A new intuitionistic fuzzy rough set approach for decision support

2012-2013 > Academic research: refereed > Chapter in an edited book (author)Accepted ManuscriptPublishe

Implementation and applications of chaotic oscillatory based neural network for wind prediction problems

Author name used in this publication: J. N. K. LIU2011-2012 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

A bank of unscented Kalman filters for multimodal human perception with mobile service robots

A new generation of mobile service robots could be ready soon to operate in human environments if they can robustly estimate position and identity of surrounding people. Researchers in this field face a number of challenging problems, among which sensor uncertainties and real-time constraints. In this paper, we propose a novel and efficient solution for simultaneous tracking and recognition of people within the observation range of a mobile robot. Multisensor techniques for legs and face detection are fused in a robust probabilistic framework to height, clothes and face recognition algorithms. The system is based on an efficient bank of Unscented Kalman Filters that keeps a multi-hypothesis estimate of the person being tracked, including the case where the latter is unknown to the robot. Several experiments with real mobile robots are presented to validate the proposed approach. They show that our solutions can improve the robot's perception and recognition of humans, providing a useful contribution for the future application of service robotics

Modeling of longitudinal polytomous outcome from complex survey data - application to investigate an association between mental distress and non-malignant respiratory diseases

<p>Abstract</p> <p>Background</p> <p>The data from longitudinal complex surveys based on multi-stage sampling designs contain cross-sectional dependencies among units due to clustered nature of the data and within-subject dependencies due to repeated measurements. Special statistical methods are required to analyze longitudinal complex survey data.</p> <p>Methods</p> <p>Statistics Canada's longitudinal National Population Health Survey (NPHS) dataset from the first five cycles (1994/1995 to 2002/2003) was used to investigate the effects of demographic, social, life-style, and health-related factors on the longitudinal changes of mental distress scores among the NPHS participants who self-reported physician diagnosed respiratory diseases, specifically asthma and chronic bronchitis. The NPHS longitudinal sample includes 17,276 persons of all ages. In this report, participants 15 years and older (n = 14,713) were considered for statistical analysis. Mental distress, an ordinal outcome variable (categories: no/low, moderate, and high) was examined. Ordered logistic regression models based on the weighted generalized estimating equations approach were fitted to investigate the association between respiratory diseases and mental distress adjusting for other covariates of interest. Variance estimates of regression coefficients were computed by using bootstrap methods. The final model was used to predict the probabilities of prevalence of no/low, moderate or high mental distress scores.</p> <p>Results</p> <p>Accounting for design effects does not vary the significance of the coefficients of the model. Participants suffering with chronic bronchitis were significantly at a higher risk (OR_adj= 1.37; 95% CI: 1.12-1.66) of reporting high levels of mental distress compared to those who did not self-report chronic bronchitis. There was no significant association between asthma and mental distress. There was a significant interaction between sex and self-perceived general health status indicating a dose-response relationship. Among females, the risk of mental distress increases with increasing deteriorating (from excellent to very poor) self-perceived general health.</p> <p>Conclusions</p> <p>A positive association was observed between the physician diagnosed self-reported chronic bronchitis and an increased prevalence of mental distress when adjusted for important covariates. Variance estimates of regression coefficients obtained from the sandwich estimator (i.e. not accounting for design effects) were similar to bootstrap variance estimates (i.e. accounting for design effects). Even though these two sets of variance estimates are similar, it is more appropriate to use bootstrap variance estimates.</p

Trends in causes of death among children under 5 in Bangladesh, 1993-2004: an exercise applying a standardized computer algorithm to assign causes of death using verbal autopsy data

<p>Abstract</p> <p>Background</p> <p>Trends in the causes of child mortality serve as important global health information to guide efforts to improve child survival. With child mortality declining in Bangladesh, the distribution of causes of death also changes. The three verbal autopsy (VA) studies conducted with the Bangladesh Demographic and Health Surveys provide a unique opportunity to study these changes in child causes of death.</p> <p>Methods</p> <p>To ensure comparability of these trends, we developed a standardized algorithm to assign causes of death using symptoms collected through the VA studies. The original algorithms applied were systematically reviewed and key differences in cause categorization, hierarchy, case definition, and the amount of data collected were compared to inform the development of the standardized algorithm. Based primarily on the 2004 cause categorization and hierarchy, the standardized algorithm guarantees comparability of the trends by only including symptom data commonly available across all three studies.</p> <p>Results</p> <p>Between 1993 and 2004, pneumonia remained the leading cause of death in Bangladesh, contributing to 24% to 33% of deaths among children under 5. The proportion of neonatal mortality increased significantly from 36% (uncertainty range [UR]: 31%-41%) to 56% (49%-62%) during the same period. The cause-specific mortality fractions due to birth asphyxia/birth injury and prematurity/low birth weight (LBW) increased steadily, with both rising from 3% (2%-5%) to 13% (10%-17%) and 10% (7%-15%), respectively. The cause-specific mortality rates decreased significantly due to neonatal tetanus and several postneonatal causes (tetanus: from 7 [4-11] to 2 [0.4-4] per 1,000 live births (LB); pneumonia: from 26 [20-33] to 15 [11-20] per 1,000 LB; diarrhea: from 12 [8-17] to 4 [2-7] per 1,000 LB; measles: from 5 [2-8] to 0.2 [0-0.7] per 1,000 LB; injury: from 11 [7-17] to 3 [1-5] per 1,000 LB; and malnutrition: from 9 [6-13] to 5 [2-7]).</p> <p>Conclusions</p> <p>Pneumonia remained the top killer of children under 5 in Bangladesh between 1993 and 2004. The increasing importance of neonatal survival is highlighted by the growing contribution of neonatal deaths and several neonatal causes. Notwithstanding the limitations, standardized computer-based algorithms remain a promising tool to generate comparable causes of child death using VA data.</p

Analysis of Semantic Heterogeneity Using a New Ontological Structure Based on Description Logics

Every information system has its own domain model for its environment and efficient task operations, which results in diverse heterogeneity, especially the semantic heterogeneity. Ontology is an agreement about shared conceptualizations, which can make the meaning of the different vocabularies used explicit, and so the semantics of diverse information systems can be captured by ontology-definitions of terms. This paper constructs an ontological structure using description logic, divides the types of heterogeneity of information system into two categories from the ontological view: conceptualization conflict and term assignment conflict, and analyses approaches to solve conflict by ontology mapping. Finally, this paper construct semantic-oriented integration architecture for heterogeneous systems based on ontology mapping, and discusses data request and response under the architecture, and proposes five rules for data search.Department of ComputingRefereed conference pape

A Study on the Reliability of Case-Based Reasoning Systems

Case-based reasoning (CBR) is a methodology for problem solving, which suggests a solution to a new problem based on the previously-solved problems and their associated solutions. A key issue in this methodology is that can we always trust the solutions suggested by a case-based reasoning system? This paper studies the reliability of CBR systems at an overall level first. Factors affecting the reliability of a CBR system are discussed in this section, especially the property that whether its case library is compatible with the foundational assumption that "similar problems have similar solutions." After that, the reliability of an individual suggested solution is studied. Some existing approaches which can be employed to estimate the reliability of a single solution are compared in this section. To illustrate these ideas, some experiments and their results are also discussed in this paper. It is shown that if a case library attains a high compatibility, then a satisfactory result can be expected, and the reliability of a CBR system at an overall level can be improved by identifying the reliable solutions.Department of ComputingRefereed conference pape