In this letter, we identified an issue where the I/O performance of specific tasks could not be guaranteed during multi-process I/O operations, despite the use of the latest storage technologies in virtualized environ...
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Cardiovascular disease holds the position of being the foremost cause of death worldwide. Heart Disease Prediction (HDP) is a difficult task as it needs advanced knowledge with better experience. Moreover, it encounte...
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At the doctoral level in the field of computerscience, the domain of literature plagiarism detection encompasses numerous plagiarism detection algorithms, but relatively fewer have focused on methods specifically des...
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Cardiovascular disease(CVD)remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis,driven by risk factors such as hypertension,high cholesterol,and irregular puls...
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Cardiovascular disease(CVD)remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis,driven by risk factors such as hypertension,high cholesterol,and irregular pulse *** diagnostic methods often struggle with the nuanced interplay of these risk factors,making early detection *** this research,we propose a novel artificial intelligence-enabled(AI-enabled)framework for CVD risk prediction that integrates machine learning(ML)with eXplainable AI(XAI)to provide both high-accuracy predictions and transparent,interpretable *** to existing studies that typically focus on either optimizing ML performance or using XAI separately for local or global explanations,our approach uniquely combines both local and global interpretability using Local Interpretable Model-Agnostic Explanations(LIME)and SHapley Additive exPlanations(SHAP).This dual integration enhances the interpretability of the model and facilitates clinicians to comprehensively understand not just what the model predicts but also why those predictions are made by identifying the contribution of different risk factors,which is crucial for transparent and informed decision-making in *** framework uses ML techniques such as K-nearest neighbors(KNN),gradient boosting,random forest,and decision tree,trained on a cardiovascular ***,the integration of LIME and SHAP provides patient-specific insights alongside global trends,ensuring that clinicians receive comprehensive and actionable *** experimental results achieve 98%accuracy with the Random Forest model,with precision,recall,and F1-scores of 97%,98%,and 98%,*** innovative combination of SHAP and LIME sets a new benchmark in CVD prediction by integrating advanced ML accuracy with robust interpretability,fills a critical gap in existing *** framework paves the way for more explainable and transparent decision-making in he
Adversarial attack is a method used to deceive machine learning models, which offers a technique to test the robustness of the given model, and it is vital to balance robustness with accuracy. Artificial intelligence ...
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For providing enhanced authentication performance, the concept of multi-biometrics authentication systems has emerged as a promising solution in today’s digital era. In the existing literature, numerous studies were ...
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Secure data sharing is the most challenging and essential problem to be addressed in cloud systems. In traditional works, various blockchain and cryptographic approaches are deployed for enabling secured data storage ...
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Recognizing emotions from electroencephalography (EEG) signals is a trustworthy and reliable method to monitor the mental health of patients and the enthusiasm of individual behavioral feelings and polarity. Recently,...
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Mehta and Panigrahi (FOCS 2012, IEEE, Piscataway, NJ, 2012, pp. 728-737) introduce the problem of online matching with stochastic rewards, where edges are associated with success probabilities and a match succeeds wit...
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For the sake of customer satisfaction and high profitable revenue, the products are designed in the Ecommerce application along with the product category. Based on the product category, prices may differ in a broad ra...
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