Understanding 'smart cities': Intertwining development drivers with desired outcomes in a multidimensional framework

The effects of successful ICT-based smart city services: From citizens' perspectives

Strategic principles for smart city development: A multiple case study analysis of European best practices

Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations.

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Big data analytics for preventive medicine

Combining co-citation clustering and text-based analysis to reveal the main development paths of smart cities

https://cyberleninka.org/article/n/1425583.pdf

Developing Smart Cities: An Integrated Framework

Customer Experience Management: An Exploratory Study on the Parameters Affecting Customer Experience for Cellular Mobile Services of a Telecom Company

Data Mining is an interesting field of research whose major objective is to find interesting and useful patterns from huge data sets. These patterns can be further used to make important decisions based on the result of the analysis. Healthcare industry today generates huge amount of data on a day to day basis. This data has to be analysed and hidden and meaningful patterns can be discovered. Data mining plays a promising and significant role in this aspect. Data Mining techniques can be used for disease prediction. In this research, the classification based data mining techniques are applied to healthcare data. This research focuses on the prediction of heart disease using three classification techniques namely Decision Trees, Naive Bayes and K Nearest Neighbour.

Prediction of Heart Disease Using Classification Based Data Mining Techniques

Purpose




The purpose of this paper is to develop and empirically test a process model (comprising of seven dimensions), for identifying online customer engagement patterns leading to recommendation These seven dimensions are communication, interaction, experience, satisfaction, continued involvement, bonding, and recommendation




Design/methodology/approach




The authors used a non-participant form of netnography for analyzing 849 comments from Australian banks Facebook pages High levels of inter-coder reliability strengthen the study’s empirical validity and ensure minimum researcher bias and maximum reliability and replicability




Findings




The authors identified 22 unique pattern of customer engagement, out of which nine patterns resulted in recommendation/advocacy Engagement pattern communication-interaction-recommendation was the fastest route to recommendation, observed in nine instances (or 2 percent) In comparison, C-I-E-S-CI-B-R was the longest route to recommendation observed in ninety-six instances (or 18 percent) Of the eight patterns that resulted in recommendation, five patterns (or 625 percent) showed bonding happening before recommendation




Research limitations/implications




The authors limited the data collection to Facebook pages of major banks in Australia The authors did not assess customer demography and did not share the findings with the banks




Practical implications




The findings will guide e-marketers on how to best engage with customers to enhance brand loyalty and continuously be in touch with their clients




Originality/value




Most models are conceptual and assume that customers typically journey through all the stages in the model The work is interesting because the empirical study found that customers travel in multiple different ways through this process It is significant because it changes the way the authors understand patterns of online customer engagement

A process model for identifying online customer engagement patterns on Facebook brand pages

Data mining is the process of analyzing different aspects of data and aggregating it into useful information. Classification is a data mining task generally used in medical data mining. The goal here is to discover new and useful patterns to provide meaningful and useful information for the users about the diabetes. Here a diabetes prediction and monitoring system is designed and implemented using ID3 classification algorithm. The symptoms causing diabetes are identified and are applied to the prediction model based on which the prediction is done. The monitoring module analyzes the laboratory test reports of the blood sugar levels of the patient and provides proper awareness messages to the patient through mail and bar chart.

http://www.mecs-press.org/ijitcs/ijitcs-v8-n11/IJITCS-V8-N11-4.pdf

Sujata Joshi

Papers

Developing Smart Cities: An Integrated Framework

Customer Experience Management: An Exploratory Study on the Parameters Affecting Customer Experience for Cellular Mobile Services of a Telecom Company

Prediction of Heart Disease Using Classification Based Data Mining Techniques

A process model for identifying online customer engagement patterns on Facebook brand pages

A Tool for Diabetes Prediction and Monitoring Using Data Mining Technique