2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)最新文献_第10页

Identification of Heart Diseases using Novel Machine Learning Method 利用新型机器学习方法识别心脏病

2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) Pub Date : 2023-05-25 DOI: 10.1109/ACCAI58221.2023.10200215

R. Veeranjaneyulu, S. Boopathi, Jonnadula Narasimharao, Keerat Kumar Gupta, R. Vijaya, K. Reddy, R. Ambika

引用次数: 0

An Analysis of Cancer Data Sets Utilizing Data Mining 基于数据挖掘的癌症数据集分析

2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) Pub Date : 2023-05-25 DOI: 10.1109/ACCAI58221.2023.10199382

B. R. N. Singh, Sripuram Sai Keerthi, V. S. Nikitha, Sama Sai Sradha, Neelagiri Shiva Rithika, Shivani Dornala

引用次数: 1

An Automated Identification of Cervical Cancer disease using Convolutional Neural Network Model 基于卷积神经网络模型的宫颈癌疾病自动识别

2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) Pub Date : 2023-05-25 DOI: 10.1109/ACCAI58221.2023.10200640

N. Meenakshisundaram, G. Ramkumar

{"title":"An Automated Identification of Cervical Cancer disease using Convolutional Neural Network Model","authors":"N. Meenakshisundaram, G. Ramkumar","doi":"10.1109/ACCAI58221.2023.10200640","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10200640","url":null,"abstract":"The cost of preventative measures is often lower than that of medical care in most nations. Early diagnosis of disease yields better treatment outcomes than late diagnosis. Unless we have a better idea of how to treat people, whatever help we can provide them would be appreciated. Among these illnesses is cervical cancer, which ranks number four on the list of the most prevalent cancers in women worldwide. Age and the usage of hormonal contraceptives are only two of the numerous variables that raise the risk of cervical cancer. Increased survival and lower mortality rates are the result of cervical cancer screenings that discover the disease at an early stage. The goal of this work is to apply machine learning methods to identify a model that can detect cervical cancer with high specificity and accuracy. Predictions of cervical cancer are made using a CNN model in this study. The Kaggle dataset of risk factors for cervical cancer, including 32 risk factors and 4 goal variables. Lastly, we compared our findings to those of other research and discovered that, based on various assessment metrics, our models performed better than those of the other studies in diagnosing cervical cancer.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130195711","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}

引用次数: 0

An Image-Processing-Based System for Object Detection 基于图像处理的目标检测系统

2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) Pub Date : 2023-05-25 DOI: 10.1109/ACCAI58221.2023.10199621

Ms. SruthyVidiyala, Ms. SwathiKadari, Ms. SushmaThippani, Ms. AnikeTejaswi, Ms. ArrabairuVeena, M. Bathula

引用次数: 1

Application of SVM Classifier to model and analyse the Popularity of Games using Players feedback 基于玩家反馈的SVM分类器在游戏流行度建模与分析中的应用

2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) Pub Date : 2023-05-25 DOI: 10.1109/ACCAI58221.2023.10200087

Divya Singh, Senthil Velan S

{"title":"Application of SVM Classifier to model and analyse the Popularity of Games using Players feedback","authors":"Divya Singh, Senthil Velan S","doi":"10.1109/ACCAI58221.2023.10200087","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10200087","url":null,"abstract":"Prediction or forecasting is the technique of uncovering the forth coming event by learning and obtaining experience through data collected from historical happenings and results. Prediction is used in almost every field today be it retail, healthcare, finance, marketing, travel, insurance, telecommunications, pharmaceuticals, language processing, and other fields. Analytics can be based on the collected data and is commonly and broadly used for analyzing and extracting knowledge obtained from data collected through social inter-networking. Social media contains abundant amount of multifaceted information allowing users to evolve into successful content creators. Henceforth, they also eventually become the web content distributors. So, an online game exists, since only a few features are becoming popular and the other remaining items are not so popular. Prediction of popularity will be highly significant in inter-networking dimensions considering the properties of caching and replication. In this paper, based on the surveys obtained about games’ popularity methods and features that have decent forecasting capacity are utilized to develop an algorithm using support vector classification to predict the popularity of the game.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114813435","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}

引用次数: 1

SVM Modeling Simulation to Evaluate the Electric Vehicle Transmitting Points 基于SVM的电动汽车发射点评价模型仿真

2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) Pub Date : 2023-05-25 DOI: 10.1109/ACCAI58221.2023.10199360

Rajanish Kumar Kaushal, Sanjay Agal, N. B., Ravinjit Singh, P. Singh

{"title":"SVM Modeling Simulation to Evaluate the Electric Vehicle Transmitting Points","authors":"Rajanish Kumar Kaushal, Sanjay Agal, N. B., Ravinjit Singh, P. Singh","doi":"10.1109/ACCAI58221.2023.10199360","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10199360","url":null,"abstract":"Green energy-based intelligent grids are needed to improve security, operation conditions, and power management. Different sources, like solar, wind turbines etc., generate green energy.This green energy will reduce pollution and improves energy production. The current research uses the machine learning model to apply green energy management in an intelligent grid by smart monitoring. The existing Support vector model will predict the need for hybrid electric vehicle (HEV) charging requirements. Coordinate and innovative/intelligent charging systems are applicable in HEVs. The dragonfly-based model is used to evaluate the best charging system for optimization purposes. Apart from this, the self-adaptive model is used to get modified or suit the best charging strategy. Simulation results obtained from the intelligent microgrid reveal the model's suitability and efficiency. By the end of the research, predict the charging requirements concerning minor errors and compare the coordinate and smart charging system performance and operational cost.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115002443","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}

引用次数: 1

Deep Learning-based Binary Classification for Spam Detection in SMS Data: Addressing Imbalanced Data with Sampling Techniques 基于深度学习的短信垃圾邮件二分类检测:用采样技术处理不平衡数据

2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) Pub Date : 2023-05-25 DOI: 10.1109/ACCAI58221.2023.10199860

M. Sethi, Naman Tyagi, Parmeet Singh Kalsi, Parupalli Atchuta Rao

引用次数: 1

Comparative Analysis of IRIS based Human Identity recognition using various Classification Algorithms 基于IRIS的不同分类算法的人体身份识别比较分析

2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) Pub Date : 2023-05-25 DOI: 10.1109/ACCAI58221.2023.10199821

P. B. Khatkale, Anupama Deshpande, Anil B. Pawar

{"title":"Comparative Analysis of IRIS based Human Identity recognition using various Classification Algorithms","authors":"P. B. Khatkale, Anupama Deshpande, Anil B. Pawar","doi":"10.1109/ACCAI58221.2023.10199821","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10199821","url":null,"abstract":"The module responsible for user safety is one of the most vital components of computer systems. It has been shown that simple passwords and logins cannot ensure great efficiency and are simple for hackers to get. The well-known alternative is biometric identity recognition. In recent years, iris as a biometrics attribute has garnered more attention. This was owing to the great efficiency and precision assured by this quantifiable characteristic. In the literature, the effects of this curiosity may be found. Several diverse ways have been offered by various writers. Neither employs discrete fast Fourier transform (DFFT) components to characterise the iris sample. In this paper, the authors offer their unique method for iris-based human identification recognition using DFFT components determined via principal component analysis. Three techniques were utilised for classification: k-nearest neighbours, support vector machines, and artificial neural networks. Tests conducted have shown that the suggested procedure may provide good results.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134603181","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}

引用次数: 0

Detection of Conjunctivitis with Facial Images Improved Accuracy using a Hessian Matrix with RNN and CNN 结合RNN和CNN的Hessian矩阵提高了面部图像结膜炎检测的准确性

2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) Pub Date : 2023-05-25 DOI: 10.1109/ACCAI58221.2023.10199393

Komari Rajesh, M. R.

{"title":"Detection of Conjunctivitis with Facial Images Improved Accuracy using a Hessian Matrix with RNN and CNN","authors":"Komari Rajesh, M. R.","doi":"10.1109/ACCAI58221.2023.10199393","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10199393","url":null,"abstract":"Convolutional Neural Network (CNN) classifiers are compared to Recurrent Neural Network (RNN) classifiers in the detection of conjunctivitis with facial images for improving accuracy using a Hessian matrix to improve system efficiency. The face data set used in this paper is the FERET face data set, which contains 200 individuals. Every person has two separate photographs of 120 people, taken at different times. For example, CNN with a variety of examples (N = 10) and RNN Classifier with a variety of examples (N = 10) techniques are used to detect conjunctivitis with facial images in order to improve accuracy using a Hessian matrix. CNN has a 95.71% accuracy rate, whereas RNN has a 91.62% accuracy rate. CNN has a precision rate of 95.03%, while the precision rate of recurrent neural networks (RNN) is 90.15%. CNN has a recall rate of 95.03%, while recurrent neural networks (RNN) have a recall rate of 90.34%. CNN has a specificity rate of 95.71%, while recurrent neural networks (RNN) have a specificity rate of 91.37%. The accuracy rate is significantly different (P 0.0581). When compared to RNN Classifier, the CNN Classifier predicts better classification in terms of detecting quality and reliability of conjunctivitis with facial images using the Hessian matrix.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133997283","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}

引用次数: 1

Innovation in Biomedical Data Transmission Using Acoustic Methods in MRI Systems 磁共振成像系统中使用声学方法的生物医学数据传输的创新

2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) Pub Date : 2023-05-25 DOI: 10.1109/ACCAI58221.2023.10199381

P. Thenmozhi, N. Pandian

引用次数: 1