Precisely identifying the fault-related operating state of the gear-box bearing represents a vital problem within industrial production. A bearing fault diagnosis method based on effectively improved NGO algorithm opt...
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ISBN:
(数字)9798350352894
ISBN:
(纸本)9798350352900
Precisely identifying the fault-related operating state of the gear-box bearing represents a vital problem within industrial production. A bearing fault diagnosis method based on effectively improved NGO algorithm optimized VMD and CNN-BILSTM-Cross Attention is proposed for bearing fault diagnosis. The method improves the NGO algorithm by introducing the SMP chaotic mapping to initialize the eagle swarm distribution, introducing the sine function and the inverse learning strategy to enhance the algorithm’s computational precision and search ability. The CNN-BILSTM model is integrated with the Cross Attention module, which aims to merge the bearing signal features in multiple ways and boost the model’s accuracy and robustness. The improved SCNGO algorithm is utilized to iteratively optimize crucial parameters, namely the intrinsic mode coefficient, and the quadratic penalty term [K, α]within the VMD denoising technology. The CNN is responsible for seizing the spatial features of the bearing vibration signal at diverse frequencies, while the BILSTM is in charge of capturing the temporal features of the same signal. Moreover, the Cross Attention mechanism is introduced to blend the spatial features extracted by the CNN and the temporal features obtained by the BILSTM. In comparison to other models, the proposed model demonstrates stronger generalization and robustness.
Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable *** Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf ***,current DL me...
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Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable *** Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf ***,current DL methods often require substantial computational resources,hindering their application on resource-constrained *** propose the Deep Tomato Detection Network(DTomatoDNet),a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome *** Convn kernels used in the proposed(DTomatoDNet)framework is 1×1,which reduces the number of parameters and helps in more detailed and descriptive feature extraction for *** proposed DTomatoDNet model is trained from scratch to determine the classification success rate.10,000 tomato leaf images(1000 images per class)from the publicly accessible dataset,covering one healthy category and nine disease categories,are utilized in training the proposed DTomatoDNet *** specifically,we classified tomato leaf images into Target Spot(TS),Early Blight(EB),Late Blight(LB),Bacterial Spot(BS),Leaf Mold(LM),Tomato Yellow Leaf Curl Virus(YLCV),Septoria Leaf Spot(SLS),Spider Mites(SM),Tomato Mosaic Virus(MV),and Tomato Healthy(H).The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%,demonstrating excellent accuracy in differentiating between tomato *** model could be used on mobile platforms because it is lightweight and designed with fewer *** farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.
In dynamic meteorological prediction, accurate rainfall forecasting is a mystery. In a complex and dynamic natural environment with unpredictable sky movements, we propose an innovative methodology that forecasts week...
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Convolutional Neural Network (CNN) is one of the deep learning architectures that is very effective for handling images. CNN is able to automatically extract important features from images, making it very suitable for...
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This paper explores strategies to improve the energy efficiency and operational longevity of Wireless Sensor Networks (WSNs) through the optimization of clustering techniques and energy efficient routing protocols. Th...
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Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to dete...
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Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or *** cardiotocography measures infer wrongly and give wrong predictions because of human *** traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as *** condition is very important to measure at numerous stages and give proper medications to the fetus for its *** the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional *** techniques play a pivotal role in detecting fetal disease in its early *** research article uses Federated machine learning(FML)and ML techniques to classify the condition of the *** study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the ***,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,***,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results.
This study presents a novel approach for brain MRI classification by integrating multiple state-of-the-art deep learning (DL) architectures, including VGG16, EfficientNet, MobileNet, AlexNet, and ResNet50, with an att...
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The application of deep learning to the classification of garbage images has been demonstrated to significantly enhance the accuracy and efficiency of classification through the extraction of features and the recognit...
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ISBN:
(数字)9798350352894
ISBN:
(纸本)9798350352900
The application of deep learning to the classification of garbage images has been demonstrated to significantly enhance the accuracy and efficiency of classification through the extraction of features and the recognition of patterns. However, in environments with limited resources or computational power, traditional models are unable to effectively handle complex trash image classification tasks. Consequently, the deployment of lightweight models in garbage image classification h as g arnered increasing interest. In this paper, the structure of the RepVGG model is optimized by combining depthwise separable convolution. Adapting the convolutional layers of the original trunk structure to training branches and replacing the convolutional kernel of the trunk with a 5 × 5 convolutional kernel results in the trunk and branches being reparameterized to a single 5 × 5 convolutional layer after training. This design enhances the image feature extraction capability at multiple levels. The inference structure retains the simple stacking of the original convolutional and ReLU layers. In this paper, the improved model is named DSC5RepVGG. The experimental results demonstrate that DSC5RepVGG reduces the number of parameters by 60.97% in comparison to RepVGG, reduces the theoretical floating-point computation by 58.22%, and improves the Top-1 accuracy by 1.29% on Kaggle’s Garbage Classification dataset.
Aims to address the problems of low accuracy of existing garbage classification and low classification ac curacy of models, large model size and difficulty of deployment on portable devices. A lightweight garbage imag...
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ISBN:
(数字)9798350352894
ISBN:
(纸本)9798350352900
Aims to address the problems of low accuracy of existing garbage classification and low classification ac curacy of models, large model size and difficulty of deployment on portable devices. A lightweight garbage image classification model is proposed by merging the improved lightweight model ShuffleNet V2 and MobileViT. Firstly, the structure of the ShuffleNet V2 model is adjusted and improved to enhance the cross-channel information interaction ability of the model, which improves the feature information interaction ability between the channels of the ShuffleNet V 2 model; secondly, the improved ShuffleNet V2 model is fused with the MobileViT model, the advantage of the MobileViT module in global feature information extraction is used to overcome the shortcomings of the ShuffleNet V2 network, which is difficult to extract global feature information. Comparative experiments are conducted on public datasets to evaluate the performance of the model. The experimental results show that the accuracy of the proposed SNViT model is significantly better than the D enseNet-121 model, and also has a better classification performance compared to EfficientNet V2, MobileNet V2 and other models, while the precision rate, recall rate and F1 value are also improved. The improved SNViT model improves the accuracy of the image classification task and achieves high classification performance, while the number of parameters is small and easy to train and deploy.
Cities are facing challenges of high rise in population number and con-sequently need to be equipped with latest smart services to provide luxuries of life to its *** integrated solutions are also a need to deal with ...
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Cities are facing challenges of high rise in population number and con-sequently need to be equipped with latest smart services to provide luxuries of life to its *** integrated solutions are also a need to deal with the social and environmental challenges,caused by increasing ***,the development of smart services’integrated network,within a city,is facing the bar-riers including;less efficient collection and sharing of data,along with inadequate collaboration of software and *** to resolve these issues,this paper recommended a solution for a synchronous functionality in the smart services’integration process through modeling *** this integration modeling solution,atfirst,the service participants,processes and tasks of smart services are identified and then standard illustrations are developed for the better understand-ing of the integrated service group *** process modeling and notation(BPMN)language based models are developed and discussed for a devised case study,to test and experiment i.e.,for remote healthcare from a smart *** research is concluded with the integration process model application for the required data sharing among different service *** outcomes of the modeling are better understanding and attaining maximum automation that can be referenced and replicated.
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