scholarly journals Detection and Analysis of Sow Targets Based on Image Vision

Agriculture ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 73
Author(s):  
Kaidong Lei ◽  
Chao Zong ◽  
Ting Yang ◽  
Shanshan Peng ◽  
Pengfei Zhu ◽  
...  
Keyword(s):  
Large Scale ◽  
Recognition Rate ◽  
Model Parameters ◽  
Livestock Farming ◽  
Data Set ◽  
Attention Model ◽  
Precision Livestock Farming ◽  
Area Distribution ◽  
Complex Scenes ◽  
Detection And Recognition

In large-scale sow production, real-time detection and recognition of sows is a key step towards the application of precision livestock farming techniques. In the pig house, the overlap of railings, floors, and sows usually challenge the accuracy of sow target detection. In this paper, a non-contact machine vision method was used for sow targets perception in complex scenarios, and the number position of sows in the pen could be detected. Two multi-target sow detection and recognition models based on the deep learning algorithms of Mask-RCNN and UNet-Attention were developed, and the model parameters were tuned. A field experiment was carried out. The data-set obtained from the experiment was used for algorithm training and validation. It was found that the Mask-RCNN model showed a higher recognition rate than that of the UNet-Attention model, with a final recognition rate of 96.8% and complete object detection outlines. In the process of image segmentation, the area distribution of sows in the pens was analyzed. The position of the sow’s head in the pen and the pixel area value of the sow segmentation were analyzed. The feeding, drinking, and lying behaviors of the sow have been identified on the basis of image recognition. The results showed that the average daily lying time, standing time, feeding and drinking time of sows were 12.67 h(MSE 1.08), 11.33 h(MSE 1.08), 3.25 h(MSE 0.27) and 0.391 h(MSE 0.10), respectively. The proposed method in this paper could solve the problem of target perception of sows in complex scenes and would be a powerful tool for the recognition of sows.

Automated Text Detection and Recognition in Annotated Biomedical Publication Images

2014 ◽  
Vol 9 (2) ◽  
pp. 34-63 ◽  
Author(s):  
Soumya De ◽  
R. Joe Stanley ◽  
Beibei Cheng ◽  
Sameer Antani ◽  
Rodney Long ◽  
...  
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Rate ◽  
Regions Of Interest ◽  
Correct Recognition ◽  
Support Vector ◽  
Data Set ◽  
Biomedical Publication ◽  
Biomedical Publications ◽  
Detection And Recognition

Images in biomedical publications often convey important information related to an article's content. When referenced properly, these images aid in clinical decision support. Annotations such as text labels and symbols, as provided by medical experts, are used to highlight regions of interest within the images. These annotations, if extracted automatically, could be used in conjunction with either the image caption text or the image citations (mentions) in the articles to improve biomedical information retrieval. In the current study, automatic detection and recognition of text labels in biomedical publication images was investigated. This paper presents both image analysis and feature-based approaches to extract and recognize specific regions of interest (text labels) within images in biomedical publications. Experiments were performed on 6515 characters extracted from text labels present in 200 biomedical publication images. These images are part of the data set from ImageCLEF 2010. Automated character recognition experiments were conducted using geometry-, region-, exemplar-, and profile-based correlation features and Fourier descriptors extracted from the characters. Correct recognition as high as 92.67% was obtained with a support vector machine classifier, compared to a 75.90% correct recognition rate with a benchmark Optical Character Recognition technique.

scholarly journals A systematic comparison of community detection algorithms for measuring selective exposure in co-exposure networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Subhayan Mukerjee
Keyword(s):  
Community Detection ◽  
Large Scale ◽  
Selective Exposure ◽  
Model Parameters ◽  
Data Set ◽  
Media Environment ◽  
Systematic Comparison ◽  
Detection Techniques ◽  
Detection Algorithms ◽  
Audience Fragmentation

AbstractThe use of community detection techniques for understanding audience fragmentation and selective exposure to information has received substantial scholarly attention in recent years. However, there exists no systematic comparison, that seeks to identify which of the many community detection algorithms are the best suited for studying these dynamics. In this paper, I address this question by proposing a formal mathematical model for audience co-exposure networks by simulating audience behavior in an artificial media environment. I show how a variety of synthetic audience overlap networks can be generated by tuning specific parameters, that control various aspects of the media environment and individual behavior. I then use a variety of community detection algorithms to characterize the level of audience fragmentation in these synthetic networks and compare their performances for different combinations of the model parameters. I demonstrate how changing the manner in which co-exposure networks are constructed significantly improves the performances of some of these algorithms. Finally, I validate these findings using a novel empirical data-set of large-scale browsing behavior. The contributions of this research are two-fold: first, it shows that two specific algorithms, FastGreedy and Multilevel are the best suited for measuring selective exposure patterns in co-exposure networks. Second, it demonstrates the use of formal modeling for informing analytical choices for better capturing complex social phenomena.

Precision Agriculture in Latvia in the Last 20 Years

2017 ◽  
Vol 8 (2) ◽  
pp. 698-702
Author(s):  
A. Gailums
Keyword(s):  
Precision Agriculture ◽  
Large Scale ◽  
Precision Farming ◽  
Livestock Farming ◽  
Precision Livestock Farming ◽  
Crop Farming ◽  
Automatic Steering ◽  
Fruit Growing

The establishment of the system Soil – Yield that occured in Latvia during the 1970–80s could be considered as the beginning of precision farming with the available technologies. The first precision farming technologies have been associated with harvesting where combines and tractors with the automatic steering were used. The precision agriculture in Latvia includes various branches. Latvia farmers are using precision crop farming, precision livestock farming, precision fruit growing, precision bee keeping, precision farming greenhouse and precision growing berries. Precision farming technologies in Latvia are introduced mainly in large scale farms, with more than 1000 ha. The most important researches in precision farming in Latvia were done in 2000s.

scholarly journals Measuring Selective Exposure: A Systematic Comparison of Community Detection Algorithms in Coexposure Networks

2020 ◽  
Author(s):  
Subhayan Mukerjee
Keyword(s):  
Community Detection ◽  
Large Scale ◽  
Selective Exposure ◽  
Model Parameters ◽  
Data Set ◽  
Media Environment ◽  
Detection Algorithms ◽  
Audience Fragmentation ◽  
The Media ◽  
Browsing Behavior

The use of network analytic techniques for understanding audience fragmentation and selective exposure to information has received substantial scholarly attention in recent years. In this paper, I propose a formal mathematical model for audience co-exposure networks by simulating audience behavior in an artificial media environment. I show how a variety of synthetic audience overlap networks can be generated by tuning specific parameters, that control various aspects of the media environment and individual behavior. I then use a variety of community detection algorithms to characterize the level of audience fragmentation in these synthetic networks and compare their performances for different combinations of the model parameters. Finally, I validate these findings using a novel empirical data-set of actual large-scale browsing behavior and demonstrate the model's utility in informing future analytical choices.

scholarly journals A Study of Spatial Attention and Squeeze Excitation Block Fusion Improved ResNet for Identifying Bank Notes

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Junjun Huo
Keyword(s):  
Spatial Attention ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Recognition System ◽  
Model Parameters ◽  
Recognition Method ◽  
Bank Note ◽  
Attention Model ◽  
Spatial Domains ◽  
Improved Model

Based on deep learning and digital image processing algorithms, we design and implement an accurate automatic recognition system for bank note text and propose an improved recognition method based on ResNet for the problems of difficult image text extraction and insufficient recognition accuracy. Firstly, a deep hyperparameterized convolution (DO-Conv) is used instead of the traditional convolution in the network to improve the recognition rate while reducing the model parameters. Then, the spatial attention model (SAM) and the squeezed excitation block (SE-Block) are fused and applied to a modified ResNet to extract detailed features of bank note images in the channel and spatial domains. Finally, the label-smoothed cross-entropy (LSCE) loss function is used to train the model to automatically calibrate the network to prevent classification errors. The experimental results demonstrate that the improved model is not easily affected by the image quality, and the model in this paper has good performance in text detection and recognition in specific business ticket scenarios.

EXPONENTIATED HALF-LOGISTIC LOMAX DISTRIBUTION WITH PROPERTIES AND APPLICATION

2019 ◽  
Vol XVI (2) ◽  
pp. 1-11
Author(s):  
Farrukh Jamal ◽  
Hesham Mohammed Reyad ◽  
Soha Othman Ahmed ◽  
Muhammad Akbar Ali Shah ◽  
Emrah Altun
Keyword(s):  
Real Data ◽  
Continuous Model ◽  
Model Parameters ◽  
Data Set ◽  
Lomax Distribution ◽  
Mathematical Properties ◽  
Proposed Model ◽  
Probability Weighted Moment ◽  
Record Statistics ◽  
Maximum Likelihood Criterion

A new three-parameter continuous model called the exponentiated half-logistic Lomax distribution is introduced in this paper. Basic mathematical properties for the proposed model were investigated which include raw and incomplete moments, skewness, kurtosis, generating functions, Rényi entropy, Lorenz, Bonferroni and Zenga curves, probability weighted moment, stress strength model, order statistics, and record statistics. The model parameters were estimated by using the maximum likelihood criterion and the behaviours of these estimates were examined by conducting a simulation study. The applicability of the new model is illustrated by applying it on a real data set.

ProGen:Provenance database generator for large-scale data set

2009 ◽  
Vol 28 (11) ◽  
pp. 2737-2740
Author(s):  
Xiao ZHANG ◽  
Shan WANG ◽  
Na LIAN
Keyword(s):  
Large Scale ◽  
Data Set ◽  
Large Scale Data ◽  
Scale Data

Integrative Data Analysis from a Unifying Research Synthesis Perspective

2018 ◽  
Author(s):  
Eun-Young Mun ◽  
Anne E. Ray
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Research Synthesis ◽  
Alcohol Intervention ◽  
Data Set ◽  
Integrative Data Analysis ◽  
Level Data ◽  
Model Complex ◽  
Wide Range ◽  
Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

Financial distress determinants among SMEs: empirical evidence from Sweden

2020 ◽  
Vol 47 (3) ◽  
pp. 547-560 ◽  
Author(s):  
Darush Yazdanfar ◽  
Peter Öhman
Keyword(s):  
Financial Crisis ◽  
Financial Distress ◽  
Large Scale ◽  
Global Financial Crisis ◽  
Binary Logistic Regression ◽  
Data Availability ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
The Global Financial Crisis

PurposeThe purpose of this study is to empirically investigate determinants of financial distress among small and medium-sized enterprises (SMEs) during the global financial crisis and post-crisis periods.Design/methodology/approachSeveral statistical methods, including multiple binary logistic regression, were used to analyse a longitudinal cross-sectional panel data set of 3,865 Swedish SMEs operating in five industries over the 2008–2015 period.FindingsThe results suggest that financial distress is influenced by macroeconomic conditions (i.e. the global financial crisis) and, in particular, by various firm-specific characteristics (i.e. performance, financial leverage and financial distress in previous year). However, firm size and industry affiliation have no significant relationship with financial distress.Research limitationsDue to data availability, this study is limited to a sample of Swedish SMEs in five industries covering eight years. Further research could examine the generalizability of these findings by investigating other firms operating in other industries and other countries.Originality/valueThis study is the first to examine determinants of financial distress among SMEs operating in Sweden using data from a large-scale longitudinal cross-sectional database.

scholarly journals Relationship between large-scale ionospheric field-aligned currents and electron/ion precipitations: DMSP observations

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Chao Xiong ◽  
Claudia Stolle ◽  
Patrick Alken ◽  
Jan Rauberg
Keyword(s):  
Time Distribution ◽  
Large Scale ◽  
Particle Flux ◽  
Particle Energy ◽  
Lower Latitude ◽  
Particle Precipitation ◽  
Data Set ◽  
The Mean ◽  
Two Parameters ◽  
Meteorological Satellite

Abstract In this study, we have derived field-aligned currents (FACs) from magnetometers onboard the Defense Meteorological Satellite Project (DMSP) satellites. The magnetic latitude versus local time distribution of FACs from DMSP shows comparable dependences with previous findings on the intensity and orientation of interplanetary magnetic field (IMF) By and Bz components, which confirms the reliability of DMSP FAC data set. With simultaneous measurements of precipitating particles from DMSP, we further investigate the relation between large-scale FACs and precipitating particles. Our result shows that precipitation electron and ion fluxes both increase in magnitude and extend to lower latitude for enhanced southward IMF Bz, which is similar to the behavior of FACs. Under weak northward and southward Bz conditions, the locations of the R2 current maxima, at both dusk and dawn sides and in both hemispheres, are found to be close to the maxima of the particle energy fluxes; while for the same IMF conditions, R1 currents are displaced further to the respective particle flux peaks. Largest displacement (about 3.5°) is found between the downward R1 current and ion flux peak at the dawn side. Our results suggest that there exists systematic differences in locations of electron/ion precipitation and large-scale upward/downward FACs. As outlined by the statistical mean of these two parameters, the FAC peaks enclose the particle energy flux peaks in an auroral band at both dusk and dawn sides. Our comparisons also found that particle precipitation at dawn and dusk and in both hemispheres maximizes near the mean R2 current peaks. The particle precipitation flux maxima closer to the R1 current peaks are lower in magnitude. This is opposite to the known feature that R1 currents are on average stronger than R2 currents.

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