Pub Date : 2022-03-16DOI: 10.1109/ICEARS53579.2022.9752039
Huynh Quoc Khanh, P. Damodharan, D.Vinoth Kumar
The most pressing global concern right now is Covid-19. Covid-19 affects the health, daily activities and movement of people, disrupts the global economy, damages the tourist sector, and constitutes a significant threat to global health. Finding a vaccine in a short amount of time is a success that leads to a quicker return to normalcy. Following the intricate developments of Covid-19, it is also vital to foresee the scenario early in order to aid in the construction of improved health facilities, take legislative measures, and avoid economic losses, particularly human losses. The Arima model is used in this article to forecast Covid-19 in India. Arima is well suited to forecasting data using two time-ordered data points. In this paper, data acquired by Indian states from January 1, 2020 to November 8, 2021 are used.
{"title":"Data acquisition based COVID-19 Spread Prediction Analysis","authors":"Huynh Quoc Khanh, P. Damodharan, D.Vinoth Kumar","doi":"10.1109/ICEARS53579.2022.9752039","DOIUrl":"https://doi.org/10.1109/ICEARS53579.2022.9752039","url":null,"abstract":"The most pressing global concern right now is Covid-19. Covid-19 affects the health, daily activities and movement of people, disrupts the global economy, damages the tourist sector, and constitutes a significant threat to global health. Finding a vaccine in a short amount of time is a success that leads to a quicker return to normalcy. Following the intricate developments of Covid-19, it is also vital to foresee the scenario early in order to aid in the construction of improved health facilities, take legislative measures, and avoid economic losses, particularly human losses. The Arima model is used in this article to forecast Covid-19 in India. Arima is well suited to forecasting data using two time-ordered data points. In this paper, data acquired by Indian states from January 1, 2020 to November 8, 2021 are used.","PeriodicalId":252961,"journal":{"name":"2022 International Conference on Electronics and Renewable Systems (ICEARS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121560926","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 : 2022-03-16DOI: 10.1109/ICEARS53579.2022.9752423
M. M. Yunus, A. Sabarudin, Nurul Izzah Hamid, A. K. M. Yusof, P. Nohuddin, M. Karim
Coronary computed tomography angiography (CCTA) has been recognized as a widely used non-invasive coronary imaging approach, which provides the assessment of luminal stenosis. However, the current interpretation of CCTA images still depends on qualitative assessment, which is prone to subjective variability and considered time-consuming. Multiple studies were conducted, venturing into the application of machine learning models specifically used for the classification of atherosclerotic plaques. Hence, this experimental study was designed to classify the atherosclerotic plaques from CCTA images using Auto-WEKA. In this study, there were 202 patients’ original CCTA images collected retrospectively from Institut Jantung Negara (IJN). Semi-auto segmentation of three main coronary arteries was performed on the axial view of CCTA multi-slice images which resulted in a sum of 606 Volume of Interest (VOI). The radiomic features included the first-order, second-order, and shape-order features were extracted from each VOI and acted as an input dataset for the automated machine learning (AutoML) tool which was Auto-WEKA to perform the classification as either normal, calcified, mixed, or non-calcified atherosclerotic plaques. In this study, the best classifier suggested among 39 machine learning methods tested by Auto-WEKA was the random forest. The classification performance was evaluated in terms of multi-class classification of confusion matrix, recall (sensitivity), precision (PPV), F-measure, inverse F-measure, accuracy, and receiver operating characteristics (ROC) curve as well as area under the curve (AUC). Overall, the results showed the highest accuracy of 87% (F-measure: 0.69; Inverse F-Measure: 0.92; AUC: 0.9278) in classifying the calcified plaques using the best classifiers suggested by Auto-WEKA compared to normal, non-calcified and mixed plaques. For the normal plaques, it showed the accuracy of 83% (F-measure: 0.85; Inverse F-Measure: 0.80; AUC: 0.9172), while the non-calcified and mixed plaques showed the accuracy of 77% (F-measure: 0.43; Inverse F-Measure: 0.85; AUC: 0.7911) and 80% (F-measure: 0.54; Inverse F-Measure: 0.87; AUC: 0.7986), respectively. In conclusion, Auto-WEKA showed promising results in obtaining the best classifier among 39 machine learning for the classification of the calcified plaques compared to normal, non-calcified, and mixed plaques based on a CCTA-based radiomic dataset.
{"title":"Automated Classification of Atherosclerosis in Coronary Computed Tomography Angiography Images Based on Radiomics Study Using Automatic Machine Learning","authors":"M. M. Yunus, A. Sabarudin, Nurul Izzah Hamid, A. K. M. Yusof, P. Nohuddin, M. Karim","doi":"10.1109/ICEARS53579.2022.9752423","DOIUrl":"https://doi.org/10.1109/ICEARS53579.2022.9752423","url":null,"abstract":"Coronary computed tomography angiography (CCTA) has been recognized as a widely used non-invasive coronary imaging approach, which provides the assessment of luminal stenosis. However, the current interpretation of CCTA images still depends on qualitative assessment, which is prone to subjective variability and considered time-consuming. Multiple studies were conducted, venturing into the application of machine learning models specifically used for the classification of atherosclerotic plaques. Hence, this experimental study was designed to classify the atherosclerotic plaques from CCTA images using Auto-WEKA. In this study, there were 202 patients’ original CCTA images collected retrospectively from Institut Jantung Negara (IJN). Semi-auto segmentation of three main coronary arteries was performed on the axial view of CCTA multi-slice images which resulted in a sum of 606 Volume of Interest (VOI). The radiomic features included the first-order, second-order, and shape-order features were extracted from each VOI and acted as an input dataset for the automated machine learning (AutoML) tool which was Auto-WEKA to perform the classification as either normal, calcified, mixed, or non-calcified atherosclerotic plaques. In this study, the best classifier suggested among 39 machine learning methods tested by Auto-WEKA was the random forest. The classification performance was evaluated in terms of multi-class classification of confusion matrix, recall (sensitivity), precision (PPV), F-measure, inverse F-measure, accuracy, and receiver operating characteristics (ROC) curve as well as area under the curve (AUC). Overall, the results showed the highest accuracy of 87% (F-measure: 0.69; Inverse F-Measure: 0.92; AUC: 0.9278) in classifying the calcified plaques using the best classifiers suggested by Auto-WEKA compared to normal, non-calcified and mixed plaques. For the normal plaques, it showed the accuracy of 83% (F-measure: 0.85; Inverse F-Measure: 0.80; AUC: 0.9172), while the non-calcified and mixed plaques showed the accuracy of 77% (F-measure: 0.43; Inverse F-Measure: 0.85; AUC: 0.7911) and 80% (F-measure: 0.54; Inverse F-Measure: 0.87; AUC: 0.7986), respectively. In conclusion, Auto-WEKA showed promising results in obtaining the best classifier among 39 machine learning for the classification of the calcified plaques compared to normal, non-calcified, and mixed plaques based on a CCTA-based radiomic dataset.","PeriodicalId":252961,"journal":{"name":"2022 International Conference on Electronics and Renewable Systems (ICEARS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114029515","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 : 2022-03-16DOI: 10.1109/ICEARS53579.2022.9752084
A. R, Nethaji Achha, Pramod Kumar P, Sai Ganesh P, Ruthvik Reddy A, Gayathri Shriya V
In the present ingenious world, every country is accelerating in the process of developing smart cities. As a part of developing smart cities, public toilets have been entrenched at every nook and corner of the country. Yet, the hygiene and cleanliness in our country are at gunpoint due to the improper maintenance of public toilets. Because of this reason, though there are many public toilets available, people are not ready to use them with the fear of getting infected or falling sick after using the public toilet that is not properly maintained. This paper proposes a new idea with the help of advancing technologies such as the Internet of Things (IoT). They are smart testing toolkits that can be installed in public toilets so that people can safely use them without any fear. It also contributes to converting the public toilets from disease transmitters to smart toilets that contribute to the health and well-being of the nation. Since prevention is better than cure, by implementing the proposed idea the transmission of diseases that are caused using ill-maintained public toilets can be prevented.
{"title":"Disease Transmission Prevention at Public Toilets with IoT-Enabled Devices in Smart Cities","authors":"A. R, Nethaji Achha, Pramod Kumar P, Sai Ganesh P, Ruthvik Reddy A, Gayathri Shriya V","doi":"10.1109/ICEARS53579.2022.9752084","DOIUrl":"https://doi.org/10.1109/ICEARS53579.2022.9752084","url":null,"abstract":"In the present ingenious world, every country is accelerating in the process of developing smart cities. As a part of developing smart cities, public toilets have been entrenched at every nook and corner of the country. Yet, the hygiene and cleanliness in our country are at gunpoint due to the improper maintenance of public toilets. Because of this reason, though there are many public toilets available, people are not ready to use them with the fear of getting infected or falling sick after using the public toilet that is not properly maintained. This paper proposes a new idea with the help of advancing technologies such as the Internet of Things (IoT). They are smart testing toolkits that can be installed in public toilets so that people can safely use them without any fear. It also contributes to converting the public toilets from disease transmitters to smart toilets that contribute to the health and well-being of the nation. Since prevention is better than cure, by implementing the proposed idea the transmission of diseases that are caused using ill-maintained public toilets can be prevented.","PeriodicalId":252961,"journal":{"name":"2022 International Conference on Electronics and Renewable Systems (ICEARS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115904139","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 : 2022-03-16DOI: 10.1109/ICEARS53579.2022.9752163
N. Mahesh, G. S. Deepak Prasath, E. Divyadharshini, V. Gokul
COVID-19 is a contagious complaint that affects the case’s lungs vigorously which results in the reduction of oxygen situations in the blood. An unforeseen drop in oxygen position in the blood will lead to Hypoxemia. So frequent monitoring of the case’s Saturation of Peripheral Oxygen (SPO2) and heart rate is needed. By recording and monitoring these parameters immediate treatment can be handed to the case in case of emergency. A covid-19 health monitoring system is developed in this project. The model consists of the temperature measurement, pulse rate measurement, and SPO2 measurement. Temperature detectors measure body temperature using the LM35 detector and Arduino, it works on the principle of resistor sensitivity of temperature. The increase in temperature level of patient is considered to be the symptom of Corona and can be measured with the help of a temperature detector. Pulse rate and SPO2 level are measured using a pulse oximeter sensor. Once recorded, the sensors shoot the data over to the Arduino UNO which in turn sends it to the the local server using a WIFI block wherein the information can be used for further analysis and visualizations.
{"title":"Implementation of Covid-19 Health Monitoring System","authors":"N. Mahesh, G. S. Deepak Prasath, E. Divyadharshini, V. Gokul","doi":"10.1109/ICEARS53579.2022.9752163","DOIUrl":"https://doi.org/10.1109/ICEARS53579.2022.9752163","url":null,"abstract":"COVID-19 is a contagious complaint that affects the case’s lungs vigorously which results in the reduction of oxygen situations in the blood. An unforeseen drop in oxygen position in the blood will lead to Hypoxemia. So frequent monitoring of the case’s Saturation of Peripheral Oxygen (SPO2) and heart rate is needed. By recording and monitoring these parameters immediate treatment can be handed to the case in case of emergency. A covid-19 health monitoring system is developed in this project. The model consists of the temperature measurement, pulse rate measurement, and SPO2 measurement. Temperature detectors measure body temperature using the LM35 detector and Arduino, it works on the principle of resistor sensitivity of temperature. The increase in temperature level of patient is considered to be the symptom of Corona and can be measured with the help of a temperature detector. Pulse rate and SPO2 level are measured using a pulse oximeter sensor. Once recorded, the sensors shoot the data over to the Arduino UNO which in turn sends it to the the local server using a WIFI block wherein the information can be used for further analysis and visualizations.","PeriodicalId":252961,"journal":{"name":"2022 International Conference on Electronics and Renewable Systems (ICEARS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131925740","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 : 2022-03-16DOI: 10.1109/ICEARS53579.2022.9751860
Chandragiri Sandeep, Yellepeddi Srikar, Kodali Rajani, D. R. Rao
In this Study, a strategy for detecting minerals in a picture collection is offered. The novelty is to perform the multi-classification of mineral based on the given image using convolutional neural network. Identification and classification of minerals are the fundamental of the mining and processing of minerals [1]. The suggested technique next detects the minerals and labels them with their relevant class labels using multiple photos from the image collection. Tensor Flow, Keras, and OpenCV are used to detect these minerals. Keras is a free and open-source Python interface for artificial neural networks. Keras is a Python library that connects to the TensorFlow library. These are photos from the Training Image dataset are fed into the training model. Our system recognizes the numerous bright variations of minerals from the training dataset using a particular collection of hand specimen photographs of minerals in seven classes: diamond, bornite, chrysocolla, malachite, muscovite, pyrite, and quartz. The model is trained until the error rate becomes insignificant. The trained model is put to the test on some real-world photos.
{"title":"Mineral Identification using CNN","authors":"Chandragiri Sandeep, Yellepeddi Srikar, Kodali Rajani, D. R. Rao","doi":"10.1109/ICEARS53579.2022.9751860","DOIUrl":"https://doi.org/10.1109/ICEARS53579.2022.9751860","url":null,"abstract":"In this Study, a strategy for detecting minerals in a picture collection is offered. The novelty is to perform the multi-classification of mineral based on the given image using convolutional neural network. Identification and classification of minerals are the fundamental of the mining and processing of minerals [1]. The suggested technique next detects the minerals and labels them with their relevant class labels using multiple photos from the image collection. Tensor Flow, Keras, and OpenCV are used to detect these minerals. Keras is a free and open-source Python interface for artificial neural networks. Keras is a Python library that connects to the TensorFlow library. These are photos from the Training Image dataset are fed into the training model. Our system recognizes the numerous bright variations of minerals from the training dataset using a particular collection of hand specimen photographs of minerals in seven classes: diamond, bornite, chrysocolla, malachite, muscovite, pyrite, and quartz. The model is trained until the error rate becomes insignificant. The trained model is put to the test on some real-world photos.","PeriodicalId":252961,"journal":{"name":"2022 International Conference on Electronics and Renewable Systems (ICEARS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132559523","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 : 2022-03-16DOI: 10.1109/ICEARS53579.2022.9752282
D. R. Rao, S. Noorjahan, Shaik Ayesha Fathima
Earth's environment and its evolution can be seen through satellite images in near real time. Through satellite imagery, remote sensing data provides crucial information that can be used for a variety of applications, including image fusion, change detection, land cover classification, agriculture, mining, disaster mitigation, and monitoring climate change. The objective of this project is to propose a method for classifying satellite images according to multiple predefined land cover classes. The proposed approach involves collecting data in image format. The data is then preprocessed using data preprocessing techniques. The processed data is fed into the proposed algorithm and the obtained result is analyzed. Some of the algorithms used in satellite imagery classification are U-Net, Random Forest, Deep Labv3, CNN (Convolutional Neural Network), ANN(Artificial neural network), Resnet etc. In this project, DeepLabv3 (Atrous convolution) algorithm is used for land cover classification. The dataset used is the deep globe land cover classification dataset. DeepLabv3 is a semantic segmentation system that uses atrous convolution to capture multi-scale context by adopting multiple atrous rates in cascade or in parallel to determine the scale of segments.
{"title":"Classification of Land Cover Usage from Satellite Images using Deep Learning Algorithms","authors":"D. R. Rao, S. Noorjahan, Shaik Ayesha Fathima","doi":"10.1109/ICEARS53579.2022.9752282","DOIUrl":"https://doi.org/10.1109/ICEARS53579.2022.9752282","url":null,"abstract":"Earth's environment and its evolution can be seen through satellite images in near real time. Through satellite imagery, remote sensing data provides crucial information that can be used for a variety of applications, including image fusion, change detection, land cover classification, agriculture, mining, disaster mitigation, and monitoring climate change. The objective of this project is to propose a method for classifying satellite images according to multiple predefined land cover classes. The proposed approach involves collecting data in image format. The data is then preprocessed using data preprocessing techniques. The processed data is fed into the proposed algorithm and the obtained result is analyzed. Some of the algorithms used in satellite imagery classification are U-Net, Random Forest, Deep Labv3, CNN (Convolutional Neural Network), ANN(Artificial neural network), Resnet etc. In this project, DeepLabv3 (Atrous convolution) algorithm is used for land cover classification. The dataset used is the deep globe land cover classification dataset. DeepLabv3 is a semantic segmentation system that uses atrous convolution to capture multi-scale context by adopting multiple atrous rates in cascade or in parallel to determine the scale of segments.","PeriodicalId":252961,"journal":{"name":"2022 International Conference on Electronics and Renewable Systems (ICEARS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130823384","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 : 2022-03-16DOI: 10.1109/ICEARS53579.2022.9751902
B. Vaishnavi, Anvitha Pamidighantam, A. Hema, V. R. Syam
The purpose of Hyperspectral image (HSI) classification is for analyzing the remotely sensed images. The need of Convolutional neural network (CNN) is that it is the most frequently worn deep learning method to process the visual data. CNN is required for HSI classification which is also seen in new projects. The 2D CNN mechanisms are widely used. Here, we have proposed a 2-D CNN model along with Support Vector Machine (SVM) and Random Forest classifies for HSI classification. To test the performance of this approach, experiments are performed over Indian Pines, University of Pavia, and Salinas Scene along with WHU-Hi-LongKou, WHU-Hi-HanChuan, and WHU-Hi-HongHu remote sensing data sets. These datasets are used for crop images classification.
{"title":"Hyperspectral Image Classification for Agricultural Applications","authors":"B. Vaishnavi, Anvitha Pamidighantam, A. Hema, V. R. Syam","doi":"10.1109/ICEARS53579.2022.9751902","DOIUrl":"https://doi.org/10.1109/ICEARS53579.2022.9751902","url":null,"abstract":"The purpose of Hyperspectral image (HSI) classification is for analyzing the remotely sensed images. The need of Convolutional neural network (CNN) is that it is the most frequently worn deep learning method to process the visual data. CNN is required for HSI classification which is also seen in new projects. The 2D CNN mechanisms are widely used. Here, we have proposed a 2-D CNN model along with Support Vector Machine (SVM) and Random Forest classifies for HSI classification. To test the performance of this approach, experiments are performed over Indian Pines, University of Pavia, and Salinas Scene along with WHU-Hi-LongKou, WHU-Hi-HanChuan, and WHU-Hi-HongHu remote sensing data sets. These datasets are used for crop images classification.","PeriodicalId":252961,"journal":{"name":"2022 International Conference on Electronics and Renewable Systems (ICEARS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130866546","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 : 2022-03-16DOI: 10.1109/icears53579.2022.9752030
{"title":"ICEARS 2022 Cover Page","authors":"","doi":"10.1109/icears53579.2022.9752030","DOIUrl":"https://doi.org/10.1109/icears53579.2022.9752030","url":null,"abstract":"","PeriodicalId":252961,"journal":{"name":"2022 International Conference on Electronics and Renewable Systems (ICEARS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131173726","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 : 2022-03-16DOI: 10.1109/ICEARS53579.2022.9752089
B. Shreevishnu, V. Ananthanarayanan
Data security refers to the path toward protecting information from unauthorized user access and data corruption all through its lifecycle. Most of security system deployed in the current system leads to loss of confidential data as the key is easily hackable because of a single algorithm usage. Diabetes-related complexities incorporate harm to large and little blood vessels. The danger of most diabetes-related inconveniences can be diminished whenever analyzed early. To overcome these two problems this project presents a medical application that analyses a patient’s medical data to give a diagnosis to check if he/she is diabetic with an efficient data security system where two security algorithms will be merged to secure the patient’s medical data stored and accessed in cloud. This research work attempts to provide an effective end to end security for medical applications.
{"title":"A Novel Hybrid Algorithm for Securing Data Over Wireless Transfer in Machine Learning Based Prediction System","authors":"B. Shreevishnu, V. Ananthanarayanan","doi":"10.1109/ICEARS53579.2022.9752089","DOIUrl":"https://doi.org/10.1109/ICEARS53579.2022.9752089","url":null,"abstract":"Data security refers to the path toward protecting information from unauthorized user access and data corruption all through its lifecycle. Most of security system deployed in the current system leads to loss of confidential data as the key is easily hackable because of a single algorithm usage. Diabetes-related complexities incorporate harm to large and little blood vessels. The danger of most diabetes-related inconveniences can be diminished whenever analyzed early. To overcome these two problems this project presents a medical application that analyses a patient’s medical data to give a diagnosis to check if he/she is diabetic with an efficient data security system where two security algorithms will be merged to secure the patient’s medical data stored and accessed in cloud. This research work attempts to provide an effective end to end security for medical applications.","PeriodicalId":252961,"journal":{"name":"2022 International Conference on Electronics and Renewable Systems (ICEARS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130717188","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 : 2022-03-16DOI: 10.1109/ICEARS53579.2022.9752048
Lakkireddy Arundhathi, Saripalli Krishnaveni, S. Vasavi
Cloud computing is one among the most crucial commercial technologies nowadays. It offers a diverse range of services. One of the most exciting and important procedures in cloud computing is virtual machine installation (VMP). Virtual Machine Placement uses evolutionary computing to lower energy consumption while lowering the total number of physical servers that are currently in use. By examining the ant colony system’s (ACS) promising performance for combinatorial issues, Order Exchange and Ant Colony System OEMACS, an approach based on ACS finds solution by combining order exchange and migration local search strategies, was developed (Order exchange and Migration Ant Colony System). From a global optimization standpoint, The OEMACS algorithm is capable of significantly lowering the active servers in number and is used for virtual machine assignment. It also aids in the reduction of the number of active servers that are underutilized. In OEMACS, artificial ants are guided to the best feasible solution using the pheromone deposition method. It also arranges virtual machines in such a way that resource waste and power consumption are reduced. On servers with homogenous and heterogeneous VM sizes, this strategy is used. OEMACS surpasses some of the previously utilized algorithms, such as standard heuristics and other evolutionary-based techniques, according to the findings.
云计算是当今最重要的商业技术之一。它提供各种各样的服务。云计算中最令人兴奋和重要的过程之一是虚拟机安装(VMP)。虚拟机布局使用进化计算来降低能耗，同时降低当前正在使用的物理服务器的总数。通过考察蚁群系统(ACS)在组合问题上的良好表现，提出了一种基于ACS的结合顺序交换和迁移局部搜索策略的求解方法(Order Exchange and migration ant colony system)。从全局优化的角度来看，OEMACS算法能够显著减少活动服务器的数量，并用于虚拟机分配。它还有助于减少未充分利用的活动服务器的数量。在OEMACS中，利用信息素沉积法引导人工蚂蚁找到最佳可行方案。它还以减少资源浪费和功耗的方式安排虚拟机。在具有同构和异构VM大小的服务器上，使用此策略。根据研究结果，OEMACS超越了以前使用的一些算法，如标准启发式和其他基于进化的技术。
{"title":"Multi-Objective Virtual Machine Placement using Order Exchange and Migration Ant Colony System algorithm","authors":"Lakkireddy Arundhathi, Saripalli Krishnaveni, S. Vasavi","doi":"10.1109/ICEARS53579.2022.9752048","DOIUrl":"https://doi.org/10.1109/ICEARS53579.2022.9752048","url":null,"abstract":"Cloud computing is one among the most crucial commercial technologies nowadays. It offers a diverse range of services. One of the most exciting and important procedures in cloud computing is virtual machine installation (VMP). Virtual Machine Placement uses evolutionary computing to lower energy consumption while lowering the total number of physical servers that are currently in use. By examining the ant colony system’s (ACS) promising performance for combinatorial issues, Order Exchange and Ant Colony System OEMACS, an approach based on ACS finds solution by combining order exchange and migration local search strategies, was developed (Order exchange and Migration Ant Colony System). From a global optimization standpoint, The OEMACS algorithm is capable of significantly lowering the active servers in number and is used for virtual machine assignment. It also aids in the reduction of the number of active servers that are underutilized. In OEMACS, artificial ants are guided to the best feasible solution using the pheromone deposition method. It also arranges virtual machines in such a way that resource waste and power consumption are reduced. On servers with homogenous and heterogeneous VM sizes, this strategy is used. OEMACS surpasses some of the previously utilized algorithms, such as standard heuristics and other evolutionary-based techniques, according to the findings.","PeriodicalId":252961,"journal":{"name":"2022 International Conference on Electronics and Renewable Systems (ICEARS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131154674","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}
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