2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)最新文献_第7页

A Survey on Advanced Text Recognition and Projection in Augmented Reality 增强现实中高级文本识别与投影的研究进展

2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) Pub Date : 2022-12-16 DOI: 10.1109/ICAC3N56670.2022.10074420

R Rishikesh Mahadevan, Abhishek S, Anudeep N, V. Vivek

{"title":"A Survey on Advanced Text Recognition and Projection in Augmented Reality","authors":"R Rishikesh Mahadevan, Abhishek S, Anudeep N, V. Vivek","doi":"10.1109/ICAC3N56670.2022.10074420","DOIUrl":"https://doi.org/10.1109/ICAC3N56670.2022.10074420","url":null,"abstract":"Augmented Reality (AR) is a technology that overlays a computer-generated image on a user’s view of the real world, thus providing a combined view. Artificial Intelligence (AI) has been used in various sectors such as gaming, manufacturing, military, and law enforcement systems. With the rapid growth of augmented reality development tools and the expansion of their functionality over the years, developers have started believing that with the application of augmented reality for AI developers can create an artificial intelligence-based app that can perform all basic tasks such as adapting to different environments. The survey focuses on studying and comparing different methods and approaches of Augmented Reality and text/image identification and mapping techniques to further combine both the approaches to achieve the end goal of obtaining AR Projections from the recognized text. The process will involve the following steps which are Handwritten text Recognition and extraction using multi model CTC. Emotion and Sentiment Analysis for text mapping and recognition using BiLSTM and SENN, Image Generation using XMC-GAN and then finally converting 2D images to 3D using COLF making it ready for the final Augmented Reality projection.","PeriodicalId":342573,"journal":{"name":"2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128678834","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

Optical Character Recognition system with Projection Profile based segmentation and Deep Learning Techniques 基于投影轮廓分割和深度学习技术的光学字符识别系统

2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) Pub Date : 2022-12-16 DOI: 10.1109/ICAC3N56670.2022.10074151

Arockia Rexi F, Lija Jacob

引用次数: 0

Drinking Water Management System 食水管理系统

2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) Pub Date : 2022-12-16 DOI: 10.1109/ICAC3N56670.2022.10074535

Isha Goel, Swarnima Shishodia, Hari Om Upadhyay, Rohit Rastogi

引用次数: 0

Brain Tumor Detection Using MRI 脑肿瘤的MRI检测

2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) Pub Date : 2022-12-16 DOI: 10.1109/ICAC3N56670.2022.10074150

Nidhi Raj Singh, Siddhant Kishore, Gururama Senthilvel.P, Elina Saxena

引用次数: 1

Grayness Comparison of UV and Latex Ink on Vinyl Substrate Printed by Large Format Printing (LFP) 大幅面印刷(LFP)乙烯基基材上UV油墨和乳胶油墨的灰度比较

2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) Pub Date : 2022-12-16 DOI: 10.1109/ICAC3N56670.2022.10074129

Sanjeev Kumar, Vishal Sharma

引用次数: 0

Analyzing the Behavioral Artifacts for Malicious Software Detection Using Machine Learning Algorithms 利用机器学习算法分析恶意软件检测的行为伪影

2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) Pub Date : 2022-12-16 DOI: 10.1109/ICAC3N56670.2022.10074264

N. Chandini, C. Mai

{"title":"Analyzing the Behavioral Artifacts for Malicious Software Detection Using Machine Learning Algorithms","authors":"N. Chandini, C. Mai","doi":"10.1109/ICAC3N56670.2022.10074264","DOIUrl":"https://doi.org/10.1109/ICAC3N56670.2022.10074264","url":null,"abstract":"Malicious software intentionally affects your computer system. Malware is analyzed using static or dynamic analysis techniques. These techniques are used to classify and predict unique patterns for correctly identifying malware. Over the last decade, many malware detection techniques have been proposed using a variety of techniques. In most cases, network-based attacks such as DDoS attacks are known network security risks. These causes usually cause network devices to overflow with more requests than they can handle, preventing the server from responding to legitimate requests. The biggest security risk is related to software. Software attacks can exploit the entire system, steal information, modify data, deny services, and endanger or damage devices. In particular, I am interested in dynamic analysis for developing malware detection systems that utilize machine learning techniques. This document proposes behavior-based malware detection techniques. To develop this model, we arrange a dynamic analysis approach and run malware samples using classifiers. To extracted for the malware detection system. To develop this technique, we create a dynamic analysis approach and run malware using classification algorithms. Computer programs connected to Internet are increasingly needed to analyze and prevent malware. The software exploits system vulnerabilities to unknowingly steal sensitive information and secretly send it to an attacker-controlled remote server. This approach uses the ML classifier to detect malware and identify the type of malware attacked in the dataset.","PeriodicalId":342573,"journal":{"name":"2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132469717","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 forest fire using support vector machine in comparison with k - nearest neighbour to measure the accuracy, precision and recall 利用支持向量机对森林火灾进行检测，并与k近邻法进行比较，以衡量其准确率、精密度和召回率

2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) Pub Date : 2022-12-16 DOI: 10.1109/ICAC3N56670.2022.10074520

Inturi Susmitha, Femila Roseline J

{"title":"Detection of forest fire using support vector machine in comparison with k - nearest neighbour to measure the accuracy, precision and recall","authors":"Inturi Susmitha, Femila Roseline J","doi":"10.1109/ICAC3N56670.2022.10074520","DOIUrl":"https://doi.org/10.1109/ICAC3N56670.2022.10074520","url":null,"abstract":"The performance of novel SVMs for the detection of forest fires is going to be evaluated as part of the proposed work's objectives. The KNN algorithm is used as a benchmark for the SVM algorithm that was just proposed. The forest fire dataset that is currently available on kaggle contains a total of 1599 samples that were gathered. These samples are broken up as follows: 70% are used for the training dataset (which has a total of 1230 records), and 30% are used for the testing dataset (which has a total of 369 records). For the purpose of quantifying the performance of the SVM algorithm, accuracy, precision, and recall values are calculated. In comparison, the Novel Linear Support vector machine attained an accuracy of 97.5 percent whereas the KNN algorithm only achieved 89.25 percent. The SVM method achieves a precision of 94.3%, whereas the KNN approach achieves a precision of 88.64%. The SVM algorithm yielded a recall of 94.8%, while the KNN algorithm only managed to achieve 87%. The achieved significant value is less than 0.001 with a probability of less than 0.05. When compared to the KNN algorithm, the SVM's accuracy, precision, and recall were all shown to be significantly higher. According to the findings of this research, the SVM algorithm performed significantly better than the KNN method when it came to the detection of forest fires using the dataset under consideration.","PeriodicalId":342573,"journal":{"name":"2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131946521","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

Object Detection using OpenCV and Deep Learning 使用OpenCV和深度学习的目标检测

2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) Pub Date : 2022-12-16 DOI: 10.1109/ICAC3N56670.2022.10074012

Tushar, Kundan Kumar, Sanjay Kumar

{"title":"Object Detection using OpenCV and Deep Learning","authors":"Tushar, Kundan Kumar, Sanjay Kumar","doi":"10.1109/ICAC3N56670.2022.10074012","DOIUrl":"https://doi.org/10.1109/ICAC3N56670.2022.10074012","url":null,"abstract":"The most often adopted methodologies for contemporary machine learning techniques to execute a variety of responsibilities on embedded devices are mobile networks and multimodal neural networks. In this research, we propose a method for identifying an item that takes into account the learning - based pretrained system MobileNetV3 for an SSD (Single Shot Multibox Detector). This set of rules is utilized for constant identification, and for webcam feed to find the explanation webcam which recognizes the thing in a video transfer. Consequently, we utilize an item identification module which could find what’s with inside the video transfer. To place into impact, the module, we integrate the MobileNetV3 and the SSD system for a quick and green profound getting to know-primarily based totally approach of item detection. The primary goal of our study is to investigate the effectiveness of an object identification technique SSD and the significance of MobileNetV3. The testing findings demonstrate that the Average Closeness of the set of rules to discover one of a kind training as car, person and cat is 97.45%, 95.67% and 85.59%, respectively. It enhances the accuracy of content detection at the processing speed necessary for practical identification and the needs of regular progress indoors and outdoors.","PeriodicalId":342573,"journal":{"name":"2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132035784","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

CNN Based Fire Detection System 基于CNN的火灾探测系统

2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) Pub Date : 2022-12-16 DOI: 10.1109/ICAC3N56670.2022.10074585

Rishav Pal, Vishal Singh Rawat, Vivek Kumar Dubey, Mukesh Chauhan

引用次数: 0

Water Quality Monitoring 水质监测

2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) Pub Date : 2022-12-16 DOI: 10.1109/icac3n56670.2022.10074257

Saurabh Singh, Anurag Singh, Vishwanath Gupta, Yogesh Kumar

引用次数: 0