This research proposes a method to provide visually impaired persons with better medical services and to mitigate their difficulties in taking medicines in their daily lives. Normally, pharmaceutical information are distributed in a form of printed materials, but it is not accessible by those who are visually impaired. Since persons who are visually impaired by accidents or diseases are required to take more types of medicines compared to other handicapped persons, and thus it is an important matter to let them have appropriate medicines at the right time. Although there are several web sites which are run by the Ministry of Health and Welfare providing pharmaceutic information, the information on those web sites are duplicated and some of the menus are similar, and thus giving users difficulties and discomfort in finding appropriate pharmaceutical information. Since ontology can define and describe the resource in an information system more clearly, in this research, an ontology consists of pharmaceutical information and knowledge on medicines is constructed to give patients more precise information more efficiently. Also, a service in which users can have voice guidance on pharmaceutical information retrieve from the ontology-based information system by contacting RFID sticker on the medicine to the reader.

Recently, technologies are being developed to recognize and authenticate users using bioinformatics to solve information security issues. Biometric information includes face, fingerprint, iris, voice, and vein. Among them, face recognition technology occupies a large part. Face recognition technology is applied in various fields. For example, it can be used for identity verification, such as a personal identification card, passport, credit card, security system, and personnel data. In addition, it can be used for security, including crime suspect search, unsafe zone monitoring, vehicle tracking crime.In this thesis, we conducted a study to recognize faces by detecting the areas of the face through a computer webcam. The purpose of this study was to contribute to the improvement in the accuracy of Recognition of Face Based on CNN Algorithms. For this purpose, We used data files provided by github to build a face recognition model. We also created data using CNN algorithms, which are widely used for image recognition. Various photos were learned by CNN algorithm. The study found that the accuracy of face recognition based on CNN algorithms was 77%. Based on the results of the study, We carried out recognition of the face according to the distance. Research findings may be useful if face recognition is required in a variety of situations. Research based on this study is also expected to improve the accuracy of face recognition.

This study perceived that there are limits to prompt and accurate monitoring when an accident occurs and the correct information of egg production stage, such as the date of spawning, cleaning, and refrigerating cannot be identified, since eggshell codes using barcode only show numbers identifying a city and province and the name of producers. To fix this problem, this study partially suggested the RFID (Radio Frequency Identification) technology and IoT-based Connected System. The proposed system in this study shares data with related agencies as the system of agricultural and livestock product information runs as the main server, and the database information of the proposed system is provided by farmhouses, distributors, and sellers. Through various media such as a webpage or mobile application built to provide the relevant information, customers can search and obtain information about agricultural and livestock products they want. Since the information on an entire process is open to the public, information ranging from simple to clear, additional ones such as hazardous elements can be viewed.

In this paper, the pattern of extracting the same expression is proposed by using the Android intelligent device to identify the facial expression. The understanding and expression of expression are very important to human computer interaction, and the technology to identify human expressions is very popular. Instead of searching for the emoticons that users often use, you can identify facial expressions with acamera, which is a useful technique that can be used now. This thesis puts forward the technology of the third data is available on the website of the set, use the content to improve the infrastructure of the facial expression recognition accuracy, in order to improve the synthesis of neural network algorithm, making the facial expression recognition model, the user's facial expressions and similar e xpressions, reached 66%.It doesn't need to search for emoticons. If you use the camera to recognize the expression, itwill appear emoticons immediately. So this service is the emoticons used when people send messages to others, and it can feel a lot of convenience. In countless emoticons, there is no need to find emoticons, which is an increasing trend in deep learning. So we need to use more suitable algorithm for expression recognition, and then improve accuracy.