Frontiers in Big Data最新文献_第6页

A novel approach to fake news classification using LSTM-based deep learning models 利用基于 LSTM 的深度学习模型进行假新闻分类的新方法

IF 3.1

Frontiers in Big Data Pub Date : 2024-01-08 DOI: 10.3389/fdata.2023.1320800

Halyna Padalko, Vasyl Chomko, D. Chumachenko

{"title":"A novel approach to fake news classification using LSTM-based deep learning models","authors":"Halyna Padalko, Vasyl Chomko, D. Chumachenko","doi":"10.3389/fdata.2023.1320800","DOIUrl":"https://doi.org/10.3389/fdata.2023.1320800","url":null,"abstract":"The rapid dissemination of information has been accompanied by the proliferation of fake news, posing significant challenges in discerning authentic news from fabricated narratives. This study addresses the urgent need for effective fake news detection mechanisms. The spread of fake news on digital platforms has necessitated the development of sophisticated tools for accurate detection and classification. Deep learning models, particularly Bi-LSTM and attention-based Bi-LSTM architectures, have shown promise in tackling this issue. This research utilized Bi-LSTM and attention-based Bi-LSTM models, integrating an attention mechanism to assess the significance of different parts of the input data. The models were trained on an 80% subset of the data and tested on the remaining 20%, employing comprehensive evaluation metrics including Recall, Precision, F1-Score, Accuracy, and Loss. Comparative analysis with existing models revealed the superior efficacy of the proposed architectures. The attention-based Bi-LSTM model demonstrated remarkable proficiency, outperforming other models in terms of accuracy (97.66%) and other key metrics. The study highlighted the potential of integrating advanced deep learning techniques in fake news detection. The proposed models set new standards in the field, offering effective tools for combating misinformation. Limitations such as data dependency, potential for overfitting, and language and context specificity were acknowledged. The research underscores the importance of leveraging cutting-edge deep learning methodologies, particularly attention mechanisms, in fake news identification. The innovative models presented pave the way for more robust solutions to counter misinformation, thereby preserving the veracity of digital information. Future research should focus on enhancing data diversity, model efficiency, and applicability across various languages and contexts.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"7 3","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139446656","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

CTAB-GAN+: enhancing tabular data synthesis. CTAB-GAN+：增强表格数据合成。

IF 3.1

Frontiers in Big Data Pub Date : 2024-01-08 eCollection Date: 2023-01-01 DOI: 10.3389/fdata.2023.1296508

Zilong Zhao, Aditya Kunar, Robert Birke, Hiek Van der Scheer, Lydia Y Chen

{"title":"CTAB-GAN+: enhancing tabular data synthesis.","authors":"Zilong Zhao, Aditya Kunar, Robert Birke, Hiek Van der Scheer, Lydia Y Chen","doi":"10.3389/fdata.2023.1296508","DOIUrl":"https://doi.org/10.3389/fdata.2023.1296508","url":null,"abstract":"The usage of synthetic data is gaining momentum in part due to the unavailability of original data due to privacy and legal considerations and in part due to its utility as an augmentation to the authentic data. Generative adversarial networks (GANs), a paragon of generative models, initially for images and subsequently for tabular data, has contributed many of the state-of-the-art synthesizers. As GANs improve, the synthesized data increasingly resemble the real data risking to leak privacy. Differential privacy (DP) provides theoretical guarantees on privacy loss but degrades data utility. Striking the best trade-off remains yet a challenging research question. In this study, we propose CTAB-GAN+ a novel conditional tabular GAN. CTAB-GAN+ improves upon state-of-the-art by (i) adding downstream losses to conditional GAN for higher utility synthetic data in both classification and regression domains; (ii) using Wasserstein loss with gradient penalty for better training convergence; (iii) introducing novel encoders targeting mixed continuous-categorical variables and variables with unbalanced or skewed data; and (iv) training with DP stochastic gradient descent to impose strict privacy guarantees. We extensively evaluate CTAB-GAN+ on statistical similarity and machine learning utility against state-of-the-art tabular GANs. The results show that CTAB-GAN+ synthesizes privacy-preserving data with at least 21.9% higher machine learning utility (i.e., F1-Score) across multiple datasets and learning tasks under given privacy budget.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"6 ","pages":"1296508"},"PeriodicalIF":3.1,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10801038/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139520685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybridization of long short-term memory neural network in fractional time series modeling of inflation 在通货膨胀的分数时间序列建模中混合使用长短期记忆神经网络

IF 3.1

Frontiers in Big Data Pub Date : 2024-01-04 DOI: 10.3389/fdata.2023.1282541

Erman Arif, Elin Herlinawati, D. Devianto, Mutia Yollanda, Dony Permana

{"title":"Hybridization of long short-term memory neural network in fractional time series modeling of inflation","authors":"Erman Arif, Elin Herlinawati, D. Devianto, Mutia Yollanda, Dony Permana","doi":"10.3389/fdata.2023.1282541","DOIUrl":"https://doi.org/10.3389/fdata.2023.1282541","url":null,"abstract":"Inflation is capable of significantly impacting monetary policy, thereby emphasizing the need for accurate forecasts to guide decisions aimed at stabilizing inflation rates. Given the significant relationship between inflation and monetary, it becomes feasible to detect long-memory patterns within the data. To capture these long-memory patterns, Autoregressive Fractionally Moving Average (ARFIMA) was developed as a valuable tool in data mining. Due to the challenges posed in residual assumptions, time series model has to be developed to address heteroscedasticity. Consequently, the implementation of a suitable model was imperative to rectify this effect within the residual ARFIMA. In this context, a novel hybrid model was proposed, with Generalized Autoregressive Conditional Heteroscedasticity (GARCH) being replaced by Long Short-Term Memory (LSTM) neural network. The network was used as iterative model to address this issue and achieve optimal parameters. Through a sensitivity analysis using mean absolute percentage error (MAPE), mean squared error (MSE), and mean absolute error (MAE), the performance of ARFIMA, ARFIMA-GARCH, and ARFIMA-LSTM models was assessed. The results showed that ARFIMA-LSTM excelled in simulating the inflation rate. This provided further evidence that inflation data showed characteristics of long memory, and the accuracy of the model was improved by integrating LSTM neural network.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"3 3","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139384694","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

Criminal clickbait: a panel data analysis on the attractiveness of online advertisements offering stolen data 犯罪点击诱饵：关于提供被盗数据的在线广告吸引力的面板数据分析

IF 3.1

Frontiers in Big Data Pub Date : 2023-12-22 DOI: 10.3389/fdata.2023.1320569

Renushka Madarie, Christianne J. de Poot, Marleen Weulen Kranenbarg

{"title":"Criminal clickbait: a panel data analysis on the attractiveness of online advertisements offering stolen data","authors":"Renushka Madarie, Christianne J. de Poot, Marleen Weulen Kranenbarg","doi":"10.3389/fdata.2023.1320569","DOIUrl":"https://doi.org/10.3389/fdata.2023.1320569","url":null,"abstract":"Few studies have examined the sales of stolen account credentials on darkweb markets. In this study, we tested how advertisement characteristics affect the popularity of illicit online advertisements offering account credentials. Unlike previous criminological research, we take a novel approach by assessing the applicability of knowledge on regular consumer behaviours instead of theories explaining offender behaviour.We scraped 1,565 unique advertisements offering credentials on a darkweb market. We used this panel data set to predict the simultaneous effects of the asking price, endorsement cues and title elements on advertisement popularity by estimating several hybrid panel data models.Most of our findings disconfirm our hypotheses. Asking price did not affect advertisement popularity. Endorsement cues, including vendor reputation and cumulative sales and views, had mixed and negative relationships, respectively, with advertisement popularity.Our results might suggest that account credentials are not simply regular products, but high-risk commodities that, paradoxically, become less attractive as they gain popularity. This study highlights the necessity of a deeper understanding of illicit online market dynamics to improve theories on illicit consumer behaviours and assist cybersecurity experts in disrupting criminal business models more effectively. We propose several avenues for future experimental research to gain further insights into these illicit processes.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"1 11","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138944240","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

Corrigendum: Non-invasive detection of anemia using lip mucosa images transfer learning convolutional neural networks. 更正：利用唇粘膜图像转移学习卷积神经网络对贫血进行无创检测。

IF 3.1

Frontiers in Big Data Pub Date : 2023-12-20 eCollection Date: 2023-01-01 DOI: 10.3389/fdata.2023.1338363

Shekhar Mahmud, Mohammed Mansour, Turker Berk Donmez, Mustafa Kutlu, Chris Freeman

引用次数: 0

Enhancing knowledge discovery from unstructured data using a deep learning approach to support subsurface modeling predictions 利用深度学习方法加强非结构化数据的知识发现，为地下建模预测提供支持

IF 3.1

Frontiers in Big Data Pub Date : 2023-12-19 DOI: 10.3389/fdata.2023.1227189

Brendan Hoover, Dakota Zaengle, M. Mark-Moser, Patrick C. Wingo, Anuj Suhag, Kelly Rose

{"title":"Enhancing knowledge discovery from unstructured data using a deep learning approach to support subsurface modeling predictions","authors":"Brendan Hoover, Dakota Zaengle, M. Mark-Moser, Patrick C. Wingo, Anuj Suhag, Kelly Rose","doi":"10.3389/fdata.2023.1227189","DOIUrl":"https://doi.org/10.3389/fdata.2023.1227189","url":null,"abstract":"Subsurface interpretations and models rely on knowledge from subject matter experts who utilize unstructured information from images, maps, cross sections, and other products to provide context to measured data (e. g., cores, well logs, seismic surveys). To enhance such knowledge discovery, we advanced the National Energy Technology Laboratory's (NETL) Subsurface Trend Analysis (STA) workflow with an artificial intelligence (AI) deep learning approach for image embedding. NETL's STA method offers a validated science-based approach of combining geologic systems knowledge, statistical modeling, and datasets to improve predictions of subsurface properties. The STA image embedding tool quickly extracts images from unstructured knowledge products like publications, maps, websites, and presentations; categorically labels the images; and creates a repository for geologic domain postulation. Via a case study on geographic and subsurface literature of the Gulf of Mexico (GOM), results show the STA image embedding tool extracts images and correctly labels them with ~90 to ~95% accuracy.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":" 31","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138962433","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

Beyond-accuracy: a review on diversity, serendipity, and fairness in recommender systems based on graph neural networks. 超越准确性：基于图神经网络的推荐系统中的多样性、偶然性和公平性综述。

IF 3.1

Frontiers in Big Data Pub Date : 2023-12-19 eCollection Date: 2023-01-01 DOI: 10.3389/fdata.2023.1251072

Tomislav Duricic, Dominik Kowald, Emanuel Lacic, Elisabeth Lex

{"title":"Beyond-accuracy: a review on diversity, serendipity, and fairness in recommender systems based on graph neural networks.","authors":"Tomislav Duricic, Dominik Kowald, Emanuel Lacic, Elisabeth Lex","doi":"10.3389/fdata.2023.1251072","DOIUrl":"10.3389/fdata.2023.1251072","url":null,"abstract":"By providing personalized suggestions to users, recommender systems have become essential to numerous online platforms. Collaborative filtering, particularly graph-based approaches using Graph Neural Networks (GNNs), have demonstrated great results in terms of recommendation accuracy. However, accuracy may not always be the most important criterion for evaluating recommender systems' performance, since beyond-accuracy aspects such as recommendation diversity, serendipity, and fairness can strongly influence user engagement and satisfaction. This review paper focuses on addressing these dimensions in GNN-based recommender systems, going beyond the conventional accuracy-centric perspective. We begin by reviewing recent developments in approaches that improve not only the accuracy-diversity trade-off but also promote serendipity, and fairness in GNN-based recommender systems. We discuss different stages of model development including data preprocessing, graph construction, embedding initialization, propagation layers, embedding fusion, score computation, and training methodologies. Furthermore, we present a look into the practical difficulties encountered in assuring diversity, serendipity, and fairness, while retaining high accuracy. Finally, we discuss potential future research directions for developing more robust GNN-based recommender systems that go beyond the unidimensional perspective of focusing solely on accuracy. This review aims to provide researchers and practitioners with an in-depth understanding of the multifaceted issues that arise when designing GNN-based recommender systems, setting our work apart by offering a comprehensive exploration of beyond-accuracy dimensions.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"6 ","pages":"1251072"},"PeriodicalIF":3.1,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10762851/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139089306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Corrigendum: Towards an understanding of global brain data governance: ethical positions that underpin global brain data governance discourse. 更正：对全球脑数据治理的理解：支撑全球脑数据治理讨论的伦理立场。

IF 3.1

Frontiers in Big Data Pub Date : 2023-12-19 eCollection Date: 2023-01-01 DOI: 10.3389/fdata.2023.1344345

Paschal Ochang, Damian Eke, Bernd Carsten Stahl

引用次数: 0

Corrigendum: Do you hear the people sing? Comparison of synchronized URL and narrative themes in 2020 and 2023 French protests. 更正：你听到人民在歌唱吗？2020 年和 2023 年法国抗议活动中同步 URL 和叙事主题的比较。

IF 3.1

Frontiers in Big Data Pub Date : 2023-12-12 eCollection Date: 2023-01-01 DOI: 10.3389/fdata.2023.1343108

Lynnette Hui Xian Ng, Kathleen M Carley

引用次数: 0

Corrigendum: Anemia detection through non-invasive analysis of lip mucosa images. 更正：通过对嘴唇粘膜图像的非侵入性分析检测贫血。

IF 3.1

Frontiers in Big Data Pub Date : 2023-12-11 eCollection Date: 2023-01-01 DOI: 10.3389/fdata.2023.1335213

Shekhar Mahmud, Turker Berk Donmez, Mohammed Mansour, Mustafa Kutlu, Chris Freeman

引用次数: 0