Secure and Efficient Data Storage Operations by Using Intelligent Classification Technique and RSA Algorithm in IoT-Based Cloud Computing

Abstract

In mobile cloud services, smartphones may depend on IoT-based cloud infrastructure and information storage tools to conduct technical errands, such as quest, information processing, and combined networks. In addition to traditional finding institutions, the smart IoT-cloud often upgrades the normal impromptu structure by treating mobile devices as corporate hubs, e.g., by identifying institutions. This has many benefits from the start, with several significant problems to be overcome in order to enhance the unwavering consistency of the cloud environment while Internet of things connects and improves decision support system of the entire network. In fact, similar issues apply to monitor loading, resistance, and other security risks in the cloud state. Right now, we are looking at changed arrangement procedures in MATLAB utilizing cardiovascular failure information and afterward protecting that information with the assistance of RSA calculation in mobile cloud. The calculations tried are SVM, RF, DT, NB, and KNN. In the outcome, the order strategies that have the best exactness result to test respiratory failure information will be recommended for use for enormous scope information. Instead, the collected data will be transferred to the mobile cloud for preservation using the RSA encryption algorithm.

Water Extracts from Waste Rocks of the Coal Industry of Chernvonograd Mining Area (Ukraine): Problems of Environmental Safety and Civil Protection

“…The content of iron (Fe tot ) in the water varies from 0.3 mg/l to 65.3 mg/l and is 20.49 mg/l on average. The results presented in the article are a contribution to the development of the theory of geochemistry of technogenesis and the theory of environmental safety, replenish the databases of pollutants that should be combined into a single database using new developments in information technologies [40,41]. In addition, the results of our research will be useful in designing new methods of coal mining and coal mining waste storage [42,43] and designing safety systems of man-made objects [44,45].…”

Diagnosis of Chronic Ischemic Heart Disease Using Machine Learning Techniques

“…All published investigations are based on a subset of the database's 14 features [ 42 ]. We utilized the previously processed UCI Cleveland dataset, which is also accessible on Kaggle.…”

An Efficient Machine Learning Model Based on Improved Features Selections for Early and Accurate Heart Disease Predication

“…MultiSURF is the most reliable feature selection algorithm explained in equations (12) and (13) and can be used for explicitly detecting pure 2way interactions across a wide range of problems. Variance threshold is efficient in eliminating all features with variance below a certain threshold evaluated from equation (20). Lastly, we used mutual information in the feature selection phase to find dimensionless quantities with units of bits that measure "how much one random variable provides information about another."…”

“…Lastly, we used mutual information in the feature selection phase to find dimensionless quantities with units of bits that measure "how much one random variable provides information about another." Mathematical modulation behind mutual information is explained in equation ( 15)- (20).…”

“…Traditional methods like angiography are regarded as the most precise practice when it comes to detecting heart abnormalities but still facing certain limitations, such as high costs, various other side effects, and a high level of technical expertise is required, and most importantly it is much expensive, computationally difficult, and take time to assess [11,12], to overcome the limitations of conventional invasive-based approaches for detecting cardiac disease. Predictive machine learning and deep learning algorithms were used to construct noninvasive Internet of Medical ing (IoMT) [13][14][15][16], smart healthcare systems such as KNN, SVM, NB, DT, LR, RF, and ANN [17][18][19][20][21][22]. As a result, the death rate among individuals with heart disease has exponentially dropped per year.…”