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

Privacy and Security in Mobile Health Technologies: Challenges and Concerns 移动医疗技术中的隐私和安全:挑战和关注

2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2022-01-26 DOI: 10.1109/CCWC54503.2022.9720863

A. Odeh, I. Keshta, Abobakr Aboshgifa, Eman Abdelfattah

引用次数: 1

Design of a Machine Learning Based Intrusion Detection Framework and Methodology for IoT Networks 基于机器学习的物联网网络入侵检测框架与方法设计

2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2022-01-26 DOI: 10.1109/CCWC54503.2022.9720857

S. RicardoManzano, N. Goel, Marzia Zaman, Rohit Joshi, Sagarika Naik

引用次数: 3

Autoregressive Modeling of Utility Customer Outages with Deep Neural Networks 基于深度神经网络的电力客户停电自回归建模

2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2022-01-26 DOI: 10.1109/CCWC54503.2022.9720799

K. Udeh, D. Wanik, D. Cerrai, Derek Aguiar, E. Anagnostou

{"title":"Autoregressive Modeling of Utility Customer Outages with Deep Neural Networks","authors":"K. Udeh, D. Wanik, D. Cerrai, Derek Aguiar, E. Anagnostou","doi":"10.1109/CCWC54503.2022.9720799","DOIUrl":"https://doi.org/10.1109/CCWC54503.2022.9720799","url":null,"abstract":"More and more frequently, electric utility emergency response personnel are required to manage the impact of severe weather events on electric distribution networks. In the US, economic losses associated with extreme weather events are estimated between $20 billion and $55 billion annually. Spatiotemporal modeling of customer outages from weather data can mitigate the economic and personal impact of adverse weather by reducing customer downtimes and increasing customer confidence in electric utility providers during a power outage event. In this paper, we consider the problem of customer outage forecasting by integrating distributed temporal and spatial weather data with deep learning prediction models. Using weather and outage data from ten random counties across New York State, we fit separate spatiotemporal models based on long short-term memory (LSTM) and convolutional neural networks (CNN) to predict customer outages over a 48 hour forecast horizon. Specifically, we consider both autoregressive and covariate-dependent signatures of variation in the development of three model architectures that predict (a) county-level outages given county-level data, (b) county-level outages given state-level data, and (c) state-level outages given state-level data. We compare our methods against statistical approaches (ARIMA, ARIMAX and VARMAX) and a persistence-based method. The results demonstrate that our method achieves better performance over the baselines in terms of root mean square error, median absolute error, Pearson correlation, and average relative error, thus providing an effective tool for electric utility companies to prepare for adverse weather events.","PeriodicalId":101590,"journal":{"name":"2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC)","volume":"7 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":"126732057","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: 3

Novel Encoding method for Quantum Error Correction 量子纠错的新编码方法

2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2022-01-26 DOI: 10.1109/CCWC54503.2022.9720880

Mummadi Swathi, Bhawana Rudra

引用次数: 6

FPGA Implementation of Radio Frequency Neural Networks 射频神经网络的FPGA实现

2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2022-01-26 DOI: 10.1109/CCWC54503.2022.9720784

Amit Bhatia, Josh Robinson, Joseph M. Carmack, Scott Kuzdeba

引用次数: 1

A Comparative Study of Solar Water Pump Storage Systems 太阳能水泵蓄能系统的比较研究

2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2022-01-26 DOI: 10.1109/CCWC54503.2022.9720912

Amirhossein Jahanfar, M. Iqbal

引用次数: 6

Offensive Language Detection on Social Media Based on Text Classification 基于文本分类的社交媒体攻击性语言检测

2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2022-01-26 DOI: 10.1109/CCWC54503.2022.9720804

P. Hajibabaee, Masoud Malekzadeh, Mohsen Ahmadi, Maryam Heidari, Armin Esmaeilzadeh, Reyhaneh Abdolazimi, James H. Jones

引用次数: 20

A Multi-level Morphological and Stochastic Tagalog Stemming Template 一个多层次的形态学和随机他加禄语词干提取模板

2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2022-01-26 DOI: 10.1109/CCWC54503.2022.9720873

G. A. Ong, Melvin A. Ballera

引用次数: 2

Ransomware Classification and Detection With Machine Learning Algorithms 基于机器学习算法的勒索软件分类与检测

2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2022-01-26 DOI: 10.1109/CCWC54503.2022.9720869

Mohammad Masum, Md Jobair Hossain Faruk, H. Shahriar, Kai Qian, D. Lo, M. I. Adnan

{"title":"Ransomware Classification and Detection With Machine Learning Algorithms","authors":"Mohammad Masum, Md Jobair Hossain Faruk, H. Shahriar, Kai Qian, D. Lo, M. I. Adnan","doi":"10.1109/CCWC54503.2022.9720869","DOIUrl":"https://doi.org/10.1109/CCWC54503.2022.9720869","url":null,"abstract":"Malicious attacks, malware, and ransomware families pose critical security issues to cybersecurity, and it may cause catastrophic damages to computer systems, data centers, web, and mobile applications across various industries and businesses. Traditional anti-ransomware systems struggle to fight against newly created sophisticated attacks. Therefore, state-of-the-art techniques like traditional and neural network-based architectures can be immensely utilized in the development of innovative ransomware solutions. In this paper, we present a feature selection-based framework with adopting different machine learning algorithms including neural network-based architectures to classify the security level for ransomware detection and prevention. We applied multiple machine learning algorithms: Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Logistic Regression (LR) as well as Neural Network (NN)-based classifiers on a selected number of features for ransomware classification. We performed all the experiments on one ransomware dataset to evaluate our proposed framework. The experimental results demonstrate that RF classifiers outperform other methods in terms of accuracy, F -beta, and precision scores.","PeriodicalId":101590,"journal":{"name":"2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC)","volume":"25 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":"121549901","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: 21

Modeling a simulated Forest to get Burning Times of Tree Species using a Digital Twin 使用数字孪生模型模拟森林以获得树种燃烧时间

2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2022-01-26 DOI: 10.1109/CCWC54503.2022.9720768

Guillermo Sanchez-Guzman, W. Velasquez, Manuel S. Alvarez‐Alvarado

引用次数: 3