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RISS 인기검색어
- 검색키워드
- 저자 : Nikhil Chaudhari (검색결과 2 건)
- 무료
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ALED System to Provide Mobile IoT Assistance for Elderly and Disabled
Nikhil Chaudhari,Akash Gupta,SSV Raju 보안공학연구지원센터 2016 International Journal of Smart Home Vol.10 No.8
The United Nations estimates that about 15 percent of the world’s population lives with some type of disability. Equally important is the increasing life expectancy rate of elderly people. People falling in any of these categories face great difficulty in living an independent life and have to be dependent on others. Internet-of-Things (IoT) presents immense potential in solving these problems. IoT is a concept by which everyday objects can communicate with each other via a network. This enables us to collect and store the data from these devices in the Cloud. Although there are existing systems that help to solve some of these problems; however, a comprehensive system is missing. In this paper, we propose a unified system ALED (Assisted Living for the Elderly and Disabled) using Google Brillo and Weave platform to solve both of these problems. The system provides a solution by collecting data from IoT devices (e.g., smart sensors, wearable devices, tags) and utilizes the Cloud to process the information from database. The system enables the elderly and disabled to live their life independently and at the same time make it more pleasing.
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Promethean Fuzzy Model to Predict Diabetic Foot Ulcer
J.Jayashree,Nikhil Chaudhari,N.Ch.S.N. Iyengar 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.11
One of the most serious complications of diabetes is Diabetic foot. The incidence of diabetic foot increases equally on a rise of diabetic's increases and it is often ignored by people leading to a rise in major amputations. Diabetic foot disease diagnosis is done by using fuzzy logic and beneficial as it incorporates the knowledge and experience of physician into fuzzy sets and rules. In this paper, we propose a fuzzy expert system framework for diabetic foot which constructs large scale knowledge based diabetic foot system. The knowledge is constructed by using the fuzzification. Fuzzy verdict mechanism uses triangular membership function with mamdanis inference. Defuzzification method is adopted to convert the fuzzy values into crisp values.
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