Vasuthep Khunthong’s research while affiliated with Kasetsart University and other places

Emergency medicine, under the National Institute for Emergency Medicine or NIEM, is medical care that covers pre-hospital services for unscheduled ill or injured patients, without discrimination. In emergency situations, if the victim is unconscious then accessing updated personal health records by the rescue team is necessary. Accessing patient records, especially in emergency cases, enables proper treatment before arriving at the hospital. This project proposes a cloud platform for storing and managing medical information, and a collaborative model for connecting and facilitating community members in updating personal health records, especially for the five most common conditions, namely heart disease, hypertension, diabetes mellitus, chronic renal failure, and pregnancy. Our main challenges in retrieving and integrating the necessary data are circumventing privacy laws, technical issues arising from different data standards, and quality of the data. For the pilot study, we have initiated a field trial in the province of Ubon Ratchathani, Thailand.

One of the most challenging knowledge services is to provide information relevant enough to support making effective decisions in real time. Even though many sources of relevant data and knowledge are available on websites at any given time, they are scattered and offer little or no information on the semantic relationships, thus making such sources hard to exploit. This paper proposes an approach to developing a spatial and time-based advisory system by using ontology for aggregating data from heterogeneous databases, and from devices such as climate sensors and mobile phones, using shallow parsing to extract the domain-specific concepts and their attributes from semi-structured text, and using production rules to activate functional knowledge formalized from natural language text that is dispersed across the Web. Precision farming for rice is used as a case study since it relies upon intensive sensing of environmental conditions of the crop, extensive data handling and processing, and farmer knowledge. This work aims to support resource-poor farmers toward higher productivity while minimizing costs. The service offered is to therefore provide personal assistance, thus enhancing a farmer's ability to apply actions effectively according to the crop calendar, i.e. the optimal use of pesticides and nutrients in heterogeneous field situations that affect crop quality and reduce risk.

This work presents a system called Rice Watch, which has been developed to track, extract and process market price information and production yield for strategic planning in the economics and marketing of rice. Even though many sources of market information, such as commodity prices and stocks, are available on websites at any given time, such information is scattered and cannot be recomputed toward providing more usable information. Moreover, it is difficult to monitor large-scale areas under cultivation for production-market planning by using only surveys or statistical methods. Therefore, this work aims to apply integrated technologies, such as Information Extraction techniques, Machine aided Translation and Multi-Temporal Profile Matching techniques on satellite images, with two main goals: to provide market-information services to farmers and entrepreneurs for planning rice cultivation with better economic returns, and also to provide estimates for rice production at each stage of rice growth for market planning.

Data sharing is essential for government agencies during disaster management as it requires high collaborative efforts among various organizations. Recently, social media have been increasingly used during emergencies and disasters for disseminating and receiving information to and from the public. By using social media for communications, the government can receive real-time data such as images, spatial data, non- spatial data, as well as textual data from the public and from organizations. The challenge lies in how to combine social media with government data, which is gathered from multiple sources, in multiple formats using multiple terminologies. This paper focuses on how to manage data acquisition and extraction from multiple sources, data integration, as well as data verification. The proposed model was designed and conceptualized by using frame- based data collection and ontology-based data integration, combined with the effective use of dynamic data from social media, with the aim of improving the disaster assistance.

Improving quality of healthcare for people with chronic conditions requires informed and knowledgeable healthcare providers and patients. Decision support and clinical information system are two of the main components to support improving chronic care. In this paper, we describe an ongoing initiative that emphasizes the need for healthcare knowledge management to support both components. Ontology-based knowledge acquisition and modeling based on knowledge engineering approach provides an effective mechanism in capturing expert opinion in form of clinical practice guidelines. The Semantic Web framework is adopted in building a knowledge management platform that allows integration between the knowledge with patient databases and supported publications. We discuss one of the challenges, which is to apply the healthcare knowledge into existing healthcare provider environments by focusing on augmenting decision making and improving quality of patient care services.

Providing information and knowledge services with collecting and maintaining weakly structured text sources is time-consuming activities. This project targets for building specific services and knowledge infrastructures to support decision-making and problem solving in Agriculture domains. It is composed of three essential components :iExtraction --a tool that semantically extracts relevant information from textual representation sources embedded in each individual and related web site and transforms them into structured and standard format, iGrid – a framework that supports virtual knowledge repository as well as discovers and integrates structured information distributed over different sources. Semantic Media Wiki is, then, applied to register and update of agricultural information as a semantic network dictionary. This constructed agricultural information is used for the reference information for interoperability for specialist and farmers, iVisualization --a sophisticated tool that visually presents information in a specific semantic network model, called PMM: Problem-huMan-Method model. Moreover, in order to invite contributions from the user community in order to share the knowledge both tacit and explicit knowledge without the language barrier, it is necessary to provide more sophisticated tools and systems, such as reverse dictionary, ontology-based knowledge sharing and machine-aided translation for sustainable development of agricultural knowledge virtual repository and services .This collaboration project is currently implemented by using Rice domain as a case study. The generated PMM consists of Rice Disease Problems identification, Rice huMan experts who could solve that disease problem and the Method for solving the disease problem both in corrective and preventive ways.

Improving quality of healthcare for people with chronic conditions requires informed and knowledgeable healthcare providers and patients. Decision support and clinical information system are two of the main components to support improving chronic care. In this paper, we describe an ongoing initiative that emphasizes the need for healthcare knowledge management to support both components. Ontology-based knowledge acquisition and modeling based on knowledge engineering approach provides an effective mechanism in capturing expert opinion in form of clinical practice guidelines. The Semantic Web framework is adopted in building a knowledge management platform that allows integration between the knowledge with patient databases and supported publications. We discuss one of the challenges, which is to apply the healthcare knowledge into existing healthcare provider environments by focusing on augmenting decision making and improving quality of patient care services.

In order to ensure long lasting solution of agricultural economy, not only Land Reform is needed. It must be accompanied by supplementary reforms, i.e., Farmer Reform and Management Reform. With the Land Reform, the farmers could cultivate their own land which is the most important factor of production. With Farmer Reform, the productivity of farming could be improved by the skilled and knowledgeable farmers. With Management Reform, agricultural efficiency both in production and delivery to the market could be improved with cost reduction and fare trade. This work, then, proposes three-platforms technologies : 1) A wireless sensor network for controlling and monitoring the Agricultural Environment such as Humidity, Soil Moisture, Soil Nutrient N-P-K , Water Quality, and etc. 2) CyberBrain which is a framework that combines Knowledge Engineering, Information and Communication technologies to provide the effective knowledge service for supporting the farmers in problem-solving, decision making and early warning, and 3) e-community for changing the model of “Training and Visit” to be the Second Life in the Virtual space and changing the collective to be the cooperative problem solving.

With the development of the Internet and the World Wide Web, the enormous amount of knowledge resources becomes the obstacle for knowledge consumers from effectively and efficiently accessing the information needed. To overcome such a problem, knowledge fusion is one of the solutions. This paper introduces the CyberBrain: a framework that combines approaches based on Knowledge Engineering and Language Engineering to provide the effective knowledge service. CyberBrain is a dynamic structure, interconnecting organization and communities. It behaves as a natural ecosystem, self-organizing, emerging and adaptive to acquire, collect, extract, and aggregate the related knowledge. With CyberBrain, appropriate and personalized knowledge services will be provided to support problem solving, decision making and early warning. At the current state, the framework is demonstrated with Rice Knowledge Portal using the PMM (Problem-Methods-Man) map generation. In addition, AGROVOC concept Server has also been used focusing primarily on the process of knowledge integration.

This paper presents a system called 1-2-3 Personalized Fertilizer, a smart mobilized fertilizing expert system. It is one of the applications based on ALRO-CyberBrain Project 1 which is a framework that emerges Knowledge Science and Engineering to Information and Communication Technology for providing the effective knowledge service. The system will provide the tailor-made fertilizer for the individual farmer. There are three main components: farmer's profile registration, inference engine with the knowledge of specific fertilizing for each soil series, and mobile interface. With only three steps: register cropping patterns, entry soil nutrient: N-P-K through mobile, and compute the tailor-made fertilizer, the farmer will get SMS (Short Message Service) back with Personalized Fertilizer formula within a few seconds together with the cost and the yield that they will gain. With the case studies on Rice Fertilizer in the Agricultural Land Reform area in Thailand, we could save cost at least $312.5 Million per year.