Abstract Chronic Diseases are a prevalent problem that affects millions of people worldwide. It is a prevalent health condition that requires careful diet and medication management and preventing chronic diseases. Traditional approaches to nutritional recommendation generation often rely on generic guidelines and population-based data, which may not account for individual dietary needs and preferences variations. In this paper, we propose a knowledge graph driven methodology for generating highly personalized nutritional recommendations that leverage the power of knowledge graphs to integrate and analyze complex data about an individual's health, lifestyle, and dietary habits. Our methodology employs a multi-step process that includes data collection and curation, knowledge graph construction, and personalized recommendation generation. We illustrate the effectiveness of our approach through a case study in which we generate personalized nutritional recommendations for a sample individual based on their specific health and dietary goals.

Wellness platform plays a vital role to prevent chronic disease. In this paper, we have introduced wellness smartphone application within the ambit of the Mining Mind project, which aims to support the people to adopt healthy behavior and lifestyle. When an unhealthy behavior is detected, personalized physical recommendations are generated automatically by the Mining Mind wellness platform. Recommendations are delivered through Push notification and display on the application main screen for the user. The application has feedback functionalities on the effectiveness of recommendation and education. The application also supports descriptive analytics of wellness goals achieved on a monthly, weekly and daily basis.

Smart gadgets play a vital role to adapt the unhealthy lifestyle by converging science and technology in this digital world. The challenge of behavior change through step-by-step coaching and guidance is realized by just-in-time intervention using mobile computing. The amalgamation of behavior change theories with digital technologies support us to mold the behavior in a scientific manner. Wellness platform based behavior analysis is performed through unbiased life-log as well as questionnaire for permanent and ad-hoc users. Daily personalized coaching has been provided to motivate the users, whereas instant context-based recommendations have supported stage-wise adaptation of healthy behavior.

The significance of social media has already been proven in provoking transformation of public opinion for developed countries in improving democratic process of elections. On the contrary, developing countries lacking basic necessities of life possess monopolistic electoral system in which candidates are elected based on tribes, family backgrounds, or landlord influences. They extort voters to cast votes against their promises for the provision of basic needs. Similarly voters also poll votes for personal interests being unaware of party manifesto or national interest. These issues can be addressed by social media, resulting as ongoing process of improvement for presently adopted electoral procedures. People of Pakistan utilized social media to garner support and campaign for political parties in General Elections 2013. Political leaders, parties, and people of Pakistan disseminated party's agenda and advocacy of party's ideology on Twitter without much campaigning cost. To study effectiveness of social media inferred from individual's political behavior, large scale analysis, sentiment detection & tweet classification was done in order to classify, predict and forecast election results. The experimental results depicts that social media content can be used as an effective indicator for capturing political behaviors of different parties Positive, negative and neutral behavior of the party followers as well as party's campaign impact can be predicted from the analysis. The analytical findings proved to be having considerable correspondence with actual results as published by Election Commission of Pakistan‥