This study compares the effectiveness of various Generative Adversarial Network architectures, including WGAN and WGAN-GP, in data clustering using the Iris dataset. Performance was evaluated with metrics such as Silh...
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We develop the general formalism that describes the inelastic scattering of free electrons by out-of-equilibrium materials. Utilizing modulated electrons further reveals previously inaccessible information and enables...
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Recognizing individuals continuously using wearable devices has several applications in personalized services and security. This paper presents a hybrid deep network architecture called SE-ResBiLSTM that integrates a ...
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A human photoplethysmogram is a biological signal widely used for heart rate estimation, and it has a great potential for a variety of physiological and mental health monitoring applications. Photoplethysmogram is a h...
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Applications and concepts of fuzzy inference systems in wireless communications are reviewed to demonstrate their effectiveness in signal processing and telecommunications. We believe this to be the first such researc...
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The fuzzy nature of the selection criteria when carrying out procedures for diagnosing the technological processes states leads to the need using expert assessments, which often turn out the only information basis for...
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This paper demonstrates that Yolo V7, the latest version of the single-stage neural network Yolo, has good recognition of lunar impact craters on the Lunar CCD data and DEM data provided by the LROC camera which carri...
This paper demonstrates that Yolo V7, the latest version of the single-stage neural network Yolo, has good recognition of lunar impact craters on the Lunar CCD data and DEM data provided by the LROC camera which carried by NASA's Lunar Reconnaissance Orbiter (LRO) used for the experiments. The results show that Yolo V7 has good results in detecting lunar impact craters based on two data types. The best identification results are based on CCD data exceeding 70%.
This paper presents a comparative analysis of the application of Variational Autoencoders (VAE) and Wasserstein Generative Adversarial Networks (WGAN) for detecting anomalous images. The performance evaluation of the ...
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The prevalence of heart disease is rising globally, with the World Health Organization reporting that cardiovascular diseases account for approximately 17.9 million deaths annually. Early diagnosis and treatment are c...
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ISBN:
(数字)9798331509675
ISBN:
(纸本)9798331509682
The prevalence of heart disease is rising globally, with the World Health Organization reporting that cardiovascular diseases account for approximately 17.9 million deaths annually. Early diagnosis and treatment are critical to mitigating risks, as studies indicate that timely intervention can reduce mortality rates by up to 30%. This study presents a predictive numerical model leveraging machine learning classifiers—Random Forest, K-Nearest Neighbours (KNN), and Logistic Regression—to enhance diagnostic accuracy. The dataset utilized is sourced from the University of California, Irvine (UCI), comprising crucial patient characteristics. A comparative analysis of classifiers is performed, evaluating their efficacy in heart disease prediction. The study provides insights into feature selection, classification performance, and the impact of different modeling approaches, contributing to improved diagnostic frameworks in medical applications. Additionally, a detailed comparison of existing methods and their limitations has been incorporated.
The study proposes a fuzzy logic system and its utilization to enhance the supply chain management using human resource management by addressing the challenges. Conventional approaches in supply chain management are i...
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