This paper presents a fault-tolerant middleware for private storage services based on a client-server model. A client-side API split files into redundant chunks, which are encoded/decoded, anonymized, and distributed ...
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In this research is applied simulation modeling of queuing systems with the help of vector quantization. A technique for adjusting the boundaries between classes with vector quantization is suggested. With this techni...
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ISBN:
(纸本)0780379632
In this research is applied simulation modeling of queuing systems with the help of vector quantization. A technique for adjusting the boundaries between classes with vector quantization is suggested. With this technique is determined the function of density distribution of the input flow. The implementation of suggested approach for simple Jackson queuing system is shown.
The Electroencephalography discipline studies a type of signals called Electroencephalograms (EEGs), which represent the electrical activity of different parts of the brain. EEGs are composed of a massive number of fe...
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Designing a story is widely considered a crafty yet critical task that requires deep specific human knowledge in order to reach a minimum quality and originality. This includes designing at a high level different elem...
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In Community question answering (QA) sites, malicious users may provide deceptive answers to promote their products or services. It is important to identify and filter out these deceptive answers. In this paper, we fi...
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For environmental protection, urban planning, monitoring, and management of the urban ecosystem, mapping urban green spaces is a crucial undertaking. A vital source of information for United Nations Sustainable Develo...
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Adaptation is one of the necessary capabilities of any expert system. In a traditional expert system, the evolving environment is often treated in a static view. And the system accepts the change negatively. Our focus...
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ISBN:
(纸本)0780386531
Adaptation is one of the necessary capabilities of any expert system. In a traditional expert system, the evolving environment is often treated in a static view. And the system accepts the change negatively. Our focus in this paper is to construct an adaptive CBR model which can learn continually through detecting feedbacks from the outside to partially release this. Knowledge base here is improved gradually so to enhance the system's adaptation of solving problems in dynamic environment.
This study analysed work activity in a hospital basement where humans and robots interacted and cooperated on logistics tasks. The robots were deployed to automate parts of courier processes and improve the work envir...
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Field tests demonstrated the value of enhancements that enable an advanced nonintrusive load monitoring system to tackle complex monitoring environments. Nonintrusive load monitoring (NILM) can determine the operating...
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Field tests demonstrated the value of enhancements that enable an advanced nonintrusive load monitoring system to tackle complex monitoring environments. Nonintrusive load monitoring (NILM) can determine the operating schedule of electrical loads in a target system from measurements made at a centralized location, such as the electric utility service entry. In contrast to other systems, NILM reduces sensor costs by using relatively few sensors.
Manual breast cancer diagnosis is time-consuming, error-prone, and requires skilled professionals, while deep learning-based computer-Aided Diagnosis (CAD) systems offer greater performance. Yet existing CAD systems o...
Manual breast cancer diagnosis is time-consuming, error-prone, and requires skilled professionals, while deep learning-based computer-Aided Diagnosis (CAD) systems offer greater performance. Yet existing CAD systems often focus on single imaging modalities, leading to reduced accuracy when applied to different image types. Furthermore, the challenges faced by current deep learning approaches in balancing local feature extraction and global context understanding hinder the development of comprehensive diagnostic tools. To address these issues, we introduce the InceptionNext-Transformer, a novel hybrid deep learning architecture designed for multi-modal breast cancer image analysis. This innovative approach combines Convolutional Neural Networks and Vision Transformers in a unique four-stage design: two initial stages with InceptionNext blocks for multi-scale feature extraction, a third stage employing self-attention to capture global dependencies, and a final stage integrating local and global data for enhanced classification. The study presents a computationally efficient CAD for breast cancer detection applicable to resource-constrained clinical environments. The model exhibited outstanding performance when assessed on seven datasets encompassing histopathology, mammography, and ultrasound imaging modalities. In binary classification using the BreakHis histopathology dataset, it attained 100% accuracy. Using the same dataset, the method achieved 98.25% accuracy for multi-class classification. In the analysis of mammography utilizing the INbreast, MIAS, and DDSM datasets, it achieved an accuracy of 99.97%. On BUSI and BLUID ultrasound datasets, the model attained an accuracy of 92.86%. These findings validate the InceptionNeXt-Transformer’s enhanced capacity for generalization across various imaging modalities. It demonstrates superior diagnostic reliability and computational efficiency, rendering it suitable for resource-limited clinical environments.
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