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Imbalanced Classification in Diabetics Using Ensembled Machine Learning

Computers, Materials & Continua 2022年第9期72卷 4397-4409页

作者： M.Sandeep Kumar Mohammad Zubair Khan sukumar rajendran Ayman Noor A.Stephen Dass J.Prabhu School of Information Technology and Engineering Vellore Institute of TechnologyVelloreTamil Nadu632014India Department of Computer Science and Information Taibah UniversityMedinaSaudi Arabia College of Computer Science and Engineering Taibah UniversityMedinaSaudi Arabia

Diabetics is one of the world’s most common diseases which are caused by continued high levels of blood *** risk of diabetics can be lowered if the diabetic is found at the early *** recent days,several machine learning models were developed to predict the diabetic presence at an early *** this paper,we propose an embedded-based machine learning model that combines the split-vote method and instance duplication to leverage an imbalanced dataset called PIMA Indian to increase the prediction of *** proposed method uses both the concept of over-sampling and under-sampling along with model weighting to increase the performance of *** measures such as Accuracy,Precision,Recall,and F1-Score are used to evaluate the *** results we obtained using K-Nearest Neighbor(kNN),Naïve Bayes(NB),Support Vector Machines(SVM),Random Forest(RF),Logistic Regression(LR),and Decision Trees(DT)were 89.32%,91.44%,95.78%,89.3%,81.76%,and 80.38%*** SVM model is more efficient than other models which are 21.38%more than exiting machine learning-based works.

关键词： Diabetics classification imbalanced data split-vote instance duplication

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Emphasizing privacy and security of edge intelligence with machine learning for healthcare

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International Journal of Intelligent Computing and Cybernetics 2022年第1期15卷 92-109页

作者： sukumar rajendran Sandeep Kumar Mathivanan Prabhu Jayagopal Kumar Purushothaman Janaki Benjula Anbu Malar Manickam Bernard Suganya Pandy Manivannan Sorakaya Somanathan Vellore Institute of Technology VelloreIndia

Purpose-Artificial Intelligence(AI)has surpassed expectations in opening up different possibilities for machines from different walks of *** service providers are *** computing reduces latency,improving availability and saving ***/methodology/approach-The exponential growth in tensor processing unit(TPU)and graphics processing unit(GPU)combined with different types of sensors has enabled the pairing of medical technology with deep learning in providing the best patient care.A significant role of pushing and pulling data from the cloud,big data comes into play as velocity,veracity and volume of data with IoT assisting doctors in predicting the abnormalities and providing customized treatment based on the patient electronic health record(EHR).Findings-The primary focus of edge computing is decentralizing and bringing intelligent IoT devices to provide real-time computing at the point of presence(PoP).The impact of the PoP in healthcare gains importance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of *** impact edge computing of the PoP in healthcare gains significance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of ***/value-The utility value of sensors data improves through the Laplacian mechanism of preserved PII response to each query from the *** scalability is at 50%with respect to the sensitivity and preservation of the PII values in the local ODL.

关键词： Edge computing Deep learning Cloud computing Fog computing Electronic health record Tensor processing unit Graphics processing unit Point of presence

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