An intensive healthcare monitoring paradigm by using IoT based machine learning strategies

Kondaka, Lakshmi Sudha; Thenmozhi, M.; Vijayakumar, K.; Kohli, Rashi

doi:10.1007/s11042-021-11111-8

Cited by 77 publications

(16 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The cloud system presented by Onasanya et al [17] uses the IoMT and wireless sensor networks to identify and treat cancer at an early stage. To better pinpoint potential COVID-19, researchers have created a second IoMT system.…”

Section: Related Workmentioning

confidence: 99%

Healthcare Monitoring using Machine Learning Based Data Analytics

Janani¹,

Subramanian²,

Karthik³

et al. 2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

View full text Add to dashboard Cite

In this paper, we develop a machine learning based healthcare monitoring and analytics from various Internet of Medical Things (IoMT) devices for possible prediction of cardiovascular risk in patients. The study uses random forest for feature selection and then the fuzzy logic classifier is used for prediction of Cardio Vascular Disease (CVD). The simulation is conducted to test the efficacy of the proposed machine learning based data analytics model over various other methods. The results show than the proposed method has higher rate of classification accuracy in classifying the CVD with higher recall and F1-score than other methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Healthcare Monitoring using Machine Learning Based Data Analytics

Janani¹,

Subramanian²,

Karthik³

et al. 2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

View full text Add to dashboard Cite

show abstract

“…A work by research group of Kondaka discuss about the role of machine learning in assessing and managing the data on cloud for prediction of disease accurately. The deep learning strategies can be utilized to reduce the flaws and errors in IoT smart health system [28]. A detailed survey was carried out by Li and co-workers on effectiveness of machine learning with big data analytics to cater the problems like cloud security, storage allocation, communicating delay, data retrieve, etc.…”

Section: Related Workmentioning

confidence: 99%

IoT-Based Smart Health Monitoring System for COVID-19

Bhardwaj¹,

Joshi²,

Gaur

2022

SN COMPUT. SCI.

View full text Add to dashboard Cite

With the commencement of the COVID-19 pandemic, social distancing and quarantine are becoming essential practices in the world. IoT health monitoring systems prevent frequent visits to doctors and meetings between patients and medical professionals. However, many individuals require regular health monitoring and observation through medical staff. In this proposed work, we have taken advantage of the technology to make patients life easier for earlier diagnosis and treatment. A smart health monitoring system is being developed using Internet of Things (IoT) technology which is capable of monitoring blood pressure, heart rate, oxygen level, and temperature of a person. This system is helpful for rural areas or villages where nearby clinics can be in touch with city hospitals about their patient health conditions. However, if any changes occur in a patient's health based on standard values, then the IoT system will alert the physician or doctor accordingly. The maximum relative error (%ϵ r ) in the measurement of heart rate, patient body temperature and SPO 2 was found to be 2.89%, 3.03%, 1.05%, respectively, which was comparable to the commercials health monitoring system. This health monitoring system based on IoT helps out doctors to collect real-time data effortlessly. The availability of high-speed internet allows the system to monitor the parameters at regular intervals. Furthermore, the cloud platform allows data storage so that previous measurements could be retrieved in the near future. This system would help in identifying and early treatment of COVID-19 individual patients.

show abstract

“…A supercapacitor is ideal in order to generate energy to apply in which a high current cycle of charging/discharging occurs often and for short periods of time [12]. Batteries, on the other hand, are better suited to systems with rare charging/discharging cycles and require longer charge/discharge than supercapacitors [38]. Combining the two to create a composite form fits the power and energy requirements of wind energy conversion system while also reduces battery stress, resulting in an increased battery life.…”

Section: Supercapacitormentioning

confidence: 99%

A Review on Frequency Stability Enhancement and Effective Energy Storage through Various Optimization Techniques

Mishra

Ralhan

Mohiddin

2022

Mobile Information Systems

View full text Add to dashboard Cite

In recent years, the applicability of these DG units to electrical Microgrids (MGs) has grown rapidly, enabling them to contribute a large percentage of the installed generating capacity. However, the fluctuating and intermittent nature of renewable generation can adversely affect electric grid stability and operations. Conventionally, to overcome these problems, batteries are employed. Nevertheless, the quick charging and discharging cycle reduces the battery life span, resulting in an economic burden and environmental damage. To resolve these problems, short-term Distributed Energy Storage (DES) systems based on advanced technologies, such as Superconducting Magnetic Energy Storage (SMES) and Supercapacitor Energy Storage (SCES), are emerging as potential alternatives. A supplementary regulator which includes storing energy as well as a flexible AC transmission system is designed to boost the minute signal reliability. The precise optimization of multiple energy device characteristics is needed for efficient performance. The artificial intelligence methods and Particle Swarm Optimization (PSO) are used to obtain the optimum parameters in the micro-hydro system. This research examines the many Energy Storage Systems (ESSs) in power systems, particularly microgrids, and demonstrates their critical role in improving the quality of electrical systems. As a result, the ESSs were divided into several technologies based on the energy storage form and the most important technological features. In this review paper, the most common classifications are introduced/presented, summarized, and compared according to their characteristics.

show abstract

An intensive healthcare monitoring paradigm by using IoT based machine learning strategies

Cited by 77 publications

References 20 publications

Healthcare Monitoring using Machine Learning Based Data Analytics

Healthcare Monitoring using Machine Learning Based Data Analytics

IoT-Based Smart Health Monitoring System for COVID-19

A Review on Frequency Stability Enhancement and Effective Energy Storage through Various Optimization Techniques

Contact Info

Product

Resources

About