2022 2nd International Conference on Intelligent Technologies (CONIT)最新文献_第7页

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

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

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

Analysis of the Parallel & Standalone Operation of PVES and BESS for Microgrid Applications with Varying Climatic Condition 不同气候条件下微电网PVES与BESS并网与单机运行分析

2022 2nd International Conference on Intelligent Technologies (CONIT) Pub Date : 2022-06-24 DOI: 10.1109/CONIT55038.2022.9847816

Nirzari Vora, Siddharth Joshi, Darshan Patel

{"title":"Analysis of the Parallel & Standalone Operation of PVES and BESS for Microgrid Applications with Varying Climatic Condition","authors":"Nirzari Vora, Siddharth Joshi, Darshan Patel","doi":"10.1109/CONIT55038.2022.9847816","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847816","url":null,"abstract":"In today's time, fuel price and shortage of conventional sources like coal are the biggest concern worldwide. Henceforth, world is moving towards adapting green energy i.e. renewable energy for the production of the electricity. Renewable sources are available in nature. One can harness in the forms of the solar energy, the wind energy, the tidal energy, the biomass energy, the geothermal energy etc. These sources are environment friendly and clean to use to produce electricity. One issue which has to be addressed while using these sources is that they are weather and location dependent. So reliability on these sources alone is less which leads to combining other source, be it conventional or other renewable sources. This combination of two or more sources to generate power is called hybrid system and in this paper, we are considering PVES (Photo-Voltaic Energy System) as main source and BESS (Battery Energy Storage System) for storage purpose. The simulations studies and analysis for the parallel & standalone Operation of PVES and BESS is performed and proposed in this paper. The system is used for the DC microgrid applications. The MATLAB simulation analysis is done by varying climatic conditions i.e. change in insolation and change in temperature.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"49 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":"117054203","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

Research on the Effects of in-Vehicle Human-Machine Interface on Drivers' Pre and Post Takeover Request Eye-tracking Characteristics 车载人机界面对驾驶员接管请求前后眼动特征的影响研究

2022 2nd International Conference on Intelligent Technologies (CONIT) Pub Date : 2022-06-24 DOI: 10.1109/CONIT55038.2022.9848040

Weimin Liu, Qingkun Li, Zhenyuan Wang, Wenjun Wang, Chao Zeng, Bo Cheng

{"title":"Research on the Effects of in-Vehicle Human-Machine Interface on Drivers' Pre and Post Takeover Request Eye-tracking Characteristics","authors":"Weimin Liu, Qingkun Li, Zhenyuan Wang, Wenjun Wang, Chao Zeng, Bo Cheng","doi":"10.1109/CONIT55038.2022.9848040","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848040","url":null,"abstract":"In-vehicle Human-Machine Interface (HMI) plays a significant role for conditionally automated vehicles in realizing effective communications from driving automation systems to drivers during either automated driving period or control transitions. The present study aimed to investigate the effects of in-vehicle HMI on drivers' eye-tracking characteristics pre and post takeover request (TOR). A driving simulator-based experiment was conducted comparing the differences of drivers' visual behaviors with or without HMI under two TB (time budget) conditions (TB = 4 s; TB = 10 s). The visual HMI adopted in the experiments consisted of vehicle status display and a bird-view depiction of the traffic situation. Experiment results showed fixations prior to the TOR were more frequently shifted from real traffic situation to HMI which was effective in indirectly maintaining drivers' mode and situation awareness. Pre TOR entropy measures indicated a more dispersed but still ordered scanning pattern in spatial sampling. Saccadic behaviors were shown to be encouraged for a less cognitively demanded but a more visually loaded acquisition of surrounding information with the assistance of HMI. Post TOR fixation measure showed a prolonged Eyes-on-Traffic-Time (EoTT) when HMI was provided. And as a physiological indicator for mental workload, blink rate and blink latency did not show an additional increase after the issue of TOR under “with HMI” condition. We conclude that the introduction of in-vehicle visual HMI can be a valid option to support drivers in both automated driving and takeover time.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"421 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":"116169778","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 Model for Optimal Assignment of Non-Uniquely Mapped NGS Reads in DNA Regions of Duplications or Deletions DNA重复或缺失区域非唯一定位NGS读段的优化分配模型

2022 2nd International Conference on Intelligent Technologies (CONIT) Pub Date : 2022-06-24 DOI: 10.1109/CONIT55038.2022.9848131

Rituparna Sinha, Rajat K. Pal, R. K. De

引用次数: 0

Prediction of Happiness Score of Countries by Considering Maximum Infection Rate of People by COVID-19 using Random Forest Algorithm 考虑COVID-19最大人群感染率的国家幸福指数随机森林算法预测

2022 2nd International Conference on Intelligent Technologies (CONIT) Pub Date : 2022-06-24 DOI: 10.1109/CONIT55038.2022.9847791

Ashish Kumar, Sudhanshu K. Mishra, Ayush Kejriwal

引用次数: 0

Optimizing Deep Neural Network using Enhanced Artificial Bee Colony Algorithm for an Efficient Intrusion Detection System 基于增强人工蜂群算法优化深度神经网络的高效入侵检测系统

2022 2nd International Conference on Intelligent Technologies (CONIT) Pub Date : 2022-06-24 DOI: 10.1109/CONIT55038.2022.9848014

Mukul Soni, Mayank Singhal, Jatin, R. Katarya

引用次数: 0

Prediction of Bipolar Disorder Using Machine Learning Techniques 使用机器学习技术预测双相情感障碍

2022 2nd International Conference on Intelligent Technologies (CONIT) Pub Date : 2022-06-24 DOI: 10.1109/CONIT55038.2022.9848137

Disha D N, S. S., Sharada U. Shenoy, Sudesh Rao

引用次数: 0

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