scholarly journals A Temporal Network Model for Livestock Trade Systems

2021 ◽  
Vol 8 ◽  
Author(s):  
Sara Ansari ◽  
Jobst Heitzig ◽  
Laura Brzoska ◽  
Hartmut H. K. Lentz ◽  
Jakob Mihatsch ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Network Model ◽  
Experimental Results ◽  
Temporal Network ◽  
Structure And Dynamics ◽  
Node Centrality ◽  
Trading Activities ◽  
Disease Spreading ◽  
Animal Trade ◽  
Livestock Trade

The movements of animals between farms and other livestock holdings for trading activities form a complex livestock trade network. These movements play an important role in the spread of infectious diseases among premises. For studying the disease spreading among animal holdings, it is of great importance to understand the structure and dynamics of the trade system. In this paper, we propose a temporal network model for animal trade systems. Furthermore, a novel measure of node centrality important for disease spreading is introduced. The experimental results show that the model can reasonably well describe these spreading-related properties of the network and it can generate crucial data for research in the field of the livestock trade system.

scholarly journals Spreading predictability in complex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Na Zhao ◽  
Jian Wang ◽  
Yong Yu ◽  
Jun-Yan Zhao ◽  
Duan-Bing Chen
Keyword(s):  
Infectious Diseases ◽  
Prediction Model ◽  
Complex Networks ◽  
State Of The Art ◽  
Experimental Results ◽  
Vaccination Strategies ◽  
Macro Scale ◽  
Good Consistency ◽  
The Future ◽  
Rumor Control

AbstractMany state-of-the-art researches focus on predicting infection scale or threshold in infectious diseases or rumor and give the vaccination strategies correspondingly. In these works, most of them assume that the infection probability and initially infected individuals are known at the very beginning. Generally, infectious diseases or rumor has been spreading for some time when it is noticed. How to predict which individuals will be infected in the future only by knowing the current snapshot becomes a key issue in infectious diseases or rumor control. In this report, a prediction model based on snapshot is presented to predict the potentially infected individuals in the future, not just the macro scale of infection. Experimental results on synthetic and real networks demonstrate that the infected individuals predicted by the model have good consistency with the actual infected ones based on simulations.

A neural network model of the structure and dynamics of human personality.

2010 ◽  
Vol 117 (1) ◽  
pp. 61-92 ◽  
Author(s):  
Stephen J. Read ◽  
Brian M. Monroe ◽  
Aaron L. Brownstein ◽  
Yu Yang ◽  
Gurveen Chopra ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Human Personality ◽  
Structure And Dynamics

scholarly journals Computer Assisted Chord Detection Using Deep Learning and YOLOV4 Neural Network Model

2021 ◽  
Vol 2083 (4) ◽  
pp. 042017
Author(s):  
Yingdong Ru
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Training Model ◽  
Experimental Results ◽  
Fine Tuning ◽  
Computer Assisted ◽  
Single Voice ◽  
Music Information ◽  
Chord Recognition

Abstract Music symbol recognition is an important part of Optical Music Recognition (OMR), Chord recognition is one of the most important research contents in the field of music information retrieval. It plays an important role in information processing, music structure analysis, and recommendation systems. Aiming at the problem of low chord recognition accuracy in the OMR recognition model, the article proposes a chord recognition method based on the YOLOV4 neural network model. First, the YOLOV4 network model is used to train single-voice scores to obtain the best training model. Then, the scores containing chords are trained through neural network fine-tuning technology. The experimental results show that the method recognizes the chords with great results, the model was tested on the test set generated by MuseScore. The experimental results show that the accuracy of note recognition is high, which can reach the accuracy of duration value of 0.96 which is higher than the accuracy of note recognition of other score recognition models.

scholarly journals Exploiting the Relationship between Pruning Ratio and Compression Effect for Neural Network Model Based on TensorFlow

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Bo Liu ◽  
Qilin Wu ◽  
Yiwen Zhang ◽  
Qian Cao
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Prediction Accuracy ◽  
Computing Time ◽  
Experimental Results ◽  
Data Set ◽  
Compression Effect ◽  
Model Based ◽  
The Relationship

Pruning is a method of compressing the size of a neural network model, which affects the accuracy and computing time when the model makes a prediction. In this paper, the hypothesis that the pruning proportion is positively correlated with the compression scale of the model but not with the prediction accuracy and calculation time is put forward. For testing the hypothesis, a group of experiments are designed, and MNIST is used as the data set to train a neural network model based on TensorFlow. Based on this model, pruning experiments are carried out to investigate the relationship between pruning proportion and compression effect. For comparison, six different pruning proportions are set, and the experimental results confirm the above hypothesis.

BP Neural Network Model for early Diagnosis of Kawasaki Disease

2012 ◽  
Vol 468-471 ◽  
pp. 723-726 ◽  
Author(s):  
Jiang Huang ◽  
Jian Feng Chen
Keyword(s):  
Neural Network ◽  
Kawasaki Disease ◽  
Early Diagnosis ◽  
Infectious Diseases ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Clinical Symptoms ◽  
Laboratory Data ◽  
Bp Neural Network Model

In order to diagnose Kawasaki Disease during early phase, clinical symptoms (temperature, rash, conjunctival injection, erythema of thelips, and oral mucosal changes) and laboratory data (white blood cell, neutrophil, platelet, high sensitive c-reactive protein, and erythrocyte sedimentation rate) of 138 children with Kawasaki disease or infectious diseases were used to develop a BP neural network model. 90 random cases were trained using MATLAB software for setting up the BP neural network model. The other 48 cases were analyzed to predict Kawasaki disease using this model. Results showed that the predict accuracy in patients with Kawasaki disease and children with infectious diseases are 95.6% and 88%, respectively. Our result indicates that the BP neural network model is likely to provide an accurate test for early diagnosis of Kawasaki disease.

A Microscale Collagen-Fibrin Interacting Network Model With Comparison to Experimental Results

2012 ◽  
Author(s):  
Jeffrey D. Hyypio ◽  
Mohammad F. Hadi ◽  
Victor K. Lai ◽  
Victor H. Barocas
Keyword(s):  
Mechanical Loading ◽  
Network Model ◽  
Computer Model ◽  
Soft Tissues ◽  
Uniaxial Extension ◽  
Tissue Mechanics ◽  
Experimental Results ◽  
Large Strains ◽  
Heterogeneous Nature ◽  
Engineered Tissue

Many native and bioengineered soft tissues are composed of two or more types of biopolymer networks that mechanically define and support the material [1]. Modeling the response of multi-network soft tissues to mechanical loading can be difficult due to the heterogeneous nature of these materials and the large strains (>1) involved. As tissues deform, the different biopolymer networks interact with one another and determine the overall stress-strain outcome for the tissue. Capturing this interaction could help improve the accuracy of a computer model to simulate the microscale behavior of soft tissues under load. We have developed a two-network model to reflect interactions between collagen and fibrin biopolymer networks loaded in uniaxial extension. The model can help improve our understanding of native and engineered tissue mechanics.

scholarly journals SOCIAL DISTANCING STRATEGIES AGAINST DISEASE SPREADING

Fractals ◽  
2013 ◽  
Vol 21 (03n04) ◽  
pp. 1350019 ◽  
Author(s):  
L. D. VALDEZ ◽  
C. BUONO ◽  
P. A. MACRI ◽  
L. A. BRAUNSTEIN
Keyword(s):  
Infectious Diseases ◽  
Healthy Individual ◽  
Percolation Theory ◽  
Quenched Disorder ◽  
Epidemic Spreading ◽  
Heterogeneous Distribution ◽  
Percolation Process ◽  
Social Distancing ◽  
Disease Spreading

The recurrent infectious diseases and their increasing impact on the society has promoted the study of strategies to slow down the epidemic spreading. In this review we outline the applications of percolation theory to describe strategies against epidemic spreading on complex networks. We give a general outlook of the relation between link percolation and the susceptible-infected-recovered model, and introduce the node void percolation process to describe the dilution of the network composed by healthy individual, i.e., the network that sustain the functionality of a society. Then, we survey two strategies: the quenched disorder strategy where an heterogeneous distribution of contact intensities is induced in society, and the intermittent social distancing strategy where health individuals are persuaded to avoid contact with their neighbors for intermittent periods of time. Using percolation tools, we show that both strategies may halt the epidemic spreading. Finally, we discuss the role of the transmissibility, i.e., the effective probability to transmit a disease, on the performance of the strategies to slow down the epidemic spreading.

Wavelet transformed Gaussian network model

2014 ◽  
Vol 13 (06) ◽  
pp. 1450053 ◽  
Author(s):  
Meng Zhan ◽  
Suhong Li ◽  
Fan Li
Keyword(s):  
Protein Dynamics ◽  
Network Model ◽  
Protein Structures ◽  
Network Models ◽  
Experimental Results ◽  
Gaussian Network Model ◽  
The Mean ◽  
Dynamics And Function ◽  
And Function ◽  
Gaussian Network

Accurate prediction of the Debye–Waller temperature factor of proteins is of significant importance in the study of protein dynamics and function. This work explores the utility of wavelets for improving the performance of Gaussian network model (GNM). We propose two wavelet transformed Gaussian network models (wtGNM), namely a scale-one wtGNM and a scale-two wtGNM. Based on a set of 113 protein structures, it shows that the mean correlation with experimental results for the scale-one wtGNM is 0.714 and that for the scale-two wtGNM is 0.738. In contrast, the mean correlation for the original GNM is 0.594. Therefore, the wtGNM is a potential algorithm for improving the GNM prediction of protein B-factors.

scholarly journals Efficacy-specific Herbal Group Detection from Traditional Chinese Medicine Prescriptions via Hierarchical Attentive Neural Network Model

2020 ◽  
Author(s):  
Li Chen ◽  
Xinglong Liu ◽  
Siyuan Zhang ◽  
Hong Yi ◽  
Yongmei Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Network Model ◽  
Neural Network Model ◽  
Experimental Results ◽  
Frequent Patterns ◽  
Two Stage ◽  
Second Stage ◽  
Group Detection

Abstract Background: Mining massive prescriptions in Traditional Chinese Medicine (TCM) accumulated in the lengthy period of several thousand years to discover essential herbal groups for distinct efficacies is of significance for TCM modernization, thus starting to draw attentions recently. However, most existing methods for the task treat herbs with different surface forms orthogonally and determine efficacy-specific herbal groups based on the raw frequencies an herbal group occur in a collection of prescriptions. Such methods entirely overlook the fact that prescriptions in TCM are formed empirically by different people at different historical stages, and thus full of herbs with different surface forms expressing the same material, or even noisy and redundant herbs.Methods: We propose a two-stage approach for efficacy-specific herbal group detection from prescriptions in TCM. For the first stage we devise a hierarchical attentive neural network model to capture essential herbs in a prescription for its efficacy, where herbs are encoded with dense real-valued vectors learned automatically to identify their differences on the semantical level. For the second stage, frequent patterns are mined to discover essential herbal groups for an efficacy from distilled prescriptions obtained in the first stage.Results: We verify the effectiveness of our proposed approach from two aspects, the first one is the ability of the hierarchical attentive neural network model to distill a prescription, and the second one is the accuracy in discovering efficacy-specific herbal groups.Conclusion: The experimental results demonstrate that the hierarchical attentive neural network model is capable to capture herbs in a prescription essential to its efficacy, and the distilled prescriptions significantly could improve the performance of efficacy-specific herbal group detection.

Phát hiện mã độc IoT botnet dựa trên đồ thị PSI với mô hình Skip-gram

2020 ◽  
Vol 7 (1) ◽  
pp. 29-36
Author(s):  
Ngô Quốc Dũng ◽  
Lê Văn Hoàng ◽  
Nguyễn Huy Trung
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Experimental Results ◽  
Data Set ◽  
Iot Devices

 Tóm tắt— Trong bài báo này, nhóm tác giả đề xuất một phương pháp phát hiện mã độc IoT botnet dựa trên đồ thị PSI (Printable String Information)  sử dụng mạng nơ-ron tích chập (Convolutional Neural Network - CNN). Thông qua việc phân tích đặc tính của Botnet trên các thiết bị IoT, phương pháp đề xuất xây dựng đồ thị để thể hiện các mối liên kết giữa các PSI, làm đầu vào cho mô hình mạng nơ-ron CNN phân lớp. Kết quả thực nghiệm trên bộ dữ liệu 10033 tập tin ELF gồm 4002 mẫu mã độc IoT botnet và 6031 tập tin lành tính cho thấy phương pháp đề xuất đạt độ chính xác (accuracy) và độ đo F1 lên tới 98,1%. Abstract— In this paper, the authors propose a method for detecting IoT botnet malware based on PSI graphs using Convolutional Neural Network (CNN). Through analyzing the characteristics of Botnet on IoT devices, the proposed method construct the graph to show the relations between PSIs, as input for the CNN neural network model. Experimental results on the 10033 data set of ELF files including 4002 IoT botnet malware samples and 6031 benign files show Accuracy and F1-score up to 98.1%. 

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