conservation goals.

Matched MeSH terms: Bayes Theorem
Fulltext Machine learning algorithm for ventilator mode selection, pressure and volume control

T A, G G, P AMD, Assaad M

PLoS One, 2024;19(3):e0299653.
PMID: 38478485 DOI: 10.1371/journal.pone.0299653

Mechanical ventilation techniques are vital for preserving individuals with a serious condition lives in the prolonged hospitalization unit. Nevertheless, an imbalance amid the hospitalized people demands and the respiratory structure could cause to inconsistencies in the patient's inhalation. To tackle this problem, this study presents an Iterative Learning PID Controller (ILC-PID), a unique current cycle feedback type controller that helps in gaining the correct pressure and volume. The paper also offers a clear and complete examination of the primarily efficient neural approach for generating optimal inhalation strategies. Moreover, machine learning-based classifiers are used to evaluate the precision and performance of the ILC-PID controller. These classifiers able to forecast and choose the perfect type for various inhalation modes, eliminating the likelihood that patients will require mechanical ventilation. In pressure control, the suggested accurate neural categorization exhibited an average accuracy rate of 88.2% in continuous positive airway pressure (CPAP) mode and 91.7% in proportional assist ventilation (PAV) mode while comparing with the other classifiers like ensemble classifier has reduced accuracy rate of 69.5% in CPAP mode and also 71.7% in PAV mode. An average accuracy of 78.9% rate in other classifiers compared to neutral network in CPAP. The neural model had an typical range of 81.6% in CPAP mode and 84.59% in PAV mode for 20 cm H2O of volume created by the neural network classifier in the volume investigation. Compared to the other classifiers, an average of 72.17% was in CPAP mode, and 77.83% was in PAV mode in volume control. Different approaches, such as decision trees, optimizable Bayes trees, naive Bayes trees, nearest neighbour trees, and an ensemble of trees, were also evaluated regarding the accuracy by confusion matrix concept, training duration, specificity, sensitivity, and F1 score.

Matched MeSH terms: Bayes Theorem
Fulltext Global, regional, and national burden of hepatitis B, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019

GBD 2019 Hepatitis B Collaborators

Lancet Gastroenterol Hepatol, 2022 Sep;7(9):796-829.
PMID: 35738290 DOI: 10.1016/S2468-1253(22)00124-8

BACKGROUND: Combating viral hepatitis is part of the UN Sustainable Development Goals (SDGs), and WHO has put forth hepatitis B elimination targets in its Global Health Sector Strategy on Viral Hepatitis (WHO-GHSS) and Interim Guidance for Country Validation of Viral Hepatitis Elimination (WHO Interim Guidance). We estimated the global, regional, and national prevalence of hepatitis B virus (HBV), as well as mortality and disability-adjusted life-years (DALYs) due to HBV, as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019. This included estimates for 194 WHO member states, for which we compared our estimates to WHO elimination targets.
METHODS: The primary data sources were population-based serosurveys, claims and hospital discharges, cancer registries, vital registration systems, and published case series. We estimated chronic HBV infection and the burden of HBV-related diseases, defined as an aggregate of cirrhosis due to hepatitis B, liver cancer due to hepatitis B, and acute hepatitis B. We used DisMod-MR 2.1, a Bayesian mixed-effects meta-regression tool, to estimate the prevalence of chronic HBV infection, cirrhosis, and aetiological proportions of cirrhosis. We used mortality-to-incidence ratios modelled with spatiotemporal Gaussian process regression to estimate the incidence of liver cancer. We used the Cause of Death Ensemble modelling (CODEm) model, a tool that selects models and covariates on the basis of out-of-sample performance, to estimate mortality due to cirrhosis, liver cancer, and acute hepatitis B.
FINDINGS: In 2019, the estimated global, all-age prevalence of chronic HBV infection was 4·1% (95% uncertainty interval [UI] 3·7 to 4·5), corresponding to 316 million (284 to 351) infected people. There was a 31·3% (29·0 to 33·9) decline in all-age prevalence between 1990 and 2019, with a more marked decline of 76·8% (76·2 to 77·5) in prevalence in children younger than 5 years. HBV-related diseases resulted in 555 000 global deaths (487 000 to 630 000) in 2019. The number of HBV-related deaths increased between 1990 and 2019 (by 5·9% [-5·6 to 19·2]) and between 2015 and 2019 (by 2·9% [-5·9 to 11·3]). By contrast, all-age and age-standardised death rates due to HBV-related diseases decreased during these periods. We compared estimates for 2019 in 194 WHO locations to WHO-GHSS 2020 targets, and found that four countries achieved a 10% reduction in deaths, 15 countries achieved a 30% reduction in new cases, and 147 countries achieved a 1% prevalence in children younger than 5 years. As of 2019, 68 of 194 countries had already achieved the 2030 target proposed in WHO Interim Guidance of an all-age HBV-related death rate of four per 100 000.
INTERPRETATION: The prevalence of chronic HBV infection declined over time, particularly in children younger than 5 years, since the introduction of hepatitis B vaccination. HBV-related death rates also decreased, but HBV-related death counts increased as a result of population growth, ageing, and cohort effects. By 2019, many countries had met the interim seroprevalence target for children younger than 5 years, but few countries had met the WHO-GHSS interim targets for deaths and new cases. Progress according to all indicators must be accelerated to meet 2030 targets, and there are marked disparities in burden and progress across the world. HBV interventions, such as vaccination, testing, and treatment, must be strategically supported and scaled up to achieve elimination.
FUNDING: Bill & Melinda Gates Foundation.

Matched MeSH terms: Bayes Theorem
New approach for sugarcane disease recognition through visible and near-infrared spectroscopy and a modified wavelength selection method using machine learning models

Ong P, Jian J, Li X, Zou C, Yin J, Ma G

Spectrochim Acta A Mol Biomol Spectrosc, 2023 Dec 05;302:123037.
PMID: 37356390 DOI: 10.1016/j.saa.2023.123037

The proliferation of pathogenic fungi in sugarcane crops poses a significant threat to agricultural productivity and economic sustainability. Early identification and management of sugarcane diseases are therefore crucial to mitigate the adverse impacts of these pathogens. In this study, visible and near-infrared spectroscopy (380-1400 nm) combined with a novel wavelength selection method, referred to as modified flower pollination algorithm (MFPA), was utilized for sugarcane disease recognition. The selected wavelengths were incorporated into machine learning models, including Naïve Bayes, random forest, and support vector machine (SVM). The developed simplified SVM model, which utilized the MFPA wavelength selection method yielded the best performances, achieving a precision value of 0.9753, a sensitivity value of 0.9259, a specificity value of 0.9524, and an accuracy of 0.9487. These results outperformed those obtained by other wavelength selection approaches, including the selectivity ratio, variable importance in projection, and the baseline method of the flower pollination algorithm.

Matched MeSH terms: Bayes Theorem
Fulltext Design and implementation of adolescent health Latin dance teaching system under artificial intelligence technology

Liu X, Soh KG, Dev Omar Dev R, Li W, Yi Q

PLoS One, 2023;18(11):e0293313.
PMID: 37917594 DOI: 10.1371/journal.pone.0293313

Since various dance teaching systems have attracted much attention with the development of Artificial Intelligence (AI) technology, this paper improves the recognition performance of Latin dance teaching systems by optimizing the action recognition model. Firstly, the object detection and action recognition technology under the current AI technology is analyzed, and the Two-stage object detection algorithm and One-stage object detection algorithm are evaluated. Secondly, the technologies and functions contained in the adolescent health Latin dance teaching system are described, including image acquisition, feature extraction, object detection, and action recognition. Finally, the action recognition algorithm is optimized based on object detection, and the rationality and feasibility of the proposed algorithm are verified by experiments. The experimental results show that the optimization algorithm can search the optimal feature subset after five iterations on Undefine Classes of 101 (UCF101) dataset, but it needs seven iterations on Human Motion Database 51 (HMDB51) dataset. Meanwhile, when using support vector machine classifier, the optimization algorithm can achieve the highest accuracy of motion recognition. Regressive Function, Multinomial Naive Bayes and Gaussian Naive Bayes Algorithms have lower prediction delay, as low as 0.01s. Therefore, this paper has certain reference significance for the design and implementation of adolescent health Latin dance teaching system.

Matched MeSH terms: Bayes Theorem
Probabilistic prediction of phosphate ion adsorption onto biochar materials using a large dataset and online deployment

Iftikhar S, Ishtiaq R, Zahra N, Ruba F, Lam SM, Abbas A, et al.

Chemosphere, 2025 Feb;370:144031.
PMID: 39732408 DOI: 10.1016/j.chemosphere.2024.144031

Phosphate (PO4(III)) contamination in water bodies poses significant environmental challenges, necessitating efficient and accurate methods to predict and optimize its removal. The current study addresses this issue by predicting the adsorption capacity of PO4(III) ions onto biochar-based materials using five probabilistic machine learning models: eXtreme Gradient Boosting LSS (XGBoostLSS), Natural Gradient Boosting, Bayesian Neural Networks (NN), Probabilistic NN, and Monte-Carlo Dropout NN. Utilizing a dataset of 2952 data points with 16 inputs, XGBoostLSS demonstrated the highest R2 (0.95) on new adsorbents. SHapely Additive exPlanations analysis showed that adsorption experimental conditions had the most significant impact (43%), followed by synthesis conditions (29%) and adsorbent characteristics (28%). Optimized conditions included an initial PO4(III) concentration of 125 mg/L, carbon content of 11.5%, oxygen content of 23%, a contact time of 1440 min, a heating rate of 5 °C/min, 200 rpm, and a surface area of 410 m2/g, using Ra-LDO adsorbent synthesized from rape cabbage feedstock. This study developed and presented a practical online framework for predicting PO4(III) removal onto biochar using a web-based graphical user interface.

Matched MeSH terms: Bayes Theorem

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