Vijay Kumar Mago

Supporting meningitis diagnosis amongst infants and children through the use of fuzzy cognitive mapping.

BackgroundMeningitis is characterized by an inflammation of the meninges, or the membranes surrounding the brain and spinal cord. Early diagnosis and treatment is crucial for a positive outcome, yet identifying meningitis is a complex process involving an array of signs and symptoms and multiple causal factors which require novel solutions to support clinical decision-making. In this work, we explore the potential of fuzzy cognitive map to assist in the modeling of meningitis, as a support tool for physicians in the accurate diagnosis and treatment of the condition.MethodsFuzzy cognitive mapping (FCM) is a method for analysing and depicting human perception of a given system. FCM facilitates the development of a conceptual model which is not limited by exact values and measurements and thus is well suited to representing relatively unstructured knowledge and associations expressed in imprecise terms. A team of doctors (physicians), comprising four paediatricians, was formed to define the multifarious signs and symptoms associated with meningitis and to identify risk factors integral to its causality, as indicators used by clinicians to identify the presence or absence of meningitis in patients. The FCM model, consisting of 20 concept nodes, has been designed by the team of paediatricians in collaborative dialogue with the research team.ResultsThe paediatricians were supplied with a form containing various input parameters to be completed at the time of diagnosing meningitis among infants and children. The paediatricians provided information on a total of 56 patient cases amongst children whose age ranged from 2 months to 7 years. The physicians’ decision to diagnose meningitis was available for each individual case which was used as the outcome measure for evaluating the model. The FCM was trained using 40 cases with an accuracy of 95%, and later 16 test cases were used to analyze the accuracy and reliability of the model. The system produced the results with sensitivity of 83.3% and specificity of 80%.ConclusionsThis work suggests that the application and development of a knowledge based system, using the formalization of FCMs for understanding the symptoms and causes of meningitis in children and infants, can provide a reliable front-end decision-making tool to better assist physicians.

Rebel with many causes: A computational model of insurgency

Attempts to model insurgency have suffered from several obstacles. Qualitative research may be vague and conflicting, while quantitative research is limited due to the difficulties of collecting sufficient data in war and inferring complex relationships. We propose an innovative combination of Fuzzy Cognitive Maps and Cellular Automata to capture this complexity. Our approach is computational, thus it can be used to develop a simulation platform in which military and political analysts can test scenarios. We take a step-by-step approach to illustrate the potential of our approach in a population-centric war, similar to the on-going campaign in Afghanistan. While the project still requires validation and improvement of the knowledge base by domain experts as well as construction of accurate simulation scenarios, this example fully specifies the general problem definition and the technical structure of the model.

Clinical decision support system for dental treatment

Abstract Background In this research, a decision making system, based on fuzzy inference mechanism as proposed by Mamdani, is presented. Literature suggests that there is a lack of consistency among dentists in choosing treatment plan(s). So, this research work aims to facilitate the dentist decide the treatment plan(s) of the broken tooth. Methods An expert system based on fuzzy logic has been designed to accept inaccurate and vague values of dental signs and symptoms associated with the broken tooth. We designed a knowledge base with 60 rules and used Mamdani inference algorithm to decide the possible one or more treatment(s) and suggest the same to the dentist. Results The results proposed by the system are compared with the dentists’ suggestions. The Chi-square test of homogeneity is conducted on 100 randomly generated sample cases with the help of three professional dentists. It is found that the results produced by the system are consistent with the treatment plan(s) proposed by the dentists. Chi-square value of the test is 3.843565 which is less than the critical value which is 12.592. Hence, we are unable to reject the null hypothesis that assumes the two populations are homogeneous with respect to treatments. Conclusions The accuracy of the proposed decision support system for the treatment of broken tooth enhances the confidence level of the dentists while making decision regarding the treatment plan(s). Simple and interactive GUI makes it easy to use.

Social interactions of eating behaviour among high school students: a cellular automata approach

BackgroundOverweight and obesity in children and adolescents is a global epidemic posing problems for both developed and developing nations. The prevalence is particularly alarming in developed nations, such as the United States, where approximately one in three school-aged adolescents (ages 12-19) are overweight or obese. Evidence suggests that weight gain in school-aged adolescents is related to energy imbalance exacerbated by the negative aspects of the school food environment, such as presence of unhealthy food choices. While a well-established connection exists between the food environment, presently there is a lack of studies investigating the impact of the social environment and associated interactions of school-age adolescents. This paper uses a mathematical modelling approach to explore how social interactions among high school adolescents can affect their eating behaviour and food choice.MethodsIn this paper we use a Cellular Automata (CA) modelling approach to explore how social interactions among school-age adolescents can affect eating behaviour, and food choice. Our CA model integrates social influences and transition rules to simulate the way individuals would interact in a social community (e.g., school cafeteria). To replicate these social interactions, we chose the Moore neighbourhood which allows all neighbours (eights cells in a two-dimensional square lattice) to influence the central cell. Our assumption is that individuals belong to any of four states; Bring Healthy, Bring Unhealthy, Purchase Healthy, and Purchase Unhealthy, and will influence each other according to parameter settings and transition rules. Simulations were run to explore how the different states interact under varying parameter settings.ResultsThis study, through simulations, illustrates that students will change their eating behaviour from unhealthy to healthy as a result of positive social and environmental influences. In general, there is one common characteristic of changes across time; students with similar eating behaviours tend to form groups, represented by distinct clusters. Transition of healthy and unhealthy eating behaviour is non-linear and a sharp change is observed around a critical point where positive and negative influences are equal.ConclusionsConceptualizing the social environment of individuals is a crucial step to increasing our understanding of obesogenic environments of high-school students, and moreover, the general population. Incorporating both contextual, and individual determinants found in real datasets, in our model will greatly enhance calibration of future models. Complex mathematical modelling has a potential to contribute to the way public health data is collected and analyzed.

The strongest does not attract all but it does attract the most - evaluating the criminal attractiveness of shopping malls using fuzzy logic

Crime attractors are locations e.g. shopping malls that attract criminally motivated offenders because of the presence of known criminal opportunities. Although there have been many studies that explore the patterns of crime in and around these locations, there are still many questions that linger. In recent years, there has been a growing interest to develop mathematical models in attempts to help answer questions about various criminological phenomena. In this paper, we are interested in applying a formal methodology to model the relative attractiveness of crime attractor locations based on characteristics of offenders and the crime they committed. To accomplish this task, we adopt fuzzy logic techniques to calculate the attractiveness of crime attractors in three suburban cities in the Metro Vancouver region of British Columbia, Canada. The fuzzy logic techniques provide results comparable with our real-life expectations that offenders do not necessarily commit significant crimes in the immediate neighbourhood of the attractors, but travel towards it, and commit crimes on the way. The results of this study could lead to a variety of crime prevention benefits and urban planning strategies.

Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies

The need for intelligent machines in areas such as medical diagnostics, biometric security systems, and image processing motivates researchers to develop and explore new techniques, algorithms, and applications in this evolving field.Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies provides a common platform for researchers to present theoretical and applied research findings for enhancing and developing intelligent systems. Through its discussions of advances in and applications of pattern recognition technologies and artificial intelligence, this reference highlights core concepts in biometric imagery, feature recognition, and other related fields, along with their applicability.

Theories and Simulations of Complex Social Systems

Research into social systems is challenging due to their complex nature. Traditional methods of analysis are often difficult to apply effectively as theories evolve over time. This can be due to a lack of appropriate data, or too much uncertainty. It can also be the result of problems which are not yet understood well enough in the general sense so that they can be classified, and an appropriate solution quickly identified. Simulation is one tool that deals well with these challenges, fits in well with the deductive process, and is useful for testing theory. This field is still relatively new, and much of the work is necessarily innovative, although it builds upon a rich and varied foundation. There are a number of existing modelling paradigms being applied to complex social systems research. Additionally, new methods and measures are being devised through the process of conducting research.We expect that readers will enjoy the collection of high quality research works from new and accomplished researchers.

Teaching Computational Modeling in the Data Science Era

Abstract Integrating data and models is an important and still challenging goal in science. Computational modeling has been taught for decades and regularly revised, for example in the 2000s where it became more inclusive of data mining. As we are now in the ‘data science’ era, we have the occasion (and often the incentive) to teach in an integrative manner computational modeling and data science. In this paper, we reviewed the content of courses and programs on computational modeling and/or data science. From this review and our teaching experience, we formed a set of design principles for an integrative course. We independently implemented these principles in two public research universities, in Canada and the US, for a course targeting graduate students and upper-division undergraduates. We discuss and contrast these implementations, and suggest ways in which the teaching of computational science can continue to be revised going forward.

Finding Trendsetters on Yelp Dataset

The search for Trendsetters in social networks turned to be a complex research topic that has gained much attention. The work here presented uses big data analytics to find who better spreads the word in a social network and is innovative in their choices. The analysis on the Yelp platform can be divided in three parts: first, we justify the use of Tips frequency as a variable to profile business popularity. Second we analyze Tips frequency to select businesses that fit a growing popularity profile. And third we graph mine the sociographs generated by the users that interacted with each selected business. Top nodes are ranked by using Indegree, Eigenvector centrality, Pagerank and a Trendsetter algorithms, and we compare the relative performance of each algorithm. Our findings indicate that the Trendsetter ranking algorithm is the most performant at finding nodes that best reflect the Trendsetter properties.

Combining association rule mining and network analysis for pharmacosurveillance

Retailers routinely use association mining to investigate trends in the use of their products. In the medical world, association mining is mostly used to identify associations between symptoms and diseases, or between drugs and adverse events. In comparison, there is a relative paucity of work that focuses on relationships between drugs exclusively. In this work, we use the Medical expenditure panel survey to examine relationships between drugs in the United States. In addition to examining the rules generated by association mining, we introduce the notion of a target drug network and demonstrate via different drugs that it can offer additional medical insight. For example, we were able to find drugs that are commonly taken together despite containing the same active compound. Future work can expand on the concept of target drug network, for example, by annotating the networks with the compounds and intended uses of each drug, to yield additional insight for pharmacosurveillance as well as pharmaceutical companies.