Sound event detection and classification present significant challenges, particularly in noisy environments with multiple overlapping sources. This paper introduces an innovative architecture for multiple sound event ...
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Model-Mediated Teleoperation (MMT) is a form of teleoperation where a model is used to display operator commands to the environment and environmental feedback to the operator. This circumvents the performance-stabilit...
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A novel electrochemical aptasensor was designed for the simultaneous detection of aflatoxin B1 (AFB1) and deoxynivalenol (DON) using dual-working microelectrodes and PDMS-based microfluidic channels. The system provid...
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Supply chain management and Hyperledger are two interconnected domains. They leverage blockchain technology to enhance efficiency, transparency, and security in supply chain operations. Together, they provide a decent...
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In multi-receiver passive radar systems, coherent detection assumes accurate synchronisation between receivers. However, achieving this in practice is challenging. By creating a virtual radar environment we can invest...
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Due to recent expansion of wireless communications, it has become impossible to cope with the allotment of the precious spectrum while resources for wireless communication are bounded and finite. Hence, the cognitive ...
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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
Multi-image steganography refers to a data-hiding scheme where a user tries to hide confidential messages within multiple images. Different from the traditional steganography which only requires the security of an ind...
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In this letter, we depart from the widely-used gradient descent-based hierarchical federated learning (FL) algorithms to develop a novel hierarchical FL framework based on the alternating direction method of multiplie...
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In particular, Rumex weed detection is regarded as a crucial step in real-world data under many circumstances. The detection task suffers from several issues, such as overlapping weeds, occlusion, varying leaf colour ...
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