Cybersecurity is a field that unifies concepts from various fundamental areas: mathematics, physics, computerscience, electronics, sociology, and management. This field is challenging for government authorities, acad...
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Explainable Artificial Intelligence (XAI) seeks to render Artificial Intelligence (AI) models transparent and comprehensible, potentially increasing trust and confidence in AI recommendations. This research explores t...
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The evolution of Python requires accurate version identification to facilitate compatibility and ongoing support. We extend previous work on deep learning models for Python version identification, where LSTM and CodeB...
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The article deals with the approaches to the definition of online education. Its basic components are described. There is considered the experience of the training of the students in the pedagogical universities to th...
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ISBN:
(数字)9798350353532
ISBN:
(纸本)9798350353549
The article deals with the approaches to the definition of online education. Its basic components are described. There is considered the experience of the training of the students in the pedagogical universities to the implementation of e-learning at school in the context of the production (pedagogical) practice.
Instead of the traditional 'system-environment' pair for objects of the regional economy, it is proposed to consider metasystems - these objects together with all interacting objects of the microenvironment. E...
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The analysis of various sources has been carried out, confirming the demand for protective solutions against information threats from fake websites of organizations. The relevance of the research topic is confirmed by...
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We explore a continuous aggregated dynamic model for developing two gas fields. The new borehole commissioning rates are the control parameters. Changes in the average flow rate of producing boreholes and current natu...
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Graph convolutional networks that leverage spatial-temporal information from skeletal data have emerged as a popular approach for 3D human pose estimation. However, comprehensively modeling consistent spatialtemporal ...
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Graph convolutional networks that leverage spatial-temporal information from skeletal data have emerged as a popular approach for 3D human pose estimation. However, comprehensively modeling consistent spatialtemporal dependencies among the body joints remains a challenging task. Current approaches are limited by performing graph convolutions solely on immediate neighbors, deploying separate spatial or temporal modules, and utilizing single-pass feedforward architectures. To solve these limitations, we propose a forward multi-scale residual graph convolutional network(FMR-GNet) for 3D pose estimation from monocular video. First, we introduce a mix-hop spatialtemporal attention graph convolution layer that effectively aggregates neighboring features with learnable weights over large receptive fields. The attention mechanism enables dynamically computing edge weights at each layer. Second,we devise a cross-domain spatial-temporal residual module to fuse multi-scale spatial-temporal convolutional features through residual connections, explicitly modeling interdependencies across spatial and temporal domains. Third, we integrate a forward dense connection block to propagate spatial-temporal representations across network layers, enabling high-level semantic skeleton information to enrich lower-level features. Comprehensive experiments conducted on two challenging 3D human pose estimation benchmarks, namely Human3.6M and MPI-INF-3DHP, demonstrate that the proposed FMR-GNet achieves superior performance, surpassing the most state-of-the-art methods.
Malaria is an important and worldwide fatal disease that has been widely reported by the World Health Organization(WHO),and it has about 219 million cases worldwide,with 435,000 of those *** common malaria diagnosis a...
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Malaria is an important and worldwide fatal disease that has been widely reported by the World Health Organization(WHO),and it has about 219 million cases worldwide,with 435,000 of those *** common malaria diagnosis approach is heavily reliant on highly trained experts,who use a microscope to examine the ***,there is a need to create an automated solution for the diagnosis of *** of the main objectives of this work is to create a design tool that could be used to diagnose malaria from the image of a blood *** this paper,we firstly developed a graphical user interface that could be used to help segment red blood cells and infected cells and allow the users to analyze the blood ***,a Feed-forward Neural Network(FNN)is designed to classify the cells into two *** achieved results show that the proposed techniques can be used to detect malaria,as it has achieved 92%accuracy with a database that contains 27,560 benchmark images.
To give shift in safety protocols, we have employed advanced deep learning algorithms and frameworks (Shrestha and Mahmood in IEEE Access 7:53,040–53,065, 2019 [25]) to construct an innovative AI model. The designed ...
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