2021 2nd International Informatics and Software Engineering Conference (IISEC)最新文献_第4页

IoT Based Classification and Price Prediction of Organically and Inorganically Grown Vegetables and Fruits 基于物联网的有机与无机蔬菜水果分类与价格预测

2021 2nd International Informatics and Software Engineering Conference (IISEC) Pub Date : 2021-12-16 DOI: 10.1109/iisec54230.2021.9672350

W. Wijekoon, Lakshan W.M Wijewardana, S.L. Wattegedara, W. Kumara, Wellalage Sasini, P. Abeygunawardhana

引用次数: 1

DeepMSWeb: A Web-Based Decision Support System via Deep Learning for Automatic Detection of MS Lesions DeepMSWeb:基于深度学习的MS病变自动检测决策支持系统

2021 2nd International Informatics and Software Engineering Conference (IISEC) Pub Date : 2021-12-16 DOI: 10.1109/iisec54230.2021.9672360

M. Yildirim, E. Dandıl

{"title":"DeepMSWeb: A Web-Based Decision Support System via Deep Learning for Automatic Detection of MS Lesions","authors":"M. Yildirim, E. Dandıl","doi":"10.1109/iisec54230.2021.9672360","DOIUrl":"https://doi.org/10.1109/iisec54230.2021.9672360","url":null,"abstract":"Multiple Sclerosis (MS) is a common neurological disorder in recent years. The diagnosis process of the disease starts with the accurate and precise detection of lesions from MR images. In addition, important achievements are achieved with computer aided decision support systems, which are used as an auxiliary secondary tool in the detection of MS. In this study, we present a web-based decision support system (DeepMSWeb) developed via deep learning for the detection of MS lesions on a publicly-available dataset. Mask R-CNN architecture, one of the deep learning models, is used in the infrastructure of DeepMSWeb, and the developed web application has a flexible and user-friendly interface. In addition, experimental studies are carried out with DeepMSWeb on the dataset consisting of MR images for the detection of MS lesions, and the detection accuracy of the application is supported by similarity measurement metrics. Radiologists who have experienced DeepMsWeb are confirmed that DeepMSWeb can be used as a decision support system for the detection of MS lesions. In addition, it is evaluated that DeepMs Web can be used in different screen sizes, is easy to use and is a fast as decision support tool.","PeriodicalId":344273,"journal":{"name":"2021 2nd International Informatics and Software Engineering Conference (IISEC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130311805","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

Cognitive Ledger Project: Towards Building Personal Digital Twins Through Cognitive Blockchain 认知分类帐项目:通过认知区块链构建个人数字孪生

2021 2nd International Informatics and Software Engineering Conference (IISEC) Pub Date : 2021-12-16 DOI: 10.1109/IISEC54230.2021.9672433

Amir-Reza Asadi

引用次数: 1

Survey on Interface Usability Evaluation for Oil and Gas Critical Control Systems 油气关键控制系统界面可用性评估调查

2021 2nd International Informatics and Software Engineering Conference (IISEC) Pub Date : 2021-12-16 DOI: 10.1109/iisec54230.2021.9672449

Layth Nabeel Alrawi, Osama Ibraheem Ashour Ashour, Abdulrahman Zeain

引用次数: 0

A Deep Neural Network Model for Stock Investment Recommendation by Considering the Stock Market as a Time Graph 考虑股市为时间图的深度神经网络股票投资推荐模型

2021 2nd International Informatics and Software Engineering Conference (IISEC) Pub Date : 2021-12-16 DOI: 10.1109/iisec54230.2021.9672444

Mustafa Mert Keskin, Muhammed Yilmaz, A. Ozbayoglu

引用次数: 0

Analysis of Lack of Cohesion in Methods (LCOM): A Case Study 方法衔接缺失分析——以英语为例

2021 2nd International Informatics and Software Engineering Conference (IISEC) Pub Date : 2021-12-16 DOI: 10.1109/iisec54230.2021.9672419

Elif Nur Haner Kirğil, Tülin Erçelebİ Ayyildiz

引用次数: 0

An Improved Transfer Learning-Based Model for Malaria Detection using Blood Smear of Microscopic Cell Images 一种基于迁移学习的基于血液涂片显微细胞图像的疟疾检测改进模型

2021 2nd International Informatics and Software Engineering Conference (IISEC) Pub Date : 2021-12-16 DOI: 10.1109/iisec54230.2021.9672447

Muhammad Bilyaminu, A. Varol

{"title":"An Improved Transfer Learning-Based Model for Malaria Detection using Blood Smear of Microscopic Cell Images","authors":"Muhammad Bilyaminu, A. Varol","doi":"10.1109/iisec54230.2021.9672447","DOIUrl":"https://doi.org/10.1109/iisec54230.2021.9672447","url":null,"abstract":"Because of insufficient medical specialists in some parts of the African and Asian continents, malaria patients' mortality rates have increased over the years. Since the people of regions generally suffer from malaria diseases, computer-aided detection (CAD) technology is required to decrease the number of casualties and reduce the waiting time for consulting by a Malaria specialist. This study shows the potential of transfer learning, a method of Deep Learning (DL) to classify the smeared blood of microscopic malaria cell images to determine whether it is parasitized or uninfected. This classification of malaria cell images will enhance the workflow of health practitioners at the frontline, especially microscopists, and provides them with a valuable alternative for malaria detection based on microscopic cell images. Although many technological advancements and evaluation techniques for identifying the infection exist, a microscopist at regions with limited resources faces challenges in improving diagnostic accuracy. We compared and evaluated a type of pre-trained CNN models, such as ResNet-50 and our appended Resnet-50+KNN. The experiment shows that our new model has the excellent capability and can perform better on malarial microscopic cell image classification with a higher accuracy rate of 98%.","PeriodicalId":344273,"journal":{"name":"2021 2nd International Informatics and Software Engineering Conference (IISEC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132680902","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

Twitter Streaming Data Analytics for Disaster Alerts 灾难警报的Twitter流数据分析

2021 2nd International Informatics and Software Engineering Conference (IISEC) Pub Date : 2021-12-16 DOI: 10.1109/iisec54230.2021.9672370

Syed Attique Shah, S. Yahia, Keegan McBride, Akhtar Jamil, D. Draheim

{"title":"Twitter Streaming Data Analytics for Disaster Alerts","authors":"Syed Attique Shah, S. Yahia, Keegan McBride, Akhtar Jamil, D. Draheim","doi":"10.1109/iisec54230.2021.9672370","DOIUrl":"https://doi.org/10.1109/iisec54230.2021.9672370","url":null,"abstract":"In today's world, disasters, both natural and manmade, are becoming increasingly frequent, and new solutions are of a compelling need to provide and disseminate information about these disasters to the public and concerned authorities in an effective and efficient manner. One of the most frequently used ways for information dissemination today is through social media, and when it comes to real-time information, Twitter is often the channel of choice. Thus, this paper discusses how Big Data Analytics (BDA) can take advantage of information streaming from Twitter to generate alerts and provide information in real-time on ongoing disasters. The paper proposes TAGS (Twitter Alert Generation System), a novel solution for collecting and analyzing social media streaming data in realtime and subsequently issue warnings related to ongoing disasters using a combination of Hadoop and Spark frameworks. The paper tests and evaluates the proposed solution using Twitter data from the 2018 earthquake in Palu City, Sulawesi, Indonesia. The proposed architecture was able to issue alert messages on various disaster scenarios and identify critical information that can be utilized for further analysis. Moreover, the performance of the proposed solution is assessed with respect to processing time and throughput that shows reliable system efficiency.","PeriodicalId":344273,"journal":{"name":"2021 2nd International Informatics and Software Engineering Conference (IISEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134387971","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

Traffic Accidents Cause and Effect Analysis: A Case Study in Chattanooga 交通事故因果分析:以查塔努加市为例

2021 2nd International Informatics and Software Engineering Conference (IISEC) Pub Date : 2021-12-16 DOI: 10.1109/iisec54230.2021.9672363

M. Messiha, Laura-Anne Fox, Serkan Varol, Bandar Aldhuwayhi, E. Kaplanoglu

{"title":"Traffic Accidents Cause and Effect Analysis: A Case Study in Chattanooga","authors":"M. Messiha, Laura-Anne Fox, Serkan Varol, Bandar Aldhuwayhi, E. Kaplanoglu","doi":"10.1109/iisec54230.2021.9672363","DOIUrl":"https://doi.org/10.1109/iisec54230.2021.9672363","url":null,"abstract":"Motor vehicle crashes are a major cause of fatality in the United States. Chattanooga ranks 4th in the state of Tennessee for traffic accidents which has continued to increase in the past decade. Using a geo-spatial regression model, this project investigated the variables related to weather, property type, collision type, driver characteristics, and spatial factors to see if they play an influencing factor in the number of accidents road occurred during different lighting conditions. The utilized dataset contains police report information on recorded traffic accidents that took place in the city of Chattanooga, TN over a three-year timespan, from January 2018- December 2020. A total of 37,053 records were analyzed and the location analysis identified hotspot locations where motor crashes and fatal accidents have occurred the most. The findings showed that a large majority of fatal accidents happened near a highly industrial area along Amnicola highway. Also, Hamilton place mall area was identified as one of the hotpots where accidents occurred more frequently than other locations. Other findings indicated that the impacts of some driver related variables and collision types on accidents were greater than other factors. The results of this study can be used by local authorities to address the issues at particular hotspot locations and to strategize road safety regulations.","PeriodicalId":344273,"journal":{"name":"2021 2nd International Informatics and Software Engineering Conference (IISEC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114219564","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

Kernel Virtual Machine based High Performance Environment for Grid and Jungle Computing 基于内核虚拟机的网格和丛林计算高性能环境

2021 2nd International Informatics and Software Engineering Conference (IISEC) Pub Date : 2021-12-16 DOI: 10.1109/iisec54230.2021.9672355

S. Bazai, Muhammad Imran Ghafoor, Mubashar Aqeel, Muhammad Sohaib Roomi

{"title":"Kernel Virtual Machine based High Performance Environment for Grid and Jungle Computing","authors":"S. Bazai, Muhammad Imran Ghafoor, Mubashar Aqeel, Muhammad Sohaib Roomi","doi":"10.1109/iisec54230.2021.9672355","DOIUrl":"https://doi.org/10.1109/iisec54230.2021.9672355","url":null,"abstract":"Grid, cluster, cloud, and jungle enable highspeed computing in current domains. To tackle the rising data and the computation on the internet by dispersing the load between numerous nodes, grid and jungle computing originated as the largest option. In this article, we demonstrate two models-grid and jungle-using virtual machines. The java based virtual machine environment is used for constructing jungle and grid models. Our main aim is to reduce power consumption, gain high performance and reduce hardware costs for building these types of settings. The approach employs KVM (Kernel-based Virtual Machine) and Open Nebula cloud for configuration and deployment of grid and jungle in a virtual environment. The performance of different algorithms in virtual machines and regular machines are tested individually. The performance of models is calculated and compared using streaming Ramanujan number, firefly technique, and finding the prime numbers for both grid and jungle environment. The results provide the high execution of KVM for Ramanujan numbers and prime numbers while firefly technique requires more execution time on grid computing.","PeriodicalId":344273,"journal":{"name":"2021 2nd International Informatics and Software Engineering Conference (IISEC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127435273","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