2023 International Conference on Smart Applications, Communications and Networking (SmartNets)最新文献

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Harnessing 5G Networks for Health Care: Challenges and Potential Applications 利用5G网络进行医疗保健:挑战和潜在应用
2023 International Conference on Smart Applications, Communications and Networking (SmartNets) Pub Date : 2023-07-25 DOI: 10.1109/SmartNets58706.2023.10215757
S. Abir, Mohammed A. Abuibaid, Jun Steed Huang, Yang Hong
{"title":"Harnessing 5G Networks for Health Care: Challenges and Potential Applications","authors":"S. Abir, Mohammed A. Abuibaid, Jun Steed Huang, Yang Hong","doi":"10.1109/SmartNets58706.2023.10215757","DOIUrl":"https://doi.org/10.1109/SmartNets58706.2023.10215757","url":null,"abstract":"The fifth generation (5G) network has revolutionized the healthcare field, offering high-speed data transfer, low latency, and improved network coverage. This has opened new opportunities to help healthcare providers break through barriers for better care delivery. The paradigm shift towards a distributed patient-centric approach in health care has driven the need for the utilization of 4G and other advanced technologies for smart healthcare. However, the limitations of fourth-generation healthcare systems require machine-to-machine (M2M) or device-to-device (D2D) communication, which can be addressed by 5G network infrastructure with its ultra-low latency, high availability, reliability, and security features. This paper provides an overview of the role of smart healthcare in transforming the healthcare industry through the integration of advanced technologies such as electronic health records (EHRs), telemedicine, internet of things (IoT), wearable devices, artificial intelligence (AI), blockchain, and 3D printing. It also discusses the key features of 5G networks, including high-speed data transfer, low latency, increased bandwidth, improved network coverage, network slicing, and improved security. Additionally, this paper proposes a 5G-enabled emergency vehicle communication system as a potential application of 5G technology in health care.","PeriodicalId":301834,"journal":{"name":"2023 International Conference on Smart Applications, Communications and Networking (SmartNets)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133164447","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
Graph Neural Network based Approach for Rumor Detection on Social Networks 基于图神经网络的社交网络谣言检测方法
2023 International Conference on Smart Applications, Communications and Networking (SmartNets) Pub Date : 2023-07-25 DOI: 10.1109/SmartNets58706.2023.10215926
Daniel Hosseini, Rong Jin
{"title":"Graph Neural Network based Approach for Rumor Detection on Social Networks","authors":"Daniel Hosseini, Rong Jin","doi":"10.1109/SmartNets58706.2023.10215926","DOIUrl":"https://doi.org/10.1109/SmartNets58706.2023.10215926","url":null,"abstract":"In today’s society, social media usage has resulted in several challenges, including the widespread dissemination of rumors - unverified or false information that can significantly impact public perception and decision-making. As a result, detecting and preventing the spread of rumors is essential. Recent scientific studies have proposed various solutions that use both machine learning and deep learning techniques to identify rumors. In this paper, we investigate an approach that employs graph neural networks to detect rumors. Specifically, we represent posts and their responses as graphs, which are then processed through a Graph Attention Network (GAT) layer. The resulting representations are fed into a dense neural network for classification. Our experiments on the PHEME dataset show that our approach achieves satisfactory performance in identifying rumors. This study also provides a promising avenue for future research in the field of rumor detection using graph neural networks.","PeriodicalId":301834,"journal":{"name":"2023 International Conference on Smart Applications, Communications and Networking (SmartNets)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130200000","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
C-V2X Link Decision with Machine Learning 基于机器学习的C-V2X链路决策
2023 International Conference on Smart Applications, Communications and Networking (SmartNets) Pub Date : 2023-07-25 DOI: 10.1109/SmartNets58706.2023.10215916
Ömer Sokmaz, Ahmet Yazar
{"title":"C-V2X Link Decision with Machine Learning","authors":"Ömer Sokmaz, Ahmet Yazar","doi":"10.1109/SmartNets58706.2023.10215916","DOIUrl":"https://doi.org/10.1109/SmartNets58706.2023.10215916","url":null,"abstract":"Vehicle-to-everything (V2X) communications standards have started to reach a maturity in recent years. The evolution period of 5G communications systems also accelerates the development of V2X concept. In this period, cellular V2X (C-V2X) standards are enhanced by the 3GPP organization. In this paper, a novel approach is proposed to decide on a communications link for two different modes (Mode 3 and Mode 4) of C-V2X systems. The proposed approach is developed in an environmental-aware perspective based on machine learning (ML) techniques.","PeriodicalId":301834,"journal":{"name":"2023 International Conference on Smart Applications, Communications and Networking (SmartNets)","volume":"260 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121145840","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 Platform for Monitoring Student Commuting in the Use of School Transport in Smart Cities - A Facial Recognition Based Approach 智能城市中使用学校交通工具的学生通勤监控平台——基于面部识别的方法
2023 International Conference on Smart Applications, Communications and Networking (SmartNets) Pub Date : 2023-07-25 DOI: 10.1109/SmartNets58706.2023.10216190
Jessé Da Costa Rocha, Marcela Alves De Souza, E. Cardoso, N. Vijaykumar, Jasmine Priscyla Leite De Araújo, C. Francês
{"title":"A Platform for Monitoring Student Commuting in the Use of School Transport in Smart Cities - A Facial Recognition Based Approach","authors":"Jessé Da Costa Rocha, Marcela Alves De Souza, E. Cardoso, N. Vijaykumar, Jasmine Priscyla Leite De Araújo, C. Francês","doi":"10.1109/SmartNets58706.2023.10216190","DOIUrl":"https://doi.org/10.1109/SmartNets58706.2023.10216190","url":null,"abstract":"This article proposes an intelligent platform for monitoring students' steps on their way to school until they leave the school to their homes. This platform can identify students and notify those responsible and competent authorities in various situations of school life, such as: entering and leaving the school bus, entering and leaving school, entering the school cafeteria, etc. The first application aims to control access to the school bus through facial recognition. In the tests carried out, the recognition system achieved excellent results in all metrics.","PeriodicalId":301834,"journal":{"name":"2023 International Conference on Smart Applications, Communications and Networking (SmartNets)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121396372","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
Impact of a Web Based Crowdfunding Application for Renewable Energy Projects in Nigeria 网络众筹对尼日利亚可再生能源项目的影响
2023 International Conference on Smart Applications, Communications and Networking (SmartNets) Pub Date : 2023-07-25 DOI: 10.1109/SmartNets58706.2023.10216253
A. Awelewa, Ebubechi Ezenwanne, Kayode Ojo, Isaac Samuel, Popoola Olawale
{"title":"Impact of a Web Based Crowdfunding Application for Renewable Energy Projects in Nigeria","authors":"A. Awelewa, Ebubechi Ezenwanne, Kayode Ojo, Isaac Samuel, Popoola Olawale","doi":"10.1109/SmartNets58706.2023.10216253","DOIUrl":"https://doi.org/10.1109/SmartNets58706.2023.10216253","url":null,"abstract":"Energy crisis is one of the major challenges confronting African countries. Nigeria has a growing energy demand, and about 70% of its citizens lack access to electricity, especially in rural areas which constitute 60% of the population in the country that depends mainly on fuel wood. The adoption of renewable energy systems as a viable alternative to solve the energy crisis has been hampered by major challenges such as inadequacy, unsustainability, and poor reliability. Access to credit and other financial services remains limited, worsening these issues and hindering progress toward the provision of affordable and clean energy. Hence, the thrust of this work is to develop a web application for crowdfunding renewable energy projects in Nigeria. The purpose is to create a financial technology in mobilizing funds from fellow citizens towards driving a stable electricity supply to every location around the country. The crowdfunding application consists of a frontend application built with react.js, a backend application built with node.js, databases, and a payment system. Two case studies are considered and economically analyzed: a wind turbine system and a wind-diesel hybrid system. The comparative analysis carried out shows that, for the two case studies, the total cost of electricity (COE), simple payback (SPB), and internal rate of return (IRR) are 12.89 $/kWh and 14.21 $/kWh, 161 years and 144 years, and 113.47% and 114.81%, respectively. Further, the sensitivity analysis results reveal that it is more cost-effective to use the wind turbine system alone than the hybrid configuration of wind and diesel.","PeriodicalId":301834,"journal":{"name":"2023 International Conference on Smart Applications, Communications and Networking (SmartNets)","volume":"442 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125748349","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
Reducing Bootstrap Overhead within VANET Blockchain Applications through Pruning 通过修剪减少VANET区块链应用程序中的引导开销
2023 International Conference on Smart Applications, Communications and Networking (SmartNets) Pub Date : 2023-07-25 DOI: 10.1109/SmartNets58706.2023.10215679
E. Bowlin, Mohammad S. Khan, Biju Bajracharya
{"title":"Reducing Bootstrap Overhead within VANET Blockchain Applications through Pruning","authors":"E. Bowlin, Mohammad S. Khan, Biju Bajracharya","doi":"10.1109/SmartNets58706.2023.10215679","DOIUrl":"https://doi.org/10.1109/SmartNets58706.2023.10215679","url":null,"abstract":"Blockchains within Vehicular Ad-hoc Networks (VANETs) have enjoyed different applications within literature. Certain characteristics, like privacy and data security, are necessary to create a secure network. Blockchains provide different characteristics that can benefit VANETs. Traditional blockchains do not translate well into the highly mobile environments like VANETs. Currently, literature yields progress in adapting these data structures into these networks. However, little discussion has been created about the bootstrapping requirements when nodes join a blockchain network. Bootstrapping requires downloading a large portion of the chain and can create large network loads depending on the chain size. This work sets out to provide a pruning technique demonstrations that prunes in specific time intervals to reduce network and storage load. This method removes unnecessary blocks that are no longer need due to age and irrelevancy to the current road conditions. A discussion on the rationale of why to prune a chain is conducted. Over all, pruning provides a reduction in the overall data sent over the network, which primarily comes from bootstrapping nodes.","PeriodicalId":301834,"journal":{"name":"2023 International Conference on Smart Applications, Communications and Networking (SmartNets)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115403646","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
Using Machine Learning and SEM to Analyze Attitudes towards adopting Metaverse in Higher Education 利用机器学习和扫描电镜分析高等教育对采用元宇宙的态度
2023 International Conference on Smart Applications, Communications and Networking (SmartNets) Pub Date : 2023-07-25 DOI: 10.1109/SmartNets58706.2023.10215936
Salman Hussain, Eman Almohsen, T. Henari, S. Shatnawi, Anwaar Buzaboon, Mohammed Fardan, Khawla Albinali
{"title":"Using Machine Learning and SEM to Analyze Attitudes towards adopting Metaverse in Higher Education","authors":"Salman Hussain, Eman Almohsen, T. Henari, S. Shatnawi, Anwaar Buzaboon, Mohammed Fardan, Khawla Albinali","doi":"10.1109/SmartNets58706.2023.10215936","DOIUrl":"https://doi.org/10.1109/SmartNets58706.2023.10215936","url":null,"abstract":"The Metaverse has become a highly discussed topic in recent times, as it has the potential to transform many aspects of our lives. From banking and investing to real estate, manufacturing, and education, the Metaverse could change how we operate in many industries. This research paper aims to investigate the level of user acceptance and attitude toward the integration of the Metaverse technology into higher education in Bahrain and Jordan by employing the Technology Acceptance Model along with three external variables, self-efficacy, subjective norms, and perceived behavior control. A two-stage analysis was performed, consisting of structural equation modeling and machine learning classification algorithms. SEM results suggest that self-efficacy and social norms positively influenced perceived usefulness and ease of use, it is also found that perceived ease of use and perceived usefulness significantly affected users’ attitudes toward using this technology. Machine learning findings supported SEM results and indicated that J48, LogitBoost, and PART classifiers have achieved the highest accuracy.","PeriodicalId":301834,"journal":{"name":"2023 International Conference on Smart Applications, Communications and Networking (SmartNets)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129390935","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
Improved k-NN Regression Model Using Random Forests for Air Pollution Prediction 基于随机森林的改进k-NN回归模型空气污染预测
2023 International Conference on Smart Applications, Communications and Networking (SmartNets) Pub Date : 2023-07-25 DOI: 10.1109/SmartNets58706.2023.10216028
Siddhartha Sharma, R. Lakshmi
{"title":"Improved k-NN Regression Model Using Random Forests for Air Pollution Prediction","authors":"Siddhartha Sharma, R. Lakshmi","doi":"10.1109/SmartNets58706.2023.10216028","DOIUrl":"https://doi.org/10.1109/SmartNets58706.2023.10216028","url":null,"abstract":"In this paper, we review various k-Nearest-Neighbor (k-NN) based models and their accuracies to develop a better model to predict concentrations of air pollutants. The proposed model splits the range of target variable values into a number of buckets first. Then, a hybrid k-NN model, which is a combination of weighted attribute k-NN and distance-weighted k-NN, and where the weights are assigned by calculating Information Gain, is used for each attribute, to calculate the target variable value of each test case. The proposed model decreases the root mean square error (RMSE) of predicted NO, NO2 and NOx values by 28.29%, 29.44%, and 16.51% respectively, compared to the state-of the-art. Similarly, the mean absolute error (MAE) values for NO, NO2, and NOx are decreased by 18.26%, 33.67%, and 14.54%, compared to the state-of the-art. This model gives good results when the size of each bucket is nearly equal.","PeriodicalId":301834,"journal":{"name":"2023 International Conference on Smart Applications, Communications and Networking (SmartNets)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121402790","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
SATA M.2 on Forensics: Trim Function Effect on Recovering Permanently Deleted Files SATA M.2 on Forensics: Trim功能对恢复永久删除文件的影响
2023 International Conference on Smart Applications, Communications and Networking (SmartNets) Pub Date : 2023-07-25 DOI: 10.1109/SmartNets58706.2023.10215536
Ruwa F. Abu Hweidi, M. Jazzar, A. Eleyan, T. Bejaoui
{"title":"SATA M.2 on Forensics: Trim Function Effect on Recovering Permanently Deleted Files","authors":"Ruwa F. Abu Hweidi, M. Jazzar, A. Eleyan, T. Bejaoui","doi":"10.1109/SmartNets58706.2023.10215536","DOIUrl":"https://doi.org/10.1109/SmartNets58706.2023.10215536","url":null,"abstract":"The spread of different types of SSD memory in a wide range of devices increases the challenge of cybercrime and forensic investigation. This is due to the features of the memory structure and how the data is recovered under such features. This research paper consists of an experiment to recover permanently deleted files in SATAM.2 SSD memory when- the Trim function is disabled and permitted with various forensic tools such as OSForensics, Autopsy, FTK and AXIOM. The experiment is applied to the NTFS file system under the Windows 11 environment. The research finds that 0% of files are recovered when the Trim function is enabled and 100% of files can be recovered if Trim function is disabled. This makes the recovery process difficult for investigators to find valid evidence. In future work, there is a need for a method that allows investigators to recover files in such conditions, and to apply more experiments to various features under different file system types and operating systems.","PeriodicalId":301834,"journal":{"name":"2023 International Conference on Smart Applications, Communications and Networking (SmartNets)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123077080","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
Sentiment Analysis of Using ChatGPT in Education ChatGPT在教学中的情感分析
2023 International Conference on Smart Applications, Communications and Networking (SmartNets) Pub Date : 2023-07-25 DOI: 10.1109/SmartNets58706.2023.10215977
Mohammad Tubishat, F. Al-Obeidat, Ahmed Shuhaiber
{"title":"Sentiment Analysis of Using ChatGPT in Education","authors":"Mohammad Tubishat, F. Al-Obeidat, Ahmed Shuhaiber","doi":"10.1109/SmartNets58706.2023.10215977","DOIUrl":"https://doi.org/10.1109/SmartNets58706.2023.10215977","url":null,"abstract":"This paper presents a study on the use of the Chat Generative Pretrained Transformer (ChatGPT) in education. In this work, we propose a sentiment analysis model of tweets related to the use of the ChatGPT in education. The purpose of this research is to identify common sentiments, topics, and perspectives that are expressed towards ChatGPT in the education field based on the data collected from Twitter. Twitter was used to collect 11830 tweets about the use of ChatGPT in education. Topics and emotions expressed in the tweets were extracted using NLP algorithms and organized into distinct groups. Also, the most frequent words in the positive and negative opinion words are determined. The findings of the paper indicate that most tweets about ChatGPT are either positive or neutral, with a small percentage expressing negative sentiments. In addition, the study analyzes the sentiments expressed in tweets about the employment of ChatGPT in education using four different classifiers: Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF). According to the results, the SVM classifier has the highest accuracy of 81.4 percent.","PeriodicalId":301834,"journal":{"name":"2023 International Conference on Smart Applications, Communications and Networking (SmartNets)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121633120","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
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