Journal of Big Data最新文献_第2页

Enhancing oil palm segmentation model with GAN-based augmentation 利用基于 GAN 的增强技术改进油棕榈树细分模型

IF 8.1 2区计算机科学

Journal of Big Data Pub Date : 2024-09-08 DOI: 10.1186/s40537-024-00990-x

Qi Bin Kwong, Yee Thung Kon, Wan Rusydiah W. Rusik, Mohd Nor Azizi Shabudin, Shahirah Shazana A. Rahman, Harikrishna Kulaveerasingam, David Ross Appleton

{"title":"Enhancing oil palm segmentation model with GAN-based augmentation","authors":"Qi Bin Kwong, Yee Thung Kon, Wan Rusydiah W. Rusik, Mohd Nor Azizi Shabudin, Shahirah Shazana A. Rahman, Harikrishna Kulaveerasingam, David Ross Appleton","doi":"10.1186/s40537-024-00990-x","DOIUrl":"https://doi.org/10.1186/s40537-024-00990-x","url":null,"abstract":"In digital agriculture, accurate crop detection is fundamental to developing automated systems for efficient plantation management. For oil palm, the main challenge lies in developing robust models that perform well in different environmental conditions. This study addresses the feasibility of using GAN augmentation methods to improve palm detection models. For this purpose, drone images of young palms (< 5 year-old) from eight different estates were collected, annotated, and used to build a baseline detection model based on DETR. StyleGAN2 was trained on the extracted palms and then used to generate a series of synthetic palms, which were then inserted into tiles representing different environments. CycleGAN networks were trained for bidirectional translation between synthetic and real tiles, subsequently utilized to augment the authenticity of synthetic tiles. Both synthetic and real tiles were used to train the GAN-based detection model. The baseline model achieved precision and recall values of 95.8% and 97.2%. The GAN-based model achieved comparable result, with precision and recall values of 98.5% and 98.6%. In the challenge dataset 1 consisting older palms (> 5 year-old), both models also achieved similar accuracies, with baseline model achieving precision and recall of 93.1% and 99.4%, and GAN-based model achieving 95.7% and 99.4%. As for the challenge dataset 2 consisting of storm affected palms, the baseline model achieved precision of 100% but recall was only 13%. The GAN-based model achieved a significantly better result, with a precision and recall values of 98.7% and 95.3%. This result demonstrates that images generated by GANs have the potential to enhance the accuracies of palm detection models.","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"25 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186336","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

AI sees beyond humans: automated diagnosis of myopia based on peripheral refraction map using interpretable deep learning 人工智能的视力超越人类：利用可解释深度学习，基于周边屈光图自动诊断近视

IF 8.1 2区计算机科学

Journal of Big Data Pub Date : 2024-09-08 DOI: 10.1186/s40537-024-00989-4

Yong Tang, Zhenghua Lin, Linjing Zhou, Weijia Wang, Longbo Wen, Yongli Zhou, Zongyuan Ge, Zhao Chen, Weiwei Dai, Zhikuan Yang, He Tang, Weizhong Lan

{"title":"AI sees beyond humans: automated diagnosis of myopia based on peripheral refraction map using interpretable deep learning","authors":"Yong Tang, Zhenghua Lin, Linjing Zhou, Weijia Wang, Longbo Wen, Yongli Zhou, Zongyuan Ge, Zhao Chen, Weiwei Dai, Zhikuan Yang, He Tang, Weizhong Lan","doi":"10.1186/s40537-024-00989-4","DOIUrl":"https://doi.org/10.1186/s40537-024-00989-4","url":null,"abstract":"The question of whether artificial intelligence (AI) can surpass human capabilities is crucial in the application of AI in clinical medicine. To explore this, an interpretable deep learning (DL) model was developed to assess myopia status using retinal refraction maps obtained with a novel peripheral refractor. The DL model demonstrated promising performance, achieving an AUC of 0.9074 (95% CI 0.83–0.97), an accuracy of 0.8140 (95% CI 0.70–0.93), a sensitivity of 0.7500 (95% CI 0.51–0.90), and a specificity of 0.8519 (95% CI 0.68–0.94). Grad-CAM analysis provided interpretable visualization of the attention of DL model and revealed that the DL model utilized information from the central retina, similar to human readers. Additionally, the model considered information from vertical regions across the central retina, which human readers had overlooked. This finding suggests that AI can indeed surpass human capabilities, bolstering our confidence in the use of AI in clinical practice, especially in new scenarios where prior human knowledge is limited.","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"23 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186335","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

Efficient microservices offloading for cost optimization in diverse MEC cloud networks 在多样化 MEC 云网络中高效卸载微服务以优化成本

IF 8.1 2区计算机科学

Journal of Big Data Pub Date : 2024-09-04 DOI: 10.1186/s40537-024-00975-w

Abdul Rasheed Mahesar, Xiaoping Li, Dileep Kumar Sajnani

{"title":"Efficient microservices offloading for cost optimization in diverse MEC cloud networks","authors":"Abdul Rasheed Mahesar, Xiaoping Li, Dileep Kumar Sajnani","doi":"10.1186/s40537-024-00975-w","DOIUrl":"https://doi.org/10.1186/s40537-024-00975-w","url":null,"abstract":"In recent years, mobile applications have proliferated across domains such as E-banking, Augmented Reality, E-Transportation, and E-Healthcare. These applications are often built using microservices, an architectural style where the application is composed of independently deployable services focusing on specific functionalities. Mobile devices cannot process these microservices locally, so traditionally, cloud-based frameworks using cost-efficient Virtual Machines (VMs) and edge servers have been used to offload these tasks. However, cloud frameworks suffer from extended boot times and high transmission overhead, while edge servers have limited computational resources. To overcome these challenges, this study introduces a Microservices Container-Based Mobile Edge Cloud Computing (MCBMEC) environment and proposes an innovative framework, Optimization Task Scheduling and Computational Offloading with Cost Awareness (OTSCOCA). This framework addresses Resource Matching, Task Sequencing, and Task Scheduling to enhance server utilization, reduce service latency, and improve service bootup times. Empirical results validate the efficacy of MCBMEC and OTSCOCA, demonstrating significant improvements in server efficiency, reduced service latency, faster service bootup times, and notable cost savings. These outcomes underscore the pivotal role of these methodologies in advancing mobile edge computing applications amidst the challenges of edge server limitations and traditional cloud-based approaches.","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"1 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186337","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

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