Pub Date : 2022-01-01DOI: 10.1504/ijesms.2022.10044577
R. K. K. Vani, Jayashree Padmanabhan
{"title":"Assessment of mental workload using XGBoost classifier from optimised EEG features","authors":"R. K. K. Vani, Jayashree Padmanabhan","doi":"10.1504/ijesms.2022.10044577","DOIUrl":"https://doi.org/10.1504/ijesms.2022.10044577","url":null,"abstract":"","PeriodicalId":51938,"journal":{"name":"International Journal of Engineering Systems Modelling and SImulation","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66733634","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 : 2022-01-01DOI: 10.1504/ijesms.2022.10045315
M. Selvi, M. Maheswari, A. Christy, Theresa M. Mercy, A. Jesudoss, V. Ulagamuthalvi
{"title":"Analysis and implementation of SQL injection attack and countermeasures using SQL injection prevention techniques","authors":"M. Selvi, M. Maheswari, A. Christy, Theresa M. Mercy, A. Jesudoss, V. Ulagamuthalvi","doi":"10.1504/ijesms.2022.10045315","DOIUrl":"https://doi.org/10.1504/ijesms.2022.10045315","url":null,"abstract":"","PeriodicalId":51938,"journal":{"name":"International Journal of Engineering Systems Modelling and SImulation","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66733967","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 : 2022-01-01DOI: 10.1504/ijesms.2022.10049166
T. Leonid, R. Jayaparvathy
{"title":"Elephant sound classification using machine learning algorithms for mitigation strategy","authors":"T. Leonid, R. Jayaparvathy","doi":"10.1504/ijesms.2022.10049166","DOIUrl":"https://doi.org/10.1504/ijesms.2022.10049166","url":null,"abstract":"","PeriodicalId":51938,"journal":{"name":"International Journal of Engineering Systems Modelling and SImulation","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66735697","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 : 2022-01-01DOI: 10.1504/ijesms.2022.10044749
Jayprabha Terdale, Amrit Ghosh
{"title":"Optical fiber-based refractive index measurement sensor","authors":"Jayprabha Terdale, Amrit Ghosh","doi":"10.1504/ijesms.2022.10044749","DOIUrl":"https://doi.org/10.1504/ijesms.2022.10044749","url":null,"abstract":"","PeriodicalId":51938,"journal":{"name":"International Journal of Engineering Systems Modelling and SImulation","volume":"625 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66733722","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 : 2022-01-01DOI: 10.1504/ijesms.2022.10045885
N. Reddy, T. Shankar, K. Sundaram
{"title":"An efficient fruit quality monitoring and classification using convolutional neural network and fuzzy system","authors":"N. Reddy, T. Shankar, K. Sundaram","doi":"10.1504/ijesms.2022.10045885","DOIUrl":"https://doi.org/10.1504/ijesms.2022.10045885","url":null,"abstract":"","PeriodicalId":51938,"journal":{"name":"International Journal of Engineering Systems Modelling and SImulation","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66734050","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 : 2022-01-01DOI: 10.1504/ijesms.2022.10044308
S. Agrawal, M. Agrawal
{"title":"Rice plant diseases detection using convolutional neural networks","authors":"S. Agrawal, M. Agrawal","doi":"10.1504/ijesms.2022.10044308","DOIUrl":"https://doi.org/10.1504/ijesms.2022.10044308","url":null,"abstract":"","PeriodicalId":51938,"journal":{"name":"International Journal of Engineering Systems Modelling and SImulation","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66733588","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 : 2021-06-07DOI: 10.1504/IJESMS.2021.10037755
P. Pragathi, A. N. Rao
The day-to-day change and evolution of chronic conditions had a high impact on the medical field. Alcohol consumption is also another important and considerable cause of the occurrence of various chronic conditions. Generally, the data that is being collected during the diagnosis can be represented in various forms such as clinical values, reports, images, etc. There is a dire need of analysing this data to let the people and health centres/institutions knowledgeable about the criticality and effect of chronic conditions. This work mainly focuses on the analysis of the mortality rate that occurs due to alcohol consumption. To achieve this, K-means clustering with linear regression technique is proposed. The linear regression model is constructed to forecast the analysis of consumers on the whole. The simulation results evaluate the model and it is observed that the coefficient of determination exhibits that the constructed model is found to be fitting precisely.
{"title":"Mortality analysis of alcohol consumption using a hybrid machine learning model","authors":"P. Pragathi, A. N. Rao","doi":"10.1504/IJESMS.2021.10037755","DOIUrl":"https://doi.org/10.1504/IJESMS.2021.10037755","url":null,"abstract":"The day-to-day change and evolution of chronic conditions had a high impact on the medical field. Alcohol consumption is also another important and considerable cause of the occurrence of various chronic conditions. Generally, the data that is being collected during the diagnosis can be represented in various forms such as clinical values, reports, images, etc. There is a dire need of analysing this data to let the people and health centres/institutions knowledgeable about the criticality and effect of chronic conditions. This work mainly focuses on the analysis of the mortality rate that occurs due to alcohol consumption. To achieve this, K-means clustering with linear regression technique is proposed. The linear regression model is constructed to forecast the analysis of consumers on the whole. The simulation results evaluate the model and it is observed that the coefficient of determination exhibits that the constructed model is found to be fitting precisely.","PeriodicalId":51938,"journal":{"name":"International Journal of Engineering Systems Modelling and SImulation","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46230665","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 : 2021-05-28DOI: 10.1504/IJESMS.2021.115529
Shruti Sharma, Y. Gupta
Epidemic diseases are the contagious or infectious diseases which are possible to be spread into the entire country, and are defined as an outbreak that occurs and affects an exceptionally high proportion of the population. However, these infectious ailments if controlled beforehand by using trending technologies for the early prediction would not turn into mortality situations. With this view, this paper is summarising the research work by using machine learning and big data handling techniques for the early prediction of epidemic diseases. The epidemic diseases especially covered in this review are influenza, malaria and dengue ailment. The diseases are compared against machine learning models used and input data contemplated. An observation for the prediction of diseases found that same factors associated with searching techniques give different results for different locations; overall searches are showing diversity and dearth in data. Moreover, dearth of data will mitigate the accuracy.
{"title":"Role of machine learning and big data in healthcare for the prediction of epidemic diseases: a survey","authors":"Shruti Sharma, Y. Gupta","doi":"10.1504/IJESMS.2021.115529","DOIUrl":"https://doi.org/10.1504/IJESMS.2021.115529","url":null,"abstract":"Epidemic diseases are the contagious or infectious diseases which are possible to be spread into the entire country, and are defined as an outbreak that occurs and affects an exceptionally high proportion of the population. However, these infectious ailments if controlled beforehand by using trending technologies for the early prediction would not turn into mortality situations. With this view, this paper is summarising the research work by using machine learning and big data handling techniques for the early prediction of epidemic diseases. The epidemic diseases especially covered in this review are influenza, malaria and dengue ailment. The diseases are compared against machine learning models used and input data contemplated. An observation for the prediction of diseases found that same factors associated with searching techniques give different results for different locations; overall searches are showing diversity and dearth in data. Moreover, dearth of data will mitigate the accuracy.","PeriodicalId":51938,"journal":{"name":"International Journal of Engineering Systems Modelling and SImulation","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2021-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47646097","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 : 2021-05-28DOI: 10.1504/IJESMS.2021.115531
Shweta Agrawal, S. Jain, Ebuka Ibeke
Networked control system (NCS) consists of controlled distributed nodes while an orchestrator functions as a central coordinator for controlling the distributed tasks. The NCSs have challenges of coordination and right execution sequencing of operations. This paper proposes a framework named controlled orchestrator (COrch) for coordinating and sequencing the tasks of NCSs. An experiment was performed with three robotic vehicles that are considered as individual control system. Furthermore, the proposed orchestrator COrch decided the sequencing of operations of the robots while performing obstacle avoidance task for spatially distributed robots in parallel. COrch is used to control this task by utilising the concept of remote method invocation (RMI) and multithreading. RMI is used to prepare the software for controlling the robots at remote end while multithreading is used to perform parallel and synchronise execution of multiple robots. The remote end software generates signals for sequential, parallel and hybrid mode execution.
{"title":"An orchestrator for networked control systems and its application to collision avoidance in multiple mobile robots","authors":"Shweta Agrawal, S. Jain, Ebuka Ibeke","doi":"10.1504/IJESMS.2021.115531","DOIUrl":"https://doi.org/10.1504/IJESMS.2021.115531","url":null,"abstract":"Networked control system (NCS) consists of controlled distributed nodes while an orchestrator functions as a central coordinator for controlling the distributed tasks. The NCSs have challenges of coordination and right execution sequencing of operations. This paper proposes a framework named controlled orchestrator (COrch) for coordinating and sequencing the tasks of NCSs. An experiment was performed with three robotic vehicles that are considered as individual control system. Furthermore, the proposed orchestrator COrch decided the sequencing of operations of the robots while performing obstacle avoidance task for spatially distributed robots in parallel. COrch is used to control this task by utilising the concept of remote method invocation (RMI) and multithreading. RMI is used to prepare the software for controlling the robots at remote end while multithreading is used to perform parallel and synchronise execution of multiple robots. The remote end software generates signals for sequential, parallel and hybrid mode execution.","PeriodicalId":51938,"journal":{"name":"International Journal of Engineering Systems Modelling and SImulation","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2021-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49268602","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 : 2021-05-28DOI: 10.1504/IJESMS.2021.10038002
T. Deepthi, K. Balamurugan, M. Uthayakumar
Visual assessment in casting is difficult to assess due to the opacity and the high working temperature. By using the simulation tool, it can be avoided through an examination on the melt runs inside the mould cavity, pressure variation, and runs velocity in the different areas of the gating framework. Assessments are conducted for decided gating design and the sprinter aspect proportions. The MAGMASOFT, a simulation tool is preferred to study the exceptional gating proportions to achieve the velocity, force exerted at the sprinter location, and ingates. A regression version is designed to pick out the real speed and the pressure of liquid metal exists in the mould hollow space. Further, the melt velocity dissected as a detail of distance covered concerning ingate for the distinct gating propositions. A high-speed thermal camera is used to verify the actual melt motion in different gating systems and contrasted with the simulated observation.
{"title":"Simulation and experimental analysis on cast metal runs behaviour rate at different gating models","authors":"T. Deepthi, K. Balamurugan, M. Uthayakumar","doi":"10.1504/IJESMS.2021.10038002","DOIUrl":"https://doi.org/10.1504/IJESMS.2021.10038002","url":null,"abstract":"Visual assessment in casting is difficult to assess due to the opacity and the high working temperature. By using the simulation tool, it can be avoided through an examination on the melt runs inside the mould cavity, pressure variation, and runs velocity in the different areas of the gating framework. Assessments are conducted for decided gating design and the sprinter aspect proportions. The MAGMASOFT, a simulation tool is preferred to study the exceptional gating proportions to achieve the velocity, force exerted at the sprinter location, and ingates. A regression version is designed to pick out the real speed and the pressure of liquid metal exists in the mould hollow space. Further, the melt velocity dissected as a detail of distance covered concerning ingate for the distinct gating propositions. A high-speed thermal camera is used to verify the actual melt motion in different gating systems and contrasted with the simulated observation.","PeriodicalId":51938,"journal":{"name":"International Journal of Engineering Systems Modelling and SImulation","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2021-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44118213","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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