Journal of Big Data最新文献

{"title":"Bilingual video captioning model for enhanced video retrieval","authors":"","doi":"10.1186/s40537-024-00878-w","DOIUrl":"https://doi.org/10.1186/s40537-024-00878-w","url":null,"abstract":"<h3>Abstract</h3> <p>Many video platforms rely on the descriptions that uploaders provide for video retrieval. However, this reliance may cause inaccuracies. Although deep learning-based video captioning can resolve this problem, it has some limitations: (1) traditional keyframe extraction techniques do not consider video length/content, resulting in low accuracy, high storage requirements, and long processing times; (2) Arabic language support in video captioning is not extensive. This study proposes a new video captioning approach that uses an efficient keyframe extraction method and supports both Arabic and English. The proposed keyframe extraction technique uses time- and content-based approaches for better quality captions, fewer storage space requirements, and faster processing. The English and Arabic models use a sequence-to-sequence framework with long short-term memory in both the encoder and decoder. Both models were evaluated on caption quality using four metrics: bilingual evaluation understudy (BLEU), metric for evaluation of translation with explicit ORdering (METEOR), recall-oriented understudy of gisting evaluation (ROUGE-L), and consensus-based image description evaluation (CIDE-r). They were also evaluated using cosine similarity to determine their suitability for video retrieval. The results demonstrated that the English model performed better with regards to caption quality and video retrieval. In terms of BLEU, METEOR, ROUGE-L, and CIDE-r, the English model scored 47.18, 30.46, 62.07, and 59.98, respectively, whereas the Arabic model scored 21.65, 36.30, 44.897, and 45.52, respectively. According to the video retrieval, the English and Arabic models successfully retrieved 67% and 40% of the videos, respectively, with 20% similarity. These models have potential applications in storytelling, sports commentaries, and video surveillance.</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"11 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139476825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cyberattack detection in wireless sensor networks using a hybrid feature reduction technique with AI and machine learning methods","authors":"Mohamed H. Behiry, Mohammed Aly","doi":"10.1186/s40537-023-00870-w","DOIUrl":"https://doi.org/10.1186/s40537-023-00870-w","url":null,"abstract":"","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"30 4","pages":"1-39"},"PeriodicalIF":8.1,"publicationDate":"2024-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139437458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gct-TTE: graph convolutional transformer for travel time estimation","authors":"Vladimir Mashurov, Vaagn Chopuryan, Vadim Porvatov, Arseny Ivanov, Natalia Semenova","doi":"10.1186/s40537-023-00841-1","DOIUrl":"https://doi.org/10.1186/s40537-023-00841-1","url":null,"abstract":"<p>This paper introduces a new transformer-based model for the problem of travel time estimation. The key feature of the proposed GCT-TTE architecture is the utilization of different data modalities capturing different properties of an input path. Along with the extensive study regarding the model configuration, we implemented and evaluated a sufficient number of actual baselines for path-aware and path-blind settings. The conducted computational experiments have confirmed the viability of our pipeline, which outperformed state-of-the-art models on both considered datasets. Additionally, GCT-TTE was deployed as a web service accessible for further experiments with user-defined routes.</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"14 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139465372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CNN-IKOA: convolutional neural network with improved Kepler optimization algorithm for image segmentation: experimental validation and numerical exploration","authors":"Mohamed Abdel-Basset, Reda Mohamed, Ibrahim Alrashdi, Karam M. Sallam, Ibrahim A. Hameed","doi":"10.1186/s40537-023-00858-6","DOIUrl":"https://doi.org/10.1186/s40537-023-00858-6","url":null,"abstract":"<p>Chest diseases, especially COVID-19, have quickly spread throughout the world and caused many deaths. Finding a rapid and accurate diagnostic tool was indispensable to combating these diseases. Therefore, scientists have thought of combining chest X-ray (CXR) images with deep learning techniques to rapidly detect people infected with COVID-19 or any other chest disease. Image segmentation as a preprocessing step has an essential role in improving the performance of these deep learning techniques, as it could separate the most relevant features to better train these techniques. Therefore, several approaches were proposed to tackle the image segmentation problem accurately. Among these methods, the multilevel thresholding-based image segmentation methods won significant interest due to their simplicity, accuracy, and relatively low storage requirements. However, with increasing threshold levels, the traditional methods have failed to achieve accurate segmented features in a reasonable amount of time. Therefore, researchers have recently used metaheuristic algorithms to tackle this problem, but the existing algorithms still suffer from slow convergence speed and stagnation into local minima as the number of threshold levels increases. Therefore, this study presents an alternative image segmentation technique based on an enhanced version of the Kepler optimization algorithm (KOA), namely IKOA, to better segment the CXR images at small, medium, and high threshold levels. Ten CXR images are used to assess the performance of IKOA at ten threshold levels (T-5, T-7, T-8, T-10, T-12, T-15, T-18, T-20, T-25, and T-30). To observe its effectiveness, it is compared to several metaheuristic algorithms in terms of several performance indicators. The experimental outcomes disclose the superiority of IKOA over all the compared algorithms. Furthermore, the IKOA-based segmented CXR images at eight different threshold levels are used to train a newly proposed CNN model called CNN-IKOA to find out the effectiveness of the segmentation step. Five performance indicators, namely overall accuracy, precision, recall, F1-score, and specificity, are used to disclose the CNN-IKOA’s effectiveness. CNN-IKOA, according to the experimental outcomes, could achieve outstanding outcomes for the images segmented at T-12, where it could reach 94.88% for overall accuracy, 96.57% for specificity, 95.40% for precision, and 95.40% for recall.</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"4 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139421099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The state of metaverse research: a bibliometric visual analysis based on CiteSpace","authors":"Huike Li, Bo Li","doi":"10.1186/s40537-024-00877-x","DOIUrl":"https://doi.org/10.1186/s40537-024-00877-x","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Objective</h3><p>To understand the current status of research in the field of metaverse, and to analyze the research progress and evolutionary trends in this field.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>Based on the bibliometric analysis, a total of 921 papers were obtained by searching the Web of science core database for the keyword \"metaverse\". CiteSpace was used to visualize and analyze the current status and trends of metaverse research in China.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Ireland is currently the country with the highest research impact. China is currently the country with the largest number of publications in this field, but the impact of the research is insufficient. The current research in the highly cited literature focuses on technical and history reviews of the metaverse as well as its development in the field of education. Artificial Intelligence and utaut2 are the underlying clusters of cited literature in this research area. Several research hotspots have been formed, such as virtual reality, augmented reality, mixed reality, digital twins and artificial intelligence.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>The current research on metaverse in various fields is basically in its infancy, but has a great potential for future development and will gradually penetrate into many different directions with many challenges.</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"50 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139422642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RPf-GCNs: reciprocal perspective driven fused GCNs for rumor detection on social media","authors":"","doi":"10.1186/s40537-023-00866-6","DOIUrl":"https://doi.org/10.1186/s40537-023-00866-6","url":null,"abstract":"<h3>Abstract</h3> <p>The earliest detection of rumors across social media is the need to the hour in present global village. User’s are seamlessly connected in an unstructured network leading to rapid flow of information. User’s on the social media with malign intents may share defamatory content to contribute towards the fifth generation media warfare. The ingress of such defamatory content into society can result in panic, uncertainty and demoralization the peoples. Due to the huge amount of content over social platforms, the detection of malicious contents is hard. Earlier research while focuses on content profiling and flow of information, however, the reciprocal perspective of the source and following contents is missing. In this research, a novel Reciprocal Perspective fused Graph Convolutional Neural Network (RPf-GCN) is proposed. The proposed framework incorporates twin GCNs to encode both the bottom-up and top-down perspectives, enhancing the understanding of rumor propagation. Moreover convolutional operation is employed to fuse reciprocal perspective, providing a holistic view of the conversations. To validate the efficacy of the proposed framework, we conducted a series of experiments using real-world datasets, including PHEME and SemEval. Experimentation performed illustrates that the proposed framework outperformed over various baselines in two different evaluation metrics namely Macro F1 (for PHEME 0.736, for SemEval 0.461) and Accuracy (for PHEME 0.748, for SemEval 0.658).</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"21 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139414365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of flight departure delays caused by weather conditions adopting data-driven approaches","authors":"Seongeun Kim, Eunil Park","doi":"10.1186/s40537-023-00867-5","DOIUrl":"https://doi.org/10.1186/s40537-023-00867-5","url":null,"abstract":"<p>In this study, we utilize data-driven approaches to predict flight departure delays. The growing demand for air travel is outpacing the capacity and infrastructure available to support it. In addition, abnormal weather patterns caused by climate change contribute to the frequent occurrence of flight delays. In light of the extensive network of international flights covering vast distances across continents and oceans, the importance of forecasting flight delays over extended time periods becomes increasingly evident. Existing research has predominantly concentrated on short-term predictions, prompting our study to specifically address this aspect. We collected datasets spanning over 10 years from three different airports such as ICN airport in South Korea, JFK and MDW airport in the United States, capturing flight information at six different time intervals (2, 4, 8, 16, 24, and 48 h) prior to flight departure. The datasets comprise 1,569,879 instances for ICN, 773,347 for JFK, and 404,507 for MDW, respectively. We employed a range of machine learning and deep learning approaches, including Decision Tree, Random Forest, Support Vector Machine, K-nearest neighbors, Logistic Regression, Extreme Gradient Boosting, and Long Short-Term Memory, to predict flight delays. Our models achieved accuracy rates of 0.749 for ICN airport, 0.852 for JFK airport, and 0.785 for MDW airport in 2-h predictions. Furthermore, for 48-h predictions, our models achieved accuracy rates of 0.748 for ICN airport, 0.846 for JFK airport, and 0.772 for MDW airport based on our experimental results. Consequently, we have successfully validated the accuracy of flight delay predictions for longer time frames. The implications and future research directions derived from these findings are also discussed.</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"209 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139414412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of customer reviews with an improved VADER lexicon classifier","authors":"Kousik Barik, Sanjay Misra","doi":"10.1186/s40537-023-00861-x","DOIUrl":"https://doi.org/10.1186/s40537-023-00861-x","url":null,"abstract":"","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"1 11","pages":"1-29"},"PeriodicalIF":8.1,"publicationDate":"2024-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139380276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain meets machine learning: a survey","authors":"Safak Kayikci, Taghi M. Khoshgoftaar","doi":"10.1186/s40537-023-00852-y","DOIUrl":"https://doi.org/10.1186/s40537-023-00852-y","url":null,"abstract":"<p>Blockchain and machine learning are two rapidly growing technologies that are increasingly being used in various industries. Blockchain technology provides a secure and transparent method for recording transactions, while machine learning enables data-driven decision-making by analyzing large amounts of data. In recent years, researchers and practitioners have been exploring the potential benefits of combining these two technologies. In this study, we cover the fundamentals of blockchain and machine learning and then discuss their integrated use in finance, medicine, supply chain, and security, including a literature review and their contribution to the field such as increased security, privacy, and decentralization. Blockchain technology enables secure and transparent decentralized record-keeping, while machine learning algorithms can analyze vast amounts of data to derive valuable insights. Together, they have the potential to revolutionize industries by enhancing efficiency through automated and trustworthy processes, enabling data-driven decision-making, and strengthening security measures by reducing vulnerabilities and ensuring the integrity of information. However, there are still some important challenges to be handled prior to the common use of blockchain and machine learning such as security issues, strategic planning, information processing, and scalable workflows. Nevertheless, until the difficulties that have been identified are resolved, their full potential will not be achieved.</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"372 1 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139374947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of random oversampling and random undersampling on the performance of prediction models developed using observational health data","authors":"Cynthia Yang, Egill A. Fridgeirsson, Jan A. Kors, Jenna M. Reps, Peter R. Rijnbeek","doi":"10.1186/s40537-023-00857-7","DOIUrl":"https://doi.org/10.1186/s40537-023-00857-7","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Background</h3><p>There is currently no consensus on the impact of class imbalance methods on the performance of clinical prediction models. We aimed to empirically investigate the impact of random oversampling and random undersampling, two commonly used class imbalance methods, on the internal and external validation performance of prediction models developed using observational health data.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>We developed and externally validated prediction models for various outcomes of interest within a target population of people with pharmaceutically treated depression across four large observational health databases. We used three different classifiers (lasso logistic regression, random forest, XGBoost) and varied the target imbalance ratio. We evaluated the impact on model performance in terms of discrimination and calibration. Discrimination was assessed using the area under the receiver operating characteristic curve (AUROC) and calibration was assessed using calibration plots.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>We developed and externally validated a total of 1,566 prediction models. On internal and external validation, random oversampling and random undersampling generally did not result in higher AUROCs. Moreover, we found overestimated risks, although this miscalibration could largely be corrected by recalibrating the models towards the imbalance ratios in the original dataset.</p><h3 data-test=\"abstract-sub-heading\">Conclusions</h3><p>Overall, we found that random oversampling or random undersampling generally does not improve the internal and external validation performance of prediction models developed in large observational health databases. Based on our findings, we do not recommend applying random oversampling or random undersampling when developing prediction models in large observational health databases.</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"27 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139102109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}