In this paper, we study a class of equations representing nonlinear diffusion on networks. A particular instance of our model could be seen as a network equivalent of the porous-medium equation. We are interested in s...
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Many real-world systems give rise to a time series of symbols. The elements in a sequence can be generated by agents walking over a networked space so that whenever a node is visited the corresponding symbol is genera...
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The agricultural sector faces significant challenges due to invasion of pests that damage crops and cause significant loss of production. Traditional methods to detect these insects are cost ineffective, thus, automat...
The agricultural sector faces significant challenges due to invasion of pests that damage crops and cause significant loss of production. Traditional methods to detect these insects are cost ineffective, thus, automated vision systems based on machine learning (ML) have recently been proposed in the literature. However, a significant issue is the lack of a prior dataset to build the ML model on. To mitigate this problem, we propose a new approach to train a model using a small initial dataset and continually improve the accuracy process by retraining it on new images labeled by a mobile application. Retraining is performed on new data, which comes from a mobile application that displays pictures of insects and prompts expert users to label them. The users’ input is used to retrain the model on new coming images. Specifically, our method trains the model on 100 initial images, and retrains it with every 100 new images. The IP102 large-scale dataset for pest recognition was used to demonstrate the effectiveness of the approach. The results show an improvement of accuracy of up to 50 percentage units for the built Convolutional Neural Network (CNN) model.
We study a predator-prey system with a generalist Leslie-Gower predator, a functional Holling type II response, and a weak Allee effect on the prey. The prey’s population often grows much faster than its predator, al...
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Phylogenetic networks play an important role in evolutionary biology as, other than phylogenetic trees, they can be used to accommodate reticulate evolutionary events such as horizontal gene transfer and hybridization...
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In this paper, we have worked on to improve the overall accuracy and the individual accuracies of the given gestures before developing the sign language gestures prediction system for that we have taken a dataset of p...
In this paper, we have worked on to improve the overall accuracy and the individual accuracies of the given gestures before developing the sign language gestures prediction system for that we have taken a dataset of pre-processed images from Kaggle and further applied CNN model with four layers of relu activation function, has three layers of pooling and has a layer of softmax activation and after execution we have got and overall accuracy of 99.95%.
Graph Neural Networks (GNNs) play a fundamental role in many deep learning problems, in particular in cheminformatics. However, typical GNNs cannot capture the concept of chirality, which means they do not distinguish...
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The weak saturation number wsat(n, F ) is the minimum number of edges in a graph on n vertices such that all the missing edges can be activated sequentially so that each new edge creates a copy of F. A usual approach ...
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When extracting any information from a data table with incomplete information, following Lipski we only know the lower and the upper bound of the information. Methods of rough sets that are applied to data tables cont...
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This paper studies stability conditions for neural networks with stochastic impulsive intensity and impulsive density. Firstly, the pth moment exponential stability conditions are given, where the impulsive intensitie...
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ISBN:
(数字)9798331508661
ISBN:
(纸本)9798331508678
This paper studies stability conditions for neural networks with stochastic impulsive intensity and impulsive density. Firstly, the pth moment exponential stability conditions are given, where the impulsive intensities of impulsive neural networks are independent and identically distributed, and the impulsive sequence is defined by impulsive density. Then, taking a linear time-varying impulsive system as an example, it is shown that impulsive sequence defined by average impulsive interval is sometime insufficient in terms of stabilizing such linear time-varying systems. Finally, two examples are presented to verify that impulsive density method is more effective and universal than average impulsive interval method when dealing with time-varying systems.
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