2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET)最新文献_第2页

Cross-validation of machine learning algorithms for malware detection using static features of Windows portable executables: A Comparative Study 使用Windows可移植可执行文件的静态特性进行恶意软件检测的机器学习算法的交叉验证:一项比较研究

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322809

Warda Aslam, M. Fraz, S.K. Rizvi, S. Saleem

{"title":"Cross-validation of machine learning algorithms for malware detection using static features of Windows portable executables: A Comparative Study","authors":"Warda Aslam, M. Fraz, S.K. Rizvi, S. Saleem","doi":"10.1109/HONET50430.2020.9322809","DOIUrl":"https://doi.org/10.1109/HONET50430.2020.9322809","url":null,"abstract":"With the expansion in the notoriety of modern technology, cyber-attacks have also increased. Traditional techniques to distinguish between malware and benign files are usually signature-based or behavior-based; the following methods can be less accurate and resource hungry. A robust technique is needed which is more efficient and takes less time as compared to traditional techniques. Machine learning can play an important role in this scenario due to its predictive capabilities based upon training. In this study, we use existing machine learning algorithms for classification and clustering using static features of malware-benign portable executables. Cross-validation is performed using two datasets; a publicly available dataset and a self-collected dataset. The self-collected dataset comprises 21,486 samples, whereas, the publicly available dataset comprises 138,047 samples. In the case of supervised classification, accuracies were observed to be above 80% whereas in the case of unsupervised F1-score above 0.9 was observed.","PeriodicalId":245321,"journal":{"name":"2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET)","volume":"179 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131486093","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

Fusing Birch with G. Boosting for improving temporal traffic congestion tailored to port gates: Case Study in Patras, Greece 促进改善港口大门的时间交通拥堵:希腊帕特雷的案例研究

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322662

A. Dimara, Dimitrios Triantafyllidis, S. Krinidis, Konstantinos Kitsikoudis, D. Ioannidis, Stavros Antipas, D. Tzovaras

{"title":"Fusing Birch with G. Boosting for improving temporal traffic congestion tailored to port gates: Case Study in Patras, Greece","authors":"A. Dimara, Dimitrios Triantafyllidis, S. Krinidis, Konstantinos Kitsikoudis, D. Ioannidis, Stavros Antipas, D. Tzovaras","doi":"10.1109/HONET50430.2020.9322662","DOIUrl":"https://doi.org/10.1109/HONET50430.2020.9322662","url":null,"abstract":"Traffic congestion is a vulnerable aspect of every port producing inconvenient situations for all stakeholders. This paper suggests a model that simulates traffic density at ports' gates into three levels: low, medium and high. Parameters that affect traffic density are clustered to strengthen potential relationship between them by using BIRCH algorithm. Furthermore, Gradient Boosting Algorithm is utilized to classify traffic volume into the three states while exploiting available historical data along with the clustered data. The aim is to propose a non-intrusive and inexpensive novel tool for traffic simulation based only on available port data while ensuring better quality of transportation particularly with regard to boarding time towards avoiding further delays. Finally, the proposed traffic simulation model is evaluated with real-time port data, while the experimental results have shown that it is a powerful tool for port traffic simulation and management.","PeriodicalId":245321,"journal":{"name":"2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127701029","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

Sampled-data Tracking and Disturbance Compensation of Nonlinear Systems 非线性系统的采样数据跟踪与扰动补偿

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322825

S. Zia, A. Qayyum, M. Malik

引用次数: 2

Passive tracking of a target based on Supervisory adaptive EKF and CKF 基于监督自适应EKF和CKF的目标被动跟踪

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322664

Meghdad Mohammad, Ali Naiari, Mehdi Hosseynzadeh

{"title":"Passive tracking of a target based on Supervisory adaptive EKF and CKF","authors":"Meghdad Mohammad, Ali Naiari, Mehdi Hosseynzadeh","doi":"10.1109/HONET50430.2020.9322664","DOIUrl":"https://doi.org/10.1109/HONET50430.2020.9322664","url":null,"abstract":"This study proposed a neural network structure for passive tracking of a target by an observer. Since the passive tracking measurement equation is nonlinear, the extended Kalman filter (EKF) and cubature Kalman filter (CKF) methods were implemented. Due to the nonlinear nature of the passive tracking measurement equation, the conventional extended and cubature Kalman filters are not good candidates where bearing-only target tracking is a standard and traditional passive tracking method. The effectiveness of Kalman filters dramatically depended on measurement noise covariance (R). Since R is challenging to be determined and changed with environmental variations, an on-line adaptive filter is proposed. The adaptive structure is founded on the double-layer perceptron neural network, where its weights were updated by the steepest descent method to tune covariance matrix R. In the numerical simulations, it is assumed that tracking of a target was carried out in an underwater environment by sonar measurement. In this paper, in addition to the proposed method, the neural network extended Kalman filter (NNEKF), neural network cubature Kalman filter (NNCKF), Sage Husa adaptive cubature Kalman filter (SHCKF) are used to track the target. The simulation results show the effectiveness of the proposed method.","PeriodicalId":245321,"journal":{"name":"2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET)","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115243625","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

A Survey of Artificial Intelligence and Internet of Things (IoT) based approaches against Covid-19 基于人工智能和物联网(IoT)的应对新冠肺炎方法综述

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322829

Osama Nadeem, Muhammad Shajee Saeed, M. Tahir, R. Mumtaz

{"title":"A Survey of Artificial Intelligence and Internet of Things (IoT) based approaches against Covid-19","authors":"Osama Nadeem, Muhammad Shajee Saeed, M. Tahir, R. Mumtaz","doi":"10.1109/HONET50430.2020.9322829","DOIUrl":"https://doi.org/10.1109/HONET50430.2020.9322829","url":null,"abstract":"Artificial Intelligence (AI) has proven to be a great instrument in the application of technology in different real life situations. In most cases, the application of AI is followed by an improvement in the living conditions and well-being for humanity. Currently, the world is facing the pandemic of Covid-19. Application of artificial intelligence (AI) can be of great significance in the fight against Covid-19 combined with other forms of technology like the Internet of Things (IoT). This paper focuses on the survey of different AI applications and methods to aid our fight against Covid-19 and winning this battle. Different application subdomains of artificial intelligence include computer vision, speech recognition, natural language processing and data analytics etc. The survey highlights areas where artificial intelligence (AI) works in tandem with internet of things (IoT) to provide efficient ways to prevent, diagnose and reduce Covid-19. The paper also presents a survey of research work and approaches on applications of technology against Covid-19 and data sources related to them. We also propose a novel approach for preliminary indication of Covid-19, which makes use of AI enabled IoT sensors, along with conversational capabilities.","PeriodicalId":245321,"journal":{"name":"2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122973383","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: 7

Liquid Metasurface Antennas 液体超表面天线

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322836

Kristina Moralic, Dylan Languasco, William Romeo, M. Mencagli

引用次数: 0

A Deep Learning Based Classifier for Crack Detection with Robots in Underground Pipes 基于深度学习的地下管道机器人裂纹检测分类器

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322665

Saffeer M. Khan, S. Haider, Ishaq Unwala

{"title":"A Deep Learning Based Classifier for Crack Detection with Robots in Underground Pipes","authors":"Saffeer M. Khan, S. Haider, Ishaq Unwala","doi":"10.1109/HONET50430.2020.9322665","DOIUrl":"https://doi.org/10.1109/HONET50430.2020.9322665","url":null,"abstract":"Underground utility pipes especially sewer pipes are prone to develop cracks due to aging, shifting soil, increased traffic, corrosion, and improper installation. A major challenge for utility operators is cost effective periodic condition monitoring of their sewer networks. The existing industry standard pipe condition monitoring system is based on passing a robot mounted closed circuit television (CCTV) camera through the pipe. The CCTV video feed is recorded and monitored by a trained operator who annotates it corresponding to the location of cracks and other structural imperfections. This system is both cost and labor intensive. In recent years, the deep learning-based systems have achieved success in vision based object detection problems. In this project, we have collected pipe crack data from extensive field trials with CCTV based systems in actual sewer networks. The noisy field data is cleaned up and used for training the convolutional neural networks. We test the proposed model with validation data to determine its accuracy and effectiveness. The results indicate that deep learning model can be effectively used to detect cracks in underground pipes.","PeriodicalId":245321,"journal":{"name":"2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124454175","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

Tree-based Routing Protocol in Wireless Sensor Networks using Optimization Algorithm Batch Particles with a Mobile Sink 基于移动Sink的批粒子优化算法的无线传感器网络树状路由协议

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322844

Arian Yousefiankalareh, A. Najari, Mehdi Hosseynzadeh

引用次数: 8

An FCR Approach Towards Detection of Outliers for Medical Data 医疗数据异常值检测的FCR方法

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322843

Sidra Iqbal, Hafiz Bahloul Ajmeri, Sumaira Bibi, Abdul Wahid

引用次数: 0

Business model for rural connectivity using multi-tenancy 5G network slicing 使用多租户5G网络切片的农村连接商业模式

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322837

Shruthi Koratagere Anantha Kumar, R. Stewart, David H. Crawford, S. Chaudhari

引用次数: 8