Cyber security is dynamic as defenders often need to adapt their defense postures. The state-ofthe-art is that the adaptation of network defense is done manually(i.e., tedious and error-prone). The ideal solution is t...
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Cyber security is dynamic as defenders often need to adapt their defense postures. The state-ofthe-art is that the adaptation of network defense is done manually(i.e., tedious and error-prone). The ideal solution is to automate adaptive network defense, which is however a difficult problem. As a first step towards automation, we propose investigating how to attain semi-automated adaptive network defense(SAND). We propose an approach extending the architecture of software-defined networking, which is centered on providing defenders with the capability to program the generation and deployment of dynamic defense rules enforced by network defense tools. We present the design and implementation of SAND, as well as the evaluation of the prototype implementation. Experimental results show that SAND can achieve agile and effective dynamic adaptations of defense rules(less than 15 ms on average for each operation), while only incurring a small performance overhead.
Recent advancements in learning-based Multi-View Stereo (MVS) methods have prominently featured transformer-based models with attention mechanisms. However, existing approaches have not thoroughly investigated the pro...
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A theoretical methodology is suggested for finding the malaria parasites’presence with the help of an intelligent hyper-parameter tuned Deep Learning(DL)based malaria parasite detection and classification(HPTDL-MPDC)...
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A theoretical methodology is suggested for finding the malaria parasites’presence with the help of an intelligent hyper-parameter tuned Deep Learning(DL)based malaria parasite detection and classification(HPTDL-MPDC)in the smear images of human peripheral *** existing approaches fail to predict the malaria parasitic features and reduce the prediction *** trained model initiated in the proposed system for classifying peripheral blood smear images into the non-parasite or parasite classes using the available online *** Adagrad optimizer is stacked with the suggested pre-trained Deep Neural Network(DNN)with the help of the contrastive divergence method to *** features are extracted from the images in the proposed system to train the DNN for initializing the visible *** smear images show the concatenated feature to be utilized as the feature vector in the proposed ***,hyper-parameters are used to fine-tune DNN to calculate the class labels’*** suggested system outperforms more modern methodologies with an accuracy of 91%,precision of 89%,recall of 93%and F1-score of 91%.The HPTDL-MPDC has the primary application in detecting the parasite of malaria in the smear images of human peripheral blood.
Food allergies are a significant concern for the community as they can have adverse effects on human health. Even a small amount of certain food items can trigger allergic reactions in the body, ranging from mild symp...
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Predictive modelling can be a huge benefit when it comes to optimizing patient flows in a hospital. Hospital beds are considered critical resources, thus the need for optimizing patient flow is evident. This paper foc...
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Localization of neurovascular bundles or vessels is critical in endoscopic surgery. It still remains challenging to identify neurovascular bundles and vessels due to limited field-of-view and tactile perception loss. ...
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Feature selection is a critical aspect of improving the interpretability of machine learning models. Genetic Programming (GP) has a built-in feature selection mechanism that explores the search space to include inform...
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Sparse multi-dimensional gene expression data refers to datasets that has a vast number of features and observations, where a substantial portion of the entries are zero or missing values. In such datasets, the number...
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In today's dynamic world, providing inclusive and personalized support for individuals with physical disabilities is imperative. With diverse needs and preferences, tailored assistance according to user personas i...
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Prevailing research concentrates on superficial features or descriptions of images, revealing a significant gap in the systematic exploration of their connotative and aesthetic attributes. Furthermore, the use of cros...
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