Pub Date : 2024-02-04DOI: 10.23919/ICACT60172.2024.10471976
Guanhua Zhu, Xiaoling Xu, Qing Zhong, Bing-Yuh Lu, Yushen Lu, Guangming Xu, Yumeng Zhou, Ziyi Jiang, Kai Sun, Minhao Wang
This study employed the time-frequency analysis to compute some of the XJTU-SY bearing datasets and aimed at the investigation of spectrogram of the raw data of the datasets. The methods of this study are divided into 4 parts: (1) spectrogram, (2) XJTU-SY bearing datasets, (3) equipment and, (4) 2D correlation. The results show the dominant reasons of malfunction of the machine occur in the duration of the 75th to 100th minutes. Both 2D correlation coefficients of the spectrograms of horizontal and vertical vibrations in the 100th and 123th minutes are larger than 0.8 because rotation of the roller entered a distinguished state of malfunction in the 100th minute. The inner damage is enhanced step by step. The interpretation of VEs and HEs is helpful to detect the fault diagnosis of the roller. The further studies will test more data, and add more algorithms for the accurate diagnosis.
本研究采用时频分析法计算了部分 XJTU-SY 轴承数据集，旨在研究数据集原始数据的频谱图。本研究的方法分为四个部分：（1）频谱图；（2）XJTU-SY 轴承数据集；（3）设备；（4）二维相关性。结果表明，机器故障的主要原因发生在第 75 分钟至第 100 分钟。第 100 分钟和第 123 分钟的水平振动和垂直振动频谱图的二维相关系数均大于 0.8，这是因为辊筒旋转在第 100 分钟进入了明显的故障状态。内部损坏逐步加剧。对 VE 和 HE 的解释有助于检测滚筒的故障诊断。进一步的研究将测试更多的数据，并增加更多的算法来进行精确诊断。
{"title":"Time-frequency Analysis for Validating Prognostics Algorithms of Rolling Element Bearings","authors":"Guanhua Zhu, Xiaoling Xu, Qing Zhong, Bing-Yuh Lu, Yushen Lu, Guangming Xu, Yumeng Zhou, Ziyi Jiang, Kai Sun, Minhao Wang","doi":"10.23919/ICACT60172.2024.10471976","DOIUrl":"https://doi.org/10.23919/ICACT60172.2024.10471976","url":null,"abstract":"This study employed the time-frequency analysis to compute some of the XJTU-SY bearing datasets and aimed at the investigation of spectrogram of the raw data of the datasets. The methods of this study are divided into 4 parts: (1) spectrogram, (2) XJTU-SY bearing datasets, (3) equipment and, (4) 2D correlation. The results show the dominant reasons of malfunction of the machine occur in the duration of the 75th to 100th minutes. Both 2D correlation coefficients of the spectrograms of horizontal and vertical vibrations in the 100th and 123th minutes are larger than 0.8 because rotation of the roller entered a distinguished state of malfunction in the 100th minute. The inner damage is enhanced step by step. The interpretation of VEs and HEs is helpful to detect the fault diagnosis of the roller. The further studies will test more data, and add more algorithms for the accurate diagnosis.","PeriodicalId":518077,"journal":{"name":"2024 26th International Conference on Advanced Communications Technology (ICACT)","volume":"17 1","pages":"327-331"},"PeriodicalIF":0.0,"publicationDate":"2024-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140528279","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}
Pub Date : 2024-02-04DOI: 10.23919/ICACT60172.2024.10471943
Shah Muhammad Imtiyaj Uddin, Md Ariful Isalm Mojumder, Rashedul Islam Sumon, Joo Mon–il, Hee-Cheol Kim
Histopathology plays a vital role in the microscopic examination of colorectal cancer tissues, with a historical focus on the tumor-stroma ratio using texture analysis. However, due to the time-consuming and labor-intensive nature of this approach, there's a need for innovative solutions. This study introduces a groundbreaking shift by employing deep transfer learning to automate tissue classification within colorectal cancer histology samples. Through a comprehensive evaluation of various pre-trained models, including ResNet50V2, VGG19, Xception, InceptionV3, and MobileNet, we have achieved remarkable results. Notably, the ResNet50V2 model stands out with an impressive accuracy of 95%. Beyond its potential to significantly enhance operational responses, this research underscores the effectiveness and consistency of transfer learning as a rapid and efficient tool for colorectal cancer detection and classification.
{"title":"Leveraging Deep Learning for Automated Analysis of Colorectal Cancer Histology Images to Elevate Diagnosis Precision","authors":"Shah Muhammad Imtiyaj Uddin, Md Ariful Isalm Mojumder, Rashedul Islam Sumon, Joo Mon–il, Hee-Cheol Kim","doi":"10.23919/ICACT60172.2024.10471943","DOIUrl":"https://doi.org/10.23919/ICACT60172.2024.10471943","url":null,"abstract":"Histopathology plays a vital role in the microscopic examination of colorectal cancer tissues, with a historical focus on the tumor-stroma ratio using texture analysis. However, due to the time-consuming and labor-intensive nature of this approach, there's a need for innovative solutions. This study introduces a groundbreaking shift by employing deep transfer learning to automate tissue classification within colorectal cancer histology samples. Through a comprehensive evaluation of various pre-trained models, including ResNet50V2, VGG19, Xception, InceptionV3, and MobileNet, we have achieved remarkable results. Notably, the ResNet50V2 model stands out with an impressive accuracy of 95%. Beyond its potential to significantly enhance operational responses, this research underscores the effectiveness and consistency of transfer learning as a rapid and efficient tool for colorectal cancer detection and classification.","PeriodicalId":518077,"journal":{"name":"2024 26th International Conference on Advanced Communications Technology (ICACT)","volume":"44 ","pages":"01-06"},"PeriodicalIF":0.0,"publicationDate":"2024-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140528120","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}
During the outbreak of COVID-19, many enterprises massively created Virtual Private Networks (VPNs) for companies to cooperate; however, these accounts lacked efficient management after the epidemic, leading to data leakage or the suffering of malicious attacks. Consequently, several firms have started to build private blockchains for data preservation and verification. Private blockchains are usually built internally in companies; once a firm's internal network opens for massive external logins, many servers and private blockchains cannot work properly to protect data. In general, private blockchains store critical personal information or relevant confidential data; once a private blockchain opens to external access, all the information in the network nodes will be exposed, making private them lose their protection functions. This paper proposes a security mechanism using a private blockchain system based on zero trust architecture. The zero trust architecture tracks every user's network conditions and analyses whether their behaviors are authorized. Additionally, the system utilizes micro-segmentation to divide the private blockchain, preventing the system from malicious attacks. The proposed system employs user multi-factor authentication to identify users, and the zero trust architecture tracks and analyzes if users' behaviors are reasonable. This method effectively ensures corporate networks' security and enables private blockchain to filter legal and authorized users to access and verify.
{"title":"A Private Blockchain System based on Zero Trust Architecture","authors":"Yao-Chung Chang, Yu-Shan Lin, Arun Kumar Sangaiahc, Hsin-Te Wu","doi":"10.23919/ICACT60172.2024.10471993","DOIUrl":"https://doi.org/10.23919/ICACT60172.2024.10471993","url":null,"abstract":"During the outbreak of COVID-19, many enterprises massively created Virtual Private Networks (VPNs) for companies to cooperate; however, these accounts lacked efficient management after the epidemic, leading to data leakage or the suffering of malicious attacks. Consequently, several firms have started to build private blockchains for data preservation and verification. Private blockchains are usually built internally in companies; once a firm's internal network opens for massive external logins, many servers and private blockchains cannot work properly to protect data. In general, private blockchains store critical personal information or relevant confidential data; once a private blockchain opens to external access, all the information in the network nodes will be exposed, making private them lose their protection functions. This paper proposes a security mechanism using a private blockchain system based on zero trust architecture. The zero trust architecture tracks every user's network conditions and analyses whether their behaviors are authorized. Additionally, the system utilizes micro-segmentation to divide the private blockchain, preventing the system from malicious attacks. The proposed system employs user multi-factor authentication to identify users, and the zero trust architecture tracks and analyzes if users' behaviors are reasonable. This method effectively ensures corporate networks' security and enables private blockchain to filter legal and authorized users to access and verify.","PeriodicalId":518077,"journal":{"name":"2024 26th International Conference on Advanced Communications Technology (ICACT)","volume":"33 1","pages":"143-146"},"PeriodicalIF":0.0,"publicationDate":"2024-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140528122","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}
Pub Date : 2024-02-04DOI: 10.23919/ICACT60172.2024.10472002
Tae-Gyu Lee
This paper proposes an effective testing method combining blockchain and metaverse technologies. This research analyses the interaction between blockchain networks and virtual reality worlds, developing a secure and efficient testing process. The paper evaluates the interaction between metaverse and blockchain in various scenarios, offering insights to enhance reliability and security. This study makes a significant contribution to strengthening safety in the digital environment through the fusion of metaverse and blockchain technologies. This study proposes key evaluation factors and formulas to objectively assess the integration of blockchain and the metaverse services. These evaluation metrics can be valuable in the development of assessment tools and simulation instruments.
{"title":"A Test Method for the Convergence of the Metaverse and Blockchain","authors":"Tae-Gyu Lee","doi":"10.23919/ICACT60172.2024.10472002","DOIUrl":"https://doi.org/10.23919/ICACT60172.2024.10472002","url":null,"abstract":"This paper proposes an effective testing method combining blockchain and metaverse technologies. This research analyses the interaction between blockchain networks and virtual reality worlds, developing a secure and efficient testing process. The paper evaluates the interaction between metaverse and blockchain in various scenarios, offering insights to enhance reliability and security. This study makes a significant contribution to strengthening safety in the digital environment through the fusion of metaverse and blockchain technologies. This study proposes key evaluation factors and formulas to objectively assess the integration of blockchain and the metaverse services. These evaluation metrics can be valuable in the development of assessment tools and simulation instruments.","PeriodicalId":518077,"journal":{"name":"2024 26th International Conference on Advanced Communications Technology (ICACT)","volume":"16 9","pages":"321-326"},"PeriodicalIF":0.0,"publicationDate":"2024-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140528280","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}
Pub Date : 2024-02-04DOI: 10.23919/ICACT60172.2024.10472000
John Raymund B. Baragas, Lea D. Austero, J. Llovido, Lany L. Maceda, Mideth B. Abisado
In spite of the broad implementation of the Universal Access to Quality Tertiary Education Act (UAQTE) - a groundbreaking legislation benefitting over 2 million students since its enactment in 2017 - a comprehensive evaluation of its outcomes has been notably absent. To bridge this gap, an extensive survey was undertaken among graduating tertiary students in selected regions of the Philippines. This strategically designed survey aimed to pinpoint overlooked aspects of UAQTE and capture firsthand insights from its recipients. The methodology employed to create this survey included focus-group discussions with various stakeholders (i.e., students, parents, faculty) and a pilot test reflecting the target demographic. To facilitate analysis of the results of 1462 responses, five regression machine learning algorithms were then employed to analyze questionnaire data. The decision tree regressor with a root-mean-squared-error of 0.6881 was found to be the best performing model describing the collected questionnaire data. Shapley explanations of the best performing model highlighted the desire of the recipient to pursue international employment as the top predictor of satisfaction in UAQTE among its recipients. Furthermore, insights from employed topic modeling among the open-ended questions in the deployed survey suggested potential inadequacy of UAQTE subsidies, specifically to recipients whose pursued degrees are in the science, technology, engineering, and mathematics courses. This substantial finding promises valuable insights into the effectiveness of the legislation and may inform future policy adjustments to better address the diverse needs of tertiary education in the Philippines. Overall, this research provides a robust framework for assessing the impact of UAQTE and showcases a methodologically sound approach in integrating machine learning and qualitative analysis.
{"title":"Leveraging Machine Learning to Uncover Key Factors Influencing Satisfaction Among Free Tertiary Education Recipients in the Philippines","authors":"John Raymund B. Baragas, Lea D. Austero, J. Llovido, Lany L. Maceda, Mideth B. Abisado","doi":"10.23919/ICACT60172.2024.10472000","DOIUrl":"https://doi.org/10.23919/ICACT60172.2024.10472000","url":null,"abstract":"In spite of the broad implementation of the Universal Access to Quality Tertiary Education Act (UAQTE) - a groundbreaking legislation benefitting over 2 million students since its enactment in 2017 - a comprehensive evaluation of its outcomes has been notably absent. To bridge this gap, an extensive survey was undertaken among graduating tertiary students in selected regions of the Philippines. This strategically designed survey aimed to pinpoint overlooked aspects of UAQTE and capture firsthand insights from its recipients. The methodology employed to create this survey included focus-group discussions with various stakeholders (i.e., students, parents, faculty) and a pilot test reflecting the target demographic. To facilitate analysis of the results of 1462 responses, five regression machine learning algorithms were then employed to analyze questionnaire data. The decision tree regressor with a root-mean-squared-error of 0.6881 was found to be the best performing model describing the collected questionnaire data. Shapley explanations of the best performing model highlighted the desire of the recipient to pursue international employment as the top predictor of satisfaction in UAQTE among its recipients. Furthermore, insights from employed topic modeling among the open-ended questions in the deployed survey suggested potential inadequacy of UAQTE subsidies, specifically to recipients whose pursued degrees are in the science, technology, engineering, and mathematics courses. This substantial finding promises valuable insights into the effectiveness of the legislation and may inform future policy adjustments to better address the diverse needs of tertiary education in the Philippines. Overall, this research provides a robust framework for assessing the impact of UAQTE and showcases a methodologically sound approach in integrating machine learning and qualitative analysis.","PeriodicalId":518077,"journal":{"name":"2024 26th International Conference on Advanced Communications Technology (ICACT)","volume":"20 4","pages":"206-210"},"PeriodicalIF":0.0,"publicationDate":"2024-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140528296","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}
Pub Date : 2024-02-04DOI: 10.23919/ICACT60172.2024.10471923
B. K. Baniya, Thomas Rush
The ubiquity of the Internet plays a pivotal role in connecting individuals and facilitating easy access to various essential services. As of 2022, the International Telecommunication Union (ITU) reports that approximately 5.3 billion people are connected to the internet, underscoring its widespread coverage and indispensability in our daily lives. This expansive coverage enables a myriad of services, including communication, e-banking, e-commerce, online social security access, medical reporting, education, entertainment, weather information, traffic monitoring, online surveys, and more. However, this open platform also exposes vulnerabilities to malicious users who actively seek to exploit weaknesses in the virtual domain, aiming to gain credentials, financial benefits, or reveal critical information through the use of malware. This constant threat poses a serious challenge in safeguarding sensitive information in cyberspace. To address this challenge, we propose the use of ensemble and deep neural network (DNN) based machine learning (ML) techniques to detect malicious intent packets before they can infiltrate or compromise systems and applications. Attackers employ various tactics to evade existing security systems, such as antivirus or intrusion detection systems, necessitating a robust defense mechanism. Our approach involves implementing an ensemble, a collection of diverse classifiers capable of capturing different attack patterns and better generalizing from highly relevant features, thus enhancing protection against a variety of attacks compared to a single classifier. Given the highly unbalanced dataset, the ensemble classifier effectively addresses this condition, and oversampling is also employed to minimize bias toward the majority class. To prevent overfitting, we utilize Random Forest (RF) and the dropout technique in the DNN. Furthermore, we introduce a DNN to assess its ability to recognize complex attack patterns and variations compared to the ensemble approach. Various metrics, such as classification accuracy, precision, recall, Fl-score, confusion matrix are utilized to measure the performance of our proposed system, with the aim of outperforming current state-of-the-art intrusion detection systems.
{"title":"Intelligent Anomaly Detection System Based on Ensemble and Deep Learning","authors":"B. K. Baniya, Thomas Rush","doi":"10.23919/ICACT60172.2024.10471923","DOIUrl":"https://doi.org/10.23919/ICACT60172.2024.10471923","url":null,"abstract":"The ubiquity of the Internet plays a pivotal role in connecting individuals and facilitating easy access to various essential services. As of 2022, the International Telecommunication Union (ITU) reports that approximately 5.3 billion people are connected to the internet, underscoring its widespread coverage and indispensability in our daily lives. This expansive coverage enables a myriad of services, including communication, e-banking, e-commerce, online social security access, medical reporting, education, entertainment, weather information, traffic monitoring, online surveys, and more. However, this open platform also exposes vulnerabilities to malicious users who actively seek to exploit weaknesses in the virtual domain, aiming to gain credentials, financial benefits, or reveal critical information through the use of malware. This constant threat poses a serious challenge in safeguarding sensitive information in cyberspace. To address this challenge, we propose the use of ensemble and deep neural network (DNN) based machine learning (ML) techniques to detect malicious intent packets before they can infiltrate or compromise systems and applications. Attackers employ various tactics to evade existing security systems, such as antivirus or intrusion detection systems, necessitating a robust defense mechanism. Our approach involves implementing an ensemble, a collection of diverse classifiers capable of capturing different attack patterns and better generalizing from highly relevant features, thus enhancing protection against a variety of attacks compared to a single classifier. Given the highly unbalanced dataset, the ensemble classifier effectively addresses this condition, and oversampling is also employed to minimize bias toward the majority class. To prevent overfitting, we utilize Random Forest (RF) and the dropout technique in the DNN. Furthermore, we introduce a DNN to assess its ability to recognize complex attack patterns and variations compared to the ensemble approach. Various metrics, such as classification accuracy, precision, recall, Fl-score, confusion matrix are utilized to measure the performance of our proposed system, with the aim of outperforming current state-of-the-art intrusion detection systems.","PeriodicalId":518077,"journal":{"name":"2024 26th International Conference on Advanced Communications Technology (ICACT)","volume":"94 ","pages":"137-142"},"PeriodicalIF":0.0,"publicationDate":"2024-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140528308","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}
In order to ensure the smooth construction of highway tunnel construction project, it is necessary to monitor and analyze the tunnel deformation. Most of the existing monitoring systems at home and abroad are for independent projects or independent equipment monitoring, the system application scope is small, and the data processing is not perfect. Based on this, this paper takes the tunnel deformation monitoring during highway tunnel construction as the research object, and adopts LSTM to predict the tunnel section deformation. The short-duration memory neural network model can learn from memory and then predict the subsequent information. After establishing the neural network model, the model parameters such as learning rate, number of hidden nodes, number of iteration steps and unit input are tested and adjusted by comparative experiment, and the best fitting effect is obtained at last. The tunnel prediction model can predict the deformation of tunnel section in real time, and has high precision. At the same time, it can leave enough reaction time for construction personnel. It can be predicted that it has good development potential in the future.
{"title":"Research on LSTM-based Model for Predicting Deformation of Tunnel Section During Construction Period","authors":"Jiwen Zhang, Kai-Qi Yuan, Jianjun Mao, Yincai Cai, Dongfeng Lei, Jinyang Deng, Ting Peng","doi":"10.23919/ICACT60172.2024.10471966","DOIUrl":"https://doi.org/10.23919/ICACT60172.2024.10471966","url":null,"abstract":"In order to ensure the smooth construction of highway tunnel construction project, it is necessary to monitor and analyze the tunnel deformation. Most of the existing monitoring systems at home and abroad are for independent projects or independent equipment monitoring, the system application scope is small, and the data processing is not perfect. Based on this, this paper takes the tunnel deformation monitoring during highway tunnel construction as the research object, and adopts LSTM to predict the tunnel section deformation. The short-duration memory neural network model can learn from memory and then predict the subsequent information. After establishing the neural network model, the model parameters such as learning rate, number of hidden nodes, number of iteration steps and unit input are tested and adjusted by comparative experiment, and the best fitting effect is obtained at last. The tunnel prediction model can predict the deformation of tunnel section in real time, and has high precision. At the same time, it can leave enough reaction time for construction personnel. It can be predicted that it has good development potential in the future.","PeriodicalId":518077,"journal":{"name":"2024 26th International Conference on Advanced Communications Technology (ICACT)","volume":"62 ","pages":"395-401"},"PeriodicalIF":0.0,"publicationDate":"2024-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140528315","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}
Generating and distributing entangled pairs between arbitrary nodes is essential to fully realize the network's capabilities, with the challenges of limited qubit resources, severe decoherence and stochastic physical mechanism. In this paper, we model the service process of quantum repeater nodes based on the concept of queuing theory to help characterize their availability. Further, we propose a link-disjoint multi-path routing algorithm, with repeaters' availability, nodes' qubit capacity, entangled links' fidelity and classical delay taken into consideration. The performance of our scheme has been evaluated with simulated environment and compared with other existing routing schemes.
{"title":"A Reliable Routing Method for Remote Entanglement Distribution under Limited Resources","authors":"Tianzhu Hu, Xiaofeng Jiang, Tianze Zhu, Xin Sun, Haomin Chen, Jian Yang","doi":"10.23919/ICACT60172.2024.10471969","DOIUrl":"https://doi.org/10.23919/ICACT60172.2024.10471969","url":null,"abstract":"Generating and distributing entangled pairs between arbitrary nodes is essential to fully realize the network's capabilities, with the challenges of limited qubit resources, severe decoherence and stochastic physical mechanism. In this paper, we model the service process of quantum repeater nodes based on the concept of queuing theory to help characterize their availability. Further, we propose a link-disjoint multi-path routing algorithm, with repeaters' availability, nodes' qubit capacity, entangled links' fidelity and classical delay taken into consideration. The performance of our scheme has been evaluated with simulated environment and compared with other existing routing schemes.","PeriodicalId":518077,"journal":{"name":"2024 26th International Conference on Advanced Communications Technology (ICACT)","volume":"98 ","pages":"360-364"},"PeriodicalIF":0.0,"publicationDate":"2024-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140528287","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}
Pub Date : 2024-02-04DOI: 10.23919/ICACT60172.2024.10471911
Narantuya Erkhembaatar, O. Bataa
The ICT Development Index (IDI) functions as an established tool for assessing the digital divide and facilitating comparisons of lCT performance within and between countries. Information entropy, representing the level of uncertainty in a random variable, can be applied across various fields, including information and communication technology (LCT), When designing data analysis using information entropy, it is essential to observe, evaluate, and utilize metrics derived from this method. The proposed methodology aims to allocate weights to the indicators within the ICT Development Index for country ranking. To assess the efficacy of this methodology, we explored its potential applications in evaluating indexes. Our model incorporates an innovative approach that combines the entropy weight coefficient method with the correlation coefficient weighting method. We present the evaluation results of the integrated calculation method in Mongolia.
{"title":"A Study on the Evaluation of the ICT Development Indexes and Some Results","authors":"Narantuya Erkhembaatar, O. Bataa","doi":"10.23919/ICACT60172.2024.10471911","DOIUrl":"https://doi.org/10.23919/ICACT60172.2024.10471911","url":null,"abstract":"The ICT Development Index (IDI) functions as an established tool for assessing the digital divide and facilitating comparisons of lCT performance within and between countries. Information entropy, representing the level of uncertainty in a random variable, can be applied across various fields, including information and communication technology (LCT), When designing data analysis using information entropy, it is essential to observe, evaluate, and utilize metrics derived from this method. The proposed methodology aims to allocate weights to the indicators within the ICT Development Index for country ranking. To assess the efficacy of this methodology, we explored its potential applications in evaluating indexes. Our model incorporates an innovative approach that combines the entropy weight coefficient method with the correlation coefficient weighting method. We present the evaluation results of the integrated calculation method in Mongolia.","PeriodicalId":518077,"journal":{"name":"2024 26th International Conference on Advanced Communications Technology (ICACT)","volume":"14 2","pages":"84-88"},"PeriodicalIF":0.0,"publicationDate":"2024-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140528283","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}
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