2021 2nd Global Conference for Advancement in Technology (GCAT)最新文献_第9页

Underwater Image Enhancement Using SIFT and HOG Transformation 基于SIFT和HOG变换的水下图像增强

2021 2nd Global Conference for Advancement in Technology (GCAT) Pub Date : 2021-10-01 DOI: 10.1109/GCAT52182.2021.9587787

Simran Choudhary, Er. Pritpal Singh

引用次数: 0

College Exam Allocation Using MongoDB and Python3 使用MongoDB和Python3分配大学考试

2021 2nd Global Conference for Advancement in Technology (GCAT) Pub Date : 2021-10-01 DOI: 10.1109/GCAT52182.2021.9587589

Nithin Sameer Yerramilli, N. Johnson, Y. Omsri Sainadh Reddy

{"title":"College Exam Allocation Using MongoDB and Python3","authors":"Nithin Sameer Yerramilli, N. Johnson, Y. Omsri Sainadh Reddy","doi":"10.1109/GCAT52182.2021.9587589","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587589","url":null,"abstract":"A college has a minimum of 4000 students. During examination activities, these students have to be allotted examination rooms in a way that there are students equal or less than the capacity of the rooms and that no student is allotted multiple rooms or no rooms at all. The faculty should be posted as Invigilators to the appropriate rooms and care should be taken such that not a faculty is not posted at multiple rooms and should have the number of duties corresponding to their experience. The Courses should also be taken care of such that no student has 2 examinations on different courses at once and that all the course exams are allotted.This is a replacement for the previous existing system[3] where everything was done manually, considering one or a batch of students at once allocated to one room at a time. It reduces the time taken by the previous system and the resources needed to carry out this task. This requires specific python distributions as well as third party database systems to be working effectively. It uses Microsoft Excel Documents as well as querying operators provided by MongoDB[4] to work efficiently. The goal is to handle the scheduling tasks required during examination activities by a college institution effectively, efficiently and reliably.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129192350","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

Design of a Photovoltaic System using MPPT based SEPIC Converter control 基于MPPT的SEPIC变换器控制光伏系统设计

2021 2nd Global Conference for Advancement in Technology (GCAT) Pub Date : 2021-10-01 DOI: 10.1109/GCAT52182.2021.9587673

Rajesh Dongare, B. N. Chaudhari

引用次数: 1

SIGNify – A mobile solution for Indian sign language using MobileNet architecture 一个使用MobileNet架构的印度手语移动解决方案

2021 2nd Global Conference for Advancement in Technology (GCAT) Pub Date : 2021-10-01 DOI: 10.1109/GCAT52182.2021.9587639

S. Mohammed Siddiq, S. Roopashree, Musfirah Suha, M. Ruthvik, Kuruva Divyasree

{"title":"SIGNify – A mobile solution for Indian sign language using MobileNet architecture","authors":"S. Mohammed Siddiq, S. Roopashree, Musfirah Suha, M. Ruthvik, Kuruva Divyasree","doi":"10.1109/GCAT52182.2021.9587639","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587639","url":null,"abstract":"Sign language assists in visual communication for the vocal and hearing-impaired population. It benefits people with other disabilities such as autism, down syndrome etc. The work recommends an intelligent system specifically to recognize the Indian Sign Language (ISL). It is an interesting and challenging problem, as the solution brings a leap in both social and technological aspects. A signer independent methodology based on different techniques of deep learning, is a real-time recognition system for Indian sign language developed to avoid the isolation of disabling groups from the rest of the society. In our work, we build and compare two pre-trained CNN models, MobileNet and InceptionV3 architectures. The suggested approach using the MobileNet model showed an accuracy of 99% on a custom-built dataset. The working CNN model can perform real-time recognition of ISL alphabets, numbers and a few selected gestures integrated into an Android mobile platform called SIGNify using React-Native, for better accessibility and user-friendly access. The study highlights a small step involved in human-computer interaction.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128668741","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: 2

Hybrid Approach for Predicting Heart Failure using Machine Learning 使用机器学习预测心力衰竭的混合方法

2021 2nd Global Conference for Advancement in Technology (GCAT) Pub Date : 2021-10-01 DOI: 10.1109/GCAT52182.2021.9587466

Yashowardhan Shinde, Aryan Kenchappago, Sumit Patil, Yash Panchwatkar, Sashikala Mishra

{"title":"Hybrid Approach for Predicting Heart Failure using Machine Learning","authors":"Yashowardhan Shinde, Aryan Kenchappago, Sumit Patil, Yash Panchwatkar, Sashikala Mishra","doi":"10.1109/GCAT52182.2021.9587466","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587466","url":null,"abstract":"The research aims on prediction of a heart failure using different machine learning algorithms and hybrid fusion techniques like majority voting of the best performing classifiers. This paper focuses on a majority based algorithm which uses the 3 best performing machine learning models out of the 10 selected machine learning models such as Logistic Regression, Decision Tree, Random Forest, Bagging Classifier, Gradient Boosting Classifier, Extreme Gradient Boosting Classifier, Extreme Gradient Boosting Random Forest, Extra Trees, Categorical Boosting Classifier and K-Nearest Neighbours. The top 3 classifiers are selected on the basis of their accuracy, f1-score and training time required. The minimum accuracy and F1-score score threshold is set to 90% so as to achieve better real world performance of the algorithm in terms of the ability to produce higher accuracy as well as the low time complexity. The proposed model combined the 3 best performing models using the voting method which yielded an Accuracy of 96.67%, a F1-Score of 95.24% and an AUC Score of 95% on the used data set.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116290567","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

Intelligent Traffic Violation Detection 智能交通违章检测

2021 2nd Global Conference for Advancement in Technology (GCAT) Pub Date : 2021-10-01 DOI: 10.1109/GCAT52182.2021.9587520

Roopa Ravish, S. Rangaswamy, Kausthub Char

{"title":"Intelligent Traffic Violation Detection","authors":"Roopa Ravish, S. Rangaswamy, Kausthub Char","doi":"10.1109/GCAT52182.2021.9587520","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587520","url":null,"abstract":"ITVD is a technique which uses Artificial Intelligence and deep learning concepts to detect the vehicle violating the traffic rules. We observe that in the past few years there has been a tremendous increase in the number of on road vehicles. The congested roads with pollution, thereby creating havoc which serves as a reason to violate the traffic rules. This in turn increases road accidents. ITVD is an algorithm which detects traffic violations such as jumping red signals, riding vehicles without helmets, driving without seat belts and vehicles stepping over the stop line during red signals. In many developing countries like India, traffic violations are monitored manually by the traffic department. Such systems make the law enforcement and traffic management difficult since it requires tracking of each vehicle without a miss. This necessitates an automated system which detects the traffic violations and abnormal events occurring on the roads. In this paper we propose the YOLOv3(You Only Look Once version3) algorithm to detect the traffic violations. This algorithm uses Convolutional Neural Networks (CNN) to detect an object and Darknet-53 as a feature extractor. The main advantage of using YOLOv3 is that it uses clustering analysis to cluster the input dataset to improve the prediction ability even with small vehicles.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116893401","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: 7

Domain Ontology Construction using Formal Concept and Relational Concept Analysis 基于形式概念和关系概念分析的领域本体构建

2021 2nd Global Conference for Advancement in Technology (GCAT) Pub Date : 2021-10-01 DOI: 10.1109/GCAT52182.2021.9587855

M. Begam

{"title":"Domain Ontology Construction using Formal Concept and Relational Concept Analysis","authors":"M. Begam","doi":"10.1109/GCAT52182.2021.9587855","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587855","url":null,"abstract":"Domain ontology construction is an important task in knowledge management applications. Knowledge representation in appropriate form is mandate requirement using which extraction of functional facts and applying them in various business operations is feasible. Semantic web technologies are boon to the technical community that develops applications based on knowledge management. Ontology is the mean by which knowledge can be captured and queried in well-defined manner. Developing domain ontologies and investigation of domain knowledge from huge data set/corpus is the tedious task. Formal Conceptual Analysis (FCA) and Relational Concept Analysis(RCA) are data analysis methods that can be applied to domain ontology construction and information retrieval. The concept lattices generated are used for domain ontology construction. We proposed a semi-automated methodology for generating concept lattices based on FCA and RCA techniques for Tree data set. Data about the for various trees found in the world are taken into consideration and their attributes are collected from Internet. We obtain concept lattices and association rules from FCA. Modeling based on RCA has been carried out and resultant concept lattices are generated.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117225109","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

Early Stage Stroke Prediction Using Artificial Neural Network 基于人工神经网络的早期中风预测

2021 2nd Global Conference for Advancement in Technology (GCAT) Pub Date : 2021-10-01 DOI: 10.1109/GCAT52182.2021.9587590

Leesa Menezes, Emmima Gnanaraj, Simran Bindra, A. Pansare

引用次数: 1

Stock Market Prediction Using Sentimental Analysis and Machine Learning 利用情感分析和机器学习进行股市预测

2021 2nd Global Conference for Advancement in Technology (GCAT) Pub Date : 2021-10-01 DOI: 10.1109/GCAT52182.2021.9587898

Urvik Shah, Bhavya Karani, Jainam V. Shah, Manoj Dhande

引用次数: 2

GlioMeNet: Brain tumor analysis using faster R-CNN GlioMeNet:使用更快的R-CNN进行脑肿瘤分析

2021 2nd Global Conference for Advancement in Technology (GCAT) Pub Date : 2021-10-01 DOI: 10.1109/GCAT52182.2021.9587605

G. Sethuram Rao, D. Vydeki

引用次数: 0