Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment.

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In this paper, we present a data analytics and visualization framework for health-shocks prediction based on large-scale health informatics dataset. The framework is developed using cloud computing services based on Amazon web services (AWS) integrated with geographical information systems (GIS) to facilitate big data capture, storage, index and visualization of data through smart devices for different stakeholders. In order to develop a predictive model for health-shocks, we have collected a unique data from 1000 households, in rural and remotely accessible regions of Pakistan, focusing on factors like health, social, economic, environment and accessibility to healthcare facilities. We have used the collected data to generate a predictive model of health-shock using a fuzzy rule summarization technique, which can provide stakeholders with interpretable linguistic rules to explain the causal factors affecting healthshocks. The evaluation of the proposed system in terms of the interpret-ability and accuracy of the generated data models for classifying health-shock shows promising results. The prediction accuracy of the fuzzy model based on a k-fold cross-validation of the data samples shows above 89% performance in predicting health-shocks based on the given factors.

a b s t r a c tBig Data has a significant impact in modern society. In this paper we investigated the importance of Big Data in modern life, and in terms of the economy, and discussed the challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explored the potential of the powerful combination of Big Data and Computational intelligence and identified a number of areas where novel applications in real world problems can be developed by utilizing these powerful tools and technologies. We presented a novel data modelling methodology which introduces a novel biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). In this paper, we have also discussed various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment.

Objective: To determine the association of breath holding spells with irondeficiency anemia in children. Study Design: Case control study. Place and Duration of Study:Military Hospital Rawalpindi from Jun 2012 to Dec 2012. Study Population: Sixty children ofeither gender meeting inclusion criteria aged 6 months to 5 years with 30 of breath holding spells incase group and 30 in control group as healthy children were included in the study after informedconsent from parents. Method: Complete blood picture and serum ferritin levels were performedof all children in both case and control groups. Tests were carried out at AFIP Rawalpindi. All datawas entered and analyzed using SPSS version 10. Frequencies and percentages were calculatedfor categorical (qualitative) variables like sex and children having iron deficiency anemia in casesand controls. Mean and Standard Deviation (SD) was calculated for numerical (quantitative)variable like Age. Odds ratio was calculated from the data of cases and controls. Regarding irondeficiency anemia p value <0.05 was considered as significant. Results: In this study, werecorded 43.33% (n=13) cases were between 0.6-3 years and 56.67% (n=17) were between 4-5years while 53.33% (n=16) controls were between 0.6-3 years and 46.67% (n=14) were between4-5 years. Mean±SD was calculated as 3.3+1.46 years in cases and 2.93+1.48 years in controlgroup. Male children were 60% (n=18) in patient group and 46.67% (n=14) in controls group.Female children were 40% (n=12) in patient and 53.33% (n=16) in control group respectively.Association of breath holding spells with iron deficiency anemia in children revealed as 56.67%(n=17) in cases and 3.33% (n=1) in control group while remaining 43.33% (n=13) in cases and96.67% (n=29) in control group had no findings of this association. P value was calculated as<0.0001 and Odds Ratio was 37.92 which shows a significant difference between the two groups.Conclusions: The association of breath holding spells with iron deficiency anemia in children issignificantly higher than healthy controls. So, it is recommended that every child who present withbreath holding spells should be evaluated for iron deficiency anemia