Displaying publications 1 - 20 of 37 in total

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  1. Aziz F, Arof H, Mokhtar N, Shah NM, Khairuddin ASM, Hanafi E, et al.
    PLoS One, 2018;13(8):e0202092.
    PMID: 30157219 DOI: 10.1371/journal.pone.0202092
    In this paper, an image-based waste collection scheduling involving a node with three waste bins is considered. First, the system locates the three bins and determines the waste level of each bin using four Laws Masks and a set of Support Vector Machine (SVM) classifiers. Next, a Hidden Markov Model (HMM) is used to decide on the number of days remaining before waste is collected from the node. This decision is based on the HMM's previous state and current observations. The HMM waste collection scheduling seeks to maximize the number of days between collection visits while preventing waste contamination due to late collection. The proposed system was trained using 100 training images and then tested on 100 test images. Each test image contains three bins that might be shifted, rotated, occluded or toppled over. The upright bins could be empty, partially full or full of garbage of various shapes and sizes. The method achieves bin detection, waste level classification and collection day scheduling rates of 100%, 99.8% and 100% respectively.
    Matched MeSH terms: Markov Chains
  2. Dawood F, Loo CK
    PLoS One, 2016;11(3):e0152003.
    PMID: 26998923 DOI: 10.1371/journal.pone.0152003
    Mirror neurons are visuo-motor neurons found in primates and thought to be significant for imitation learning. The proposition that mirror neurons result from associative learning while the neonate observes his own actions has received noteworthy empirical support. Self-exploration is regarded as a procedure by which infants become perceptually observant to their own body and engage in a perceptual communication with themselves. We assume that crude sense of self is the prerequisite for social interaction. However, the contribution of mirror neurons in encoding the perspective from which the motor acts of others are seen have not been addressed in relation to humanoid robots. In this paper we present a computational model for development of mirror neuron system for humanoid based on the hypothesis that infants acquire MNS by sensorimotor associative learning through self-exploration capable of sustaining early imitation skills. The purpose of our proposed model is to take into account the view-dependency of neurons as a probable outcome of the associative connectivity between motor and visual information. In our experiment, a humanoid robot stands in front of a mirror (represented through self-image using camera) in order to obtain the associative relationship between his own motor generated actions and his own visual body-image. In the learning process the network first forms mapping from each motor representation onto visual representation from the self-exploratory perspective. Afterwards, the representation of the motor commands is learned to be associated with all possible visual perspectives. The complete architecture was evaluated by simulation experiments performed on DARwIn-OP humanoid robot.
    Matched MeSH terms: Markov Chains
  3. Apenteng OO, Osei PP, Oduro B, Kwabla MP, Ismail NA
    Infect Dis Model, 2020;5:755-765.
    PMID: 33073067 DOI: 10.1016/j.idm.2020.09.009
    Malaysia is faced with a high HIV/AIDS burden that poses a public health threat. We constructed and applied a compartmental model to understand the spread and control of HIV/AIDS in Malaysia. A simple model for HIV and AIDS disease that incorporates condom and uncontaminated needle-syringes interventions and addresses the relative impact of given treatment therapy for infected HIV newborns on reducing HIV and AIDS incidence is presented. We demonstrated how treatment therapy for new-born babies and the use of condoms or uncontaminated needle-syringes impact the dynamics of HIV in Malaysia. The model was calibrated to HIV and AIDS incidence data from Malaysia from 1986 to 2011. The epidemiological parameters are estimated using Bayesian inference via Markov chain Monte Carlo simulation method. The reproduction number optimal for control of the HIV/AIDS disease obtained suggests that the disease-free equilibrium was unstable during the 25 years. However, the results indicated that the use of condoms and uncontaminated needle-syringes are pivotal intervention control strategies; a comprehensive adoption of the intervention may help stop the spread of HIV disease. Treatment therapy for newborn babies is also of high value; it reduces the epidemic peak. The combined effect of condom use or uncontaminated needle-syringe is more pronounced in controlling the spread of HIV/AIDS.
    Matched MeSH terms: Markov Chains
  4. Yen FY, Chong KM, Ha LM
    PLoS One, 2013;8(6):e65440.
    PMID: 23755231 DOI: 10.1371/journal.pone.0065440
    This paper proposes three synthetic-type control charts to monitor the mean time-between-events of a homogenous Poisson process. The first proposed chart combines an Erlang (cumulative time between events, Tr ) chart and a conforming run length (CRL) chart, denoted as Synth-Tr chart. The second proposed chart combines an exponential (or T) chart and a group conforming run length (GCRL) chart, denoted as GR-T chart. The third proposed chart combines an Erlang chart and a GCRL chart, denoted as GR-Tr chart. By using a Markov chain approach, the zero- and steady-state average number of observations to signal (ANOS) of the proposed charts are obtained, in order to evaluate the performance of the three charts. The optimal design of the proposed charts is shown in this paper. The proposed charts are superior to the existing T chart, Tr chart, and Synth-T chart. As compared to the EWMA-T chart, the GR-T chart performs better in detecting large shifts, in terms of the zero- and steady-state performances. The zero-state Synth-T4 and GR-Tr (r = 3 or 4) charts outperform the EWMA-T chart for all shifts, whereas the Synth-Tr (r = 2 or 3) and GR-T 2 charts perform better for moderate to large shifts. For the steady-state process, the Synth-Tr and GR-Tr charts are more efficient than the EWMA-T chart in detecting small to moderate shifts.
    Matched MeSH terms: Markov Chains*
  5. Chan KL, Rosli R, Tatarinova TV, Hogan M, Firdaus-Raih M, Low EL
    BMC Bioinformatics, 2017 Jan 27;18(Suppl 1):1426.
    PMID: 28466793 DOI: 10.1186/s12859-016-1426-6
    BACKGROUND: Gene prediction is one of the most important steps in the genome annotation process. A large number of software tools and pipelines developed by various computing techniques are available for gene prediction. However, these systems have yet to accurately predict all or even most of the protein-coding regions. Furthermore, none of the currently available gene-finders has a universal Hidden Markov Model (HMM) that can perform gene prediction for all organisms equally well in an automatic fashion.

    RESULTS: We present an automated gene prediction pipeline, Seqping that uses self-training HMM models and transcriptomic data. The pipeline processes the genome and transcriptome sequences of the target species using GlimmerHMM, SNAP, and AUGUSTUS pipelines, followed by MAKER2 program to combine predictions from the three tools in association with the transcriptomic evidence. Seqping generates species-specific HMMs that are able to offer unbiased gene predictions. The pipeline was evaluated using the Oryza sativa and Arabidopsis thaliana genomes. Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis showed that the pipeline was able to identify at least 95% of BUSCO's plantae dataset. Our evaluation shows that Seqping was able to generate better gene predictions compared to three HMM-based programs (MAKER2, GlimmerHMM and AUGUSTUS) using their respective available HMMs. Seqping had the highest accuracy in rice (0.5648 for CDS, 0.4468 for exon, and 0.6695 nucleotide structure) and A. thaliana (0.5808 for CDS, 0.5955 for exon, and 0.8839 nucleotide structure).

    CONCLUSIONS: Seqping provides researchers a seamless pipeline to train species-specific HMMs and predict genes in newly sequenced or less-studied genomes. We conclude that the Seqping pipeline predictions are more accurate than gene predictions using the other three approaches with the default or available HMMs.

    Matched MeSH terms: Markov Chains
  6. Juhan N, Zubairi YZ, Khalid ZM, Mahmood Zuhdi AS
    Iran J Public Health, 2020 Sep;49(9):1642-1649.
    PMID: 33643938 DOI: 10.18502/ijph.v49i9.4080
    Background: Identifying risk factors associated with mortality is important in providing better prognosis to patients. Consistent with that, Bayesian approach offers a great advantage where it rests on the assumption that all model parameters are random quantities and hence can incorporate prior knowledge. Therefore, we aimed to develop a reliable model to identify risk factors associated with mortality among ST-Elevation Myocardial Infarction (STEMI) male patients using Bayesian approach.

    Methods: A total of 7180 STEMI male patients from the National Cardiovascular Disease Database-Acute Coronary Syndrome (NCVD-ACS) registry for the years 2006-2013 were enrolled. In the development of univariate and multivariate logistic regression model for the STEMI patients, Bayesian Markov Chain Monte Carlo (MCMC) simulation approach was applied. The performance of the model was assessed through convergence diagnostics, overall model fit, model calibration and discrimination.

    Results: A set of six risk factors for cardiovascular death among STEMI male patients were identified from the Bayesian multivariate logistic model namely age, diabetes mellitus, family history of CVD, Killip class, chronic lung disease and renal disease respectively. Overall model fit, model calibration and discrimination were considered good for the proposed model.

    Conclusion: Bayesian risk prediction model for CVD male patients identified six risk factors associated with mortality. Among the highest risks were Killip class (OR=18.0), renal disease (2.46) and age group (OR=2.43) respectively.

    Matched MeSH terms: Markov Chains
  7. El-Sharnouby S, Fischer B, Magbanua JP, Umans B, Flower R, Choo SW, et al.
    PLoS One, 2017;12(3):e0172725.
    PMID: 28282436 DOI: 10.1371/journal.pone.0172725
    It is now well established that eukaryote genomes have a common architectural organization into topologically associated domains (TADs) and evidence is accumulating that this organization plays an important role in gene regulation. However, the mechanisms that partition the genome into TADs and the nature of domain boundaries are still poorly understood. We have investigated boundary regions in the Drosophila genome and find that they can be identified as domains of very low H3K27me3. The genome-wide H3K27me3 profile partitions into two states; very low H3K27me3 identifies Depleted (D) domains that contain housekeeping genes and their regulators such as the histone acetyltransferase-containing NSL complex, whereas domains containing moderate-to-high levels of H3K27me3 (Enriched or E domains) are associated with regulated genes, irrespective of whether they are active or inactive. The D domains correlate with the boundaries of TADs and are enriched in a subset of architectural proteins, particularly Chromator, BEAF-32, and Z4/Putzig. However, rather than being clustered at the borders of these domains, these proteins bind throughout the H3K27me3-depleted regions and are much more strongly associated with the transcription start sites of housekeeping genes than with the H3K27me3 domain boundaries. While we have not demonstrated causality, we suggest that the D domain chromatin state, characterised by very low or absent H3K27me3 and established by housekeeping gene regulators, acts to separate topological domains thereby setting up the domain architecture of the genome.
    Matched MeSH terms: Markov Chains
  8. Farrukh Mukhamedov
    MyJurnal
    In the present paper we provide a construction of Quantum Markov chain on a Cayley tree. Moreover, we give a concrete example of such chains, which is shift invariant and has the clustering property
    Matched MeSH terms: Markov Chains
  9. Raouf MA, Hashim F, Liew JT, Alezabi KA
    PLoS One, 2020;15(8):e0237386.
    PMID: 32790697 DOI: 10.1371/journal.pone.0237386
    The IEEE 802.11ah standard relies on the conventional distributed coordination function (DCF) as a backoff selection method. The DCF is utilized in the contention-based period of the newly introduced medium access control (MAC) mechanism, namely restricted access window (RAW). Despite various advantages of RAW, DCF still utilizes the legacy binary exponential backoff (BEB) algorithm, which suffers from a crucial disadvantage of being prone to high probability of collisions with high number of contending stations. To mitigate this issue, this paper investigates the possibility of replacing the existing exponential sequence (i.e., as in BEB) with a better pseudorandom sequence of integers. In particular, a new backoff algorithm, namely Pseudorandom Sequence Contention Algorithm (PRSCA) is proposed to update the CW size and minimize the collision probability. In addition, the proposed PRSCA incorporates a different approach of CW freezing mechanism and backoff stage reset process. An analytical model is derived for the proposed PRSCA and presented through a discrete 2-D Markov chain model. Performance evaluation demonstrates the efficiency of the proposed PRSCA in reducing collision probability and improving saturation throughput, network throughput, and access delay performance.
    Matched MeSH terms: Markov Chains
  10. 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: Markov Chains
  11. Alyousifi Y, Ibrahim K, Kang W, Zin WZW
    Environ Monit Assess, 2020 Oct 21;192(11):719.
    PMID: 33083907 DOI: 10.1007/s10661-020-08666-8
    An environmental problem which is of concern across the globe nowadays is air pollution. The extent of air pollution is often studied based on data on the observed level of air pollution. Although the analysis of air pollution data that is available in the literature is numerous, studies on the dynamics of air pollution with the allowance for spatial interaction effects through the use of the Markov chain model are very limited. Accordingly, this study aims to explore the potential impact of spatial dependence over time and space on the distribution of air pollution based on the spatial Markov chain (SMC) model using the longitudinal air pollution index (API) data. This SMC model is pertinent to be applied since the daily data of API from 2012 to 2014 that have been gathered from 37 different air quality stations in Peninsular Malaysia is found to exhibit the property of spatial autocorrelation. Based on the spatial transition probability matrices found from the SMC model, specific characteristics of air pollution are studied in the regional context. These characteristics are the long-run proportion and the mean first passage time for each state of air pollution. It is found that the probability for a particular station's state to remain good is 0.814 if its neighbors are in a good state of air pollution and 0.7082 if its neighbors are in a moderate state. For a particular station having neighbors in a good state of air pollution, the proportion of time for it to continue being in a good state is 0.6. This proportion reduces to 0.4, 0.01, and 0 for the cell of moderate, unhealthy, and very unhealthy states, respectively. In addition, there exists a significant spatial dependence of API, indicating that air pollution for a particular station is dependent on the states of the neighboring stations.
    Matched MeSH terms: Markov Chains
  12. Gandola AE, Dainelli L, Zimmermann D, Dahlui M, Detzel P
    Nutrients, 2019 May 30;11(6).
    PMID: 31151244 DOI: 10.3390/nu11061235
    This study evaluated the cost-effectiveness of the consumption of a milk powder product fortified with potassium (+1050.28 mg/day) and phytosterols (+1200 mg/day) to lower systolic blood pressure and low-density lipoprotein cholesterol, respectively, and, therefore, the risk of myocardial infarction (MI) and stroke among the 35-75-year-old population in Malaysia. A Markov model was created against a do-nothing option, from a governmental perspective, and with a time horizon of 40 years. Different data sources, encompassing clinical studies, practice guidelines, grey literature, and statistical yearbooks, were used. Sensitivity analyses were performed to evaluate the impact of uncertainty on the base case estimates. With an incremental cost-effectiveness ratio equal to international dollars (int$) 22,518.03 per quality-adjusted life-years gained, the intervention can be classified as very cost-effective. If adopted nationwide, it would help prevent at least 13,400 MIs, 30,500 strokes, and more than 10,600 and 17,100 MI- and stroke-related deaths. The discounted cost savings generated for the health care system by those who consume the fortified milk powder would amount to int$8.1 per person, corresponding to 0.7% of the total yearly health expenditure per capita. Sensitivity analyses confirmed the robustness of the results. Together with other preventive interventions, the consumption of milk powder fortified with potassium and phytosterols represents a cost-effective strategy to attenuate the rapid increase in cardiovascular burden in Malaysia.
    Matched MeSH terms: Markov Chains
  13. Permsuwan U, Chaiyakunapruk N, Dilokthornsakul P, Thavorn K, Saokaew S
    Appl Health Econ Health Policy, 2016 Jun;14(3):281-92.
    PMID: 26961276 DOI: 10.1007/s40258-016-0228-3
    BACKGROUND: Even though Insulin glargine (IGlar) has been available and used in other countries for more than a decade, it has not been adopted into Thai national formulary. This study aimed to evaluate the long-term cost effectiveness of IGlar versus neutral protamine Hagedorn (NPH) insulin in type 2 diabetes from the perspective of Thai Health Care System.

    METHODS: A validated computer simulation model (the IMS CORE Diabetes Model) was used to estimate the long-term projection of costs and clinical outcomes. The model was populated with published characteristics of Thai patients with type 2 diabetes. Baseline risk factors were obtained from Thai cohort studies, while relative risk reduction was derived from a meta-analysis study conducted by the Canadian Agency for Drugs and Technology in Health. Only direct costs were taken into account. Costs of diabetes management and complications were obtained from hospital databases in Thailand. Both costs and outcomes were discounted at 3 % per annum and presented in US dollars in terms of 2014 dollar value. Incremental cost-effectiveness ratio (ICER) was calculated. One-way and probabilistic sensitivity analyses were also performed.

    RESULTS: IGlar is associated with a slight gain in quality-adjusted life years (0.488 QALYs), an additional life expectancy (0.677 life years), and an incremental cost of THB119,543 (US$3522.19) compared with NPH insulin. The ICERs were THB244,915/QALY (US$7216.12/QALY) and THB176,525/life-year gained (LYG) (US$5201.09/LYG). The ICER was sensitive to discount rates and IGlar cost. At the acceptable willingness to pay of THB160,000/QALY (US$4714.20/QALY), the probability that IGlar was cost effective was less than 20 %.

    CONCLUSIONS: Compared to treatment with NPH insulin, treatment with IGlar in type 2 diabetes patients who had uncontrolled blood glucose with oral anti-diabetic drugs did not represent good value for money at the acceptable threshold in Thailand.

    Matched MeSH terms: Markov Chains
  14. Chong HY, Mohamed Z, Tan LL, Wu DBC, Shabaruddin FH, Dahlui M, et al.
    Br J Dermatol, 2017 Oct;177(4):1102-1112.
    PMID: 28346659 DOI: 10.1111/bjd.15498
    BACKGROUND: A strong association has been documented between HLA-B*15:02 and carbamazepine-induced severe cutaneous adverse reactions (SCARs) in Asians. Human leucocyte antigen testing is potentially valuable in many countries to facilitate early recognition of patient susceptibility to SCARs.

    OBJECTIVES: To determine the cost-effectiveness of universal HLA-B*15:02 screening in preventing carbamazepine-induced Stevens-Johnson syndrome/toxic epidermal necrolysis in an ethnically diverse Malaysian population.

    METHODS: A hybrid model of a decision tree and Markov model was developed to evaluate three strategies for treating newly diagnosed epilepsy among adults: (i) carbamazepine initiation without HLA-B*15:02 screening (current practice); (ii) universal HLA-B*15:02 screening prior to carbamazepine initiation; and (iii) alternative treatment [sodium valproate (VPA)] prescribing without HLA-B*15:02 screening. Base-case analysis and sensitivity analyses were performed over a lifetime time horizon. Incremental cost-effectiveness ratios were calculated.

    RESULTS: Both universal HLA-B*15:02 screening and VPA prescribing were dominated by current practice. Compared with current practice, universal HLA-B*15:02 screening resulted in a loss of 0·0255 quality-adjusted life years (QALYs) at an additional cost of 707 U.S. dollars (USD); VPA prescribing resulted in a loss of 0·2622 QALYs at an additional cost of USD 4127, owing to estimated differences in antiepileptic treatment efficacy.

    CONCLUSIONS: Universal HLA-B*15:02 screening is unlikely to be a cost-effective intervention in Malaysia. However, with the emergence of an ethnically diverse population in many other countries, this may render HLA-B*15:02 screening a viable intervention when an increasing proportion of the population is at risk and an equally effective yet safer antiepileptic drug is available.

    Matched MeSH terms: Markov Chains
  15. de Vries B, Narayan R, McGeechan K, Santiagu S, Vairavan R, Burke M, et al.
    Acta Obstet Gynecol Scand, 2018 Jun;97(6):668-676.
    PMID: 29450884 DOI: 10.1111/aogs.13310
    INTRODUCTION: Cesarean section rates continue to increase globally. Prediction of intrapartum cesarean section could lead to preventive measures. Our aim was to assess the association between sonographically measured cervical length at 37 weeks of gestation and cesarean section among women planning a vaginal birth. The population was women with a low-risk pregnancy or with gestational diabetes.

    MATERIAL AND METHODS: This was a prospective cohort study conducted in a tertiary referral hospital in Sydney, Australia. In all, 212 women with a low-risk pregnancy or with gestational diabetes were recruited including 158 nulliparous and 54 parous women. Maternal demographic, clinical and ultrasound characteristics were collected at 37 weeks of gestation. Semi-Bayesian logistic regression and Markov chain Monte Carlo simulation were used to assess the relation between cervical length and cesarean section in labor.

    RESULTS: Rates of cesarean section were 5% (2/55) for cervical length ≤20 mm, 17% (17/101) for cervical length 20-32 mm, and 27% (13/56) for cervical length >32 mm. These rates were 4, 22 and 33%, respectively, in nulliparous women. In the semi-Bayesian analysis, the odds ratio for cesarean section was 6.2 (95% confidence interval 2.2-43) for cervical length 20-32 mm and 10 (95% confidence interval 4.8-74) for cervical length >32 mm compared with the lowest quartile of cervical length, after adjusting for maternal age, parity, height, prepregnancy body mass index, gestational diabetes, induction of labor, neonatal sex and birthweight centile.

    CONCLUSIONS: Cervical length at 37 weeks of gestation is associated with intrapartum cesarean section.

    Matched MeSH terms: Markov Chains
  16. Lim KK, Yoon SY, Mohd Taib NA, Shabaruddin FH, Dahlui M, Woo YL, et al.
    Appl Health Econ Health Policy, 2018 06;16(3):395-406.
    PMID: 29572724 DOI: 10.1007/s40258-018-0384-8
    OBJECTIVE: Previous studies showed that offering BRCA mutation testing to population subgroups at high risk of harbouring the mutation may be cost effective, yet no evidence is available for low- or middle-income countries (LMIC) and in Asia. We estimated the cost effectiveness of BRCA mutation testing in early-stage breast cancer patients with high pre-test probability of harbouring the mutation in Malaysia, an LMIC in Asia.

    METHODS: We developed a decision analytic model to estimate the lifetime costs and quality-adjusted life-years (QALYs) accrued through BRCA mutation testing or routine clinical surveillance (RCS) for a hypothetical cohort of 1000 early-stage breast cancer patients aged 40 years. In the model, patients would decide whether to accept testing and to undertake risk-reducing mastectomy, oophorectomy, tamoxifen, combinations or neither. We calculated the incremental cost-effectiveness ratio (ICER) from the health system perspective. A series of sensitivity analyses were performed.

    RESULTS: In the base case, testing generated 11.2 QALYs over the lifetime and cost US$4815 per patient whereas RCS generated 11.1 QALYs and cost US$4574 per patient. The ICER of US$2725/QALY was below the cost-effective thresholds. The ICER was sensitive to the discounting of cost, cost of BRCA mutation testing and utility of being risk-free, but the ICERs remained below the thresholds. Probabilistic sensitivity analysis showed that at a threshold of US$9500/QALY, 99.9% of simulations favoured BRCA mutation testing over RCS.

    CONCLUSIONS: Offering BRCA mutation testing to early-stage breast cancer patients identified using a locally-validated risk-assessment tool may be cost effective compared to RCS in Malaysia.

    Matched MeSH terms: Markov Chains
  17. Suhana O, Nazni WA, Apandi Y, Farah H, Lee HL, Sofian-Azirun M
    Heliyon, 2019 Dec;5(12):e02682.
    PMID: 31867449 DOI: 10.1016/j.heliyon.2019.e02682
    Chikungunya virus (CHIKV) is maintained in the sylvatic cycle in West Africa and is transmitted by Aedes mosquito species to monkeys. In 2006, four verified CHIKV isolates were obtained during a survey of arboviruses in monkeys (Macaca fascicularis) in Pahang state, Peninsular Malaysia. RNA was extracted from the CHIKV isolates and used in reverse transcription polymerase chain reactions (RT-PCR) to amplify PCR fragments for sequencing. Nucleic acid primers were designed to generate overlapping PCR fragments that covered the whole viral sequence. A total of 11,238 base pairs (bp) corresponding to open reading frames (ORFs) from our isolates and 47 other registered isolates in the National Center for Biotechnology Information (NCBI) were used to elucidate sequences, amino acids, and phylogenetic relationships and to estimate divergence times by using MEGA 7.0 and the Bayesian Markov chain Monte Carlo method. Phylogenetic analysis revealed that all CHIKV isolates could be classified into the Asian genotype and clustered with Bagan Panchor clades, which are associated with the chikungunya outbreak reported in 2006, with sequence and amino acid similarities of 99.9% and 99.7%, respectively. Minor amino acid differences were found between human and non-human primate isolates. Amino acid analysis showed a unique amino acid at position 221 in the nsP1region, at which a glycine (G) was found only in monkey isolates, whereas arginine (R) was found at the same position only in human isolates. The time to the most recent common ancestor (MRCA) estimation indicated that CHIKV probably started to diverge from human to non-human primates in approximately 2004 in Malaysia. The results suggested that CHIKV in non-human primates probably resulted from the spillover of the virus from humans. The study will be helpful in understanding the movement and evolution of CHIKV in Malaysia and globally.
    Matched MeSH terms: Markov Chains
  18. Aziz F, Arof H, Mokhtar N, Mubin M
    J Neural Eng, 2014 Oct;11(5):056018.
    PMID: 25188730 DOI: 10.1088/1741-2560/11/5/056018
    This paper presents a wheelchair navigation system based on a hidden Markov model (HMM), which we developed to assist those with restricted mobility. The semi-autonomous system is equipped with obstacle/collision avoidance sensors and it takes the electrooculography (EOG) signal traces from the user as commands to maneuver the wheelchair. The EOG traces originate from eyeball and eyelid movements and they are embedded in EEG signals collected from the scalp of the user at three different locations. Features extracted from the EOG traces are used to determine whether the eyes are open or closed, and whether the eyes are gazing to the right, center, or left. These features are utilized as inputs to a few support vector machine (SVM) classifiers, whose outputs are regarded as observations to an HMM. The HMM determines the state of the system and generates commands for navigating the wheelchair accordingly. The use of simple features and the implementation of a sliding window that captures important signatures in the EOG traces result in a fast execution time and high classification rates. The wheelchair is equipped with a proximity sensor and it can move forward and backward in three directions. The asynchronous system achieved an average classification rate of 98% when tested with online data while its average execution time was less than 1 s. It was also tested in a navigation experiment where all of the participants managed to complete the tasks successfully without collisions.
    Matched MeSH terms: Markov Chains*
  19. Zourmand A, Ting HN, Mirhassani SM
    J Voice, 2013 Mar;27(2):201-9.
    PMID: 23473455 DOI: 10.1016/j.jvoice.2012.12.006
    Speech is one of the prevalent communication mediums for humans. Identifying the gender of a child speaker based on his/her speech is crucial in telecommunication and speech therapy. This article investigates the use of fundamental and formant frequencies from sustained vowel phonation to distinguish the gender of Malay children aged between 7 and 12 years. The Euclidean minimum distance and multilayer perceptron were used to classify the gender of 360 Malay children based on different combinations of fundamental and formant frequencies (F0, F1, F2, and F3). The Euclidean minimum distance with normalized frequency data achieved a classification accuracy of 79.44%, which was higher than that of the nonnormalized frequency data. Age-dependent modeling was used to improve the accuracy of gender classification. The Euclidean distance method obtained 84.17% based on the optimal classification accuracy for all age groups. The accuracy was further increased to 99.81% using multilayer perceptron based on mel-frequency cepstral coefficients.
    Matched MeSH terms: Markov Chains
  20. Yap KL, Chong YW, Liu W
    PLoS One, 2020;15(1):e0227982.
    PMID: 31978101 DOI: 10.1371/journal.pone.0227982
    The rapid increase in the usage of the mobile internet has led to a great expansion of cellular data networks in order to provide better quality of service. However, the cost to expand the cellular network is high. One of the solutions to provide affordable wireless connectivity is the deployment of a WiFi access point to offload users' data usage. Nevertheless, the frequent and inefficient handover process between the WiFi AP and cellular network, especially when the mobile device is on the go, may degrade the network performance. Mobile devices do not have the intelligence to select the optimal network to enhance the quality of service (QoS). This paper presents an enhanced handover mechanism using mobility prediction (eHMP) to assist mobile devices in the handover process so that users can experience seamless connectivity. eHMP is tested in two wireless architectures, homogeneous and heterogeneous networks. The network performance significantly improved when eHMP is used in a homogeneous network, where the network throughput increases by 106% and the rate of retransmission decreases by 85%. When eHMP is used in a heterogeneous network, the network throughput increases by 55% and the retransmission rate decreases by 75%. The findings presented in this paper reveal that mobility prediction coupled with the multipath protocol can improve the QoS for mobile devices. These results will contribute to a better understanding of how the network service provider can offload traffic to the WiFi network without experiencing performance degradation.
    Matched MeSH terms: Markov Chains
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