Cloud and IoT based privacy preserved e-Healthcare system using secured storage algorithm and deep learning

2020 ◽  
Vol 39 (3) ◽  
pp. 3011-3023
Author(s):  
T. Munirathinam ◽  
Sannasi Ganapathy ◽  
Arputharaj Kannan
Keyword(s):  
Deep Learning ◽  
Disease Severity ◽  
Healthcare System ◽  
Data Storage ◽  
Original Data ◽  
Medical Data ◽  
Heart Beat ◽  
Current Status ◽  
Learning Approach ◽  
Proposed Model

Rapid introduction of new diseases and the severity improvement of existing dead diseases due to the bad food habits and lacking of awareness over the health conscious food items those are available in the market. The Internet of Things (IoT) gets more attention for reducing the disease severity by knowing the current status of their disease according to the dynamic inputs of human body through IoT devices today. Moreover, the combination of IoT and cloud computing technologies are playing major roles in e-health services. In this scenario, security is a major issue in the process of data storage and communication. For this purpose, we propose a new e-healthcare system for monitoring the dead disease level by using the technologies such as IoT and Cloud with the help of deep learning approach and fuzzy rules with temporal features. In this system, the medical data is retrieved from various located patients who are utilizing the e-healthcare assisting devices. First, the retrieved and encrypted data is stored in cloud by applying a newly proposed secured cloud storage algorithm. Second, the stored data can be retrieved the data as original data by applying the decryption process. Third, a new cloud framework is introduced for predicting the status of heart beat rates and diabetes levels by using the medical data that is created by applying the UCI Repository dataset. In addition, a new deep learning approach which applies the Convolutional Neural Network for predicting the disease severity. The experimental results are obtained by conducting various experiments for the proposed model by using the dataset and the hospital patient records. The proposed model results outperforms the available disease prediction systems in terms of prediction accuracy.

ECG Image Classification Using Deep Learning Approach

2021 ◽  
pp. 343-357
Author(s):  
Pratik Kanani ◽  
Mamta Chandraprakash Padole
Keyword(s):  
Deep Learning ◽  
Sampling Frequency ◽  
Heart Beat ◽  
Learning Approach ◽  
Recording Device ◽  
Noisy Signal ◽  
Arrhythmia Detection ◽  
Ecg Signals ◽  
Ecg Arrhythmia ◽  
Deep Learning Model

Cardiovascular diseases are a major cause of death worldwide. Cardiologists detect arrhythmias (i.e., abnormal heart beat) with the help of an ECG graph, which serves as an important tool to recognize and detect any erratic heart activity along with important insights like skipping a beat, a flutter in a wave, and a fast beat. The proposed methodology does ECG arrhythmias classification by CNN, trained on grayscale images of R-R interval of ECG signals. Outputs are strictly in the terms of a label that classify the beat as normal or abnormal with which abnormality. For training purpose, around one lakh ECG signals are plotted for different categories, and out of these signal images, noisy signal images are removed, then deep learning model is trained. An image-based classification is done which makes the ECG arrhythmia system independent of recording device types and sampling frequency. A novel idea is proposed that helps cardiologists worldwide, although a lot of improvements can be done which would foster a “wearable ECG Arrhythmia Detection device” and can be used by a common man.

scholarly journals Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 982 ◽  
Author(s):  
Hyo Lee ◽  
Ihsan Ullah ◽  
Weiguo Wan ◽  
Yongbin Gao ◽  
Zhijun Fang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Large Scale ◽  
Recognition Rate ◽  
Experimental Results ◽  
Learning Approach ◽  
Deep Model ◽  
Proposed Model ◽  
Real Time Applications ◽  
Model Recognition

Make and model recognition (MMR) of vehicles plays an important role in automatic vision-based systems. This paper proposes a novel deep learning approach for MMR using the SqueezeNet architecture. The frontal views of vehicle images are first extracted and fed into a deep network for training and testing. The SqueezeNet architecture with bypass connections between the Fire modules, a variant of the vanilla SqueezeNet, is employed for this study, which makes our MMR system more efficient. The experimental results on our collected large-scale vehicle datasets indicate that the proposed model achieves 96.3% recognition rate at the rank-1 level with an economical time slice of 108.8 ms. For inference tasks, the deployed deep model requires less than 5 MB of space and thus has a great viability in real-time applications.

scholarly journals Deep Learning and Autoregressive Approach for Prediction of Time Series Data

2021 ◽  
Vol 3 (2) ◽  
pp. 1
Author(s):  
Akhter Mohiuddin Rather
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Statistical Models ◽  
Time Complexity ◽  
Time Series Data ◽  
Series Data ◽  
Learning Approach ◽  
Proposed Model ◽  
Artificial Neural

Fractional This paper proposes a deep learning approach for prediction of nonstationary data. A new regression scheme has been used in the proposed model. Any non-stationary data can be used to test the efficiency of the proposed model, however in this work stock data has been used due to the fact that stock data has a property of being nonlinear or non-stationary in nature. Beside using proposed model, predictions were also obtained using some statistical models and artificial neural networks. Traditional statistical models did not yield any expected results; artificial neural networks resulted into high time complexity. Therefore, deep learning approach seemed to be the best method as of today in dealing with such problems wherein time complexity and excellent predictions are of concern.

scholarly journals A New Deep Learning Approach for Anomaly Base IDS using Memetic Classifier

2017 ◽  
Vol 12 (5) ◽  
pp. 677 ◽  
Author(s):  
Shahriar Mohammadi ◽  
Amin Namadchian
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Large Scale ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
Learning Approach ◽  
Detection Systems ◽  
Video Mining ◽  
Proposed Model ◽  
Evaluation Algorithm

A model of an intrusion-detection system capable of detecting attack in computer networks is described. The model is based on deep learning approach to learn best features of network connections and Memetic algorithm as final classifier for detection of abnormal traffic.One of the problems in intrusion detection systems is large scale of features. Which makes typical methods data mining method were ineffective in this area. Deep learning algorithms succeed in image and video mining which has high dimensionality of features. It seems to use them to solve the large scale of features problem of intrusion detection systems is possible. The model is offered in this paper which tries to use deep learning for detecting best features.An evaluation algorithm is used for produce final classifier that work well in multi density environments.We use NSL-KDD and Kdd99 dataset to evaluate our model, our findings showed 98.11 detection rate. NSL-KDD estimation shows the proposed model has succeeded to classify 92.72% R2L attack group.

scholarly journals DEEP LEARNING APPROACH FOR URBAN MAPPING

2022 ◽  
Vol XLVI-4/W3-2021 ◽  
pp. 261-266
Author(s):  
C. Najjaj ◽  
H. Rhinane ◽  
A. Hilali
Keyword(s):  
Deep Learning ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Semantic Segmentation ◽  
Learning Approach ◽  
Building Detection ◽  
Building Extraction ◽  
Great Precision ◽  
Proposed Model ◽  
Weight Model

Abstract. Researchers in computer vision and machine learning are becoming increasingly interested in image semantic segmentation. Many methods based on convolutional neural networks (CNNs) have been proposed and have made considerable progress in the building extraction mission. This other methods can result in suboptimal segmentation outcomes. Recently, to extract buildings with a great precision, we propose a model which can recognize all the buildings and present them in mask with white and the other classes in black. This developed network, which is based on U-Net, will boost the model's sensitivity. This paper provides a deep learning approach for building detection on satellite imagery applied in Casablanca city, Firstly, to begin we describe the terminology of this field. Next, the main datasets exposed in this project which’s 1000 satellite imagery. Then, we train the model UNET for 25 epochs on the training and validation datasets and testing the pretrained weight model with some unseen satellite images. Finally, the experimental results show that the proposed model offers good performance obtained as a binary mask that extract all the buildings in the region of Casablanca with a higher accuracy and entirety to achieve an average F1 score on test data of 0.91.

scholarly journals A Deep Learning Prediction Model Based on Extreme-Point Symmetric Mode Decomposition and Cluster Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Guohui Li ◽  
Songling Zhang ◽  
Hong Yang
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Extreme Point ◽  
Prediction Models ◽  
Original Data ◽  
Symmetric Mode ◽  
Intrinsic Mode Functions ◽  
Model Based ◽  
Mode Decomposition ◽  
Proposed Model

Aiming at the irregularity of nonlinear signal and its predicting difficulty, a deep learning prediction model based on extreme-point symmetric mode decomposition (ESMD) and clustering analysis is proposed. Firstly, the original data is decomposed by ESMD to obtain the finite number of intrinsic mode functions (IMFs) and residuals. Secondly, the fuzzy c-means is used to cluster the decomposed components, and then the deep belief network (DBN) is used to predict it. Finally, the reconstructed IMFs and residuals are the final prediction results. Six kinds of prediction models are compared, which are DBN prediction model, EMD-DBN prediction model, EEMD-DBN prediction model, CEEMD-DBN prediction model, ESMD-DBN prediction model, and the proposed model in this paper. The same sunspots time series are predicted with six kinds of prediction models. The experimental results show that the proposed model has better prediction accuracy and smaller error.

scholarly journals Character Recognition of Components Mounted on Printed Circuit Board Using Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2921
Author(s):  
Sumyung Gang ◽  
Ndayishimiye Fabrice ◽  
Daewon Chung ◽  
Joonjae Lee
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Printed Circuit Boards ◽  
Printed Circuit Board ◽  
Environmental Changes ◽  
Original Data ◽  
Circuit Board ◽  
Circuit Boards ◽  
Printed Circuit ◽  
Proposed Model

As the size of components mounted on printed circuit boards (PCBs) decreases, defect detection becomes more important. The first step in an inspection involves recognizing and inspecting characters printed on parts attached to the PCB. In addition, since industrial fields that produce PCBs can change very rapidly, the style of the collected data may vary between collection sites and collection periods. Therefore, flexible learning data that can respond to all fields and time periods are needed. In this paper, large amounts of character data on PCB components were obtained and analyzed in depth. In addition, we proposed a method of recognizing characters by constructing a dataset that was robust with various fonts and environmental changes using a large amount of data. Moreover, a coreset capable of evaluating an effective deep learning model and a base set using n-pick sampling capable of responding to a continuously increasing dataset were proposed. Existing original data and the EfficientNet B0 model showed an accuracy of 97.741%. However, the accuracy of our proposed model was increased to 98.274% for the coreset of 8000 images per class. In particular, the accuracy was 98.921% for the base set with only 1900 images per class.

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