Beluga whale acoustic signal classification using deep learning neural network models

Pashto is one of the most ancient and historical languages in the world and is spoken in Pakistan and Afghanistan. Various languages like Urdu, English, Chinese, and Japanese have OCR applications, but very little work has been conducted on the Pashto language in this perspective. It becomes more difficult for OCR applications to recognize handwritten characters and digits, because handwriting is influenced by the writer’s hand dynamics. Moreover, there was no publicly available dataset for handwritten Pashto digits before this study. Due to this, there was no work performed on the recognition of Pashto handwritten digits and characters combined. To achieve this objective, a dataset of Pashto handwritten digits consisting of 60,000 images was created. The trio deep learning Convolutional Neural Network, i.e., CNN, LeNet, and Deep CNN were trained and tested with both Pashto handwritten characters and digits datasets. From the simulations, the Deep CNN achieved 99.42 percent accuracy for Pashto handwritten digits, 99.17 percent accuracy for handwritten characters, and 70.65 percent accuracy for combined digits and characters. Similarly, LeNet and CNN models achieved slightly less accuracies (LeNet; 98.82, 99.15, and 69.82 percent and CNN; 98.30, 98.74, and 66.53 percent) for Pashto handwritten digits, Pashto characters, and the combined Pashto digits and characters recognition datasets, respectively. Based on these results, the Deep CNN model is the best model in terms of accuracy and loss as compared to the other two models.

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Feature extraction-based image steganalysis using deep learning

WEENTECH Proceedings in Energy ◽

10.32438/wpe.182021 ◽

2021 ◽

pp. 188-198

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Secure Communication ◽

Information Technologies ◽

Network Models ◽

Error Rates ◽

Multimedia Data ◽

Secret Message ◽

Neural Network Models

The innovations in advanced information technologies has led to rapid delivery and sharing of multimedia data like images and videos. The digital steganography offers ability to secure communication and imperative for internet. The image steganography is essential to preserve confidential information of security applications. The secret image is embedded within pixels. The embedding of secret message is done by applied with S-UNIWARD and WOW steganography. Hidden messages are reveled using steganalysis. The exploration of research interests focused on conventional fields and recent technological fields of steganalysis. This paper devises Convolutional neural network models for steganalysis. Convolutional neural network (CNN) is one of the most frequently used deep learning techniques. The Convolutional neural network is used to extract spatio-temporal information or features and classification. We have compared steganalysis outcome with AlexNet and SRNeT with same dataset. The stegnalytic error rates are compared with different payloads.

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SEALing Neural Network Models in Encrypted Deep Learning Accelerators

10.1109/dac18074.2021.9586199 ◽

2021 ◽

Author(s):

Pengfei Zuo ◽

Yu Hua ◽

Ling Liang ◽

Xinfeng Xie ◽

Xing Hu ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Network Models ◽

Neural Network Models

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Application of Deep Learning for Characterization of Drivers’ Engagement in Secondary Tasks in In-Vehicle Systems

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198120926507 ◽

2020 ◽

Vol 2674 (8) ◽

pp. 429-440

Author(s):

Osama A. Osman ◽

Hesham Rakha

Keyword(s):

Neural Network ◽

Deep Learning ◽

Time Series Data ◽

Short Term Memory ◽

Network Models ◽

Sensor Technology ◽

Series Data ◽

Neural Network Models ◽

Secondary Tasks ◽

Driving Assistance

Distracted driving (i.e., engaging in secondary tasks) is an epidemic that threatens the lives of thousands every year. Data collected from vehicular sensor technologies and through connectivity provide comprehensive information that, if used to detect driver engagement in secondary tasks, could save thousands of lives and millions of dollars. This study investigates the possibility of achieving this goal using promising deep learning tools. Specifically, two deep neural network models (a multilayer perceptron neural network model and a long short-term memory networks [LSTMN] model) were developed to identify three secondary tasks: cellphone calling, cellphone texting, and conversation with adjacent passengers. The Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS) time series data, collected using vehicle sensor technology, were used to train and test the model. The results show excellent performance for the developed models, with a slight improvement for the LSTMN model, with overall classification accuracies ranging between 95 and 96%. Specifically, the models are able to identify the different types of secondary tasks with high accuracies of 100% for calling, 96%–97% for texting, 90%–91% for conversation, and 95%–96% for the normal driving. Based on this performance, the developed models improve on the results of a previous model developed by the author to classify the same three secondary tasks, which had an accuracy of 82%. The model is promising for use in in-vehicle driving assistance technology to report engagement in unlawful tasks or alert drivers to take over control in level 1 and 2 automated vehicles.

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Deep Learning-based Integrated Stacked Model for the Stock Market Prediction

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a1823.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 5167-5174 ◽

Cited By ~ 2

Keyword(s):

Neural Network ◽

Time Series ◽

Deep Learning ◽

Stock Market ◽

Network Models ◽

Time Series Forecasting ◽

Convolution Neural Network ◽

Neural Network Models ◽

Stock Market Prediction ◽

Market Data

Recently, the stock market prediction has become one of the essential application areas of time-series forecasting research. The successful prediction of the stock market can be better guided to the investors to maximize their profit and to minimize the risk of investment. The stock market data are very much complex, non-linear and dynamic. Due to this reason, still, it is a challenging task. In recent time, deep learning method has become one of the most popular machine learning methods for time-series forecasting due to their temporal feature extraction capabilities. In this paper, we have proposed a novel Deep Learning-based Integrated Stacked Model (DISM) that integrates both the 1D Convolution neural network and LSTM recurrent neural network to find the spatial and temporal features from the stock market data. Our proposed DISM is applied to forecast the stock market. Here, we have also compared our proposed DISM with the single structured stacked LSTM, and 1D Convolution neural network models, and some other statistical models. We have observed that our proposed DISM produces better results in terms of accuracy and stability.

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Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network

Applied Sciences ◽

10.3390/app11157147 ◽

2021 ◽

Vol 11 (15) ◽

pp. 7147

Author(s):

Jinmo Gu ◽

Jinhyuk Na ◽

Jeongeun Park ◽

Hayoung Kim

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

False Positive Rate ◽

Direct Marketing ◽

Network Models ◽

Learning Models ◽

Insurance Policy ◽

Neural Network Models ◽

Potential Customers

Outbound telemarketing is an efficient direct marketing method wherein telemarketers solicit potential customers by phone to purchase or subscribe to products or services. However, those who are not interested in the information or offers provided by outbound telemarketing generally experience such interactions negatively because they perceive telemarketing as spam. In this study, therefore, we investigate the use of deep learning models to predict the success of outbound telemarketing for insurance policy loans. We propose an explainable multiple-filter convolutional neural network model called XmCNN that can alleviate overfitting and extract various high-level features using hundreds of input variables. To enable the practical application of the proposed method, we also examine ensemble models to further improve its performance. We experimentally demonstrate that the proposed XmCNN significantly outperformed conventional deep neural network models and machine learning models. Furthermore, a deep learning ensemble model constructed using the XmCNN architecture achieved the lowest false positive rate (4.92%) and the highest F1-score (87.47%). We identified important variables influencing insurance policy loan prediction through the proposed model, suggesting that these factors should be considered in practice. The proposed method may increase the efficiency of outbound telemarketing and reduce the spam problems caused by calling non-potential customers.

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Understanding fully-connected and convolution allayers in unsupervised learning using face images

10.5753/wvc.2019.7621 ◽

2019 ◽

Author(s):

Lucas Fontes Buzuti ◽

Carlos Eduardo Thomaz

Keyword(s):

Neural Network ◽

Deep Learning ◽

Unsupervised Learning ◽

Facial Expressions ◽

Multivariate Statistics ◽

Network Models ◽

Neural Network Models ◽

Face Images ◽

Convolutional Autoencoder ◽

Fully Connected

The goal of this paper is to implement and compare two unsupervised models of deep learning: Autoencoder and Convolutional Autoencoder. These neural network models have been trained to learn regularities in well-framed face images with different facial expressions. The Autoencoder's basic topology is addressed here, composed of encoding and decoding multilayers. This paper approaches these automatic codings using multivariate statistics to visually understand the bottleneck differences between the fully-connected and convolutional layers and the corresponding importance of the dropout strategy when applied in a model.

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Tongue fissure visualization by using deep learning – an example of the application of artificial intelligence in traditional medicine

10.21203/rs.2.19210/v2 ◽

2020 ◽

Author(s):

Wen-Hsien Chang ◽

Han-Kuei Wu ◽

Lun-chien Lo ◽

William W. L. Hsiao ◽

Hsueh-Ting Chu ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Transfer Learning ◽

Network Model ◽

Neural Network Model ◽

Graphics Processing Units ◽

Network Models ◽

Neural Network Models ◽

The Neural Network ◽

Objective Evidence

Abstract Background: Traditional Chinese medicine (TCM) describes physiological and pathological changes inside and outside the human body by the application of four methods of diagnosis. One of the four methods, tongue diagnosis, is widely used by TCM physicians, since it allows direct observations that prevent discrepancies in the patient’s history and, as such, provides clinically important, objective evidence. The clinical significance of tongue features has been explored in both TCM and modern medicine. However, TCM physicians may have different interpretations of the features displayed by the same tongue, and therefore intra- and inter-observer agreements are relatively low. If an automated interpretation system could be developed, more consistent results could be obtained, and learning could also be more efficient. This study will apply a recently developed deep learning method to the classification of tongue features, and indicate the regions where the features are located.Methods: A large number of tongue photographs with labeled fissures were used. Transfer learning was conducted using the ImageNet-pretrained ResNet50 model to determine whether tongue fissures were identified on a tongue photograph. Often, the neural network model lacks interpretability, and users cannot understand how the model determines the presence of tongue fissures. Therefore, Gradient-weighted Class Activation Mapping (Grad-CAM) was also applied to directly mark the tongue features on the tongue image. Results: Only 6 epochs were trained in this study and no graphics processing units (GPUs) were used. It took less than 4 minutes for each epoch to be trained. The correct rate for the test set was approximately 70%. After the model training was completed, Grad-CAM was applied to localize tongue fissures in each image. The neural network model not only determined whether tongue fissures existed, but also allowed users to learn about the tongue fissure regions.Conclusions: This study demonstrated how to apply transfer learning using the ImageNet-pretrained ResNet50 model for the identification and localization of tongue fissures and regions. The neural network model built in this study provided interpretability and intuitiveness, (often lacking in general neural network models), and improved the feasibility for clinical application.

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Four-Dimension Deep Learning Method for Flower Quality Grading with Depth Information

Electronics ◽

10.3390/electronics10192353 ◽

2021 ◽

Vol 10 (19) ◽

pp. 2353

Author(s):

Xinyan Sun ◽

Zhenye Li ◽

Tingting Zhu ◽

Chao Ni

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Classification Accuracy ◽

Network Models ◽

Depth Image ◽

Depth Information ◽

Neural Network Models ◽

Flower Bud ◽

Flower Quality

Grading the quality of fresh cut flowers is an important practice in the flower industry. Based on the flower maturing status, a classification method based on deep learning and depth information was proposed for the grading of flower quality. Firstly, the RGB image and the depth image of a flower bud were collected and transformed into fused RGBD information. Then, the RGBD information of a flower was set as inputs of a convolutional neural network to determine the flower bud maturing status. Four convolutional neural network models (VGG16, ResNet18, MobileNetV2, and InceptionV3) were adjusted for a four-dimensional (4D) RGBD input to classify flowers, and their classification performances were compared with and without depth information. The experimental results show that the classification accuracy was improved with depth information, and the improved InceptionV3 network with RGBD achieved the highest classification accuracy (up to 98%), which means that the depth information can effectively reflect the characteristics of the flower bud and is helpful for the classification of the maturing status. These results have a certain significance for the intelligent classification and sorting of fresh flowers.

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Statistically-informed deep learning for gravitational wave parameter estimation

Machine Learning: Science and Technology ◽

10.1088/2632-2153/ac3843 ◽

2021 ◽

Author(s):

Hongyu Shen ◽

Eliu Huerta ◽

Eamonn O’Shea ◽

Prayush Kumar ◽

Zhizhen Zhao

Keyword(s):

Neural Network ◽

Parameter Estimation ◽

Deep Learning ◽

Gravitational Wave ◽

Network Models ◽

Wave Parameter ◽

Posterior Distributions ◽

Neural Network Models ◽

The Masses ◽

Bayesian Methodologies

Abstract We introduce deep learning models to estimate the masses of the binary components of black hole mergers, (m1, m2), and three astrophysical properties of the post-merger compact remnant, namely, the final spin, af, and the frequency and damping time of the ringdown oscillations of the fundamental (l=m=2) bar mode, (ωR, ωI). Our neural networks combine a modified WaveNet architecture with contrastive learning and normalizing flow. We validate these models against a Gaussian conjugate prior family whose posterior distribution is described by a closed analytical expression. Upon confirming that our models produce statistically consistent results, we used them to estimate the astrophysical parameters (m1, m2, af, ωR, ωI) of five binary black holes: GW150914, GW170104, GW170814, GW190521 and GW190630. We use PyCBC Inference to directly compare traditional Bayesian methodologies for parameter estimation with our deep learning based posterior distributions. Our results show that our neural network models predict posterior distributions that encode physical correlations, and that our data-driven median results and 90\% confidence intervals are similar to those produced with gravitational wave Bayesian analyses. This methodology requires a single V100 NVIDIA GPU to produce median values and posterior distributions within two milliseconds for each event. This neural network, and a tutorial for its use, are available at the Data and Learning Hub for Science.

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