Pub Date : 2021-11-24DOI: 10.1109/citisia53721.2021.9719962
Bishwajeet Roy, P. Prasad, Angelika Maag
The emergence of the Corona Virus (COVID-19) pandemic has resulted in a technological revolution bringing with it changes such as working from home, online shopping, online health consultation and many others were now taking place on a daily basis. One of the major impacts of COVID-19 was on public health due to travel restrictions and lockdowns restricting travel to medical clinics, stressing hospital capacity to the limit and throwing into strong relief the benefits of Telemedicine and Telehealth. This technological advancement has evolved over the past four decades with considerable success; however, challenges remain regarding data transfer, audiovisual streaming, power consumption, the impact of adverse climatic conditions on performance, area of coverage, security, privacy, and wearable sensors integration. This research aims to identify and critically analyse these issues. Prior research will be extracted from peer-reviewed journals published in scientific and medical data bases during 2017 and 2021. The resulting data will be compiled into graphic representations for further analysis. This paper makes a significant contribution to the body of knowledge in the field of wireless Telemedicine and Telehealth through detailed identification of the issues that are plaguing this field.
{"title":"A Review on Wireless Telemedicine Technology Challenges and Possible Solution","authors":"Bishwajeet Roy, P. Prasad, Angelika Maag","doi":"10.1109/citisia53721.2021.9719962","DOIUrl":"https://doi.org/10.1109/citisia53721.2021.9719962","url":null,"abstract":"The emergence of the Corona Virus (COVID-19) pandemic has resulted in a technological revolution bringing with it changes such as working from home, online shopping, online health consultation and many others were now taking place on a daily basis. One of the major impacts of COVID-19 was on public health due to travel restrictions and lockdowns restricting travel to medical clinics, stressing hospital capacity to the limit and throwing into strong relief the benefits of Telemedicine and Telehealth. This technological advancement has evolved over the past four decades with considerable success; however, challenges remain regarding data transfer, audiovisual streaming, power consumption, the impact of adverse climatic conditions on performance, area of coverage, security, privacy, and wearable sensors integration. This research aims to identify and critically analyse these issues. Prior research will be extracted from peer-reviewed journals published in scientific and medical data bases during 2017 and 2021. The resulting data will be compiled into graphic representations for further analysis. This paper makes a significant contribution to the body of knowledge in the field of wireless Telemedicine and Telehealth through detailed identification of the issues that are plaguing this field.","PeriodicalId":252063,"journal":{"name":"2021 6th International Conference on Innovative Technology in Intelligent System and Industrial Applications (CITISIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128847944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-24DOI: 10.1109/citisia53721.2021.9719986
{"title":"[Copyright notice]","authors":"","doi":"10.1109/citisia53721.2021.9719986","DOIUrl":"https://doi.org/10.1109/citisia53721.2021.9719986","url":null,"abstract":"","PeriodicalId":252063,"journal":{"name":"2021 6th International Conference on Innovative Technology in Intelligent System and Industrial Applications (CITISIA)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125119519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-24DOI: 10.1109/citisia53721.2021.9719939
Syed Sikandar Ali, Nabil Giweli, A. Dawoud, P. Prasad
The rapid growth in Internet of Things IoT applications has increased the demand for Wireless Sensor Network (WSN) as an essential supportive Ad-hoc network class in the IoT stack. However, managing the network lifetime related to power consumption and network capacity is still a significant challenge that affects WSN functionality. Wireless network capacity is primarily affected by available bandwidth, error rate, and Signal to Noise Ratio (SNR). These factors have more profound effects on WSN because of limitations in power supplies and the ad-hoc mode implemented in WSN. Hence, it is essential to maintain the network lifetime and capacity to use WSN in real-world IoT applications. Data aggregation techniques with efficiently collecting and aggregating packets will help to reduce power consumption and reduce network traffic congestions.This study aims to systematically analyze and review the data aggregation techniques used in WSN. The paper presents a comprehensive survey based on the current work, component classification, and evaluation table. Additionally, an analysis based on the data aggregation technique is conducted for improving the network capacity based on the existing technologies. Also, the study proposes an aggregation framework based on the literature study, identifying the significant components used for obtaining an enhanced solution for improving network capacity in a WSN with the help of the data aggregation technique.
{"title":"Data Aggregation Techniques in Wireless Sensors Networks: A survey","authors":"Syed Sikandar Ali, Nabil Giweli, A. Dawoud, P. Prasad","doi":"10.1109/citisia53721.2021.9719939","DOIUrl":"https://doi.org/10.1109/citisia53721.2021.9719939","url":null,"abstract":"The rapid growth in Internet of Things IoT applications has increased the demand for Wireless Sensor Network (WSN) as an essential supportive Ad-hoc network class in the IoT stack. However, managing the network lifetime related to power consumption and network capacity is still a significant challenge that affects WSN functionality. Wireless network capacity is primarily affected by available bandwidth, error rate, and Signal to Noise Ratio (SNR). These factors have more profound effects on WSN because of limitations in power supplies and the ad-hoc mode implemented in WSN. Hence, it is essential to maintain the network lifetime and capacity to use WSN in real-world IoT applications. Data aggregation techniques with efficiently collecting and aggregating packets will help to reduce power consumption and reduce network traffic congestions.This study aims to systematically analyze and review the data aggregation techniques used in WSN. The paper presents a comprehensive survey based on the current work, component classification, and evaluation table. Additionally, an analysis based on the data aggregation technique is conducted for improving the network capacity based on the existing technologies. Also, the study proposes an aggregation framework based on the literature study, identifying the significant components used for obtaining an enhanced solution for improving network capacity in a WSN with the help of the data aggregation technique.","PeriodicalId":252063,"journal":{"name":"2021 6th International Conference on Innovative Technology in Intelligent System and Industrial Applications (CITISIA)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125678092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-24DOI: 10.1109/citisia53721.2021.9719947
Junyan Li
The dark web has become a major trading platform for cybercriminals, with its anonymity and encrypted content nature make it possible to exchange hacked information and sell illegal goods without being traced. The types of items traded on the dark web have increased with the number of users and demands. In recent years, in addition to the main items sold in the past, including drugs, firearms and child pornography, a growing number of cybercriminals are targeting various types of private information, including different types of account data, identity information and visual data etc. This paper will further discuss the issue of threat detection in the dark web by reviewing the past literature on the subject. An approach is also proposed to identify criminals who commit crimes offline or on the surface network by using private information purchased from the dark web and the original sources of information on the dark web by building a database based on historical victim records for keyword matching and traffic analysis.
{"title":"Threats and data trading detection methods in the dark web","authors":"Junyan Li","doi":"10.1109/citisia53721.2021.9719947","DOIUrl":"https://doi.org/10.1109/citisia53721.2021.9719947","url":null,"abstract":"The dark web has become a major trading platform for cybercriminals, with its anonymity and encrypted content nature make it possible to exchange hacked information and sell illegal goods without being traced. The types of items traded on the dark web have increased with the number of users and demands. In recent years, in addition to the main items sold in the past, including drugs, firearms and child pornography, a growing number of cybercriminals are targeting various types of private information, including different types of account data, identity information and visual data etc. This paper will further discuss the issue of threat detection in the dark web by reviewing the past literature on the subject. An approach is also proposed to identify criminals who commit crimes offline or on the surface network by using private information purchased from the dark web and the original sources of information on the dark web by building a database based on historical victim records for keyword matching and traffic analysis.","PeriodicalId":252063,"journal":{"name":"2021 6th International Conference on Innovative Technology in Intelligent System and Industrial Applications (CITISIA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122259092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-24DOI: 10.1109/CITISIA53721.2021.9719915
S. Devi, A. Alsadoon, Sreekanth Gopalakrishnan Nair, A. Dawoud, Oday Al-Jerew, Benoy Varghese, P. Prasad
Healthcare systems have severe privacy and security concerns. Several solutions had been proposed to address the privacy and security issues; however, these solutions have several limitations. Block-chain technology has limited applicability in healthcare systems security because of many limitations in confidentiality and integrity of patient data. This research aims to improve privacy for efficient data collection with keyless signature infrastructure to create a reliable and secure environment. The proposed system consists of an enhanced privacy and block validity that generates a hash signature, block header, and blocks of data along with its public and private key pairs that ensure data authenticity. Differential privacy is used to increase the specific time for creating the file because it provides the contribution proof of each file. Our simulation studies results show minimum intrusion and found the probability of falsification attack in the range of 0-1. It provides an increase in differential privacy by 34.5 -35.6% and block validity by 13- 13.5%. The proposed system focuses on data transmission techniques that permit data subjects to monitor, agree, and notify the processing of their sensitive data r. Finally, this study enhances the security ad privacy issues of data transmission with blockchain technology during data transmission in healthcare systems.
{"title":"Utilizing Blockchain to Enhance the Privacy and Block Validity in Healthcare Systems","authors":"S. Devi, A. Alsadoon, Sreekanth Gopalakrishnan Nair, A. Dawoud, Oday Al-Jerew, Benoy Varghese, P. Prasad","doi":"10.1109/CITISIA53721.2021.9719915","DOIUrl":"https://doi.org/10.1109/CITISIA53721.2021.9719915","url":null,"abstract":"Healthcare systems have severe privacy and security concerns. Several solutions had been proposed to address the privacy and security issues; however, these solutions have several limitations. Block-chain technology has limited applicability in healthcare systems security because of many limitations in confidentiality and integrity of patient data. This research aims to improve privacy for efficient data collection with keyless signature infrastructure to create a reliable and secure environment. The proposed system consists of an enhanced privacy and block validity that generates a hash signature, block header, and blocks of data along with its public and private key pairs that ensure data authenticity. Differential privacy is used to increase the specific time for creating the file because it provides the contribution proof of each file. Our simulation studies results show minimum intrusion and found the probability of falsification attack in the range of 0-1. It provides an increase in differential privacy by 34.5 -35.6% and block validity by 13- 13.5%. The proposed system focuses on data transmission techniques that permit data subjects to monitor, agree, and notify the processing of their sensitive data r. Finally, this study enhances the security ad privacy issues of data transmission with blockchain technology during data transmission in healthcare systems.","PeriodicalId":252063,"journal":{"name":"2021 6th International Conference on Innovative Technology in Intelligent System and Industrial Applications (CITISIA)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134623121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-24DOI: 10.1109/citisia53721.2021.9719894
Tonny Kurniawan, Hervinsen Zhang, Rico Wijaya, J. Linggarjati, Savita Sondhi, R. Hedwig
Living healthy is a current lifestyle especially in big cities. People are concerned about the number of calories they consume per day, and they even hire nutritionists and personal trainers to help them in monitoring their healthy life. In this research, the researcher is using a calorie monitoring dining table with a revolving stand or tray on a table to hold condiments. This device, which is commonly called as Lazy Susan, is modified by adding Raspberry Pi and load cell sensor to calculate the number of calories transferred from serving plate to user’s plate. Users can select the menu and the Lazy Susan will rotate to serve the desired menu while also calculating the calories consumed by the user. The information will be transferred to the user’s Android smartphone and will be adjusted according to the user’s activity. The error reading rate of the system is 6% to 7% and the total price of the product is $1,308, which is cheaper than the "made to measureautomated Lazy Susan dining table" available in the market.
{"title":"Lazy Susan Calorie Monitoring Dining Table Based on Raspberry Pi","authors":"Tonny Kurniawan, Hervinsen Zhang, Rico Wijaya, J. Linggarjati, Savita Sondhi, R. Hedwig","doi":"10.1109/citisia53721.2021.9719894","DOIUrl":"https://doi.org/10.1109/citisia53721.2021.9719894","url":null,"abstract":"Living healthy is a current lifestyle especially in big cities. People are concerned about the number of calories they consume per day, and they even hire nutritionists and personal trainers to help them in monitoring their healthy life. In this research, the researcher is using a calorie monitoring dining table with a revolving stand or tray on a table to hold condiments. This device, which is commonly called as Lazy Susan, is modified by adding Raspberry Pi and load cell sensor to calculate the number of calories transferred from serving plate to user’s plate. Users can select the menu and the Lazy Susan will rotate to serve the desired menu while also calculating the calories consumed by the user. The information will be transferred to the user’s Android smartphone and will be adjusted according to the user’s activity. The error reading rate of the system is 6% to 7% and the total price of the product is $1,308, which is cheaper than the \"made to measureautomated Lazy Susan dining table\" available in the market.","PeriodicalId":252063,"journal":{"name":"2021 6th International Conference on Innovative Technology in Intelligent System and Industrial Applications (CITISIA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131972365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-24DOI: 10.1109/citisia53721.2021.9719989
A. Halabi-Echeverry, Juan C. Aldana-Bernal, D. Villate-Daza, S. Islam
This paper provides an approach to Port2Port Business Process Intelligence (BPIs) helping decision makers in tackling constant changes in governance responsibilities. This consideration leads to the need for Port2Port technological solutions among ports and development of capabilities on sharing information, planning and execution in a collaborative way. It is offered three Port2Port BPIs: 1) Control process for greenhouse gas emissions coming from ships, 2) The process for monitoring ballast Waters, 3) Sanitation Performance Compliance under COVID19 situation. The identification and selection of learning tasks have been integrated into the conceptualisation scheme, suggesting the exploitation of Deep reinforcement Learning (RL) to capture important aspects of the real problem facing the learning agents interacting with its environment to achieve the proposed goals.
{"title":"Towards Business Process Intelligence to Port2Port Governance Responsibility based on Learning Algorithms","authors":"A. Halabi-Echeverry, Juan C. Aldana-Bernal, D. Villate-Daza, S. Islam","doi":"10.1109/citisia53721.2021.9719989","DOIUrl":"https://doi.org/10.1109/citisia53721.2021.9719989","url":null,"abstract":"This paper provides an approach to Port2Port Business Process Intelligence (BPIs) helping decision makers in tackling constant changes in governance responsibilities. This consideration leads to the need for Port2Port technological solutions among ports and development of capabilities on sharing information, planning and execution in a collaborative way. It is offered three Port2Port BPIs: 1) Control process for greenhouse gas emissions coming from ships, 2) The process for monitoring ballast Waters, 3) Sanitation Performance Compliance under COVID19 situation. The identification and selection of learning tasks have been integrated into the conceptualisation scheme, suggesting the exploitation of Deep reinforcement Learning (RL) to capture important aspects of the real problem facing the learning agents interacting with its environment to achieve the proposed goals.","PeriodicalId":252063,"journal":{"name":"2021 6th International Conference on Innovative Technology in Intelligent System and Industrial Applications (CITISIA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129360275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-24DOI: 10.1109/citisia53721.2021.9719909
R. Rajat, Priyanka Jaroli, Naveen Kumar, R. Kaushal
Nowadays everything is digitalized in the world. In the digitalization world E-commerce take a unique place for people. People are not going anywhere and buy all the thing at home using this E-commerce platform. For selecting the platform generally used the reviews of the people which are already buy from there. The paper proposes a sentiment analysis of the large amazon real dataset based on the counter vectorizer (CV) and term frequency inverse document frequency (TF-IDF) and logistic regressor. Firstly, take the dataset from the amazon E-commerce into JSON format and load the dataset and split the dataset into train test model. Secondly, take out the features using the counter vectorizer and term frequency inverse document frequency (TF-IDF). Finally, logistic regressor (LR) is used and measure the positive and negative sentiment of the review. simulation result represents the model accuracy score, precision, recall, confusion matrix of the implemented approach.
{"title":"A Sentiment Analysis of Amazon Review Data Using Machine Learning Model","authors":"R. Rajat, Priyanka Jaroli, Naveen Kumar, R. Kaushal","doi":"10.1109/citisia53721.2021.9719909","DOIUrl":"https://doi.org/10.1109/citisia53721.2021.9719909","url":null,"abstract":"Nowadays everything is digitalized in the world. In the digitalization world E-commerce take a unique place for people. People are not going anywhere and buy all the thing at home using this E-commerce platform. For selecting the platform generally used the reviews of the people which are already buy from there. The paper proposes a sentiment analysis of the large amazon real dataset based on the counter vectorizer (CV) and term frequency inverse document frequency (TF-IDF) and logistic regressor. Firstly, take the dataset from the amazon E-commerce into JSON format and load the dataset and split the dataset into train test model. Secondly, take out the features using the counter vectorizer and term frequency inverse document frequency (TF-IDF). Finally, logistic regressor (LR) is used and measure the positive and negative sentiment of the review. simulation result represents the model accuracy score, precision, recall, confusion matrix of the implemented approach.","PeriodicalId":252063,"journal":{"name":"2021 6th International Conference on Innovative Technology in Intelligent System and Industrial Applications (CITISIA)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123454742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-24DOI: 10.1109/citisia53721.2021.9719884
Kaleeem Shaik Kaleeem, Anuradha Samkham Raju, Nabil Giweli, A. Dawoud, P. Prasad, Mousa Abu Kashef
Smart cities are one of the main areas where IoT showed great success, collecting and processing enormous amounts of data to facilitate different applications. Smart parking is one of the evolving smart cities applications. The rapid increase in the population also results in a high number of vehicles that may lead to traffic congestion in urban cities. This traffic congestion is increasing the problem of urban mobility. Urban mobility may have an adverse impact on the quality of life as well as the economy. This problem can be mitigated with the efficient management of the parking systems. This work aims to review the IoT-based regression techniques used in smart parking systems to overcome the problem of urban mobility. IoT-based regression techniques are used to predict parking space in the parking area so that the drivers can get parking space on time and the problem of urban mobility can be mitigated. It was identified that regression techniques efficiently predicted parking space availability. This study provides a comprehensive review of regression techniques used in IoT smart parking applications. The system architecture was also presented to get a better understanding of this work.
{"title":"IoT Regression Techniques In Smart Parking Systems: Survey","authors":"Kaleeem Shaik Kaleeem, Anuradha Samkham Raju, Nabil Giweli, A. Dawoud, P. Prasad, Mousa Abu Kashef","doi":"10.1109/citisia53721.2021.9719884","DOIUrl":"https://doi.org/10.1109/citisia53721.2021.9719884","url":null,"abstract":"Smart cities are one of the main areas where IoT showed great success, collecting and processing enormous amounts of data to facilitate different applications. Smart parking is one of the evolving smart cities applications. The rapid increase in the population also results in a high number of vehicles that may lead to traffic congestion in urban cities. This traffic congestion is increasing the problem of urban mobility. Urban mobility may have an adverse impact on the quality of life as well as the economy. This problem can be mitigated with the efficient management of the parking systems. This work aims to review the IoT-based regression techniques used in smart parking systems to overcome the problem of urban mobility. IoT-based regression techniques are used to predict parking space in the parking area so that the drivers can get parking space on time and the problem of urban mobility can be mitigated. It was identified that regression techniques efficiently predicted parking space availability. This study provides a comprehensive review of regression techniques used in IoT smart parking applications. The system architecture was also presented to get a better understanding of this work.","PeriodicalId":252063,"journal":{"name":"2021 6th International Conference on Innovative Technology in Intelligent System and Industrial Applications (CITISIA)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125326459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-24DOI: 10.1109/CITISIA53721.2021.9719973
Neelam Maharjan, Binod Syangtan, Amr Alchouemi, Moshiur Bhuiyan
With the advancement of the development in the technology the majority of Type 2 Diabetes Mellitus(T2DM) screening tests in today with the use of multivariate technology and techniques data mining has provided strength and opportunities to the study of dynamic interaction between large variables. Machine learning approach used to predict the early onset of diabetes mellitus (DM). This algorithm has increased the accuracy to forecast the risk of diabetes using classifier models. It predicts the increase of blood glucose whereas deep learning of neural network probabilistic modelling was designed. Since diabetes mellitus is one of the most common chronic condition which has the highest death rate. In order to improve the quality of life of individual with diabetes and to eliminate complication, preventing glycaemic levels from reaching the physiological range in fundamental. The review of 12 paper aim is to provide classification techniques using machine learning methods. It involves the approach provided to collect the pre-processing to obtain relevant characteristics to measure their significant features that is classified through the performance based on precision, sensitivity, specificity and area under the curve and trained through SVM to identify the related features. Out of 30 paper completion 12 paper were nominated to reviewed which mainly focused on prediction model to build into support scheme for diagnosis or integrated with current information system for healthcare.
{"title":"Predicting Hypoglycaemia Using Classification","authors":"Neelam Maharjan, Binod Syangtan, Amr Alchouemi, Moshiur Bhuiyan","doi":"10.1109/CITISIA53721.2021.9719973","DOIUrl":"https://doi.org/10.1109/CITISIA53721.2021.9719973","url":null,"abstract":"With the advancement of the development in the technology the majority of Type 2 Diabetes Mellitus(T2DM) screening tests in today with the use of multivariate technology and techniques data mining has provided strength and opportunities to the study of dynamic interaction between large variables. Machine learning approach used to predict the early onset of diabetes mellitus (DM). This algorithm has increased the accuracy to forecast the risk of diabetes using classifier models. It predicts the increase of blood glucose whereas deep learning of neural network probabilistic modelling was designed. Since diabetes mellitus is one of the most common chronic condition which has the highest death rate. In order to improve the quality of life of individual with diabetes and to eliminate complication, preventing glycaemic levels from reaching the physiological range in fundamental. The review of 12 paper aim is to provide classification techniques using machine learning methods. It involves the approach provided to collect the pre-processing to obtain relevant characteristics to measure their significant features that is classified through the performance based on precision, sensitivity, specificity and area under the curve and trained through SVM to identify the related features. Out of 30 paper completion 12 paper were nominated to reviewed which mainly focused on prediction model to build into support scheme for diagnosis or integrated with current information system for healthcare.","PeriodicalId":252063,"journal":{"name":"2021 6th International Conference on Innovative Technology in Intelligent System and Industrial Applications (CITISIA)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132088155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：