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There is a rapidly growing interest in health care due to the recent development of IT convergence technologies according to the 4th industrial revolution. More services for personal health management of users are available and studies on the establishment of knowledge base for an efficient health log data management in the health care field are being carried out with the emergence of block chain technology which is the next generation information security technology. In this paper, a knowledge-based block chain network for health log data management mobile service is suggested. The user’s log data and context information are applied to block chain technology that is difficult to forge and falsify in the knowledge-based health platform, enabling a large amount of users’ log data and context information accumulated continuously to be stored in a block in the knowledge base using the side chain structure that stores information through the configuration of knowledge-based data transaction. This enables high expandability and security to be secured in mobile environment as well. The result of comparative evaluation with the existing ontology knowledge model for verifying the validity shows that the suggested method presented approximately 16.5% higher performance in accuracy and reproducibility.

Due to recent growing interest, the importance of preventive and efficient healthcare using big data scattered throughout various IoT devices is being emphasized in healthcare, as well in the IT field. The analysis of information in healthcare is mainly prediction using a user’s basic information and static data from a knowledge base. In this study, a knowledge-based dynamic cluster model using a convolutional neural network (CNN) is suggested for healthcare recommendations. The suggested method carries out a process to extend static data and a previous knowledge base from an ontology-based ambient-context knowledge base beyond knowledge-based healthcare management, which was the focus of previous study. It is possible to acquire and expand a large amount of high-quality information by reproducing inferred knowledge using a CNN, which is a deep-learning algorithm. A dynamic cluster model is developed, and the accuracy of the predictions is improved in order to enable recommendations on healthcare according to a user environment that changes over time and based on environmental factors as dynamic elements, rather than static elements. Also, the accuracy of the predictions is verified through a performance evaluation between the suggested method and the previous method to validate effectiveness, and an approximate 13% performance improvement was confirmed. Currently, the acquisition of knowledge from unstructured data is in its early stages. It is expected that symbolic knowledge-acquisition technology from unstructured information that is produced and that changes in real time, and the dynamic cluster model method suggested in this study, will become the core technologies that promote the development of healthcare management technology.

As the basic paradigm of health management has changed from diagnosis and treatment to preventative management, health improvement and management has received growing attention in societies around the world. Recently the number of obese youth has risen globally and obesity has caused serious problems regarding almost all of the diseases of these days. This study presents dietary nutrition recommendations based on knowledge for obese youth. The knowledge-based dietary nutrition recommendations herein include not only static dietary nutritional data but also individualized diet menus for them by utilizing knowledge-based context data through a collaborative filtering method. The suggested method utilizes the basic information on obese youth, forms a similarity clustering with a high correlation, applies the similarity weight on {user-menu} matrix within the similarity clustering and utilizes the knowledge based collaborative filtering to recommend the dietary nutritional menu. Also by using the knowledge-based context-aware modeling, the study constitutes a {user-menu} merge matrix and solves the sparse problem of previous recommendation system. The suggested method herein, unlike the conventional uniformed dietary nutrition recommendations for obesity management, is capable of providing the personalized recommendations. Also through mobile devices, users can receive personalized recipes and menus anytime and anywhere. By using the proposed method, the researcher develops a mobile application of dietary nutrition recommendation service for obese management. A mobile interface will be built herein and applied in an experiment to test its logical validity and effectiveness.

With the development of information technology, ambient intelligence has been combined with various application areas so as to create new convergence service industries. Through IT convergence, human-oriented technologies for improving people’s quality of life has continued to be developed. Healthcare service that has been provided along with the development of various smart IT devices makes it possible to realize more efficient healthcare of people. Therefore, along with such a medical service, the advanced lifecare service for physical and mental health has been demanded. In order to meet the healthcare demands, an advanced healthcare platform has been developed. Lifecare service has been expanded to healthcare, the disease with the highest mortality induced by complications so that the service for disease survivals have been offered. Accordingly, a big-data integration and advanced healthcare platform based on patients’ life logs are developed in order for health service. In this platform, it is possible to establish an optimized model with the knowledge base and predict diseases and complications and judge a degree of risk with the use of information filtering. The conventional filtering based on a data model using scatter life logs makes use of user attribute information only for clustering so that it has low accuracy. Also, in calculating the similarity of actual users, such a method does not apply social relationships. Therefore, this study proposes a social mining based cluster process for big-data integration. The proposed method uses conventional static model information and the information extracted from the social network in order to create reliable user modeling and applies a different level of weight depending on users’ relations. In the clustering process for disease survivals’ health conditions, it is possible to predict their health risk. Based on the risk and expectation of healthcare event occurrence, their health conditions can be improved. Lifecare forecasting model that uses social relation performs social sequence mining using PrefixSpan to complement the weak point that spends a long time to scan it repeatedly in the candidate pattern. For performance evaluation, the social mining based cluster process was compared with a conventional cluster method. More specifically, the estimation accuracy of the conventional model-based cluster method was compared with the accuracy of the social mining based cluster process. As a result, the proposed method in the mining-based healthcare platform had better performance than the conventional model-based cluster method.

Due to the development of IT convergence technologies, increased attention has focused on smart health service platforms to detect emergency situations related to chronic disease, telemedicine, silvercare, and wellness. Moreover, there is a high demand for technologies that can properly judge a situation and provide suitable countermeasures or health information if an emergency situation occurs. In this paper, we propose the sequential pattern analysis based bio-detection for smart health services. A smart health service platform is able to save bio-images and their locations detected in a smart health surveillance area where CCD cameras are installed. When a person’s figure is saved, the route tracing detects any movement and then traces its location. In addition, the platform analyzes the perceived bio-images and sequential patterns in order to determine whether or not the emergency situation is normal. Using AprioirAll algorithm-based sequential pattern profile analysis, bio-detection can detect a user who is undergoing an emergency based on abnormal patterns. It performs this task by managing information obtained from data and trace analyses, and it starts bio-detection only when there are patterns not conforming to sequential patterns. In other words, bio-detection detects the maximum sequence that can satisfy the minimum support in a given transaction. Sequential pattern profile analysis based on life-logs can analyze normal and abnormal profiles to provide health guidelines.

As IT convergence technique develops, medical technology and apparatus are being modernized opening the era that we can obtain variable information easily anywhere, anytime thanks to wireless communication developed, further. These social changes enabled us to obtain information related to health more efficiently. Modern society is rapidly aging and more people experience chronic diseases because of their wrong eating habit, obesity and insufficient exercise. Thus a demand for health improvement and management at a certain term is increasing rather than complete therapy. Previously, major medical institutions managed personal medical history regarding patients mainly in health management but it is not changing its method to self-utilization and management by individual patient as of now along with medical institutions as fusion technology develops, and individual health record information can easily be checked anywhere, anytime through personal health record (PHR) platform. Unlike developing speed of related technology, however, there is a limitation in expansion, development of individual health record service, personal information security currently. In this paper, we propose mobile service regarding life style improvement targeting high risk chronic diseases based on PHR platform. PHR platform determines high blood pressure, diabetes, hyperlipidemia diseases which are three main chronic diseases using users’ data and can monitor chronic diseases in portable mobile device. Also, the service provides by organically, mutually connected form through feedback towards input from health states of users in mobile device. By proposing contents about service based on efficient individual health record through mobile device that maximized transportability based on PHR platform, proposed method will contribute to industry development and activation of application service development of individual health record. Increase in consistency and reliability through standardization of afterwards health management service is expected to contribute to reduction in social cost and improvement of national health being the basis to realize communication activation of health record between medical institutions, efficient management and education of patients, reduction in dual examinations.

These days, with the development of information technology, new paradigms have been created through academical and technological convergence in various areas. The IT convergence draws much attention as the next generation technology for disaster prevention and management in the construction and transportation area. Along with global warming, global climate changes and unusual weather occur around the world, and consequently disasters become more huge. IT convergence based disaster management service makes it possible to quickly respond to unexpected disasters in the ubiquitous environment and mitigate the disasters. Although research on disaster prevention and management has constantly been conducted, it is relatively slow to develop the technology for disaster prediction and prevention. For efficient safety and disaster prevention and management in the next generation IT convergence, it is essential to establish a systematic disaster prevention technology and a disaster prevention information system. In this paper, we proposed ontology-driven slope modeling for disaster management service through the convergence of construction, transportation technology and IT. User profile, environment information, location information, weather index, slope stability, disaster, statistics and analysis of disasters, and forest fire disaster index are used to build internal context information, external context information, and service context information. Ontology-based context awareness modeling of the landslides and disasters generated is constructed, and relevant rules are generated by inference engine. Based on the ontology of external and internal context awareness, the rules of service inference derived by inference engine are produced using protégé 5.0. According to the service inference rules, disaster control services best fitting for users’ environment is provided. By addressing the social issues related to disaster prevention and response and judging the potential risk of disasters, the proposed method can contribute to improving the safety of the public and the quality of their life. Social consensus on the necessity of prevention of urban climate disasters can be formed easily, and a ripple effect is expected on the situational response to natural disaster.

In regards to information technology of modern society, IT convergence technology is applied in various fields. Particularly, active studies are conducted for creative design products with the use of IT convergence technology in design industry which transforms sensibility of people into various expressions. People started to put high significance to design elements and sensibility accordingly with diverse and distinctive lifestyle and active studies are also conducted on sensibility engineering interaction method which connects sensibility of people with design to satisfy such demand. Also, since distributed processing became available, advancement from server centered information processing and network, such strength is applied to design industry as well. The purpose of this study lies in recommending and proposing P2P context awareness based sensibility design using color and bio-signal analysis. In order to express design that coincides with distinctive and differentiated sensibility of people, the proposed method analyzes relation between visual sensibility and color design with the use of statistic analysis tool R 3.1.0 and SPSS 21.0 and the clustering of users with similar sensibility is conducted with the use of P2P network based context awareness. It recommends color design that coincides with the sensibility of new user by using the P2P network based collaborative filtering and applying it to color design based on clustered users. Proposed method reduces the time and cost spent to estimate design that satisfies the sensibility and requirement of user and supports companies to have concrete and clarified grasp on ambiguous personal requirement of user.

The modern society has been developing new paradigms in diverse fields through IT convergence based on information technique development. In the field of construction/transportation, such IT convergence has been attracting attention as a new generation technology for disaster prevention and management. Researches on disaster prevention and management are continuously being performed. However, the development of safety technology and simulation for prediction and prevention is comparatively slow. For the new generation IT convergence to efficiently secure safety and manage disaster prevention, it is more important than anything else to construct systematic disaster prevention system and information technology. In this study, we suggested the associative context mining for ontology-driven hidden knowledge discovery. Such method reasons potential new knowledge information through the association rule mining in the ontology-driven context modeling, a preexisting research, and uses the semantic reasoning engine to create and apply rules to the context simulation. The ontology knowledge base consists of internal, external, and service context information such as user profile, weather index, industry index, location information, environment information, and comprehensive disaster situation. Apriori mining algorithm of the association rule is applied to reason the potential relationship among internal, external, and service context information and discovers and applies hidden knowledge to the semantic reasoning engine. The accuracy and validity are verified through evaluating the performance of the developed ontology-driven associative context simulation. Such developed simulation is expected contribute to enhancing public safety and quality of life through determining potential risk involved in disaster prevention and quick response.