Deep learning(DL) systems exhibit multiple behavioral characteristics such as correctness, robustness, and fairness. Ensuring that these behavioral characteristics function properly is crucial for maintaining the accu...
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In today's digital landscape, ensuring the security of data transmitted and stored in the cloud is paramount. Traditional encryption methods face challenges in scalability and susceptibility to quantum threats. Th...
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Ensuring the well-being of foetuses during pregnancy is crucial for preventing child and maternal mortality, aligning with the United Nations' Sustainable Development Goals. Utilizing a dataset of 2126 records fro...
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Network attack detection and mitigation require packet collection,pre-processing,feature analysis,classification,and *** for these tasks sometimes become complex or inefficient when applied to real-time data *** mitig...
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Network attack detection and mitigation require packet collection,pre-processing,feature analysis,classification,and *** for these tasks sometimes become complex or inefficient when applied to real-time data *** mitigate hybrid assaults,this study designs an efficient forensic layer employing deep learning pattern analysis and multidomain feature *** this paper,we provide a novel multidomain feature extraction method using Fourier,Z,Laplace,Discrete Cosine Transform(DCT),1D Haar Wavelet,Gabor,and Convolutional *** method dragon fly optimisation reduces feature dimensionality and improves feature selection *** selected features are fed into VGGNet and GoogLeNet models using binary cascaded neural networks to analyse network traffic patterns,detect anomalies,and warn network *** suggested model tackles the inadequacies of existing approaches to hybrid threats,which are growing more common and challenge conventional security *** model integrates multidomain feature extraction,deep learning pattern analysis,and the forensic layer to improve intrusion detection and prevention *** diverse attack scenarios,our technique has 3.5% higher accuracy,4.3% higher precision,8.5% higher recall,and 2.9% lower delay than previous models.
In today’s world, there are many people suffering from mentalhealth problems such as depression and anxiety. If these conditions are notidentified and treated early, they can get worse quickly and have far-reachingne...
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In today’s world, there are many people suffering from mentalhealth problems such as depression and anxiety. If these conditions are notidentified and treated early, they can get worse quickly and have far-reachingnegative effects. Unfortunately, many people suffering from these conditions,especially depression and hypertension, are unaware of their existence until theconditions become chronic. Thus, this paper proposes a novel approach usingBi-directional Long Short-Term Memory (Bi-LSTM) algorithm and GlobalVector (GloVe) algorithm for the prediction and treatment of these *** and fitness bands can be equipped with these algorithms whichcan share data with a variety of IoT devices and smart systems to betterunderstand and analyze the user’s condition. We compared the accuracy andloss of the training dataset and the validation dataset of the two modelsnamely, Bi-LSTM without a global vector layer and with a global vector *** was observed that the model of Bi-LSTM without a global vector layer hadan accuracy of 83%,while Bi-LSTMwith a global vector layer had an accuracyof 86% with a precision of 86.4%, and an F1 score of 0.861. In addition toproviding basic therapies for the treatment of identified cases, our model alsohelps prevent the deterioration of associated conditions, making our methoda real-world solution.
Diffusion tensor imaging (DTI) is a neuroimaging approach that lets in for the visualization and quantification of the structural integrity of white depend fibers in the brain. In latest years, DTI has come to be an e...
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In Information Centric Networking(ICN)where content is the object of exchange,in-network caching is a unique functional feature with the ability to handle data storage and distribution in remote sensing satellite *** ...
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In Information Centric Networking(ICN)where content is the object of exchange,in-network caching is a unique functional feature with the ability to handle data storage and distribution in remote sensing satellite *** up cache space at any node enables users to access data nearby,thus relieving the processing pressure on the ***,the existing caching strategies still suffer from the lack of global planning of cache contents and low utilization of cache resources due to the lack of fine-grained division of cache *** address the issues mentioned,a cooperative caching strategy(CSTL)for remote sensing satellite networks based on a two-layer caching model is *** two-layer caching model is constructed by setting up separate cache spaces in the satellite network and the ground *** caching of popular contents in the region at the ground station to reduce the access delay of users.A content classification method based on hierarchical division is proposed in the satellite network,and differential probabilistic caching is employed for different levels of *** cached content is also dynamically adjusted by analyzing the subsequent changes in the popularity of the cached *** the two-layer caching model,ground stations and satellite networks collaboratively cache to achieve global planning of cache contents,rationalize the utilization of cache resources,and reduce the propagation delay of remote sensing *** results show that the CSTL strategy not only has a high cache hit ratio compared with other caching strategies but also effectively reduces user request delay and server load,which satisfies the timeliness requirement of remote sensing data transmission.
Various topological rough group structures, such as S-topological rough groups, s-topological rough groups, Irr-topological rough groups, and irresolute topological rough groups, naturally arise in numerous topologica...
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This paper presents a new IoT-based approach for agricultural pest control, incorporating machine learning techniques. Traditional pest monitoring methods are labor-intensive and often result in the overuse of pestici...
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Time series have numerous applications in finance, healthcare, IoT, and smart city. In many of these applications, time series typically contain personal data, so privacy infringement may occur if they are released di...
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