scholarly journals A Spatiotemporal Convolutional Network for Multi-Behavior Recognition of Pigs

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2381
Author(s):  
Dan Li ◽  
Kaifeng Zhang ◽  
Zhenbo Li ◽  
Yifei Chen
Keyword(s):  
Network Architecture ◽  
Statistical Data ◽  
State Of The Art ◽  
Behavior Recognition ◽  
Convolutional Network ◽  
Test Set ◽  
Time Consumption ◽  
And Behavior ◽  
Human Eyes

The statistical data of different kinds of behaviors of pigs can reflect their health status. However, the traditional behavior statistics of pigs were obtained and then recorded from the videos through human eyes. In order to reduce labor and time consumption, this paper proposed a pig behavior recognition network with a spatiotemporal convolutional network based on the SlowFast network architecture for behavior classification of five categories. Firstly, a pig behavior recognition video dataset (PBVD-5) was built by cutting short clips from 3-month non-stop shooting videos, which was composed of five categories of pig’s behavior: feeding, lying, motoring, scratching and mounting. Subsequently, a SlowFast network based spatiotemporal convolutional network for the pig’s multi-behavior recognition (PMB-SCN) was proposed. The results of the networks with variant architectures of the PMB-SCN were implemented and the optimal architecture was compared with the state-of-the-art single stream 3D convolutional network in our dataset. Our 3D pig behavior recognition network showed a top-1 accuracy of 97.63% and a views accuracy of 96.35% on the test set of PBVD and a top-1 accuracy of 91.87% and a views accuracy of 84.47% on a new test set collected from a completely different pigsty. The experimental results showed that this network provided remarkable ability of generalization and possibility for the subsequent pig detection and behavior recognition simultaneously.

A Modified Convolutional Neural Network for Resting-State EEG-Based Schizophrenia Classification with Weighted Electrodes

2020 ◽  
Vol 10 (3) ◽  
pp. 681-687
Author(s):  
Danyang Ma ◽  
Genke Yang ◽  
Zeya Li ◽  
Haichun Liu ◽  
Changchun Pan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Resting State ◽  
Classification Accuracy ◽  
Pathological Study ◽  
Severe Mental Disorder ◽  
Time Consumption ◽  
Weight Value ◽  
And Behavior

Schizophrenia is a severe mental disorder that can result in hallucinations, delusions, and extremely disordered thinking and behavior. While electroencephalography (EEG) has been used as an auxiliary tool for diagnostic purposes in several recent studies, all EEG channels are treated homogeneously without addressing the dominance of certain channels. The main purpose of this study is to obtain the weight value of each channel as the quantitative representation of influence of each scalp area on the classification of schizophrenia phases, and then to apply the weight values to improve the accuracy of classification. We propose a new convolutional neural network (CNN) structure based on AlexNet to derive weight values as weight layer and classify the samples better. Our results show that the modified CNN structure achieves better performance in terms of time consumption and classification accuracy compared with the original classifier. Also, the visualization of the weight layer in our model indicates possible correlations between scalp areas and schizophrenia conditions, which may benefit future pathological study.

scholarly journals Using Non-Additive Entropy to Enhance Convolutional Neural Features for Texture Recognition

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1259
Author(s):  
Joao Florindo ◽  
Konradin Metze
Keyword(s):  
Plant Species ◽  
Leaf Surface ◽  
State Of The Art ◽  
Local Entropy ◽  
Texture Recognition ◽  
Convolutional Network ◽  
Alternative Representation ◽  
Scanned Image ◽  
Deep Learning Features

Here we present a study on the use of non-additive entropy to improve the performance of convolutional neural networks for texture description. More precisely, we introduce the use of a local transform that associates each pixel with a measure of local entropy and use such alternative representation as the input to a pretrained convolutional network that performs feature extraction. We compare the performance of our approach in texture recognition over well-established benchmark databases and on a practical task of identifying Brazilian plant species based on the scanned image of the leaf surface. In both cases, our method achieved interesting performance, outperforming several methods from the state-of-the-art in texture analysis. Among the interesting results we have an accuracy of 84.4% in the classification of KTH-TIPS-2b database and 77.7% in FMD. In the identification of plant species we also achieve a promising accuracy of 88.5%. Considering the challenges posed by these tasks and results of other approaches in the literature, our method managed to demonstrate the potential of computing deep learning features over an entropy representation.

scholarly journals Statistical Analysis of NMR Metabolic Fingerprints: Established Methods and Recent Advances

Metabolites ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 47 ◽  
Author(s):  
Helena Zacharias ◽  
Michael Altenbuchinger ◽  
Wolfram Gronwald
Keyword(s):  
Data Analysis ◽  
Statistical Analysis ◽  
Statistical Data ◽  
State Of The Art ◽  
Data Normalization ◽  
Statistical Data Analysis ◽  
Metabolomics Data ◽  
Pros And Cons ◽  
Zero Sum

In this review, we summarize established and recent bioinformatic and statistical methods for the analysis of NMR-based metabolomics. Data analysis of NMR metabolic fingerprints exhibits several challenges, including unwanted biases, high dimensionality, and typically low sample numbers. Common analysis tasks comprise the identification of differential metabolites and the classification of specimens. However, analysis results strongly depend on the preprocessing of the data, and there is no consensus yet on how to remove unwanted biases and experimental variance prior to statistical analysis. Here, we first review established and new preprocessing protocols and illustrate their pros and cons, including different data normalizations and transformations. Second, we give a brief overview of state-of-the-art statistical analysis in NMR-based metabolomics. Finally, we discuss a recent development in statistical data analysis, where data normalization becomes obsolete. This method, called zero-sum regression, builds metabolite signatures whose estimation as well as predictions are independent of prior normalization.

scholarly journals Long-term recurrent convolutional network violent Behaviour recognition with attention mechanism

2021 ◽  
Vol 336 ◽  
pp. 05013
Author(s):  
Qiming Liang ◽  
Yong Li ◽  
Kaikai Yang ◽  
Xipeng Wang ◽  
Zhi Li
Keyword(s):  
Feature Extraction ◽  
Network Architecture ◽  
Violent Behavior ◽  
Attention Mechanism ◽  
Background Information ◽  
Violent Behaviour ◽  
Behavior Recognition ◽  
Convolutional Network ◽  
Video Information

Violent behavior recognition is an important direction of behavior recognition research. For traditional violent behavior recognition algorithms, there is too much background information when processing video information, which will cause greater interference in feature extraction, so the recognition accuracy is not high. Improved on the basis of effective recurrent convolutional network, a long-term recurrent convolutional network with attention mechanism is proposed. In the video preprocessing stage, a variety of attention mechanisms are introduced. In the feature extraction stage, the lightweight end-to-side neural network architecture GhostNet and convLSTM are selected to build a long-term recurrent convolutional network. The global average pooling and fully connected layer are used in the classification process. The combined approach realizes the classification of behaviours. The final results show that in the Hockey dataset, the algorithm in this paper has increased by 0.4% compared to LRCN, in the RWF-2000 dataset with more samples, it has increased by 10.5% compared to LRCN, and has increased by 1.75% compared to I3D, indicating that the algorithm in this paper can effectively suppress the background information. Interference, improve the performance of the algorithm.

scholarly journals Evaluation of scratch and pre-trained convolutional neural networks for the classification of Tomato plant diseases

2021 ◽  
Vol 10 (2) ◽  
pp. 467
Author(s):  
Mohammad Amimul Ihsan Aquil ◽  
Wan Hussain Wan Ishak
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Tomato Plant ◽  
State Of The Art ◽  
Plant Diseases ◽  
Fine Tuning ◽  
Convolutional Network ◽  
Tomato Diseases

<span id="docs-internal-guid-01580d49-7fff-6f2a-70d1-7893ec0a6e14"><span>Plant diseases are a major cause of destruction and death of most plants and especially trees. However, with the help of early detection, this issue can be solved and treated appropriately. A timely and accurate diagnosis is critical in maintaining the quality of crops. Recent innovations in the field of deep learning (DL), especially in convolutional neural networks (CNNs) have achieved great breakthroughs across different applications such as the classification of plant diseases. This study aims to evaluate scratch and pre-trained CNNs in the classification of tomato plant diseases by comparing some of the state-of-the-art architectures including densely connected convolutional network (Densenet) 120, residual network (ResNet) 101, ResNet 50, ReseNet 30, ResNet 18, squeezenet and Vgg.net. The comparison was then evaluated using a multiclass statistical analysis based on the F-Score, specificity, sensitivity, precision, and accuracy. The dataset used for the experiments was drawn from 9 classes of tomato diseases and a healthy class from PlantVillage. The findings show that the pretrained Densenet-120 performed excellently with 99.68% precision, 99.84% F-1 score, and 99.81% accuracy, which is higher compared to its non-trained based model showing the effectiveness of using a combination of a CNN model with fine-tuning adjustment in classifying crop diseases.</span></span>

scholarly journals Spectral-Spatial Classification of Hyperspectral Images: Three Tricks and a New Learning Setting

2018 ◽  
Vol 10 (7) ◽  
pp. 1156 ◽  
Author(s):  
Jacopo Acquarelli ◽  
Elena Marchiori ◽  
Lutgarde Buydens ◽  
Thanh Tran ◽  
Twan Laarhoven
Keyword(s):  
Network Architecture ◽  
Data Augmentation ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Hyperspectral Images ◽  
Class Label ◽  
Spatial Classification ◽  
Increased Risk ◽  
New Learning

Spectral-spatial classification of hyperspectral images has been the subject of many studies in recent years. When there are only a few labeled pixels for training and a skewed class label distribution, this task becomes very challenging because of the increased risk of overfitting when training a classifier. In this paper, we show that in this setting, a convolutional neural network with a single hidden layer can achieve state-of-the-art performance when three tricks are used: a spectral-locality-aware regularization term and smoothing- and label-based data augmentation. The shallow network architecture prevents overfitting in the presence of many features and few training samples. The locality-aware regularization forces neighboring wavelengths to have similar contributions to the features generated during training. The new data augmentation procedure favors the selection of pixels in smaller classes, which is beneficial for skewed class label distributions. The accuracy of the proposed method is assessed on five publicly available hyperspectral images, where it achieves state-of-the-art results. As other spectral-spatial classification methods, we use the entire image (labeled and unlabeled pixels) to infer the class of its unlabeled pixels. To investigate the positive bias induced by the use of the entire image, we propose a new learning setting where unlabeled pixels are not used for building the classifier. Results show the beneficial effect of the proposed tricks also in this setting and substantiate the advantages of using labeled and unlabeled pixels from the image for hyperspectral image classification.

scholarly journals Developing Deep Learning Models for the Classification of Pediatric Elbow Radiographic Abnormalities of Comparable Performance to Physicians: Strategies for Model Optimization With Small Sized Development Sets

2022 ◽  
Author(s):  
Mark B. TAN ◽  
Russ Y. CHUA ◽  
Qiao FAN ◽  
Marielle V. FORTIER ◽  
Pearlly P. CHANG
Keyword(s):  
Network Architecture ◽  
Model Performance ◽  
P Value ◽  
Model Optimization ◽  
Rater Agreement ◽  
Method Performance ◽  
Test Set ◽  
Physician Group ◽  
Comparable Performance

Abstract BackgroundTo compare the performance of an AI model based on strategies designed to overcome small sized development sets to pediatric ER physicians at a classification triage task of pediatric elbow radiographs. Methods1,314 pediatric elbow lateral radiographs (mean age: 8.2 years) were retrospectively retrieved, binomially classified based on their annotation as normal or abnormal (with pathology), and randomly partitioned into a development set (993 images), tuning set (109 images), second tuning set (100 images) and test set (112 images). The AI model was trained on the development set and utilized the EfficientNet B1 compound scaling network architecture and online augmentations. Its performance on the test set was compared to a group of five physicians (inter-rater agreement: fair). Statistical analysis: AUC of AI model - DeLong method. Performance of AI model and physician groups - McNemar test. ResultsAccuracy of the model on the test set - 0.804 (95% CI, 0.718 - 0.873), AUROC - 0.872 (95% CI, 0.831 - 0.947). AI model performance compared to the physician group on the test set - sensitivity 0.790 (95% CI 0.684 to 0.895) vs 0.649 (95% CI 0.525 to 0.773), p value 0.088; specificity 0.818 (95% CI 0.716 to 0.920) vs 0.873 (95% CI 0.785 to 0.961), p value 0.439.ConclusionsThe AI model for elbow radiograph triage designed with strategies to optimize performance for a small sized development set showed comparable performance to physicians.

scholarly journals DASOT: A Unified Framework Integrating Data Association and Single Object Tracking for Online Multi-Object Tracking

2020 ◽  
Vol 34 (07) ◽  
pp. 10672-10679
Author(s):  
Qi Chu ◽  
Wanli Ouyang ◽  
Bin Liu ◽  
Feng Zhu ◽  
Nenghai Yu
Keyword(s):  
Object Tracking ◽  
Network Architecture ◽  
State Of The Art ◽  
Data Association ◽  
Computational Cost ◽  
Single Object ◽  
Unified Framework ◽  
Convolutional Network ◽  
Comparable Performance ◽  
Feature Sharing

In this paper, we propose an online multi-object tracking (MOT) approach that integrates data association and single object tracking (SOT) with a unified convolutional network (ConvNet), named DASOTNet. The intuition behind integrating data association and SOT is that they can complement each other. Following Siamese network architecture, DASOTNet consists of the shared feature ConvNet, the data association branch and the SOT branch. Data association is treated as a special re-identification task and solved by learning discriminative features for different targets in the data association branch. To handle the problem that the computational cost of SOT grows intolerably as the number of tracked objects increases, we propose an efficient two-stage tracking method in the SOT branch, which utilizes the merits of correlation features and can simultaneously track all the existing targets within one forward propagation. With feature sharing and the interaction between them, data association branch and the SOT branch learn to better complement each other. Using a multi-task objective, the whole network can be trained end-to-end. Compared with state-of-the-art online MOT methods, our method is much faster while maintaining a comparable performance.

scholarly journals MulStepNET: stronger multi-step graph convolutional networks via multi-power adjacency matrix combination

2021 ◽  
Author(s):  
Xun Liu ◽  
Fangyuan Lei ◽  
Guoqing Xia
Keyword(s):  
Adjacency Matrix ◽  
Network Architecture ◽  
State Of The Art ◽  
Expressive Power ◽  
Structured Data ◽  
Convolutional Network ◽  
Graph Representations ◽  
Convolutional Networks ◽  
Feature Information ◽  
Real World Problems

AbstractGraph convolutional networks (GCNs) have become the de facto approaches and achieved state-of-the-art results for circumventing many real-world problems on graph-structured data. However, these networks are usually shallow due to the over-smoothing of GCNs with many layers, which limits the expressive power of learning graph representations. The current methods of solving the limitations have the bottlenecks of high complexity and many parameters. Although Simple Graph Convolution (SGC) reduces the complexity and parameters, it fails to distinguish the feature information of neighboring nodes at different distances. To tackle the limits, we propose MulStepNET, a stronger multi-step graph convolutional network architecture, that can capture more global information, by simultaneously combining multi-step neighborhoods information. When compared to existing methods such as GCN and MixHop, MulStepNET aggregates neighborhoods information at more distant distances via multi-power adjacency matrix while fitting fewest parameters and being computationally more efficient. Experiments on citation networks including Pubmed, Cora, and Citeseer demonstrate that the proposed MulStepNET model improves over SGC by 2.8, 3.3, and 2.1% respectively while keeping similar stability, and achieves better performance in terms of accuracy and stability compared to other baselines.

Small Industry, Gimmicks and Moscow Statistics, or How the Small Russian Enterprise Came into the World (Russian Physiological Sketch, 1830-1840)

2009 ◽  
pp. 123-129
Author(s):  
Yu. Golubitsky
Keyword(s):  
Small Businesses ◽  
Statistical Data ◽  
Russian Enterprise ◽  
Small Enterprises ◽  
Business Practices ◽  
Way Of Life ◽  
The World ◽  
Small Industry ◽  
Xix Century

The article considers business practices of Moscow small industry in the XIX century, basing upon physiological sketches of N. Polevoy and I. Kokorev, statistical data and the classification of professions are also presented. The author claims that the heroes of the analyzed sketches are the forefathers of Moscow small businesses and shows what a deep similarity their occupations and a way of life bear to the present-day routine existence of small enterprises.

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