2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)最新文献_第6页

Design of IoT Based Indoor Air Purifier 基于物联网的室内空气净化器设计

2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917953

C. Kousalya, Udaya Mouni Boppana, Raja Kondapalli, Md. Tabil Ahammed, Carmela Gaile S. Gallardo, Priyadharshini Balaji, Maharin Afroj

引用次数: 3

Single Identity System for Identification papers based on Blockchain 基于区块链的身份证件单一身份系统

2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917828

Hariharasudan V, Suhail Javed Quraishi, Shahil Sinha

{"title":"Single Identity System for Identification papers based on Blockchain","authors":"Hariharasudan V, Suhail Javed Quraishi, Shahil Sinha","doi":"10.1109/ICICICT54557.2022.9917828","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917828","url":null,"abstract":"The fast rise of the internet has both advantages and problems. Almost everything has flaws, but they will be addressed or repaired in the long term. As a result, our personally identifiable information (PII) should be kept highly secure. Our identities are the most important things that offer us with an identity as well as certain rewards. We have been asked to give proof of our identification. Any of these is acceptable, including buying a sim card. Will occasionally receive unsolicited calls from people with advertisements or credit card offers. Have you ever considered how these folks obtained their information? We disclose every piece of information about ourselves. There are certain tried-and-true strategies for protecting our identity. However, present solutions are not completely reliable in terms of preventing data tampering. Here comes blockchain, which will play a significant role in data security and guaranteeing that data is not tampered with. In this proposal, a blockchain-based identity management system with a validator that ensures our identity is confirmed without revealing any of our personally identifiable information.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126837985","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

Detection of Brain Tumor & Intra-Tumoral Structures from the Brain MR Images 从脑磁共振图像中检测脑肿瘤及肿瘤内结构

2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917845

Mukesh M Goswami, B. Rao

引用次数: 0

Ensemble Learning Methods for Machine Fault Diagnosis 机械故障诊断的集成学习方法

2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917966

Joyal P Jose, T. Ananthan, N. Prakash

引用次数: 0

An optimal Kernelized Fuzzy C-Means for Automated Segmentation of Breast MRIs 一种用于乳腺mri自动分割的最优核化模糊c均值

2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917876

Sathya Arumugam

{"title":"An optimal Kernelized Fuzzy C-Means for Automated Segmentation of Breast MRIs","authors":"Sathya Arumugam","doi":"10.1109/ICICICT54557.2022.9917876","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917876","url":null,"abstract":"Medical image segmentation is an indispensable process in screening and determining various structures in the breast Magnetic Resonance Images. Although Fuzzy C-Means method has proven to have high capacity of segmenting the medical images, it yet faces some challenges such as noise sensitivity, computational complexity, and etc. Moreover, random initialization of cluster centers can let the clustering process easily fall onto the local minimum, leading to accuracy degradation in image segmentation. To mitigate the above issues, this paper introduces an optimal Fuzzy C-Means method based on minimal spanning tree. The proposed method adopts a robust initialization which automatically decides the number of clusters and initial cluster centers from the given dataset. This improves the segmentation performance significantly. In addition, by deciding the window size of pixel neighbor and the weights of neighbor memberships, the proposed approach adaptively incorporates spatial information to the clustering process and increases the algorithm robustness to noise pixels. To estimate the performance of the proposed method, experimental work is executed on synthetic image, and real breast MRIs. The proposed method is validated by comparing the results with that of the existing methods in the various cluster validity functions.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126847912","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 Pilot Investigation on the Performance of Auditory Stimuli based on EEG Signals Classification for BCI Applications 基于脑电信号分类的脑机接口听觉刺激性能初步研究

2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917870

E. G. Kanaga, M. R. Thanka, J. Anitha, Jeslin Lois. S

{"title":"A Pilot Investigation on the Performance of Auditory Stimuli based on EEG Signals Classification for BCI Applications","authors":"E. G. Kanaga, M. R. Thanka, J. Anitha, Jeslin Lois. S","doi":"10.1109/ICICICT54557.2022.9917870","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917870","url":null,"abstract":"Brain Computer Interface (BCI) is a communication pathway between the external devices and the brain signals that doesn’t require any physical activity of the muscular system. Such systems are the only mode of communication for people affected by a number of motor disabilities. In some medical conditions, the person is conscious and awake, but all of his voluntary muscles are paralyzed. Some patients retain vertical eye movement and partially recover from the muscular paralysis. For such patients, there are numerous communication devices available in the market. But for patients who are affected completely, that means the eyes, as well as the muscular activity, is completely paralyzed, hearing is the only mode for them to communicate. In this paper, various auditory stimuli are explored that can be used in BCI applications. In order to create an auditory simulation, comforting sounds to the users such as music and natural sounds are used. This work uses 6 different sounds as auditory stimuli and the brain signals are recorded using an electroencephalogram. The auditory signals are further classified with various classification algorithms such as multi-layer perceptron, random forest, and decision trees. The performance has been analyzed in terms of accuracy, precision, and recall. The average accuracy of 91.56% has been obtained for the random forest, 86.78% for decision trees and 89.92% for multi-layer perceptron. Random forest shows the best classification accuracy when compared to other two classifiers while classifying auditory stimuli-based EEG signals.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"60 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114125138","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

Effect of U-Net Hyperparameter Optimisation in Polyp Segmentation from Colonoscopy Images U-Net超参数优化在结肠镜图像息肉分割中的效果

2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917700

R. Karthikha, D. Najumnissa, S. Syed Rafiammal

引用次数: 0

Security Threats & Attacks in IoV Environment: Open Research Issues and Challenges 车联网环境中的安全威胁与攻击:开放研究问题与挑战

2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917816

Abhay Garg, Aditya Chauhan, P. G. Shambharkar

引用次数: 1

A Hybrid Approach for predicting COVID19 using Multiple Convolution Neural Networks and Self Attention Maps 基于多重卷积神经网络和自注意图的covid - 19混合预测方法

2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917910

Bhargava Satya Nunna, S. Kompella, Suresh Chittineni, Srinivas Gorla

{"title":"A Hybrid Approach for predicting COVID19 using Multiple Convolution Neural Networks and Self Attention Maps","authors":"Bhargava Satya Nunna, S. Kompella, Suresh Chittineni, Srinivas Gorla","doi":"10.1109/ICICICT54557.2022.9917910","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917910","url":null,"abstract":"Covid-19, the most infectious ailment effected due to severe acute respiratory syndrome, which has hindered the health of the people worldwide by causing severe respiratory problems and also lead to extent of death. This infectious syndrome needs to be monitored and detected at right time to prevent the growth of Covid-19 pandemic so as to cure the disease through an accurate diagnosis and proper medication. To address this current issue, a Convolution neural network model (CNN) integrated to self-attention has been proposed. The convolution operator is limited to local receptive field being the disadvantage of CNN. So, we have incorporated self attention mechanism between image representations at deep layers so that the model could learn both local and long range dependencies of the image. Therefore, efficacy of the proposed model has been illustrated through the experimental results and had proven to be progressive in detecting Covid-19 infection by equipping the self-attention module to CNN architecture.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116697336","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

ANN and Deep Learning Classifiers for BCI applications 脑机接口应用的人工神经网络和深度学习分类器

2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917834

K. S. Aswin, Manav Purushothaman, Polisetty Sritharani, Angel T. S

引用次数: 0