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

Objective: To study the types, etiology and long term neurodevelopmentaloutcome in neonates with seizures. Study Design: A descriptive cross-sectional study. Placeand Duration of Study: PNS Shifa Naval hospital Karachi from Jan 2011 to Feb 2014. StudyPopulation: Ninety six neonates of either gender presented with seizures at NICU PNS ShifaNaval hospital Karachi were studied. Method: All neonates with seizures were evaluated.The seizures were classified according to the simiology. They were investigated according toNICU protocol to confirm the underlying diagnosis and timely management. The patients afterdischarge were regularly followed up for one year to assess the long term neurodevelopmentaloutcome. Results: A total of 96 neonates with seizures were studied and it was observedthat 60 (62.5%) were male babies and 56 (58.33%) were term with a male to female ratio of1.6:1. Majority of the neonatal seizures were seen in 1stweek of life (85%). The most commontype of seizures was clonic 40 (41.67%) followed by subtle 20 (20.84%), mixed 16 (16.67%),tonic 10 (10.41%), myoclonic 5 (5.20%) and unclassified 5 (5.20%). Antiepileptics were usedin 82 (85.41%) patients. Phenobarbitone 49 (59.76%) was most commonly prescribed drug.The most common cause of seizures was birth asphyxia 48 (50%) followed by metabolic 16(16.68%), sepsis 10 (10.41%), intracranial hemorrhage 6 (6.25%), bilirubin encephalopathy 4(4.16%), inborn errors of metabolism 2 (2.08%), birth trauma 2 (2.08%) and unknown etiology 5(5.20%). 25 (26.04%) patients develop adverse neurodevelopmental outcome i.e. cerebral palsywith epilepsy 10 (40%) and cerebral palsy without epilepsy 05 (20%), developmental delay 10(40%). Mortality in the study was 12 (12.5%). Conclusions: Clonic seizures are commonestin neonates apart from infants and children who have GTCS. The most common etiology ofseizures in neonates is birth asphyxia. Phenobarbitone is still the most commonly prescribedantiepileptic. Quick assessment, timely diagnosis and aggressive management according tothe etiology are necessary to prevent the morbidity and mortality associated with neonatalseizures. Long term neurodevelopmental outcome is worse in patients with birth asphyxiaespecially with low Apgar score at 5 minutes. Normal delivery and birth asphyxia were the majorrisk factors for cerebral palsy