1 Introduction The Internet of Things(IoT)has facilitated the development of numerous fields in our ***,some equipment in IoT environment lacks sufficient storage and data processing capabilities[1].A feasible strateg...
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1 Introduction The Internet of Things(IoT)has facilitated the development of numerous fields in our ***,some equipment in IoT environment lacks sufficient storage and data processing capabilities[1].A feasible strategy is to leverage the powerful computing capabilities of cloud servers to process the data within the IoT devices.
Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory,...
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Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a
Reinforcement learning (RL) and imitation learning (IL) are quite two useful machine learning techniques that were shown to be potential in enhancing navigation performance. Basically, both of these methods try to fin...
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The success of vision transformer demonstrates that the transformer structure is also suitable for various vision tasks, including high-level classification tasks and low-level dense prediction tasks. Salient object d...
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As a deep learning network with an encoder-decoder architecture,UNet and its series of improved versions have been widely used in medical image segmentation with great ***,when used to segment targets in 3D medical im...
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As a deep learning network with an encoder-decoder architecture,UNet and its series of improved versions have been widely used in medical image segmentation with great ***,when used to segment targets in 3D medical images such as magnetic resonance imaging(MRI),computed tomography(CT),these models do not model the relevance of images in vertical space,resulting in poor accurate analysis of consecutive slices of the same *** the other hand,the large amount of detail lost during the encoding process makes these models incapable of segmenting small-scale tumor *** at the scene of small-scale target segmentation in 3D medical images,a fully new neural network model SUNet++is proposed on the basis of UNet and UNet++.SUNet++improves the existing models mainly in three aspects:1)the modeling strategy of slice superposition is used to thoroughly excavate the three dimensional information of the data;2)by adding an attention mechanism during the decoding process,small scale targets in the picture are retained and amplified;3)in the up-sampling process,the transposed convolution operation is used to further enhance the effect of the *** order to verify the effect of the model,we collected and produced a dataset of hyperintensity MRI liver-stage images containing over 400 cases of liver *** results on both public and proprietary datasets demonstrate the superiority of SUNet++in small-scale target segmentation of three-dimensional medical images.
As industrialization and informatization becomemore deeply intertwined,industrial control networks have entered an era of *** connection between industrial control networks and the external internet is becoming increa...
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As industrialization and informatization becomemore deeply intertwined,industrial control networks have entered an era of *** connection between industrial control networks and the external internet is becoming increasingly close,which leads to frequent security *** paper proposes a model for the industrial control *** includes a malware containment strategy that integrates intrusion detection,quarantine,and ***,the role of keynodes in the spreadofmalware is studied,a comparisonexperiment is conducted to validate the impact of the containment *** addition,the dynamic behavior of the model is analyzed,the basic reproduction number is computed,and the disease-free and endemic equilibrium of the model is also obtained by the basic reproduction ***,through simulation experiments,the effectiveness of the containment strategy is validated,the influence of the relevant parameters is analyzed,and the containment strategy is *** otherwords,selective immunity to key nodes can effectively suppress the spread ofmalware andmaintain the stability of industrial control *** earlier the immunization of key nodes,the *** the time exceeds the threshold,immunizing key nodes is almost *** analysis provides a better way to contain the malware in the industrial control network.
Handling the massive amount of data generated by Smart Mobile Devices(SMDs)is a challenging computational *** Computing is an emerging computation paradigm that is employed to conquer this *** can bring computation po...
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Handling the massive amount of data generated by Smart Mobile Devices(SMDs)is a challenging computational *** Computing is an emerging computation paradigm that is employed to conquer this *** can bring computation power closer to the end devices to reduce their computation latency and energy ***,this paradigm increases the computational ability of SMDs by collaboration with edge *** is achieved by computation offloading from the mobile devices to the edge nodes or ***,not all applications benefit from computation offloading,which is only suitable for certain types of *** properties,SMD capability,wireless channel state,and other factors must be counted when making computation offloading ***,optimization methods are important tools in scheduling computation offloading tasks in Edge Computing *** this paper,we review six types of optimization methods-they are Lyapunov optimization,convex optimization,heuristic techniques,game theory,machine learning,and *** each type,we focus on the objective functions,application areas,types of offloading methods,evaluation methods,as well as the time complexity of the proposed *** discuss a few research problems that are still *** purpose for this review is to provide a concise summary that can help new researchers get started with their computation offloading researches for Edge Computing networks.
Confidentiality of maintaining the Electronic Health Records of patients is a major concern to both the patient and Doctor. Sharing the data on cloud is one of the most efficient technology infrastructures with extens...
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Contact angle is an essential parameter to characterize substrate *** measurement of contact angle in experiment and simulation is a complex and time-consuming *** this paper,an improved method of measuring contact an...
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Contact angle is an essential parameter to characterize substrate *** measurement of contact angle in experiment and simulation is a complex and time-consuming *** this paper,an improved method of measuring contact angle in multiphase lattice Boltzmann simulations is proposed,which can accurately obtain the real-time contact angle at a low temperature and larger density *** three-phase contact point is determined by an extrapolation,and its position is not affected by the local deformation of flow field in the three-phase contact region.A series of simulations confirms that the present method has high accuracy and *** contact angle keeps an excellent linear relationship with the chemical potential of the surface,so that it is very convenient to specify the wettability of a *** real-time contact angle measurement enables us to obtain the dynamic contact angle hysteresis on chemically heterogeneous surface,while the mechanical analyses can be effectively implemented at the moving contact line.
The recent development of the Internet of Things(IoTs)resulted in the growth of IoT-based DDoS *** detection of Botnet in IoT systems implements advanced cybersecurity measures to detect and reduce malevolent botnets ...
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The recent development of the Internet of Things(IoTs)resulted in the growth of IoT-based DDoS *** detection of Botnet in IoT systems implements advanced cybersecurity measures to detect and reduce malevolent botnets in interconnected *** detection models evaluate transmission patterns,network traffic,and device behaviour to detect deviations from usual *** learning(ML)techniques detect patterns signalling botnet activity,namely sudden traffic increase,unusual command and control patterns,or irregular device *** addition,intrusion detection systems(IDSs)and signature-based techniques are applied to recognize known malware signatures related to *** ML and deep learning(DL)techniques have been developed to detect botnet attacks in IoT *** overcome security issues in an IoT environment,this article designs a gorilla troops optimizer with DL-enabled botnet attack detection and classification(GTODL-BADC)*** GTODL-BADC technique follows feature selection(FS)with optimal DL-based classification for accomplishing security in an IoT *** data preprocessing,the min-max data normalization approach is primarily *** GTODL-BADC technique uses the GTO algorithm to select features and elect optimal feature ***,the multi-head attention-based long short-term memory(MHA-LSTM)technique was applied for botnet ***,the tree seed algorithm(TSA)was used to select the optimum hyperparameter for the MHA-LSTM *** experimental validation of the GTODL-BADC technique can be tested on a benchmark *** simulation results highlighted that the GTODL-BADC technique demonstrates promising performance in the botnet detection process.
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