This generation faces existential threats because of the global assault novel Corona virus 2019 (i.e., COVID-19). With more than thirteen million infected and nearly 600000 fatalities in 188 countries/regions, COVID-19 is worst calamity since World War II. These misfortunes are traced to various reasons, including late detection latent or asymptomatic carriers, migration, inadequate isolation people. makes detection, containment, mitigation priorities contain exposure via quarantine,...

Federated learning (FL) is an emerging technology for empowering various applications that generate large amounts of data in intelligent cyber–physical systems (ICPS). Though FL can address users' concerns about privacy, its maintenance still depends on efficient incentive mechanisms. For long-term incentivization to participants federation under dynamic environments, deep reinforcement as a promising has been extensively studied. However, the nonstationary problem caused by heterogeneity...

In this paper, a novel method to verify the infrared finger-vein patterns is proposed for biometric purposes. Firstly, we select parameters Gabor filter with eight orientations exploit network, then extract vein by fusion of two distinct orientation results. Secondly, utilize SIFT features offset effect images rotation and shift impact during verification. Finally, number matching between registered test finger calculated as similarity measurement personal identification. The experiment...

Generative Adversarial Network (GAN) and its variants serve as a perfect representation of the data generation model, providing researchers with large amount high-quality generated data. They illustrate promising direction for research limited availability. When GAN learns semantic-rich distribution from dataset, density tends to concentrate on training Due gradient parameters deep neural network contain samples, they can easily remember samples. is applied private or sensitive data,...

In the explosive growth of time-series data (TSD), scale TSD suggests that and capability many Internet Things (IoT)-based applications has already been exceeded. Moreover, redundancy persists in due to correlation between information acquired via different sources. this article, we propose a cohort dominant set selection algorithms for electricity consumption with focus on discriminating is small but capable representing kernel carried by an arbitrarily error rate less than <inline-formula...

Federated learning (FL) has recently been proposed as an emerging paradigm to build machine models using distributed training datasets that are locally stored and maintained on different devices in 5G networks while providing privacy preservation for participants. In FL, the central aggregator accumulates local updates uploaded by participants update a global model. However, there two critical security threats: poisoning membership inference attacks. These attacks may be carried out...

Federated Edge Learning (FEL) allows edge nodes to train a global deep learning model collaboratively for computing in the Industrial Internet of Things (IIoT), which significantly promotes development 4.0. However, FEL faces two critical challenges: communication overhead and data privacy. suffers from expensive when training large-scale multi-node models. Furthermore, due vulnerability gradient leakage label-flipping attacks, process is easily compromised by adversaries. To address these...

In the era of Industry 4.0, Industrial Internet Things (IIoT) has been applied to help physical entities access real-time data in network and share critical information. However, both decentralization IIoT heterogeneity different devices constituting also pose a serious threat secure communication between entities. Although encryption technologies can ensure confidentiality data, security key becomes more important for IIoT. Recently, some asymmetric group agreement (AGKA) protocols have...

Tomatoes are a crop of significant economic importance, and disease during growth poses substantial threat to yield quality. In this paper, we propose IBSA_Net, tomato leaf recognition network that employs transfer learning small sample data, while introducing the Shuffle Attention mechanism enhance feature representation. The model is optimized by employing IBMax module increase receptive field adding HardSwish function ConvBN layer improve stability speed. To address challenge poor...

Empowered by promising artificial intelligence, the traditional Internet of Things is evolving into Artificial Intelligence (AIoT), which an important enabling technology for Industry 4.0. Collaborative learning a key AIoT to build machine (ML) models on distributed datasets. However, there are two critical concerns collaborative AIoT: privacy leakage sensitive data and dishonest computation. Specifically, contains information users, cannot be openly shared model learning. Furthermore,...

In this study, computer vision applicable to traditional agriculture was used achieve accurate identification of rice leaf diseases with complex backgrounds. The researchers developed the RiceDRA-Net deep residual network model and it identify four different diseases. disease test set a background named CBG-Dataset, new single constructed, SBG-Dataset, based on original dataset. Res-Attention module 3 × convolutional kernels denser connections compared other attention mechanisms reduce...

Based on the current research wine grape variety recognition task, it has been found that traditional deep learning models relying only a single feature (e.g., fruit or leaf) for classification can face great challenges, especially when there is high degree of similarity between varieties. In order to effectively distinguish these similar varieties, this study proposes multisource information fusion method, which centered SynthDiscrim algorithm, aiming achieve more comprehensive and accurate...

Transient stability assessment (TSA) plays an important role to ensure the safe operation of power system in Internet Energy (IoE). Many time-domain simulation (TDS)-based and transient energy function (TEF)-based methods have been proposed assess system. With wide area measurement (WAMS) phasor measure units (PMUs) applied observe real-time data, TSA based on machine learning data-driven are continuously studied. These kinds can only when subjected large disturbances. However, these cannot...

Revealing the in-depth structure–property relationship and designing specific capacity electrodes are particularly important for supercapacitors. Despite many efforts made to tune composition electronic structure of cobalt oxide pseudocapacitance, insight into [CoO]6 octahedron from microstructure is still insufficient. Herein, we present a tunable in LiCoO2 by chemical delithiation process. The c-strained strain induced form higher valence Co ions, (003) crystalline layer spacing increases...

Currently there are fewer depth models applied to pepper picking detection, while the existing generalized neural networks have problems such as large model parameters, long training time, and low accuracy.In order solve above problems, this paper proposes a Yolo-chili target detection algorithm for chili detection. First, classical yolov5 is used benchmark model, an adaptive spatial feature pyramid structure combining attention mechanism idea of multi-scale prediction introduced improve...

In this paper, a new finger vein recognition method based on Gabor wavelet and Local Binary Pattern (GLBP) is proposed. the scheme, magnitude operator are combined, so feature vector has excellent stability. We introduce Block-based Linear Discriminant Analysis (BLDA) to reduce dimensionality of GLBP enhance its discriminability at same time. The results an experiment show that proposed approach performance compared other competitive approaches in current literatures.