Pub Date : 2021-12-10DOI: 10.1109/SMART52563.2021.9676253
Anant Kumar Jayswal, D. K. Lobiyal
Distributed resources in a cloud environment come with issues of reliability and heterogeneous performance of every data center. This behavior of the cloud comes with many issues like trust, fault, and reliability in the cloud. So, to overcome this, reliable task scheduling is required, which considers all the conduct of the cloud. In such a situation, the position, and the executives of the allocation of resources between on-premises, datacenters and cloud data centers is a fundamental problem. Distributing in the cloud places a heavy burden on the execution of the cloud and the supervision of resource and task performance. Various static and dynamic algorithms may be used to resolve this problem. For better cloud presentation in terms of several tasks failed, execution duration, and start time, a fault-aware bat algorithm inspired by undertaking distribution algorithm for cloud infrastructure is suggested in this paper.
{"title":"Fault Aware BAT Algorithm for Task Scheduling in Cloud","authors":"Anant Kumar Jayswal, D. K. Lobiyal","doi":"10.1109/SMART52563.2021.9676253","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676253","url":null,"abstract":"Distributed resources in a cloud environment come with issues of reliability and heterogeneous performance of every data center. This behavior of the cloud comes with many issues like trust, fault, and reliability in the cloud. So, to overcome this, reliable task scheduling is required, which considers all the conduct of the cloud. In such a situation, the position, and the executives of the allocation of resources between on-premises, datacenters and cloud data centers is a fundamental problem. Distributing in the cloud places a heavy burden on the execution of the cloud and the supervision of resource and task performance. Various static and dynamic algorithms may be used to resolve this problem. For better cloud presentation in terms of several tasks failed, execution duration, and start time, a fault-aware bat algorithm inspired by undertaking distribution algorithm for cloud infrastructure is suggested in this paper.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129277297","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9676267
Vivek Jain, Shivani Chouhan, K. Goyal
The cloud based innovative applications are increasing regularly and hence the data and job load also increasing proportionally. Cloud based service providers are also increasing their infrastructure and service facility to serve in a much better way to its clients. The job processing load will also increase the waiting time and hence affect the service response time at user’s end. So, it is always a matter of great importance that which job scheduling algorithm should be applied to serve the client in an efficient manner. This is the main motivation for framing this research paper. In this paper, we are taking the main five heuristic job scheduling algorithms like FCFS (First Come First Server), SJF (Shortest Job First), MaxMin, MinMin, and Saffrage for analyzing on the pre-decided cloud infrastructure. Among these heuristic algorithm, MaxMin algorithm outperforms than others in all the test cases i.e. with the cloudlet load of 100, 200, 300, …, 1000 cloudlets. Hence we can say that the MaxMin is the best scheduling algorithm among these five heuristic job scheduling algorithms.
基于云的创新应用程序正在定期增加，因此数据和工作负载也成比例地增加。基于云的服务提供商也在增加他们的基础设施和服务设施，以便更好地为客户提供服务。作业处理负载还会增加等待时间，从而影响用户端的服务响应时间。因此，采用何种作业调度算法来高效地为客户端服务一直是一个非常重要的问题。这是构建这篇研究论文的主要动机。本文采用FCFS (First Come First Server)、SJF (Shortest job First)、MaxMin、MinMin和Saffrage五种主要的启发式作业调度算法，在预先确定的云基础架构上进行分析。在这些启发式算法中，MaxMin算法在100,200,300，…，1000个云负载的所有测试用例中都优于其他算法。因此，在这五种启发式作业调度算法中，MaxMin算法是最优调度算法。
{"title":"Analyzing Heuristic Job Scheduling Algorithms by Varying Cloudlet Load in a Cloud Infrastructure","authors":"Vivek Jain, Shivani Chouhan, K. Goyal","doi":"10.1109/SMART52563.2021.9676267","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676267","url":null,"abstract":"The cloud based innovative applications are increasing regularly and hence the data and job load also increasing proportionally. Cloud based service providers are also increasing their infrastructure and service facility to serve in a much better way to its clients. The job processing load will also increase the waiting time and hence affect the service response time at user’s end. So, it is always a matter of great importance that which job scheduling algorithm should be applied to serve the client in an efficient manner. This is the main motivation for framing this research paper. In this paper, we are taking the main five heuristic job scheduling algorithms like FCFS (First Come First Server), SJF (Shortest Job First), MaxMin, MinMin, and Saffrage for analyzing on the pre-decided cloud infrastructure. Among these heuristic algorithm, MaxMin algorithm outperforms than others in all the test cases i.e. with the cloudlet load of 100, 200, 300, …, 1000 cloudlets. Hence we can say that the MaxMin is the best scheduling algorithm among these five heuristic job scheduling algorithms.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117344595","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9676233
Appoorva Bansal, Anand Kr. Shukla, S. Bansal
When we are dealing with the huge amount of data we have to look forward to the techniques like machine learning, predictive analysis, pattern recognition etc. Specially in the health sector machine learning is growing too fast and also give productive challenges. For predicting future, machine learning algorithm plays an important role, through which system can learn and become productive by the passing time. Method and Techniques of Machine Learning are used in various fields. Among those Health care is of the field which takes lots of help from the techniques of predictions. Through the techniques of predictive analysis in health care, effective treatment and risk factor can be managed effectively among patients and improve the quality of health care. As per the modern scenario, there is a need of huge improvement in the healthcare in term of cost and other factors. Today healthcare sector faces problem in the electronic data management, disease prediction as per the symptoms, patient classification, computer based diagnosis, risk factor etc. These challenges can be solved with the help of the machine learning tools and techniques. In this paper the focus to study the various machine learning method for the predictive analysis. Its includes various application area of machine learning, but mainly highlighting the role of machine learning in health care sectors.
{"title":"Machine Learning Methods for Predictive Analytics in Health Care","authors":"Appoorva Bansal, Anand Kr. Shukla, S. Bansal","doi":"10.1109/SMART52563.2021.9676233","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676233","url":null,"abstract":"When we are dealing with the huge amount of data we have to look forward to the techniques like machine learning, predictive analysis, pattern recognition etc. Specially in the health sector machine learning is growing too fast and also give productive challenges. For predicting future, machine learning algorithm plays an important role, through which system can learn and become productive by the passing time. Method and Techniques of Machine Learning are used in various fields. Among those Health care is of the field which takes lots of help from the techniques of predictions. Through the techniques of predictive analysis in health care, effective treatment and risk factor can be managed effectively among patients and improve the quality of health care. As per the modern scenario, there is a need of huge improvement in the healthcare in term of cost and other factors. Today healthcare sector faces problem in the electronic data management, disease prediction as per the symptoms, patient classification, computer based diagnosis, risk factor etc. These challenges can be solved with the help of the machine learning tools and techniques. In this paper the focus to study the various machine learning method for the predictive analysis. Its includes various application area of machine learning, but mainly highlighting the role of machine learning in health care sectors.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"305 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114524421","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 : 2021-12-10DOI: 10.1109/smart52563.2021.9675303
{"title":"TRACK IX: Education 4.0 [Breaker page]","authors":"","doi":"10.1109/smart52563.2021.9675303","DOIUrl":"https://doi.org/10.1109/smart52563.2021.9675303","url":null,"abstract":"","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127229810","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9676223
Majharul Islam, Md. Sanzidul Islam, M. A. Siddik, Sadia Sultana Sharmin Mousumi
in the twenty-first century, seems many students are reluctant to participate in classroom matters day by day. Some days ago, a course teacher is providing a lecture, on the most important topics that will be implemented in every step in life that class majority students were nonexistent. We got motivation why it’s happened and find out a lot of reasons behind uninterested in class after interrogation. Who are belonging age range 20-25, most of the time they face an obstacle and suffer and other teenagers. The fundamental purpose of this research to take action based on behind reasons, so that students can move in classroom learning matters. In this article, solve will be described and how to motivate students. Then it will be much helpful and far informative for teacher, students, guardians and also education specialist [8]. In this research shown causes, and also recommended how to overcome and solve those reluctant and spread knowledge and enlighten the nation. The aim of the research to help and inform provide to students avoid his/her predicted absent in classroom using Machine learning, particularly we used Liner Algorithm. Then, students will participate in classroom lectures as well as social activity and world would be more peaceful. This research achieved more than the accuracy of 95.9%.
{"title":"Analyzing the Cause of Students Reluctant to Participate in the Classroom: Machine Learning Approach","authors":"Majharul Islam, Md. Sanzidul Islam, M. A. Siddik, Sadia Sultana Sharmin Mousumi","doi":"10.1109/SMART52563.2021.9676223","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676223","url":null,"abstract":"in the twenty-first century, seems many students are reluctant to participate in classroom matters day by day. Some days ago, a course teacher is providing a lecture, on the most important topics that will be implemented in every step in life that class majority students were nonexistent. We got motivation why it’s happened and find out a lot of reasons behind uninterested in class after interrogation. Who are belonging age range 20-25, most of the time they face an obstacle and suffer and other teenagers. The fundamental purpose of this research to take action based on behind reasons, so that students can move in classroom learning matters. In this article, solve will be described and how to motivate students. Then it will be much helpful and far informative for teacher, students, guardians and also education specialist [8]. In this research shown causes, and also recommended how to overcome and solve those reluctant and spread knowledge and enlighten the nation. The aim of the research to help and inform provide to students avoid his/her predicted absent in classroom using Machine learning, particularly we used Liner Algorithm. Then, students will participate in classroom lectures as well as social activity and world would be more peaceful. This research achieved more than the accuracy of 95.9%.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127240359","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 : 2021-12-10DOI: 10.1109/smart52563.2021.9675312
{"title":"TRACK XIV: Biomedical Engineering and Healthcare Technologies [Breaker page]","authors":"","doi":"10.1109/smart52563.2021.9675312","DOIUrl":"https://doi.org/10.1109/smart52563.2021.9675312","url":null,"abstract":"","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127537539","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9676224
B. Kakkar, P. Johri
Blockchain can be considered chain connecting blocks with a hash, grouped. Each contains its independent data and reference of the previous linked data. It creates a network controlled by the participants as to what they want to store and with whom they want to share. Blockchain technology can restructure the healthcare sector and upgrade the current situation, as it is secure, transparent, and reliable and stores real-time data. It can benefit healthcare by transforming the documentation of health records to electronic health records and remote patient monitoring sharing of these records to the concerned authorities (like government, hospitals, pharmaceutical industry, medical clinics, and insurance companies) using smart contracts. Blockchain provides an environment to keep medical data safe secure. It can keep data incorruptible, decentralized, and transparent log, allowing Blockchain’s extensive use for security applications. In this paper, the authors had discussed the working and features of Blockchain, implementation of Blockchain in healthcare, various Blockchain enabled healthcare system, blockchain model and EHR management, and various challenges in Blockchain.
{"title":"Blockchain: A Healthcare Perspective","authors":"B. Kakkar, P. Johri","doi":"10.1109/SMART52563.2021.9676224","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676224","url":null,"abstract":"Blockchain can be considered chain connecting blocks with a hash, grouped. Each contains its independent data and reference of the previous linked data. It creates a network controlled by the participants as to what they want to store and with whom they want to share. Blockchain technology can restructure the healthcare sector and upgrade the current situation, as it is secure, transparent, and reliable and stores real-time data. It can benefit healthcare by transforming the documentation of health records to electronic health records and remote patient monitoring sharing of these records to the concerned authorities (like government, hospitals, pharmaceutical industry, medical clinics, and insurance companies) using smart contracts. Blockchain provides an environment to keep medical data safe secure. It can keep data incorruptible, decentralized, and transparent log, allowing Blockchain’s extensive use for security applications. In this paper, the authors had discussed the working and features of Blockchain, implementation of Blockchain in healthcare, various Blockchain enabled healthcare system, blockchain model and EHR management, and various challenges in Blockchain.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126995916","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9676264
Khyati, Ashendra Kumar Saxena
This paper gives a review on the basic Economic Order Quantity (EOQ) model, which is used to improve management efficiency in modern businesses. Inventories are company’s assets, and as such, they constitute an investment. Because such investment necessitates a financial commitment, a company must keep enough inventories. For businesses with complex supply networks and production processes, balancing the risks of inventory gluts and shortages is extremely tough. To achieve these balances, businesses have devised inventory management techniques.
{"title":"Review on EOQ Models for Instanteneous and Non-Instanteneous Deteriorating Items","authors":"Khyati, Ashendra Kumar Saxena","doi":"10.1109/SMART52563.2021.9676264","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676264","url":null,"abstract":"This paper gives a review on the basic Economic Order Quantity (EOQ) model, which is used to improve management efficiency in modern businesses. Inventories are company’s assets, and as such, they constitute an investment. Because such investment necessitates a financial commitment, a company must keep enough inventories. For businesses with complex supply networks and production processes, balancing the risks of inventory gluts and shortages is extremely tough. To achieve these balances, businesses have devised inventory management techniques.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121506156","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9676318
Aaditya Jain, Anubhuti Singh, Ishika Jain
In modern days technology machine learning is one of the best discovery is in science, and it is one of the critical inventions in intellectualize, which is made artificially. To make this type of revolution, any product the programming behind it is not so complicated and even this type of programming language are used daily. So it is quite sure that it is not so difficult to execute as the program behind it is quite familiar. According to some specific case studies when every human individual is searching some queries through searching instrument via browser, the desired result comes conveniently than ever before, and all of this revolution only can happen because in present times machine level language learning has gone so far and not only that but also very few important researches are also happening around the whole world to improve this specific section of artificial intelligence better than ever. In modern days internet surfing cannot be more convenient without machine learning and artificial intelligence. This study reflects the idea if using machine learning in the application of Artificial Intelligence (A). For the last few years one of the biggest tech giants Google and along with Microsoft are usually implementing machine learning and artificial intelligence in their search engine and as well as their software-based products and not only that but they are also investing in this type of technology to improve as much as it can be in upcoming future as well. Not only in Google and Microsoft but also present generation social media is an essential thing in daily life, and by naming social media, the first thing that comes into mind is Facebook. This particular study reflects the explanation of using machine learning features in the application of Artificial Intelligence. This study explores the classifications of software categories where machine learning can be used. As the data are being more significant every day, this study shows it's necessary to improve machine learning algorithms so that they can be able to handle extensive data and return error-less and more precise output.
{"title":"Use of Machine Learning in Application of Artificial Intelligence","authors":"Aaditya Jain, Anubhuti Singh, Ishika Jain","doi":"10.1109/SMART52563.2021.9676318","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676318","url":null,"abstract":"In modern days technology machine learning is one of the best discovery is in science, and it is one of the critical inventions in intellectualize, which is made artificially. To make this type of revolution, any product the programming behind it is not so complicated and even this type of programming language are used daily. So it is quite sure that it is not so difficult to execute as the program behind it is quite familiar. According to some specific case studies when every human individual is searching some queries through searching instrument via browser, the desired result comes conveniently than ever before, and all of this revolution only can happen because in present times machine level language learning has gone so far and not only that but also very few important researches are also happening around the whole world to improve this specific section of artificial intelligence better than ever. In modern days internet surfing cannot be more convenient without machine learning and artificial intelligence. This study reflects the idea if using machine learning in the application of Artificial Intelligence (A). For the last few years one of the biggest tech giants Google and along with Microsoft are usually implementing machine learning and artificial intelligence in their search engine and as well as their software-based products and not only that but they are also investing in this type of technology to improve as much as it can be in upcoming future as well. Not only in Google and Microsoft but also present generation social media is an essential thing in daily life, and by naming social media, the first thing that comes into mind is Facebook. This particular study reflects the explanation of using machine learning features in the application of Artificial Intelligence. This study explores the classifications of software categories where machine learning can be used. As the data are being more significant every day, this study shows it's necessary to improve machine learning algorithms so that they can be able to handle extensive data and return error-less and more precise output.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125102743","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 : 2021-12-10DOI: 10.1109/smart52563.2021.9676196
{"title":"TRACK VI: Governance, Risk and Compliance [Breaker page]","authors":"","doi":"10.1109/smart52563.2021.9676196","DOIUrl":"https://doi.org/10.1109/smart52563.2021.9676196","url":null,"abstract":"","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123151589","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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