Cardiovascular disease (CVD) is a prominent cause of death worldwide. This alarming need requires an accurate prediction model using machine learning that can detect and help prevent or mitigate the risk. This study f...
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Cardiovascular disease (CVD) is a prominent cause of death worldwide. This alarming need requires an accurate prediction model using machine learning that can detect and help prevent or mitigate the risk. This study focuses on this issue and has come up with new dimensional capabilities to enhance the K-Nearest Neighbors (KNN) algorithm to predict cardiovascular diseases at an early stage by incorporating various techniques for data preprocessing and feature selection thereby improving the efficiency of the model. The proposed model identifies the most relevant features using Principal Component Analysis. The main innovation revolves around fine tuning the hyperparameter of K-Nearest Neighbors, specifically the choice of neighbors (K), using a data driven approach to ensure accuracy across different datasets. The performance of the optimized K-Nearest Neighbors algorithm is evaluated using the Framingham heart disease dataset. This model achieved an impressive prediction accuracy of 92.46% and outperformed methods that solely rely on traditional K-Nearest Neighbors. As machine learning techniques plays an important role in the development of prediction models for early detection and prevention of cardiovascular disease, this model can be considered as a valuable tool for healthcare professionals and researchers. The core contribution of this study lies in offering a comprehensive optimization of the traditional K-Nearest Neighbors (KNN) algorithm. This includes robust data preprocessing using the Hampel filter for outlier removal, feature selection through Principal Component Analysis (PCA), and performance enhancement using grid search for hyperparameter tuning combined with 10-fold cross-validation. Unlike prior studies that apply KNN with minimal adjustments, this research emphasizes the importance of an end-to-end machine learning pipeline. This holistic refinement significantly improves the predictive performance and reliability of KNN for cardiovascular diseas
Mental health conditions have become a growing problem;it increases the likelihood of premature death for patients, and imposes a high economic burden on the world. However, some studies have shown that if patients ar...
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Presently, when the Internet of Things (IoT) makes virtually everything smart by improving every aspect of our life, continuous development in this area is imperative. As IoT deals with the Low-Power Lossy Networks (L...
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A visible light communication (VLC) provides potential and effective communication paradigm due to the demand of high data-rate applications. VLC networks, consisting of multiple light emitting diodes (LEDs) and it pr...
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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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1 *** Activity Recognition(GAR),which aims to identify activities performed collectively in videos,has gained significant attention *** conventional action recognition centered on single individuals,GAR explores the c...
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1 *** Activity Recognition(GAR),which aims to identify activities performed collectively in videos,has gained significant attention *** conventional action recognition centered on single individuals,GAR explores the complex interactions between multiple individuals.
Multi-focus image fusion is a technique that combines multiple out-of-focus images to enhance the overall image quality. It has gained significant attention in recent years, thanks to the advancements in deep learning...
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The fast pace of modern life caused people to experience more pressure from their surrounding environments. As a result, depression has emerged as one of the most common diseases. To detect depression, psychiatrists n...
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Electronic Health Records (EHR) are crucial for the success of digital healthcare, with a focus on putting consumers at the center of this transformation. However, the digitalization of healthcare records brings along...
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Existing 3D object detection suffers from expensive annotation costs and poor transferability to unknown data due to the domain gap, Unsupervised Domain Adaptation (UDA) aims to generalize detection models trained in ...
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Existing 3D object detection suffers from expensive annotation costs and poor transferability to unknown data due to the domain gap, Unsupervised Domain Adaptation (UDA) aims to generalize detection models trained in labeled source domains to perform robustly on unexplored target domains, providing a promising solution for cross-domain 3D object detection. Although Self-Training (ST) based cross-domain 3D detection methods with the assistance of pseudo-labeling techniques have achieved remarkable progress, they still face the issue of low-quality pseudo-labels when there are significant domain disparities due to the absence of a process for feature distribution alignment. While Adversarial Learning (AL) based methods can effectively align the feature distributions of the source and target domains, the inability to obtain labels in the target domain forces the adoption of asymmetric optimization losses, resulting in a challenging issue of source domain bias. To overcome these limitations, we propose a novel unsupervised domain adaptation framework for 3D object detection via collaborating ST and AL, dubbed as STAL3D, unleashing the complementary advantages of pseudo labels and feature distribution alignment. Additionally, a Background Suppression Adversarial Learning (BS-AL) module and a Scale Filtering Module (SFM) are designed tailored for 3D cross-domain scenes, effectively alleviating the issues of the large proportion of background interference and source domain size bias. Our STAL3D achieves state-of-the-art performance on multiple cross-domain tasks and even surpasses the Oracle results on Waymo $\rightarrow$ KITTI and Waymo $\rightarrow$ KITTI-rain. IEEE
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