Conventional deep learning architectures do not adequately address the requirements of wearable high-precision medical devices such as blood pressure (BP) monitors. This paper presents a novel hybrid deep learning arc...
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ISBN:
(纸本)9798350375220;9798350375213
Conventional deep learning architectures do not adequately address the requirements of wearable high-precision medical devices such as blood pressure (BP) monitors. This paper presents a novel hybrid deep learning architecture that leverages advancements in sensors and signal processing modules for cuffless and continuous BP monitoring devices, emphasizing enhanced precision in an energy constrained system. The proposed architecture comprises a combination of a convolutional neural network and a bidirectional gated recurrent unit. The proposed model adopts a data-driven end-to-end approach to directly process raw photoplethysmography (PPG) signals, enabling simultaneous estimation of systolic BP and diastolic BP without the need for feature extraction. Performance evaluation was conducted using the Multiparameter intelligent Monitoring in Intensive Care II dataset, yielding small mean errors of 0.664 mmHg and -0.028 mmHg for the estimated and reference SBP and DBP, respectively.
Due to high data availability, it is difficult to classify/process images with higher speed and accuracy. The generation of semantic and human-face images has been a very important problem in artificial intelligence a...
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The abnormal growth of skin cells that are exposed to the sun is identified as skin cancer. Even though skin cancer is curable, late diagnosis and improper treatment can lead to severe effects and death. Melanoma has ...
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Servo motors, with their strong load capacity, reliable operation, and high efficiency, are widely used in electrical production and daily life. As one of the main power sources in modern industry, a motor failure can...
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ISBN:
(纸本)9798350375145;9798350375138
Servo motors, with their strong load capacity, reliable operation, and high efficiency, are widely used in electrical production and daily life. As one of the main power sources in modern industry, a motor failure can paralyze operating mechanisms and even threaten life safety. Therefore, this paper conducts intelligent diagnosis research on motor drive systems based on neural networks. Firstly, finite element method simulation is used to obtain fault data of the servo motor and collect sufficient samples. Secondly, addressing the slow convergence speed caused by the deepening of traditional convolutional neural networks (CNN), this paper combines residual learning with convolutional neural networks, proposing a residual learning-based convolutional neural network with Wide Kernel (R-WDCNN) model for fault classification. Finally, compared with traditional convolutional neural networks, the R-WDCNN algorithm achieves higher recognition accuracy and faster convergence speed.
Plant leaf diseases must be accurately and quickly detected in order to minimize financial losses and increase agricultural productivity. However, farmers39; dependence on manual, conventional methods might occasion...
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The growing complexity of recruitment processes necessitates advanced solutions for resume screening and ranking. In this paper, we present ResuMatcher, an AI-powered resume ranking system that leverages the power of ...
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With around 1.6 million deaths yearly, primarily in low-resource environments, abstract-tuberculosis (TB) is a serious worldwide health issue. While human analysis can be error-prone and labor-intensive, early discove...
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In the rapidly evolving field of machine learning, training models with datasets from various locations and organizations presents significant challenges due to privacy and legal concerns. The exploration of effective...
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ISBN:
(数字)9798400712487
ISBN:
(纸本)9798400712487
In the rapidly evolving field of machine learning, training models with datasets from various locations and organizations presents significant challenges due to privacy and legal concerns. The exploration of effective collaborative training settings, which are capable of leveraging valuable knowledge from distributed and isolated datasets, is increasingly *** study investigates key factors that impact the effectiveness of collaborative training methods in code next-token prediction, as well as the correctness and utility of the generated code, showing the promise of such methods. Additionally, we evaluate the memorization of different participant training data across various collaborative training settings, including centralized, federated, and incremental training, showing their potential risks in leaking data. Our findings indicate that the size and diversity of code datasets are pivotal factors influencing the success of collaborative trained code models. We demonstrate that federated learning achieves competitive performance compared to centralized training while offering better data protection, as evidenced by lower memorization ratios in the generated code. However, federated learning can still produce verbatim code snippets from hidden training data, potentially violating data privacy or copyright. Our study further explores the patterns of effectiveness and memorization in incremental learning, emphasizing the importance of the sequence in which individual participant datasets are introduced. Also, we identify the memorization phenomenon of cross-organizational clones as a prevalent challenge in both centralized and federated learning scenarios. Our findings highlight the persistent risk of data leakage during inference, even when training data remains unseen. We conclude with strategic recommendations for practitioners and researchers to optimize the use of multisource datasets, thereby propelling the cross-organizational collaboration forward.
The design of energy-efficient indoor lighting systems is a very interesting research challenge. Although this field has experienced significant developments, Reinforcement learning (RL) remains relatively under explo...
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Artificial Intelligence has been a boon to healthcare for quite a long time. While AI has the potential to assist in several domains, blood smear analysis has several challenges that need to be addressed to ensure the...
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