Spectral variability often occurs in hyperspectral remote sensing scenes. The traditional hyperspectral endmember extraction method ignores the influence of spectral variability, resulting in low accuracy of mixed pix...
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This research introduces a novel machine learning methodology for classifying electrocardiogram (ECG) images, integrating deep learning models like VGG16, Inception V3, and a custom CNN, alongside ensemble methods suc...
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In recent years, smart home assistants have become increasingly popular among consumers worldwide. These intelligent devices, powered by artificial intelligence and voice recognition technology, Smart home assistants ...
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Dear Editor,This letter presents an intelligent model predictive control algorithm inspired by biological regulatory mechanism and operational research. In terms of overall architecture, based on biological regulatory...
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Dear Editor,This letter presents an intelligent model predictive control algorithm inspired by biological regulatory mechanism and operational research. In terms of overall architecture, based on biological regulatory system and operational research theory, priority factor module and central coordination module are innovatively added on the basis structure of heuristic dynamic programming to carry out overall regulation of the system.
PDF malware is a significant threat to computer security. The purpose of this study is to introduce a new approach for improving the security of PDF readers. The method utilizes transfer learning by leveraging existin...
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Convolutional neural networks(CNNs)are well suited to bearing fault classification due to their ability to learn discriminative spectro-temporal ***,gathering sufficient cases of faulty conditions in real-world engine...
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Convolutional neural networks(CNNs)are well suited to bearing fault classification due to their ability to learn discriminative spectro-temporal ***,gathering sufficient cases of faulty conditions in real-world engineering scenarios to train an intelligent diagnosis system is *** paper proposes a fault diagnosis method combining several augmentation schemes to alleviate the problem of limited fault *** begin by identifying relevant parameters that influence the construction of a *** leverage the uncertainty principle in processing time-frequency domain signals,making it impossible to simultaneously achieve good time and frequency resolutions.A key determinant of this phenomenon is the window function's choice and length used in implementing the shorttime Fourier *** Gaussian,Kaiser,and rectangular windows are selected in the experimentation due to their diverse *** overlap parameter's size also influences the outcome and resolution of the spectrogram.A 50%overlap is used in the original data transformation,and±25%is used in implementing an effective augmentation policy to which two-stage regular CNN can be applied to achieve improved *** best model reaches an accuracy of 99.98%and a cross-domain accuracy of 92.54%.When combined with data augmentation,the proposed model yields cutting-edge results.
Prediction of the nutrient deficiency range and control of it through application of an appropriate amount of fertiliser at all growth stages is critical to achieving a qualitative and quantitative *** fertiliser in op...
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Prediction of the nutrient deficiency range and control of it through application of an appropriate amount of fertiliser at all growth stages is critical to achieving a qualitative and quantitative *** fertiliser in optimum amounts will protect the environment’s condition and human health *** identification also prevents the disease’s occurrence in groundnut crops.A convo-lutional neural network is a computer vision algorithm that can be replaced in the place of human experts and laboratory methods to predict groundnut crop nitro-gen nutrient deficiency through image *** chlorophyll and nitrogen are proportionate to one another,the Smart Nutrient Deficiency Prediction System(SNDP)is proposed to detect and categorise the chlorophyll concentration range via which nitrogen concentration can be *** model’sfirst part is to per-form preprocessing using Groundnut Leaf Image Preprocessing(GLIP).Then,in the second part,feature extraction using a convolution process with Non-negative ReLU(CNNR)is done,and then,in the third part,the extracted features areflat-tened and given to the dense layer(DL)***,the Maximum Margin clas-sifier(MMC)is deployed and takes the input from DL for the classification process tofind *** dataset used in this work has no visible symptoms of a deficiency with three categories:low level(LL),beginning stage of low level(BSLL),and appropriate level(AL).This model could help to predict nitrogen deficiency before perceivable *** performance of the implemented model is analysed and compared with ImageNet pre-trained *** result shows that the CNNR-MMC model obtained the highest training and validation accuracy of 99%and 95%,respectively,compared to existing pre-trained models.
Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power sta...
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Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power stations).To solve the problem,we propose an energy harvesting based task scheduling and resource management framework to provide robust and low-cost edge computing services for smart ***,we formulate an energy consumption minimization problem with regard to task offloading,time switching,and resource allocation for mobile devices,which can be decoupled and transformed into a typical knapsack ***,solutions are derived by two different ***,we deploy renewable energy and energy storage units at edge servers to tackle intermittency and instability ***,we design an energy management algorithm based on sampling average approximation for edge computing servers to derive the optimal charging/discharging strategies,number of energy storage units,and renewable energy *** simulation results show the efficiency and superiority of our proposed framework.
The dialects of a language hold a significant place in speech processing (SP) applications. The objective of dialect identification is to categorize speech sample data into a specific dialect of a speaker's spoken...
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Lithium batteries have the advantages of safe and reliable power supply, low maintenance costs, small footprint, often used as the preferred solution for power supply in data centers. To solve the problems of non-line...
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