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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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.
Aspect-based sentiment analysis is one of the famous and practical subjects in natural language processing. Traditional sentiment analysis assigns a polarity to the whole text or document and does not consider the asp...
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With the recent advancements of deep learning-based methods in image classification,the requirement of a huge amount of training data is inevitable to avoid overfitting ***,supervised deep learning models require labe...
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With the recent advancements of deep learning-based methods in image classification,the requirement of a huge amount of training data is inevitable to avoid overfitting ***,supervised deep learning models require labelled datasets for *** such a huge amount of labelled data requires considerable human effort and *** this scenario,self-supervised models are becoming popular because of their ability to learn even from unlabelled ***,the efficient transfer of knowledge learned by self-supervised models into a target task,is an unsolved *** paper proposes a method for the efficient transfer of know-ledge learned by a self-supervised model,into a target *** such as the number of layers,the number of units in each layer,learning rate,and dropout are automatically tuned in these fully connected(FC)layers using a Bayesian optimization technique called the tree-structured parzen estimator(TPE)approach *** evaluate the performance of the proposed method,state-of-the-art self-supervised models such as SimClr and SWAV are used to extract the learned *** are carried out on the CIFAR-10,CIFAR-100,and Tiny ImageNet *** proposed method outperforms the baseline approach with margins of 2.97%,2.45%,and 0.91%for the CIFAR-100,Tiny ImageNet,and CIFAR-10 datasets,respectively.
Automatic skin lesion subtyping is a crucial step for diagnosing and treating skin cancer and acts as a first level diagnostic aid for medical experts. Although, in general, deep learning is very effective in image pr...
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Automatic skin lesion subtyping is a crucial step for diagnosing and treating skin cancer and acts as a first level diagnostic aid for medical experts. Although, in general, deep learning is very effective in image processing tasks, there are notable areas of the processing pipeline in the dermoscopic image regime that can benefit from refinement. Our work identifies two such areas for improvement. First, most benchmark dermoscopic datasets for skin cancers and lesions are highly imbalanced due to the relative rarity and commonality in the occurrence of specific lesion types. Deep learning methods tend to exhibit biased performance in favor of the majority classes with such datasets, leading to poor generalization. Second, dermoscopic images can be associated with irrelevant information in the form of skin color, hair, veins, etc.;hence, limiting the information available to a neural network by retaining only relevant portions of an input image has been successful in prompting the network towards learning task-relevant features and thereby improving its performance. Hence, this research work augments the skin lesion characterization pipeline in the following ways. First, it balances the dataset to overcome sample size biases. Two balancing methods, synthetic minority oversampling TEchnique (SMOTE) and Reweighting, are applied, compared, and analyzed. Second, a lesion segmentation stage is introduced before classification, in addition to a preprocessing stage, to retain only the region of interest. A baseline segmentation approach based on Bi-Directional ConvLSTM U-Net is improved using conditional adversarial training for enhanced segmentation performance. Finally, the classification stage is implemented using EfficientNets, where the B2 variant is used to benchmark and choose between the balancing and segmentation techniques, and the architecture is then scaled through to B7 to analyze the performance boost in lesion classification. From these experiments, we find
Federated Learning (FL) offers significant advancements in user/data privacy, learning quality, model efficiency, scalability, and network communication latency. However, it faces notable security challenges, particul...
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In the realm of medical datasets, particularly when considering diabetes, the occurrence of data incompleteness is a prevalent issue. Unveiling valuable patterns through medical data analysis is crucial for early and ...
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Photovoltaic(PV)modules age with time for various reasons such as corroded joints and terminals and glass coating defects,and their ageing degrades the PV array *** the help of the PV array numerical model,this paper ...
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Photovoltaic(PV)modules age with time for various reasons such as corroded joints and terminals and glass coating defects,and their ageing degrades the PV array *** the help of the PV array numerical model,this paper explores the effects of PV module ageing on the PV array power,and the power gains and costs of rearranging and recabling aged PV modules in a PV *** numerical PV array model is first revised to account for module ageing,rearrangement and recabling,with the relevant equations presented *** updated numerical model is then used to obtain the array powers for seven different PV *** power results are then analysed in view of the attributes of the seven PV array examples.A guiding method to recommend recabling after rearranging aged modules is then proposed,leading to further significant power gains,while eliminating intra-row *** certain conditions are met,it was shown that recabling PV modules after rearranging them may lead to further significant power gains,reaching 57%and 98%in two considered PV array *** gains are possible in other arrays.A cost-benefit analysis weighing annual power gains versus estimated recabling costs is also given for the seven considered PV array examples to guide recabling decisions based on technical and economic *** the considered examples,recabling costs can be recovered in<4 *** with the powers of the aged arrays,power gains due to our proposed rearranging and recabling the PV arrays ranged between 73%and 131%in the considered examples—well over the gains reported in the ***,the cost of our static module rearrangement and recabling method outshines the costs of dynamic reconfiguration methods recently published in the literature.
Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series *** to the challenges associated with annotating anomaly events,time series reconstructi...
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Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series *** to the challenges associated with annotating anomaly events,time series reconstruction has become a prevalent approach for unsupervised anomaly ***,effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time *** this paper,we propose a cross-dimension attentive feature fusion network for time series anomaly detection,referred to as ***,a series and feature mixing block is introduced to learn representations in 1D ***,a fast Fourier transform is employed to convert the time series into 2D space,providing the capability for 2D feature ***,a cross-dimension attentive feature fusion mechanism is designed that adaptively integrates features across different dimensions for anomaly *** results on real-world time series datasets demonstrate that CAFFN performs better than other competing methods in time series anomaly detection.
The healthcare industry has witnessed an increase in the use of cloud storage, resulting in a significant demand for safeguarding medical records from potential attackers. In response to this challenge, reversible dat...
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