scholarly journals Convolutional Neural Network Analysis of Social Novelty Preference using DeepLabCut

2019 ◽  
Author(s):  
Nicholas B. Worley ◽  
Anthony Djerdjaj ◽  
John P. Christianson
Keyword(s):  
Neural Network ◽  
Social Interaction ◽  
Social Behavior ◽  
Convolutional Neural Network ◽  
Strongly Correlated ◽  
Novelty Preference ◽  
Accurate Identification ◽  
Laboratory Rodents ◽  
Human Effort ◽  
Social Decision Making

AbstractThe description and quantification of social behavior in laboratory rodents is central to basic and translational research. Conventional ethological approaches to social behavior are fraught with challenges including bias, significant human effort and temporal accuracy. Here we show proof of principle that machine learning can be applied to laboratory tests of social decision making. Rats underwent social novelty preference tests which were scored both by hand and again by a convolutional neural network generated in the DeepLabCut computer vision package of Mathis and colleagues. The CNN generated temporally (30Hz) and locally (<5pixels) accurate identification of rat nose, eye and ear positions which were then used to compute social interaction and topography heat maps. In sum, hand- and computer-scoring were strongly correlated, and each identified significant preferences to interact with novel conspecifics which sets the stage for applying DeepLabCut analysis to other types of social interaction in the future.

scholarly journals A Deep Convolutional Neural Network Model for Intelligent Discrimination between Coal and Rocks in Coal Mining Face

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Lei Si ◽  
Xiangxiang Xiong ◽  
Zhongbin Wang ◽  
Chao Tan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Deep Convolutional Neural Network ◽  
Accurate Identification ◽  
Experiment System ◽  
Speed Up ◽  
Novel Method ◽  
Coal Rock ◽  
Mining Face

Accurate identification of the distribution of coal seam is a prerequisite for realizing intelligent mining of shearer. This paper presents a novel method for identifying coal and rock based on a deep convolutional neural network (CNN). Three regularization methods are introduced in this paper to solve the overfitting problem of CNN and speed up the convergence: dropout, weight regularization, and batch normalization. Then the coal-rock image information is enriched by means of data augmentation, which significantly improves the performance. The shearer cutting coal-rock experiment system is designed to collect more real coal-rock images, and some experiments are provided. The experiment results indicate that the network we designed has better performance in identifying the coal-rock images.

scholarly journals Adolescent sleep is critical for the developmental shaping of social novelty preference

2020 ◽  
Author(s):  
Wen-Jie Bian ◽  
Luis de Lecea
Keyword(s):  
Social Interaction ◽  
Social Behavior ◽  
Sleep Problems ◽  
Critical Role ◽  
Sleep Onset ◽  
Young Patients ◽  
Autism Spectrum ◽  
Sleep Disruption ◽  
Novelty Preference ◽  
Adolescent Sleep

AbstractSleep takes one-third of our lives, yet its functions remain largely unknown. A large proportion of young patients with neurodevelopmental disorders such as autism spectrum disorders (ASDs) and schizophrenia have sleep problems, including delayed sleep onset, shortened sleep duration and sleep fragmentation, which have been linked to social interaction deficit, a shared symptom of these disorders. However, the causal relationship between sleep disruption and social defects as well as the underlying mechanisms have not yet been established despite its importance in understanding the etiology of these disorders and developing potential therapeutic means. Here using the three-chamber social interaction test, we found that developmental sleep disruption (SD) in adolescent mice caused significant and long-lasting impairment in the preference towards social novelty during adult social interactions without affecting the overall sociality. Interestingly, SD performed in the adulthood did not induce any social defect, indicating a critical period within adolescence during which sleep shapes social novelty preference. Furthermore, by analyzing the adolescent sleep and adult social behavior in a mouse model of Shank3 mutation that mimics a genetic aberrance in ASDs, we found that the development of sociality is correlated with adolescent NREM sleep while social novelty preference is correlated with adolescent REM sleep. Collectively, these results demonstrate a critical role of adolescent sleep in the forming of social novelty preference and the developmental shaping of social behavior.

scholarly journals A Novel Approach for Biofilm Detection Based on a Convolutional Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 881 ◽  
Author(s):  
Giovanni Dimauro ◽  
Francesca Deperte ◽  
Rosalia Maglietta ◽  
Mario Bove ◽  
Fabio La Gioia ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Accurate Identification ◽  
Protective Function ◽  
Bacterial Colony ◽  
Novel Approach ◽  
Nasal Cytology ◽  
Nasal Region ◽  
Validation Set

Rhinology studies anatomy, physiology and diseases affecting the nasal region: one of the most modern techniques to diagnose these diseases is nasal cytology or rhinocytology, which involves analyzing the cells contained in the nasal mucosa under a microscope and researching of other elements such as bacteria, to suspect a pathology. During the microscopic observation, bacteria can be detected in the form of biofilm, that is, a bacterial colony surrounded by an organic extracellular matrix, with a protective function, made of polysaccharides. In the field of nasal cytology, the presence of biofilm in microscopic samples denotes the presence of an infection. In this paper, we describe the design and testing of interesting diagnostic support, for the automatic detection of biofilm, based on a convolutional neural network (CNN). To demonstrate the reliability of the system, alternative solutions based on isolation forest and deep random forest techniques were also tested. Texture analysis is used, with Haralick feature extraction and dominant color. The CNN-based biofilm detection system shows an accuracy of about 98%, an average accuracy of about 100% on the test set and about 99% on the validation set. The CNN-based system designed in this study is confirmed as the most reliable among the best automatic image recognition technologies, in the specific context of this study. The developed system allows the specialist to obtain a rapid and accurate identification of the biofilm in the slide images.

scholarly journals Identification and Classification of Atmospheric Particles Based on SEM Images Using Convolutional Neural Network with Attention Mechanism

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Changchang Yin ◽  
Xuezhen Cheng ◽  
Xilu Liu ◽  
Meng Zhao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Morphological Characteristics ◽  
Attention Mechanism ◽  
Atmospheric Particles ◽  
Spherical Particles ◽  
Sem Images ◽  
Accurate Identification ◽  
Focusing Effect

Accurate identification and classification of atmospheric particulates can provide the basis for their source apportionment. Most current research studies mainly focus on the classification of atmospheric particles based on the energy spectrum of particles, which has the problems of low accuracy and being time-consuming. It is necessary to study the classification method of atmospheric particles with higher accuracy. In this paper, a convolutional neural network (CNN) model with attention mechanism is proposed to identify and classify the scanning electron microscopy (SEM) images of atmospheric particles. First, this work established a database, Qingdao 2016–2018, for atmospheric particles classification research. This database consists of 3469 SEM images of single particulates. Secondly, by analyzing the morphological characteristics of single particle SEM images, it can be divided into four categories: fibrous particles, flocculent particles, spherical particles, and mineral particles. Thirdly, by introducing attention mechanism into convolutional neural network, an Attention-CNN model for the identification and classification of the four types of atmospheric particles based on the SEM images is established. Finally, the Attention-CNN model is trained and tested based on the SEM images database, and the results of identification and classification for four types of particles are obtained. Under the same SEM images database, the classification results from Attention-CNN are compared with those of CNN and SVM. It is found that Attention-CNN has higher classification accuracy and reduces significantly the misclassification number of particles, which shows the focusing effect of attention mechanism.

scholarly journals Convolutional Neural Network-Based Remote Sensing Images Segmentation Method for Extracting Winter Wheat Spatial Distribution

2018 ◽  
Vol 8 (10) ◽  
pp. 1981 ◽  
Author(s):  
Chengming Zhang ◽  
Shuai Gao ◽  
Xiaoxia Yang ◽  
Feng Li ◽  
Maorui Yue ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Spatial Distribution ◽  
Winter Wheat ◽  
Convolutional Neural Network ◽  
Hybrid Structure ◽  
Remote Sensing Images ◽  
Accurate Identification ◽  
Edge Pixel ◽  
Wheat Field

When extracting winter wheat spatial distribution by using convolutional neural network (CNN) from Gaofen-2 (GF-2) remote sensing images, accurate identification of edge pixel is the key to improving the result accuracy. In this paper, an approach for extracting accurate winter wheat spatial distribution based on CNN is proposed. A hybrid structure convolutional neural network (HSCNN) was first constructed, which consists of two independent sub-networks of different depths. The deeper sub-network was used to extract the pixels present in the interior of the winter wheat field, whereas the shallower sub-network extracts the pixels at the edge of the field. The model was trained by classification-based learning and used in image segmentation for obtaining the distribution of winter wheat. Experiments were performed on 39 GF-2 images of Shandong province captured during 2017–2018, with SegNet and DeepLab as comparison models. As shown by the results, the average accuracy of SegNet, DeepLab, and HSCNN was 0.765, 0.853, and 0.912, respectively. HSCNN was equally as accurate as DeepLab and superior to SegNet for identifying interior pixels, and its identification of the edge pixels was significantly better than the two comparison models, which showed the superiority of HSCNN in the identification of winter wheat spatial distribution.

scholarly journals A Movie Recommendation System Based on A Convolutional Neural Network

2021 ◽  
Vol 9 (12) ◽  
pp. 1445-1455
Author(s):  
Jyoti Kumari
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Cosine Similarity ◽  
The Other ◽  
Human Effort ◽  
Movie Recommendation ◽  
Content Based Filtering

Abstract: Due to its vast applications in several sectors, the recommender system has gotten a lot of interest and has been investigated by academics in recent years. The ability to comprehend and apply the context of recommendation requests is critical to the success of any current recommender system. Nowadays, the suggestion system makes it simple to locate the items we require. Movie recommendation systems are intended to assist movie fans by advising which movie to see without needing users to go through the time-consuming and complicated method of selecting a film from a large number of thousands or millions of options. The goal of this research is to reduce human effort by recommending movies based on the user's preferences. This paper introduces a method for a movie recommendation system based on a convolutional neural network with individual features layers of users and movies performed by analyzing user activity and proposing higher-rated films to them. The proposed CNN approach on the MovieLens-1m dataset outperforms the other conventional approaches and gives accurate recommendation results. Keywords: Recommender system, convolutional neural network, movielens-1m, cosine similarity, Collaborative filtering, content-based filtering.

scholarly journals Recognition of Handwritten Digits using Convolutional Neural Network and Linear Binary Pattern

2019 ◽  
Vol 9 (1) ◽  
pp. 4368-4372
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Support Vector ◽  
Handwritten Documents ◽  
Python Language ◽  
Digital Format ◽  
Proposed Model ◽  
Human Effort

Over the past few years there has been a tremendous developments observed in the field of computer technology and artificial intelligence, especially the use of machine learning concepts in Research and Industries. The human effort can be further more reduced in recognition, learning, predicting and many other areas using machine learning and deep learning. Any information which has been handwritten documents consisting of digits in digital form like images, recognizing such digits is a challenging task. The proposed system can recognize any handwritten digits in the document which has been converted into digital format. The proposed model includes Convolutional Neural Network (CNN), a deep learning approach with Linear Binary Pattern (LBP) used for feature extraction. In order to classify more effectively we also have used Support Vector Machine to recognize mere similar digits like 1 and 7, 5 and 6 and many others. The proposed system CNN and LBP is implemented on python language; also the system is tested with different images of handwritten digits taken from MNIST dataset. By using proposed model we could able to achieve 98.74% accuracy in predicting the digits in image format.

scholarly journals Identification of Apple Tree Leaf Diseases Based on Deep Learning Models

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1065
Author(s):  
Xiaofei Chao ◽  
Guoying Sun ◽  
Hongke Zhao ◽  
Min Li ◽  
Dongjian He
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Early Diagnosis ◽  
Convolutional Neural Network ◽  
Apple Tree ◽  
Support Vector ◽  
Detection Accuracy ◽  
Accurate Identification ◽  
Tree Leaf ◽  
Spread Of Infection

Early diagnosis and accurate identification of apple tree leaf diseases (ATLDs) can control the spread of infection, to reduce the use of chemical fertilizers and pesticides, improve the yield and quality of apple, and maintain the healthy development of apple cultivars. In order to improve the detection accuracy and efficiency, an early diagnosis method for ATLDs based on deep convolutional neural network (DCNN) is proposed. We first collect the images of apple tree leaves with and without diseases from both laboratories and cultivation fields, and establish dataset containing five common ATLDs and healthy leaves. The DCNN model proposed in this paper for ATLDs recognition combines DenseNet and Xception, using global average pooling instead of fully connected layers. We extract features by the proposed convolutional neural network then use a support vector machine to classify the apple leaf diseases. Including the proposed DCNN, several DCNNs are trained for ATLDs recognition. The proposed network achieves an overall accuracy of 98.82% in identifying the ATLDs, which is higher than Inception-v3, MobileNet, VGG-16, DenseNet-201, Xception, VGG-INCEP. Moreover, the proposed model has the fastest convergence rate, and a relatively small number of parameters and high robustness compared with the mentioned models. This research indicates that the proposed deep learning model provides a better solution for ATLDs control. It could be also integrated into smart apple cultivation systems.

scholarly journals Apple Leaf Diseases Recognition Based on An Improved Convolutional Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3535 ◽  
Author(s):  
Qian Yan ◽  
Baohua Yang ◽  
Wenyan Wang ◽  
Bing Wang ◽  
Peng Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Learning Strategy ◽  
Convergence Speed ◽  
Model Parameters ◽  
Accurate Identification ◽  
Training Time ◽  
Proposed Model ◽  
Improved Model ◽  
Fully Connected

Scab, frogeye spot, and cedar rust are three common types of apple leaf diseases, and the rapid diagnosis and accurate identification of them play an important role in the development of apple production. In this work, an improved model based on VGG16 is proposed to identify apple leaf diseases, in which the global average poling layer is used to replace the fully connected layer to reduce the parameters and a batch normalization layer is added to improve the convergence speed. A transfer learning strategy is used to avoid a long training time. The experimental results show that the overall accuracy of apple leaf classification based on the proposed model can reach 99.01%. Compared with the classical VGG16, the model parameters are reduced by 89%, the recognition accuracy is improved by 6.3%, and the training time is reduced to 0.56% of that of the original model. Therefore, the deep convolutional neural network model proposed in this work provides a better solution for the identification of apple leaf diseases with higher accuracy and a faster convergence speed.

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