Development of Decision Support System Platform for Daily Dietary Plan

Background:

Solving many health issues needs accurate and significant information in food consumption. Recently, data analysis and communication provided outstanding and robust approaches to fulfill the necessity of scientific information and help in decision making in many fields. Many evidence reported that with little information better decisions can be achieved.

Objective:

This research aimed to develop the Decision Support System (DSS) for the daily dietary plan to practically help users in food consumption and health care.

Methods:

The system consists of 1,940 cuisine items, including Thai and English menus. In this system, the user can set the daily dietary plan by selecting menu items with food specific and total calories. Overall calories of selected menu items would be calculated automatically. The user can see the normal range of calories required based on gender with the help of the baseline (normal office person).

Results:

This system can help users to become familiar with a better daily dietary plan, food calories, and health care easily. Furthermore, experts (doctors) can improve their learning experiences by formulating and adjusting the Decision Support System (DSS) for patients in special need. The easiness and usefulness of this system were evaluated by 119 users on Likert scale (1=least, 5=most). The result on average was 4.58.

Conclusion:

The Decision Support System (DSS) for the daily dietary plan was developed. The accessibility to the system is via personal computer (PC), smartphone, and tablet with internet connection. For future work, this DSS can improve by connecting the platform with health care providers via sharing the data for more online support.

Characterizing the Capabilities of Internet of Things Analytics through Taxonomy and Reference Architecture

The increasing prevalence of business cases utilizing Internet of Things (IoT) analytics, coupled with the diversity of IoT analytics platforms and their capabilities, poses an immense challenge for organizations seeking to make the best choice of IoT analytics platform for their specific use cases. Aiming to characterize the capabilities of IoT analytics, this article presents a reference architecture for IoT analytics platforms created through a qualitative content analysis of online reviews and published implementation architectures of IoT analytics platforms. A further contribution is a taxonomy of the functional and cross-functional capabilities of IoT analytics platforms derived from the analysis of published use cases and related business surveys. Both the reference architecture and the associated taxonomy provide a theoretical basis for further research into IoT analytics capabilities and should therefore facilitate the evaluation, selection and adoption of IoT analytics solutions through a unified description of their capabilities and functional requirements.