Detection of Bleeding Events in Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders: Deep Learning Approach

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.

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.

Detection of Bleeding Events in Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders: Deep Learning Approach (Preprint)

10.2196/preprints.10788 ◽

2018 ◽

Author(s):

Rumeng Li ◽

Baotian Hu ◽

Feifan Liu ◽

Weisong Liu ◽

Francesca Cunningham ◽

...

Keyword(s):

Neural Network ◽

Electronic Health Record ◽

Convolutional Neural Network ◽

Language Processing ◽

Characteristic Curve ◽

Bleeding Event ◽

Support Vector ◽

Health Record ◽

Bleeding Events ◽

Electronic Health

BACKGROUND Bleeding events are common and critical and may cause significant morbidity and mortality. High incidences of bleeding events are associated with cardiovascular disease in patients on anticoagulant therapy. Prompt and accurate detection of bleeding events is essential to prevent serious consequences. As bleeding events are often described in clinical notes, automatic detection of bleeding events from electronic health record (EHR) notes may improve drug-safety surveillance and pharmacovigilance. OBJECTIVE We aimed to develop a natural language processing (NLP) system to automatically classify whether an EHR note sentence contains a bleeding event. METHODS We expert annotated 878 EHR notes (76,577 sentences and 562,630 word-tokens) to identify bleeding events at the sentence level. This annotated corpus was used to train and validate our NLP systems. We developed an innovative hybrid convolutional neural network (CNN) and long short-term memory (LSTM) autoencoder (HCLA) model that integrates a CNN architecture with a bidirectional LSTM (BiLSTM) autoencoder model to leverage large unlabeled EHR data. RESULTS HCLA achieved the best area under the receiver operating characteristic curve (0.957) and F1 score (0.938) to identify whether a sentence contains a bleeding event, thereby surpassing the strong baseline support vector machines and other CNN and autoencoder models. CONCLUSIONS By incorporating a supervised CNN model and a pretrained unsupervised BiLSTM autoencoder, the HCLA achieved high performance in detecting bleeding events.

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.

Efficient Imbalanced Multimedia Concept Retrieval by Deep Learning on Spark Clusters

Deep Learning and Neural Networks ◽

10.4018/978-1-7998-0414-7.ch017 ◽

2020 ◽

pp. 274-294

Author(s):

Yilin Yan ◽

Min Chen ◽

Saad Sadiq ◽

Mei-Ling Shyu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Imbalanced Data ◽

Network Models ◽

Multimedia Data ◽

Neural Network Models ◽

Minority Class ◽

Imbalanced Data Classification

The classification of imbalanced datasets has recently attracted significant attention due to its implications in several real-world use cases. The classifiers developed on datasets with skewed distributions tend to favor the majority classes and are biased against the minority class. Despite extensive research interests, imbalanced data classification remains a challenge in data mining research, especially for multimedia data. Our attempt to overcome this hurdle is to develop a convolutional neural network (CNN) based deep learning solution integrated with a bootstrapping technique. Considering that convolutional neural networks are very computationally expensive coupled with big training datasets, we propose to extract features from pre-trained convolutional neural network models and feed those features to another full connected neutral network. Spark implementation shows promising performance of our model in handling big datasets with respect to feasibility and scalability.

Speech Command Recognition using Artificial Neural Networks

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.4.2.358 ◽

2020 ◽

Vol 4 (2) ◽

pp. 73

Author(s):

Sushan Poudel ◽

Dr. R Anuradha

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Networks ◽

Convolutional Neural Network ◽

Recurrent Neural Network ◽

Network Models ◽

Recognition System ◽

Neural Network Models ◽

Artificial Neural

Speech is one of the most effective way for human and machine to interact. This project aims to build Speech Command Recognition System that is capable of predicting the predefined speech commands. Dataset provided by Google’s TensorFlow and AIY teams is used to implement different Neural Network models which include Convolutional Neural Network and Recurrent Neural Network combined with Convolutional Neural Network. The combination of Convolutional and Recurrent Neural Network outperforms Convolutional Neural Network alone by 8% and achieved 96.66% accuracy for 20 labels.

Subclass Separation of White Blood Cell Images Using Convolutional Neural Network Models

Elektronika ir Elektrotechnika ◽

10.5755/j01.eie.25.5.24358 ◽

2019 ◽

Vol 25 (5) ◽

pp. 63-68 ◽

Cited By ~ 7

Author(s):

Mesut Togacar ◽

Burhan Ergen ◽

Mehmet Emre Sertkaya

Keyword(s):

Neural Network ◽

Deep Learning ◽

Immune System ◽

Blood Cell ◽

Convolutional Neural Network ◽

White Blood Cell ◽

Blood Cells ◽

White Blood Cells ◽

Network Models ◽

Neural Network Models

The white blood cells produced in the bone marrow and lymphoid tissue known as leucocytes are an important part of the immune system to protect the body against foreign invaders and infectious disease. These cells, which do not have color, have a few days or several weeks of life. A lot of clinic experience is required for a doctor to detect the amount of white blood cells in human blood and classify it. Thus, early and accurate diagnosis can be made in the formation of various disease types, including infection on the immune system, such as anemia and leukemia, while evaluating and determining the disease of a patient. The white blood cells can be separated into four subclasses, such as Eosinophil, Lymphocyte, Monocyte, and Neutrophil. This study focuses on the separation of the white blood cell images by the classification process using convolutional neural network models, which is a deep learning model. A deep learning network, which is slow in the training step due to the complex architecture, but fast in the test step, is used for the feature extraction instead of intricate methods. For the subclass separation of white blood cells, the experimental results show that the AlexNet architecture gives the correct recognition rate among the convolutional neural network architectures tested in the study. Various classifiers are performed on the features derived from the AlexNet architecture to evaluate the classification performance. The best performance in the classification of white blood cells is given by the quadratic discriminant analysis classifier with the accuracy of 97.78 %.

A Deep Learning Approach for Prediction of Stock Price Based on Neural Network Models: LSTM and GRU

10.1109/icccnt51525.2021.9579782 ◽

2021 ◽

Author(s):

Vijayakumar Polepally ◽

N. Shiva Nandini Reddy ◽

M. Sindhuja ◽

N. Anjali ◽

K. Jeevan Reddy

Keyword(s):

Neural Network ◽

Deep Learning ◽

Stock Price ◽

Network Models ◽

Learning Approach ◽

Neural Network Models

Deep Learning With TensorFlow: A Review

Journal of Educational and Behavioral Statistics ◽

10.3102/1076998619872761 ◽

2019 ◽

Vol 45 (2) ◽

pp. 227-248 ◽

Cited By ~ 4

Author(s):

Bo Pang ◽

Erik Nijkamp ◽

Ying Nian Wu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Network Models ◽

Optimization Method ◽

Stochastic Gradient Descent ◽

Processing Unit ◽

Neural Network Models ◽

Core Concepts ◽

High Level

This review covers the core concepts and design decisions of TensorFlow. TensorFlow, originally created by researchers at Google, is the most popular one among the plethora of deep learning libraries. In the field of deep learning, neural networks have achieved tremendous success and gained wide popularity in various areas. This family of models also has tremendous potential to promote data analysis and modeling for various problems in educational and behavioral sciences given its flexibility and scalability. We give the reader an overview of the basics of neural network models such as the multilayer perceptron, the convolutional neural network, and stochastic gradient descent, the most commonly used optimization method for neural network models. However, the implementation of these models and optimization algorithms is time-consuming and error-prone. Fortunately, TensorFlow greatly eases and accelerates the research and application of neural network models. We review several core concepts of TensorFlow such as graph construction functions, graph execution tools, and TensorFlow’s visualization tool, TensorBoard. Then, we apply these concepts to build and train a convolutional neural network model to classify handwritten digits. This review is concluded by a comparison of low- and high-level application programming interfaces and a discussion of graphical processing unit support, distributed training, and probabilistic modeling with TensorFlow Probability library.