Journal of Big Data最新文献_第3页

Predicting startup success using two bias-free machine learning: resolving data imbalance using generative adversarial networks 利用两种无偏差机器学习预测初创企业的成功：利用生成式对抗网络解决数据不平衡问题

IF 8.1 2区计算机科学

Journal of Big Data Pub Date : 2024-09-03 DOI: 10.1186/s40537-024-00993-8

Jungryeol Park, Saesol Choi, Yituo Feng

引用次数: 0

CTGAN-ENN: a tabular GAN-based hybrid sampling method for imbalanced and overlapped data in customer churn prediction CTGAN-ENN：一种基于表格 GAN 的混合采样方法，适用于客户流失预测中的不平衡和重叠数据

IF 8.1 2区计算机科学

Journal of Big Data Pub Date : 2024-09-02 DOI: 10.1186/s40537-024-00982-x

I Nyoman Mahayasa Adiputra, Paweena Wanchai

{"title":"CTGAN-ENN: a tabular GAN-based hybrid sampling method for imbalanced and overlapped data in customer churn prediction","authors":"I Nyoman Mahayasa Adiputra, Paweena Wanchai","doi":"10.1186/s40537-024-00982-x","DOIUrl":"https://doi.org/10.1186/s40537-024-00982-x","url":null,"abstract":"Class imbalance is one of many problems of customer churn datasets. One of the common problems is class overlap, where the data have a similar instance between classes. The prediction task of customer churn becomes more challenging when there is class overlap in the data training. In this research, we suggested a hybrid method based on tabular GANs, called CTGAN-ENN, to address class overlap and imbalanced data in datasets of customers that churn. We used five different customer churn datasets from an open platform. CTGAN is a tabular GAN-based oversampling to address class imbalance but has a class overlap problem. We combined CTGAN with the ENN under-sampling technique to overcome the class overlap. CTGAN-ENN reduced the number of class overlaps by each feature in all datasets. We investigated how effective CTGAN-ENN is in each machine learning technique. Based on our experiments, CTGAN-ENN achieved satisfactory results in KNN, GBM, XGB and LGB machine learning performance for customer churn predictions. We compared CTGAN-ENN with common over-sampling and hybrid sampling methods, and CTGAN-ENN achieved outperform results compared with other sampling methods and algorithm-level methods with cost-sensitive learning in several machine learning algorithms. We provide a time consumption algorithm between CTGAN and CTGAN-ENN. CTGAN-ENN achieved less time consumption than CTGAN. Our research work provides a new framework to handle customer churn prediction problems with several types of imbalanced datasets and can be useful in real-world data from customer churn prediction.","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"78 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186338","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}

引用次数: 0

Cartographies of warfare in the Indian subcontinent: Contextualizing archaeological and historical analysis through big data approaches 印度次大陆的战争地图：通过大数据方法对考古和历史分析进行语境分析

IF 8.1 2区计算机科学

Journal of Big Data Pub Date : 2024-08-29 DOI: 10.1186/s40537-024-00962-1

Monica L. Smith, Connor Newton

{"title":"Cartographies of warfare in the Indian subcontinent: Contextualizing archaeological and historical analysis through big data approaches","authors":"Monica L. Smith, Connor Newton","doi":"10.1186/s40537-024-00962-1","DOIUrl":"https://doi.org/10.1186/s40537-024-00962-1","url":null,"abstract":"Some of the most notable human behavioral palimpsests result from warfare and its durable traces in the form of defensive architecture and strategic infrastructure. For premodern periods, this architecture is often understudied at the large scale, resulting in a lack of appreciation for the enormity of the costs and impacts of military spending over the course of human history. In this article, we compare the information gleaned from the study of the fortified cities of the Early Historic period of the Indian subcontinent (c. 3rd century BCE to 4th century CE) with the precolonial medieval era (9-17th centuries CE). Utilizing in-depth archaeological and historical studies along with local sightings and citizen-science blogs to create a comprehensive data set and map series in a “big-data” approach that makes use of heterogeneous data sets and presence-absence criteria, we discuss how the architecture of warfare shifted from an emphasis on urban defense in the Early Historic period to an emphasis on territorial offense and defense in the medieval period. Many medieval fortifications are known from only local reports and have minimal identifying information but can still be studied in the aggregate using a least-shared denominator approach to quantification and mapping.","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"14 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186358","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}

引用次数: 0

Automated subway touch button detection using image process 利用图像处理自动检测地铁触摸按钮

IF 8.1 2区计算机科学

Journal of Big Data Pub Date : 2024-08-29 DOI: 10.1186/s40537-024-00941-6

Junfeng An, Mengmeng Lu, Gang Li, Jiqiang Liu, Chongqing Wang

引用次数: 0

Cybersecurity vulnerabilities and solutions in Ethiopian university websites 埃塞俄比亚大学网站的网络安全漏洞和解决方案

IF 8.1 2区计算机科学

Journal of Big Data Pub Date : 2024-08-23 DOI: 10.1186/s40537-024-00980-z

Ali Yimam Eshetu, Endris Abdu Mohammed, Ayodeji Olalekan Salau

{"title":"Cybersecurity vulnerabilities and solutions in Ethiopian university websites","authors":"Ali Yimam Eshetu, Endris Abdu Mohammed, Ayodeji Olalekan Salau","doi":"10.1186/s40537-024-00980-z","DOIUrl":"https://doi.org/10.1186/s40537-024-00980-z","url":null,"abstract":"This study investigates the causes and countermeasures of cybercrime vulnerabilities, specifically focusing on selected 16 Ethiopian university websites. This study uses a cybersecurity awareness survey, and automated vulnerability assessment and penetration testing (VAPT) technique tools, namely, Nmap, Nessus, and Vega, to identify potential security threats and vulnerabilities. The assessment was performed according to the ISO/IEC 27001 series of standards, ensuring a comprehensive and globally recognized approach to information security. The results of this study provide valuable insights into the current state of cybersecurity in Ethiopian universities and reveals a range of issues, from outdated software and poor password management to a lack of encryption and inadequate access control. Vega vulnerability assessment reports 11,286 total findings, and Nessus identified a total of 1749 vulnerabilities across all the websites of the institutions examined. Based on these findings, the study proposes counteractive measures tailored to the specific needs of each identified defect. These recommendations aim to strengthen the security posture of the university websites, thereby protecting sensitive data and maintaining the trust of students, staff, and other stakeholders. The study emphasizes the need for proactive cybersecurity measures in the realm of higher education and presents a strategic plan for universities to improve their digital security.","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"9 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186360","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}

引用次数: 0

Crude oil price forecasting using K-means clustering and LSTM model enhanced by dense-sparse-dense strategy 使用 K-均值聚类和通过密集-稀疏-密集策略增强的 LSTM 模型预测原油价格

IF 8.1 2区计算机科学

Journal of Big Data Pub Date : 2024-08-17 DOI: 10.1186/s40537-024-00977-8

Alireza Jahandoost, Farhad Abedinzadeh Torghabeh, Seyyed Abed Hosseini, Mahboobeh Houshmand

{"title":"Crude oil price forecasting using K-means clustering and LSTM model enhanced by dense-sparse-dense strategy","authors":"Alireza Jahandoost, Farhad Abedinzadeh Torghabeh, Seyyed Abed Hosseini, Mahboobeh Houshmand","doi":"10.1186/s40537-024-00977-8","DOIUrl":"https://doi.org/10.1186/s40537-024-00977-8","url":null,"abstract":"Crude oil is an essential energy source that affects international trade, transportation, and manufacturing, highlighting its importance to the economy. Its future price prediction affects consumer prices and the energy markets, and it shapes the development of sustainable energy. It is essential for financial planning, economic stability, and investment decisions. However, reaching a reliable future prediction is an open issue because of its high volatility. Furthermore, many state-of-the-art methods utilize signal decomposition techniques, which can lead to increased prediction time. In this paper, a model called K-means-dense-sparse-dense long short-term memory (K-means-DSD-LSTM) is proposed, which has three main training phrases for crude oil price forecasting. In the first phase, the DSD-LSTM model is trained. Afterwards, the training part of the data is clustered using the K-means algorithm. Finally, a copy of the trained DSD-LSTM model is fine-tuned for each obtained cluster. It helps the models predict that cluster better while they are generalizing the whole dataset quite well, which diminishes overfitting. The proposed model is evaluated on two famous crude oil benchmarks: West Texas Intermediate (WTI) and Brent. Empirical evaluations demonstrated the superiority of the DSD-LSTM model over the K-means-LSTM model. Furthermore, the K-means-DSD-LSTM model exhibited even stronger performance. Notably, the proposed method yielded promising results across diverse datasets, achieving competitive performance in comparison to existing methods, even without employing signal decomposition techniques.","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"5 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186362","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}

引用次数: 0

Rs-net: Residual Sharp U-Net architecture for pavement crack segmentation and severity assessment Rs-net：用于路面裂缝细分和严重程度评估的残差夏普 U-Net 架构

IF 8.1 2区计算机科学

Journal of Big Data Pub Date : 2024-08-17 DOI: 10.1186/s40537-024-00981-y

Luqman Ali, Hamad AlJassmi, Mohammed Swavaf, Wasif Khan, Fady Alnajjar

{"title":"Rs-net: Residual Sharp U-Net architecture for pavement crack segmentation and severity assessment","authors":"Luqman Ali, Hamad AlJassmi, Mohammed Swavaf, Wasif Khan, Fady Alnajjar","doi":"10.1186/s40537-024-00981-y","DOIUrl":"https://doi.org/10.1186/s40537-024-00981-y","url":null,"abstract":"U-net, a fully convolutional network-based image segmentation method, has demonstrated widespread adaptability in the crack segmentation task. The combination of the semantically dissimilar features of the encoder (shallow layers) and the decoder (deep layers) in the skip connections leads to blurry features map and leads to undesirable over- or under-segmentation of target regions. Additionally, the shallow architecture of the U-Net model prevents the extraction of more discriminatory information from input images. This paper proposes a Residual Sharp U-Net (RS-Net) architecture for crack segmentation and severity assessment in pavement surfaces to address these limitations. The proposed architecture uses residual block in the U-Net model to extract a more insightful representation of features. In addition to that, a sharpening kernel filter is used instead of plain skip connections to generate a fine-tuned encoder features map before combining it with decoder features maps to reduce the dissimilarity between them and smoothes artifacts in the network layers during early training. The proposed architecture is also integrated with various morphological operations to assess the severity of cracks and categorize them into hairline, medium, and severe labels. Experiments results demonstrated that the RS-Net model has promising segmentation performance, outperforming earlier U-Net variations on testing data for crack segmentation and severity assessment, with a promising accuracy (>0.97)","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"80 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186359","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}

引用次数: 0

Internet of things and ensemble learning-based mental and physical fatigue monitoring for smart construction sites 基于物联网和集合学习的智能建筑工地身心疲劳监测

IF 8.1 2区计算机科学

Journal of Big Data Pub Date : 2024-08-16 DOI: 10.1186/s40537-024-00978-7

Bubryur Kim, K. R. Sri Preethaa, Sujeen Song, R. R. Lukacs, Jinwoo An, Zengshun Chen, Euijung An, Sungho Kim

{"title":"Internet of things and ensemble learning-based mental and physical fatigue monitoring for smart construction sites","authors":"Bubryur Kim, K. R. Sri Preethaa, Sujeen Song, R. R. Lukacs, Jinwoo An, Zengshun Chen, Euijung An, Sungho Kim","doi":"10.1186/s40537-024-00978-7","DOIUrl":"https://doi.org/10.1186/s40537-024-00978-7","url":null,"abstract":"The construction industry substantially contributes to the economic growth of a country. However, it records a large number of workplace injuries and fatalities annually due to its hesitant adoption of automated safety monitoring systems. To address this critical concern, this study presents a real-time monitoring approach that uses the Internet of Things and ensemble learning. This study leverages wearable sensor technology, such as photoplethysmography and electroencephalography sensors, to continuously track the physiological parameters of construction workers. The sensor data is processed using an ensemble learning approach called the ChronoEnsemble Fatigue Analysis System (CEFAS), comprising deep autoregressive and temporal fusion transformer models, to accurately predict potential physical and mental fatigue. Comprehensive evaluation metrics, including mean square error, mean absolute scaled error, and symmetric mean absolute percentage error, demonstrated the superior prediction accuracy and reliability of the proposed model compared to standalone models. The ensemble learning model exhibited remarkable precision in predicting physical and mental fatigue, as evidenced by the mean square errors of 0.0008 and 0.0033, respectively. The proposed model promptly recognizes potential hazards and irregularities, considerably enhancing worker safety and reducing on-site risks.","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"42 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186361","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}

引用次数: 0

Toward a globally lunar calendar: a machine learning-driven approach for crescent moon visibility prediction 实现全球月历：新月能见度预测的机器学习驱动方法

IF 8.1 2区计算机科学

Journal of Big Data Pub Date : 2024-08-12 DOI: 10.1186/s40537-024-00979-6

Samia Loucif, Murad Al-Rajab, Raed Abu Zitar, Mahmoud Rezk

{"title":"Toward a globally lunar calendar: a machine learning-driven approach for crescent moon visibility prediction","authors":"Samia Loucif, Murad Al-Rajab, Raed Abu Zitar, Mahmoud Rezk","doi":"10.1186/s40537-024-00979-6","DOIUrl":"https://doi.org/10.1186/s40537-024-00979-6","url":null,"abstract":"This paper presents a comprehensive approach to harmonizing lunar calendars across different global regions, addressing the long-standing challenge of variations in new crescent Moon sightings that mark the beginning of lunar months. We propose a machine learning (ML)-based framework to predict the visibility of the new crescent Moon, representing a significant advancement toward a globally unified lunar calendar. Our study utilized a dataset covering various countries globally, making it the first to analyze all 12 lunar months over a span of 13 years. We applied a wide array of ML algorithms and techniques. These techniques included feature selection, hyperparameter tuning, ensemble learning, and region-based clustering, all aimed at maximizing the model’s performance. The overall results reveal that the gradient boosting (GB) model surpasses all other models, achieving the highest F1 score of 0.882469 and an area under the curve (AUC) of 0.901009. However, with selected features identified through the ANOVA F-test and optimized parameters, the Extra Trees model exhibited the best performance with an F1 score of 0.887872, and an AUC of 0.906242. We expanded our analysis to explore ensemble models, aiming to understand how a combination of models might boost predictive accuracy. The Ensemble Model exhibited a slight improvement, with an F1 score of 0.888058 and an AUC of 0.907482. Additionally, the geographical segmentation of the dataset enhanced predictive performance in certain areas, such as Africa and Asia. In conclusion, ML techniques can provide efficient and reliable tool for predicting the new crescent Moon visibility that would support the decisions of marking the beginning of new lunar months.","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"4 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186363","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}

引用次数: 0

Enhancing K-nearest neighbor algorithm: a comprehensive review and performance analysis of modifications 增强 K 近邻算法：对修改的全面回顾和性能分析

IF 8.1 2区计算机科学

Journal of Big Data Pub Date : 2024-08-11 DOI: 10.1186/s40537-024-00973-y

Rajib Kumar Halder, Mohammed Nasir Uddin, Md. Ashraf Uddin, Sunil Aryal, Ansam Khraisat

{"title":"Enhancing K-nearest neighbor algorithm: a comprehensive review and performance analysis of modifications","authors":"Rajib Kumar Halder, Mohammed Nasir Uddin, Md. Ashraf Uddin, Sunil Aryal, Ansam Khraisat","doi":"10.1186/s40537-024-00973-y","DOIUrl":"https://doi.org/10.1186/s40537-024-00973-y","url":null,"abstract":"The k-Nearest Neighbors (kNN) method, established in 1951, has since evolved into a pivotal tool in data mining, recommendation systems, and Internet of Things (IoT), among other areas. This paper presents a comprehensive review and performance analysis of modifications made to enhance the exact kNN techniques, particularly focusing on kNN Search and kNN Join for high-dimensional data. We delve deep into 31 kNN search methods and 12 kNN join methods, providing a methodological overview and analytical insight into each, emphasizing their strengths, limitations, and applicability. An important feature of our study is the provision of the source code for each of the kNN methods discussed, fostering ease of experimentation and comparative analysis for readers. Motivated by the rising significance of kNN in high-dimensional spaces and a recognized gap in comprehensive surveys on exact kNN techniques, our work seeks to bridge this gap. Additionally, we outline existing challenges and present potential directions for future research in the domain of kNN techniques, offering a holistic guide that amalgamates, compares, and dissects existing methodologies in a coherent manner.<h3 data-test=\"abstract-sub-heading\">Graphical Abstract</h3>\u0000","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"22 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141930358","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}

引用次数: 0