Reducing a node’s power consumption is a difficult task for extending the network’s lifetime because the nodes are resource-constrained (i.e., limited battery power, processing capacity, storage, and non-rechargeabl...
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Suicide is a serious issue around the world and is a leading cause of death in US. In the past 20 years, the suicide rate has seen a significant increase of 35%. With the rapid development of information technology, m...
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To generate dance that temporally and aesthetically matches the music is a challenging problem in three ***,the generated motion should be beats-aligned to the local musical ***,the global aesthetic style should be ma...
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To generate dance that temporally and aesthetically matches the music is a challenging problem in three ***,the generated motion should be beats-aligned to the local musical ***,the global aesthetic style should be matched between motion and *** third,the generated motion should be diverse and *** address these challenges,we propose ReChoreoNet,which re-choreographs high-quality dance motion for a given piece of music.A data-driven learning strategy is proposed to efficiently correlate the temporal connections between music and motion in a progressively learned cross-modality embedding *** beats-aligned content motion will be subsequently used as autoregressive context and control signal to control a normalizing-flow model,which transfers the style of a prototype motion to the final generated *** addition,we present an aesthetically labelled music-dance repertoire(MDR)for both efficient learning of the cross-modality embedding,and understanding of the aesthetic connections between music and *** demonstrate that our repertoire-based framework is robustly extensible in both content and *** quantitative and qualitative experiments have been carried out to validate the efficiency of our proposed model.
The earthquake early warning (EEW) system provides advance notice of potentially damaging ground shaking. In EEW, early estimation of magnitude is crucial for timely rescue operations. A set of thirty-four features is...
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The earthquake early warning (EEW) system provides advance notice of potentially damaging ground shaking. In EEW, early estimation of magnitude is crucial for timely rescue operations. A set of thirty-four features is extracted using the primary wave earthquake precursor signal and site-specific information. In Japan's earthquake magnitude dataset, there is a chance of a high imbalance concerning the earthquakes above strong impact. This imbalance causes a high prediction error while training advanced machine learning or deep learning models. In this work, Conditional Tabular Generative Adversarial Networks (CTGAN), a deep machine learning tool, is utilized to learn the characteristics of the first arrival of earthquake P-waves and generate a synthetic dataset based on this information. The result obtained using actual and mixed (synthetic and actual) datasets will be used for training the stacked ensemble magnitude prediction model, MagPred, designed specifically for this study. There are 13295, 3989, and 1710 records designated for training, testing, and validation. The mean absolute error of the test dataset for single station magnitude detection using early three, four, and five seconds of P wave are 0.41, 0.40, and 0.38 MJMA. The study demonstrates that the Generative Adversarial Networks (GANs) can provide a good result for single-station magnitude prediction. The study can be effective where less seismic data is available. The study shows that the machine learning method yields better magnitude detection results compared with the several regression models. The multi-station magnitude prediction study has been conducted on prominent Osaka, Off Fukushima, and Kumamoto earthquakes. Furthermore, to validate the performance of the model, an inter-region study has been performed on the earthquakes of the India or Nepal region. The study demonstrates that GANs can discover effective magnitude estimation compared with non-GAN-based methods. This has a high potential
Lymphoma is a type of malignant tumor that develops from lymphoid hematopoietic tissues. The precise diagnosis of lymphomas is one of the challenging tasks because of the similarity within the morphological features a...
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Fish classification and object detection are crucial tasks in the fishery industry. The use of computer vision and deep learning techniques can help automate these tasks and improve the efficiency of the fishery indus...
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Weather variability significantly impacts crop yield, posing challenges for large-scale agricultural operations. This study introduces a deep learning-based approach to enhance crop yield prediction accuracy. A Multi-...
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Advancements in smart applications highlight the need for increased processing and storage capacity at Smart Devices (SDs). To tackle this, Edge computing (EC) is enabled to offload SD workloads to distant edge server...
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The segmentation of head and neck(H&N)tumors in dual Positron Emission Tomography/Computed Tomogra-phy(PET/CT)imaging is a critical task in medical imaging,providing essential information for diagnosis,treatment p...
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The segmentation of head and neck(H&N)tumors in dual Positron Emission Tomography/Computed Tomogra-phy(PET/CT)imaging is a critical task in medical imaging,providing essential information for diagnosis,treatment planning,and outcome *** by the need for more accurate and robust segmentation methods,this study addresses key research gaps in the application of deep learning techniques to multimodal medical ***,it investigates the limitations of existing 2D and 3D models in capturing complex tumor structures and proposes an innovative 2.5D UNet Transformer model as a *** primary research questions guiding this study are:(1)How can the integration of convolutional neural networks(CNNs)and transformer networks enhance segmentation accuracy in dual PET/CT imaging?(2)What are the comparative advantages of 2D,2.5D,and 3D model configurations in this context?To answer these questions,we aimed to develop and evaluate advanced deep-learning models that leverage the strengths of both CNNs and *** proposed methodology involved a comprehensive preprocessing pipeline,including normalization,contrast enhancement,and resampling,followed by segmentation using 2D,2.5D,and 3D UNet Transformer *** models were trained and tested on three diverse datasets:HeckTor2022,AutoPET2023,and *** was assessed using metrics such as Dice Similarity Coefficient,Jaccard Index,Average Surface Distance(ASD),and Relative Absolute Volume Difference(RAVD).The findings demonstrate that the 2.5D UNet Transformer model consistently outperformed the 2D and 3D models across most metrics,achieving the highest Dice and Jaccard values,indicating superior segmentation *** instance,on the HeckTor2022 dataset,the 2.5D model achieved a Dice score of 81.777 and a Jaccard index of 0.705,surpassing other model *** 3D model showed strong boundary delineation performance but exhibited variability across datasets,while the
This article introduces a novel approach to bolster the robustness of Deep Neural Network (DNN) models against adversarial attacks named "Targeted Adversarial Resilience Learning (TARL)". The initial ev...
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