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2021 International Conference on Intelligent Technologies (CONIT)最新文献

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Driver Distraction and Drowsiness Detection System 驾驶员分心和困倦检测系统
2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498348
G. Roshini, Y. Kavya, R. Hareesh, M. Suma, N. Sunny
{"title":"Driver Distraction and Drowsiness Detection System","authors":"G. Roshini, Y. Kavya, R. Hareesh, M. Suma, N. Sunny","doi":"10.1109/CONIT51480.2021.9498348","DOIUrl":"https://doi.org/10.1109/CONIT51480.2021.9498348","url":null,"abstract":"The major cause of deaths in our world is a car accident. Nearly around 1.5 billion people die due to car accidents and majority are happening just due to a simple factor that is drowsiness of driver. Most of the people travel for long distances without any sleep and using mobile phones while driving this results to the issue of tiredness and as a result to the drowsiness. This can be avoided just by alerting the driver when there is any such case of occurrence. So we are proposing a system which can alert the driver using a alarm when the driver gets distracted or feels drowsy. The aim of this project is to build the driver distraction prediction system that will detect whether persons eyes are closed or not.","PeriodicalId":426131,"journal":{"name":"2021 International Conference on Intelligent Technologies (CONIT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133536979","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: 3
Depression Detection from Tweets Along with Clinical Tests 从推特和临床测试中检测抑郁症
2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498486
Sarthak Maniar, Kaustubh K. Patil, B. Rao, R. Shankarmani
{"title":"Depression Detection from Tweets Along with Clinical Tests","authors":"Sarthak Maniar, Kaustubh K. Patil, B. Rao, R. Shankarmani","doi":"10.1109/CONIT51480.2021.9498486","DOIUrl":"https://doi.org/10.1109/CONIT51480.2021.9498486","url":null,"abstract":"Social media has become a massive surge in this generation for everyone to communicate with others, which on one half connects the world while on the other half has a depressing side with people suffering from mental illness. People nowadays express their thoughts and feelings more easily on social media platforms. In addition, numerous studies have proven that by analysing social media posts, we may identify people with mental problems using machine learning. Twitter is one such platform that covers a wide target audience from all parts of the world and from the tweets we can detect the early stage of depression by analyzing its linguistic markers and emotions. Using a sentimental analysis dataset we have created a model with the help of Naïve Bayes Classifier which will support our primary model that will recognize different emotions from the tweets. We've also performed clinical tests by using the MBTI Types (Myer Briggs Type Indicator), a well-known personality test that identifies a person's traits by indicating one of 16 types.","PeriodicalId":426131,"journal":{"name":"2021 International Conference on Intelligent Technologies (CONIT)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130467109","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 Novel Approach in Cyber Security for securing the Workplace of the Future in large Industry Setups 在大型工业设置中确保未来工作场所的网络安全的新方法
2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498468
S. Vadlamudi, Suman De
{"title":"A Novel Approach in Cyber Security for securing the Workplace of the Future in large Industry Setups","authors":"S. Vadlamudi, Suman De","doi":"10.1109/CONIT51480.2021.9498468","DOIUrl":"https://doi.org/10.1109/CONIT51480.2021.9498468","url":null,"abstract":"Cyber Security as a topic has been a major course of concern for any product offering. Digitization has been on the rise and with the impact of the COVID19 scenario, the need for realizing business requirements over a virtual setup has become critical. This has led to massive cyber-attacks that leads to stolen assets, lost reputation, lost business, financial losses, impact on the stock market, and theft of intellectual property so on. It is essential to build a security strategy to navigate through challenging times, recover quickly and continue to deliver the intended outcome. Security efficiency can be achieved by balancing and optimizing the relationships between people, processes, and technology. Investing in sustainable security culture is necessary as it transforms security from a one-time event into a lifecycle that generates security returns forever. In this paper, we explore an approach to achieve security culture changes among people that results in the change of process & technology for a secure environment. This proposed paper also explores the impacts of people, processes, and technology and how each of these pillars contributes to a creation of a secure workplace of the future.","PeriodicalId":426131,"journal":{"name":"2021 International Conference on Intelligent Technologies (CONIT)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130668098","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
Hand Gesture Recognition System using Modified SVM and Hybrid Ensemble Classifier 基于改进支持向量机和混合集成分类器的手势识别系统
2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498381
M. P. Athul Vijay, S. Kanagalakshmi, M. S. Subodh Raj, S. N. George
{"title":"Hand Gesture Recognition System using Modified SVM and Hybrid Ensemble Classifier","authors":"M. P. Athul Vijay, S. Kanagalakshmi, M. S. Subodh Raj, S. N. George","doi":"10.1109/CONIT51480.2021.9498381","DOIUrl":"https://doi.org/10.1109/CONIT51480.2021.9498381","url":null,"abstract":"Hand Gesture Recognition (HGR) methods have gained tremendous interest in the past few years. The technique of HGR allows humans to connect with the system and interact naturally, thereby avoiding the involvement of any mechanical amenities. Automatic control of home appliances in smart home is an important application of a HGR system. In this paper, we propose a new HGR system using Speeded Up Robust Feature (SURF) as the feature descriptor. Bag of Feature (BoF) algorithm is employed to generate visual histogram of the SURF features and to generate a unified vector by mapping to the visual vocabulary. The initial stage of classification is performed by the proposed modified Support Vector Machine (SVM) classifier. In the second stage a classifier fusion model called as hybrid ensemble classifier obtained by combining K-Nearest Neighbour (KNN) and the modified SVM classifier is used. The experimental results show that the proposed hybrid ensemble and the modified SVM classifier provides better results compared to the individual classifiers.","PeriodicalId":426131,"journal":{"name":"2021 International Conference on Intelligent Technologies (CONIT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116857963","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
Wrong Side Vehicle Detection 侧错车辆检测
2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498416
Anirudh Goyal, Antriksh Mangal, A. R. Kulkarni
{"title":"Wrong Side Vehicle Detection","authors":"Anirudh Goyal, Antriksh Mangal, A. R. Kulkarni","doi":"10.1109/CONIT51480.2021.9498416","DOIUrl":"https://doi.org/10.1109/CONIT51480.2021.9498416","url":null,"abstract":"With the advent of Intelligent Transportation Systems, traffic management can be made more robust and advanced. This can alleviate problems such as wrong side driving, which is one of the leading causes of traffic congestion and road accidents, thus improving the road transport efficiency. In this paper, we present a novel solution to reduce the number of accidents caused by wrong side vehicles by alerting the other road users and warning the driver to revert back. Present day solutions are expensive and difficult to setup. Using low-cost hardware and pragmatic design, we are able to provide economic viability to our solution.","PeriodicalId":426131,"journal":{"name":"2021 International Conference on Intelligent Technologies (CONIT)","volume":"61 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134389286","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
Age Estimation in Juveniles using Convolution Neural Network 基于卷积神经网络的青少年年龄估计
2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498483
Ravi Sharma, Nitish Pandey, Y. S. Thakur, A. Gangwar, S. Suman
{"title":"Age Estimation in Juveniles using Convolution Neural Network","authors":"Ravi Sharma, Nitish Pandey, Y. S. Thakur, A. Gangwar, S. Suman","doi":"10.1109/CONIT51480.2021.9498483","DOIUrl":"https://doi.org/10.1109/CONIT51480.2021.9498483","url":null,"abstract":"Age estimation models have been developed by many but no one has been able to build it with much accuracy and precision. This becomes even more challenging while estimating age of juveniles. This project aims to find a solution to this challenging problem. Juvenile age estimation can be used in multiple domains. It can be used majorly to stop criminal activities affecting children and teenagers. Faces of the subjects are captured and then the images are analysed using neural networks. The project is based mainly on concepts of machine learning like computer vision and convolution neural networks. This project has enormous scope as the algorithm applied can be continuously optimised as per requirements. Convolution Neural networks are highly used for tasks involving image analysis and identification. Computer vision deals with how the computer perceives and visualises the input provided to it. The fundamental concepts used in any machine learning based image analysis are Computer vision and Convolution Neural networks. These fundamentals have been developed by researchers across the world but still have some improvements to be done. With accurate dataset and proper optimisation of algorithms it is possible to create an age estimation model more precise and accurate than the existing ones.","PeriodicalId":426131,"journal":{"name":"2021 International Conference on Intelligent Technologies (CONIT)","volume":"283 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131851883","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
Implementation of a Web-based Technology for Readymade Garments Manufacturing Defects 成衣制造缺陷的基于网络技术的实现
2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498295
Lubba Saha, Subir Dey Raju, Mushfiqur Rahman Chowdhury, K. Salam
{"title":"Implementation of a Web-based Technology for Readymade Garments Manufacturing Defects","authors":"Lubba Saha, Subir Dey Raju, Mushfiqur Rahman Chowdhury, K. Salam","doi":"10.1109/CONIT51480.2021.9498295","DOIUrl":"https://doi.org/10.1109/CONIT51480.2021.9498295","url":null,"abstract":"Readymade garments (RMG) manufacturing defects carry a significant role in reducing the quality of the products. Real-time production data tracking can play an important role to track those defects. A web-based software like InTrack can be a pioneer to produce that trackable data. InTrack is a Quality Control (QC) web-application to track RMG manufacturing defects using production line data. It requires complex architectural decisions for the backend and intricate management of frontend states and components. We have studied in-depth into the engineering of such a data intensive web-application and applicable for the RMG industies.","PeriodicalId":426131,"journal":{"name":"2021 International Conference on Intelligent Technologies (CONIT)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130267448","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
Performace Testing Approach for Enterprise Application comprising Serverless Component 包含无服务器组件的企业应用程序的性能测试方法
2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498446
Deepak Khatri, S. Khatri, Deepti Mishra
{"title":"Performace Testing Approach for Enterprise Application comprising Serverless Component","authors":"Deepak Khatri, S. Khatri, Deepti Mishra","doi":"10.1109/CONIT51480.2021.9498446","DOIUrl":"https://doi.org/10.1109/CONIT51480.2021.9498446","url":null,"abstract":"Performance testing validates non-functional requirement covering load test, scalability test, stress test etc. Performance testing of an application, which is using micro service base architecture and deployed part of the functionalities on serverless computing, need to be validated at system level for specified business scenarios. Various performance testing strategies are being used to simulate and measure the performance for serverless computing architecture.Serverless computing is trending nowadays, where developer needs to focus on his code rather than worrying about server management. In serverless computing, Application is nothing but collection of one or more functions, written for specific business functionality, which triggers on an event. The developer just needs to upload the code for execution. The performance of the serverless computing may vary due to dynamic configuration of the solution, technologies and different technology used by the service provider. [1] This paper is based on performance testing experiment conducted of a real business application built on complex architecture containing serverless component. Various past and recent work was also reviewed for performance testing approach of serverless computing application. It demonstrates the performance testing and monitoring of serverless computing application.","PeriodicalId":426131,"journal":{"name":"2021 International Conference on Intelligent Technologies (CONIT)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127618395","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
Person Detection, Tracking and Following in a Differential Drive Mobile Robot 差动驱动移动机器人的人检测、跟踪与跟踪
2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498493
Srikrishna Sowrirajan, Aswin Gururaj Prakash, S. R. Naven, V. Gopi, S. Deivalakshmi
{"title":"Person Detection, Tracking and Following in a Differential Drive Mobile Robot","authors":"Srikrishna Sowrirajan, Aswin Gururaj Prakash, S. R. Naven, V. Gopi, S. Deivalakshmi","doi":"10.1109/CONIT51480.2021.9498493","DOIUrl":"https://doi.org/10.1109/CONIT51480.2021.9498493","url":null,"abstract":"One primary application of robotics is the application of the robots as a personal assistant. Thus this would require the robot to perceive the environment and perform actions required by the person. One such application is a person following. Other applications are following a soldier on a battlefield and a worker in a factory carrying tools. We will take the relatively simple problem of carrying luggage. To do this, we addressed three sub-problems Detection of a Human, Tracking a human, Following a Human. And the additional requirement is that it must be done in real-time and use the robot’s on-board compute capabilities. We chose hardware and were able to achieve the required outcome in both simulation and real life.","PeriodicalId":426131,"journal":{"name":"2021 International Conference on Intelligent Technologies (CONIT)","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127789873","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 Neural Networks Based Error Level Analysis for Lossless Image Compression Based Forgery Detection 基于深度神经网络的无损图像压缩伪造检测误差分析
2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498357
Chintakrindi Geaya Sri, Shahana Bano, T. Deepika, Nehanth Kola, Yerramreddy Lakshmi Pranathi
{"title":"Deep Neural Networks Based Error Level Analysis for Lossless Image Compression Based Forgery Detection","authors":"Chintakrindi Geaya Sri, Shahana Bano, T. Deepika, Nehanth Kola, Yerramreddy Lakshmi Pranathi","doi":"10.1109/CONIT51480.2021.9498357","DOIUrl":"https://doi.org/10.1109/CONIT51480.2021.9498357","url":null,"abstract":"The proposed model is implemented in deep learning based on counterfeit feature extraction and Error Level Analysis (ELA) techniques. Error level analysis is used to improve the efficiency of distinguishing copy-move images produced by Deep Fake from the real ones. Error Level Analysis is used on images in-depth for identifying whether the photograph has long passed through changing. This Model uses CNN on the dataset of images for training and to test the dataset for identifying the forged image. Convolution neural network (CNN) can extract the counterfeit attribute and detect if images are false. In the proposed approach after the tests were carried out, it is displayed with the pie chart representation based on percentage the image is detected. It also detects different image compression ratios using the ELA process. The results of the assessments display the effectiveness of the proposed method.","PeriodicalId":426131,"journal":{"name":"2021 International Conference on Intelligent Technologies (CONIT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128520913","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
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