Novel framework based on deep learning and cloud analytics for smart patient monitoring and recommendation (SPMR)

Motwani, Anand; Shukla, Piyush Kumar; Pawar, Mahesh

doi:10.1007/s12652-020-02790-6

Cited by 39 publications

(30 citation statements)

References 37 publications

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“…A function bag is a technique for extracting the characteristics of an object from its surroundings. According to the classification theory (Word Bag), once numerous representative keywords have been excluded from the object, a dictionary is generated, and an object is calculated based on the number of events that occur in order to get the attribute vector [ 29 ]. When it comes to developing a robust vocabulary, a large quantity of data is necessary, which means a large dataset.…”

Section: Existing Methodsmentioning

confidence: 99%

Hybrid Encryption Method for Health Monitoring Systems Based on Machine Learning

Malmurugan

Nelson

Altuwairiqi

et al. 2022

Computational Intelligence and Neuroscience

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Numerous forms of disasters and vandalism can occur in transmission lines, which makes them vulnerable. As a result, the transmission pipes must be protected by a reliable monitoring system. When a wireless sensor network is built from disparate devices that are positioned at varying distances from one another, it can be used to monitor physical and environmental conditions in the surrounding environment. In addition to the built-in sensor on the exterior of a pipeline and sensors positioned to support bridge structures, wireless sensor networks have a range of other applications. Other uses include robotics, healthcare, environmental monitoring, and a variety of other areas of technology. It is feasible to use wireless sensor networks to monitor temperature and pressure, as well as leak detection and transmission line sabotage, among other applications. There are several different sorts of attacks that can be launched against wireless sensor networks. When it comes to information security in wireless sensor networks, cryptographic approaches play a critical role in ensuring the integrity of the data. Different types of cryptographic algorithms are now available for use in order to maintain network security. Specific difficulties must be addressed, though, and these are as follows: To strengthen the power of these algorithms, a unique hybrid encryption approach for monitoring energy transmission lines and increasing the security of wireless sensor networks is created in this study. While wireless sensor networks are being used to monitor transmission pipelines, the proposed hybrid encryption method ensures that data is transferred securely and promptly. The proposed method must follow three cryptographic principles: integrity, secrecy, and authenticity. All of the subtleties and underlying principles of the algorithm are explained in detail so that the algorithm can be put into action immediately after it is introduced.

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Section: Existing Methodsmentioning

confidence: 99%

Hybrid Encryption Method for Health Monitoring Systems Based on Machine Learning

Malmurugan

Nelson

Altuwairiqi

et al. 2022

Computational Intelligence and Neuroscience

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“…Chen et al ( 42 ) concluded that feature extraction might improve the performance of deep neural networks. In their work, Motwani et al ( 43 ) suggested a framework based on a deep neural network for intelligent patient monitoring. Motwani et al ( 44 ) used DL with cost optimization for remote patient monitoring and recommendation.…”

Section: Research Motivationmentioning

confidence: 99%

A Novel Extra Tree Ensemble Optimized DL Framework (ETEODL) for Early Detection of Diabetes

Arya

Sastry

Motwani

et al. 2022

Front. Public Health

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Diabetes has been recognized as a global medical problem for more than half a century. Patients with diabetes can benefit from the Internet of Things (IoT) devices such as continuous glucose monitoring (CGM), intelligent pens, and similar devices. Smart devices generate continuous data streams that must be processed in real-time to benefit the users. The amount of medical data collected is vast and heterogeneous since it is gathered from various sources. An accurate diagnosis can be achieved through a variety of scientific and medical techniques. It is necessary to process this streaming data faster to obtain relevant and significant knowledge. Recently, the research has concentrated on improving the prediction model's performance by using ensemble-based and Deep Learning (DL) approaches. However, the performance of the DL model can degrade due to overfitting. This paper proposes the Extra-Tree Ensemble feature selection technique to reduce the input feature space with DL (ETEODL), a predictive framework to predict the likelihood of diabetes. In the proposed work, dropout layers follow the hidden layers of the DL model to prevent overfitting. This research utilized a dataset from the UCI Machine learning (ML) repository for an Early-stage prediction of diabetes. The proposed scheme results have been compared with state-of-the-art ML algorithms, and the comparison validates the effectiveness of the predictive framework. This proposed work, which outperforms the other selected classifiers, achieves a 97.38 per cent accuracy rate. F1-Score, precision, and recall percent are 96, 97.7, and 97.7, respectively. The comparison unveils the superiority of the suggested approach. Thus, the proposed method effectively improves the performance against the earlier ML techniques and recent DL approaches and avoids overfitting.

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“…Nonlinear estimators based on KLMSs were proposed by Singh et al [ 18 ], and they outperformed traditional estimators. KLMSs estimators have poor selections of system parameter, and to overcome their limitations, nonlinear estimators, namely, EKFs and UKFs, were used in this study [ 28 ]. EKFs were selected due to their ease in implementations, but suffered from inadequate representations of nonlinear functions for 1 st order linearization, while UKFs outperformed EKFs by providing stableness by treating nonlinearities precisely.…”

Section: Literature Reviewmentioning

confidence: 99%

A Cloud-Based Machine Learning Approach to Reduce Noise in ECG Arrhythmias for Smart Healthcare Services

Jain

Alsanie

Gago

et al. 2022

Computational Intelligence and Neuroscience

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ECG (electrocardiogram) identifies and traces targets and is commonly employed in cardiac disease detection. It is necessary for monitoring precise target trajectories. Estimations of ECG are nonlinear as the parameters TDEs (time delays) and Doppler shifts are computed on receipt of echoes where EKFs (extended Kalman filters) and electrocardiogram have not been examined for computations. ECG, certain times, results in poor accuracies and low SNRs (signal-to-noise ratios), especially while encountering complicated environments. This work proposes to track online filter performances while using optimization techniques to enhance outcomes with the removal of noise in the signal. The use of cost functions can assist state corrections while lowering costs. A new parameter is optimized using IMCEHOs (Improved Mutation Chaotic Elephant Herding Optimizations) by linearly approximating system nonlinearity where multi-iterative function (Optimized Iterative UKFs) predicts a target’s unknown parameters. To obtain optimal solutions theoretically, multi-iterative function takes less iteration, resulting in shorter execution times. The proposed multi-iterative function provides numerical approximations, which are derivative-free implementations. Signals are updated in the cloud environment; the updates are received by the patients from home. The simulation evaluation results with estimators show better performances in terms of reduced NMSEs (normalized mean square errors), RMSEs (root mean squared errors), SNRs, variances, and better accuracies than current approaches. Machine learning algorithms have been used to predict the stages of heart disease, which is updated to the patient in the cloud environment. The proposed work has a 91.0% accuracy rate with an error rate of 0.05% by reducing noise levels.

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Novel framework based on deep learning and cloud analytics for smart patient monitoring and recommendation (SPMR)

Cited by 39 publications

References 37 publications

Hybrid Encryption Method for Health Monitoring Systems Based on Machine Learning

Hybrid Encryption Method for Health Monitoring Systems Based on Machine Learning

A Novel Extra Tree Ensemble Optimized DL Framework (ETEODL) for Early Detection of Diabetes

A Cloud-Based Machine Learning Approach to Reduce Noise in ECG Arrhythmias for Smart Healthcare Services

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