BiGRU-Attention Based Cow Behavior Classification Using Video Data for Precision Livestock Farming

2021 ◽  
Vol 64 (6) ◽  
pp. 1823-1833
Author(s):  
Yangyang Guo ◽  
Yongliang Qiao ◽  
Salah Sukkarieh ◽  
Lilong Chai ◽  
Dongjian He
Keyword(s):  
Deep Learning ◽  
Animal Behavior ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Video Data ◽  
List Type ◽  
Livestock Farming ◽  
Precision Livestock Farming ◽  
Temporal Features ◽  
Gated Recurrent Unit

HighlightsBiGRU-attention based cow behavior classification was proposed.Key spatial-temporal features were captured for behavior representation.BiGRU-attention achieved >82% classification accuracy on calf and adult cow datasets.The proposed method could be used for similar animal behavior classification.Abstract. Animal behavior consists of time series activities, which can reflect animals’ health and welfare status. Monitoring and classifying animal behavior facilitates management decisions to optimize animal performance, welfare, and environmental outcomes. In recent years, deep learning methods have been applied to monitor animal behavior worldwide. To achieve high behavior classification accuracy, a BiGRU-attention based method is proposed in this article to classify some common behaviors, such as exploring, feeding, grooming, standing, and walking. In our work, (1) Inception-V3 was first applied to extract convolutional neural network (CNN) features for each image frame in videos, (2) bidirectional gated recurrent unit (BiGRU) was used to further extract spatial-temporal features, (3) an attention mechanism was deployed to allocate weights to each of the extracted spatial-temporal features according to feature similarity, and (4) the weighted spatial-temporal features were fed to a Softmax layer for behavior classification. Experiments were conducted on two datasets (i.e., calf and adult cow), and the proposed method achieved 82.35% and 82.26% classification accuracy on the calf and adult cow datasets, respectively. In addition, in comparison with other methods, the proposed BiGRU-attention method outperformed long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and BiGRU. Overall, the proposed BiGRU-attention method can capture key spatial-temporal features to significantly improve animal behavior classification, which is favorable for automatic behavior classification in precision livestock farming. Keywords: BiGRU, Cow behavior, Deep learning, LSTM, Precision livestock farming.

scholarly journals Automated Individual Cattle Identification Using Video Data: A Unified Deep Learning Architecture Approach

2021 ◽  
Vol 2 ◽  
Author(s):  
Yongliang Qiao ◽  
Cameron Clark ◽  
Sabrina Lomax ◽  
He Kong ◽  
Daobilige Su ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Attention Mechanism ◽  
Video Data ◽  
Identification Accuracy ◽  
Sequence Length ◽  
Livestock Farming ◽  
Precision Livestock Farming ◽  
Learning Framework ◽  
Spatio Temporal

Individual cattle identification is a prerequisite and foundation for precision livestock farming. Existing methods for cattle identification require radio frequency or visual ear tags, all of which are prone to loss or damage. Here, we propose and implement a new unified deep learning approach to cattle identification using video analysis. The proposed deep learning framework is composed of a Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) with a self-attention mechanism. More specifically, the Inception-V3 CNN was used to extract features from a cattle video dataset taken in a feedlot with rear-view. Extracted features were then fed to a BiLSTM layer to capture spatio-temporal information. Then, self-attention was employed to provide a different focus on the features captured by BiLSTM for the final step of cattle identification. We used a total of 363 rear-view videos from 50 cattle at three different times with an interval of 1 month between data collection periods. The proposed method achieved 93.3% identification accuracy using a 30-frame video length, which outperformed current state-of-the-art methods (Inception-V3, MLP, SimpleRNN, LSTM, and BiLSTM). Furthermore, two different attention schemes, namely, additive and multiplicative attention mechanisms were compared. Our results show that the additive attention mechanism achieved 93.3% accuracy and 91.0% recall, greater than multiplicative attention mechanism with 90.7% accuracy and 87.0% recall. Video length also impacted accuracy, with video sequence length up to 30-frames enhancing identification performance. Overall, our approach can capture key spatio-temporal features to improve cattle identification accuracy, enabling automated cattle identification for precision livestock farming.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

Tobacco Leaf Grading Based on Deep Convolutional Neural Networks and Machine Vision

2021 ◽  
Vol 65 (1) ◽  
pp. 11-22
Author(s):  
Mengyao Lu ◽  
Shuwen Jiang ◽  
Cong Wang ◽  
Dong Chen ◽  
Tian’en Chen
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Classification Accuracy ◽  
Classification Model ◽  
List Type ◽  
Tobacco Leaves ◽  
Tobacco Leaf ◽  
Grading Model

HighlightsA classification model for the front and back sides of tobacco leaves was developed for application in industry.A tobacco leaf grading method that combines a CNN with double-branch integration was proposed.The A-ResNet network was proposed and compared with other classic CNN networks.The grading accuracy of eight different grades was 91.30% and the testing time was 82.180 ms, showing a relatively high classification accuracy and efficiency.Abstract. Flue-cured tobacco leaf grading is a key step in the production and processing of Chinese-style cigarette raw materials, directly affecting cigarette blend and quality stability. At present, manual grading of tobacco leaves is dominant in China, resulting in unsatisfactory grading quality and consuming considerable material and financial resources. In this study, for fast, accurate, and non-destructive tobacco leaf grading, 2,791 flue-cured tobacco leaves of eight different grades in south Anhui Province, China, were chosen as the study sample, and a tobacco leaf grading method that combines convolutional neural networks and double-branch integration was proposed. First, a classification model for the front and back sides of tobacco leaves was trained by transfer learning. Second, two processing methods (equal-scaled resizing and cropping) were used to obtain global images and local patches from the front sides of tobacco leaves. A global image-based tobacco leaf grading model was then developed using the proposed A-ResNet-65 network, and a local patch-based tobacco leaf grading model was developed using the ResNet-34 network. These two networks were compared with classic deep learning networks, such as VGGNet, GoogLeNet-V3, and ResNet. Finally, the grading results of the two grading models were integrated to realize tobacco leaf grading. The tobacco leaf classification accuracy of the final model, for eight different grades, was 91.30%, and grading of a single tobacco leaf required 82.180 ms. The proposed method achieved a relatively high grading accuracy and efficiency. It provides a method for industrial implementation of the tobacco leaf grading and offers a new approach for the quality grading of other agricultural products. Keywords: Convolutional neural network, Deep learning, Image classification, Transfer learning, Tobacco leaf grading

Deep Learning Approach for the Morphological Synthesis in Malayalam and Tamil at the Character Level

2021 ◽  
Vol 20 (6) ◽  
pp. 1-17
Author(s):  
B. Premjith ◽  
K. P. Soman
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Conditional Random Field ◽  
Short Term ◽  
Long Short Term Memory ◽  
Target Languages ◽  
Main Components ◽  
Learning Architectures ◽  
Gated Recurrent Unit ◽  
Character Sequence

Morphological synthesis is one of the main components of Machine Translation (MT) frameworks, especially when any one or both of the source and target languages are morphologically rich. Morphological synthesis is the process of combining two words or two morphemes according to the Sandhi rules of the morphologically rich language. Malayalam and Tamil are two languages in India which are morphologically abundant as well as agglutinative. Morphological synthesis of a word in these two languages is challenging basically because of the following reasons: (1) Abundance in morphology; (2) Complex Sandhi rules; (3) The possibilty in Malayalam to form words by combining words that belong to different syntactic categories (for example, noun and verb); and (4) The construction of a sentence by combining multiple words. We formulated the task of the morphological generation of nouns and verbs of Malayalam and Tamil as a character-to-character sequence tagging problem. In this article, we used deep learning architectures like Recurrent Neural Network (RNN) , Long Short-Term Memory Networks (LSTM) , Gated Recurrent Unit (GRU) , and their stacked and bidirectional versions for the implementation of morphological synthesis at the character level. In addition to that, we investigated the performance of the combination of the aforementioned deep learning architectures and the Conditional Random Field (CRF) in the morphological synthesis of nouns and verbs in Malayalam and Tamil. We observed that the addition of CRF to the Bidirectional LSTM/GRU architecture achieved more than 99% accuracy in the morphological synthesis of Malayalam and Tamil nouns and verbs.

scholarly journals A Hybrid Optimized LSTM Models for Human Activity Recognition with IOT Devices

2021 ◽  
pp. 182-189
Author(s):  
S. Arokiaraj ◽  
Dr. N. Viswanathan
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Short Term Memory ◽  
The Other ◽  
Learning Models ◽  
Computational Overhead ◽  
Temporal Features ◽  
Human Movements ◽  
Proposed Model ◽  
Iot Devices

With the advent of Internet of things(IoT),HA (HA) recognition has contributed the more application in health care in terms of diagnosis and Clinical process. These devices must be aware of human movements to provide better aid in the clinical applications as well as user’s daily activity.Also , In addition to machine and deep learning algorithms, HA recognition systems has significantly improved in terms of high accurate recognition. However, the most of the existing models designed needs improvisation in terms of accuracy and computational overhead. In this research paper, we proposed a BAT optimized Long Short term Memory (BAT-LSTM) for an effective recognition of human activities using real time IoT systems. The data are collected by implanting the Internet of things) devices invasively. Then, proposed BAT-LSTM is deployed to extract the temporal features which are then used for classification to HA. Nearly 10,0000 dataset were collected and used for evaluating the proposed model. For the validation of proposed framework, accuracy, precision, recall, specificity and F1-score parameters are chosen and comparison is done with the other state-of-art deep learning models. The finding shows the proposed model outperforms the other learning models and finds its suitability for the HA recognition.

scholarly journals Optimasi Deep Learning untuk Prediksi Saham di Masa Pandemi Covid-19

2021 ◽  
Vol 7 (2) ◽  
pp. 133
Author(s):  
Widi Hastomo ◽  
Adhitio Satyo Bayangkari Karno ◽  
Nawang Kalbuana ◽  
Ervina Nisfiani ◽  
Lussiana ETP
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Hidden Layer ◽  
Gated Recurrent Unit ◽  
Blue Chip

Penelitian ini bertujuan untuk meningkatkan akurasi dengan menurunkan tingkat kesalahan prediksi dari 5 data saham blue chip di Indonesia. Dengan cara mengkombinasikan desain 4 hidden layer neural nework menggunakan Long Short Term Memory (LSTM) dan Gated Recurrent Unit (GRU). Dari tiap data saham akan dihasilkan grafik rmse-epoch yang dapat menunjukan kombinasi layer dengan akurasi terbaik, sebagai berikut; (a) BBCA dengan layer LSTM-GRU-LSTM-GRU (RMSE=1120,651, e=15), (b) BBRI dengan layer LSTM-GRU-LSTM-GRU (RMSE =110,331, e=25), (c) INDF dengan layer GRU-GRU-GRU-GRU (RMSE =156,297, e=35 ), (d) ASII dengan layer GRU-GRU-GRU-GRU (RMSE =134,551, e=20 ), (e) TLKM dengan layer GRU-LSTM-GRU-LSTM (RMSE =71,658, e=35 ). Tantangan dalam mengolah data Deep Learning (DL) adalah menentukan nilai parameter epoch untuk menghasilkan prediksi akurasi yang tinggi.

Screening children at risk for developmental disabilities based on face landmark from video data of mobile-based application: Cross-Sectional Study (Preprint)

2021 ◽  
Author(s):  
Yu Rang Park ◽  
Sang Ho Hwang ◽  
Yeonsoo Yu ◽  
Jichul Kim ◽  
Taeyeop Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Developmental Disabilities ◽  
Early Detection ◽  
Short Term Memory ◽  
Preliminary Evidence ◽  
Cross Sectional Study ◽  
Important Variable ◽  
Video Data ◽  
Classification Model ◽  
Cross Sectional

BACKGROUND Early detection and intervention of developmental disabilities (DDs) are critical for improving the long-term outcomes of the afflicted children. Mobile-based applications are easily accessible and may thus help the early identification of DDs. OBJECTIVE We aimed to identify facial expression and head pose based on face landmark data extracted from face recording videos and to differentiate the characteristics between children with DDs and those without. METHODS Eighty-nine children (DD, n=33; typically developing, n=56) were included in the analysis. Using the mobile-based application, we extracted facial landmarks and head poses from the recorded videos and performed Long Short-Term Memory(LSTM)-based DD classification. RESULTS Stratified k-fold cross-validation showed that the average values of accuracy, precision, recall, and f1-score of the LSTM based deep learning model of DD children were 88%, 91%,72%, and 80%, respectively. Through the interpretation of prediction results using SHapley Additive exPlanations (SHAP), we confirmed that the nodding head angle variable was the most important variable. All of the top 10 variables of importance had significant differences in the distribution between children with DDs and those without (p<0.05). CONCLUSIONS Our results provide preliminary evidence that the deep-learning classification model using mobile-based children’s video data could be used for the early detection of children with DDs.

scholarly journals EEG and Deep Learning Based Brain Cognitive Function Classification

Computers ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 104
Author(s):  
Saraswati Sridhar ◽  
Vidya Manian
Keyword(s):  
Deep Learning ◽  
Motor Imagery ◽  
Cognitive Aging ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Principal Component ◽  
Learning System ◽  
Detection Accuracy ◽  
Band Power ◽  
Signal Features

Electroencephalogram signals are used to assess neurodegenerative diseases and develop sophisticated brain machine interfaces for rehabilitation and gaming. Most of the applications use only motor imagery or evoked potentials. Here, a deep learning network based on a sensory motor paradigm (auditory, olfactory, movement, and motor-imagery) that employs a subject-agnostic Bidirectional Long Short-Term Memory (BLSTM) Network is developed to assess cognitive functions and identify its relationship with brain signal features, which is hypothesized to consistently indicate cognitive decline. Testing occurred with healthy subjects of age 20–40, 40–60, and >60, and mildly cognitive impaired subjects. Auditory and olfactory stimuli were presented to the subjects and the subjects imagined and conducted movement of each arm during which Electroencephalogram (EEG)/Electromyogram (EMG) signals were recorded. A deep BLSTM Neural Network is trained with Principal Component features from evoked signals and assesses their corresponding pathways. Wavelet analysis is used to decompose evoked signals and calculate the band power of component frequency bands. This deep learning system performs better than conventional deep neural networks in detecting MCI. Most features studied peaked at the age range 40–60 and were lower for the MCI group than for any other group tested. Detection accuracy of left-hand motor imagery signals best indicated cognitive aging (p = 0.0012); here, the mean classification accuracy per age group declined from 91.93% to 81.64%, and is 69.53% for MCI subjects. Motor-imagery-evoked band power, particularly in gamma bands, best indicated (p = 0.007) cognitive aging. Although the classification accuracy of the potentials effectively distinguished cognitive aging from MCI (p < 0.05), followed by gamma-band power.

Sign in / Sign up

Export Citation Format

Share Document