While passive side-channel attacks and active fault attacks have been studied intensively in the last few decades, strong attackers combining these attacks have only been studied relatively recently. Due to its simpli...
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Heterogeneous networks are promising solutions for enhancing network performance of LTE-A mobile networks by deploying small cells within the area of the serving macro cells. The goal of deploying such networks is to ...
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3D point cloud classification requires distinct models from 2D image classification due to the divergent characteristics of the respective input data. While 3D point clouds are unstructured and sparse, 2D images are s...
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This article presents an initial solution based selective harmonic elimination (SHE) method for multilevel inverter (MLI) that aims to solve SHE problem with high accuracy while significantly reducing the number of it...
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The breach of data confidentiality, integrity, and availability due to cyberattacks can adversely impact the operation of grid-connected Photovoltaic (PV) inverters. Detecting such attacks based on their signatures or...
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Learning from unlabeled data or self-learning, can substantially reduce the complexity of machine learning (ML) utilization in real-time deployment. While the development of un/semisupervised algorithms shows promisin...
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Despite its advantage of preserving data privacy, federated learning (FL) could suffer from the limited computation resources of the distributed clients particularly when they are connected by wireless networks. By im...
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This paper advances the schedulability analysis of the Adaptive Mixed-Criticality for Weakly Hard Real-Time Systems (AMC-WH) which allows a specified number of consecutive low-criticality (LO) jobs of tasks to be skip...
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The time-sensitive Internet of Things (IoT) applications within 5G and edge computing environments presents unique challenges in network resource management. Current systems struggle with efficiently managing the high...
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Segmenting brain tumors in Magnetic Resonance Imaging(MRI)volumes is challenging due to their diffuse and irregular ***,2D and 3D deep neural networks have become famous for medical image segmentation because of the a...
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Segmenting brain tumors in Magnetic Resonance Imaging(MRI)volumes is challenging due to their diffuse and irregular ***,2D and 3D deep neural networks have become famous for medical image segmentation because of the availability of labelled ***,3D networks can be computationally expensive and require significant training *** research proposes a 3D deep learning model for brain tumor segmentation that uses lightweight feature extraction modules to improve performance without compromising contextual information or *** proposed model,called Hybrid Attention-Based Residual Unet(HA-RUnet),is based on the Unet architecture and utilizes residual blocks to extract low-and high-level features from MRI *** and Squeeze-Excitation(SE)modules are also integrated at different levels to learn attention-aware features adaptively within local and global receptive *** proposed model was trained on the BraTS-2020 dataset and achieved a dice score of 0.867,0.813,and 0.787,as well as a sensitivity of 0.93,0.88,and 0.83 for Whole Tumor,Tumor Core,and Enhancing Tumor,on test dataset *** results show that the proposed HA-RUnet model outperforms the ResUnet and AResUnet base models while having a smaller number of parameters than other state-of-the-art ***,the proposed HA-RUnet model can improve brain tumor segmentation accuracy and facilitate appropriate diagnosis and treatment planning for medical practitioners.
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