Pub Date : 2021-03-10DOI: 10.1080/24751839.2021.1893496
Péter Marjai, P. Lehotay-Kéry, A. Kiss
ABSTRACT In today's rushing world, there's an ever-increasing usage of networking equipment. These devices log their operations; however, there could be errors that result in the restart of the given device. There could be different patterns before different errors. Our main goal is to predict the upcoming error based on the log lines of the actual file. To achieve this, we use document similarity. One of the key concepts of information retrieval is document similarity which is an indicator of how analogous (or different) documents are. In this paper, we are studying the effectiveness of prediction based on cosine similarity, Jaccard similarity, and Euclidean distance of rows before restarts. We use different features like TFIDF, Doc2Vec, LSH, and others in conjunction with these distance measures. Since networking devices produce lots of log files, we use Spark for Big data computing.
{"title":"Document similarity for error prediction","authors":"Péter Marjai, P. Lehotay-Kéry, A. Kiss","doi":"10.1080/24751839.2021.1893496","DOIUrl":"https://doi.org/10.1080/24751839.2021.1893496","url":null,"abstract":"ABSTRACT In today's rushing world, there's an ever-increasing usage of networking equipment. These devices log their operations; however, there could be errors that result in the restart of the given device. There could be different patterns before different errors. Our main goal is to predict the upcoming error based on the log lines of the actual file. To achieve this, we use document similarity. One of the key concepts of information retrieval is document similarity which is an indicator of how analogous (or different) documents are. In this paper, we are studying the effectiveness of prediction based on cosine similarity, Jaccard similarity, and Euclidean distance of rows before restarts. We use different features like TFIDF, Doc2Vec, LSH, and others in conjunction with these distance measures. Since networking devices produce lots of log files, we use Spark for Big data computing.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2021-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24751839.2021.1893496","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49431722","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 : 2021-03-10DOI: 10.1080/24751839.2021.1893495
E. Yılmaz, M. Trocan
ABSTRACT Differential diagnosis of malignant melanoma, which is the cause of more than 75% of deaths amongst skin lesions, is vital for patients. Artificial intelligence-based decision support systems developed for the analysis of medical images are in the solution of such problems. In recent years, various deep learning algorithms have been developed to be used for this purpose. In our previous study, we compared the performances of AlexNet, GoogLeNet and ResNet-50 for the differential diagnosis of benign and malignant melanoma on International Skin Imaging Collaboration: Melanoma Project (ISIC) dataset. In this study, we proposed a CNN model by modifying the GoogLeNet algorithm and we compared the performance of this model with the previous results. For the experiments, we used 19,373 benign and 2197 malignant diagnosed dermoscopy images obtained from this public archive. We compared the performance results according to the eight different performance metrics including polygon area metric (PAM), classification accuracy (CA), sensitivity (SE), specificity (SP), area under curve (AUC), kappa (K), F measure metric (FM) and time complexity (TC) measures. According to the results, our proposed CNN achieved the best classification accuracy with 0.9309 and decreased the time complexity of GoogLeNet from 283 min 50 to 256 min 26 s.
摘要恶性黑色素瘤是导致75%以上皮肤病变死亡的原因，对患者进行鉴别诊断至关重要。为分析医学图像而开发的基于人工智能的决策支持系统正是解决这些问题的方法。近年来，各种深度学习算法已被开发用于此目的。在我们之前的研究中，我们在国际皮肤成像合作组织：黑色素瘤项目（ISIC）数据集上比较了AlexNet、GoogLeNet和ResNet-50在良恶性黑色素瘤鉴别诊断方面的性能。在这项研究中，我们通过修改GoogLeNet算法提出了一个CNN模型，并将该模型的性能与之前的结果进行了比较。在实验中，我们使用了从这个公共档案中获得的19373张良性和2197张恶性诊断的皮肤镜图像。我们根据八种不同的性能指标比较了性能结果，包括多边形面积指标（PAM）、分类准确度指标（CA）、敏感性指标（SE）、特异性指标（SP）、曲线下面积指标（AUC）、kappa指标（K）、F度量指标（FM）和时间复杂性指标（TC）。根据结果，我们提出的CNN获得了最佳的分类精度，为0.9309，并将GoogLeNet的时间复杂度从283降低 最小50至256 最小26 s
{"title":"A modified version of GoogLeNet for melanoma diagnosis","authors":"E. Yılmaz, M. Trocan","doi":"10.1080/24751839.2021.1893495","DOIUrl":"https://doi.org/10.1080/24751839.2021.1893495","url":null,"abstract":"ABSTRACT Differential diagnosis of malignant melanoma, which is the cause of more than 75% of deaths amongst skin lesions, is vital for patients. Artificial intelligence-based decision support systems developed for the analysis of medical images are in the solution of such problems. In recent years, various deep learning algorithms have been developed to be used for this purpose. In our previous study, we compared the performances of AlexNet, GoogLeNet and ResNet-50 for the differential diagnosis of benign and malignant melanoma on International Skin Imaging Collaboration: Melanoma Project (ISIC) dataset. In this study, we proposed a CNN model by modifying the GoogLeNet algorithm and we compared the performance of this model with the previous results. For the experiments, we used 19,373 benign and 2197 malignant diagnosed dermoscopy images obtained from this public archive. We compared the performance results according to the eight different performance metrics including polygon area metric (PAM), classification accuracy (CA), sensitivity (SE), specificity (SP), area under curve (AUC), kappa (K), F measure metric (FM) and time complexity (TC) measures. According to the results, our proposed CNN achieved the best classification accuracy with 0.9309 and decreased the time complexity of GoogLeNet from 283 min 50 to 256 min 26 s.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2021-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24751839.2021.1893495","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43177130","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 : 2021-02-01DOI: 10.1080/24751839.2021.1874252
L. Nemes, A. Kiss
ABSTRACT The prediction and speculation about the values of the stock market especially the values of the worldwide companies are a really interesting and attractive topic. In this article, we cover the topic of the stock value changes and predictions of the stock values using fresh scraped economic news about the companies. We are focussing on the headlines of economic news. We use numerous different tools to the sentiment analysis of the headlines. We consider BERT as the baseline and compare the results with three other tools, VADER, TextBlob, and a Recurrent Neural Network, and compare the sentiment results to the stock changes of the same period. The BERT and RNN were much more accurate, these tools were able to determine the emotional values without neutral sections, in contrast to the other two tools. Comparing these results with the movement of stock market values in the same time periods, we can establish the moment of the change occurred in the stock values with sentiment analysis of economic news headlines. Also we discovered a significant difference between the different models in terms of the effect of emotional values on the change in the value of the stock market by the correlation matrices.
{"title":"Prediction of stock values changes using sentiment analysis of stock news headlines","authors":"L. Nemes, A. Kiss","doi":"10.1080/24751839.2021.1874252","DOIUrl":"https://doi.org/10.1080/24751839.2021.1874252","url":null,"abstract":"ABSTRACT The prediction and speculation about the values of the stock market especially the values of the worldwide companies are a really interesting and attractive topic. In this article, we cover the topic of the stock value changes and predictions of the stock values using fresh scraped economic news about the companies. We are focussing on the headlines of economic news. We use numerous different tools to the sentiment analysis of the headlines. We consider BERT as the baseline and compare the results with three other tools, VADER, TextBlob, and a Recurrent Neural Network, and compare the sentiment results to the stock changes of the same period. The BERT and RNN were much more accurate, these tools were able to determine the emotional values without neutral sections, in contrast to the other two tools. Comparing these results with the movement of stock market values in the same time periods, we can establish the moment of the change occurred in the stock values with sentiment analysis of economic news headlines. Also we discovered a significant difference between the different models in terms of the effect of emotional values on the change in the value of the stock market by the correlation matrices.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24751839.2021.1874252","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41788841","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 : 2021-01-22DOI: 10.1080/24751839.2021.1871819
Vimal Kumar, N. Tyagi
ABSTRACT The bandwidth-intensive applications on Smart-Mobile-Devices (SMDs) are increasing with SMD's colossal growth. The overlapped cellular and non-cellular networks, in hot-spot-places, and SMDs capabilities are significant reasons for this growth. SMD's interfaces-RAT (Radio-Access-Technology) can have complementary link characteristics. The end-users can avail always-best-connectivity (ABC) on their SMDs with complementary RAT characteristics. This paper proposes an Integrated-multi-RAT-utilization (Im-Ru) framework for multipath-computing support to realize ABC for the end-users. The Im-Ru framework has two approaches. The First is a hybrid-RAT-discovery model based on SMD's interfaces, current-location, and identification using ANDSF and MIIS servers. The second is the user's preference-based RAT-selection using weighted-RAT-parameters. We observe that the Im-Ru framework for multipath-computing is useful in future 5G-NR networks. We analyzed the Im-Ru's performance related to average-throughput improvement over the existing approaches for SMD's different speeds and observed a significant improvement. The experimental results show that Im-Ru is more reliable by realizing lower packet-loss and delay than existing work.
{"title":"An integrated-multi-RAT framework for multipath-computing in heterogeneous-wireless network","authors":"Vimal Kumar, N. Tyagi","doi":"10.1080/24751839.2021.1871819","DOIUrl":"https://doi.org/10.1080/24751839.2021.1871819","url":null,"abstract":"ABSTRACT The bandwidth-intensive applications on Smart-Mobile-Devices (SMDs) are increasing with SMD's colossal growth. The overlapped cellular and non-cellular networks, in hot-spot-places, and SMDs capabilities are significant reasons for this growth. SMD's interfaces-RAT (Radio-Access-Technology) can have complementary link characteristics. The end-users can avail always-best-connectivity (ABC) on their SMDs with complementary RAT characteristics. This paper proposes an Integrated-multi-RAT-utilization (Im-Ru) framework for multipath-computing support to realize ABC for the end-users. The Im-Ru framework has two approaches. The First is a hybrid-RAT-discovery model based on SMD's interfaces, current-location, and identification using ANDSF and MIIS servers. The second is the user's preference-based RAT-selection using weighted-RAT-parameters. We observe that the Im-Ru framework for multipath-computing is useful in future 5G-NR networks. We analyzed the Im-Ru's performance related to average-throughput improvement over the existing approaches for SMD's different speeds and observed a significant improvement. The experimental results show that Im-Ru is more reliable by realizing lower packet-loss and delay than existing work.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24751839.2021.1871819","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48410749","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 : 2021-01-01DOI: 10.1080/24751839.2021.1963912
Jose Fabio Ribeiro Bezerra
{"title":"Content-based fake news classification through modified voting ensemble","authors":"Jose Fabio Ribeiro Bezerra","doi":"10.1080/24751839.2021.1963912","DOIUrl":"https://doi.org/10.1080/24751839.2021.1963912","url":null,"abstract":"","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60140756","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 : 2020-12-25DOI: 10.1080/24751839.2020.1843121
Redwan Hasif Alvi, M. H. Rahman, Adib Al Shaeed Khan, R. Rahman
ABSTRACT Neonatal sepsis that is a major threat for maternal and neonatal health worldwide. In this work we design non-invasive, deep learning classification models for predicting accurately and efficiently the early-onset sepsis in neonates in Neonatal Intensive Care Units. By non-invasive, it means that no external instrument or foreign body is introduced when taking data for the classifier. Moreover, the data collected for the purpose of predicting and classifying subjects with neonatal sepsis is in the form of tabular, structured data. The deep learning classification models we design and propose in this are known for working with time series, sequential or image data. Hence, the objective of the current research is to propose such a model that makes use of the powerful tools inherent in Neural Networks for pattern recognition, and use them to outperform traditional machine learning algorithms to detect early-onset neonatal sepsis. Real life neonatal sepsis data samples from two different hospitals are used (Crecer’s Hospital Centre in Cartagena-Colombia and Children’s Hospital of Philadelphia) to make the evaluation of the Neural Networks as authentic as possible.
{"title":"Deep learning approach on tabular data to predict early-onset neonatal sepsis","authors":"Redwan Hasif Alvi, M. H. Rahman, Adib Al Shaeed Khan, R. Rahman","doi":"10.1080/24751839.2020.1843121","DOIUrl":"https://doi.org/10.1080/24751839.2020.1843121","url":null,"abstract":"ABSTRACT Neonatal sepsis that is a major threat for maternal and neonatal health worldwide. In this work we design non-invasive, deep learning classification models for predicting accurately and efficiently the early-onset sepsis in neonates in Neonatal Intensive Care Units. By non-invasive, it means that no external instrument or foreign body is introduced when taking data for the classifier. Moreover, the data collected for the purpose of predicting and classifying subjects with neonatal sepsis is in the form of tabular, structured data. The deep learning classification models we design and propose in this are known for working with time series, sequential or image data. Hence, the objective of the current research is to propose such a model that makes use of the powerful tools inherent in Neural Networks for pattern recognition, and use them to outperform traditional machine learning algorithms to detect early-onset neonatal sepsis. Real life neonatal sepsis data samples from two different hospitals are used (Crecer’s Hospital Centre in Cartagena-Colombia and Children’s Hospital of Philadelphia) to make the evaluation of the Neural Networks as authentic as possible.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2020-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24751839.2020.1843121","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46500511","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 : 2020-12-25DOI: 10.1080/24751839.2020.1857039
E. Szwarc, I. Nielsen, Czeslaw Smutnicki, G. Bocewicz, Z. Banaszak, J. Bilski
ABSTRACT Selection of competent employees is one of the numerous factors that determine the success of a project. The literature describes many approaches that help decision makers to recruit candidates with the required skills. Only a few of them consider the disruptions that can occur during the implementation of a project, such as employee absenteeism and fluctuations in the duration of activities, etc. Collectively, what these approaches amount to is proactive planning of employee teams with redundant competences. Searching for competence frameworks robust to disruptions involves time-consuming calculations, which do not guarantee that an admissible solution will be found. In view of this, in the present study, we propose sufficient conditions, the fulfilment of which guarantees the existence of such a solution. By testing these conditions, one can determine whether there exists an admissible solution, i.e. whether it is at all worth searching for a robust competence framework. The possibilities of practical application of the proposed method are illustrated with an example.
{"title":"Competence-oriented project team planning – university case study","authors":"E. Szwarc, I. Nielsen, Czeslaw Smutnicki, G. Bocewicz, Z. Banaszak, J. Bilski","doi":"10.1080/24751839.2020.1857039","DOIUrl":"https://doi.org/10.1080/24751839.2020.1857039","url":null,"abstract":"ABSTRACT Selection of competent employees is one of the numerous factors that determine the success of a project. The literature describes many approaches that help decision makers to recruit candidates with the required skills. Only a few of them consider the disruptions that can occur during the implementation of a project, such as employee absenteeism and fluctuations in the duration of activities, etc. Collectively, what these approaches amount to is proactive planning of employee teams with redundant competences. Searching for competence frameworks robust to disruptions involves time-consuming calculations, which do not guarantee that an admissible solution will be found. In view of this, in the present study, we propose sufficient conditions, the fulfilment of which guarantees the existence of such a solution. By testing these conditions, one can determine whether there exists an admissible solution, i.e. whether it is at all worth searching for a robust competence framework. The possibilities of practical application of the proposed method are illustrated with an example.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2020-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24751839.2020.1857039","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44402716","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 : 2020-12-02DOI: 10.1080/24751839.2020.1847490
Inga Vagale, E. Lipenbergs, V. Bobrovs, G. Ivanovs
ABSTRACT In modern world, technology plays a significant role. Upcoming services, demanding a specific level of service quality that should be guaranteed no matter what, will impose obligations to network performance and capacity. European strategy for broadband development prescribes a set of quality indicators that networks should correspond. But imposed obligations themselves don’t guarantee the persistent level of quality. European initiatives of geographical mapping of broadband access, which are developed in order to monitor the development of internet access services, propose guidance for gathering and representation of estimated QoS parameters at the so-called QoS-1 level, whereas monitoring of actual network performance on QoS-2 level and representation of real and objective internet quality indicators rests undefined. Besides ensuring that quality is described in meaningful and comparable manner the general measurement methodology that suits various purposes should be established. This research is aimed to develop principles of monitoring and objective representation of internet access service quality parameters in specific location on QoS-2 level, as well as to establish joint mechanism to obtain the quality of service data that would be appropriate for different needs. This research is directed to the mobile internet access service.
{"title":"Development of internet measurement principles for representation of measured provision of service (QoS-2)","authors":"Inga Vagale, E. Lipenbergs, V. Bobrovs, G. Ivanovs","doi":"10.1080/24751839.2020.1847490","DOIUrl":"https://doi.org/10.1080/24751839.2020.1847490","url":null,"abstract":"ABSTRACT In modern world, technology plays a significant role. Upcoming services, demanding a specific level of service quality that should be guaranteed no matter what, will impose obligations to network performance and capacity. European strategy for broadband development prescribes a set of quality indicators that networks should correspond. But imposed obligations themselves don’t guarantee the persistent level of quality. European initiatives of geographical mapping of broadband access, which are developed in order to monitor the development of internet access services, propose guidance for gathering and representation of estimated QoS parameters at the so-called QoS-1 level, whereas monitoring of actual network performance on QoS-2 level and representation of real and objective internet quality indicators rests undefined. Besides ensuring that quality is described in meaningful and comparable manner the general measurement methodology that suits various purposes should be established. This research is aimed to develop principles of monitoring and objective representation of internet access service quality parameters in specific location on QoS-2 level, as well as to establish joint mechanism to obtain the quality of service data that would be appropriate for different needs. This research is directed to the mobile internet access service.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24751839.2020.1847490","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42374035","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 : 2020-11-27DOI: 10.1080/24751839.2020.1847507
Jiho Cho, Hongsuk Yi, Heejin Jung, Khac-Hoai Nam Bui
ABSTRACT Recently, with the rapid development of deep learning models, traffic analysis using image datasets recently has attracted more attention. Specifically, the network traffic can be represented to images as the input for deep learning models to provide various applications (e.g. Spatio-Temporal traffic forecasting). In this study, we propose a new image generation approach for traffic density classification in terms of large-scale road network. Particularly, traffic volume and speed are at certain areas able to be measured by using surveillance systems (e.g. loop detectors). However, measuring the density is difficult which depends on the spatial correlation from the perspective of the network. Consequently, an effective image generation approach, based on information arrival and departure time of vehicles, is proposed to deal with this problem. Regarding the experiment, traffic density classification using a convolutional neural network is executed on roadside equipment data of 11 continuous intersections for evaluating the effectiveness of the proposed approach.
{"title":"An image generation approach for traffic density classification at large-scale road network","authors":"Jiho Cho, Hongsuk Yi, Heejin Jung, Khac-Hoai Nam Bui","doi":"10.1080/24751839.2020.1847507","DOIUrl":"https://doi.org/10.1080/24751839.2020.1847507","url":null,"abstract":"ABSTRACT Recently, with the rapid development of deep learning models, traffic analysis using image datasets recently has attracted more attention. Specifically, the network traffic can be represented to images as the input for deep learning models to provide various applications (e.g. Spatio-Temporal traffic forecasting). In this study, we propose a new image generation approach for traffic density classification in terms of large-scale road network. Particularly, traffic volume and speed are at certain areas able to be measured by using surveillance systems (e.g. loop detectors). However, measuring the density is difficult which depends on the spatial correlation from the perspective of the network. Consequently, an effective image generation approach, based on information arrival and departure time of vehicles, is proposed to deal with this problem. Regarding the experiment, traffic density classification using a convolutional neural network is executed on roadside equipment data of 11 continuous intersections for evaluating the effectiveness of the proposed approach.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2020-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24751839.2020.1847507","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43212639","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 : 2020-11-27DOI: 10.1080/24751839.2020.1847379
Botambu Collins, Dinh Tuyen Hoang, N. Nguyen, D. Hwang
ABSTRACT Social media following its introduction has witnessed a lot of scholarly attention in recent years due to its growing popularity. These various social media sites have become the mecca of information because of their less costly and easy accessibility. Although these sites were developed to enhance our lives, they are seen as both angelic and vicious. Growing misinformation and fake content by malicious users have not only plagued our online social media ecosystem into chaos, but it also meted untold suffering to humankind. Recently, social media has witnessed a reverberation amid the proliferation of fake news which has made people reluctant to engage in genuine news sharing for fear that such information is false. Consequently, there is a dire need for these fake content to be detected and removed from social media. This study explores the various methods of combating fake news on social media such as Natural Language Processing, Hybrid model. We surmised that detecting fake news is a challenging and complex issue, however, it remains a workable task. Revelation in this study holds that the application of hybrid-machine learning techniques and the collective effort of humans could stand a higher chance of fighting misinformation on social media.
{"title":"Trends in combating fake news on social media – a survey","authors":"Botambu Collins, Dinh Tuyen Hoang, N. Nguyen, D. Hwang","doi":"10.1080/24751839.2020.1847379","DOIUrl":"https://doi.org/10.1080/24751839.2020.1847379","url":null,"abstract":"ABSTRACT Social media following its introduction has witnessed a lot of scholarly attention in recent years due to its growing popularity. These various social media sites have become the mecca of information because of their less costly and easy accessibility. Although these sites were developed to enhance our lives, they are seen as both angelic and vicious. Growing misinformation and fake content by malicious users have not only plagued our online social media ecosystem into chaos, but it also meted untold suffering to humankind. Recently, social media has witnessed a reverberation amid the proliferation of fake news which has made people reluctant to engage in genuine news sharing for fear that such information is false. Consequently, there is a dire need for these fake content to be detected and removed from social media. This study explores the various methods of combating fake news on social media such as Natural Language Processing, Hybrid model. We surmised that detecting fake news is a challenging and complex issue, however, it remains a workable task. Revelation in this study holds that the application of hybrid-machine learning techniques and the collective effort of humans could stand a higher chance of fighting misinformation on social media.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2020-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24751839.2020.1847379","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45998250","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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