Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as *** has been seen as a robust solution to relevant challenges.A significant delay can ha...
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Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as *** has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud ***,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing *** proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution ***,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating *** study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam *** outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection *** excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage *** efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and *** simulated data indicates that the new MCWOA outpaces other methods across all *** study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).
Extracting useful details from images is essential for the Internet of Things ***,in real life,various external environments,such as badweather conditions,will cause the occlusion of key target information and image d...
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Extracting useful details from images is essential for the Internet of Things ***,in real life,various external environments,such as badweather conditions,will cause the occlusion of key target information and image distortion,resulting in difficulties and obstacles to the extraction of key information,affecting the judgment of the real situation in the process of the Internet of Things,and causing system decision-making errors and *** this paper,we mainly solve the problem of rain on the image occlusion,remove the rain grain in the image,and get a clear image without ***,the single image deraining algorithm is studied,and a dual-branch network structure based on the attention module and convolutional neural network(CNN)module is proposed to accomplish the task of rain *** order to complete the rain removal of a single image with high quality,we apply the spatial attention module,channel attention module and CNN module to the network structure,and build the network using the coder-decoder *** the experiment,with the structural similarity(SSIM)and the peak signal-to-noise ratio(PSNR)as evaluation indexes,the training and testing results on the rain removal dataset show that the proposed structure has a good effect on the single image deraining task.
Large-scale neural networks-based federated learning(FL)has gained public recognition for its effective capabilities in distributed ***,the open system architecture inherent to federated learning systems raises concer...
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Large-scale neural networks-based federated learning(FL)has gained public recognition for its effective capabilities in distributed ***,the open system architecture inherent to federated learning systems raises concerns regarding their vulnerability to potential *** attacks turn into a major menace to federated learning on account of their concealed property and potent destructive *** altering the local model during routine machine learning training,attackers can easily contaminate the global *** detection and aggregation solutions mitigate certain threats,but they are still insufficient to completely eliminate the influence generated by ***,federated unlearning that can remove unreliable models while maintaining the accuracy of the global model has become a *** some existing federated unlearning approaches are rather difficult to be applied in large neural network models because of their high computational ***,we propose SlideFU,an efficient anti-poisoning attack federated unlearning *** primary concept of SlideFU is to employ sliding window to construct the training process,where all operations are confined within the *** design a malicious detection scheme based on principal component analysis(PCA),which calculates the trust factors between compressed models in a low-cost way to eliminate unreliable *** confirming that the global model is under attack,the system activates the federated unlearning process,calibrates the gradients based on the updated direction of the calibration *** on two public datasets demonstrate that our scheme can recover a robust model with extremely high efficiency.
The most widely farmed fruit in the world is *** the production and quality of the mangoes are hampered by many *** diseases need to be effectively controlled and ***,a quick and accurate diagnosis of the disorders is...
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The most widely farmed fruit in the world is *** the production and quality of the mangoes are hampered by many *** diseases need to be effectively controlled and ***,a quick and accurate diagnosis of the disorders is *** convolutional neural networks,renowned for their independence in feature extraction,have established their value in numerous detection and classification ***,it requires large training datasets and several parameters that need careful *** proposed Modified Dense Convolutional Network(MDCN)provides a successful classification scheme for plant diseases affecting mango *** model employs the strength of pre-trained networks and modifies them for the particular context of mango leaf diseases by incorporating transfer learning *** data loader also builds mini-batches for training the models to reduce training ***,optimization approaches help increase the overall model’s efficiency and lower computing *** employed on the MangoLeafBD Dataset consists of a total of 4,000 *** the experimental results,the proposed system is compared with existing techniques and it is clear that the proposed algorithm surpasses the existing algorithms by achieving high performance and overall throughput.
In this paper, different from previous traditional multi-exposure image fusion (MEF) algorithms that use handdesigned feature extraction approaches or deep learning-based algorithms that utilize convolutional neural n...
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Due to their biological interpretability,memristors are widely used to simulate synapses between artificial neural *** a type of neural network whose dynamic behavior can be explained,the coupling of resonant tunnelin...
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Due to their biological interpretability,memristors are widely used to simulate synapses between artificial neural *** a type of neural network whose dynamic behavior can be explained,the coupling of resonant tunneling diode-based cellular neural networks(RTD-CNNs)with memristors has rarely been reported in the ***,this paper designs a coupled RTD-CNN model with memristors(RTD-MCNN),investigating and analyzing the dynamic behavior of the *** on this model,a simple encryption scheme for the protection of digital images in police forensic applications is *** results show that the RTD-MCNN can have two positive Lyapunov exponents,and its output is influenced by the initial values,exhibiting ***,a set of amplitudes in its output sequence is affected by the internal parameters of the memristor,leading to nonlinear ***,the rich dynamic behaviors described above make the RTD-MCNN highly suitable for the design of chaos-based encryption schemes in the field of privacy *** tests and security analyses validate the effectiveness of this scheme.
Missing values pose a formidable obstacle in multivariate time series analysis. Existing imputation methods rely on entangled representations that struggle to simultaneously capture multiple orthogonal time-series pat...
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Instance co-segmentation aims to segment the co-occurrent instances among two *** task heavily relies on instance-related cues provided by co-peaks,which are generally estimated by exhaustively exploiting all paired c...
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Instance co-segmentation aims to segment the co-occurrent instances among two *** task heavily relies on instance-related cues provided by co-peaks,which are generally estimated by exhaustively exploiting all paired candidates in point-to-point ***,such patterns could yield a high number of false-positive co-peaks,resulting in over-segmentation whenever there are mutual *** tackle with this issue,this paper proposes an instance co-segmentation method via tensor-based salient co-peak search(TSCPS-ICS).The proposed method explores high-order correlations via triple-to-triple matching among feature maps to find reliable co-peaks with the help of co-saliency *** proposed method is shown to capture more accurate intra-peaks and inter-peaks among feature maps,reducing the false-positive rate of co-peak *** having accurate co-peaks,one can efficiently infer responses of the targeted *** on four benchmark datasets validate the superior performance of the proposed method.
Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detecti...
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Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.
Accurate localization of tropical cyclone (TC) centers is crucial for intensity estimation and track prediction. While traditional methodologies primarily rely on visible light and infrared (IR) imagery, the potential...
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