2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC)最新文献_第9页

Development of Scoliotic Spine Severity Detection using Deep Learning Algorithms 基于深度学习算法的脊柱侧凸严重程度检测的发展

2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2022-01-26 DOI: 10.1109/CCWC54503.2022.9720906

Nahid A. Makhdoomi, T. S. Gunawan, Nur Hanani Idris, Othman Omran Khalifa, Rajandra Kumar Karupiah, Arif Bramantoro, Farah Diyana Abdul Rahman, Z. Zakaria

{"title":"Development of Scoliotic Spine Severity Detection using Deep Learning Algorithms","authors":"Nahid A. Makhdoomi, T. S. Gunawan, Nur Hanani Idris, Othman Omran Khalifa, Rajandra Kumar Karupiah, Arif Bramantoro, Farah Diyana Abdul Rahman, Z. Zakaria","doi":"10.1109/CCWC54503.2022.9720906","DOIUrl":"https://doi.org/10.1109/CCWC54503.2022.9720906","url":null,"abstract":"According to research conducted by Johns Hopkins' Division of Pediatric Orthopedic Surgery, around three million new instances of Scoliosis are identified each year, with the majority of cases affecting children between the ages of 10 and 12. The current method of diagnosing and treating Scoliosis, which includes spinal injections, back braces, and a variety of other types of surgery, may have resulted in inconsistencies and ineffective treatment by professionals. Other scoliosis diagnosis methods have been developed since the technology's invention. Using Convolutional Neural Network (CNN), this research will integrate an artificial intelligence-assisted method for detecting and classifying Scoliosis illness types. The software model will include an initialization phase, preprocessing the dataset, segmentation of features, performance measurement, and severity classification. The neural network used in this study is U-Net, which was developed specifically for biomedical picture segmentation. It has demonstrated reliable and accurate results, with prediction accuracy reaching 94.42%. As a result, it has been established that employing an algorithm helped by artificial intelligence provides a higher level of accuracy in detecting Scoliosis than manual diagnosis by professionals.","PeriodicalId":101590,"journal":{"name":"2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129479402","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

MPTCP Performance Simulation in Multiple LEO Satellite Environment 多LEO卫星环境下MPTCP性能仿真

2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2022-01-26 DOI: 10.1109/CCWC54503.2022.9720772

Chol Hyun Park, Phillipe Austria, Yoohwan Kim, Ju-Yeon Jo

引用次数: 4

BBR Congestion Control Analysis with Multipath TCP (MPTCP) and Asymmetrical Latency Subflow 基于MPTCP和非对称延迟子流的BBR拥塞控制分析

2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2022-01-26 DOI: 10.1109/CCWC54503.2022.9720867

Phillipe Austria, Chol Hyun Park, Ju-Yeon Jo, Yoohwan Kim, Rahul Sundaresan, K. Pham

引用次数: 1

A Survey on Mobile Malware Detection Methods using Machine Learning 基于机器学习的移动恶意软件检测方法综述

2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2022-01-26 DOI: 10.1109/CCWC54503.2022.9720753

Mina Esmail Zadeh Nojoo Kambar, Armin Esmaeilzadeh, Yoohwan Kim, K. Taghva

引用次数: 19

Solutions for Closing Usage Gap in Rural Areas in West Africa 缩小西非农村地区使用差距的解决方案

2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2022-01-26 DOI: 10.1109/CCWC54503.2022.9720816

Ida Sèmévo Tognisse, Jules R. Dégila, A. Kora

引用次数: 0

A Framework for Open Source Intelligence Penetration Testing of Virtual Health Care Systems 虚拟医疗系统的开源智能渗透测试框架

2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2022-01-26 DOI: 10.1109/CCWC54503.2022.9720785

C. DeCusatis, Patrick Peko, Jordan Irving, Maxwell Teache, Christopher Laibach, Jason Hodge

引用次数: 2

Smart Helmet with Rear View and Accident Detection System for Increased Safety 带后视镜和事故检测系统的智能头盔，提高安全性

2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2022-01-26 DOI: 10.1109/CCWC54503.2022.9720833

Ajan Ahmed, Mohammad Monirujjaman Khan, Rajesh Dey, Ipseeta Nanda

引用次数: 1

Using EEG and fNIRS Signals as Polygraph 利用脑电图和近红外光谱信号作为测谎仪

2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2022-01-26 DOI: 10.1109/CCWC54503.2022.9720780

M. Khalil, Maria Ramirez, K. George

引用次数: 1

Employability Prediction of Engineering Graduates Using Ensemble Classification Modeling 基于集成分类模型的工程毕业生就业能力预测

2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2022-01-26 DOI: 10.1109/CCWC54503.2022.9720783

R. R. Maaliw, K. Quing, A. Lagman, Bernard Ugalde, Melvin A. Ballera, Michael Angelo D. Ligayo

引用次数: 3

Detecting and Classifying Self-Deleting Windows Malware Using Prefetch Files 使用预取文件检测和分类自删Windows恶意软件

2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2022-01-26 DOI: 10.1109/CCWC54503.2022.9720874

Adam Duby, Teryl Taylor, Gedare Bloom, Yanyan Zhuang

{"title":"Detecting and Classifying Self-Deleting Windows Malware Using Prefetch Files","authors":"Adam Duby, Teryl Taylor, Gedare Bloom, Yanyan Zhuang","doi":"10.1109/CCWC54503.2022.9720874","DOIUrl":"https://doi.org/10.1109/CCWC54503.2022.9720874","url":null,"abstract":"Malware detection and analysis can be a burdensome task for incident responders. As such, research has turned to machine learning to automate malware detection and malware family classification. Existing work extracts and engineers static and dynamic features from the malware sample to train classifiers. Despite promising results, such techniques assume that the analyst has access to the malware executable file. Self-deleting malware invalidates this assumption and requires analysts to find forensic evidence of malware execution for further analysis. In this paper, we present and evaluate an approach to detecting malware that executed on a Windows target and further classify the malware into its associated family to provide semantic insight. Specifically, we engineer features from the Windows prefetch file, a file system forensic artifact that archives process information. Results show that it is possible to detect the malicious artifact with 99% accuracy; furthermore, classifying the malware into a fine-grained family has comparable performance to techniques that require access to the original executable. We also provide a thorough security discussion of the proposed approach against adversarial diversity.","PeriodicalId":101590,"journal":{"name":"2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC)","volume":"127 1-3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123575755","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: 4