Application of Internet of Things on the Healthcare Field Using Convolutional Neural Network Processing

Mohana, J.; Yakkala, Bhaskarrao; Vimalnath, S.; Mansingh, P. M. Benson; Yuvaraj, N.; Srihari, K.; Sasikala, G.; Mahalakshmi, V.; Abdullah, R. Yasir; Sundramurthy, Venkatesa Prabhu

doi:10.1155/2022/1892123

Cited by 26 publications

(10 citation statements)

References 19 publications

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“…Among the most common NTDs types are borne viruses regarded as a virus collection and naturally transmit between the hosts. Although the clinical appearance of these disorders is well known [ 2 , 3 ], it is difficult to make an appropriate diagnosis of these conditions. In order to make an accurate arboviral diagnosis, a range of factors must be considered, the most prevalent of which are included in the scientific literature.…”

Section: Introductionmentioning

confidence: 99%

Recurrent Neural Networks for Feature Extraction from Dengue Fever

Daniel¹,

Sherly²,

Ponnuramu

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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Dengue fever modelling in endemic locations is critical to reducing outbreaks and improving vector-borne illness control. Early projections of dengue are a crucial tool for disease control because of the unavailability of treatments and universal vaccination. Neural networks have made significant contributions to public health in a variety of ways. In this paper, we develop a deep learning modelling using random forest (RF) that helps extract the features of the dengue fever from the text datasets. The proposed modelling involves the data collection, preprocessing of the input texts, and feature extraction. The extracted features are studied to test how well the feature extraction using RF is effective on dengue datasets. The simulation result shows that the proposed method achieves higher degree of accuracy that offers an improvement of more than 12% than the existing methods in extracting the features from the input datasets than the other feature extraction methods. Further, the study reduces the errors associated with feature extraction that is 10% lesser than the other existing methods, and this shows the efficacy of the model.

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Section: Introductionmentioning

confidence: 99%

Recurrent Neural Networks for Feature Extraction from Dengue Fever

Daniel¹,

Sherly²,

Ponnuramu

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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show abstract

“…Unfortunately, the difficulty in examining the ECG signal pattern is not only confined to this scenario. ECG signals consist of short durations and small amplitudes which can be measured in milliseconds and millivolts with great intra-and interobserver variability that can affect the perceptibility of such signs [6,7]. It is a time-consuming process to scrutinize numerous ECG signals and the high chances are high for likely misunderstanding of important data.…”

Section: Introductionmentioning

confidence: 99%

Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

Obayya¹,

Nemri²,

Alharbi³

et al. 2023

Computers, Materials &Amp; Continua

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With new developments experienced in Internet of Things (IoT), wearable, and sensing technology, the value of healthcare services has enhanced. This evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based healthcare. Biomedical Electrocardiogram (ECG) signals are generally utilized in examination and diagnosis of Cardiovascular Diseases (CVDs) since it is quick and non-invasive in nature. Due to increasing number of patients in recent years, the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from patients. In such scenario computer-assisted automated diagnostic tools are important for classification of ECG signals. The current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECG Signal Classification (IBADL-BECGC) approach. To accomplish this, the proposed IBADL-BECGC model initially pre-processes the input signals. Besides, IBADL-BECGC model applies NasNet model to derive the features from test ECG signals. In addition, Improved Bat Algorithm (IBA) is employed to optimally fine-tune the hyperparameters related to NasNet approach. Finally, Extreme Learning Machine (ELM) classification algorithm is executed to perform ECG classification method. The presented IBADL-BECGC model was experimentally validated utilizing benchmark dataset. The comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.

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“…9 Similarly, the IoT cloud computing platform has been integrated with several areas of nursing, resulting in a more efficient, more intelligent and more user-friendly IN model. 10,11 However, the application of IoT cloud-based IN to the monitoring and nursing of pediatric pneumonia sepsis treated with HGG has not been reported in China or abroad.…”

Section: Introductionmentioning

confidence: 99%

Application of intelligent nursing based on cloud computing of internet of things in children with pneumonia and sepsis treated with human gamma globulin

Qin¹,

Liu

Shao³

2023

Eur J Inflamm

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Purpose: To explore the application of intelligent nursing (IN) based on the Internet of Things (IoT) in children with pneumonia and sepsis treated with human gamma globulin (HGG). Methods: A total of 200 children with pneumonia combined with sepsis who attended the First People’s Hospital of Shangqiu from January 1, 2020 to February 13, 2022 were consecutively collected. Children were randomly divided into IN group and routine nursing (RN) group, with 100 children in each group. All children received standard anti-infection treatment along with intravenous HGG. In IN group, IN measures based on the IoT cloud computing platform monitored the treatment process of children with HGG throughout the whole process, while children in the RN group only received RN measures. Information on both groups was collected from the medical records, such as gender, age, duration of hospitalization, fever, antibiotic use, serological indicators, pulmonary function indicators, immune function indicators and adverse effects of HGG. Multi-factorial logistic regression was performed to access the correlation between IN and the duration of hospitalization and a range of other factors studied above. Results: After adjusting for numerous confounding factors, multifactorial logistic regression revealed that the application of IN was associated with a shorter duration of hospitalization ( p = .030) and lower white blood cell (WBC) and creatinine (Cr) levels in post-treatment children ( p = .003, p = .010). It was also associated with higher levels of forced expiratory volume in one second (FEV1), forced vital capacity (FVC) and peak expiratory flow (PEV) after treatment ( p = .014, p = .001, p = .002) and higher levels of immune CD4+/CD8+ ratio after treatment ( p = .001) and reduced symptoms of vomiting among the adverse effects ( p = .047). Conclusion: The IoT cloud-based IN model significantly improved the efficacy of HGG in the treatment of pneumonia sepsis in children and reduce occurrence of some adverse reactions.

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Application of Internet of Things on the Healthcare Field Using Convolutional Neural Network Processing

Cited by 26 publications

References 19 publications

Recurrent Neural Networks for Feature Extraction from Dengue Fever

Recurrent Neural Networks for Feature Extraction from Dengue Fever

Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

Application of intelligent nursing based on cloud computing of internet of things in children with pneumonia and sepsis treated with human gamma globulin

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