2022 2nd International Conference on Intelligent Technologies (CONIT)最新文献_第9页

A Novel Deep Learning Algorithm for Covid Detection and Classification 一种新型的Covid检测与分类深度学习算法

2022 2nd International Conference on Intelligent Technologies (CONIT) Pub Date : 2022-06-24 DOI: 10.1109/CONIT55038.2022.9847880

S. Selvi, Nikhil Agarwal, Paarth Barkur, Yash Mishra, Abhsihek Kumar

{"title":"A Novel Deep Learning Algorithm for Covid Detection and Classification","authors":"S. Selvi, Nikhil Agarwal, Paarth Barkur, Yash Mishra, Abhsihek Kumar","doi":"10.1109/CONIT55038.2022.9847880","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847880","url":null,"abstract":"The prediction of future development of a natural phenomenon is one of the main objectives of recent technology, but this is a great challenge when dealing with an epidemic or pandemic. This proved to be particularly true in the case of Covid-19 global pandemic that the world is suffering and facing since January 2020. The response to the virus infection are partially known, however the immune system is mostly affected especially in patients with pre-existing respiratory or systemic diseases. Most infections by coronavirus are mild and self-treated. Therefore, in early stages of the disease, it will be misleading to estimate the real spread of the virus based on the reports of hospital. Moreover, such reports vary according to how measurements are performed, and the number of tests related only to the number of symptomatic patients. Despite all this, the large amount of official data published in last months, and updated daily has motivated various mathematical models, which are required to predict the evolution of an epidemic and plan effective control strategies. Due to the incompleteness of the data and intrinsic complexity, predicting the evolution, the peak or the end of the pandemic is a challenge. In this paper, a deep learning based approach is proposed aiming to evaluate a-priori risk of an epidemic caused by Covid-19. The proposed algorithm leverages image processing and deep learning algorithms to detect Covid and differentiate between normal, Covid affected, lung opacity and viral pneumonia affected chest x-rays. This results in setting strategies to prevent or decrease the impact of future epidemic waves. The accuracy for the proposed algorithm is 95.01% and Recall is 98.5% on validation data. The inference is that combining image processing with deep learning can improve performance of Covid detection.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122147999","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 Reliable Software Defifined Networking based Framework for IoT Devices 基于物联网设备的可靠软件定义网络框架

2022 2nd International Conference on Intelligent Technologies (CONIT) Pub Date : 2022-06-24 DOI: 10.1109/CONIT55038.2022.9848104

S. Anand, Neha Manjunath

{"title":"A Reliable Software Defifined Networking based Framework for IoT Devices","authors":"S. Anand, Neha Manjunath","doi":"10.1109/CONIT55038.2022.9848104","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848104","url":null,"abstract":"With the help of IoT (Internet of Things) devices, the world is becoming more connected. To accomplish this, a vast amount of data must be safely stored and accessed, yet IoT devices have limited memory and computing time. As a result, a huge storage room with secure space for storage is required. SDN (Software-Defined Networking) is a revolutionary network technology that incorporates a new paradigm of unsecured apps and Internet-of- Things (IoT) services. Enemies hoping to upset the activity of an IoT framework can use the malevolent bundle change assault (MP A), a basic however powerful assault that has recently been found in loT in light of remote sensor organizations. We offer a strategy for securing and dependably conveying information within the sight of dynamic aggressors to oppose MP As that takes advantage of SDN's programmability and flexibility. Our method ensures that loT devices are aware of any changes. The suggested solution's effectiveness and performance were assessed in a series of extensive tests using a prototype implementation. The findings show that even if malicious forwarding devices only modify a small percentage of the data, they may be reliably and promptly identified and circumvented. We examined the exhibition of our proposed framework utilizing OMNeT++ to recreate our whole situation and affirmed that the framework is secure and dependable in loT applications.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"17 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126965318","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

Short Term Load Forecasting using Machine Learning Techniques 利用机器学习技术进行短期负荷预测

2022 2nd International Conference on Intelligent Technologies (CONIT) Pub Date : 2022-06-24 DOI: 10.1109/CONIT55038.2022.9848160

Sonakshi Dua, Shaurya Gautam, Mahi Garg, Rajendra Mahla, Mrityunjay Chaudhary, S. Vadhera

引用次数: 1

Analysis of Software Bug Prediction and Tracing Models from a Statistical Perspective Using Machine Learning 用机器学习从统计角度分析软件Bug预测和跟踪模型

2022 2nd International Conference on Intelligent Technologies (CONIT) Pub Date : 2022-06-24 DOI: 10.1109/CONIT55038.2022.9848385

Darshana N. Tambe, L. Ragha

{"title":"Analysis of Software Bug Prediction and Tracing Models from a Statistical Perspective Using Machine Learning","authors":"Darshana N. Tambe, L. Ragha","doi":"10.1109/CONIT55038.2022.9848385","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848385","url":null,"abstract":"Software is the heart of over 99% of all modern-day devices which include smartphones, personal computers, internet of things (IoT) networks, etc. This software is built by a team of engineers which divide the final product into multiple smaller components and these components are integrated together to build the final software, due to which inherent interfacing vulnerabilities & bugs are injected into it. Multiple bugs are also injected into the system due to inexperience or mistakes made by software engineers & programmers. To identify these mistakes, a wide variety of bug prediction & tracing models are proposed by researchers, which assist programmers to predict & track these bugs. But these models have large variations in terms of accuracy, precision, recall, delay, computational complexity, cost of deployment and other performance metrics, due to which it is ambiguous for software designers to identify best bug tracing method(s) for their application deployments. To reduce this ambiguity, a discussion about design of different bug tracing & prediction models and their statistical comparison is done in this paper. This comparison includes evaluation of accuracy, precision, recall, computational complexity and scalability under different scenarios. Based on this comparison, in this paper experiments were performed on five publically available datasets from NASA MDP repository using different algorithms i.e. DRF, LSVM, LR, RF, and kNN. From the results it was observed that kNN algorithm outperforms average 98.8% accuracy on these five datasets and hence kNN were considered to be the most significant with its selected features. In the future, this performance can be improved via use of CNN & LSTM based models, which can utilize the base kNN layer, and estimate highly dense features for efficient classification performance.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"359 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114079192","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

Study of Object Detection with Faster RCNN 基于快速RCNN的目标检测研究

2022 2nd International Conference on Intelligent Technologies (CONIT) Pub Date : 2022-06-24 DOI: 10.1109/CONIT55038.2022.9847725

S. Bhatlawande, S. Shilaskar, Mohit Agrawal, Varad Ashtekar, Mahesh Badade, Shwetambari Belote, Jyoti Madake

引用次数: 0

Comparison of Variants of Yen's Algorithm for Finding K-Simple Shortest Paths 寻找k -简单最短路径的Yen算法的变种比较

2022 2nd International Conference on Intelligent Technologies (CONIT) Pub Date : 2022-06-24 DOI: 10.1109/CONIT55038.2022.9847738

P. B. Niranjane, S. Amdani

引用次数: 3

Brain Tumor Detection Application Based On Convolutional Neural Network 基于卷积神经网络的脑肿瘤检测应用

2022 2nd International Conference on Intelligent Technologies (CONIT) Pub Date : 2022-06-24 DOI: 10.1109/CONIT55038.2022.9848177

Suman Pokhrel, Laxmi Kanta Dahal, N. Gupta, Rijesh Shrestha, Anshul Srivastava, Akash Bhasney

{"title":"Brain Tumor Detection Application Based On Convolutional Neural Network","authors":"Suman Pokhrel, Laxmi Kanta Dahal, N. Gupta, Rijesh Shrestha, Anshul Srivastava, Akash Bhasney","doi":"10.1109/CONIT55038.2022.9848177","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848177","url":null,"abstract":"A brain tumor is a collection, or mass, of abnormal cells in your brain. Your skull, which encloses your brain, is very rigid. Any growth inside such a restricted space can cause problems. Magnetic resonance imaging (MRI) is a non-invasive method for producing three-dimensional (3D) tomographic images of the human body. MRI is most often used for the detection of tumors, lesions, and other abnormalities in soft tissues, such as the brain. Clinically, radiologists qualitatively analyze films produced by MRI scanners. Brain Tumor segmentation is one of the most crucial and arduous tasks in the terrain of medical image processing as a human-assisted manual classification can result in inaccurate prediction and diagnosis. Moreover, it is an aggravating task when there is a large amount of data present to be assisted. Brain tumors have high diversity in appearance and there is a similarity between tumor and normal tissues and thus the extraction of tumor regions from images becomes unyielding. We implemented various state of the art Neural Networks like MobileN etV2, MobileNetV3 small, MobileNetV3 large, VGG16, VGG19 and our Custom CNN model. Among these models CNN was able to get the Highest amount of accuracy. Our proposed method consists of a Convolutional Neural Network (CNN) (which is implemented using Keras and Tensor flow) that is integrated to a full featured cross-platform desktop application(which is implemented using PyQt5 and MariaDB) that can be easily used in hospitals as well as local clinics. The main aim of this project is to distinguish between normal and abnormal pixels, and classify a tumor affected brain using real-world datasets.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"242 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114145394","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

Comparative Study and Review on Successive Approximation/Stochastic Approximation Analog to Digital Converters for Biomedical Applications 生物医学应用中连续逼近/随机逼近模数转换器的比较研究与综述

2022 2nd International Conference on Intelligent Technologies (CONIT) Pub Date : 2022-06-24 DOI: 10.1109/CONIT55038.2022.9847947

G. Snehalatha, J. Selvakumar, Esther Rani Thuraka

引用次数: 0

A Comparative Study on Change-Point Detection Methods in Time Series Data 时间序列数据变化点检测方法的比较研究

2022 2nd International Conference on Intelligent Technologies (CONIT) Pub Date : 2022-06-24 DOI: 10.1109/CONIT55038.2022.9848051

Aditya Pushkar, Muktesh Gupta, Rajesh Wadhvani, Manasi Gyanchandani

引用次数: 2

MPPT Algorithms with LCL Filter for Grid Connected PV System 并网光伏系统的LCL滤波MPPT算法

2022 2nd International Conference on Intelligent Technologies (CONIT) Pub Date : 2022-06-24 DOI: 10.1109/CONIT55038.2022.9847960

Shivam Dutt Jha, Siddharth, Siddharth Chowdhary, Kuldeep Singh

引用次数: 0