Pub Date : 2020-10-09DOI: 10.1109/ICSTCEE49637.2020.9276931
Akshay Kotian, Sourabh Patil, Nikhil Prajapati, Y. Mane
An Intrusion Detection System is model which monitors the network security from various type of Attacks. Intrusion Detection plays an important role in order to provide Network Security. In this paper we implement an Intrusion Detection System by building a Deep Learning Model using Feed Forward Neural Network (FFNN) and Long Short Term Memory Neural Network(LSTM). The study of model is based on Binary Classification and Multiclass Classification. The Model is implemented on Realtime Datasets or Dynamic Datasets. There is an comparative study between Feed Forward Neural Network and Long Short Term Memory Neural Network. The Intrusion Detection System(IDS) model improves the acccuracy and enlarge the further implementation for an Intrusion Detection Systems.
{"title":"Realtime Detection Of Network Anomalies Using Neural Network","authors":"Akshay Kotian, Sourabh Patil, Nikhil Prajapati, Y. Mane","doi":"10.1109/ICSTCEE49637.2020.9276931","DOIUrl":"https://doi.org/10.1109/ICSTCEE49637.2020.9276931","url":null,"abstract":"An Intrusion Detection System is model which monitors the network security from various type of Attacks. Intrusion Detection plays an important role in order to provide Network Security. In this paper we implement an Intrusion Detection System by building a Deep Learning Model using Feed Forward Neural Network (FFNN) and Long Short Term Memory Neural Network(LSTM). The study of model is based on Binary Classification and Multiclass Classification. The Model is implemented on Realtime Datasets or Dynamic Datasets. There is an comparative study between Feed Forward Neural Network and Long Short Term Memory Neural Network. The Intrusion Detection System(IDS) model improves the acccuracy and enlarge the further implementation for an Intrusion Detection Systems.","PeriodicalId":113845,"journal":{"name":"2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129335722","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 : 2020-10-09DOI: 10.1109/ICSTCEE49637.2020.9277041
S. Giraddi, S. Seeri, P. Hiremath, Jayalaxmi G.N
Deep learning techniques are used widespread for image recognition and classification problems. Gradually, deep learning architectures have modified to comprise more layers and become more robust model for classification problems. In this paper, the base VGG16 model is fine-tuned for the classification flowers into five categories, namely, Daisy, Dandelion, Sunflower, Rose and Tulip flowers. The fine-tuned VGG16 model is trained using 3520 flower images. The model is achieved a classification accuracy of 97.67% for validation set and 95.00% for testing dataset. The Kaggle dataset is used for training, validation and testing of the proposed fine-tuned VGG16 model. The goal of this work is to show that a proper modified VGG16 deep model, which is, pre-trained on ImageNet for image classification can be used for other image data set using very small dataset without over fitting. The VGG16 model uses mall size 3x3 filters.
{"title":"Flower Classification using Deep Learning models","authors":"S. Giraddi, S. Seeri, P. Hiremath, Jayalaxmi G.N","doi":"10.1109/ICSTCEE49637.2020.9277041","DOIUrl":"https://doi.org/10.1109/ICSTCEE49637.2020.9277041","url":null,"abstract":"Deep learning techniques are used widespread for image recognition and classification problems. Gradually, deep learning architectures have modified to comprise more layers and become more robust model for classification problems. In this paper, the base VGG16 model is fine-tuned for the classification flowers into five categories, namely, Daisy, Dandelion, Sunflower, Rose and Tulip flowers. The fine-tuned VGG16 model is trained using 3520 flower images. The model is achieved a classification accuracy of 97.67% for validation set and 95.00% for testing dataset. The Kaggle dataset is used for training, validation and testing of the proposed fine-tuned VGG16 model. The goal of this work is to show that a proper modified VGG16 deep model, which is, pre-trained on ImageNet for image classification can be used for other image data set using very small dataset without over fitting. The VGG16 model uses mall size 3x3 filters.","PeriodicalId":113845,"journal":{"name":"2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE)","volume":"298 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114105076","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 : 2020-10-09DOI: 10.1109/ICSTCEE49637.2020.9277184
Poorva Thosar, J. Patil, Mishail Singh, Swaraj Thamke, S. Gonge
The direct measurement of the temperature of a permanent magnet synchronous motor (PMSM) is difficult due to its complexity of the construction of the motor. It is difficult to embed thermal sensors in the motor. Thus, the temperature of various components of the motor must be modeled from other parameters. The classical methods of thermal modeling lack accuracy and require expertise on heat models as well as knowledge of the individual motor construction. In this paper, efficient and fast predictive linear models are evaluated. Linear regression with gradient descent and normal equations is evaluated to predict the dynamic temperatures inside PMSM. The features used to train the data are selected as per correlation analysis. Results are further optimized using regularization techniques such as L1 and L2 regularization. K-nearest neighbor regression is evaluated, and then different predictive models are compared.
{"title":"Prediction of Motor Temperature using Linear Regression","authors":"Poorva Thosar, J. Patil, Mishail Singh, Swaraj Thamke, S. Gonge","doi":"10.1109/ICSTCEE49637.2020.9277184","DOIUrl":"https://doi.org/10.1109/ICSTCEE49637.2020.9277184","url":null,"abstract":"The direct measurement of the temperature of a permanent magnet synchronous motor (PMSM) is difficult due to its complexity of the construction of the motor. It is difficult to embed thermal sensors in the motor. Thus, the temperature of various components of the motor must be modeled from other parameters. The classical methods of thermal modeling lack accuracy and require expertise on heat models as well as knowledge of the individual motor construction. In this paper, efficient and fast predictive linear models are evaluated. Linear regression with gradient descent and normal equations is evaluated to predict the dynamic temperatures inside PMSM. The features used to train the data are selected as per correlation analysis. Results are further optimized using regularization techniques such as L1 and L2 regularization. K-nearest neighbor regression is evaluated, and then different predictive models are compared.","PeriodicalId":113845,"journal":{"name":"2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123905574","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 : 2020-10-09DOI: 10.1109/ICSTCEE49637.2020.9276922
M. Meraj, S. P. Singh, P. Johri, M. Quasim
Smart Internet of Things for Disease Tracking is a solution for efficient tracking of diseases and therefore allows to detection patterns of various diseases. The plan is a broad network of smart devices that will process and interpret the entered data automatically. The computer then transfers the data to the main backend, which could be data from the Department of Health. This will warn of the disease spreading. Once trends and analyzes reach the spine, it is easy to take steps to end the rapid spread of the disease and prevent it throughout the country and around the world. It also lets patients have the illness identified as soon as possible. To counter this serious problem, many people are utilizing the Internet of Things (IoT) to capture sensory data in real-time, but that was not practical until recently. This involves monitoring individuals, medical facilities, environments, and even distant areas of the world in certain situations. This paper explores the Detection and Prediction of Infectious Diseases patterns through IoT Sensors.
{"title":"An investigation on infectious disease patterns using Internet of Things (IoT)","authors":"M. Meraj, S. P. Singh, P. Johri, M. Quasim","doi":"10.1109/ICSTCEE49637.2020.9276922","DOIUrl":"https://doi.org/10.1109/ICSTCEE49637.2020.9276922","url":null,"abstract":"Smart Internet of Things for Disease Tracking is a solution for efficient tracking of diseases and therefore allows to detection patterns of various diseases. The plan is a broad network of smart devices that will process and interpret the entered data automatically. The computer then transfers the data to the main backend, which could be data from the Department of Health. This will warn of the disease spreading. Once trends and analyzes reach the spine, it is easy to take steps to end the rapid spread of the disease and prevent it throughout the country and around the world. It also lets patients have the illness identified as soon as possible. To counter this serious problem, many people are utilizing the Internet of Things (IoT) to capture sensory data in real-time, but that was not practical until recently. This involves monitoring individuals, medical facilities, environments, and even distant areas of the world in certain situations. This paper explores the Detection and Prediction of Infectious Diseases patterns through IoT Sensors.","PeriodicalId":113845,"journal":{"name":"2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE)","volume":"227 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116428800","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 : 2020-10-09DOI: 10.1109/ICSTCEE49637.2020.9277404
Ricardo Martins, F. Caldeira, Filipe Sá, Maryam Abbasi, Pedro Martins
The motivation for developing a self-service platform for employees arises precisely from the idea that in all organizations there are tasks that could be automated in order to redirect work resources to more important tasks. The proposed application consists of the development of a self-service platform, for personal information and scheduling tasks, aimed at the employees instead of all the solutions that are in the market that aim their platform to the Human Resources. We focus on the employers giving them more responsibility to make their own personal management like, change their personal info, book their vacations and other, giving to the Human Resources the tasks of managing all these actions made by the employers. At the end of the work, it is expected that the final solution to be considered as an example of success with regards to the theme of business automation and innovation, using the low-code application Outsystems to perform the full proposed application development.
{"title":"An overview on how to develop a low-code application using OutSystems","authors":"Ricardo Martins, F. Caldeira, Filipe Sá, Maryam Abbasi, Pedro Martins","doi":"10.1109/ICSTCEE49637.2020.9277404","DOIUrl":"https://doi.org/10.1109/ICSTCEE49637.2020.9277404","url":null,"abstract":"The motivation for developing a self-service platform for employees arises precisely from the idea that in all organizations there are tasks that could be automated in order to redirect work resources to more important tasks. The proposed application consists of the development of a self-service platform, for personal information and scheduling tasks, aimed at the employees instead of all the solutions that are in the market that aim their platform to the Human Resources. We focus on the employers giving them more responsibility to make their own personal management like, change their personal info, book their vacations and other, giving to the Human Resources the tasks of managing all these actions made by the employers. At the end of the work, it is expected that the final solution to be considered as an example of success with regards to the theme of business automation and innovation, using the low-code application Outsystems to perform the full proposed application development.","PeriodicalId":113845,"journal":{"name":"2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE)","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127370477","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 : 2020-10-09DOI: 10.1109/ICSTCEE49637.2020.9276861
P. Desai
In the new hypercompetitive world; businesses should focus on cost savings, improved productivity and reduce manual tasks. Hence the need to automate processes to take care of the repetitive work. Robotic Process Automation (RPA) provides the capability to mimic repetitive human tasks with the use of software robots or botsThe RPA bot acts like a human operator intervention into business process such as click of a mouse, interacting with other documents, extracting required information, providing good Object Character Recognition, interacting with ERP, CRM and other legacy systems in an automated way. The name robotic suggests that they need to be productive, giving good return on investment, providing better employee satisfaction, minimize human intervention and finally offering better customer experience. Many organizations are unable to decide if RPA is required, hence there is a gap in finalizing the need. This paper deals providing RPA solutions considering pre-requisite for organization having existing automation and organization that are new to automation, it also deals with areas that need focus namely business growth, functionality, employee productivity, scalability, improve customer experience along with bots and their performance.
{"title":"Robotic process automation: RPA Pre-requisite and pivotal points : Special Issue: Special issue:IAISCT(SS4)","authors":"P. Desai","doi":"10.1109/ICSTCEE49637.2020.9276861","DOIUrl":"https://doi.org/10.1109/ICSTCEE49637.2020.9276861","url":null,"abstract":"In the new hypercompetitive world; businesses should focus on cost savings, improved productivity and reduce manual tasks. Hence the need to automate processes to take care of the repetitive work. Robotic Process Automation (RPA) provides the capability to mimic repetitive human tasks with the use of software robots or botsThe RPA bot acts like a human operator intervention into business process such as click of a mouse, interacting with other documents, extracting required information, providing good Object Character Recognition, interacting with ERP, CRM and other legacy systems in an automated way. The name robotic suggests that they need to be productive, giving good return on investment, providing better employee satisfaction, minimize human intervention and finally offering better customer experience. Many organizations are unable to decide if RPA is required, hence there is a gap in finalizing the need. This paper deals providing RPA solutions considering pre-requisite for organization having existing automation and organization that are new to automation, it also deals with areas that need focus namely business growth, functionality, employee productivity, scalability, improve customer experience along with bots and their performance.","PeriodicalId":113845,"journal":{"name":"2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE)","volume":"200 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126264467","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 : 2020-10-09DOI: 10.1109/ICSTCEE49637.2020.9277030
Ramesh Chundi, Vishwanath R. Hulipalled, J. B. Simha
Usage of social media has become more widespread to express sentiment, emotion about public events, government policies, product reviews etc. Performing Sentiment Analysis (SA) on social media data will give more and more insights about user’s behavior. Multilingual society like India, it is very common to use code switch text in social media to express their views. Switching between languages while communicating is refer as code mixing or code switching. Analyzing this code switch text and getting the useful information from this too harder because of its unstructured linguistic nature. In this paper, we proposed a hybrid model called SAEKCS for sentiment analysis on Kannada-English code switch text. Our proposed model uses deep learning techniques like Convolutional Neural Network (CNN) and Bidirectional Long Short Term Memory (BiLSTM) for sentiment analysis in code switch text. Our experimental results shows that 77.6% of accuracy and 69.6% of coverage. These results are much better than existing works [17] [18].
{"title":"SAEKCS: Sentiment Analysis for English – Kannada Code SwitchText Using Deep Learning Techniques","authors":"Ramesh Chundi, Vishwanath R. Hulipalled, J. B. Simha","doi":"10.1109/ICSTCEE49637.2020.9277030","DOIUrl":"https://doi.org/10.1109/ICSTCEE49637.2020.9277030","url":null,"abstract":"Usage of social media has become more widespread to express sentiment, emotion about public events, government policies, product reviews etc. Performing Sentiment Analysis (SA) on social media data will give more and more insights about user’s behavior. Multilingual society like India, it is very common to use code switch text in social media to express their views. Switching between languages while communicating is refer as code mixing or code switching. Analyzing this code switch text and getting the useful information from this too harder because of its unstructured linguistic nature. In this paper, we proposed a hybrid model called SAEKCS for sentiment analysis on Kannada-English code switch text. Our proposed model uses deep learning techniques like Convolutional Neural Network (CNN) and Bidirectional Long Short Term Memory (BiLSTM) for sentiment analysis in code switch text. Our experimental results shows that 77.6% of accuracy and 69.6% of coverage. These results are much better than existing works [17] [18].","PeriodicalId":113845,"journal":{"name":"2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126340592","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 : 2020-10-09DOI: 10.1109/ICSTCEE49637.2020.9277300
Neetu Faujdar, Aparna Sinha, H. Sharma, Eshaan Verma
Based on the generalized definition of a network, we know that it is made up of a number of nodes. Sending and receiving of data takes place via these nodes. This process is characterized by the permission provided and requirement of sharing data. Basically, networking helps the nodes across the globe to connect and enable data transfer. Software Defined Networking (SDN) is a smart networking technique. It brings together other disciplines with networking for example, programming, research etc. The SDN model is predominantly controlled by a central unit called controller. All the communication takes place via this controller; however, it has a disadvantage. If the controller anyhow fails or is hacked, the entire system will either fail or get corrupted. In this paper disadvantage of the SDN has overcome with the relevant solution.
{"title":"Network Security in Software defined Networks (SDN)","authors":"Neetu Faujdar, Aparna Sinha, H. Sharma, Eshaan Verma","doi":"10.1109/ICSTCEE49637.2020.9277300","DOIUrl":"https://doi.org/10.1109/ICSTCEE49637.2020.9277300","url":null,"abstract":"Based on the generalized definition of a network, we know that it is made up of a number of nodes. Sending and receiving of data takes place via these nodes. This process is characterized by the permission provided and requirement of sharing data. Basically, networking helps the nodes across the globe to connect and enable data transfer. Software Defined Networking (SDN) is a smart networking technique. It brings together other disciplines with networking for example, programming, research etc. The SDN model is predominantly controlled by a central unit called controller. All the communication takes place via this controller; however, it has a disadvantage. If the controller anyhow fails or is hacked, the entire system will either fail or get corrupted. In this paper disadvantage of the SDN has overcome with the relevant solution.","PeriodicalId":113845,"journal":{"name":"2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126462124","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 : 2020-10-09DOI: 10.1109/ICSTCEE49637.2020.9277484
A. Khalyasmaa, S. Eroshenko, A. Bramm, D. Tran, Piepur Chakravarthi Teja, R. Hariprakash
The paper describes the methodology for consideration of renewable energy sources in strategic plans of fuel and energy sector development. The inclusion of renewable energy projects in energy development programs is carried out based on the results of an integrated ranking of technical, economic and environmental criteria. For power generation facilities based on renewable energy sources, the energy potential is additionally addressed in accordance with the list of power generation technologies under consideration, which makes it possible to obtain the values of the installed capacity utilization factors, and, as a consequence, to judge on the technological and commercial feasibility of renewable energy projects implementation. The paper provides an example of a ranked list formulation of renewable energy projects for a real regional power system.
{"title":"Strategic planning of renewable energy sources implementation following the country-wide goals of energy sector development","authors":"A. Khalyasmaa, S. Eroshenko, A. Bramm, D. Tran, Piepur Chakravarthi Teja, R. Hariprakash","doi":"10.1109/ICSTCEE49637.2020.9277484","DOIUrl":"https://doi.org/10.1109/ICSTCEE49637.2020.9277484","url":null,"abstract":"The paper describes the methodology for consideration of renewable energy sources in strategic plans of fuel and energy sector development. The inclusion of renewable energy projects in energy development programs is carried out based on the results of an integrated ranking of technical, economic and environmental criteria. For power generation facilities based on renewable energy sources, the energy potential is additionally addressed in accordance with the list of power generation technologies under consideration, which makes it possible to obtain the values of the installed capacity utilization factors, and, as a consequence, to judge on the technological and commercial feasibility of renewable energy projects implementation. The paper provides an example of a ranked list formulation of renewable energy projects for a real regional power system.","PeriodicalId":113845,"journal":{"name":"2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116902858","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 : 2020-10-09DOI: 10.1109/ICSTCEE49637.2020.9276944
A. Venkatachalam, R. Lathamanju, M. Shobana, A. Sandanakaruppan
Cloud computing is an innovation that is of expanding request nowadays. Here, resources are multiplexed from physical machines to virtual machines through virtualization technology. Cloud computing gives different sorts of administrations to clients. In Cloud Computing, the supplier progressively distributes the resources. Doing as such, the service provider ought to have some information about the future asset needs. They can be determined utilizing the load prediction calculations. A calculation named long short-term memory (LSTM) neural system is utilized to analyze the load, which is proficient as far as both expanding and diminishing need of resources. The predicted results of the LSTM model is helpful for optimizing the service response time and also fulfils the Service Level Agreement (SLA) contracted by the user.
{"title":"Improving elasticity in cloud with predictive algorithms","authors":"A. Venkatachalam, R. Lathamanju, M. Shobana, A. Sandanakaruppan","doi":"10.1109/ICSTCEE49637.2020.9276944","DOIUrl":"https://doi.org/10.1109/ICSTCEE49637.2020.9276944","url":null,"abstract":"Cloud computing is an innovation that is of expanding request nowadays. Here, resources are multiplexed from physical machines to virtual machines through virtualization technology. Cloud computing gives different sorts of administrations to clients. In Cloud Computing, the supplier progressively distributes the resources. Doing as such, the service provider ought to have some information about the future asset needs. They can be determined utilizing the load prediction calculations. A calculation named long short-term memory (LSTM) neural system is utilized to analyze the load, which is proficient as far as both expanding and diminishing need of resources. The predicted results of the LSTM model is helpful for optimizing the service response time and also fulfils the Service Level Agreement (SLA) contracted by the user.","PeriodicalId":113845,"journal":{"name":"2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114339576","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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