scholarly journals Classifying Ingestive Behavior of Dairy Cows via Automatic Sound Recognition

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5231
Author(s):  
Guoming Li ◽  
Yijie Xiong ◽  
Qian Du ◽  
Zhengxiang Shi ◽  
Richard S. Gates
Keyword(s):  
Deep Learning ◽  
Dairy Cows ◽  
Dairy Cow ◽  
Short Term Memory ◽  
Classification Performance ◽  
Ingestive Behavior ◽  
Sound Recognition ◽  
Forage Species ◽  
Audio Files ◽  
Ingestive Behaviors

Determining ingestive behaviors of dairy cows is critical to evaluate their productivity and health status. The objectives of this research were to (1) develop the relationship between forage species/heights and sound characteristics of three different ingestive behaviors (bites, chews, and chew-bites); (2) comparatively evaluate three deep learning models and optimization strategies for classifying the three behaviors; and (3) examine the ability of deep learning modeling for classifying the three ingestive behaviors under various forage characteristics. The results show that the amplitude and duration of the bite, chew, and chew-bite sounds were mostly larger for tall forages (tall fescue and alfalfa) compared to their counterparts. The long short-term memory network using a filtered dataset with balanced duration and imbalanced audio files offered better performance than its counterparts. The best classification performance was over 0.93, and the best and poorest performance difference was 0.4–0.5 under different forage species and heights. In conclusion, the deep learning technique could classify the dairy cow ingestive behaviors but was unable to differentiate between them under some forage characteristics using acoustic signals. Thus, while the developed tool is useful to support precision dairy cow management, it requires further improvement.

scholarly journals Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature

2017 ◽  
Vol 2017 ◽  
pp. 1-22 ◽  
Author(s):  
Jihyun Kim ◽  
Thi-Thu-Huong Le ◽  
Howon Kim
Keyword(s):  
Deep Learning ◽  
Power Consumption ◽  
Distribution System ◽  
Short Term Memory ◽  
Electrical Power ◽  
Electricity Consumption ◽  
Classification Performance ◽  
Robust Solution ◽  
Proposed Model ◽  
Load Monitoring

Monitoring electricity consumption in the home is an important way to help reduce energy usage. Nonintrusive Load Monitoring (NILM) is existing technique which helps us monitor electricity consumption effectively and costly. NILM is a promising approach to obtain estimates of the electrical power consumption of individual appliances from aggregate measurements of voltage and/or current in the distribution system. Among the previous studies, Hidden Markov Model (HMM) based models have been studied very much. However, increasing appliances, multistate of appliances, and similar power consumption of appliances are three big issues in NILM recently. In this paper, we address these problems through providing our contributions as follows. First, we proposed state-of-the-art energy disaggregation based on Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model and additional advanced deep learning. Second, we proposed a novel signature to improve classification performance of the proposed model in multistate appliance case. We applied the proposed model on two datasets such as UK-DALE and REDD. Via our experimental results, we have confirmed that our model outperforms the advanced model. Thus, we show that our combination between advanced deep learning and novel signature can be a robust solution to overcome NILM’s issues and improve the performance of load identification.

scholarly journals Payload-Based Traffic Classification Using Multi-Layer LSTM in Software Defined Networks

2019 ◽  
Vol 9 (12) ◽  
pp. 2550 ◽  
Author(s):  
Lim ◽  
Kim ◽  
Kim ◽  
Hong ◽  
Han
Keyword(s):  
Deep Learning ◽  
Network Traffic ◽  
Short Term Memory ◽  
Single Layer ◽  
Classification Performance ◽  
Traffic Classification ◽  
Software Defined Networks ◽  
Learning Models ◽  
Network Operator ◽  
Network Traffic Classification

Recently, with the advent of various Internet of Things (IoT) applications, a massive amount of network traffic is being generated. A network operator must provide different quality of service, according to the service provided by each application. Toward this end, many studies have investigated how to classify various types of application network traffic accurately. Especially, since many applications use temporary or dynamic IP or Port numbers in the IoT environment, only payload-based network traffic classification technology is more suitable than the classification using the packet header information as well as payload. Furthermore, to automatically respond to various applications, it is necessary to classify traffic using deep learning without the network operator intervention. In this study, we propose a traffic classification scheme using a deep learning model in software defined networks. We generate flow-based payload datasets through our own network traffic pre-processing, and train two deep learning models: 1) the multi-layer long short-term memory (LSTM) model and 2) the combination of convolutional neural network and single-layer LSTM models, to perform network traffic classification. We also execute a model tuning procedure to find the optimal hyper-parameters of the two deep learning models. Lastly, we analyze the network traffic classification performance on the basis of the F1-score for the two deep learning models, and show the superiority of the multi-layer LSTM model for network packet classification.

scholarly journals Prediction of Long Non-Coding RNAs Based on Deep Learning

Genes ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 273 ◽  
Author(s):  
Xiu-Qin Liu ◽  
Bing-Xiu Li ◽  
Guan-Rong Zeng ◽  
Qiao-Yue Liu ◽  
Dong-Mei Ai
Keyword(s):  
Deep Learning ◽  
High Throughput Sequencing ◽  
Short Term Memory ◽  
Rapid Development ◽  
Classification Performance ◽  
Sequencing Technology ◽  
Learning Framework ◽  
Non Coding Rnas ◽  
Set Up ◽  
Deep Learning Model

With the rapid development of high-throughput sequencing technology, a large number of transcript sequences have been discovered, and how to identify long non-coding RNAs (lncRNAs) from transcripts is a challenging task. The identification and inclusion of lncRNAs not only can more clearly help us to understand life activities themselves, but can also help humans further explore and study the disease at the molecular level. At present, the detection of lncRNAs mainly includes two forms of calculation and experiment. Due to the limitations of bio sequencing technology and ineluctable errors in sequencing processes, the detection effect of these methods is not very satisfactory. In this paper, we constructed a deep-learning model to effectively distinguish lncRNAs from mRNAs. We used k-mer embedding vectors obtained through training the GloVe algorithm as input features and set up the deep learning framework to include a bidirectional long short-term memory model (BLSTM) layer and a convolutional neural network (CNN) layer with three additional hidden layers. By testing our model, we have found that it obtained the best values of 97.9%, 96.4% and 99.0% in F1score, accuracy and auROC, respectively, which showed better classification performance than the traditional PLEK, CNCI and CPC methods for identifying lncRNAs. We hope that our model will provide effective help in distinguishing mature mRNAs from lncRNAs, and become a potential tool to help humans understand and detect the diseases associated with lncRNAs.

scholarly journals HADLN: Hybrid Attention-Based Deep Learning Network for Automated Arrhythmia Classification

2021 ◽  
Vol 12 ◽  
Author(s):  
Mingfeng Jiang ◽  
Jiayan Gu ◽  
Yang Li ◽  
Bo Wei ◽  
Jucheng Zhang ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Deep Learning ◽  
Short Term Memory ◽  
Classification Performance ◽  
Attention Mechanism ◽  
Learning Network ◽  
Deep Convolution Neural Network ◽  
Deep Learning Network ◽  
Deep Learning Model ◽  
Fusion Features

In recent years, with the development of artificial intelligence, deep learning model has achieved initial success in ECG data analysis, especially the detection of atrial fibrillation. In order to solve the problems of ignoring the correlation between contexts and gradient dispersion in traditional deep convolution neural network model, the hybrid attention-based deep learning network (HADLN) method is proposed to implement arrhythmia classification. The HADLN can make full use of the advantages of residual network (ResNet) and bidirectional long–short-term memory (Bi-LSTM) architecture to obtain fusion features containing local and global information and improve the interpretability of the model through the attention mechanism. The method is trained and verified by using the PhysioNet 2017 challenge dataset. Without loss of generality, the ECG signal is classified into four categories, including atrial fibrillation, noise, other, and normal signals. By combining the fusion features and the attention mechanism, the learned model has a great improvement in classification performance and certain interpretability. The experimental results show that the proposed HADLN method can achieve precision of 0.866, recall of 0.859, accuracy of 0.867, and F1-score of 0.880 on 10-fold cross-validation.

scholarly journals Road Surface Classification Using a Deep Ensemble Network with Sensor Feature Selection

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4342 ◽  
Author(s):  
Jongwon Park ◽  
Kyushik Min ◽  
Hayoung Kim ◽  
Woosung Lee ◽  
Gaehwan Cho ◽  
...  
Keyword(s):  
Feature Selection ◽  
Deep Learning ◽  
Short Term Memory ◽  
Classification Performance ◽  
Sensor Data ◽  
Short Term ◽  
Feature Selection Technique ◽  
Long Short Term Memory ◽  
Deep Learning Network ◽  
Real Vehicle

Deep learning is a fast-growing field of research, in particular, for autonomous application. In this study, a deep learning network based on various sensor data is proposed for identifying the roads where the vehicle is driving. Long-Short Term Memory (LSTM) unit and ensemble learning are utilized for network design and a feature selection technique is applied such that unnecessary sensor data could be excluded without a loss of performance. Real vehicle experiments were carried out for the learning and verification of the proposed deep learning structure. The classification performance was verified through four different test roads. The proposed network shows the classification accuracy of 94.6% in the test data.

Effects of Retinoic Acid and 1,25-Dihydroxyvitamin D3 on IFN-gamma Secretion by Mononuclear Leukocytes from Nulliparous and Postparturient Dairy Cattle

2000 ◽  
Vol 70 (3) ◽  
pp. 92-101 ◽  
Author(s):  
Burim Ametaj ◽  
Brian Nonnecke ◽  
Ronald Horst ◽  
Donald Beitz
Keyword(s):  
Retinoic Acid ◽  
Dairy Cattle ◽  
Dairy Cows ◽  
Dairy Cow ◽  
Pokeweed Mitogen ◽  
Mononuclear Leukocytes ◽  
Combined Effects ◽  
Ifn Gamma ◽  
Dihydroxyvitamin D ◽  
1,25 Dihydroxyvitamin D

Individual and combined effects of several isomers of retinoic acid (RA) and 1,25-dihydroxyvitamin D3 (1,25-(OH)2D3) on interferon-gamma (IFN-gamma) secretion by blood mononuclear leukocytes (MNL) from nulliparous and postparturient Holstein cattle were evaluated in vitro. In the first experiment, effects on incubation period (24 to 72 hours) and time of supplementation (0 to 32 hours) with all-trans, 9-cis, 13-cis-, and 9,13-dicis-RAs (0 to 100 nM) on IFN-gamma secretion by pokeweed mitogen (PWM)-stimulated (0 and 10 mug/ml) MNL from nulliparous cattle were evaluated. In the second experiment, MNL from postparturient cows (bled at 0, 2, 4, and 16 days postpartum) were stimulated with PWM (0 and 10 mug/ml) in the presence of RA isomers (9-cis- or 9,13-dicis-RA; 0 to 100 nM), 1,25-(OH)2D3 (0 to 100 nM), or with combinations of these metabolites. The results show that individual isomers of RA had no effect on IFN-gamma secretion by PWM-stimulated MNL from nulliparous or postparturient cows. Furthermore 1,25-dihydroxyvitamin D3 inhibited IFN-gamma secretion by MNL from nulliparous and postparturient dairy cows; however, the degree of inhibition was greater when 9-cis- and 9,13-dicis-RA were also present in the cultures. Finally mononuclear leukocytes from postparturient dairy cows produced substantially less IFN-gamma than did MNL from nulliparous cattle. It is concluded that retinoic acids individually did not affect the capacity of leukocytes from dairy cattle to secrete IFN-gamma. This result is in marked contrast to studies in monogastric species indicating that RAs inhibit IFN-gamma secretion by peripheral blood T cells. Inhibition of IFN-gamma secretion by 1,25-(OH)2D3 was potentiated by 9-cis- and 9,13-di-cis-retinoics acids, suggesting that an excess of dietary vitamins A and D may compromise further the naturally immunosuppressed postparturient dairy cow. Additional research is necessary to determine if the combined effects of these metabolites on IFN-gamma secretion represent an increased susceptibility of the dairy cow to infectious diseases during the periparturient period. Lower secretion of IFN-gamma by MNL from postpartutient dairy cows, relative to nulliparous cattle, suggests that recently-calved cows are naturally immunosuppressed.

Advancing dairy farming profitability through research

2001 ◽  
pp. 17-22
Author(s):  
J.R. Caradus ◽  
D.A. Clark
Keyword(s):  
Research And Development ◽  
Dairy Cow ◽  
Low Cost ◽  
Pasture Management ◽  
Nitrogen Fertiliser ◽  
Grass Endophyte ◽  
Forage Species ◽  
Development Outcomes ◽  
Dairy Cow Nutrition ◽  
The Impact

The New Zealand dairy industry recognises that to remain competitive it must continue to invest in research and development. Outcomes from research have ensured year-round provision of low-cost feed from pasture while improving productivity. Some of these advances, discussed in this paper, include the use of white clover in pasture, understanding the impacts of grass endophyte, improved dairy cow nutrition, the use of alternative forage species and nitrogen fertiliser to improve productivity, demonstration of the impact of days-in-milk on profitability, and the use of feed budgeting and appropriate pasture management. Keywords: dairy, profitability, research and development

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

A Study on the Auxiliary Diagnosis of Thyroid Disease Images Based on Multiple Dimensional Deep Learning Algorithms

2020 ◽  
Vol 16 (3) ◽  
pp. 199-205
Author(s):  
Yuejun Liu ◽  
Yifei Xu ◽  
Xiangzheng Meng ◽  
Xuguang Wang ◽  
Tianxu Bai
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Region Of Interest ◽  
Classification Performance ◽  
Thyroid Diseases ◽  
Great Success ◽  
Learning Models ◽  
Good Classification Performance ◽  
Spect Images

Background: Medical imaging plays an important role in the diagnosis of thyroid diseases. In the field of machine learning, multiple dimensional deep learning algorithms are widely used in image classification and recognition, and have achieved great success. Objective: The method based on multiple dimensional deep learning is employed for the auxiliary diagnosis of thyroid diseases based on SPECT images. The performances of different deep learning models are evaluated and compared. Methods: Thyroid SPECT images are collected with three types, they are hyperthyroidism, normal and hypothyroidism. In the pre-processing, the region of interest of thyroid is segmented and the amount of data sample is expanded. Four CNN models, including CNN, Inception, VGG16 and RNN, are used to evaluate deep learning methods. Results: Deep learning based methods have good classification performance, the accuracy is 92.9%-96.2%, AUC is 97.8%-99.6%. VGG16 model has the best performance, the accuracy is 96.2% and AUC is 99.6%. Especially, the VGG16 model with a changing learning rate works best. Conclusion: The standard CNN, Inception, VGG16, and RNN four deep learning models are efficient for the classification of thyroid diseases with SPECT images. The accuracy of the assisted diagnostic method based on deep learning is higher than that of other methods reported in the literature.

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

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