Recent years have witnessed the rapid growth of social network services and consequently, research problems investigated in this area. Community detection is one of the most important problems in social networks. A go...
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Recent years have witnessed the rapid growth of social network services and consequently, research problems investigated in this area. Community detection is one of the most important problems in social networks. A good community can be defined as a group of vertices that are highly connected and loosely connected to the vertices outside the group. Community detection includes exploring the community partitioning in social networks. Regarding the fact that social networks are huge, having complete information about the whole network is almost impossible. As a result, the problem of local community detection has become more popular in recent years. This problem can be defined as the detection of a community for a given node by using local information. Many networks contain both positive and negative relations. A community in signed networks is defined as a group of nodes that are densely connected by positive links within the community and negative links between communities. In this paper, considering the problem of local community detection in signed networks, a new fast algorithm, noted as $Alg_{SP}$ , is developed to identify a dense community for a given node in signed networks. Experimental results show that the proposed algorithm can detect the ground-truth communities independently from the starting nodes.
Accurate and efficient diagnosis of COVID-19 remains a significant challenge due to the limitations of current detection methods, such as blood tests and chest scans, which can be time-consuming and error-prone. This ...
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Space systems enable essential communications, navigation, imaging and sensing for a variety of domains, including agriculture, commerce, transportation, and emergency operations by first responders. Protecting the cy...
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Accurate weekly electricity load prediction is of utmost importance for electricity providers to ensure uninterrupted power supply to customers. This study applies an Artificial Neural Network (ANN) to achieve precise...
Accurate weekly electricity load prediction is of utmost importance for electricity providers to ensure uninterrupted power supply to customers. This study applies an Artificial Neural Network (ANN) to achieve precise weekly electricity load prediction. The dataset used for the ANN model consists of three months’ worth of data, including daily workload profiles, holiday work profiles, temperature, and humidity. For model training, 90% of the data is utilized with the Levenberg-Marquardt algorithm, while the remaining 10% is used for testing. The Mean Average Percentage Error (MAPE) is employed as the error metric. Based on the test results, the weekly load prediction error rate using ANN is determined to be 1.78% based on the MAPE value.
This research proposes an IoT based technique for predicting rainfall forecast in coastal regions using a deep reinforcement learning model. The proposed technique utilizes Long Short-Term Memory (LSTM) networks to ca...
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The Internet of Things(IoT)has enabled various intelligent services,and IoT service range has been steadily extended through long range wide area communication technologies,which enable very long distance wireless dat...
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The Internet of Things(IoT)has enabled various intelligent services,and IoT service range has been steadily extended through long range wide area communication technologies,which enable very long distance wireless data ***-nodes are connected to a gateway with a single *** consume very low-power,using very low data rate to deliver *** long transmission time is consequently needed for each data packet transmission in long range wide area networks,data transmission should be efficiently ***,this paper proposes a multicast uplink data transmission mechanism particularly for bad network *** delay will be increased if only retransmissions are used under bad network ***,employing multicast techniques in bad network conditions can significantly increase packet delivery ***,retransmission can be reduced and hence transmission efficiency ***,the proposed method adopts multicast uplink after network condition *** predict network conditions,the proposed method uses a deep neural network *** proposed method performance was verified by comparison with uplink unicast transmission only,confirming significantly improved performance.
Photon counting computed tomography (PCCT) is an emerging spectral CT technology with significant potential for revolutionizing clinical CT applications. However, noise amplification during signal decomposition signif...
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ISBN:
(数字)9798350388152
ISBN:
(纸本)9798350388169
Photon counting computed tomography (PCCT) is an emerging spectral CT technology with significant potential for revolutionizing clinical CT applications. However, noise amplification during signal decomposition significantly limits the utility of basis material images. While data-driven supervised learning has been successful in reducing noise in conventional CT images, applying this method to PCCT is challenging due to the early phase of clinical studies and difficulty in gathering sufficient clinical data for training. To solve this issue, this paper proposes a projection-domain noise propagation model for noise suppression during material decomposition. Firstly, an analytical model is derived from the decomposition of dual-energy materials to describe in detail the propagation of noise from the detector domain to the projection domain. Such model can accurately describe the variance of each pixel in the basis material images. Secondly, we incorporate this statistical model into self-supervised learning network and define a joint optimization strategy via maximizing the constrained log-likelihood with Gaussian statistics. For self-supervised learning, we suggest to construct pseudo training pairs to learn denoising solely from noisy samples. Extensive analyses on real data demonstrate that the proposed method is promising for improving the virtual monochromatic imaging (VMI) quality of PCCT. Our method uses a small amount of experimental data and could be implemented in a real clinical setting.
To satisfy the low delay, low jitter, and high success rate requirements for in-vehicle networks, IEEE 802.1 Task Group proposed Time-Sensitive Networking (TSN), which has aroused increasing attention in managing time...
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Visual place recognition techniques based on deep learning, which have imposed themselves as the state-of-the-art in recent years, do not generalize well to environments visually different from the training set. Thus,...
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Automated test case generation is critical to support the testing of large-scale systems without guaranteeing good coverage while reducing the manual effort of writing test cases. The coverage of test data also relies...
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