Displaying publications 1 - 20 of 390 in total

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  1. Syahrom A, Abdul Kadir MR, Harun MN, Öchsner A
    Med Eng Phys, 2015 Jan;37(1):77-86.
    PMID: 25523865 DOI: 10.1016/j.medengphy.2014.11.001
    Artificial bone is a suitable alternative to autografts and allografts, however their use is still limited. Though there were numerous reports on their structural properties, permeability studies of artificial bones were comparably scarce. This study focused on the development of idealised, structured models of artificial cancellous bone and compared their permeability values with bone surface area and porosity. Cancellous bones from fresh bovine femur were extracted and cleaned following an established protocol. The samples were scanned using micro-computed tomography (μCT) and three-dimensional models of the cancellous bones were reconstructed for morphology study. Seven idealised and structured cancellous bone models were then developed and fabricated via rapid prototyping technique. A test-rig was developed and permeability tests were performed on the artificial and real cancellous bones. The results showed a linear correlation between the permeability and the porosity as well as the bone surface area. The plate-like idealised structure showed a similar value of permeability to the real cancellous bones.
    Matched MeSH terms: Linear Models
  2. Maiwall R, Sarin SK, Kumar S, Jain P, Kumar G, Bhadoria AS, et al.
    Liver Int, 2017 Oct;37(10):1497-1507.
    PMID: 28393476 DOI: 10.1111/liv.13443
    BACKGROUND AND AIM: There is limited data on predictors of acute kidney injury in acute on chronic liver failure. We developed a PIRO model (Predisposition, Injury, Response, Organ failure) for predicting acute kidney injury in a multicentric cohort of acute on chronic liver failure patients.

    PATIENTS AND METHODS: Data of 2360 patients from APASL-ACLF Research Consortium (AARC) was analysed. Multivariate logistic regression model (PIRO score) was developed from a derivation cohort (n=1363) which was validated in another prospective multicentric cohort of acute on chronic liver failure patients (n=997).

    RESULTS: Factors significant for P component were serum creatinine[(≥2 mg/dL)OR 4.52, 95% CI (3.67-5.30)], bilirubin [(<12 mg/dL,OR 1) vs (12-30 mg/dL,OR 1.45, 95% 1.1-2.63) vs (≥30 mg/dL,OR 2.6, 95% CI 1.3-5.2)], serum potassium [(<3 mmol/LOR-1) vs (3-4.9 mmol/L,OR 2.7, 95% CI 1.05-1.97) vs (≥5 mmol/L,OR 4.34, 95% CI 1.67-11.3)] and blood urea (OR 3.73, 95% CI 2.5-5.5); for I component nephrotoxic medications (OR-9.86, 95% CI 3.2-30.8); for R component,Systemic Inflammatory Response Syndrome,(OR-2.14, 95% CI 1.4-3.3); for O component, Circulatory failure (OR-3.5, 95% CI 2.2-5.5). The PIRO score predicted acute kidney injury with C-index of 0.95 and 0.96 in the derivation and validation cohort. The increasing PIRO score was also associated with mortality (P

    Matched MeSH terms: Linear Models
  3. Gijsberts CM, Groenewegen KA, Hoefer IE, Eijkemans MJ, Asselbergs FW, Anderson TJ, et al.
    PLoS One, 2015;10(7):e0132321.
    PMID: 26134404 DOI: 10.1371/journal.pone.0132321
    BACKGROUND: Clinical manifestations and outcomes of atherosclerotic disease differ between ethnic groups. In addition, the prevalence of risk factors is substantially different. Primary prevention programs are based on data derived from almost exclusively White people. We investigated how race/ethnic differences modify the associations of established risk factors with atherosclerosis and cardiovascular events.

    METHODS: We used data from an ongoing individual participant meta-analysis involving 17 population-based cohorts worldwide. We selected 60,211 participants without cardiovascular disease at baseline with available data on ethnicity (White, Black, Asian or Hispanic). We generated a multivariable linear regression model containing risk factors and ethnicity predicting mean common carotid intima-media thickness (CIMT) and a multivariable Cox regression model predicting myocardial infarction or stroke. For each risk factor we assessed how the association with the preclinical and clinical measures of cardiovascular atherosclerotic disease was affected by ethnicity.

    RESULTS: Ethnicity appeared to significantly modify the associations between risk factors and CIMT and cardiovascular events. The association between age and CIMT was weaker in Blacks and Hispanics. Systolic blood pressure associated more strongly with CIMT in Asians. HDL cholesterol and smoking associated less with CIMT in Blacks. Furthermore, the association of age and total cholesterol levels with the occurrence of cardiovascular events differed between Blacks and Whites.

    CONCLUSION: The magnitude of associations between risk factors and the presence of atherosclerotic disease differs between race/ethnic groups. These subtle, yet significant differences provide insight in the etiology of cardiovascular disease among race/ethnic groups. These insights aid the race/ethnic-specific implementation of primary prevention.

    Matched MeSH terms: Linear Models
  4. Rohana J, Hasmawati J, Zulkifli SZ
    Singapore Med J, 2007 Mar;48(3):191-4.
    PMID: 17342285
    We report part of the findings of a study conducted to determine the correlation between bone mineral content (BMC) and biochemical bone markers in very low birth weight (VLBW) infants.
    Matched MeSH terms: Linear Models
  5. Mohd Tahir Ismail, Zaidi Isa
    Sains Malaysiana, 2006;35:55-62.
    The behaviour of many financial time series cannot be modeled solely by linear time series model. Phenomena such as mean reversion, volatility of stock markets and structural breaks cannot be modelled implicitly using simple linear time series model. Thus, to overcome this problem, nonlinear time series models are typically designed to accommodate these nonlinear features in the data. In this paper, we use portmanteau test and structural change test to detect nonlinear feature in three ASEAN countries exchange rates (Malaysia, Singapore and Thailand). It is found that the null hypothesis of linearity is rejected and there is evidence of structural breaks in the exchange rates series. Therefore, the decision of using regime switching model in this study is justified. Using model selection criteria (AIC, SBC, HQC), we compare the in-sample fitting between two types of regime switching model. The two regime switching models we considered were the Self-Exciting Threshold Autoregressive (SETAR) model and the Markov switching Autoregressive (MS-AR) model where these models can explain the abrupt changes in a time series but differ as how they model the movement between regimes. From the AIC, SBC and HQC values, it is found that the MS -AR model is the best fitted model for all the return series. In addition, the regime switching model also found to perform better than simple autoregressive model in in-sample fitting. This result justified that nonlinear model give better in-sample fitting than linear model.
    Matched MeSH terms: Linear Models
  6. Noraishah Othman, Siti Kartom Kamarudin, Muhd Noor Md Yunus, Abd. Halim Shamsuddin, Siti Rozaimah, Zahirah Yaakob
    MyJurnal
    The production of carbon dioxide from Karas woods under argon atmosphere was investigated using a direct pyrolysis-combustion approach. Direct burning was used in this study, using argon for yrolysis and oxygen during combustion to look at the yield of carbon dioxide, produced at different parameters, such as the temperature, retention time and flow rate of argon, as the carrier gas. In this study, a new methodology, 23 response surface central composite design was successfully employed for the experimental design and analysis of results. Central composite experimental design and response surface method were utilized to determine the best operating condition for a maximum carbon dioxide production. Appropriate predictable empirical linear model was developed by incorporating interaction effects of all the variables involved. The results of the analysis revealed that linear equation models fitted well with the experimental for carbon dioxide yield. Nevertheless, the R-Squared obtained using the direct pyrolysis-combustion was 0.7118, indicating that the regression line was not at the best-fitted line.
    Matched MeSH terms: Linear Models
  7. Krupa BN, Mohd Ali MA, Zahedi E
    Physiol Meas, 2009 Aug;30(8):729-43.
    PMID: 19550027 DOI: 10.1088/0967-3334/30/8/001
    Cardiotocograph (CTG) is widely used in everyday clinical practice for fetal surveillance, where it is used to record fetal heart rate (FHR) and uterine activity (UA). These two biosignals can be used for antepartum and intrapartum fetal monitoring and are, in fact, nonlinear and non-stationary. CTG recordings are often corrupted by artifacts such as missing beats in FHR, high-frequency noise in FHR and UA signals. In this paper, an empirical mode decomposition (EMD) method is applied on CTG signals. A recursive algorithm is first utilized to eliminate missing beats. High-frequency noise is reduced using EMD followed by the partial reconstruction (PAR) method, where the noise order is identified by a statistical method. The obtained signal enhancement from the proposed method is validated by comparing the resulting traces with the output obtained by applying classical signal processing methods such as Butterworth low-pass filtering, linear interpolation and a moving average filter on 12 CTG signals. Three obstetricians evaluated all 12 sets of traces and rated the proposed method, on average, 3.8 out of 5 on a scale of 1(lowest) to 5 (highest).
    Matched MeSH terms: Linear Models
  8. Azam Rahim, Maimaiti, Namaitijiag, Abuduli, Maihebureti, Zafar Ahmed
    MyJurnal
    Asthma is one of the most common chronic diseases in the world. It is estimated that around 300 million people in the world currently have asthma. The prevalence of asthma is increasing in most of countries, especially among children. The burden of severe asthma is considerable high in the Middle East courtiers including Iran. This study to investigate the effect of patients' knowledge about Inhaled Corticosteroids (ICS), attitude and health beliefs toward ICS, and behavioral intention to adhere to prescribed ICS in adult asthmatic patients in Yazd city, Iran. A cross sectional study was conducted from August 2008 to January 2009 in three private allergy and asthma clinics, located in Yazd city of Iran, using Structured face to face interviews using a questionnaire by the researcher. The results showed that the majority of patients (55.5%) were not adherent to their prescribed ICS. Patients’ knowledge toward ICS did not have effect on medication adherence behavior, while patients with positive attitude toward ICS were better adherent with their medication. Linear regression model identifies intention to comply with treatment and positive attitude toward ICS as predictors for adherence behavior. This study shows the relationship between medications beliefs, attitude, behavioral intention, and medication adherence. A better understanding of patient's medication beliefs, and attitude and their effect on compliance may help health care system to promote adherence.
    Matched MeSH terms: Linear Models
  9. Mirhosseini H, Tan CP, Hamid NS, Yusof S
    J Agric Food Chem, 2007 Sep 19;55(19):7659-66.
    PMID: 17708646
    The possible relationships between the main emulsion components (namely, Arabic gum, xanthan gum, and orange oil) and the physicochemical properties of orange beverage emulsion were evaluated by using response surface methodology. The physicochemical emulsion property variables considered as response variables were emulsion stability, viscosity, fluid behavior, zeta-potential, and electrophoretic mobility. The independent variables had the most and least significant ( p < 0.05) effect on viscosity and zeta-potential, respectively. The quadratic effect of orange oil and Arabic gum, the interaction effect of Arabic gum and xanthan gum, and the main effect of Arabic gum were the most significant ( p < 0.05) effects on turbidity loss rate, viscosity, viscosity ratio, and mobility, respectively. The main effect of Arabic gum was found to be significant ( p < 0.05) in all response variables except for turbidity loss rate. The nonlinear regression equations were significantly ( p < 0.05) fitted for all response variables with high R (2) values (>0.86), which had no indication of lack of fit. The results indicated that a combined level of 10.78% (w/w) Arabic gum, 0.56% (w/w) xanthan gum, and 15.27% (w/w) orange oil was predicted to provide the overall optimum region in terms of physicochemical properties studied. No significant ( p > 0.05) difference between the experimental and the predicted values confirmed the adequacy of response surface equations.
    Matched MeSH terms: Linear Models
  10. Nur Arina Bazilah Kamisan, Muhammad Hisyam Lee, Suhartono Suhartono, Abdul Ghapor Hussin, Yong Zulina Zubairi
    Sains Malaysiana, 2018;47:419-426.
    Forecasting a multiple seasonal data is differ from a usual seasonal data since it contains more than one cycle in a
    data. Multiple linear regression (MLR) models have been used widely in load forecasting because of its usefulness in the
    forecast a linear relationship with other factors but MLR has a disadvantage of having difficulties in modelling a nonlinear
    relationship between the variables and influencing factors. Neural network (NN) model, on the other hand, is a good
    model for modelling a nonlinear data. Therefore, in this study, a combination of MLR and NN models has proposed this
    combination to overcome the problem. This hybrid model is then compared with MLR and NN models to see the performance
    of the hybrid model. RMSE is used as a performance indicator and a proposed graphical error plot is introduce to see the
    error graphically. From the result obtained this model gives a better forecast compare to the other two models.
    Matched MeSH terms: Linear Models
  11. Keong KM, Aziz I, Yin Wei CC
    J Orthop Surg (Hong Kong), 2017 01 01;25(1):2309499016684431.
    PMID: 29185383 DOI: 10.1177/2309499016684431
    PURPOSE: This study aims to derive a formula to predict post-operative height increment in Lenke 1 and Lenke 2 adolescent idiopathic scoliosis (AIS) patients using preoperative radiological parameters.

    METHODS: This study involved 70 consecutive Lenke 1 and 2 AIS patients who underwent scoliosis correction with alternate-level pedicle screw instrumentation. Preoperative parameters that were measured included main thoracic (MT) Cobb angle, proximal thoracic (PT) Cobb angle, lumbar Cobb angle as well as thoracic kyphosis. Side-bending flexibility (SBF) and fulcrum-bending flexibility (FBF) were derived from the measurements. Preoperative height and post-operative height increment was measured by an independent observer using a standardized method.

    RESULTS: MT Cobb angle and FB Cobb angle were significant predictors ( p < 0.001) of height increment from multiple linear regression analysis ( R = 0.784, R2 = 0.615). PT Cobb angle, lumbar, SB Cobb angle, preoperative height and number of fused segment were not significant predictors for the height increment based on the multivariable analysis. Increase in post-operative height could be calculated by the formula: Increase in height (cm) = (0.09 × preoperative MT Cobb angle) - (0.04 x FB Cobb angle) - 0.5.

    CONCLUSION: The proposed formula of increase in height (cm) = (0.09 × preoperative MT Cobb angle) - (0.04 × FB Cobb angle) - 0.5 could predict post-operative height gain to within 5 mm accuracy in 51% of patients, within 10 mm in 70% and within 15 mm in 86% of patients.

    Matched MeSH terms: Linear Models
  12. Yii MK
    Asian J Surg, 2003 Jul;26(3):149-53.
    PMID: 12925289 DOI: 10.1016/S1015-9584(09)60374-2
    Abdominal aortic aneurysm (AAA) repairs represent a significant workload in vascular surgery in Asia. This study aimed to audit AAA surgery and evaluate the application of the Portsmouth Physiological and Operative Severity Score for the Enumeration of Mortality and Morbidity (P-POSSUM) in an Asian vascular unit for standard of care. Eighty-five consecutive surgical patients with AAA from a prospective vascular database from July 1996 to December 2001 in Sarawak were available for analysis. Comparisons between predicted deaths by P-POSSUM and observed deaths in both urgency of surgery categories (elective, urgent, emergency ruptures) and risk range groups (0-5%, >5-15%, >15-50%, >50-100%) were made. No significant difference was found between the predicted and observed rates of death for elective, urgent and emergency AAA repairs. The observed mortality rates were 5%, 18% and 30%, respectively. The observed rates of death were also comparable to P-POSSUM predicted rates of death in the various risk range groups. The POSSUM score used with the P-POSSUM mortality equation is easy to use and applicable as a comparative vascular auditing tool in Asia.
    Matched MeSH terms: Linear Models
  13. Jun Zhao, Feifei Wang, Yifan Lu
    Sains Malaysiana, 2017;46:2223-2229.
    Formation lithology identification is an indispensable link in oil and gas exploration. Precision of the traditional recognition method is difficult to guarantee when trying to identify lithology of particular formation with strong heterogeneity and complex structure. In order to remove this defect, multivariate membership function discrimination method is proposed, which regard to lithology identification as a linear model in the fuzzy domain and obtain aimed result with the multivariate membership function established. It is indicated by the test on lower carboniferous Bachu group bioclastic limestone section and Donghe sandstone section reservoir on T Field H area that satisfactory accuracy can be achieved in both clastic rock and carbonate formation and obvious advantages are unfold when dealing with complex formations, which shows a good application prospect and provides a new thought to solve complex problems on oilfield exploration and development with fuzzy theory.
    Matched MeSH terms: Linear Models
  14. Aldubai SAR, Aljohani AM, Alghamdi AG, Alghamdi KS, Ganasegeran K, Yenbaawi AM
    J Family Med Prim Care, 2019 02;8(2):657-662.
    PMID: 30984690 DOI: 10.4103/jfmpc.jfmpc_268_18
    Background and Aim: Burnout is a common problem for interns and residents. It has been associated with physical and mental health of health care providers as well as low job satisfaction and medical errors. Few studies have investigated this problem among residents. This study aimed to determine the prevalence of burnout and its associated factors among family residents in Al Madina city, Saudi Arabia.

    Materials and Methods: This cross-sectional study was conducted among 75 residents in the family medicine residency programs in Al Madina, Saudi Arabia. A self-administered questionnaire was used that includes questions on sociodemographic characteristics and sources of stress and burnout. T test, analysis of variance (ANOVA) test, and multiple linear regression analysis were employed.

    Results: Majority were female (54.7%) and aged 26 to 30 years (84.0%). The significant predictors of burnout in the final model were "tests/examinations" (P = 0.014), "large amount of content to be learnt" (P = 0.016), "unfair assessment from superiors" (P = 0.001), "work demands affect personal/home life" (P = 0.001), and "lack of support from superiors" (P = 0.006).

    Conclusion: Burnout is present among family medicine residents at a relatively high percentage. This situation is strongly triggered by work-related stressors, organizational attributes, and system-related attributes, but not socio-demographics of the respondents. Systemic changes to relieve the workload of family medicine residents are recommended to promote effective management of burnout.

    Matched MeSH terms: Linear Models
  15. Wong HY, Subramaniyan M, Bullen C, Amer Siddiq AN, Danaee M, Yee A
    Tob Induc Dis, 2019;17:65.
    PMID: 31582954 DOI: 10.18332/tid/111355
    INTRODUCTION: The mobile-phone-based Bedfont iCOTM Smokerlyzer® is of unknown validity and reproducibility compared to the widely-used piCO+ Smokerlyzer®. We aimed to compare the validity and reproducibility of the iCOTM Smokerlyzer® with the piCO+ Smokerlyzer® among patients reducing or quitting tobacco smoking.

    METHODS: Methadone-maintained therapy (MMT) users from three centers in Malaysia had their exhaled carbon monoxide (eCO) levels recorded via the piCO+ and iCOTM Smokerlyzers®, their nicotine dependence assessed with the Malay version of the Fagerström Test for Nicotine Dependence (FTND-M), and daily tobacco intake measured via the Opiate Treatment Index (OTI) Tobacco Q-score. Pearson partial correlations were used to compare the eCO results of both devices, as well as the corresponding FTND-M scores.

    RESULTS: Among the 146 participants (mean age 47.9 years, 92.5% male, and 73.3% Malay ethnic group) most (55.5%) were moderate smokers (6-19 cigarettes/day). Mean eCO categories were significantly correlated between both devices (r=0.861, p<0.001), and the first and second readings were significantly correlated for each device (r=0.94 for the piCO+ Smokerlyzer®, p<0.001; r=0.91 for the iCOTM Smokerlyzer®, p<0.001). Exhaled CO correlated positively with FTND-M scores for both devices. The post hoc analysis revealed a significantly lower iCOTM Smokerlyzer® reading of 0.82 (95% CI: 0.69-0.94, p<0.001) compared to that of the piCO+ Smokerlyzer®, and a significant intercept of -0.34 (95% CI: -0.61 - -0.07, p=0.016) on linear regression analysis, suggesting that there may be a calibration error in one or more of the iCOTM Smokerlyzer® devices.

    CONCLUSIONS: The iCOTM Smokerlyzer® readings are highly reproducible compared to those of the piCO+ Smokerlyzer®, but calibration guidelines are required for the mobile-phone-based device. Further research is required to assess interchangeability.

    Matched MeSH terms: Linear Models
  16. Ataie-Jafari A, Loke SC, Rahmat AB, Larijani B, Abbasi F, Leow MK, et al.
    Clin Nutr, 2013 Dec;32(6):911-7.
    PMID: 23395257 DOI: 10.1016/j.clnu.2013.01.012
    This participant-blinded parallel-group randomized placebo-controlled study demonstrated that alfacalcidol (vitamin D analogue) preserves beta cell function in newly diagnosed type 1 diabetes (T1DM) in children.
    Matched MeSH terms: Linear Models
  17. Gopinath SCB, Ismail ZH, Shapiai MI, Yasin MNM
    PMID: 34009645 DOI: 10.1002/bab.2196
    Current developments in sensors and actuators are heralding a new era to facilitate things to happen effortlessly and efficiently with proper communication. On the other hand, Internet of Things (IoT) has been boomed up with er potential and occupies a wide range of disciplines. This study has choreographed to design of an algorithm and a smart data-processing scheme to implement the obtained data from the sensing system to transmit to the receivers. Technically, it is called "telediagnosis" and "remote digital monitoring," a revolution in the field of medicine and artificial intelligence. For the proof of concept, an algorithmic approach has been implemented for telediagnosis with one of the degenerative diseases, that is, Parkinson's disease. Using the data acquired from an improved interdigitated electrode, sensing surface was evaluated with the attained sensitivity of 100 fM (n = 3), and the limit of detection was calculated with the linear regression value coefficient. By the designed algorithm and data processing with the assistance of IoT, further validation was performed and attested the coordination. This proven concept can be ideally used with all sensing strategies for immediate telemedicine by end-to-end communications.
    Matched MeSH terms: Linear Models
  18. Oettli P, Behera SK, Yamagata T
    Sci Rep, 2018 02 02;8(1):2271.
    PMID: 29396527 DOI: 10.1038/s41598-018-20298-0
    The influence of local conditions and remote climate modes on the interannual variability of oil palm fresh fruit bunches (FFB) total yields in Malaysia and two major regions (Peninsular Malaysia and Sabah/Sarawak) is explored. On a country scale, the state of sea-surface temperatures (SST) in the tropical Pacific Ocean during the previous boreal winter is found to influence the regional climate. When El Niño occurs in the Pacific Ocean, rainfall in Malaysia reduces but air temperature increases, generating a high level of water stress for palm trees. As a result, the yearly production of FFB becomes lower than that of a normal year since the water stress during the boreal spring has an important impact on the total annual yields of FFB. Conversely, La Niña sets favorable conditions for palm trees to produce more FFB by reducing chances of water stress risk. The region of the Leeuwin current also seems to play a secondary role through the Ningaloo Niño/ Niña in the interannual variability of FFB yields. Based on these findings, a linear model is constructed and its ability to reproduce the interannual signal is assessed. This model has shown some skills in predicting the total FFB yield.
    Matched MeSH terms: Linear Models
  19. Kamaruzaman S, Sanagi MM, Endud S, Wan Ibrahim WA, Yahaya N
    PMID: 24140656 DOI: 10.1016/j.jchromb.2013.09.017
    Mesoporous silica material, MCM-41, was utilized for the first time as an adsorbent in solid phase membrane tip extraction (SPMTE) of non-steroidal anti-inflammatory drugs (NSAIDs) in urine prior to high performance liquid chromatography-ultraviolet (HPLC-UV) analysis. The prepared MCM-41 material was enclosed in a polypropylene membrane tip and used as an adsorbent in SPMTE. Four NSAIDs namely ketoprofen, diclofenac, mefenamic acid and naproxen were selected as model analytes. Several important parameters, such as conditioning solvent, sample pH, salting-out effect, sample volume, extraction time, desorption solvent and desorption time were optimized. Under the optimum extraction conditions, the MCM-41-SPMTE method showed good linearity in the range of 0.01-10μg/mL with excellent correlation coefficients (r=0.9977-0.9995), acceptable RSDs (0.4-9.4%, n=3), good limits of detection (5.7-10.6μg/L) and relative recoveries (81.4-108.1%). The developed method showed a good tolerance to biological sample matrices.
    Matched MeSH terms: Linear Models
  20. Hariharan M, Chee LS, Yaacob S
    J Med Syst, 2012 Jun;36(3):1309-15.
    PMID: 20844933 DOI: 10.1007/s10916-010-9591-z
    Acoustic analysis of infant cry signals has been proven to be an excellent tool in the area of automatic detection of pathological status of an infant. This paper investigates the application of parameter weighting for linear prediction cepstral coefficients (LPCCs) to provide the robust representation of infant cry signals. Three classes of infant cry signals were considered such as normal cry signals, cry signals from deaf babies and babies with asphyxia. A Probabilistic Neural Network (PNN) is suggested to classify the infant cry signals into normal and pathological cries. PNN is trained with different spread factor or smoothing parameter to obtain better classification accuracy. The experimental results demonstrate that the suggested features and classification algorithms give very promising classification accuracy of above 98% and it expounds that the suggested method can be used to help medical professionals for diagnosing pathological status of an infant from cry signals.
    Matched MeSH terms: Linear Models
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