Disease diagnosis is a challenging task due to large number of associated factors. Uncertainty in the process arises from inaccuracy patient attributes, missing data, and limitation medical expert's ability define cause effect relationships when there are multiple interrelated variables. This paper aims demonstrate an integrated view deploying smart disease using Internet Things (IoT) empowered...

This paper is on the suitability ofusing Fuzzy Cognitive Mapping technique (FCM) along with Fuzzy inference system(FIS),for the diagnosis of human disease.The diagnosis is based on the symptoms and the biochemical analysis of blood.Health care professionals use symptoms and signs as clues that can help to determine the most likely diagnosis when illness is present..Biochemistry involves in the ...

In this work a novel Multi-Sensor Data Fusion (MSDF) architecture is presented. First, each measurement-vector coming from each sensor is fed to a Fuzzy Logic-based Adaptive Kalman Filter (FL-AKF); thus there are N sensors and N FL-AKFs working in parallel. The adaptation in each FL-AKF is in the sense of dynamically tuning the measurement noise covariance matrix R employing a fuzzy inference s...

A Fuzzy Inference System was developed to aid in the diagnosis of Pulmonary Embolism using ventilation-perfusion scans and correlated chest x-rays. The diagnosis achieved needed to be accurate and reliable, comparable to that of a nuclear medicine radiologist. The Mamdani fuzzy model has been employed to implement the inference system. Sources of expertise included the criteria defined by PIOPE...

Fuzzy systems address the imprecision of the input and output variables, which formally describe notions like “rather warm” or “pretty cold”, while provide a behaviour that depends on fuzzy data. This class of systems are classically represented by means of Fuzzy Inference Systems (FIS), a computing framework based on the concepts of fuzzy if-then rules and fuzzy reasoning. Even if FIS are larg...

The key issue of inventory management is the problem of safety stock control. The existence of imprecise data makes this control complex. Fuzzy logic (FL) is widely used to develop expert system, due to its ability in representing imprecise data. Therefore, in this study a fuzzy logic system and theory have been used that incorporate the linguistic variable more practically and also help in eli...

Churn prediction is a useful tool to predict customer at churn risk. By accurate prediction of churners and non-churners, a company can use the limited marketing resource efficiently to target the churner customers in a retention marketing campaign. Accuracy is not the only important aspect in evaluating a churn prediction models. Churn prediction models should be both accurate and comprehensib...