This paper presents a novel medical imaging framework, Efficient Parallel Deep Transfer SubNet+-based Explainable Model (EPDTNet + -EM), designed to improve the detection and classification of abnormalities in medical...
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Alzheimer's disease is a common and complex brain disorder that primarily affects the elderly. Because it is progressing and has few effective therapies, it requires a thorough understanding of the condition;our s...
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Pancreatic cancer's devastating impact and low survival rates call for improved detection methods. While Artificial Intelligence has shown remarkable progress, its increasing complexity has led to "black box&...
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The most common type of malignant brain tumor, gliomas, has a variety of grades that significantly impact a patient’s chance of survival. Accurate segmentation of brain tumor regions from MRI images is crucial for en...
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作者:
Yao, YuhangJafar, Syed A.
Department of Electrical Engineering and Computer Science IrvineCA92697 United States
The optimal quantum communication cost of computing a classical sum of distributed sources is studied over a quantum erasure multiple access channel (QEMAC). K classical messages comprised of finite-field symbols are ...
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Today, machine learning is used in a broad variety of applications. Convolution neural networks (CNN), in particular, are widely used to analyze visual data. The fashion industry is catching up to the growing usage of...
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Deep models have been successful in almost every research field and they are capable of handling complex problem statements. But most of the deep neural networks are huge in size with millions/billions of parameters r...
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Deep models have been successful in almost every research field and they are capable of handling complex problem statements. But most of the deep neural networks are huge in size with millions/billions of parameters requiring heavy resources and computations to be installed in edge devices. In this paper, we present an efficient co-teaching strategy consisting of multiple small networks performing mutually at runtime to consistently improve the efficiency and generalization ability of neural networks. Unlike existing distillation mechanism, that utilizes large capacity pre-train teacher model to transfer knowledge to a smaller network unidirectionally, proposed framework treats all the networks as 'teacher' (student-sized) and co-teach them allowing them to compute concurrently and quickly with better generalizations. We have carefully divided the backbone network into small network using depth scaling with regularizations. Multiple small networks are used during the co-teaching process and the proposed AdaCoRCE loss is used to make the network learn from each other. During training, these networks are provided with the two different views of same data to increase their diversity. Co-teaching scheme allows model to fetch stronger and unique representation of knowledge by using different data views and AdaCoRCE loss. This paper provides a generalized framework that could be applied to various network structures (e.g., MobileNets, ResNet, MixNet, etc.) and it demonstrates efficient performance on variety of histology image datasets. In this paper we have used four different publicly available histology dataset on two types of diseases to evaluate the performance of proposed technique. Analysis on colorectal cancer and breast cancer histology images suggests that the proposed model enhances the overall performance of the model in terms of accuracy, GFLOPs and inference time. Further, the proposed framework is also analyzed using benchmark cifar-10 dataset and compariso
The increase in number of people using the Internet leads to increased cyberattack *** Persistent Threats,or APTs,are among the most dangerous targeted *** attacks utilize various advanced tools and techniques for att...
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The increase in number of people using the Internet leads to increased cyberattack *** Persistent Threats,or APTs,are among the most dangerous targeted *** attacks utilize various advanced tools and techniques for attacking targets with specific *** countries with advanced technologies,like the US,Russia,the UK,and India,are susceptible to this targeted *** is a sophisticated attack that involves multiple stages and specific ***,TTP(Tools,Techniques,and Procedures)involved in the APT attack are commonly new and developed by an attacker to evade the security ***,APTs are generally implemented in multiple *** one of the stages is detected,we may apply a defense mechanism for subsequent stages,leading to the entire APT attack *** detection at the early stage of APT and the prediction of the next step in the APT kill chain are ongoing *** survey paper will provide knowledge about APT attacks and their essential *** follows the case study of known APT attacks,which will give clear information about the APT attack process—in later sections,highlighting the various detection methods defined by different researchers along with the limitations of the *** used in this article comes from the various annual reports published by security experts and blogs and information released by the enterprise networks targeted by the attack.
Intrusion detection is a prominent factor in the cybersecurity domain that prevents the network from malicious attacks. Cloud security is not satisfactory for securing the user’s information because it is based on st...
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As internet use in communication networks has grown, fake news has become a big problem. The misleading heading of the news loses the trust of the reader. Many techniques have emerged, but they fail because fraudsters...
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