Search

Aiming at the problem that DGA domain name is difficult to detect effectively, a hybrid model based on bidirectional gated recurrent unity and multi-channel convolutional neural network is proposed for DGA domain name detection. The model consists of three parts: the character embedding layer, the feature extraction layer and the classification prediction layer. The character embedding layer completes the automatic encoding of the input characters; the feature extraction layer uses BiGRU to learn the dependency between data features and the MCNN uses different neural network channels to learn information from various aspects to obtain deep hidden information for automatic extraction. The characteristics of the input characters; the classification layer uses a three-layer fully connected neural network to achieve automatic prediction classification of DGA domain names. The experimental results show that the model achieves an accuracy of 92.27%, which improves the accuracy of detection compared with other deep learning methods.

There is a lack of knowledge about which risk factors are more important in West Nile virus (WNV) transmission and risk magnitude. A better understanding of the risk factors is of great help in developing effective new technologies and appropriate prevention strategies for WNV infection. A contribution analysis of all risk factors in WNV infection would identify those major risk factors. Based on the identified major risk factors, measures to control WNV proliferation could be directed toward those significant risk factors, thus improving the effectiveness and efficiency in developing WNV control and prevention strategies. Neural networks have many generally accepted advantages over conventional analytical techniques, for instance, ability to automatically learn the relationship between the inputs and outputs from training data, powerful generalization ability, and capability of handling nonlinear interactions. In this article, a neural network model was developed for analysis of risk factors in WNV infection. To reveal the relative contribution of the input variables, the neural network was trained using an algorithm called structural learning with forgetting. During the learning, weak neural connections are forced to fade away while a skeletal network with strong connections emerges. The significant risk factors can be identified by analyzing this skeletal network. The proposed approach is tested with the dead bird surveillance data in Ontario, Canada. The results demonstrate the effectiveness of the proposed approach.

A technique of intelligent computation of moisture content in shrinkable biomaterials from multiple predictors was developed. All measurable predictors were structured in three sets: biomaterial properties (volume, density, porosity, diffusivity); drying conditions (time, air temperature, humidity, velocity, pressure); and drying technologies. Two typical drying models were considered: time-dependent (thermodynamical) and time-independent (relational). The relationship between predictors and moisture content was established on the basis of multi-factorial linear regression (MLR) and neural networks (NN). Accuracy of statistical approximation was strongly dependent on drying model and chosen set of predictors. Time-independent models demonstrated better accuracy (MSE = 0.214) than time-dependent models (MSE = 0.254). Redundant predictors did not affect the accuracy and generalization ability of statistical models. Results of NN training and testing showed superior accuracy with respect to statistical model...

In livestock farming, odour measurement and reduction are necessary for a cleaner environment, lower health risks to humans, and higher quality of livestock production. There have been many studies on modelling of livestock farm odour by analysing the chemical components in odorous air. It is suggested that the component analysis approaches should be extended to factors such as temperature, relative humidity, and airflow speed. In this paper, a neuro-fuzzy-based method for analysing odour generation factors to the perception of livestock farm odour was proposed. The proposed approach incorporates neuro-adaptive learning techniques into fuzzy logic method. Rather than choosing the parameters associated with a given membership function by trail and error, these parameters could be tuned automatically in a systematic manner so as to adjust the membership functions of the input/output variables for optimal system performance. A multi-factor livestock farm odour model was developed, and both numeric factors and linguistic factors were considered. The proposed approach was tested with a livestock farm odour database. The results demonstrated the effectiveness of the proposed approach in comparison to a typical neural network model.

In pig farming, odour measurement and reduction are necessary for a cleaner environment, lower health risks to humans, and higher quality of pig production. There have been many studies on the modelling of pork farm odour by analysing the chemical components in odorous air. It is suggested that the component analysis approach should be extended to factors such as temperature, relative humidity, and airflow speed. However, previous component and factor analysis did not examine which components or factors contribute significantly to a complex odour. A relative contribution analysis of potential odour components and factors to the perception of odour would allow identification of significant odour components and major contributing factors. Odour reduction practice for pork farms could then be directed towards the significant components and factors, thus improving the efficiency in developing odour measurement and reduction technologies. It is generally accepted that neural networks have several advantages over conventional techniques, for instance, their ability to automatically learn the relationship between the inputs and outputs without any previous knowledge of the system being studied, their powerful generalisation ability, and their capability of handling non-linear interactions. However, typical neural network models suffer from the so-called ‘black box’ problem, i.e. it offers no information about the system other than the input/output relationship. In this paper, existing methods for odour strength prediction were reviewed, and a neural network based multi-component multi-factor odour model was developed. To reveal the relative contribution of the inputs, the neural network was trained using an algorithm called structural learning with forgetting. By applying the structural learning with forgetting based algorithm, unnecessary neural connections faded away and a skeletal network emerged. The resulted skeletal network enabled an analysis of the contribution of components and factors. The effectiveness of the proposed approach was demonstrated by simulation and comparison studies.

Final prediction accuracy is greatly influenced by the predictive error of individual RBF network output in the process of RBF Neural Network Ensemble Prediction. The predictive value of individual RBF network model with the help of SAS statistical analysis software is analyzed, and the prediction values that are unreliable are removed out, Therefore the final prediction accuracy is increased. The PMI is predicted based on the improved model and former model, respectively. The results show that the relative prediction error is reduced by about 0.2%.

Detecting and monitoring odour around livestock farms are difficult. In this paper, a dynamic neural network based model is proposed to locate odour dispersion around livestock facilities. The proposed dispersion model can dynamically represent complex or non-steady-state meteorological and topographical features in and around livestock farm areas. The proposed approach can also model odour dispersions from multiple odour sources, and from various types of sources such like point source, line source, and area source. In addition, the proposed model simulates the odour dispersion through the dynamic neural activity landscape, without explicitly additional models of the dynamic environment, odour sources, and farming activities.

A novel neural network based approach for risk factor analysis of infection of West Nile virus (WNV) is proposed. A multi-factor risk analysis model is developed and learnt by an algorithm called structural learning with forgetting. Through the learning, unnecessary connections fade away and a skeletal network emerges. By analyzing the resulted skeletal networks, significant risk factors can be identified, and thus a more thorough understanding of WNV transmission mechanism can be obtained. The proposed approach is tested with a dead birds surveillance data. The results demonstrate the effectiveness of the proposed approach.

A novel neural network based approach to analysing contributions of odour components and factors to the perception of pork farm odour is proposed. A multi-component multi-factor odour analysis model is developed and learnt by an algorithm called structural learning with forgetting. Through the learning, unnecessary connections fade away and a skeletal network emerges. By analysing the resulting skeletal networks significant odour components and factors can be identified, and thus a more thorough understanding of odour model can be obtained. The proposed approach is tested with a pork farm odour database. The results demonstrate the effectiveness of the proposed approach.