Due to the rapid development of information technology, health monitoring research demands are growing tremendously. People prefer to use wearable devices that consist of sensors, which help in monitoring the health of individuals. This study aims to develop a system that remotely monitors the physiological aspects of individuals, such as blood oxygen saturation and pulse measurement, using wearable sensors and a new cloud-based fuzzy logic system, and generates necessary alarms. Monitoring health remotely based on wearable and non-invasive sensors provides a cost-effective solution, which in turn permits the elderly people to continue to live in his/her home environment as opposed to spending on expensive healthcare facilities. The developed system shall permit smooth physiological monitoring of an individual’s health by healthcare personnel in real-time. The developed system will not only assess health conditions but also provide feedback from distant facilities.
Moreover, the developed rule-based fuzzy system can infer an individual’s health condition. The study uses pulse rate and oxygen saturation data, which are processed by a fuzzy system on the cloud, and the acquired results are displayed on a mobile application.

A cloud-based pervasive application for monitoring oxygen saturation and heart rate using fuzzy-as-a-service

The increase of the adoption of IoT devices and the contemporary problem of food production have given rise to numerous applications of IoT in agriculture. These applications typically comprise a set of sensors that are installed in open fields and measure metrics, such as temperature or humidity, which are used for irrigation control systems. Though useful, most contemporary systems have high installation and maintenance costs, and they do not offer automated control or, if they do, they are usually not interpretable, and thus cannot be trusted for such critical applications. In this work, we design Vital, a system that incorporates a set of low-cost sensors, a robust data store, and most importantly an explainable AI decision support system. Our system outputs a fuzzy rule-base, which is interpretable and allows fully automating the irrigation of the fields. Upon evaluating Vital in two pilot cases, we conclude that it can be effective for monitoring open-field installations.

Versatile Internet of Things for Agriculture: An eXplainable AI Approach

In fuzzy hypothesis testing we use
fuzzy test statistics produced by fuzzy estimators and fuzzy critical values.
In this paper we use the non-asymptotic fuzzy estimators in fuzzy hypothesis testing. These are triangular shaped fuzzy numbers that generalize the fuzzy estimators based on confidence intervals in such a way that eliminates discontinuities and ensures compact support.
Our approach is particularly useful in critical situations, where
subtle fuzzy comparisons between almost equal statistical quantities have to be made. In such cases the hypotheses tests that use non-asymptotic fuzzy estimators give better results than the previous approaches, since they give us the possibility of partial rejection or not of the null hypothesis.

Hypotheses tests using non-asymptotic fuzzy estimators and fuzzy critical values

In this presentation, we investigate a special case of an open problem that is related to the exchange principle, a property of fuzzy implications. We focus on the cases of (S,N)- implications and the preservation of the exchange principle via lattice operations. We present and prove some sufficient conditions such that the exchange principle is preserved under the join and meet operations if we use (S,N)-implications.

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A cloud-based pervasive application for monitoring oxygen saturation and heart rate using fuzzy-as-a-service

Versatile Internet of Things for Agriculture: An eXplainable AI Approach

Hypotheses tests using non-asymptotic fuzzy estimators and fuzzy critical values

Preservation of the Exchange Principle via Lattice Operations on (S,N)-Implications

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