2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)最新文献_第2页

Vehicular Ad Hoc Network performance enhancement using VOA based Road Side Unit Deployment 使用基于VOA的道路侧单元部署增强车辆自组织网络性能

2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917926

Ayushi Sharma, Kavita Pandey

引用次数: 0

Image Encryption Using Homomorphic Transform 基于同态变换的图像加密

2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917824

Ankit Vishnoi, A. Aggarwal, Ajay Prasad, M. Prateek, S. Aggarwal

引用次数: 3

Analysis of the Effect of Black Box Adversarial Attacks on Medical Image Classification Models 黑箱对抗性攻击对医学图像分类模型的影响分析

2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917603

P. S, A. V, Sreeratcha B, Preeti Krishnaveni Ra, Snofy D. Dunston, M. Rajam V.

{"title":"Analysis of the Effect of Black Box Adversarial Attacks on Medical Image Classification Models","authors":"P. S, A. V, Sreeratcha B, Preeti Krishnaveni Ra, Snofy D. Dunston, M. Rajam V.","doi":"10.1109/ICICICT54557.2022.9917603","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917603","url":null,"abstract":"In the field of medical science, the reliability of the results produced by deep learning classifiers on disease diagnosis plays a crucial role. The reliability of the classifier substantially reduces by the presence of adversarial examples. The adversarial examples mislead the classifiers to give wrong prediction with equal or more confidence than the actual prediction. The adversarial attacks in the black box type is done by creating a pseudo model that resembles the target model. From the pseudo model, the attack is created and is transferred to the target model. In this work, the Fast Gradient Sign Method and its variants Momentum Iterative Fast Gradient Sign Method, Projected Gradient Descent and Basic Iterative Method are used to create adversarial examples on a target VGG-16 model. The datasets used are Diabetic Retinopathy 2015 Data Colored Resized and SARS-CoV-2 CT Scan Dataset. The experimentation revealed that the transferability of attack is true for the above described attack methods on a VGG-16 model. Also, the Projected Gradient Descent attack provides a higher success in attack in comparison with the other methods experimented in this work.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"44 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":"130581106","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: 5

Standalone solar photovoltaic system for indigenous people of Vellagevi village, Kodaikanal Kodaikanal Vellagevi村土著居民的独立太阳能光伏系统

2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917590

J. Vishnupriyan, P. Partheeban, A. Dhanasekaran, M. Shiva

引用次数: 0

UNet with Two-Fold Training for Effective Segmentation of Lung Section in Chest X-Ray 基于双重训练的UNet有效分割胸部x线肺切片

2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917585

V. Rajinikanth, Seifedine Kadry, R. Damaševičius, J. Gnanasoundharam, Mazin Abed Mohammed, G. Glan Devadhas

{"title":"UNet with Two-Fold Training for Effective Segmentation of Lung Section in Chest X-Ray","authors":"V. Rajinikanth, Seifedine Kadry, R. Damaševičius, J. Gnanasoundharam, Mazin Abed Mohammed, G. Glan Devadhas","doi":"10.1109/ICICICT54557.2022.9917585","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917585","url":null,"abstract":"Segmentation and evaluation of the Region of Interest (ROI) in medical imaging is a prime task for disease screening and decision-making. Due to accuracy, Convolutional-Neural-Network (CNN) based ROI segmentation has been widely employed in recent years to evaluate a class of medical images recorded using chosen modality. The proposed work aims to demonstrate the segmentation performance of the UNet scheme with a one-fold and two-fold training process. To experimentally verify the merit of the proposed scheme, segmentation of the lung section from the chest X-ray is studied. This research includes the following parts; (i) Resizing the test image and image mask to pixels, (ii) Training the UNet with one-fold and two-fold approaches, (iii) Extracting the ROI, (iv) Comparing the ROI with the mask to compute the image metrics and (v) Validating and confirming the segmentation performance of UNet. The performance of UNet is then verified with UNet+ and UNet++. The investigational ending substantiates that the proposed approach helps to get better Jaccard (>95%), Dice ((>97%), and Accuracy (>98%) in two-fold training compared to other methods considered in this study.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"132 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":"121400633","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: 6

Prediction Of Solar Power Generation Based On Machine Learning Algorithm 基于机器学习算法的太阳能发电预测

2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917846

Rinshy Annie Varughese, Dr. R. Karpagam

{"title":"Prediction Of Solar Power Generation Based On Machine Learning Algorithm","authors":"Rinshy Annie Varughese, Dr. R. Karpagam","doi":"10.1109/ICICICT54557.2022.9917846","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917846","url":null,"abstract":"Energy demand is growing and by 2050 solar energy will account for 11% total electricity production. It has emerged as one of the most potential sources of alternative energy Even though the usage of solar energy in residential places has increased, yet they are regarded as unpredictable and irregular power sources because the generated power output depends on the geographical region, atmospheric conditions, which can vary drastically. Depending upon the weather conditions solar panels will work differently. Since the power generation mostly depends on weather conditions it is necessary to consider weather conditions. Because of the unpredictability in photovoltaic generations, it is crucial to examine the effects of environmental circumstances on solar power system using machine learning based approach. The machine learning algorithm shows great results in anticipating the power with weather conditions as input models. The approach uses different databases, input, and mathematical relationships to predict the solar power generated. Various machine learning algorithm would be applied to get the patterns and to obtain the results with maximum accuracy and efficiency. This study demonstrates how a variety of machine learning techniques may be used to predict the amount of energy a solar panel provides. Various models were applied to the database and the most appropriate machine learning predictive model was identified through coefficient of determination analysis. The results obtained after comparing the data for different years are furnished. Temperature, relative humidity, pressure, and wind speed are the independent factors, with power generated as the dependent variable. The proposed model has provided prediction results with good accuracy.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"156 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":"121478812","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

Analysis of Neural Networks for Object Detection using Image Processing Techniques 利用图像处理技术进行目标检测的神经网络分析

2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917833

R. Praveena, T. Babu, K. Sakthimurugan, G. Sudha, M. Birunda, J. Surendiran

引用次数: 2

Analysis of Total Harmonic Distortion of Three-Phase Induction Motor Drive for Different PWM Techniques 不同PWM技术下三相感应电动机驱动的总谐波畸变分析

2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917623

Bashir N. Jamadar, Balasaheb B. Vhankhande, D. S. Sutrave

引用次数: 0

Optimal Sizing and Operation of Solar PV powered EV Battery Swapping Station for Indian Petroleum Retail Outlet 印度石油零售网点太阳能光伏电动汽车电池交换站的优化规模与运行

2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917711

Vigneshkumar B, Anu G. Kumar

{"title":"Optimal Sizing and Operation of Solar PV powered EV Battery Swapping Station for Indian Petroleum Retail Outlet","authors":"Vigneshkumar B, Anu G. Kumar","doi":"10.1109/ICICICT54557.2022.9917711","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917711","url":null,"abstract":"India has enormous potential for implementing solar PV and Electric vehicles (EVs); however, the stochastic nature of PV and EV complicates their integration into the distribution system. Moreover, EVs presently have significant drawbacks, such as long charging time and a limited range. This paper presents a case study on developing a cost-effective model for implementing EV charger-installed PV-powered Petroleum Retail Outlets (PRO) in India. The case study considers the design and operational aspects of five evolutionary stages of PV-based Battery Swapping Station (BSS) deployment in PRO. The optimal charging methodology for the PV-based BSS under Real-time pricing (RTP) for demand and ToD based Feed-in Tariff (ToD-FIT) for solar feed-in is obtained using a COIN-OR-based Linear Programming algorithm. The optimized solar PV capacity payback period and LCOE are computed for different cases. The study result shows that the proposed model benefits all stakeholders from a capital expenditure and operational expenditure perspective. It is replicable across India as it gives stakeholders an optimal techno-economic model to implement PV-based BSS at PROs.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"10 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":"127730193","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

Modelling an Adaptive Clustering Model for the Mining Community Using Learning Approaches 基于学习方法的采矿社区自适应聚类模型建模

2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917762

Prathima. Y, B. Murugan

{"title":"Modelling an Adaptive Clustering Model for the Mining Community Using Learning Approaches","authors":"Prathima. Y, B. Murugan","doi":"10.1109/ICICICT54557.2022.9917762","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917762","url":null,"abstract":"Data clustering is a crucial phase in data analysis, widely concentrated by data mining communities. Many previous algorithms based on data clustering are related to the endless models that look for sparsity and higher dimensional issues and try to avoid the sequence of information and the data structural data. The recurrent and convolutional neural networks work on deep learning-based models concerning the data as sequences. Yet, the explanation of outcomes and the supervised signals are lacking. The adaptive data clustering model (ADCM) technique is proposed in this system to incorporate the pre-trained data encoders into data clustering tasks. This model depends on the representation of a sequence that breaks the dependencies on the supervision. The proposed system provides experimental outcomes that perform better than the traditional data clustering algorithm and the modern data model, pre-trained on the complete datasets. Additionally, the clustering result explains the significant understanding of the deep learning technique principles. The clustering approach proposes the description model that assists the users in understanding the quality and meaning of the outcome of the clustering process.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"47 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":"121968557","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