Displaying publications 1 - 20 of 867 in total

  1. Rakami NMHN, Ismail NAH, Abu Kassim NL, Idrus F
    J Appl Meas, 2020;21(1):91-100.
    PMID: 32129771
    This paper describes the process of assessing the unidimensionality and validity of egalitarian education (EE) items based on the Rasch measurement model. Egalitarian education was measured by a self-developed 5 EE items of Likert-scale format. The process of assessing the validity of EE items involved a collection of data from 400 Malay teachers, who are teaching in government school around peninsular of Malaysia where the measurement of construct validity for the overall EE items were established using Winsteps. Various Rasch measurement tools were utilized to demonstrate the true unidimensionality and validity measure of the EE items and in meeting the needs of the Rasch measurement model. The findings show that the validity and unidimensionality of EE items can be truly established and can satisfy the characteristics of the Rasch measurement model.
    Matched MeSH terms: Logistic Models*
  2. Algamal ZY, Lee MH
    Comput Biol Med, 2015 Dec 1;67:136-45.
    PMID: 26520484 DOI: 10.1016/j.compbiomed.2015.10.008
    Cancer classification and gene selection in high-dimensional data have been popular research topics in genetics and molecular biology. Recently, adaptive regularized logistic regression using the elastic net regularization, which is called the adaptive elastic net, has been successfully applied in high-dimensional cancer classification to tackle both estimating the gene coefficients and performing gene selection simultaneously. The adaptive elastic net originally used elastic net estimates as the initial weight, however, using this weight may not be preferable for certain reasons: First, the elastic net estimator is biased in selecting genes. Second, it does not perform well when the pairwise correlations between variables are not high. Adjusted adaptive regularized logistic regression (AAElastic) is proposed to address these issues and encourage grouping effects simultaneously. The real data results indicate that AAElastic is significantly consistent in selecting genes compared to the other three competitor regularization methods. Additionally, the classification performance of AAElastic is comparable to the adaptive elastic net and better than other regularization methods. Thus, we can conclude that AAElastic is a reliable adaptive regularized logistic regression method in the field of high-dimensional cancer classification.
    Matched MeSH terms: Logistic Models
  3. Habshah, M., Syaiba, B.A.
    It is now evident that the estimation of logistic regression parameters, using Maximum LikelihoodEstimator (MLE), suffers a huge drawback in the presence of outliers. An alternative approach is touse robust logistic regression estimators, such as Mallows type leverage dependent weights estimator(MALLOWS), Conditionally Unbiased Bounded Influence Function estimator (CUBIF), Bianco andYohai estimator (BY), and Weighted Bianco and Yohai estimator (WBY). This paper investigates therobustness of the preceding robust estimators by using real data sets and Monte Carlo simulations. Theresults indicate that the MLE behaves poorly in the presence of outliers. On the other hand, the WBYestimator is more efficient than the other existing robust estimators. Thus, it is suggested that the WBYestimator be employed when outliers are present in the data to obtain a reliable estimate.
    Matched MeSH terms: Logistic Models
  4. Norhayati Rosli, Arifah Bahar, Yeak SH, Haliza Abdul Rahman, Madihah Md. Salleh
    Stochastic differential equations play a prominent role in many application areas including finance, biology and epidemiology. By incorporating random elements to ordinary differential equation system, a system of stochastic differential equations (SDEs) arises. This leads to a more complex insight of the physical phenomena than their deterministic counterpart. However, most of the SDEs do not have an analytical solution where numerical method is the best way to resolve this problem. Recently, much work had been done in applying numerical methods for solving SDEs. A very general class of Stochastic Runge-Kutta, (SRK) had been studied and 2-stage SRK with order convergence of 1.0 and 4-stage SRK with order convergence of 1.5 were discussed. In this study, we compared the performance of Euler-Maruyama, 2-stage SRK and 4-stage SRK in approximating the strong solutions of stochastic logistic model which describe the cell growth of C. acetobutylicum P262. The MS-stability functions of these schemes were calculated and regions of MS-stability are given. We also perform the comparison for the performance of these methods based on their global errors.
    Matched MeSH terms: Logistic Models
  5. Law ZK, Dineen R, England TJ, Cala L, Mistri AK, Appleton JP, et al.
    Transl Stroke Res, 2021 04;12(2):275-283.
    PMID: 32902808 DOI: 10.1007/s12975-020-00845-6
    Neurological deterioration is common after intracerebral hemorrhage (ICH). We aimed to identify the predictors and effects of neurological deterioration and whether tranexamic acid reduced the risk of neurological deterioration. Data from the Tranexamic acid in IntraCerebral Hemorrhage-2 (TICH-2) randomized controlled trial were analyzed. Neurological deterioration was defined as an increase in National Institutes of Health Stroke Scale (NIHSS) of ≥ 4 or a decline in Glasgow Coma Scale of ≥ 2. Neurological deterioration was considered to be early if it started ≤ 48 h and late if commenced between 48 h and 7 days after onset. Logistic regression was used to identify predictors and effects of neurological deterioration and the effect of tranexamic acid on neurological deterioration. Of 2325 patients, 735 (31.7%) had neurological deterioration: 590 (80.3%) occurred early and 145 (19.7%) late. Predictors of early neurological deterioration included recruitment from the UK, previous ICH, higher admission systolic blood pressure, higher NIHSS, shorter onset-to-CT time, larger baseline hematoma, intraventricular hemorrhage, subarachnoid extension and antiplatelet therapy. Older age, male sex, higher NIHSS, previous ICH and larger baseline hematoma predicted late neurological deterioration. Neurological deterioration was independently associated with a modified Rankin Scale of > 3 (aOR 4.98, 3.70-6.70; p 
    Matched MeSH terms: Logistic Models
  6. Nhu VH, Shirzadi A, Shahabi H, Singh SK, Al-Ansari N, Clague JJ, et al.
    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: Logistic Models*
  7. Philipson CD, Dent DH, O'Brien MJ, Chamagne J, Dzulkifli D, Nilus R, et al.
    Ecol Evol, 2014 Sep;4(18):3675-88.
    PMID: 25478157 DOI: 10.1002/ece3.1186
    A life-history trade-off between low mortality in the dark and rapid growth in the light is one of the most widely accepted mechanisms underlying plant ecological strategies in tropical forests. Differences in plant functional traits are thought to underlie these distinct ecological strategies; however, very few studies have shown relationships between functional traits and demographic rates within a functional group. We present 8 years of growth and mortality data from saplings of 15 species of Dipterocarpaceae planted into logged-over forest in Malaysian Borneo, and the relationships between these demographic rates and four key functional traits: wood density, specific leaf area (SLA), seed mass, and leaf C:N ratio. Species-specific differences in growth rates were separated from seedling size effects by fitting nonlinear mixed-effects models, to repeated measurements taken on individuals at multiple time points. Mortality data were analyzed using binary logistic regressions in a mixed-effects models framework. Growth increased and mortality decreased with increasing light availability. Species differed in both their growth and mortality rates, yet there was little evidence for a statistical interaction between species and light for either response. There was a positive relationship between growth rate and the predicted probability of mortality regardless of light environment, suggesting that this relationship may be driven by a general trade-off between traits that maximize growth and traits that minimize mortality, rather than through differential species responses to light. Our results indicate that wood density is an important trait that indicates both the ability of species to grow and resistance to mortality, but no other trait was correlated with either growth or mortality. Therefore, the growth mortality trade-off among species of dipterocarp appears to be general in being independent of species crossovers in performance in different light environments.
    Matched MeSH terms: Logistic Models
  8. Gunasekaran S, Venkatesh B, Sagar BS
    Int J Neural Syst, 2004 Apr;14(2):139-45.
    PMID: 15112371
    Training methodology of the Back Propagation Network (BPN) is well documented. One aspect of BPN that requires investigation is whether or not the BPN would get trained for a given training data set and architecture. In this paper the behavior of the BPN is analyzed during its training phase considering convergent and divergent training data sets. Evolution of the weights during the training phase was monitored for the purpose of analysis. The evolution of weights was plotted as return map and was characterized by means of fractal dimension. This fractal dimensional analysis of the weight evolution trajectories is used to provide a new insight to understand the behavior of BPN and dynamics in the evolution of weights.
    Matched MeSH terms: Logistic Models
  9. Sajid MR, Muhammad N, Zakaria R, Shahbaz A, Bukhari SAC, Kadry S, et al.
    Interdiscip Sci, 2021 Jun;13(2):201-211.
    PMID: 33675528 DOI: 10.1007/s12539-021-00423-w
    BACKGROUND: In the broader healthcare domain, the prediction bears more value than an explanation considering the cost of delays in its services. There are various risk prediction models for cardiovascular diseases (CVDs) in the literature for early risk assessment. However, the substantial increase in CVDs-related mortality is challenging global health systems, especially in developing countries. This situation allows researchers to improve CVDs prediction models using new features and risk computing methods. This study aims to assess nonclinical features that can be easily available in any healthcare systems, in predicting CVDs using advanced and flexible machine learning (ML) algorithms.

    METHODS: A gender-matched case-control study was conducted in the largest public sector cardiac hospital of Pakistan, and the data of 460 subjects were collected. The dataset comprised of eight nonclinical features. Four supervised ML algorithms were used to train and test the models to predict the CVDs status by considering traditional logistic regression (LR) as the baseline model. The models were validated through the train-test split (70:30) and tenfold cross-validation approaches.

    RESULTS: Random forest (RF), a nonlinear ML algorithm, performed better than other ML algorithms and LR. The area under the curve (AUC) of RF was 0.851 and 0.853 in the train-test split and tenfold cross-validation approach, respectively. The nonclinical features yielded an admissible accuracy (minimum 71%) through the LR and ML models, exhibiting its predictive capability in risk estimation.

    CONCLUSION: The satisfactory performance of nonclinical features reveals that these features and flexible computational methodologies can reinforce the existing risk prediction models for better healthcare services.

    Matched MeSH terms: Logistic Models
  10. Islahudin F, Lee FY, Tengku Abd Kadir TNI, Abdullah MZ, Makmor-Bakry M
    Res Social Adm Pharm, 2021 10;17(10):1831-1840.
    PMID: 33589374 DOI: 10.1016/j.sapharm.2021.02.002
    BACKGROUND: An adherence model is required to optimise medication management among chronic kidney disease (CKD) patients, as current assessment methods overestimate the true adherence of CKD patients with complex regimens. An approach to assess adherence to individual medications is required to assist pharmacists in addressing non-adherence.

    OBJECTIVE: To develop an adherence prediction model for CKD patients.

    METHODS: This multi-centre, cross-sectional study was conducted in 10 tertiary hospitals in Malaysia using simple random sampling of CKD patients with ≥1 medication (sample size = 1012). A questionnaire-based collection of patient characteristics, adherence (defined as ≥80% consumption of each medication for the past one month), and knowledge of each medication (dose, frequency, indication, and administration) was performed. Continuous data were converted to categorical data, based on the median values, and then stratified and analysed. An adherence prediction model was developed through multiple logistic regression in the development group (n = 677) and validated on the remaining one-third of the sample (n = 335). Beta-coefficient values were then used to determine adherence scores (ranging from 0 to 7) based on the predictors identified, with lower scores indicating poorer medication adherence.

    RESULTS: Most of the 1012 patients had poor medication adherence (n = 715, 70.6%) and half had good medication knowledge (n = 506, 50%). Multiple logistic regression analysis determined 4 significant predictors of adherence: ≤7 medications (constructed score = 2, p 

    Matched MeSH terms: Logistic Models
  11. MyJurnal
    The study investigated socio-demographic factors and product attributes affecting purchase decision of special rice by Malaysian consumer. The primary data were analyzed by using binary logit model.
    Demographic factors and consumer preference for special rice (with reference to basmati rice) attributes were identified to affect purchasing behavior for special rice. Size of household, marital status, number of children, household income and gender of consumers are the main socio-demographic factors that significantly influence households’ choices of special rice for home consumption in the Klang Valley area. The findings also suggest that product attributes such as flavor and aroma, availability, brand name and quality also influence the frequent purchasing of Basmati rice among the Malaysian consumers. However price and easy preparation are not significant in influencing the frequent purchasing of Basmati rice since most consumers are aware that special rice such as Basmati is expensive and all rice has to be prepared in a usual way.
    Matched MeSH terms: Logistic Models
  12. Runge-Ranzinger S, Kroeger A, Olliaro P, McCall PJ, Sánchez Tejeda G, Lloyd LS, et al.
    PLoS Negl Trop Dis, 2016 Sep;10(9):e0004916.
    PMID: 27653786 DOI: 10.1371/journal.pntd.0004916
    BACKGROUND: Dengue is an increasingly incident disease across many parts of the world. In response, an evidence-based handbook to translate research into policy and practice was developed. This handbook facilitates contingency planning as well as the development and use of early warning and response systems for dengue fever epidemics, by identifying decision-making processes that contribute to the success or failure of dengue surveillance, as well as triggers that initiate effective responses to incipient outbreaks.

    METHODOLOGY/PRINCIPAL FINDINGS: Available evidence was evaluated using a step-wise process that included systematic literature reviews, policymaker and stakeholder interviews, a study to assess dengue contingency planning and outbreak management in 10 countries, and a retrospective logistic regression analysis to identify alarm signals for an outbreak warning system using datasets from five dengue endemic countries. Best practices for managing a dengue outbreak are provided for key elements of a dengue contingency plan including timely contingency planning, the importance of a detailed, context-specific dengue contingency plan that clearly distinguishes between routine and outbreak interventions, surveillance systems for outbreak preparedness, outbreak definitions, alert algorithms, managerial capacity, vector control capacity, and clinical management of large caseloads. Additionally, a computer-assisted early warning system, which enables countries to identify and respond to context-specific variables that predict forthcoming dengue outbreaks, has been developed.

    CONCLUSIONS/SIGNIFICANCE: Most countries do not have comprehensive, detailed contingency plans for dengue outbreaks. Countries tend to rely on intensified vector control as their outbreak response, with minimal focus on integrated management of clinical care, epidemiological, laboratory and vector surveillance, and risk communication. The Technical Handbook for Surveillance, Dengue Outbreak Prediction/ Detection and Outbreak Response seeks to provide countries with evidence-based best practices to justify the declaration of an outbreak and the mobilization of the resources required to implement an effective dengue contingency plan.

    Matched MeSH terms: Logistic Models
  13. Bukhari MM, Ghazal TM, Abbas S, Khan MA, Farooq U, Wahbah H, et al.
    Comput Intell Neurosci, 2022;2022:3606068.
    PMID: 35126487 DOI: 10.1155/2022/3606068
    Smart applications and intelligent systems are being developed that are self-reliant, adaptive, and knowledge-based in nature. Emergency and disaster management, aerospace, healthcare, IoT, and mobile applications, among them, revolutionize the world of computing. Applications with a large number of growing devices have transformed the current design of centralized cloud impractical. Despite the use of 5G technology, delay-sensitive applications and cloud cannot go parallel due to exceeding threshold values of certain parameters like latency, bandwidth, response time, etc. Middleware proves to be a better solution to cope up with these issues while satisfying the high requirements task offloading standards. Fog computing is recommended middleware in this research article in view of the fact that it provides the services to the edge of the network; delay-sensitive applications can be entertained effectively. On the contrary, fog nodes contain a limited set of resources that may not process all tasks, especially of computation-intensive applications. Additionally, fog is not the replacement of the cloud, rather supplement to the cloud, both behave like counterparts and offer their services correspondingly to compliance the task needs but fog computing has relatively closer proximity to the devices comparatively cloud. The problem arises when a decision needs to take what is to be offloaded: data, computation, or application, and more specifically where to offload: either fog or cloud and how much to offload. Fog-cloud collaboration is stochastic in terms of task-related attributes like task size, duration, arrival rate, and required resources. Dynamic task offloading becomes crucial in order to utilize the resources at fog and cloud to improve QoS. Since this formation of task offloading policy is a bit complex in nature, this problem is addressed in the research article and proposes an intelligent task offloading model. Simulation results demonstrate the authenticity of the proposed logistic regression model acquiring 86% accuracy compared to other algorithms and confidence in the predictive task offloading policy by making sure process consistency and reliability.
    Matched MeSH terms: Logistic Models
  14. Yuan CJ, Varathan KD, Suhaimi A, Ling LW
    J Rehabil Med, 2023 Jan 09;55:jrm00348.
    PMID: 36306152 DOI: 10.2340/jrm.v54.2432
    OBJECTIVE: To explore machine learning models for predicting return to work after cardiac rehabilitation.

    SUBJECTS: Patients who were admitted to the University of Malaya Medical Centre due to cardiac events.

    METHODS: Eight different machine learning models were evaluated. The models included 3 different sets of features: full features; significant features from multiple logistic regression; and features selected from recursive feature extraction technique. The performance of the prediction models with each set of features was compared.

    RESULTS: The AdaBoost model with the top 20 features obtained the highest performance score of 92.4% (area under the curve; AUC) compared with other prediction models.

    CONCLUSION: The findings showed the potential of using machine learning models to predict return to work after cardiac rehabilitation.

    Matched MeSH terms: Logistic Models
  15. Mahmud SZ, Joanita S, Khairun Nisa J, Balkish MN, Tahir A
    Med J Malaysia, 2013 Apr;68(2):125-8.
    PMID: 23629557 MyJurnal
    Extensive literature reviews showed that pacifier usage is associated with early cessation of breast feeding, as well as respiratory infection. This cross sectional study was a part of the bigger study of The Third National Health Morbidity Survey conducted throughout Malaysia in 2006. Survival and pearson cox regression was done to find association between pacifier user and breast feeding duration. Logistic Regression was done to find association between variables of interest. The prevalence of pacifier use was 32.9%. Chinese children reported significantly higher usage of pacifier (95% CI; 47.5, 58.7) as well as those resided in urban area (95% CI;32.5,37.7). One third of pacifier user had stopped breastfeeding at 6 months of age. Those with pacifier users were significantly shorter in breast feeding duration and significantly associated with non exclusivity in breastfeeding. Those without pacifier user were significantly associated with ever breast fed.(p value=0.001). There was no significant association between pacifier use with acute respiratory infection. Factors such as ethnicity and residential are non modifiable whereas modifiable factor such as pacifier use is certainly needed to be addressed at maternal and child health care level.
    Study name: National Health and Morbidity Survey (NHMS-2006)
    Matched MeSH terms: Logistic Models
  16. Boo NY, Lim SM, Koh KT, Lau KF, Ravindran J
    Med J Malaysia, 2008 Oct;63(4):306-10.
    PMID: 19385490 MyJurnal
    This study aimed to identify the risk factors which were significantly associated with low birth weight (LBW, <2500 g) infants among the Malaysian population. This was a case-control study carried out at the Tuanku Jaafar Hospital, Seremban, Malaysia over a five-month period. Cases were all infants born with birth weight less than 2500 g. Control infant were selected with the help a random sampling table from among infants with birth weight of > or =2500 g born on the same day in the hospital. Of 3341 livebirths delivered in the hospital, 422 (12.6%) were LBW infants. Logistic regression analysis showed that, after controlling for various potential confounders, the only significant risk factors associated with infants of LBW were gestational age (adjusted odds ratio (OR)=0.6, 95% C.I.: 0.5, 0.6; < 0.0001), maternal pre-pregnancy weight (adjusted OR = 0.97, 95% C.I.: 0.95, 0.99; p < 0.0001), nulliparity (adjusted OR = 3.4, 95% C.I.: 2.2, 5.1; p < 0.0001), previous history of LBW infants (adjusted OR = 2.3, 95% C.I.: 1.4, 3.8; p=0.001) and PIH during current pregnancy (adjusted OR=3.3, 95% C.I.: 1.6, 6.6; p = 0.001). A number of potentially preventable or treatable risk factors were identified to be associated with LBW infants in Malaysia.
    Matched MeSH terms: Logistic Models
  17. Lujan-Barroso L, Zhang W, Olson SH, Gao YT, Yu H, Baghurst PA, et al.
    Pancreas, 2016 11;45(10):1401-1410.
    PMID: 27088489
    OBJECTIVES: We aimed to evaluate the relation between menstrual and reproductive factors, exogenous hormones, and risk of pancreatic cancer (PC).

    METHODS: Eleven case-control studies within the International Pancreatic Cancer Case-control Consortium took part in the present study, including in total 2838 case and 4748 control women. Pooled estimates of odds ratios (ORs) and their 95% confidence intervals (CIs) were calculated using a 2-step logistic regression model and adjusting for relevant covariates.

    RESULTS: An inverse OR was observed in women who reported having had hysterectomy (ORyesvs.no, 0.78; 95% CI, 0.67-0.91), remaining significant in postmenopausal women and never-smoking women, adjusted for potential PC confounders. A mutually adjusted model with the joint effect for hormone replacement therapy (HRT) and hysterectomy showed significant inverse associations with PC in women who reported having had hysterectomy with HRT use (OR, 0.64; 95% CI, 0.48-0.84).

    CONCLUSIONS: Our large pooled analysis suggests that women who have had a hysterectomy may have reduced risk of PC. However, we cannot rule out that the reduced risk could be due to factors or indications for having had a hysterectomy. Further investigation of risk according to HRT use and reason for hysterectomy may be necessary.

    Matched MeSH terms: Logistic Models
  18. Murtaza G, Khan MY, Azhar S, Khan SA, Khan TM
    Saudi Pharm J, 2016 Mar;24(2):220-5.
    PMID: 27013915 DOI: 10.1016/j.jsps.2015.03.009
    Drug-drug interactions (DDIs) may result in the alteration of therapeutic response. Sometimes they may increase the untoward effects of many drugs. Hospitalized cardiac patients need more attention regarding drug-drug interactions due to complexity of their disease and therapeutic regimen. This research was performed to find out types, prevalence and association between various predictors of potential drug-drug interactions (pDDIs) in the Department of Cardiology and to report common interactions. This study was performed in the hospitalized cardiac patients at Ayub Teaching Hospital, Abbottabad, Pakistan. Patient charts of 2342 patients were assessed for pDDIs using Micromedex® Drug Information. Logistic regression was applied to find predictors of pDDIs. The main outcome measure in the study was the association of the potential drug-drug interactions with various factors such as age, gender, polypharmacy, and hospital stay of the patients. We identified 53 interacting-combinations that were present in total 5109 pDDIs with median number of 02 pDDIs per patient. Overall, 91.6% patients had at least one pDDI; 86.3% were having at least one major pDDI, and 84.5% patients had at least one moderate pDDI. Among 5109 identified pDDIs, most were of moderate (55%) or major severity (45%); established (24.2%), theoretical (18.8%) or probable (57%) type of scientific evidence. Top 10 common pDDIs included 3 major and 7 moderate interactions. Results obtained by multivariate logistic regression revealed a significant association of the occurrence of pDDIs in patient with age of 60 years or more (p 
    Matched MeSH terms: Logistic Models
  19. Karim A, Salleh R, Khan MK
    PLoS One, 2016;11(3):e0150077.
    PMID: 26978523 DOI: 10.1371/journal.pone.0150077
    Botnet phenomenon in smartphones is evolving with the proliferation in mobile phone technologies after leaving imperative impact on personal computers. It refers to the network of computers, laptops, mobile devices or tablets which is remotely controlled by the cybercriminals to initiate various distributed coordinated attacks including spam emails, ad-click fraud, Bitcoin mining, Distributed Denial of Service (DDoS), disseminating other malwares and much more. Likewise traditional PC based botnet, Mobile botnets have the same operational impact except the target audience is particular to smartphone users. Therefore, it is import to uncover this security issue prior to its widespread adaptation. We propose SMARTbot, a novel dynamic analysis framework augmented with machine learning techniques to automatically detect botnet binaries from malicious corpus. SMARTbot is a component based off-device behavioral analysis framework which can generate mobile botnet learning model by inducing Artificial Neural Networks' back-propagation method. Moreover, this framework can detect mobile botnet binaries with remarkable accuracy even in case of obfuscated program code. The results conclude that, a classifier model based on simple logistic regression outperform other machine learning classifier for botnet apps' detection, i.e 99.49% accuracy is achieved. Further, from manual inspection of botnet dataset we have extracted interesting trends in those applications. As an outcome of this research, a mobile botnet dataset is devised which will become the benchmark for future studies.
    Matched MeSH terms: Logistic Models
  20. Goh HT, Nadarajah M, Hamzah NB, Varadan P, Tan MP
    PM R, 2016 12;8(12):1173-1180.
    PMID: 27268565 DOI: 10.1016/j.pmrj.2016.05.012
    BACKGROUND: Falls are common after stroke, with potentially serious consequences. Few investigations have included age-matched control participants to directly compare fall characteristics between older adults with and without stroke. Further, fear of falling, a significant psychological consequence of falls, has only been examined to a limited degree as a risk factor for future falls in a stroke population.

    OBJECTIVE: To compare the fall history between older adults with and without a previous stroke and to identify the determinants of falls and fear of falling in older stroke survivors.

    DESIGN: Case-control observational study.

    SETTING: Primary teaching hospital.

    PARTICIPANTS: Seventy-five patients with stroke (mean age ± standard deviation, 66 ± 7 years) and 50 age-matched control participants with no previous stroke were tested.

    METHODS: Fall history, fear of falling, and physical, cognitive, and psychological function were assessed. A χ2 test was performed to compare characteristics between groups, and logistic regression was performed to determine the risk factors for falls and fear of falling.

    MAIN OUTCOME MEASURES: Fall events in the past 12 months, Fall Efficacy Scale-International, Berg Balance Scale, Functional Ambulation Category, Fatigue Severity Scale, Montreal Cognitive Assessment, and Patient Healthy Questionnaire-9 were measured for all participants. Fugl-Meyer Motor Assessment was used to quantify severity of stroke motor impairments.

    RESULTS: Twenty-three patients and 13 control participants reported at least one fall in the past 12 months (P = .58). Nine participants with stroke had recurrent falls (≥2 falls) compared with none of the control participants (P < .01). Participants with stroke reported greater concern for falling than did nonstroke control participants (P < .01). Female gender was associated with falls in the nonstroke group, whereas falls in the stroke group were not significantly associated with any measured outcomes. Fear of falling in the stroke group was associated with functional ambulation level and balance. Functional ambulation level alone explained 22% of variance in fear of falling in the stroke group.

    CONCLUSIONS: Compared with persons without a stroke, patients with stroke were significantly more likely to experience recurrent falls and fear of falling. Falls in patients with stroke were not explained by any of the outcome measures used, whereas fear of falling was predicted by functional ambulation level. This study has identified potentially modifiable risk factors with which to devise future prevention strategies for falls in patients with stroke.


    Matched MeSH terms: Logistic Models
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