Roads are the foundation of national transportation, and the pavement will inevitably be damaged after long-term use, so it is essential to separate the damage and repair it. However, due to the unclear pictures taken...
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This research paper presents a novel approach using the grasshopper algorithm to improve the energy efficiency of virtual machine (VM) migration in cloud computing. This methodology reframes resource allocation techni...
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Epileptic seizures impact a patient's physical function and can cause irreversible damage to the brain. Timely detection of these seizures is crucial for administering appropriate antiepileptic treatment in the me...
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Hyperspectral images (HSIs) provide rich spectral information, but acquiring high-resolution data is costly and challenging, making spectral super-resolution essential. Inspired by the near-linear efficiency of state ...
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In the current digital era, millions of people are affected by increased levels of stress, anxiety, and desperation making mental health a critical worldwide concern. Proactive approaches to early detection of these i...
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Accurate soil analysis is crucial for optimizing crop cultivation and management since soil quality and texture are so important in the agricultural industry. We have incorporated deep learning (DL) into agriculture f...
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The broad learning system(BLS) based on the minimum mean square error(MMSE) criterion can achieve outstanding performance without spending too much time in various machine learning ***, when data are polluted by non-G...
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The broad learning system(BLS) based on the minimum mean square error(MMSE) criterion can achieve outstanding performance without spending too much time in various machine learning ***, when data are polluted by non-Gaussian noise, the stability of BLS may be destroyed because the MMSE criterion is sensitive to outliers. Different from the MMSE criterion, the minimum error entropy(MEE) criterion utilizes the kernel function to capture high-dimensional information and decrease the negative influence of outliers, which will make BLS more discriminative and robust. Nevertheless, the computational complexity of MEE is high due to a double summation of the data size. To solve these issues, this paper proposes a new robust BLS variant based on the quantized minimum error entropy(QMEE) criterion, in which a quantization operation is used to reduce the computational complexity of MEE. The proposed model BLS-QMEE is optimized by the fixed-point iterative method, and a sufficient condition for its convergence is provided. Compared with the standard BLS and other existing robust variants of BLS, BLS-QMEE performs more satisfactorily without consuming too much time. The desirable performance of BLS-QMEE is verified by various experiments on function approximation, several public datasets, and a practical application.
Numerous disorders that cannot be diagnosed medically have emerged throughout the world, including Autism Spectrum Disorder (ASD). It impacts on the numerous aspects of behavior, such as social and language abilities ...
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Financial Fraud Detection Model (FFDM) is used to develop an advanced detection framework utilizing Graph Neural Networks (GNNs) to accurately identify fraudulent transactions within the transactions. Traditional frau...
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Elevators serve as vital components in modern buildings, yet optimizing passengers' waiting time remains a crucial challenge. This study proposes a machine learning-based approach to enhance the efficiency of elev...
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