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
Android applications are becoming increasingly powerful in recent years. While their functionality is still of paramount importance to users, the energy efficiency of these applications is also gaining more and more a...
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Android applications are becoming increasingly powerful in recent years. While their functionality is still of paramount importance to users, the energy efficiency of these applications is also gaining more and more attention. Researchers have discovered various types of energy defects in Android applications, which could quickly drain the battery power of mobile devices. Such defects not only cause inconvenience to users, but also frustrate Android developers as diagnosing the energy inefficiency of a software product is a non-trivial task. In this work, we perform a literature review to understand the state of the art of energy inefficiency diagnosis for Android applications. We identified 55 research papers published in recent years and classified existing studies from four different perspectives, including power estimation method, hardware component, types of energy defects, and program analysis approach. We also did a cross-perspective analysis to summarize and compare our studied techniques. We hope that our review can help structure and unify the literature and shed light on future research, as well as drawing developers' attention to build energy-efficient Android applications.
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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In supervised learning algorithms, the class imbalance problem often leads to generating results biased towards the majority classes. Present methods used to deal with the class imbalance problem ignore a principal as...
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Recent advances in generative models have revolutionized the technology employed for image synthesis quite significantly, and two paradigms—GANs and diffusion-based models—are leading the pack of innovation. This pa...
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Skin cancer is a highly prevalent form of cancer that frequently arises from the proliferation of abnormal cells on the skin. Early detection is crucial for effective treatment and better prognosis. Traditional diagno...
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While Brain, emotion, and behaviours abnormalities are hallmarks of schizophrenia, a complex and long-lasting mental illness. Its symptoms are divided into three categories: positive, negative, and cognitive. Positive...
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The purpose of this article is to see how machine learning (ML) algorithms and applications are used in the COVID-19 inquiry and for other purposes. The available traditional methods for COVID-19 international epidemi...
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Safety of railway is the major problem worldwide. It has problems like cracks or any fault in the railway tracks. These problems can cause severe accidents if it is not detected regularly and early. In traditional fau...
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As the world is becoming more visually oriented, visually impaired people are struggling to access and understand visual content. This paper aims to represent the model created to assist blind and visually impaired pe...
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