2021 International Conference on Computing, Communication and Green Engineering (CCGE)最新文献_第7页

Empirical Study of Awareness towards Blended e-learning Gateways during Covid-19 Lockdown Covid-19封锁期间对混合电子学习网关意识的实证研究

2021 International Conference on Computing, Communication and Green Engineering (CCGE) Pub Date : 2021-09-23 DOI: 10.1109/CCGE50943.2021.9776386

M. Dadhich, Ruchi Doshi, S. Mathur, Rajesh Meena, Rajat Kumar Gujral, P. Dhotre

{"title":"Empirical Study of Awareness towards Blended e-learning Gateways during Covid-19 Lockdown","authors":"M. Dadhich, Ruchi Doshi, S. Mathur, Rajesh Meena, Rajat Kumar Gujral, P. Dhotre","doi":"10.1109/CCGE50943.2021.9776386","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776386","url":null,"abstract":"One of the most remarkable changes in the academic Diaspora is the international creation of virtual platforms, which has given rise to a new edge system of learning. Covid-19 presents a unique and severe problem on every front. The nationwide shutdown by the administration aims to control the diffusion of Covid-19 at education institutions across the country. Many (local, national, and worldwide) institutions have implemented a reliable and beneficial contactless atmosphere for students and faculties to maintain the continuity of learning. As a result, teachers and students are greatly influenced by the new-age virtual teaching method adopted and implemented. The survey respondents were picked by a combination of online surveys and personality tests, and then the questionnaire they were given included both closed- and open-ended items. The numbers of university and secondary school portals have recently seen an upward trend. So, to better investigate the abilities of teachers and learners to identify the efficiency of dominating content delivery methods, a hybrid approach of the exploratory study was employed. Students and faculty, 140 each who have taken web-based learning at 25 Indian institutions, are sampled using a snowball sampling methodology. The results of the t-test demonstrated a considerable divergence in teaching-learning impressions between faculty and students on three manifests ($mathrm{p} < 0.005$). Learners' responses differed from faculty responses, and statistically significant differences were found, such as scientific material can be taught effectively online, improved technocratic pedagogy is the core part of e-learning, reliance on computers/connectivity.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126612552","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

Effect of Watershed Characteristics on a Rainfall Runoff Analysis and Hydrological Model Selection - A review 流域特征对降雨径流分析和水文模型选择的影响综述

2021 International Conference on Computing, Communication and Green Engineering (CCGE) Pub Date : 2021-09-23 DOI: 10.1109/CCGE50943.2021.9776398

Aparna S. Nagure, S. Shahapure

{"title":"Effect of Watershed Characteristics on a Rainfall Runoff Analysis and Hydrological Model Selection - A review","authors":"Aparna S. Nagure, S. Shahapure","doi":"10.1109/CCGE50943.2021.9776398","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776398","url":null,"abstract":"The rainfall-runoff analysis and modeling have been the subject of a large number of research activities and a range of types of models have been developed in the last few decades, to predict the runoff well in advance to avoid the huge amount of losses due to floods. However, all these research activities are focused on the result and accuracy of models and their comparative study. It often remains unclear which model is best under which conditions. It is necessary to select the appropriate rainfall-runoff model for the watershed area according to its physical/chemical/biological characteristics. In this paper, one of the significant characteristics of the watershed that is the size of the case study area is selected as a parameter to understand how it affects the selection of the model. To understand this, 42 research papers published between 2000 to 2019 have been reviewed and categorized according to the size of the watershed, climatic conditions, and type of models used for rainfall-runoff analysis. The result obtained indicates that for major research work, black box models or data-driven models have been used for the watershed of size ranging between 250 km2 to 10000 km2. Similarly, maximum work is carried out for medium size watershed areas.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128961435","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

Hateful Meme Prediction Model Using Multimodal Deep Learning 基于多模态深度学习的仇恨模因预测模型

2021 International Conference on Computing, Communication and Green Engineering (CCGE) Pub Date : 2021-09-23 DOI: 10.1109/CCGE50943.2021.9776440

Md. Rekib Ahmed, Neeraj Bhadani, I. Chakraborty

{"title":"Hateful Meme Prediction Model Using Multimodal Deep Learning","authors":"Md. Rekib Ahmed, Neeraj Bhadani, I. Chakraborty","doi":"10.1109/CCGE50943.2021.9776440","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776440","url":null,"abstract":"With the emergence of deep neural networks along with high-end computers that can process deep architectures, there has been a lot of research when Computer Vision and Natural Language Processing has been fused into a single problem. To enable students and researchers to deep dive into multimodal deep learning Facebook AI Research team published a dataset on hateful meme classification “The Hateful Meme Challenge Dataset” in May 2020 that gave us the motivation to test ourselves and an opportunity to learn more about the dataset. The rise of communication on the internet with memes as a medium, they have been used to convey incorrect information, political agendas and also has led to cyberbullying, trolling etc. This results in the need of creating an automated tool that can detect such hateful content published on the internet and remove it at the root level before it does any harm. This paper intends to adopt Unimodal Text and Image models using Bert, LSTM and VGG16, Resnet50, SE-Resnet50, XSE-Resnet architectures and combining them into Multimodal models for effective prediction of a hateful meme. The paper compares various architectures both unimodal models and multimodal models on the evaluation metrics AUC-ROC score, F1 score and accuracy score.)","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121943102","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

OpenStack Cloud Deployment for Scientific Applications OpenStack科学应用云部署

2021 International Conference on Computing, Communication and Green Engineering (CCGE) Pub Date : 2021-09-23 DOI: 10.1109/CCGE50943.2021.9776387

Mitali Patil, Harsha Kalmath, Khushboo Chamedia, Shreya Pandey, Shilpa Deshpande, N. Kurkure, G. Misra

引用次数: 0

Deepfake Image Detection using CNNs and Transfer Learning 使用cnn和迁移学习的深度假图像检测

2021 International Conference on Computing, Communication and Green Engineering (CCGE) Pub Date : 2021-09-23 DOI: 10.1109/CCGE50943.2021.9776410

Niteesh Kumar, Pranav P, Vishal Nirney, G. V.

{"title":"Deepfake Image Detection using CNNs and Transfer Learning","authors":"Niteesh Kumar, Pranav P, Vishal Nirney, G. V.","doi":"10.1109/CCGE50943.2021.9776410","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776410","url":null,"abstract":"Headways in deep learning has enabled the creation of fraudulent digital content with ease. This fraudulent digital content created is entirely indistinguishable from the original digital content. This close identicalness has what it takes to cause havoc. This fraudulent digital content, popularly known as deepfakes having the potential to change the truth and decay faith, can leave impressions on a large scale and even our daily lives. Deepfake is composed of two words, the first being deep: deep learning and the second being fake: fake digital content. Artificial intelligence forming the nucleus of any deepfake formulation technology empowers it to dodge most of the deepfake detection techniques through learning. This ability of deepfakes to learn and elude detection technologies is a matter of significant concern. In this research work, we focus on our efforts towards the detection of deepfake images. We follow two approaches for deepfake image detection, and the first is to build a custom CNN based deep learning network to detect deepfake images, and the second is to use the concept of transfer learning.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"113 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133351012","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

A Progressive Web App for Virtual Campus Tour 一个先进的网络应用程序的虚拟校园参观

2021 International Conference on Computing, Communication and Green Engineering (CCGE) Pub Date : 2021-09-23 DOI: 10.1109/CCGE50943.2021.9776419

Harsh D Shah, Vinayak Tupe, Amit Rathod, Sohel Shaikh, Nilesh J. Uke

{"title":"A Progressive Web App for Virtual Campus Tour","authors":"Harsh D Shah, Vinayak Tupe, Amit Rathod, Sohel Shaikh, Nilesh J. Uke","doi":"10.1109/CCGE50943.2021.9776419","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776419","url":null,"abstract":"Virtual Tour can be created stored in some MB's or GB's and can be accessed by a user from any corner of the world having a strong internet connection. Many colleges have represented their campus in digital format so many student's can have an idea how college campus looks. The way of representing virtual tour of most of the colleges are the same using a 360-degree virtual tour which are the 2D images stitched together to form a long continuous image. But our virtual has real objects that are represented in a 3D Gaming environment. We have combined the idea of 3D Gaming and 360-degree images to create an actual campus environment where user can move around. We have used First Person Perspective Approach which results in when the user controls it he feels that he is walking on a real college campus. For this, we have developed our 3D Model using as popular open-source modelling tool Blender2.8. And for giving a taste of gaming we are exporting our model into the web using the Babylon.js library which is new in the market but provides all assets to develop a 3D game. So are represent our virtual in a unique way where a user has all control and can roam inside the college campus smoothly.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134233580","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

Irrigation to Smart Irrigation and Tube Well Users 灌溉到智能灌溉和管井用户

2021 International Conference on Computing, Communication and Green Engineering (CCGE) Pub Date : 2021-09-23 DOI: 10.1109/CCGE50943.2021.9776479

Swati V. Patel, Satyen Parikh, Savan H. Patel

引用次数: 1

Self-Mining Blockchain Mobile Unified Payment Interface 自挖掘区块链移动统一支付接口

2021 International Conference on Computing, Communication and Green Engineering (CCGE) Pub Date : 2021-09-23 DOI: 10.1109/CCGE50943.2021.9776418

Kuldeep Hule, Arjun Dashrath, Ashwin Gupta

引用次数: 2

Applying SMOTE with Decision Tree Classifier for Campus Placement Prediction 基于决策树分类器的SMOTE校园布局预测

2021 International Conference on Computing, Communication and Green Engineering (CCGE) Pub Date : 2021-09-23 DOI: 10.1109/CCGE50943.2021.9776360

Vikas Rattan, Shikha Sharma, R. Mittal, Varun Malik

引用次数: 2

Modeling the Prediction of Continued Usage of COVID-19 mhealth App in India 对印度COVID-19移动医疗应用程序持续使用情况的建模预测

2021 International Conference on Computing, Communication and Green Engineering (CCGE) Pub Date : 2021-09-23 DOI: 10.1109/CCGE50943.2021.9776421

R. Mittal, A. Mittal, Arun Aggarwal

引用次数: 1