Pub Date : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9753547
S. B. V., Sanjeev Sharma, K. Swathi, Korapati Reddy Yamini, Chokkam Preethi Kiran, Kamineni Chandrika
The world is moving to Internet of Things (IoT) remote monitoring technology and quick control of objects as well more precisely. IoT can be beneficial in medicine as well health care facilities as it allows for long-term research of chronic diseases, vital symptom monitoring, emergency perception, diagnosis and prediction of patient level or disease. Internet of Things becomes transparent and is useful in the context of health care and venerable care, i.e., in most cases, they are activities that require the full presence of a caretaker or medical personal. The purpose of this survey is to enlighten the significance and categories of IoT-include adult health care programs. This paper consolidates a summary of research that report on the development and use of IoT-include health care adult programs. The paper covers with various available IoT based techniques used for health care applications.
{"title":"Review on IoT based Healthcare systems","authors":"S. B. V., Sanjeev Sharma, K. Swathi, Korapati Reddy Yamini, Chokkam Preethi Kiran, Kamineni Chandrika","doi":"10.1109/ICACTA54488.2022.9753547","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753547","url":null,"abstract":"The world is moving to Internet of Things (IoT) remote monitoring technology and quick control of objects as well more precisely. IoT can be beneficial in medicine as well health care facilities as it allows for long-term research of chronic diseases, vital symptom monitoring, emergency perception, diagnosis and prediction of patient level or disease. Internet of Things becomes transparent and is useful in the context of health care and venerable care, i.e., in most cases, they are activities that require the full presence of a caretaker or medical personal. The purpose of this survey is to enlighten the significance and categories of IoT-include adult health care programs. This paper consolidates a summary of research that report on the development and use of IoT-include health care adult programs. The paper covers with various available IoT based techniques used for health care applications.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120989615","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 : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9753265
Divya Thakur, Nitika Kapoor
The term data mining refers to the practice of effectively extracting beneficial data from a large amount of data. Predicting a student's academic performance is the most complex and experimental study topic in educational data mining. Multiple factors have non-linear effects on performance, making this topic more appealing to researchers. researchers. This interest is enhanced by the increased availability of educational datasets, particularly in virtual education. There are several educational data mining surveys in the literature portion, we will only focus on student performance analysis and prediction. Data mining pursue a massive volume of dynamically created data for patterns and trends that are helpful and understandable to users. It can successfully utilize raw data generated by universities in examining hidden patterns and connections among the parameters that are used to estimate student performance and behaviour. Educational data mining bridges between the two disciplines: on the one hand is education and on the other in computer science. Educational actors (students, teachers, and administrators) have been benefitted as they are provided with the relevant information in which they have to act upon and thereby end up promoting quality-based innovations in this domain The main objectives of the system are to study existing data mining approaches in the educational domain and to analyze and compare the results of these approaches. We employed Support Vector Machine (SVM) and Naive Bayes (NB) to predict student performance in this paper.
{"title":"Predicting Student's Performance using Data Mining Algorithm","authors":"Divya Thakur, Nitika Kapoor","doi":"10.1109/ICACTA54488.2022.9753265","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753265","url":null,"abstract":"The term data mining refers to the practice of effectively extracting beneficial data from a large amount of data. Predicting a student's academic performance is the most complex and experimental study topic in educational data mining. Multiple factors have non-linear effects on performance, making this topic more appealing to researchers. researchers. This interest is enhanced by the increased availability of educational datasets, particularly in virtual education. There are several educational data mining surveys in the literature portion, we will only focus on student performance analysis and prediction. Data mining pursue a massive volume of dynamically created data for patterns and trends that are helpful and understandable to users. It can successfully utilize raw data generated by universities in examining hidden patterns and connections among the parameters that are used to estimate student performance and behaviour. Educational data mining bridges between the two disciplines: on the one hand is education and on the other in computer science. Educational actors (students, teachers, and administrators) have been benefitted as they are provided with the relevant information in which they have to act upon and thereby end up promoting quality-based innovations in this domain The main objectives of the system are to study existing data mining approaches in the educational domain and to analyze and compare the results of these approaches. We employed Support Vector Machine (SVM) and Naive Bayes (NB) to predict student performance in this paper.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"784 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133322987","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 : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9753207
T. Maheswari, E. I. Rashmi, M. Hasanthi, R. Elakkiya
SA is commonly known as Sentimental analysis is a continuous field of research on analysis consumer emotions on purchase of products. This survey paper tackles an inclusive survey of mobile phone reviews. Customer segmentation is a vital part of every company deciding their product outreach. In this machine learning project, we will utilize nltk for clustering, for handling unlabeled datasets. The acquired findings illustrate the efficacy of the solution, which has a high level of accuracy in both mobile classification and user segmentation.
{"title":"Customer Segmentation Based on Sentimental Analysis","authors":"T. Maheswari, E. I. Rashmi, M. Hasanthi, R. Elakkiya","doi":"10.1109/ICACTA54488.2022.9753207","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753207","url":null,"abstract":"SA is commonly known as Sentimental analysis is a continuous field of research on analysis consumer emotions on purchase of products. This survey paper tackles an inclusive survey of mobile phone reviews. Customer segmentation is a vital part of every company deciding their product outreach. In this machine learning project, we will utilize nltk for clustering, for handling unlabeled datasets. The acquired findings illustrate the efficacy of the solution, which has a high level of accuracy in both mobile classification and user segmentation.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114153668","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 : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9753607
T. M. Sheeba, S. Albert Antony Raj, M. Anand
Pigment epithelial detachment(PED) is a disorder in retina that happens when RPE layers of cells at the back side of the eye come apart, or get teared. The bend of layers in the retina, as well as fluid, proteins, tissue, or blood vessels, is a defining feature of PED disease, which occurs most frequently in the macula. PED can disturb the vision of the people which is often depict dark shadow, blurry vision or partial loss of vision. The optical coherence tomography (OCT) is a trend set of high resolution and non-invasive imaging modality that expedite the structure of the retina. OCT non-invasively yields cross-sectional volume of images with tissues. The major objective of this research paper is to study, state of art and to classify the retinal layer segmentation techniques, PED fluid segmentation and classification of diseases in retinal OCT images. The medical industry is suffering with more critical patients and the cases are increasing in eye diseases double the number as of now. The artificial intelligence (AI) techniques help the health sector with a great and accurate automatic detection of disease. The image classification and pattern recognition are transforming the industry with artificial intelligence techniques. Many studies are being conducted employing image processing to aid in the early diagnosis of this disease. Image processing techniques have advanced as a result of the introduction of artificial intelligence and machine learning. In this review paper, the structure classification methods and the image segmentation method that are best available existing research is discussed. This review summarizes all the recent algorithms that suits for the application of machine learning algorithms for predicting retinal diseases in OCT images. The algorithms discussed from existing research paper, produce the readers to identify the best accurate algorithm for retinal classification of infected eye and normal eye, precision and less processing time for layer segmentation. The effective methods to differentiate the neurosensory retinal detachment associated sub-retinal fluid from the sub-retinal pigment epithelium fluid are discussed in deep. The many algorithms, outcomes and imaging techniques developed over the years for the early diagnosis of Pigment Epithelial Detachment are discussed in this article.
{"title":"Pigment Epithelial Detachment Detection: A Review of Imaging Techniques and Algorithms","authors":"T. M. Sheeba, S. Albert Antony Raj, M. Anand","doi":"10.1109/ICACTA54488.2022.9753607","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753607","url":null,"abstract":"Pigment epithelial detachment(PED) is a disorder in retina that happens when RPE layers of cells at the back side of the eye come apart, or get teared. The bend of layers in the retina, as well as fluid, proteins, tissue, or blood vessels, is a defining feature of PED disease, which occurs most frequently in the macula. PED can disturb the vision of the people which is often depict dark shadow, blurry vision or partial loss of vision. The optical coherence tomography (OCT) is a trend set of high resolution and non-invasive imaging modality that expedite the structure of the retina. OCT non-invasively yields cross-sectional volume of images with tissues. The major objective of this research paper is to study, state of art and to classify the retinal layer segmentation techniques, PED fluid segmentation and classification of diseases in retinal OCT images. The medical industry is suffering with more critical patients and the cases are increasing in eye diseases double the number as of now. The artificial intelligence (AI) techniques help the health sector with a great and accurate automatic detection of disease. The image classification and pattern recognition are transforming the industry with artificial intelligence techniques. Many studies are being conducted employing image processing to aid in the early diagnosis of this disease. Image processing techniques have advanced as a result of the introduction of artificial intelligence and machine learning. In this review paper, the structure classification methods and the image segmentation method that are best available existing research is discussed. This review summarizes all the recent algorithms that suits for the application of machine learning algorithms for predicting retinal diseases in OCT images. The algorithms discussed from existing research paper, produce the readers to identify the best accurate algorithm for retinal classification of infected eye and normal eye, precision and less processing time for layer segmentation. The effective methods to differentiate the neurosensory retinal detachment associated sub-retinal fluid from the sub-retinal pigment epithelium fluid are discussed in deep. The many algorithms, outcomes and imaging techniques developed over the years for the early diagnosis of Pigment Epithelial Detachment are discussed in this article.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114561985","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 : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9753504
M. Devi, R. Parthasarathy, B. Deepa, M. Shashwenth
Battling the progressing Covid sickness 2019 (COVID-19) pandemic requests precise, quick, and point-of-care testing with quick outcomes to anticipate stages for isolation and therapy. The preliminary test to detect COVID-19 is a Swab test and also a Blood test, but these tests will take more than 2 days to receive the results and there is also a risk of transmission of the virus while collecting the samples. To predict the stages of COVID-19's effects on the human lungs accurately for further treatment for further diagnosis on a radiological image, medical experts need a high level of precision. We utilize image processing techniques and convolutional networks to analyze CT images of COVID-19 affected human lungs in this paper for the detection of pulmonary abnormalities in the early stage, Chest X-Ray is not exact. So, we are using Computed Tomography (CT) imaging especially for identifying the stages of lung anomalies. We present and discuss the scoring systems which cause the severity in lungs of COVID-19 patients every day. This will be accurate for predicting the stages of COVID-19 for early treatment and also to protect the uninfected population.
{"title":"Predicting The Stages Of Covid-19 Affected Patients Using CNN With CT Scan","authors":"M. Devi, R. Parthasarathy, B. Deepa, M. Shashwenth","doi":"10.1109/ICACTA54488.2022.9753504","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753504","url":null,"abstract":"Battling the progressing Covid sickness 2019 (COVID-19) pandemic requests precise, quick, and point-of-care testing with quick outcomes to anticipate stages for isolation and therapy. The preliminary test to detect COVID-19 is a Swab test and also a Blood test, but these tests will take more than 2 days to receive the results and there is also a risk of transmission of the virus while collecting the samples. To predict the stages of COVID-19's effects on the human lungs accurately for further treatment for further diagnosis on a radiological image, medical experts need a high level of precision. We utilize image processing techniques and convolutional networks to analyze CT images of COVID-19 affected human lungs in this paper for the detection of pulmonary abnormalities in the early stage, Chest X-Ray is not exact. So, we are using Computed Tomography (CT) imaging especially for identifying the stages of lung anomalies. We present and discuss the scoring systems which cause the severity in lungs of COVID-19 patients every day. This will be accurate for predicting the stages of COVID-19 for early treatment and also to protect the uninfected population.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123402953","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 : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9752987
B. S, J. R
Cloud storage service providers accommodate customers by offering adequate storage based on customer requirements. Multi-tenant architecture to provide low-cost resource provision. In a multitenant system single instance shares with multiple customers, similar applications data has been used multiple customers so Deduplication techniques eliminate redundant data to improve storage efficiency and Bandwidth. To improve the confidentiality of the user privacy data, convergent encryption with the deduplication techniques has been proposed to encrypt the user data before uploading it to the third-party CS Sometimes Confidentiality of the user data leads to issues with this deduplication method. To overcome these issues this paper proposed dynamic ownership management of server-side deduplication with the blowfish encryption algorithm. In this model to support tenant block-level deduplication. The proposed method computational storage efficiency and security level.
{"title":"Secure Deduplication with Dynamic Updates in Multi-Tenant Cloud Environment","authors":"B. S, J. R","doi":"10.1109/ICACTA54488.2022.9752987","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9752987","url":null,"abstract":"Cloud storage service providers accommodate customers by offering adequate storage based on customer requirements. Multi-tenant architecture to provide low-cost resource provision. In a multitenant system single instance shares with multiple customers, similar applications data has been used multiple customers so Deduplication techniques eliminate redundant data to improve storage efficiency and Bandwidth. To improve the confidentiality of the user privacy data, convergent encryption with the deduplication techniques has been proposed to encrypt the user data before uploading it to the third-party CS Sometimes Confidentiality of the user data leads to issues with this deduplication method. To overcome these issues this paper proposed dynamic ownership management of server-side deduplication with the blowfish encryption algorithm. In this model to support tenant block-level deduplication. The proposed method computational storage efficiency and security level.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115991235","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 : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9753135
G. D. Devi, S. Kamalakkannan
In an organization, employees are the major and important resources and may quit the job unpredictably which may produce immense cost. In general, the employee attitude and their effort are influenced by their personality traits but the job satisfaction may result for an individual observations from an organization based on the environment conditions. Meanwhile, the hiring of new employee may consume time and cost. Similarly, recently hired employee may need to put certain efforts for being productive. The job satisfaction of the employee is one of the factor for leaving out from the organization. The employee attrition prediction and its reasons to leave the organization required to be performed from Human Resource Management (HRM) perspective. This kind of prediction has to be progressed from HRM for analyzing the best and experienced employee's reason for leaving their organization using various data mining technique but the exact prediction is not obtained. This can be analyzed by seeing some experienced and best employee leaving their organization. Therefore, this paper has attempted for developing an ensemble model which assist in providing an accurate prediction of the employee attrition based on the HR analytics dataset. The proposed research work focus in analyzing the job satisfaction mentioned by the employee in the “Employee Attrition” has been considered by predicting the dataset using Weighed Average Mechanism (WAM) in ensemble method with Logistic Regression (LR). Moreover, the performance evaluation of proposed ensemble method attaints the higher accuracy of 98.2% which outperforms the other three existing methods for analyzing the better prediction of job satisfaction from the employees.
{"title":"Prediction of Job Satisfaction from the Employee Using Ensemble Method","authors":"G. D. Devi, S. Kamalakkannan","doi":"10.1109/ICACTA54488.2022.9753135","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753135","url":null,"abstract":"In an organization, employees are the major and important resources and may quit the job unpredictably which may produce immense cost. In general, the employee attitude and their effort are influenced by their personality traits but the job satisfaction may result for an individual observations from an organization based on the environment conditions. Meanwhile, the hiring of new employee may consume time and cost. Similarly, recently hired employee may need to put certain efforts for being productive. The job satisfaction of the employee is one of the factor for leaving out from the organization. The employee attrition prediction and its reasons to leave the organization required to be performed from Human Resource Management (HRM) perspective. This kind of prediction has to be progressed from HRM for analyzing the best and experienced employee's reason for leaving their organization using various data mining technique but the exact prediction is not obtained. This can be analyzed by seeing some experienced and best employee leaving their organization. Therefore, this paper has attempted for developing an ensemble model which assist in providing an accurate prediction of the employee attrition based on the HR analytics dataset. The proposed research work focus in analyzing the job satisfaction mentioned by the employee in the “Employee Attrition” has been considered by predicting the dataset using Weighed Average Mechanism (WAM) in ensemble method with Logistic Regression (LR). Moreover, the performance evaluation of proposed ensemble method attaints the higher accuracy of 98.2% which outperforms the other three existing methods for analyzing the better prediction of job satisfaction from the employees.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"1100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122914708","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 : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9753636
A. Pant, Sanjay Sharma, M. Bansal, Mandeep Narang
Air pollution is a significant challenge in a populated area. This paper focuses on predicting air quality index using supervised machine learning techniques in the capital city of Uttarakhand state, India, i.e., Dehradun based on the available pollutants (PM10, PM2.5, SO2, NO2). The result shows that the decision tree classifier is more accurate, with an accuracy of 98.63%. In contrast, the logistic regression is the least one with an accuracy of 91.78% for air quality prediction. The study also finds that the AQI level is low in May due to high temperatures. The study also finds that the Himalayan drugs-ISBT area is in the poor range of AQI for the capital city of Uttarakhand state.
{"title":"Comparative Analysis of Supervised Machine Learning Techniques for AQI Prediction","authors":"A. Pant, Sanjay Sharma, M. Bansal, Mandeep Narang","doi":"10.1109/ICACTA54488.2022.9753636","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753636","url":null,"abstract":"Air pollution is a significant challenge in a populated area. This paper focuses on predicting air quality index using supervised machine learning techniques in the capital city of Uttarakhand state, India, i.e., Dehradun based on the available pollutants (PM10, PM2.5, SO2, NO2). The result shows that the decision tree classifier is more accurate, with an accuracy of 98.63%. In contrast, the logistic regression is the least one with an accuracy of 91.78% for air quality prediction. The study also finds that the AQI level is low in May due to high temperatures. The study also finds that the Himalayan drugs-ISBT area is in the poor range of AQI for the capital city of Uttarakhand state.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129099968","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 : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9753219
Srinivasan L, Vismaye M, Keerthishree V, H. R, Pradeepthi K
Mobile applications are now a part of our life in many ways like ordering food, watching news, booking tickets etc. The same mobile applications can be used for betterment of the disabilities in human too. Our approach deals with a mobile application that will help disabled people to communicate with others without any problem. The inefficiency eventualities blanked in this project help humans having speech impairment, vision impairment, listening impairment to deal with real lifestyle troubles easily. Application is created using mobile sensors. In this research paper a vital assessment of present technologies and lots of issues of disabled people has been presented and pointed out for further development. Furthermore, the proposed technique should gain better performance and efficiency.
{"title":"Soft Support: Specially Abled Communication","authors":"Srinivasan L, Vismaye M, Keerthishree V, H. R, Pradeepthi K","doi":"10.1109/ICACTA54488.2022.9753219","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753219","url":null,"abstract":"Mobile applications are now a part of our life in many ways like ordering food, watching news, booking tickets etc. The same mobile applications can be used for betterment of the disabilities in human too. Our approach deals with a mobile application that will help disabled people to communicate with others without any problem. The inefficiency eventualities blanked in this project help humans having speech impairment, vision impairment, listening impairment to deal with real lifestyle troubles easily. Application is created using mobile sensors. In this research paper a vital assessment of present technologies and lots of issues of disabled people has been presented and pointed out for further development. Furthermore, the proposed technique should gain better performance and efficiency.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131219033","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 : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9753050
D. S., Hari Krishna A, D. S, Prabha D
Medical handling of entities exists as a very meaningful request field of intellectual activity. Afterwards, data excavating can play a generous impersonation of a character to learn secret news from the extremely large patient healing and medical care dataset that doctors commonly get from people being treated for medical problems to catch pieces of information about the indicative information in visible form and to kill exact situation plans. Data excavating may be sorted by type as the system draws out secret facts from an extremely large dataset. The data excavation strategy is related to and makes use of widely popular miscellaneous circumstances and extent. Using the information in visible form, excavating plan, we concede the possibility of expressing an outcome in advance, categorizing, separating, refining and clustering information in visible form. The objective states the treasure is subject to a series of actions to achieve the result of a preparation set, which holds a set of attributes and an aim. Data excavating is acceptable for excavating fashionable information in the visible form if the dataset is extremely large, but we can also have sexual relations by way of machine intelligence accompanying a narrow dataset. Because of the difference in the never-ending ailment dataset, machine intelligence algorithms are best suited to make or improve the precision or correctness of problem declarations made in advance, which happens without a doubt, accompanying the declaration made in advance of 99.9% of our projected idea, utilizing random area with a large number of trees.
{"title":"A Novel Approach on Chronic Kidney Disease Prediction Using Machine Learning","authors":"D. S., Hari Krishna A, D. S, Prabha D","doi":"10.1109/ICACTA54488.2022.9753050","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753050","url":null,"abstract":"Medical handling of entities exists as a very meaningful request field of intellectual activity. Afterwards, data excavating can play a generous impersonation of a character to learn secret news from the extremely large patient healing and medical care dataset that doctors commonly get from people being treated for medical problems to catch pieces of information about the indicative information in visible form and to kill exact situation plans. Data excavating may be sorted by type as the system draws out secret facts from an extremely large dataset. The data excavation strategy is related to and makes use of widely popular miscellaneous circumstances and extent. Using the information in visible form, excavating plan, we concede the possibility of expressing an outcome in advance, categorizing, separating, refining and clustering information in visible form. The objective states the treasure is subject to a series of actions to achieve the result of a preparation set, which holds a set of attributes and an aim. Data excavating is acceptable for excavating fashionable information in the visible form if the dataset is extremely large, but we can also have sexual relations by way of machine intelligence accompanying a narrow dataset. Because of the difference in the never-ending ailment dataset, machine intelligence algorithms are best suited to make or improve the precision or correctness of problem declarations made in advance, which happens without a doubt, accompanying the declaration made in advance of 99.9% of our projected idea, utilizing random area with a large number of trees.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133183504","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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