Pub Date : 2022-10-01DOI: 10.1109/msmc.2022.3211365
{"title":"Call for Papers: Special Issue on Human-centered Collaborative Systems","authors":"","doi":"10.1109/msmc.2022.3211365","DOIUrl":"https://doi.org/10.1109/msmc.2022.3211365","url":null,"abstract":"","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"16 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87294615","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 : 2022-10-01DOI: 10.1109/MSMC.2022.3183806
R. Balamurugan, Kshitiz Choudhary, S. Raja
Floods are one of the deadliest disasters in the coastal areas of India. Consistently, flood, the most widely recognized catastrophe in India, has an enormous effect on the nation’s property and lives. Therefore, this article is focused on developing an effective flood-prediction system using machine learning (ML) algorithms that can help with preventing the loss of human lives and property. We will use k-nearest neighbors (KNNs), support vector machines (SVMs), random forests (RFs), and decision trees (DTs) to build our ML models. And to resolve the issue of oversampling and low accuracy, a stacking classifier will be used. For comparison among these models, we will use accuracy, f1-scores, recall, and precision. The results indicate that stacked models are best for predicting floods due to real-time rainfall in that area. It is noted that Andhra Pradesh achieves the highest accuracy of 97.91%, whereas Orissa achieves an accuracy of 92.36%, lowest among the eight coastal states.
{"title":"Prediction of Flooding Due to Heavy Rainfall in India Using Machine Learning Algorithms: Providing Advanced Warning","authors":"R. Balamurugan, Kshitiz Choudhary, S. Raja","doi":"10.1109/MSMC.2022.3183806","DOIUrl":"https://doi.org/10.1109/MSMC.2022.3183806","url":null,"abstract":"Floods are one of the deadliest disasters in the coastal areas of India. Consistently, flood, the most widely recognized catastrophe in India, has an enormous effect on the nation’s property and lives. Therefore, this article is focused on developing an effective flood-prediction system using machine learning (ML) algorithms that can help with preventing the loss of human lives and property. We will use k-nearest neighbors (KNNs), support vector machines (SVMs), random forests (RFs), and decision trees (DTs) to build our ML models. And to resolve the issue of oversampling and low accuracy, a stacking classifier will be used. For comparison among these models, we will use accuracy, f1-scores, recall, and precision. The results indicate that stacked models are best for predicting floods due to real-time rainfall in that area. It is noted that Andhra Pradesh achieves the highest accuracy of 97.91%, whereas Orissa achieves an accuracy of 92.36%, lowest among the eight coastal states.","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"23 1","pages":"26-33"},"PeriodicalIF":3.2,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85119255","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 : 2022-10-01DOI: 10.1109/msmc.2022.3205500
Haibin Zhu
{"title":"Getting to Know Our Volunteers","authors":"Haibin Zhu","doi":"10.1109/msmc.2022.3205500","DOIUrl":"https://doi.org/10.1109/msmc.2022.3205500","url":null,"abstract":"","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"21 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74169384","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 : 2022-10-01DOI: 10.1109/MSMC.2022.3192658
Wenbing Zhao
Blockchain has become one of the hottest research areas in recent years. The technology could potentially lead to a new generation of decentralized applications and decentralized autonomous organizations. Unfortunately, there is simply too much misinformation regarding blockchain. Most notably, blockchain has been used as a buzzword synonymous with data immutability and trust. In fact, this is far from the truth. In this article, we provide a concise description of exactly what blockchain technology is, including its design principle, building blocks, core innovations, and benefits. This is followed by an analysis of data immutability. We show that to create an insurmountable barrier against attacks on data immutability, decentralization and system scale are both necessary. Based on this analysis, we further dissect what benefits private and consortium blockchain could actually offer when decentralization is removed. We show that private and consortium blockchain cannot offer data immutability and trust as many works in the literature have claimed or implied. Instead, the centralized version of blockchain technology provides an elegant solution to achieving fault tolerance and atomic contract execution, which could make private and consortium blockchain useful for enterprises that would like to provide high availability to their customers and for their internal operations.
{"title":"On Blockchain: Design Principle, Building Blocks, Core Innovations, and Misconceptions","authors":"Wenbing Zhao","doi":"10.1109/MSMC.2022.3192658","DOIUrl":"https://doi.org/10.1109/MSMC.2022.3192658","url":null,"abstract":"Blockchain has become one of the hottest research areas in recent years. The technology could potentially lead to a new generation of decentralized applications and decentralized autonomous organizations. Unfortunately, there is simply too much misinformation regarding blockchain. Most notably, blockchain has been used as a buzzword synonymous with data immutability and trust. In fact, this is far from the truth. In this article, we provide a concise description of exactly what blockchain technology is, including its design principle, building blocks, core innovations, and benefits. This is followed by an analysis of data immutability. We show that to create an insurmountable barrier against attacks on data immutability, decentralization and system scale are both necessary. Based on this analysis, we further dissect what benefits private and consortium blockchain could actually offer when decentralization is removed. We show that private and consortium blockchain cannot offer data immutability and trust as many works in the literature have claimed or implied. Instead, the centralized version of blockchain technology provides an elegant solution to achieving fault tolerance and atomic contract execution, which could make private and consortium blockchain useful for enterprises that would like to provide high availability to their customers and for their internal operations.","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"22 1","pages":"6-14"},"PeriodicalIF":3.2,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86753079","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 : 2022-10-01DOI: 10.1109/msmc.2022.3205492
Celal Savur, F. Sahin
{"title":"The 17th IEEE International Conference on Systems and Systems Engineering [Conference Reports]","authors":"Celal Savur, F. Sahin","doi":"10.1109/msmc.2022.3205492","DOIUrl":"https://doi.org/10.1109/msmc.2022.3205492","url":null,"abstract":"","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"205 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74849728","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 : 2022-10-01DOI: 10.1109/MSMC.2022.3198027
F. Shakiba, S. Azizi, Mengchu Zhou
Deep learning methods have shown great promise in high-voltage transmission lines’ (TLs’) intelligent inspections. The expansion of power systems, including TLs, has brought the problem of insulator fault detection into account more than before. In this article, a novel transfer learning framework based on a pretrained VGG-19 deep convolutional neural network (CNN) is proposed to detect “missing faults” (broken insulators) in aerial images. In this procedure, a well-known large imagery dataset called ImageNet is used to train VGG-19, and then the knowledge of this deep CNN is transferred. By using a few layers for a fine-tuning purpose, the newly built deep CNN is capable of distinguishing the corrupted and intact insulators. This method is able to diagnose these faults using the aerial images taken from TLs in different environments. The original dataset used in this article is the Chinese Power Line Insulator Dataset (CPLID), which is an imbalanced dataset and includes only 3,808 insulator images. Therefore, a random image-augmentation procedure is proposed and applied to generate a more suitable dataset with 16,720 images. This new dataset allows us to offer higher detection accuracy than the original one because it is a balanced dataset. Training a deep CNN by using it gives more power to the system for detecting the corrupted insulators in different situations such as rotated, dark, and blurry images with complex backgrounds. The comparison results of this study show the advantages of the proposed method over various existing ones.
{"title":"A Transfer Learning-Based Method to Detect Insulator Faults of High-Voltage Transmission Lines via Aerial Images: Distinguishing Intact and Broken Insulator Images","authors":"F. Shakiba, S. Azizi, Mengchu Zhou","doi":"10.1109/MSMC.2022.3198027","DOIUrl":"https://doi.org/10.1109/MSMC.2022.3198027","url":null,"abstract":"Deep learning methods have shown great promise in high-voltage transmission lines’ (TLs’) intelligent inspections. The expansion of power systems, including TLs, has brought the problem of insulator fault detection into account more than before. In this article, a novel transfer learning framework based on a pretrained VGG-19 deep convolutional neural network (CNN) is proposed to detect “missing faults” (broken insulators) in aerial images. In this procedure, a well-known large imagery dataset called ImageNet is used to train VGG-19, and then the knowledge of this deep CNN is transferred. By using a few layers for a fine-tuning purpose, the newly built deep CNN is capable of distinguishing the corrupted and intact insulators. This method is able to diagnose these faults using the aerial images taken from TLs in different environments. The original dataset used in this article is the Chinese Power Line Insulator Dataset (CPLID), which is an imbalanced dataset and includes only 3,808 insulator images. Therefore, a random image-augmentation procedure is proposed and applied to generate a more suitable dataset with 16,720 images. This new dataset allows us to offer higher detection accuracy than the original one because it is a balanced dataset. Training a deep CNN by using it gives more power to the system for detecting the corrupted insulators in different situations such as rotated, dark, and blurry images with complex backgrounds. The comparison results of this study show the advantages of the proposed method over various existing ones.","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"23 1","pages":"15-25"},"PeriodicalIF":3.2,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86067747","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 : 2022-10-01DOI: 10.1109/MSMC.2022.3188406
Shoayee Alotaibi, Kusum Yadav
This article aims to address the issues above by defining a social network information transmission model with the amalgamation of explainable artificial intelligence (XAI) compatible with the paranormal connection. It suggests a way of information transmission called local greedy that aids in the preservation of user privacy. Its impact acts as a buffer between the conflicting interests of privacy protection and information dissemination. Aiming at the enumeration problem of seed set selection, an incremental technique is presented for constructing seed sets to minimize time overhead; a local influence subgraph method for computing nodes is also proposed to evaluate the influence of seed set propagation rapidly. The group meets privacy protection conditions. A strategy is presented to determine the upper bound on the likelihood of a node leaking state without resorting to the time-consuming Monte Carlo approach with XAI on the crawled Sina Weibo dataset. The suggested technique is validated experimentally and by example analysis, and the findings demonstrate its usefulness.
{"title":"Explainable Artificial-Intelligence-Based Privacy Preservation Approach for Information Dissemination on Social Networks: An Incremental Technique","authors":"Shoayee Alotaibi, Kusum Yadav","doi":"10.1109/MSMC.2022.3188406","DOIUrl":"https://doi.org/10.1109/MSMC.2022.3188406","url":null,"abstract":"This article aims to address the issues above by defining a social network information transmission model with the amalgamation of explainable artificial intelligence (XAI) compatible with the paranormal connection. It suggests a way of information transmission called local greedy that aids in the preservation of user privacy. Its impact acts as a buffer between the conflicting interests of privacy protection and information dissemination. Aiming at the enumeration problem of seed set selection, an incremental technique is presented for constructing seed sets to minimize time overhead; a local influence subgraph method for computing nodes is also proposed to evaluate the influence of seed set propagation rapidly. The group meets privacy protection conditions. A strategy is presented to determine the upper bound on the likelihood of a node leaking state without resorting to the time-consuming Monte Carlo approach with XAI on the crawled Sina Weibo dataset. The suggested technique is validated experimentally and by example analysis, and the findings demonstrate its usefulness.","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"21 1","pages":"44-47"},"PeriodicalIF":3.2,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85184520","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 : 2022-10-01DOI: 10.1109/msmc.2022.3211385
{"title":"Call for Papers: Special Issue on Federated Learning for Cybersecurity Management in the era of AI","authors":"","doi":"10.1109/msmc.2022.3211385","DOIUrl":"https://doi.org/10.1109/msmc.2022.3211385","url":null,"abstract":"","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"54 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82630523","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 : 2022-10-01DOI: 10.1109/msmc.2022.3198845
G. Dhiman, A. Nagar, Seifedine Kadry
{"title":"Explainable Artificial Intelligence for the Social Internet of Things: Analysis and Modeling Using Collaborative Technologies [Special Section Editorial]","authors":"G. Dhiman, A. Nagar, Seifedine Kadry","doi":"10.1109/msmc.2022.3198845","DOIUrl":"https://doi.org/10.1109/msmc.2022.3198845","url":null,"abstract":"","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"19 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82951716","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 : 2022-10-01DOI: 10.1109/msmc.2022.3211386
{"title":"Call for Papers: Special Issue on Cooperative design, visualization, engineering, and applications","authors":"","doi":"10.1109/msmc.2022.3211386","DOIUrl":"https://doi.org/10.1109/msmc.2022.3211386","url":null,"abstract":"","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"64 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74379074","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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