2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)最新文献_第5页

A multinomial technique for detecting fake news using the Naive Bayes Classifier 一种使用朴素贝叶斯分类器检测假新闻的多项技术

2021 International Conference on Computational Intelligence and Computing Applications (ICCICA) Pub Date : 2021-11-26 DOI: 10.1109/iccica52458.2021.9697244

Ashwini S. Yerlekar, N. Mungale, Sampada S. Wazalwar

引用次数: 3

Lightweight Blockchain Secured Framework for Smart Precise Farming System 智能精准农业系统的轻量级区块链安全框架

2021 International Conference on Computational Intelligence and Computing Applications (ICCICA) Pub Date : 2021-11-26 DOI: 10.1109/iccica52458.2021.9697241

Jayant P. Mehare, M. Bartere

{"title":"Lightweight Blockchain Secured Framework for Smart Precise Farming System","authors":"Jayant P. Mehare, M. Bartere","doi":"10.1109/iccica52458.2021.9697241","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697241","url":null,"abstract":"Smart farming is a technology framework that links the many components of agriculture to generate new possibilities. This link is made possible by the Internet of Things (IoT), which gives physical items a digital identity. Some research have suggested combining Blockchain technology with Internet of Thing in various case studies such as orchestration, access, and replicated storage layer. Owing to its redundancies and conventional consensus that requires a lot of processing. Blockchain is insufficient for the majority of linked items due to capacity limitations. This article tackles these issues by providing a new lightweight Blockchain architecture with a lightweight consensus and structure. According to their processing and storage capabilities, the proposed architecture of framework displays a linked object role hierarchy. We presented a security framework that blends the blockchain technology with IoT devices to provide a secure communication platform in Smart Precise Farming System and to take use of the capabilities of high-capacity objects like Edge and Fog computing nodes.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"488 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122728600","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

A Study of Various Algorithms for Facial Expression Recognition: A Review 各种面部表情识别算法的研究综述

2021 International Conference on Computational Intelligence and Computing Applications (ICCICA) Pub Date : 2021-11-26 DOI: 10.1109/iccica52458.2021.9697318

Devasena G, V. V

引用次数: 2

GCL And ILM Layer Extraction From OCT Images For Glaucoma Detection 从OCT图像中提取GCL和ILM层用于青光眼检测

2021 International Conference on Computational Intelligence and Computing Applications (ICCICA) Pub Date : 2021-11-26 DOI: 10.1109/iccica52458.2021.9697135

Gangadevi C. Bedke, M. Jadhav, Pramodini A. Punde, Monali D. Rathod, Swapnil Dongaonkar

引用次数: 0

A Systematic Review of Blockchain-Based System: Transaction Throughput Latency and Challenges 基于区块链的系统综述:交易吞吐量延迟和挑战

2021 International Conference on Computational Intelligence and Computing Applications (ICCICA) Pub Date : 2021-11-26 DOI: 10.1109/iccica52458.2021.9697142

M. Gracy, B. Rebecca Jeyavadhanam

{"title":"A Systematic Review of Blockchain-Based System: Transaction Throughput Latency and Challenges","authors":"M. Gracy, B. Rebecca Jeyavadhanam","doi":"10.1109/iccica52458.2021.9697142","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697142","url":null,"abstract":"Blockchain is a relatively new technology that is already gaining traction in the academic community. A peer-topeer network structure underpins blockchain technology. It is made up of blocks, which are inventories that develop as new accounts are added to the system. Blockchain's stability is ensured by cryptographic technology. Although blockchain is rapidly developing and providing us with a secure and convenient service, it is not without its drawbacks. Bitcoin suffers from low throughput and high latency, both of which are detrimental to the scalability of the blockchain. The transaction rate of Bitcoin is 4.6 transactions per second. Visa conducts 1,700 transactions every second on average. Since the number of transactions on the blockchain is increasing every day, it's critical to address scalability problems until the system hits its limit. Privacy leakage and selfish mining are explored in detail, along with potential solutions. As a future direction, we envisage focusing more on improving the efficiency of throughput and latency with the suggested solutions of authentication, monitoring selection for an epoch, and mass mining using modified proof of work algorithm.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131316273","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

Recurrent Neural Networks Based Approach for Intrusion Detection System 基于递归神经网络的入侵检测系统

2021 International Conference on Computational Intelligence and Computing Applications (ICCICA) Pub Date : 2021-11-26 DOI: 10.1109/iccica52458.2021.9697281

Arjita Shrivastava, Yogadhar Pandey

引用次数: 0

Possibilities and Apprehensions in the Landscape of Artificial Intelligence in Education 人工智能在教育领域的可能性和忧虑

2021 International Conference on Computational Intelligence and Computing Applications (ICCICA) Pub Date : 2021-11-26 DOI: 10.1109/iccica52458.2021.9697272

Ashraf Alam

{"title":"Possibilities and Apprehensions in the Landscape of Artificial Intelligence in Education","authors":"Ashraf Alam","doi":"10.1109/iccica52458.2021.9697272","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697272","url":null,"abstract":"Artificial intelligence (AI) may be utilized outside of traditional computer settings and is also readily available in low-cost smart devices, making AI easily accessible to general population. Built-in capabilities for conducting complicated computer operations (edge computing), cloud-based services for collaboratively addressing difficult issues, access to huge quantities of open and closed data resources, and conciliatory accession for agile network connections are all available on these low-cost devices. Education is helped by AI in at least two ways: (1) the educational process – assistance and modifications to pedagogy and educator's routine function; and (2) the educational ambit and content – what kind of education is needed. Author, in this article, explores the challenges and potentialities that AI offers in the field of education. While the focus is on AI's participation, it may be difficult to differentiate it from other technological advancements, especially when it comes to work life. Author conclude that AI (and associated technological advancements) will substitute some professions (didactics will not be required), that other professions will transform impressively (didactic materials will need to be updated), and that a significant number of novel vocations will be created (new-fangled didactics must be constituted). In educational operations – the task itself – AI will be a reformer as well as a facilitator, altering the characteristics and division of labor.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134266429","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: 30

Question Answering System for low resource language using Transfer Learning 基于迁移学习的低资源语言问答系统

2021 International Conference on Computational Intelligence and Computing Applications (ICCICA) Pub Date : 2021-11-26 DOI: 10.1109/iccica52458.2021.9697268

Aarushi Phade, Y. Haribhakta

{"title":"Question Answering System for low resource language using Transfer Learning","authors":"Aarushi Phade, Y. Haribhakta","doi":"10.1109/iccica52458.2021.9697268","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697268","url":null,"abstract":"This paper proposes a Question Answering System for Marathi language using Transfer Learning. A well performing Question Answering system leverages the word embeddings used in the system. Producing word embeddings for a language from the scratch is a drawn-out task and requires tremendous dataset and huge computing resources. Utilizing word embeddings created from a limited dataset in NLP tasks prompts average per-formance. Instead utilizing word embeddings from pre-trained models saves a lot of time, and gives great performance, since these models have more learnable parameters and are trained on huge datasets. Our framework uses Multilingual BERT model as pre-trained source model having 110M parameters which leads to effective word representation. We have fine-tuned this BERT model for QAS with the assistance of a small, custom dataset similar to SQuAD, intended for this framework. The system uses Bert-score and F1-score as its evaluation methods. It achieves F1-score of 56.7% and Bert-score of 69.08%. The system being the first of its kind in Marathi language lays the groundwork for future research.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133483775","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

Identification and Revocation of Faulty Nodes from Deployment Area 部署区域故障节点的识别与撤销

2021 International Conference on Computational Intelligence and Computing Applications (ICCICA) Pub Date : 2021-11-26 DOI: 10.1109/iccica52458.2021.9697242

D. Prasad, A. Bindal, Afshan Hassan, K. Gupta, A. Garg

引用次数: 0

Melody generation using LSTM and BI-LSTM Network 使用LSTM和BI-LSTM网络生成旋律

2021 International Conference on Computational Intelligence and Computing Applications (ICCICA) Pub Date : 2021-11-26 DOI: 10.1109/iccica52458.2021.9697286

Satish Chikkamath, N. S. R

引用次数: 6