The applications of IoT have been employed in diverse domains like industries, clinical care, and farming, and so forth. Nowadays, the constitution of this technology is more prevalent in clinical observation, where the wearable devices have stimulated the development of the Internet of Medical Things (IoMT). In the process of reducing the death rate, it is necessary to detect the disease at an earlier stage. The cardiac disease prediction is a major defect in the examination of the dataset in clinics. The research proposed aims to recognize the important cardiac complaint prediction characteristics by utilizing machine‐learning methodologies. Numerous projects have been established regarding the diagnosis of cardiac complaints, which results in low accuracy rate. Thus, for improving the accuracy of prediction and for cardiac complaint investigation this article utilized a fuzzy c‐means neural network (FNN) and a deep convolution neural network for feature extraction. From the clinical dataset, data were obtained for the risk prediction of cardiac complaints that includes blood pressure (BP), age, sex, chest pain, cholesterol, blood sugar, and so forth. The hearts condition is recognized by categorizing the sensor data received by FNN. The evaluation performances were carried out and the results revealed that FNN is good in predicting the cardiac complaints. In addition to this, the proposed model achieves better accuracy than the other approaches through the demonstration of simulation results. The proposed approach attains the accuracy rate of 86.4% and F1‐score of 97%, precision 76.2%, and 64.6% of FPR.
Online transaction grows in enormous rate because of the strength of usage by the user. User always use online mode to pay the amount to the respective merchant. Various method of payment is available in the market but credit card is so popular due to the pre credit is assigned to the customer by banker. Card user gets extra time for paying the payment which gives comfortable live to them. Security of the card suffers in various factors such as theft, fraud, illegal access, so it is protected by using modern algorithm with automated capability. Artificial Intelligent algorithms are applied to detect the fraud but that is not achieving enough accuracy. This type of problem is overcome by using location based risk identification model with multidimensional features for analysis. Three phases of processing is carried out namely feature management, risk management and Location awareness. The focus of the model is to protect the credit card frauds in multi level security by identifying the source and location of access. It achieves high level of security when compared to all exiting algorithms with reliable manner.
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