The diagnosis of brain tumors dominates the medical sector and great accuracy along with efficiency in their methods to detect the tumor is required. The ability to diagnose brain cancers from MRI data has been greatl...
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This paper presents the design and implementation of a rural education platform utilizing Improved Support Vector Machine (ISVM) and Deep Neural network (DNN) methodologies within a cloud computing framework. The plat...
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In semi-supervised medical image segmentation, appropriately merging labeled and unlabeled data before network training instead of using them separately can effectively reduce knowledge loss, mitigate distribution dis...
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In view of technology characteristic of Continuously Variable Phosphorous Getting Process Using a Porous Silicon Layer (PSL-CVTPDG), a prediction model based on Artificial Neural network(ANN) is put forward for predic...
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The paper introduced the Psi-zeta computation of network efficiency under a scenario of log-normal fading and spectrum sharing interferences. This model is aimed to improve different performance metrics such as throug...
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ISBN:
(数字)9798331543891
ISBN:
(纸本)9798331543907
The paper introduced the Psi-zeta computation of network efficiency under a scenario of log-normal fading and spectrum sharing interferences. This model is aimed to improve different performance metrics such as throughput, Signal-to-Interference-plus-Noise-Ratio (SINR), energy efficiency and finally outage probability also reducing. Simulations demonstrate that when evaluating the overall performance, Psi-zeta is superior to traditional methods by managing interference and resource allocation agilely. The results, especially under high load and heavy interference conditions, exhibit remarkable enhancements in network performance. The strong modifiability and expandability of psi-zeta show that it can be a good candidate for improving network efficiency in future wireless networks like 6G, where parameters like performance, reliability and energy efficiency are the key factors. This work has significant potential to enable the next-generation communication systems.
For speaker recognition, in addition to recognition accuracy, large-scale speaker recognition faces another challenge: fast search of speaker databases. In this paper, we propose a voiceprint recognition system based ...
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A digital library is an application of technology to traditional libraries, providing access to various resources like books, magazines, and newspapers in digital formats. This allows users to access information anyti...
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Facial expression recognition plays a key role in promoting the development of comprehensive intelligence and building friendly human-computer interaction. Due to the interference of feature noise in expression data, ...
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ISBN:
(数字)9798331521950
ISBN:
(纸本)9798331521967
Facial expression recognition plays a key role in promoting the development of comprehensive intelligence and building friendly human-computer interaction. Due to the interference of feature noise in expression data, the lightweight facial expression recognition model with fewer parameters is difficult to learn more expression features through simple training, which limits the improvement of its recognition performance. An efficient facial expression recognition network based on Spot-adaptive Knowledge Distillation is proposed in this paper. Inspired by VoVNetV2, the network designed in this paper is lightweightly improved using Depthwise Separable Convolution and the parameter-free SimAM attention mechanism, reducing the number of parameters to 0.21 M. To further improve the recognition accuracy of the model, Spot-adaptive Knowledge Distillation is employed to improve the characterization ability of the model. The recognition accuracies of the student network designed in this paper on the KDEF and RAF-DB datasets are 93.05% and 81.17% respectively after spot-adaptive distillation.
Transmission line insulators are essential parts which ensure power distribution systems are reliable. On the other hand, insulator failures can result in serious issues including power disruptions and inefficient sys...
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Large Language Models (LLMs) have significantly transformed our daily life and established a new paradigm in natural language processing (NLP). However, the predominant pretraining of LLMs on extensive web-based texts...
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