Abnormal level of stress is the root indicator factor to have significant impact over the health of heart and there is a close relationship between the stress levels with heart rate. Review of the existing literature showcase that there has been various work that has been carried out towards investigation of considering heart rate with an internet-of-things (IoT) system. Apart from this, existing system doesnt offer any instantaneous solution where certain intimation is offered in real-time to the user with wearables as a solution to control the stress condition. Therefore, the current paper introduces a novel framework where the sampled heart rates of the patients are captured by IoT deivices. The aggregated data are further forwarded to the cloud analytic system that uses correlation to extract the appropriate message. The system after being applied with teh machine learning approach could further extract the elite outcome followed by forwarding the contextual data to teh user. Using an analytical modelliig, the proposed system shows that it offers better accuracy and reduced processing time when compared with other machine learning approach and thereby it proves to be cost effective solution in IoT system over medical case study.
Survey of IOT based Patient Health Monitoring Systemdbpublications
The Internet of things has provided a promising opportunity and applications for medical services is one of the most important way or solution for taking care of population which is in rapid growth. Internet of things consists of communication and sensors; wireless body area network is highly suitable tool for the medical IOT device. In this survey we discuss mainly on practical issues for implementation of WBAN to health care service tool for the medical devices. The IoT applications are key enabling technologies in industries. A main aim of this survey paper is that it summarizes the present state-of-the-art IOT in industries and also in workflow hospitals systematically. In recent years wide range of opportunity and powerful of IOT applications are developed in industry. The health monitoring system is a big challenge for several researchers. In this paper introduced on the survey of different IOT applications are used for the health monitoring system. The IoT applications are used to decrease the problems which are related to health care system.
Real-time Heart Pulse Monitoring Technique Using Wireless Sensor Network and ...IJECEIAES
Wireless Sensor Networks (WSNs) for healthcare have emerged in the recent years. Wireless technology has been developed and used widely for different medical fields. This technology provides healthcare services for patients, especially who suffer from chronic diseases. Services such as catering continuous medical monitoring and get rid of disturbance caused by the sensor of instruments. Sensors are connected to a patient by wires and become bed-bound that less from the mobility of the patient. In this paper, proposed a real-time heart pulse monitoring system via conducted an electronic circuit architecture to measure Heart Pulse (HP) for patients and display heart pulse measuring via smartphone and computer over the network in real-time settings. In HP measuring application standpoint, using sensor technology to observe heart pulse by bringing the fingerprint to the sensor via used Arduino microcontroller with Ethernet shield to connect heart pulse circuit to the internet and send results to the web server and receive it anywhere. The proposed system provided the usability by the user (userfriendly) not only by the specialist. Also, it offered speed andresults accuracy, the highest availability with the user on an ongoing basis, and few cost.
There are a number of scopes for IoT in order to make a difference in lives of patients. The devices can capture as well as monitor related data regarding patient and allows the providers to obtain the insights without bringing the patients visiting. The procedure can assist the patient results as well as preventing the possible communications for the process that involves risk. However, lack of electronic health record (EHR) system integration is one of the major issues faced while using IoT in healthcare. Some of the EHR systems allow the patients importing data into the record. However, it remains limited to a few dominant where the EHR players as well as leaves providers unspecific of the processing data that can be helpful for the organization to use the process. The challenges for interoperability in order to keep data in distinctive medical devices depend on the purpose and ordering physician. by Vishal Dineshkumar Soni 2018. An IoT Based Patient Health Monitoring System. International Journal on Integrated Education. 1, 1 (Dec. 2018), 43-48. DOI:https://doi.org/10.31149/ijie.v1i1.481. https://journals.researchparks.org/index.php/IJIE/article/view/481/458 https://journals.researchparks.org/index.php/IJIE/article/view/481
Predictive Data Mining for Converged Internet ofJames Kang
Kang, J. J., Adibi, S., Larkin, H., & Luan, T. (2016). Predictive data mining for Converged Internet of Things: A Mobile Health perspective. In Telecommunication Networks and Applications Conference (ITNAC), 2015 International (pp. 5-10). IEEE Xplore: IEEE. doi: http://dx.doi.org/10.1109/ATNAC.2015.7366781
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASESamsjournal
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform
machine learning on the data by training a model and finally develop a Plotly Dash analytical application
integrating the model for visualization near real-time.Findings show that emergency cases are detected
using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The
model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT
gadgets and machine learning classification.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASESpijans
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform
machine learning on the data by training a model and finally develop a Plotly Dash analytical application
integrating the model for visualization near real-time.Findings show that emergency cases are detected
using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The
model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT
gadgets and machine learning classification.
Ecis final paper-june2017_two way architecture between iot sensors and cloud ...Oliver Neuland
Improving health care with IoT - Research into a weight monitoring bed - ECIS 2017 paper.
Resulting from smart furniture applications research project in Germany, Oliver Neuland and partners from AUT developed a smart bed concept which utilizes weight monitoring for AAL and elderly care. Initially strategies were applied to find meaningful use cases, later a prototype was developed. Here a paper presented during ECIS in Portugal which describes the architecture of the prototype.
Survey of IOT based Patient Health Monitoring Systemdbpublications
The Internet of things has provided a promising opportunity and applications for medical services is one of the most important way or solution for taking care of population which is in rapid growth. Internet of things consists of communication and sensors; wireless body area network is highly suitable tool for the medical IOT device. In this survey we discuss mainly on practical issues for implementation of WBAN to health care service tool for the medical devices. The IoT applications are key enabling technologies in industries. A main aim of this survey paper is that it summarizes the present state-of-the-art IOT in industries and also in workflow hospitals systematically. In recent years wide range of opportunity and powerful of IOT applications are developed in industry. The health monitoring system is a big challenge for several researchers. In this paper introduced on the survey of different IOT applications are used for the health monitoring system. The IoT applications are used to decrease the problems which are related to health care system.
Real-time Heart Pulse Monitoring Technique Using Wireless Sensor Network and ...IJECEIAES
Wireless Sensor Networks (WSNs) for healthcare have emerged in the recent years. Wireless technology has been developed and used widely for different medical fields. This technology provides healthcare services for patients, especially who suffer from chronic diseases. Services such as catering continuous medical monitoring and get rid of disturbance caused by the sensor of instruments. Sensors are connected to a patient by wires and become bed-bound that less from the mobility of the patient. In this paper, proposed a real-time heart pulse monitoring system via conducted an electronic circuit architecture to measure Heart Pulse (HP) for patients and display heart pulse measuring via smartphone and computer over the network in real-time settings. In HP measuring application standpoint, using sensor technology to observe heart pulse by bringing the fingerprint to the sensor via used Arduino microcontroller with Ethernet shield to connect heart pulse circuit to the internet and send results to the web server and receive it anywhere. The proposed system provided the usability by the user (userfriendly) not only by the specialist. Also, it offered speed andresults accuracy, the highest availability with the user on an ongoing basis, and few cost.
There are a number of scopes for IoT in order to make a difference in lives of patients. The devices can capture as well as monitor related data regarding patient and allows the providers to obtain the insights without bringing the patients visiting. The procedure can assist the patient results as well as preventing the possible communications for the process that involves risk. However, lack of electronic health record (EHR) system integration is one of the major issues faced while using IoT in healthcare. Some of the EHR systems allow the patients importing data into the record. However, it remains limited to a few dominant where the EHR players as well as leaves providers unspecific of the processing data that can be helpful for the organization to use the process. The challenges for interoperability in order to keep data in distinctive medical devices depend on the purpose and ordering physician. by Vishal Dineshkumar Soni 2018. An IoT Based Patient Health Monitoring System. International Journal on Integrated Education. 1, 1 (Dec. 2018), 43-48. DOI:https://doi.org/10.31149/ijie.v1i1.481. https://journals.researchparks.org/index.php/IJIE/article/view/481/458 https://journals.researchparks.org/index.php/IJIE/article/view/481
Predictive Data Mining for Converged Internet ofJames Kang
Kang, J. J., Adibi, S., Larkin, H., & Luan, T. (2016). Predictive data mining for Converged Internet of Things: A Mobile Health perspective. In Telecommunication Networks and Applications Conference (ITNAC), 2015 International (pp. 5-10). IEEE Xplore: IEEE. doi: http://dx.doi.org/10.1109/ATNAC.2015.7366781
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASESamsjournal
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform
machine learning on the data by training a model and finally develop a Plotly Dash analytical application
integrating the model for visualization near real-time.Findings show that emergency cases are detected
using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The
model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT
gadgets and machine learning classification.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASESpijans
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform
machine learning on the data by training a model and finally develop a Plotly Dash analytical application
integrating the model for visualization near real-time.Findings show that emergency cases are detected
using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The
model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT
gadgets and machine learning classification.
Ecis final paper-june2017_two way architecture between iot sensors and cloud ...Oliver Neuland
Improving health care with IoT - Research into a weight monitoring bed - ECIS 2017 paper.
Resulting from smart furniture applications research project in Germany, Oliver Neuland and partners from AUT developed a smart bed concept which utilizes weight monitoring for AAL and elderly care. Initially strategies were applied to find meaningful use cases, later a prototype was developed. Here a paper presented during ECIS in Portugal which describes the architecture of the prototype.
An innovative IoT service for medical diagnosis IJECEIAES
Due to the misdiagnose of diseases that increased recently in a scarily manner, many researchers devoted their efforts and deployed technologies to improve the medical diagnosis process and reducing the resulted risk. Accordingly, this paper proposed architecture of a cyber-medicine service for medical diagnosis, based internet of things (IoT) and cloud infrastructure (IaaS). This service offers a shared environment for medical data, and extracted knowledge and findings between patients and doctors in an interactive, secured, elastic and reliable way. It predicts the medical diagnosis and provides an appropriate treatment for the given symptoms and medical conditions based on multiple classifiers to assure high accuracy. Moreover, it entails different functionalities such as on-demand searching for scientific papers and diseases description for unrecognized combination of symptoms using web crawler to enrich the results. Where such searching results from crawler, are processed, analyzed and added to the resident knowledge base (KB) to achieve adaptability and subsidize the service predictive ability.
An intelligent approach to take care of mother and baby healthIJECEIAES
This is the era of technology and is widely used in every sector. In Bangladesh the use of technology is increasing day by day in many sectors. Health sector is one of them. This research is designed and developed to help the pregnant women to get weekly information on development and conditions of their health and the growing child inside their womb. This system will notify expectant mothers automatically about their health checkup date and time. It provides general and special health information to the expectant mothers. It is designed with user friendly interface so that an expectant mother can use this system very effectively. This system allows a unique secure login system and provides a unique suggestion to the expectant mothers.This system is very user friendly and useful.
Artificial intelligence (AI), machine learning, and data science have started to shape the delivery of health services. We see this in every critical step, from patient scheduling management to physically assisted surgery.
Artificial Intelligence (AI) has revolutionized in information technology.AI is a subfield of computer science that includes the creation of intelligent machines and software that work and react like human beings. AI and its Applications gets used in various fields of life of humans as expert system solve the complex problems in various areas as science, engineering, business, medicine, video games and Advertising. But “Do any traffic lights use Artificial Intelligence??”, I thought a lot of this when waiting in a red light. This paper gives an overview of Artificial Intelligence and its applications used in human life. This will explore the current use of Artificial Intelligence technologies in Network Intrusion for protecting computer and communication networks from intruders, in the medical area-medicine, to improve hospital inpatient care, for medical image classification, in the accounting databases to mitigate the problems of it, in the computer games, and in Advertising. Also, it will show artificial intelligence principle and how they were applying in traffic signal control, how they solve some traffic problem in actual. This paper gives an introduction to a self-learning system based on RBF neural network and how the system can simulate the traffic police’s experience. This paper is focusing on how to evaluate the effect of the control with the changing of the traffic and adjust the signal with the different techniques of Artificial Intelligence.
Medical System and Artificial Intelligence: How AI assists hospital-dependent...AI Publications
The main objective of this study is to examine how artificial intelligence assists hospital-dependent patients and explore the role of artificial intelligence in the medical system. Hospital-dependent patients have become common in current society due to the elderly with multiple chronic conditions and the COVID-19 infection patient. Thus, it is undeniable that the medical field is lacking healthcare workers. However, in a globalized world, artificial intelligence, the field of science and engineering technology that makes intelligent machines perform given tasks, is chosen to be used as a tool for assisting hospital-dependent patients and collecting databases from the patients. Nevertheless, the paper will cover the use of artificial intelligence in the medical system, hospital-dependent patients as well as provide both positive and negative aspects and the comparison of using artificial intelligence instead of human intelligence. To conclude, we detail how artificial intelligence can take part in the medical system, assist hospital-dependent patients and study further the future of artificial intelligence in the medical system.
Accessing Information of Emergency Medical Services through Internet of ThingsIJARIIT
IoT is the advanced technology which is use in daily life. IoT make easy to connect different smart devices with
each other by using the internet. IOT is given the ability to computer system to run application program from different
vendors. So in this paper we are accessing the data based on IoT technology for emergency medical services. The fast
development of Internet of Thing.
An Intelligent Healthcare Serviceto Monitor Vital Signs in Daily Life – A Cas...IJERA Editor
Vital signs monitoring for elderly in daily life environment is a promising concept that efficiently can provide medical services to people at home. However, make the system self-served and functioning as personalized provision makes the challenge even larger. This paper presents a case study on a Health-IoT system where an intelligent healthcare service is developed to monitor vital signs in daily life. Here, a generic Health-IoT framework with a Clinical Decision Support System (CDSS) is presented. The generic framework is mainly focused on the supporting sensors, communication media, secure and safe data communication, cloud-based storage, and remote accesses of the data. The CDSS is used to provide a personalized report on persons‟ health condition based on daily basis observation on vital signs. Six participants, from Spain (n=3) and Slovenia (n=3) have been using the proposed healthcare system for eight weeks (e.g. 300+ health measurements) in their home environments to monitor their health. The sensitivity, specificity and overall accuracy of the DSS‟s classification are achieved as 90%, 97% and 96% respectively while k=2 i.e., top 2 most similar retrieved cases are considered. The initial user evaluation resultdemonstrates the feasibility and performance of the implemented system through the proposed framework.
Outline
Value Based Healthcare System – How it is seen today
Healthcare Challenge & IoT as a Solution
IoT – Big Data Structure
Recent Trends in IoT Big Data Analytics
Challenges & Our Future
In-depth Knowledge of
What causes the most premature death?
Distribution of Disease burden from 1990 - 2020
Challenges in Healthcare
Future Healthcare
IoT Machine Talking to Machine
Prediction of IoT Usage
About PEPGRA HEALTHCARE,
A leading healthcare communication firm with years of excellence serving clients with a dedicated team of Medical, Regulatory and Scientific writers specialized in all therapeutic areas.
Contact us at :
UK: +44-1143520021
US/Canada: +1-972-502-9262
India: +91-8754446690
info@pepgra.com
www.pepgra.com
Understanding Physicians' Adoption of Health Cloudscsandit
Recently proposed health applications are able to enforce essential advancements in the
healthcare sector. The design of these innovative solutions is often enabled through the cloud
computing model. With regards to this technology, high concerns about information security
and privacy are common in practice. These concerns with respect to sensitive medical
information could be a hurdle to successful adoption and consumption of cloud-based health
services, despite high expectations and interest in these services. This research attempts to
understand behavioural intentions of healthcare professionals to adopt health clouds in their
clinical practice. Based on different established theories on IT adoption and further related
theoretical insights, we develop a research model and a corresponding instrument to test the
proposed research model using the partial least squares (PLS) approach. We suppose that
healthcare professionals’ adoption intentions with regards to health clouds will be formed by
their outweighing two conflicting beliefs which are performance expectancy and medical
information security and privacy concerns associated with the usage of health clouds. We
further suppose that security and privacy concerns can be explained through perceived risks.
An innovative IoT service for medical diagnosis IJECEIAES
Due to the misdiagnose of diseases that increased recently in a scarily manner, many researchers devoted their efforts and deployed technologies to improve the medical diagnosis process and reducing the resulted risk. Accordingly, this paper proposed architecture of a cyber-medicine service for medical diagnosis, based internet of things (IoT) and cloud infrastructure (IaaS). This service offers a shared environment for medical data, and extracted knowledge and findings between patients and doctors in an interactive, secured, elastic and reliable way. It predicts the medical diagnosis and provides an appropriate treatment for the given symptoms and medical conditions based on multiple classifiers to assure high accuracy. Moreover, it entails different functionalities such as on-demand searching for scientific papers and diseases description for unrecognized combination of symptoms using web crawler to enrich the results. Where such searching results from crawler, are processed, analyzed and added to the resident knowledge base (KB) to achieve adaptability and subsidize the service predictive ability.
An intelligent approach to take care of mother and baby healthIJECEIAES
This is the era of technology and is widely used in every sector. In Bangladesh the use of technology is increasing day by day in many sectors. Health sector is one of them. This research is designed and developed to help the pregnant women to get weekly information on development and conditions of their health and the growing child inside their womb. This system will notify expectant mothers automatically about their health checkup date and time. It provides general and special health information to the expectant mothers. It is designed with user friendly interface so that an expectant mother can use this system very effectively. This system allows a unique secure login system and provides a unique suggestion to the expectant mothers.This system is very user friendly and useful.
Artificial intelligence (AI), machine learning, and data science have started to shape the delivery of health services. We see this in every critical step, from patient scheduling management to physically assisted surgery.
Artificial Intelligence (AI) has revolutionized in information technology.AI is a subfield of computer science that includes the creation of intelligent machines and software that work and react like human beings. AI and its Applications gets used in various fields of life of humans as expert system solve the complex problems in various areas as science, engineering, business, medicine, video games and Advertising. But “Do any traffic lights use Artificial Intelligence??”, I thought a lot of this when waiting in a red light. This paper gives an overview of Artificial Intelligence and its applications used in human life. This will explore the current use of Artificial Intelligence technologies in Network Intrusion for protecting computer and communication networks from intruders, in the medical area-medicine, to improve hospital inpatient care, for medical image classification, in the accounting databases to mitigate the problems of it, in the computer games, and in Advertising. Also, it will show artificial intelligence principle and how they were applying in traffic signal control, how they solve some traffic problem in actual. This paper gives an introduction to a self-learning system based on RBF neural network and how the system can simulate the traffic police’s experience. This paper is focusing on how to evaluate the effect of the control with the changing of the traffic and adjust the signal with the different techniques of Artificial Intelligence.
Medical System and Artificial Intelligence: How AI assists hospital-dependent...AI Publications
The main objective of this study is to examine how artificial intelligence assists hospital-dependent patients and explore the role of artificial intelligence in the medical system. Hospital-dependent patients have become common in current society due to the elderly with multiple chronic conditions and the COVID-19 infection patient. Thus, it is undeniable that the medical field is lacking healthcare workers. However, in a globalized world, artificial intelligence, the field of science and engineering technology that makes intelligent machines perform given tasks, is chosen to be used as a tool for assisting hospital-dependent patients and collecting databases from the patients. Nevertheless, the paper will cover the use of artificial intelligence in the medical system, hospital-dependent patients as well as provide both positive and negative aspects and the comparison of using artificial intelligence instead of human intelligence. To conclude, we detail how artificial intelligence can take part in the medical system, assist hospital-dependent patients and study further the future of artificial intelligence in the medical system.
Accessing Information of Emergency Medical Services through Internet of ThingsIJARIIT
IoT is the advanced technology which is use in daily life. IoT make easy to connect different smart devices with
each other by using the internet. IOT is given the ability to computer system to run application program from different
vendors. So in this paper we are accessing the data based on IoT technology for emergency medical services. The fast
development of Internet of Thing.
An Intelligent Healthcare Serviceto Monitor Vital Signs in Daily Life – A Cas...IJERA Editor
Vital signs monitoring for elderly in daily life environment is a promising concept that efficiently can provide medical services to people at home. However, make the system self-served and functioning as personalized provision makes the challenge even larger. This paper presents a case study on a Health-IoT system where an intelligent healthcare service is developed to monitor vital signs in daily life. Here, a generic Health-IoT framework with a Clinical Decision Support System (CDSS) is presented. The generic framework is mainly focused on the supporting sensors, communication media, secure and safe data communication, cloud-based storage, and remote accesses of the data. The CDSS is used to provide a personalized report on persons‟ health condition based on daily basis observation on vital signs. Six participants, from Spain (n=3) and Slovenia (n=3) have been using the proposed healthcare system for eight weeks (e.g. 300+ health measurements) in their home environments to monitor their health. The sensitivity, specificity and overall accuracy of the DSS‟s classification are achieved as 90%, 97% and 96% respectively while k=2 i.e., top 2 most similar retrieved cases are considered. The initial user evaluation resultdemonstrates the feasibility and performance of the implemented system through the proposed framework.
Outline
Value Based Healthcare System – How it is seen today
Healthcare Challenge & IoT as a Solution
IoT – Big Data Structure
Recent Trends in IoT Big Data Analytics
Challenges & Our Future
In-depth Knowledge of
What causes the most premature death?
Distribution of Disease burden from 1990 - 2020
Challenges in Healthcare
Future Healthcare
IoT Machine Talking to Machine
Prediction of IoT Usage
About PEPGRA HEALTHCARE,
A leading healthcare communication firm with years of excellence serving clients with a dedicated team of Medical, Regulatory and Scientific writers specialized in all therapeutic areas.
Contact us at :
UK: +44-1143520021
US/Canada: +1-972-502-9262
India: +91-8754446690
info@pepgra.com
www.pepgra.com
Understanding Physicians' Adoption of Health Cloudscsandit
Recently proposed health applications are able to enforce essential advancements in the
healthcare sector. The design of these innovative solutions is often enabled through the cloud
computing model. With regards to this technology, high concerns about information security
and privacy are common in practice. These concerns with respect to sensitive medical
information could be a hurdle to successful adoption and consumption of cloud-based health
services, despite high expectations and interest in these services. This research attempts to
understand behavioural intentions of healthcare professionals to adopt health clouds in their
clinical practice. Based on different established theories on IT adoption and further related
theoretical insights, we develop a research model and a corresponding instrument to test the
proposed research model using the partial least squares (PLS) approach. We suppose that
healthcare professionals’ adoption intentions with regards to health clouds will be formed by
their outweighing two conflicting beliefs which are performance expectancy and medical
information security and privacy concerns associated with the usage of health clouds. We
further suppose that security and privacy concerns can be explained through perceived risks.
There are a number of scopes for IoT in order to make a difference in lives of patients. The devices can capture as well as monitor related data regarding patient and allows the providers to obtain the insights without bringing the patients visiting. The procedure can assist the patient results as well as preventing the possible communications for the process that involves risk. However, lack of electronic health record (EHR) system integration is one of the major issues faced while using IoT in healthcare. Some of the EHR systems allow the patients importing data into the record. However, it remains limited to a few dominant where the EHR players as well as leaves providers unspecific of the processing data that can be helpful for the organization to use the process. The challenges for interoperability in order to keep data in distinctive medical devices depend on the purpose and ordering physician.
Big data analytics and internet of things for personalised healthcare: opport...IJECEIAES
With the increasing use of technologies and digitally driven healthcare systems worldwide, there will be several opportunities for the use of big data in personalized healthcare. In addition, With the advancements and availability of internet of things (IoT) based point-of-care (POC) technologies, big data analytics and artificial intelligence (AI) can provide useful methods and solutions in monitoring, diagnosis, and self-management of health issues for a better personalized healthcare. In this paper, we identify the current personalized healthcare trends and challenges. Then, propose an architecture to support big data analytics using POC test results of an individual. The proposed architecture can facilitate an integrated and self-managed healthcare as well as remote patient care by adapting three popular machine learning algorithms to leverage the current trends in IoT, big data infrastructures and data analytics for advancing personalized healthcare of the future.
Efficient machine learning classifier to detect and monitor COVID-19 cases b...IJECEIAES
In this research work, coronavirus disease 2019 (COVID-19) has been considered to help mankind survive the present-day pandemic. This research is helpful to monitor the patients newly infected by the virus, and patients who have already recovered from the disease, and also to study the flow of virus from similar health issues. In this paper, an internet of things (IoT) framework has been developed for the early detection of suspected cases. This framework is used for collecting and uploading symptoms (data) through sensor devices to the physician, data analytics center, cloud, and isolation/health centers. The symptoms of the first wave, second wave, and omicron are used to identify the suspects. Five machine learning algorithms which are considered to be the best in the existing literature have been used to find the best machine learning classifier in this research work. The proposed framework is used for the rapid detection of COVID-19 cases from real-world COVID-19 symptoms to mitigate the spread in society. This model also monitors the affected patient who has undergone treatment and recovered. It also collects data for analysis to perform further improvements in algorithms based on daily updated information from patients to provide better solutions to mankind.
January_2024 Top 10 Read Articles in Computer Networks & Communications.pdfIJCNCJournal
The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.
Smart health monitoring system using IoT based smart fitness mirrorTELKOMNIKA JOURNAL
The smart fitness mirror proposed in this researchaims to provide the users with a platform to monitor their health and fitness status on a daily basis. The system employs a number of sensors to monitor the body mass index (BMI) and amount of body fat present in the user’s body. A weight scale consisting of four load sensors has been implemented to obtain the weight of the user whereas an ultrasonic sensor has been used to measure the height of the user. In addition, four electrode plates have been implemented on the foot weight scale to infuse a small amount of electric current (1mA) for BIA (bioelectrical impedance analysis) to estimate the amount of body fat percentage, lean body mass and total body water. An IR temperature sensor has been implemented in the research to measure the temperature of the user’s body from the forehead. Tests conducted on the system illustrate that it is able to accurately compute the body mass index and perform a bioelectrical impedance analysis on the user. The system is able to achieve a 92.5 % and 93.7 % accuracy in determining the body mass index and body fat percentage respectively. An accuracy of 95.3 % was observed in the determination of the body temperature.
Internet of Things IoT Based Healthcare Monitoring System using NodeMCU and A...ijtsrd
Today Internet has become one of the important parts of daily life. It has changed how people live, work, play and learn. Internet serves for many purposes educations, finance, Business, Industries, Entertainment, Social Networking, etc. The IoT is connected objects to the Internet and used to control of those objects or remote monitoring. A health care monitoring system is necessary to constantly monitor the patient's physiological parameters. The main advantage of this system is that the results can be viewed at any time and place. The doctors can be notified by using mobile phones messages if patient health is abnormal. In this system, heartbeat sensor, temperature sensor and blood pressure sensor are used. The system can analyze the signal to detect normal or abnormal conditions. In the system, the internet of things IoT is becoming a major platform for many services and applications. The IoT is generally considered as connecting objects to the Internet and using that connection for control of those objects or remote monitoring. Khin Thet Wai | Nyan Phyo Aung | Lwin Lwin Htay "Internet of Things (IoT) Based Healthcare Monitoring System using NodeMCU and Arduino UNO" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26482.pdfPaper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/26482/internet-of-things-iot-based-healthcare-monitoring-system-using-nodemcu-and-arduino-uno/khin-thet-wai
IoT and Mobile Application Based Model for Healthcare Management Systemijtsrd
The last decade has witnessed extensive research in the field of healthcare services and their technological upgradation. To be more specific, the Internet of Things IoT has shown potential application in connecting various medical devices, sensors, and healthcare professionals to provide quality medical services in a remote location. This has improved patient safety, reduced healthcare costs, enhanced the accessibility of healthcare services, and increased operational efficiency in the healthcare industry. The current study gives an up to date summary of the potential healthcare applications of IoT HIoT based technologies. It is necessary to develop an innovative solution in the Smart Building context that increases guests’ hospitality level during the pandemics in locations like hotels, conference locations, campuses, and hospitals. The solution supports features intending to control the number of occupants by online appointments, smart navigation, and queue management in the building through mobile phones and navigation to the desired location by highlighting interests and facilities. Moreover, checking the space occupancy, and automatic adjustment of the environmental features are the abilities that can be added to the proposed design in the future development. The proposed solution can address all mentioned issues regarding the smart building by integrating and utilizing various data sources collected by the internet of things IoT sensors. Then, storing and processing collected data in servers and finally sending the desired information to the end users. Consequently, through the integration of multiple IoT technologies, a unique platform with minimal hardware usage and maximum adaptability for smart management of general and healthcare services in hospital buildings will be created. Dr. Rajendra Kumar Bharti "IoT and Mobile Application Based Model for Healthcare Management System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-6 , October 2022, URL: https://www.ijtsrd.com/papers/ijtsrd51889.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/51889/iot-and-mobile-application-based-model-for-healthcare-management-system/dr-rajendra-kumar-bharti
Internet of things based electrocardiogram monitoring system using machine l...IJECEIAES
In Bangladesh’s rural regions, almost 30% of the population lives in poverty. Rural residents also have restricted access to nursing and diagnostic services due to obsolete healthcare infrastructure. Consequently, as cardiac failure occurs, they usually fail to call the services and adopt the facilities. The internet of things (IoT) offers a massive advantage in addressing cardiac problems. This study proposed a smart IoT-based electrocardiogram (ECG) monitoring system for heart patients. The system is divided into several parts: ECG sensing network (data acquisition), IoT cloud (data transmission), result analysis (data prediction) and monetization. P, Q, R, S, and T are ECG signal properties fetched, pre-processed, analyzed and predicted to age level for future health management. ECG data are saved in the cloud and accessible via message queuing telemetry transport (MQTT) and hypertext transfer protocol (HTTP) servers. The linear regression method is utilized to determine the impact of electrocardiogram signal characteristics and error rate. The prediction was made to see how much variation there was in PQRST regularity and its sufficiency to be utilized in an ECG monitoring device. Recognizing the quality parameter values, acceptable outcomes are achieved. The proposed system will diminish future medical costs and difficulties for heart patients.
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Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
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discussion of application idea concerning with the IoT system viz. i) body wearable, ii) remote health
monitoring, iii) equipments to maintain vital statistics, iv) patient-oriented medicine, v) monitoring of
medical assets, and vi) advanced analytics of the medical data [7].
At present, there has been various research work being carried out towards the discussion of
the various strategies adopted for improving medical sector with inclusion of IoT [8-10]. However, there are
various practical problems associated with the usage of the medical IoT. The first problem of medical IoT is
the usage of sensors and body wearable. Irrespective of evolution of various forms of sensors in micro sizes,
it cannot be used for all the medical condition. Another biggest problem is that different number of sensor
acquires different data form and hence body wearable could offer discomfort and burden over the patient just
for acquiring different type of bio-signals if they are required. Another problem associated with the medical
IoT is the mechanism of transmission. Once the IoT device captures the information from the sensors or
actuators, it usually forwards the information without any forms of processing on it. This causes a problem when
there is a need of performing processing on the acquired data. The third problem associated with the medical
IoT is the application design of the analytical program. Development of sophisticated analytics calls for
comprehensive consideration of intention of the application design. At present, majority of the current
application calls for acquiring the data followed by storing and managing the data in server end. However,
certain system uses such analyzed data for the purpose of subjecting the data to the analysis where certain
information is derived and passed on to the service provider in order to act upon. However, such application
normally does no communicate with the user and such information is not even user friendly. Therefore, this
paper presents a discussion of the novel solution that perform forwarding of the contextual message to
the patient for the purpose of resisting the possible situation of stress. Section 1 discusses about the existing
literatures where different techniques are discussed for detection schemes used in power transmission lines
followed by discussion of research problems and proposed solution. Section 2 discusses about algorithm
implementation followed by discussion of result analysis in Section 3. Finally, the conclusive remarks are
provided in Section 4.
This section discusses about the existing approaches where researchers have used heartbeat data
combined with IoT for facilitating healthcare sector with better advantage of clinical guidance. The recent
work of Majumder et al. [11] have developed a predictive system for assiting forecasting of the warning for
heart attack using wearable device over IoT. Zhang et al. [12] have developed a remotely tracking of patient
health using IoT integrated with sensor network. Discussion of wearable device towards the usage in
healthcare system in carried out by Al-Eidan et al. [13]. Similar form of discussion has been carried out by
Meharouech et al. [14]. Mahmoud et al. [15] have performed an investigation towards cloud-of-things
architecture associated with the healthcare system. The authors have mainly emphasized over energy
efficiency factor in this presented system. Espinilla et al. [16] has discussed about the decision making
framework that is capable of extracting knowledge over the bio-signals captured from the wearable
applications in IoT. Study towards integration of wearbale device and IoT was also carried out by
Monton et al. [17] using hardware-based approach in order to offer reduction in delay.
Another framework developed by Pasha et al. [18] emphasizing over the interoperability in
the system design of healthcare. Adoption of fog computing integrated with an IoT towards developing
a better form of healthcare system was carried out y Paul et al. [19] for increasing the efficiency of
the system associated with the healthcare monitoring system. Wu et al. [20] has presented model for retaining
energy efficiency of the system developed with wearable IoT system. The authors have claimed of their
enhanced applicability towards mobile application during critical situation. Guan et al. [21] have presented
another system design which assists the elderly patient remotely where a home gateway system is positioned
to trace the critical limits of the heartbeats signals. A specific prototype of healthcare monitoring has been
developed by Desai and Toravi [22] using hardware-based system. Ali et al. [23] have developed a system
that conjoins sensor network with mobile device for monitoring patient health condition on the basis of its
pulse. Similar form of study has been also carried out by Poorvi et al. [24] and Kumar et al. [25].
Sathishkumar et al. [26] have developed a prototype using IoT integrated with heartbeat sensor in order to
resist uneven situation that could make the road safety vulnerable. The study carried out by Irawan and
Juhana [27] using nearly the similar approach for tracking the heart rate of the patient using signal processing
approach. Discussion of the healthcare-based application has been carried out by Rodriques [28] as well as
there are also researchers that has focused on the security aspect of similar form of integrated IoT sensor
healthcare application as that seen in work of Yeh [29]. The work carried out by Fouhad [30] has developed
nearly similar prototype application focusing on the supportability of the telemedicine. Therefore, there are
various studies being carried out where heartbeat sensors were used for developing an IoT framework for
healthcare sector. Hamza et al. [31] have introduced a technique for smart car parking based on cloud method
containing various types of sensor. The PIRs has been employed to detect the object motion.Bhagchandani
and Augustine [32] have discussed a holistic solution by using the IOT method including data analytics.
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The IoT allows real-time capturing and calculation of the medical data from smart sensors installed in
wearable devices.The work done by Wani and Revathi [33] the expansion of ransomware attacks for
ransomwares threatening IoT. An identification tool for IoTransomware attack is demonstareted that is
designed after reviewed of ransomware for the IoT. The presented technique shows the present IoT traffic
through SDN gateway. It utilizes policies borders in SDN organize for sensing of ransomware in the IoT.
The potential research problems associated with the existing system are as follows:
Existing approaches of using wearable devices and IoT is restricted only by receiving the heartbeat signal
while not processing them for better clinical / motivational inference.
At present, the studies do not discrete use machine learning techniques to arrive at user friendly
suggestion in faster track meant exclusively for user.
Assessing the reliability of the study using accuracy parameter and faster receiving of the signals in
presence of a computational modeling is absent.
Existing approaches mainly uses prototyping scheme where only narrowed area of implementation can be
assessed.
Therefore, the problem statement of the proposed study can be stated as “Developing a framework
that can facilitate user-friendly inference to countermeasure against their stress level with cost effective
modeling is a complex and challenging task”. The next section highlights the solution for this.
The prime aim of the proposed system is to offer a novel design of a framework that offers
contextual services considering the case study of healthcare sector integrated with IoT system.
The implementation of the proposed system is carried out using analytical research methodology where
the prime concern is to offer accurate forwarding of the contextual messages to the user under variable stress
condition in order to beat the critical. The schematic diagram of the proposed system is shown in Figure 1.
Acquisition of
Signals
Signal
Classification
1
2
Processing
Signals
3
Transmitting
Signals
4
Analytical
Operation
5
Machine
Learning
6
Extract
Contextual
Message
8
Forward
Message to User
9
Heart beat
Figure 1. Proposed schema of implemented methodology
Figure 1 highlights the adopted schema of implementation. The core modules of the proposed
system are acquisition of signals, processing of signals, transmitting the signals, followed by analysis of
the signal. The proposed system considers the input as heartbeat of the user as the core indicators of stress.
It is known that different levels of heartbeat consider different levels of stress. An IoT environment is
constructed that connects the user’s wearble device, extracts all the continuous data of heartbeat, followed by
further processing the data. The proposed system than perform matching with the data with the clinical
reference in order to ensure if the heartbeat signals obtains are critical or under critical. Only the critical data
are extracted and indexed for shapping it in the form of query signals. The newly indexed query signals are
then transmitted to the cloud-application where the analytics are running. The on-cloud analytics retains
the list of all the specific messages in the form of clinical stress hierarchy. Correlation-based analysis is
performed in order to match the filtered query signals with that maintained in the cloud. After the outcome of
specific message is obtained, a machine learning approach is offered to perform training towards the obtained
correlated data. This training process finally offers accurate contextual message that is forwarded to the user
via network over the communication device of the user.
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2. SYSTEM DESIGN
The complete design of the proposed system is constructed using divide and conquers approach that
mainly emphasize over obtaining accuracy of the vital stat e.g. heart beat of the user. Different individual
modules of design have been constructed in order to develop the proposed model. This section discusses
about assumption-dependencies, implementation strategy, and execution flow of proposed system as follows:
2.1. Assumptions and dependencies
The first assumption of the proposed study is that there is a synchronized set up of the wearable
body sensors with the various IoT devices forming a smart healthcare analytic system. There are also good
possibilities of using different forms of wearable sensors connected with each other and form a body area
network. The second assumption of the proposed system is that heartbeat data is continuously monitored by
the sensors; however, the filtration of the acquired signal corresponding to the severity of the stress condition
is carried out by the sensor and not by the IoT gateway that receives the signal. The third assumption of
the study is that complete data is forwarded through a secured and error free communication channel over
predefined IoT environment. Similarly, there are various dependencies of the proposed system e.g. i)
an error-free communication channel is required for performing transmission of the acquired heartbeat signal
to the destination node, ii) There is also a dependency of elastic storage capacity in order to retain all
the trained information obtained from the training phase. The trained data will be used for performing
validation of accuracy as performance measures of proposed forecasted contextual message. iii) The final
dependency of the proposed system is that there should be a hierarchical list of clinical/motivational
suggested message that are specific to actual medical context of the captured heartbeat of the user.
2.2. Implementation strategy
The implementation of the proposed system is carried out by sequential module design which is
meant for performing following steps of operation with defined strategies:
Acquisition of signals
The proposed study considers that a user is wearing a body wearable sensor that can measure
the true heart beats shown in Figure 2. The study considers that there are three categories of heartbeat viz. i)
category-1: High beat rate (>100 beats per minute), ii) category-2: Medium beat rate (100-60 beats per
minute), and iii) category-3: (<60 beats per minute). The heart beat is a continuous signal which the sensor
captures followed by segregating the signals on the basis of the categories and then it is further subjected to
processing in the next step.
Figure 2. Acquisition of signal
Processing the signals
The sensors are also considered to posses a capability to perform processing of the heart beat signals
that corresponds to unique levels of stress of the user. For this purpose, the system considers that fact of
possible situation of stress level is associated with either tachycardia or bradycardia, which is both considered
as lethal conditional. Such condition will require an immediate medical attention. All the signals of
heartbeats will be forwarded to different carriers of communication channel between wearable sensor and
nearest IoT device (or IoT gateway system). It is to be understood that IoT gateway also acquire signals from
other mobile users and will be responsible for further processing it. By processing, it will mean that a module
is constructed that considers the category-1 and category-3 signals and time-stamps it. This signal is then
further indexed in the form of two specific query systems. The first query is formulated for category-1 signal
while the second query is formulated for cateogory-3 signal. Therefore, the processing of this signal is
carried out for only critical data i.e. for category-1 and category-3 while leaving the non-critical signal i.e.
category-2. Figure 3 offers pictorial representation of this operation.
Heat Beat
Classification
Category-3 (Lower Heat beat)
Category-2 (Medium Heat beat)
Category-1 (High Heat beat)
User with
Wearable
Sensor
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Transmitting the signals
The transmission of the filtered signal from the prior stage is carried out in this module. The filtered
signal mainly consists of indexed signal of category-1 and category-3 where the indexing will mean
embedding the respective heartbeat with time stamp and user identity. The fused data is now forwarded to
the IoT gateway node followed by forwarding the same to the access point and edge server. However, while
doing so, there is quite a possibility that any of this networking device (e.g. gateway node, access point, edge
server) is found to be currently processing a job. In such condition, an adaptive queue system is considered
where the incoming task is reposited for immediate processing. Another interesting part of this transmission
module is that it can also support prioritizing the any specific incoming signals using two attributes. The first
attribute for prioritizing the incoming signal will be based on certain cut-off level of heart beat. An additional
much-critical cut-off level can be given to further develop an additional queue for such incoming signals and
they will be first processed followed by prior queue. The second attribute for prioritizing will be wait-time of
queue (such situation can occur due to network problems or high density traffic). If a signal packet is found
to wait for more than a specific duration of time, it will be soon pushed to priority queue. The end result of
this module is that the fused data are successfully transmitted to the cloud application that runs analytics for
further analyzing the fused signals. Figure 4 represents the pictorial way of this transmission process.
Figure 3. Processing the signals
Figure 4. Transmission of signals
Analysis of signals
This is the last phase of implementation that emphasize on yielding a definitive motivational /
clinical message (or suggestion) to the respective users on the basis of obtained signals shown in Figure 5.
The study consider a present of distributed motivational / clinical message maintained over the storage units
with a preamble matching with specific condition of the heart beat. The proposed system applies a correlation
of the obtained signal with the correlated value of the messages and only the highly correlated value is
shortlisted. However, in order to prevent any form of false positive, the proposed sysem applies machine
learning algorithm that performs training operation in order to validate the outcome resulted from correlation
and finally obtain the elite message to be forwarded to user.
Figure 5. Analysis of signals
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2.3. Execution flow
The proposed system constructs an algorithm that is responsible for transmitting the contextual
message over IoT environment on the basis of the heart beat data. The algorithm takes the input of d (sensory
data) and H/M/L (high/medium/low heartbeat) which after processing will yield an outcome of μ (contextual
message). The steps of algorithmic execution are as follows:
Algorithm for forwarding contextual message via IoT
Input: d (sensory data), H/M/L (high/medium/low heartbeat),
Output: μ (contextual message)
Start
1. Fori =1:(dmax)T
2. If 1<i<dlow
3. Flag dL
4. ElseIfdlow<i<dmed
5. Flag dM
6. ElseIfdmed<i<dhigh
7. Flag dH
8. End
9. Ford=[L H]
10. compared with scond
11. τ = gen [squeryHsqueryM]
12.End
13.applyC=f(τ, dblist)
14. μg(C)
End
The discussion of the execution flow of the proposed algorithm is as follows: Assuming that a body
wearable sensor device has captured heart-beat data d using a sample period of T and the sampled data is
forwarded to the IoT gateway node in the proximity. Consider that dmax is the maximum size of the sampled
heat-beat data; the algorithm evaluates the type of the heart beat data in terms of cut-off. If the heart-beat is
found to be with low cut-off dlow (Line-2) than the filtered data d is flagged as L (Line-3). Similarly,
the heart-beat data d is compared with medium (dmed) and high (dhigh) cut-off to flag M and H respectively
(Line-5 and Line-6). For an effective analysis, the algorithm ignores M signal but considers L and H signal.
A temporary buffer space is created that retains only L and H (Line-9). The algorithm also constructs
a clinical condition scond to define the degree of criticality of the L and H type of signal of heartbeat and this
condition is a referential point of stress factor too. A consideration of tachycardia (<60 beats per minute) and
bradycardia (more than 100 beats per minute) is also considered while forming the conditional function scond
(Line-10) in order to further confirm the severity of stress as both the conditions are considered to be
dangerous. The system then formulates a function to generate the normalized heart rate corresponding to H
and M and is represented as squeryH and squeryM respectively (Line-11). The query matrix τ retained
the structured form of the query signal squeryH and squeryMand this message is forwarded to the cloud
aanalytical application via gateway node, access point, and edge server. This process of query generation is
repeated for all the value of sampled sensory data M and H (Line-9 to Line-12). After the matrix τ is obtained
in the cloud analytical application it matches with the hierarchical list of pre-defined messages dblist. It should
be noted that there are different forms of prediefined messages unique made for each categories of queries.
The algorithm than applies correlation function f(x) over received data τ with list of pre-defined messages
dblist (Line-13). As correlation results in sorted value of higher correlated data as an output; therefore there
are chances that such outcome could also have outliers. Apart from this, as there are various types of disease
condition that could also have similar pattern of heart beat. Therefore, forwarding same contextual message
could be proved as false positive. This problem is solved using machine learning approach, where training is
carried out considering specific charecteristics of user’s data along with the generated heart beat data in order
to conclude the elite outcome. The elite outcome will mean that the algorithm successfully concluded about
one message from the list of message that is highly suitable for the user in its current health condition or
called as real-time contextual data. The proposed system applies a machine learning function g(x)
considering the contextual data C as the input argument in order to generate an elite outcome μ (Line-14).
Therefore, the proposed execution of the algorithm is highly a progressive step with an inclusion of only one
iterative step during training process which also maintains a higher degree of cost effectiveness while
processing the heartbeat signal for the purpose of forwarding the correct contextual messages to the user via
any communicating devices.
3. RESULT ANALYSIS
The scripting of the proposed system has been carried out using MATLAB considering the heart rate
dataset [34]. As the proposed system deals with offering the precise contextual message for user, therefore,
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it is essential to validate if the correct contextual data has been received and if it has been received on time.
Therefore, the core performance parameter considered are accuracy of choosing the right contextual message
and processing time that defines the duration spend from acquiring the heart beat data to receiving of
contextual message to the user. The proposed system also performs comparative analysis with the existing
machine learning approach to find the best approach.
The study outcome clearly shows that deep neural network offers better advantage with respect to
the accuracy shown in Figure 6 and processing time shown in Figure 7. The core reason for the higher
accuracy is its capability to recognize the error even if the error is outside of the tolerance level. This feature
is not present in other forms of machine learning approach. The prime reason behind this is increased
training time involved in neural network and support vector machine (SVM) for matching the best result with
the correlated data maintained over the list. Therefore, it can be said that proposed system offers
cost effective solution where the contextual data is accurately generated as well as incurs less time to
forward the correct contextual message to the user and hence it offers practical advantage over critical
clinical requirement.
Figure 6. Comparative analysis of accuracy Figure 7. Comparative analysis of processing time
4. CONCLUSION
Acquisition of the bio-signals can be suitably used for controlling stress factor with an aid of IoT.
However, processing the bio-signals like heartbeats is an important task that requires a cost effective method
for transforming the signals over the presence of analytics. Therefore, the proposed system introduces
a mechanism that extracts the heartbeat signal, processes it, and transmits it to the edge server via IoT
gateway system. The proposed system uses correlation-based approach in order to extract the definitive
motivational/clinical suggestion that are meant to be directly forward to the user in order to instantly control
the level of stress. Further machine learning approach is used for further extracting the accurate contextual
message. The simulated outcome of the study proves that proposed system offer good accuracy and reduced
processing time to do the entire operation.
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BIOGRAPHY OF AUTHOR
Eisha Akanksha, she is an associate professor, Department of Electronics & Communication,
CMRIT, Bengaluru, India. Her areas of interest are wireless communications, Light Fidelity,
Internet of Things and Cloud Computing. She has around 6 years of experience in teaching field.
She has worked 2.4 Years of Industry Experience- Worked as software engineer in Sasken
Technologies Bangalore, India.