Iterative Dichotomiser Posteriori Method Based Service Attack Detection in Cloud Computing

Dhiyanesh, B.; Karthick, K.; Radha, R.; Venaik, Anita

doi:10.32604/csse.2023.024691

Cited by 9 publications

(1 citation statement)

References 18 publications

(17 reference statements)

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“…Platform as a service (PaaS) is a software development framework, and it is delivered over the internet to personal computers and mobile devices [26,27]. It presents a platform utilizing software components and tools from the other two layers within the cloud computing architecture.…”

Section: Introductionmentioning

confidence: 99%

Secured Framework for Assessment of Chronic Kidney Disease in Diabetic Patients

Aldossary¹

2023

Intelligent Automation &Amp; Soft Computing

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With the emergence of cloud technologies, the services of healthcare systems have grown. Simultaneously, machine learning systems have become important tools for developing matured and decision-making computer applications. Both cloud computing and machine learning technologies have contributed significantly to the success of healthcare services. However, in some areas, these technologies are needed to provide and decide the next course of action for patients suffering from diabetic kidney disease (DKD) while ensuring privacy preservation of the medical data. To address the cloud data privacy problem, we proposed a DKD prediction module in a framework using cloud computing services and a data control scheme. This framework can provide improved and early treatment before end-stage renal failure. For prediction purposes, we implemented the following machine learning algorithms: support vector machine (SVM), random forest (RF), decision tree (DT), naïve Bayes (NB), deep learning (DL), and k nearest neighbor (KNN). These classification techniques combined with the cloud computing services significantly improved the decision making in the progress of DKD patients. We applied these classifiers to the UCI Machine Learning Repository for chronic kidney disease using various clinical features, which are categorized as single, combination of selected features, and all features. During single clinical feature experiments, machine learning classifiers SVM, RF, and KNN outperformed the remaining classification techniques, whereas in combined clinical feature experiments, the maximum accuracy was achieved for the combination of DL and RF. All the feature experiments presented increased accuracy and increased F-measure metrics from SVM, DL, and RF.

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Section: Introductionmentioning

confidence: 99%