2021 International Conference on Advances in Computing, Communication, and Control (ICAC3)最新文献_第6页

Glaucoma Identification based on Segmentation and Fusion Techniques 基于分割和融合技术的青光眼识别

2021 International Conference on Advances in Computing, Communication, and Control (ICAC3) Pub Date : 2021-12-03 DOI: 10.1109/icac353642.2021.9697174

Abdulraheem Shaik, K. R. Chandra, B. E. Raju, Prudhvi Raj Budumuru

{"title":"Glaucoma Identification based on Segmentation and Fusion Techniques","authors":"Abdulraheem Shaik, K. R. Chandra, B. E. Raju, Prudhvi Raj Budumuru","doi":"10.1109/icac353642.2021.9697174","DOIUrl":"https://doi.org/10.1109/icac353642.2021.9697174","url":null,"abstract":"Prepared professionals assess computerised images obtained from the retina. Diabetic retinopathy progression is assessed based on its severity, which determines how often exams are performed. In any event, a significant shortage of experienced observers prompted PC-assisted verification. Appraisal of veins network assumes a significant part in an assortment of clinical issues. Signs of a few vascular problems, like diabetic retinopathy, rely upon identification of the veins organization. In this work green channel of the fundus RGB picture was utilized for acquiring the hints of veins. The calculation created utilized morphological activity to smoothen the foundation, permitting veins, to be seen obviously. Circle organizing components were utilized in this work. The calculation executed has utilized modules, for example, contrast improvement, foundation rejection and thresholding. The procedures depicted in the paper depend on morphological activity and apply on freely accessible DRIVE, diaretdb0, diaretdb1 information bases and pictures from eye emergency clinic. Test results acquired by utilizing dim scale/green-channel pictures have been introduced. The executed calculation has been demonstrated to be an exceptionally successful technique for characterizing retinal veins. The executed calculation being basic and simple to carry out, is most appropriate for quick preparing applications.","PeriodicalId":196238,"journal":{"name":"2021 International Conference on Advances in Computing, Communication, and Control (ICAC3)","volume":"45 51","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120889099","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: 4

Process Control & Inspection using 5s Method and Computation with Pareto Analysis 用5s法进行过程控制与检验，用帕累托分析法进行计算

2021 International Conference on Advances in Computing, Communication, and Control (ICAC3) Pub Date : 2021-12-03 DOI: 10.1109/icac353642.2021.9697131

Jerin Beno, M. Rao, Jason Beno, S. Das

引用次数: 0

Credit Card Fraud Detection Using Machine Learning 使用机器学习的信用卡欺诈检测

2021 International Conference on Advances in Computing, Communication, and Control (ICAC3) Pub Date : 2021-12-03 DOI: 10.1109/icac353642.2021.9697227

D. Prajapati, A. Tripathi, Jeel Mehta, Kirtan Jhaveri, V. Kelkar

{"title":"Credit Card Fraud Detection Using Machine Learning","authors":"D. Prajapati, A. Tripathi, Jeel Mehta, Kirtan Jhaveri, V. Kelkar","doi":"10.1109/icac353642.2021.9697227","DOIUrl":"https://doi.org/10.1109/icac353642.2021.9697227","url":null,"abstract":"From the day when payment systems emerged, there have been people willing to find novel ways to access someone’s finances illegally. This menacing hazards has grown in the current period, as the majority of transactions are now completed entirely online using credit card information. Frauds due to Credit Cards is a broad phrase that refers to any type of fraud involving a payment card, specifically a credit cards.The solitary purpose of such transgressions is usually to gain goods and services, or to make a huge payment to another account without the owner’s consent. According to the Nilson Report, By 2025, due to credit card fraud the United States has been projected to suffer losses up to 12.5 billion dollars. Using Machine learning algorithms to detect Credit card fraud is a process in which the data is investigated through various techniques to achieve the best possible outcomes in detecting and impeding fraudulent transactions. In order to evaluate different algorithms which accurately detect credit card fraud we have used techniques such as Random Forest, XGBoost, ANN (Artificial Neural Network). The results of these models can be used to effectively detect any credit card transaction happening whether a genuine one or fraudulent.","PeriodicalId":196238,"journal":{"name":"2021 International Conference on Advances in Computing, Communication, and Control (ICAC3)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123252927","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

A Comparative Study of Amazon Product Reviews Using Sentiment Analysis 基于情感分析的亚马逊产品评论对比研究

2021 International Conference on Advances in Computing, Communication, and Control (ICAC3) Pub Date : 2021-12-03 DOI: 10.1109/icac353642.2021.9697155

Ansh Gupta, Aryan Rastogi, Avita Katal

{"title":"A Comparative Study of Amazon Product Reviews Using Sentiment Analysis","authors":"Ansh Gupta, Aryan Rastogi, Avita Katal","doi":"10.1109/icac353642.2021.9697155","DOIUrl":"https://doi.org/10.1109/icac353642.2021.9697155","url":null,"abstract":"Online shopping is an electronic business that allows people from all over the world to buy goods of their interest via web and various applications. Nowadays, these facilities are provided by famous E-commerce platforms such as Amazon, Flipkart, Snapdeal etc. Online shopping is one of the best businesses running over the Internet and hence it becomes the prime responsibility of these platforms to provide the best-rated products at the most feasible price. This paper provides a mechanism that can be used by various online shopping platforms to analyze the reviews given by the buyers, using sentimental analysis in order to maintain good service amongst their users. Sentimental Analysis is one of the most trending research areas in the domain of Natural Language Processing. It is defined as the technique that helps in the analysis of people’s emotions, sentiments from written text. In this paper, various Machine Learning classification algorithms have been used for finding the polarity of the reviews. Specifically, comparative analysis of algorithms such as Stochastic Gradient Descent, Logistic Regression, Multinomial Naive Bayes, and Support Vector Machine has been done. Performance evaluation of these algorithms has been done on the basis of the accuracy achieved. The observed results show that the Stochastic Gradient Descent with Bag of Words model outperforms other algorithms and shows the highest accuracy of 88.76%.","PeriodicalId":196238,"journal":{"name":"2021 International Conference on Advances in Computing, Communication, and Control (ICAC3)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131662287","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

Authenticity of System Users via Mouse Handling Method 通过鼠标处理方法实现系统用户的真实性

2021 International Conference on Advances in Computing, Communication, and Control (ICAC3) Pub Date : 2021-12-03 DOI: 10.1109/icac353642.2021.9697250

Kiran Kamble, Nandinee Mudegol, Pooja Mundada, Abhijeet Urunkar

引用次数: 0

Campus Safety and Hygiene Detection System using Computer Vision 基于计算机视觉的校园安全卫生检测系统

2021 International Conference on Advances in Computing, Communication, and Control (ICAC3) Pub Date : 2021-12-03 DOI: 10.1109/icac353642.2021.9697148

Nikhil Raote, Mohd Saad Khan, Zaid Siddique, A. Tripathy, Phiroj Shaikh

{"title":"Campus Safety and Hygiene Detection System using Computer Vision","authors":"Nikhil Raote, Mohd Saad Khan, Zaid Siddique, A. Tripathy, Phiroj Shaikh","doi":"10.1109/icac353642.2021.9697148","DOIUrl":"https://doi.org/10.1109/icac353642.2021.9697148","url":null,"abstract":"The recent spread of severe acute respiratory syndrome coronavirus 2 and its associated coronavirus disease has caused extensive public health concerns. University campuses are at higher risks since a lot of students are present inside the campus at a given point of time. Places where there are a lot of chances of spread of the infection in the campus include the entrance gate, canteen, library, photocopy center, seminar hall, etc. Strict actions must be taken against the violations of the covid-19 protocols which will ensure health safety and maintain hygiene in the campus. Doing this manually will be a tedious task. Owing to this problem, an attempt has been made to design a system to tackle the problem of following all the protocols and making everyone aware about the situation in the campus. This work proposes a system which will continuously monitor all these activities with the help of Computer Vision and Deep Learning. The collected CCTV cameras data has been checked in the real time mode using various object detection and object tracking models to identify and track the objects visible in the frame. This approach uses MobileNet and SSD Architecture along with the objection detection models to predict the desired output. Finally, based on the output the system checks for any violations and if encountered then it sends a text alert to the concerned authority.","PeriodicalId":196238,"journal":{"name":"2021 International Conference on Advances in Computing, Communication, and Control (ICAC3)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127737704","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