Deep Learning Based Adaptive Recurrent Neural Network for Detection of Myocardial Infarction

Myocardial infarction (MI) may precipitate severe health damage and lead to irreversible death of the heart muscle, the result of prolonged lack of oxygen if it is not treated in a timely manner. Lack of accurate and early detection techniques for this heart disease has reduced the efficiency of MI diagnosis. In this paper, the design, and implementation of an efficient deep learning algorithm called Adaptive Recurrent neural network (ARNN) is proposed for the MI detection. The main objective of the proposed work is the accurate identification of MI disease using ECG signals. ECG signal denoising has been performed using the Multi-Notch filter, which removes the specified noise frequency range. Discrete wavelet transform (DWT) is utilized for performing the feature extraction that decomposes the ECG signal into varied scales with waveletfiltering bank. After the extraction of specific QRS features, classification of the defected and normal ECG arrhythmic beat has been performed using the deep learning-based ARNN classifier. The MIT-BIH database has been used for testing and training data. The performance of the proposed algorithm is evaluated based on classification accuracy. Results that are attained include the classification accuracy of about 99.21%, 99% of sensitivity and 99.4% of specificity with PPV and NPV of about 99.4 and 99.01 values indicate the enhanced performance of our proposed work compared with the conventional LSTM-CAE and LSTM-CNN techniques.

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Developing an Individual Glucose Prediction Model Using Recurrent Neural Network

Sensors ◽

10.3390/s20226460 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6460

Author(s):

Dae-Yeon Kim ◽

Dong-Sik Choi ◽

Jaeyun Kim ◽

Sung Wan Chun ◽

Hyo-Wook Gil ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Prediction Model ◽

Glucose Level ◽

Recurrent Neural Network ◽

Learning Algorithm ◽

Percentage Error ◽

Glucose Prediction

In this study, we propose a personalized glucose prediction model using deep learning for hospitalized patients who experience Type-2 diabetes. We aim for our model to assist the medical personnel who check the blood glucose and control the amount of insulin doses. Herein, we employed a deep learning algorithm, especially a recurrent neural network (RNN), that consists of a sequence processing layer and a classification layer for the glucose prediction. We tested a simple RNN, gated recurrent unit (GRU), and long-short term memory (LSTM) and varied the architectures to determine the one with the best performance. For that, we collected data for a week using a continuous glucose monitoring device. Type-2 inpatients are usually experiencing bad health conditions and have a high variability of glucose level. However, there are few studies on the Type-2 glucose prediction model while many studies performed on Type-1 glucose prediction. This work has a contribution in that the proposed model exhibits a comparative performance to previous works on Type-1 patients. For 20 in-hospital patients, we achieved an average root mean squared error (RMSE) of 21.5 and an Mean absolute percentage error (MAPE) of 11.1%. The GRU with a single RNN layer and two dense layers was found to be sufficient to predict the glucose level. Moreover, to build a personalized model, at most, 50% of data are required for training.

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Evaluasi Performasi Ruang Warna pada Klasifikasi Diabetic Retinophaty Menggunakan Convolution Neural Network

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2021834459 ◽

2021 ◽

Vol 8 (3) ◽

pp. 619

Author(s):

Candra Dewi ◽

Andri Santoso ◽

Indriati Indriati ◽

Nadia Artha Dewi ◽

Yoke Kusuma Arbawa

Keyword(s):

Neural Network ◽

Diabetic Retinopathy ◽

Deep Learning ◽

Convolutional Neural Network ◽

Learning Algorithm ◽

Color Space ◽

Training Data ◽

Color Spaces ◽

Deep Learning Algorithm ◽

Optimal Accuracy

Semakin meningkatnya jumlah penderita diabetes menjadi salah satu faktor penyebab semakin tingginya penderita penyakit diabetic retinophaty. Salah satu citra yang digunakan oleh dokter mata untuk mengidentifikasi diabetic retinophaty adalah foto retina. Dalam penelitian ini dilakukan pengenalan penyakit diabetic retinophaty secara otomatis menggunakan citra fundus retina dan algoritme Convolutional Neural Network (CNN) yang merupakan variasi dari algoritme Deep Learning. Kendala yang ditemukan dalam proses pengenalan adalah warna retina yang cenderung merah kekuningan sehingga ruang warna RGB tidak menghasilkan akurasi yang optimal. Oleh karena itu, dalam penelitian ini dilakukan pengujian pada berbagai ruang warna untuk mendapatkan hasil yang lebih baik. Dari hasil uji coba menggunakan 1000 data pada ruang warna RGB, HSI, YUV dan L*a*b* memberikan hasil yang kurang optimal pada data seimbang dimana akurasi terbaik masih dibawah 50%. Namun pada data tidak seimbang menghasilkan akurasi yang cukup tinggi yaitu 83,53% pada ruang warna YUV dengan pengujian pada data latih dan akurasi 74,40% dengan data uji pada semua ruang warna. AbstractIncreasing the number of people with diabetes is one of the factors causing the high number of people with diabetic retinopathy. One of the images used by ophthalmologists to identify diabetic retinopathy is a retinal photo. In this research, the identification of diabetic retinopathy is done automatically using retinal fundus images and the Convolutional Neural Network (CNN) algorithm, which is a variation of the Deep Learning algorithm. The obstacle found in the recognition process is the color of the retina which tends to be yellowish red so that the RGB color space does not produce optimal accuracy. Therefore, in this research, various color spaces were tested to get better results. From the results of trials using 1000 images data in the color space of RGB, HSI, YUV and L * a * b * give suboptimal results on balanced data where the best accuracy is still below 50%. However, the unbalanced data gives a fairly high accuracy of 83.53% with training data on the YUV color space and 74,40% with testing data on all color spaces.

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Extended Kalman Filter In Recurrent Neural Network: USDIDR Forecasting Case Study

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) ◽

10.22146/ijccs.47802 ◽

2019 ◽

Vol 13 (3) ◽

pp. 293

Author(s):

Muhammad Asaduddin Hazazi ◽

Agus Sihabuddin

Keyword(s):

Neural Network ◽

Kalman Filter ◽

Extended Kalman Filter ◽

Recurrent Neural Network ◽

Learning Algorithm ◽

Regularization Parameter ◽

Training Data ◽

Stochastic Gradient Descent ◽

Computational Time ◽

Currency Exchange

Artificial Neural Networks (ANN) especially Recurrent Neural Network (RNN) have been widely used to predict currency exchange rates. The learning algorithm that is commonly used in ANN is Stochastic Gradient Descent (SGD). One of the advantages of SGD is that the computational time needed is relatively short. But SGD also has weaknesses, including SGD requiring several hyperparameters such as the regularization parameter. Besides that SGD relatively requires a lot of epoch to reach convergence. Extended Kalman Filter (EKF) as a learning algorithm on RNN is used to replace SGD with the hope of a better level of accuracy and convergence rate. This study uses IDR / USD exchange rate data from 31 August 2015 to 29 August 2018 with 70% data as training data and 30% data as test data. This research shows that RNN-EKF produces better convergent speeds and better accuracy compared to RNN-SGD.

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Comparative Analysis of Three Improved Deep Learning Architectures for Music Genre Classification

International Journal of Information Technology and Computer Science ◽

10.5815/ijitcs.2021.02.01 ◽

2021 ◽

Vol 13 (2) ◽

pp. 1-14

Author(s):

Quazi Ghulam Rafi ◽

◽

Mohammed Noman ◽

Sadia Zahin Prodhan ◽

Sabrina Alam ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Comparative Analysis ◽

Recurrent Neural Network ◽

Classification Accuracy ◽

Neural Net ◽

Learning Models ◽

Genre Classification ◽

Music Genre ◽

Music Genre Classification

Among the many music information retrieval (MIR) tasks, music genre classification is noteworthy. The categorization of music into different groups that came to existence through a complex interplay of cultures, musicians, and various market forces to characterize similarities between compositions and organize collections is known as a music genre. The past researchers extracted various hand-crafted features and developed classifiers based on them. But the major drawback of this approach was the requirement of field expertise. However, in recent times researchers, because of the remarkable classification accuracy of deep learning models, have used similar models for MIR tasks. Convolutional Neural Net- work (CNN), Recurrent Neural Network (RNN), and the hybrid model, Convolutional - Recurrent Neural Network (CRNN), are such prominently used deep learning models for music genre classification along with other MIR tasks and various architectures of these models have achieved state-of-the-art results. In this study, we review and discuss three such architectures of deep learning models, already used for music genre classification of music tracks of length of 29-30 seconds. In particular, we analyze improved CNN, RNN, and CRNN architectures named Bottom-up Broadcast Neural Network (BBNN) [1], Independent Recurrent Neural Network (IndRNN) [2] and CRNN in Time and Frequency dimensions (CRNN- TF) [3] respectively, almost all of the architectures achieved the highest classification accuracy among the variants of their base deep learning model. Hence, this study holds a comparative analysis of the three most impressive architectural variants of the main deep learning models that are prominently used to classify music genre and presents the three architecture, hence the models (CNN, RNN, and CRNN) in one study. We also propose two ways that can improve the performances of the RNN (IndRNN) and CRNN (CRNN-TF) architectures.

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Construction of cardiac arrhythmia prediction model using deep learning and gradient boosting

JURNAL INFOTEL ◽

10.20895/infotel.v13i3.683 ◽

2021 ◽

Vol 13 (3) ◽

pp. 114-119

Author(s):

Dhanar Bintang Pratama ◽

Favian Dewanta ◽

Syamsul Rizal

Keyword(s):

Neural Network ◽

Deep Learning ◽

Prediction Model ◽

Medical Staff ◽

Heart Attack ◽

Learning Algorithm ◽

Gradient Boosting ◽

Ecg Signal ◽

Deep Learning Algorithm ◽

Boosting Algorithm

Arrhythmia is a condition in which the rhythm of heartbeat becomes irregular. This condition in extreme cases can lead to fatal heart attack accidents. In order to reduce heart attack risk, appropriate early treatments should be conducted right after getting results of Arrhythmia condition, which is generated by electrocardiography ECG tools. However, reading ECG results should be done by qualified medical staff in order to diagnose the existence of arrhythmia accurately. This paper proposes a deep learning algorithm method to classify and detect the existence of arrhythmia from ECG reading. Our proposed method relies on Convolutional Neural Network (CNN) to extract feature from a single lead ECG signal and also Gradient Boosting algorithm to predict the final outcome of single lead ECG reading. This method achieved the accuracy of 96.18% and minimized the number of parameters used in CNN Layer.

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Representation Learning for Motor Imagery Recognition with Deep Neural Network

Electronics ◽

10.3390/electronics10020112 ◽

2021 ◽

Vol 10 (2) ◽

pp. 112

Author(s):

Fangzhou Xu ◽

Fenqi Rong ◽

Yunjing Miao ◽

Yanan Sun ◽

Gege Dong ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Motor Imagery ◽

Learning Algorithm ◽

Representation Learning ◽

Training Data ◽

Gradient Boosting ◽

Future Research ◽

Deep Learning Algorithm ◽

The Brain

This study describes a method for classifying electrocorticograms (ECoGs) based on motor imagery (MI) on the brain–computer interface (BCI) system. This method is different from the traditional feature extraction and classification method. In this paper, the proposed method employs the deep learning algorithm for extracting features and the traditional algorithm for classification. Specifically, we mainly use the convolution neural network (CNN) to extract the features from the training data and then classify those features by combing with the gradient boosting (GB) algorithm. The comprehensive study with CNN and GB algorithms will profoundly help us to obtain more feature information from brain activities, enabling us to obtain the classification results from human body actions. The performance of the proposed framework has been evaluated on the dataset I of BCI Competition III. Furthermore, the combination of deep learning and traditional algorithms provides some ideas for future research with the BCI systems.

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Automatic Defects Segmentation and Identification by Deep Learning Algorithm with Pulsed Thermography: Synthetic and Experimental Data

Big Data and Cognitive Computing ◽

10.3390/bdcc5010009 ◽

2021 ◽

Vol 5 (1) ◽

pp. 9

Author(s):

Qiang Fang ◽

Clemente Ibarra-Castanedo ◽

Xavier Maldague

Keyword(s):

Neural Network ◽

Experimental Data ◽

Neural Networks ◽

Deep Learning ◽

Infrared Thermography ◽

Learning Algorithm ◽

Synthetic Data ◽

Training Data ◽

Experimental Database ◽

The Neural Network

In quality evaluation (QE) of the industrial production field, infrared thermography (IRT) is one of the most crucial techniques used for evaluating composite materials due to the properties of low cost, fast inspection of large surfaces, and safety. The application of deep neural networks tends to be a prominent direction in IRT Non-Destructive Testing (NDT). During the training of the neural network, the Achilles heel is the necessity of a large database. The collection of huge amounts of training data is the high expense task. In NDT with deep learning, synthetic data contributing to training in infrared thermography remains relatively unexplored. In this paper, synthetic data from the standard Finite Element Models are combined with experimental data to build repositories with Mask Region based Convolutional Neural Networks (Mask-RCNN) to strengthen the neural network, learning the essential features of objects of interest and achieving defect segmentation automatically. These results indicate the possibility of adapting inexpensive synthetic data merging with a certain amount of the experimental database for training the neural networks in order to achieve the compelling performance from a limited collection of the annotated experimental data of a real-world practical thermography experiment.

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Aspect Based Emotion Detection and Topic Modeling on Social Media Reviews

10.3233/apc210242 ◽

2021 ◽

Author(s):

Ganesh N. Jorvekar ◽

Mohit Gangwar

Keyword(s):

Neural Network ◽

Social Media ◽

Deep Learning ◽

Recurrent Neural Network ◽

Learning Algorithm ◽

Emotion Detection ◽

Natural Language Text ◽

Deep Learning Algorithm ◽

User Comments ◽

Deep Learning Model

In recent years, the number of user comments and text materials has increased dramatically. Analysis of the emotions has drawn interest from researchers. Earlier research in the field of artificial-intelligence concentrate on identification of emotion and exploring the explanation the emotions can’t recognized or misrecognized. The association between the emotions leads to the understanding of emotion loss. In this Work we are trying to fill the gap between emotional recognition and emotional co-relation mining through social media reviews of natural language text. The association between emotions, represented as the emotional uncertainty and evolution, is mainly triggered by cognitive bias in the human emotion. Numerous types of features and Recurrent neural-network (RNN) as deep learning model provided to mine the emotion co-relation from emotion detection using text. The rule on conflict of emotions is derived on a symmetric basis. TF-IDF, NLP Features and Co-relation features has used for feature extraction as well as section and Recurrent Neural Network (RNN) and Hybrid deep learning algorithm for classification has used to demonstrates the entire research experiments. Finally evaluate the system performance with various existing system and show the effectiveness of proposed system.

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Nepali Text Document Classification Using Deep Neural Network

Tribhuvan University Journal ◽

10.3126/tuj.v33i1.28677 ◽

2019 ◽

Vol 33 (1) ◽

pp. 11-22

Author(s):

Sanjeev Subba ◽

Nawaraj Paudel ◽

Tej Bahadur Shahi

Keyword(s):

Neural Network ◽

Deep Learning ◽

Recurrent Neural Network ◽

Online News ◽

Learning Technologies ◽

Training Data ◽

Text Documents ◽

Text Document ◽

Classification Framework ◽

Text Document Classification

An automated text classification is a well-studied problem in text mining which generally demands the automatic assignment of a label or class to a particular text documents on the basis of its content. To design a computer program that learns the model form training data to assign the specific label to unseen text document, many researchers has applied deep learning technologies. For Nepali language, this is first attempt to use deep learning especially Recurrent Neural Network (RNN) and compare its performance to traditional Multilayer Neural Network (MNN). In this study, the Nepali texts were collected from online News portals and their pre-processing and vectorization was done. Finally deep learning classification framework was designed and experimented for ten experiments: five for Recurrent Neural Network and five for Multilayer Neural Network. On comparing the result of the MNN and RNN, it can be concluded that RNN outperformed the MNN as the highest accuracy achieved by MNN is 48 % and highest accuracy achieved by RNN is 63%.

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Improved COVID-19 detection using data augmentation Deep Convolution GAN and classifier DenseNet.

10.21203/rs.3.rs-235624/v1 ◽

2021 ◽

Author(s):

Debmitra Ghosh

Keyword(s):

Neural Network ◽

Deep Learning ◽

Classification Accuracy ◽

Deep Neural Network ◽

Data Augmentation ◽

Training Data ◽

X Rays ◽

X Ray ◽

Chest X Ray ◽

Synthetic Images

Abstract SARS-CoV-2 or severe acute respiratory syndrome coronavirus 2 is considered to be the cause of Coronavirus (COVID-19) which is a viral disease. The rapid spread of COVID-19 is having a detrimental effect on the global economy and health. A chest X-ray of infected patients can be considered as a crucial step in the battle against COVID-19. On retrospections, it is found that abnormalities exist in chest X-rays of patients suggestive of COVID-19. This sparked the introduction of a variety of deep learning systems and studies which have shown that the accuracy of COVID-19 patient detection through the use of chest X-rays is strongly optimistic. Although there are certain shortcomings like deep learning networks like convolutional neural networks (CNNs) need a substantial amount of training data but the outbreak is recent, so it is large datasets of radiographic images of the COVID-19 infected patients are not available in such a short time. Here, in this research, we present a method to generate synthetic chest X-ray (CXR) images by developing a Deep Convolution Generative Adversarial Network-based model. In addition, we demonstrate that the synthetic images produced from DCGAN can be utilized to enhance the performance of CNN for COVID-19 detection. Classification using CNN alone yielded 85% accuracy. Although there are several models available, we chose MobileNet as it is a lightweight deep neural network, with fewer parameters and higher classification accuracy. Here we are using a deep neural network-based model to diagnose COVID-19 infected patients through radiological imaging of 5,859 Chest X-Ray images. We are using a Deep Convolutional Neural Network and a pre-trained model “DenseNet 121” for two new label classes (COVID-19 and Normal). To improve the classification accuracy, in our work we have further reduced the number of network parameters by introducing dense blocks that are proposed in DenseNets into MobileNet. By adding synthetic images produced by DCGAN, the accuracy increased to 97%. Our goal is to use this method to speed up COVID-19 detection and lead to more robust systems of radiology.

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