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There has been an increase in credit card fraud as e-commerce has become more widespread. Financial transactions are essential to our economy, so detecting bank fraud is essential. Experiments on automated and real-time fraud detection are needed here. There are numerous machine learning techniques for identifying credit card fraud, and the most prevalent are support vector machine (SVM), logic regression, and random forest. When models penalise all errors equally during training, the quality of these detection approaches becomes crucial. This paper uses an innovative sensing method to judge the classification algorithm by considering the misclassification cost and at the same time by employing SVM hyperparameter optimization using grid search cross-validation and separating the hyperplane using the theory of reproducing kernels like linear, Gaussian, and polynomial, and the robustness is maintained. Because of this, credit card fraud has been identified significantly more successful than in the past.

Population at risk can benefit greatly from remote health monitoring because it allows for early detection and treatment. Because of recent advances in Internet-of-Things (IoT) paradigms, such monitoring systems are now available everywhere. Due to the essential nature of the patients being monitored, these systems demand a high level of quality in aspects such as availability and accuracy. In health applications, where a lot of data are accessible, deep learning algorithms have the potential to perform well. In this paper, we develop a deep learning architecture called the convolutional neural network (CNN), which we examine in this study to see if it can be implemented. The study uses the IoT system with a centralised cloud server, where it is considered as an ideal input data acquisition module. The study uses cloud computing resources by distributing CNN operations to the servers with outsourced fitness functions to be performed at the edge. The results of the simulation show that the proposed method achieves a higher rate of classifying the input instances from the data acquisition tools than other methods. From the results, it is seen that the proposed CNN achieves an average accurate rate of 99.6% on training datasets and 86.3% on testing datasets.

Patients suffering from diseases that occur due to spreading of virus like fever and cold will have decrease in body temperature. They feel cold in the normal body and room temperature conditions. For the comfort of these patients, an electric under blanket is designed which warms up the patient to maintain the normal body temperature. The heated under body supports include a heater assembly and a layer of compressible support material. The heater assembly includes a flexible heating element, multiplex polyester, and a temperature sensor. The flexible heater element may include a fabric, which coated with a conductive or semiconductive polymer. The heated under body support may also include a water resistant shell, whereas it may encase the heater assembly and the compressible support material. The material used for outer shell and inner heating element has simulated in COMSOL tool for analyzing the heat transfer between them. The proto type model has simulated in PROTEUS software, which includes Arduino UNO and thermistor. This analysis will give the result whether the material can be used as the under garment for warming the patient.