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 key objective of intrusion detection systems(IDS)is to protect the particular host or network by investigating and predicting the network traffic as an attack or *** IDS uses many methods of machine learning(ML)to...
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The key objective of intrusion detection systems(IDS)is to protect the particular host or network by investigating and predicting the network traffic as an attack or *** IDS uses many methods of machine learning(ML)to learn from pastexperience attack *** based and identify the new *** though these methods are effective,but they have to suffer from large computational costs due to considering all the traffic features,***,emerging technologies like the Internet of Things(Io T),big data,*** getting advanced day by day;as a result,network traffics are also increasing ***,the issue of computational cost needs to be addressed ***,in this research,firstly,the ML methods have been used with the feature selection technique(FST)to reduce the number of features by picking out only the important ones from NSL-KDD,CICIDS2017,and CIC-DDo S2019datasets later that helped to build IDSs with lower cost but with the higher performance which would be appropriate for vast scale *** experimental result demonstrated that the proposed model *** tree(DT)with Recursive feature elimination(RFE)performs better than other classifiers with RFE in terms of accuracy,specificity,precision,sensitivity,F1-score,and G-means on the investigated datasets.
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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Detecting and promptly identifying cracks on road surfaces is of paramount importance for preserving infrastructure integrity and ensuring the safety of road users, including both drivers and pedestrians. Presently, t...
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Antenna optimization using machine learning is a rapidly evolving field that leverages the power of artificial intelligence to design and improve antenna systems. Antenna optimization is a process of modifying antenna...
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In 2018, there were 1 million occurrences of non-melanoma cancer and 288,000 occurrences of malignant skin cancer (MM) recorded worldwide. Given the aging of the population and limited resources for medical care, a co...
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In the era of advancement in technology and modern agriculture, early disease detection of potato leaves will improve crop yield. Various researchers have focussed on disease due to different types of microbial infect...
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With the profound use of digital contents in education and social media, multimedia content have become a prevalent means of communication and with such rapid increase, information security is still a major concern. T...
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Data collection using mobile sink(s) has proven to reduce energy consumption and enhance the network lifetime of wireless sensor networks. Generally speaking, a mobile sink (MS) traverses the network region, sojournin...
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Suicide is a significant public health issue that devastates individuals and society. Early warning systems are crucial in preventing suicide. The purpose of this research is to create a deep learning model to identif...
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