International Journal of Data Science and Analytics最新文献

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Automatic identification of rank correlation between image sequences 图像序列间秩相关的自动识别
International Journal of Data Science and Analytics Pub Date : 2023-09-15 DOI: 10.1007/s41060-023-00450-4
Lior Shamir
{"title":"Automatic identification of rank correlation between image sequences","authors":"Lior Shamir","doi":"10.1007/s41060-023-00450-4","DOIUrl":"https://doi.org/10.1007/s41060-023-00450-4","url":null,"abstract":"","PeriodicalId":45667,"journal":{"name":"International Journal of Data Science and Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135436799","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
Study of violence against women and its characteristics through the application of text mining techniques 通过应用文本挖掘技术研究对妇女的暴力行为及其特征
International Journal of Data Science and Analytics Pub Date : 2023-09-14 DOI: 10.1007/s41060-023-00448-y
E. M. A. Stephanie, L. G. B. Ruiz, M. A. Vila, M. C. Pegalajar
{"title":"Study of violence against women and its characteristics through the application of text mining techniques","authors":"E. M. A. Stephanie, L. G. B. Ruiz, M. A. Vila, M. C. Pegalajar","doi":"10.1007/s41060-023-00448-y","DOIUrl":"https://doi.org/10.1007/s41060-023-00448-y","url":null,"abstract":"The Internet provides a wide variety of information that can be collected and studied, creating a massive data repository. Among the data available on the Internet, we can find articles about Violence against Women (VAW) published in the digital press, which are of great societal interest. In this work, we utilized Web scraping techniques to gather VAW-related news from the internet. Applying Text Mining techniques, we conducted a study on VAW and its characteristics. Our work comprises an exploratory analysis and the application of Topic Modelling to VAW events to identify latent topics and their semantic structures. We employed classification algorithms on a set of VAW press articles to determine the type of violence they refer to, namely physical, psychological, sexual, or a combination of them. We proposed two methodologies to target the data: the first one is based on dictionaries of VAW types, while the second approach extends the former by using the predominant violence to identify other associated types. Furthermore, we implemented two feature selection techniques: TF-IDF and $${Chi}^{2}$$ . Then, we applied Support Vector Machine, Decision Tree, Bayesian Networks, XGBoost Classifier, Random Forest, and Artificial Neural Networks. The results obtained showed that the classifiers achieved better performance when using $${Chi}^{2}$$ . The Boost Classifier demonstrated the best performance, followed by Random Forest.","PeriodicalId":45667,"journal":{"name":"International Journal of Data Science and Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134912231","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
Through the looking glass: evaluating post hoc explanations using transparent models 透过镜子:使用透明模型评估事后解释
International Journal of Data Science and Analytics Pub Date : 2023-09-12 DOI: 10.1007/s41060-023-00445-1
Mythreyi Velmurugan, Chun Ouyang, Renuka Sindhgatta, Catarina Moreira
{"title":"Through the looking glass: evaluating post hoc explanations using transparent models","authors":"Mythreyi Velmurugan, Chun Ouyang, Renuka Sindhgatta, Catarina Moreira","doi":"10.1007/s41060-023-00445-1","DOIUrl":"https://doi.org/10.1007/s41060-023-00445-1","url":null,"abstract":"Abstract Modern machine learning methods allow for complex and in-depth analytics, but the predictive models generated by these methods are often highly complex and lack transparency. Explainable Artificial Intelligence (XAI) methods are used to improve the interpretability of these complex “black box” models, thereby increasing transparency and enabling informed decision-making. However, the inherent fitness of these explainable methods, particularly the faithfulness of explanations to the decision-making processes of the model, can be hard to evaluate. In this work, we examine and evaluate the explanations provided by four XAI methods, using fully transparent “glass box” models trained on tabular data. Our results suggest that the fidelity of explanations is determined by the types of variables used, as well as the linearity of the relationship between variables and model prediction. We find that each XAI method evaluated has its own strengths and weaknesses, determined by the assumptions inherent in the explanation mechanism. Thus, though such methods are model-agnostic, we find significant differences in explanation quality across different technical setups. Given the numerous factors that determine the quality of explanations, including the specific explanation-generation procedures implemented by XAI methods, we suggest that model-agnostic XAI methods may still require expert guidance for implementation.","PeriodicalId":45667,"journal":{"name":"International Journal of Data Science and Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135878622","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
A new robust bootstrapped singular value decomposition algorithm using the sample myriad estimate 一种基于样本无数次估计的鲁棒自适应奇异值分解算法
International Journal of Data Science and Analytics Pub Date : 2023-09-09 DOI: 10.1007/s41060-023-00444-2
Chisimkwuo John, Emmanuel J. Ekpenyong, Charles Chinedu Nworu, Chukwuemeka O. Omekara
{"title":"A new robust bootstrapped singular value decomposition algorithm using the sample myriad estimate","authors":"Chisimkwuo John, Emmanuel J. Ekpenyong, Charles Chinedu Nworu, Chukwuemeka O. Omekara","doi":"10.1007/s41060-023-00444-2","DOIUrl":"https://doi.org/10.1007/s41060-023-00444-2","url":null,"abstract":"","PeriodicalId":45667,"journal":{"name":"International Journal of Data Science and Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136192381","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
Hyperparameter analysis of wide-kernel CNN architectures in industrial fault detection: an exploratory study 工业故障检测中广核CNN结构的超参数分析:探索性研究
International Journal of Data Science and Analytics Pub Date : 2023-09-07 DOI: 10.1007/s41060-023-00440-6
Jurgen van den Hoogen, Dan Hudson, Stefan Bloemheuvel, Martin Atzmueller
{"title":"Hyperparameter analysis of wide-kernel CNN architectures in industrial fault detection: an exploratory study","authors":"Jurgen van den Hoogen, Dan Hudson, Stefan Bloemheuvel, Martin Atzmueller","doi":"10.1007/s41060-023-00440-6","DOIUrl":"https://doi.org/10.1007/s41060-023-00440-6","url":null,"abstract":"Abstract Industrial fault detection has become more data-driven due to advancements in automated data analysis using deep learning. Such methods make it possible to extract useful features, e. g., from time series data retrieved from sensors, which is typically of complex nature. This allows for effective fault detection and prognostics that boost the efficiency and productivity of industrial equipment. This work explores the influence of a variety of architectural hyperparameters on the performance of one-dimensional convolutional neural networks (CNN). Using a multi-method approach, this paper focuses specifically on wide-kernel CNN models for industrial fault detection, that have proven to perform well for tasks such as classifying vibration signals retrieved from sensors. By varying hyperparameters such as the kernel size, stride and number of filters, an extensive hyperparameter space search was conducted; to identify optimal settings, we collected a total of 12,960 different combinations on three datasets into a model hyperparameter dataset, with their respective performance on the underlying fault detection task. Afterwards, this dataset was explored with follow-up analysis including statistical, feature, pattern and hyperparameter impact analysis. We find that although performance varies substantially depending on hyperparameter choices, there is no single simple strategy to optimise performance across the three datasets. However, an optimal setting in terms of performance can be found in the number of filters used in the later layers of the architecture for all datasets. Furthermore, hyperparameter importance differs across and within the datasets, and we found nonlinear relationships between hyperparameter settings and performance. Our analysis highlights key considerations when applying a wide-kernel CNN architecture to new data within the field of industrial fault detection. This supports practitioners who wish to apply and train state-of-the-art convolutional learning methods to apply to similar fault detection settings, e. g., vibration data arising from new combinations of sensors and/or machinery in the context of bearing faults.","PeriodicalId":45667,"journal":{"name":"International Journal of Data Science and Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135046940","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
Optimization of Dynamic Time Warping Algorithm for Abnormal Signal Detection 异常信号检测的动态时间翘曲算法优化
IF 2.4
International Journal of Data Science and Analytics Pub Date : 2023-09-07 DOI: 10.1007/s41060-023-00446-0
Yuru Teng, Guotao Wang, Cailing He, Yaoyang Wu, Chaoran Li
{"title":"Optimization of Dynamic Time Warping Algorithm for Abnormal Signal Detection","authors":"Yuru Teng, Guotao Wang, Cailing He, Yaoyang Wu, Chaoran Li","doi":"10.1007/s41060-023-00446-0","DOIUrl":"https://doi.org/10.1007/s41060-023-00446-0","url":null,"abstract":"","PeriodicalId":45667,"journal":{"name":"International Journal of Data Science and Analytics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74060407","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
Establishing FAIR (Findable, Accessible, Interoperable and Reusable) principles for estuarine organisms exposed to engineered nanomaterials 为接触工程纳米材料的河口生物建立FAIR(可查找、可获取、可互操作和可重复使用)原则
IF 2.4
International Journal of Data Science and Analytics Pub Date : 2023-08-26 DOI: 10.1007/s41060-023-00447-z
A. Barrick, I. Métais, H. Ettajani, J. Marion, A. Châtel
{"title":"Establishing FAIR (Findable, Accessible, Interoperable and Reusable) principles for estuarine organisms exposed to engineered nanomaterials","authors":"A. Barrick, I. Métais, H. Ettajani, J. Marion, A. Châtel","doi":"10.1007/s41060-023-00447-z","DOIUrl":"https://doi.org/10.1007/s41060-023-00447-z","url":null,"abstract":"","PeriodicalId":45667,"journal":{"name":"International Journal of Data Science and Analytics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79820342","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
Recent advances and future challenges in federated recommender systems 联合推荐系统的最新进展和未来挑战
IF 2.4
International Journal of Data Science and Analytics Pub Date : 2023-08-25 DOI: 10.1007/s41060-023-00442-4
Marko Harasic, Felix-Sebastian Keese, Denny Mattern, A. Paschke
{"title":"Recent advances and future challenges in federated recommender systems","authors":"Marko Harasic, Felix-Sebastian Keese, Denny Mattern, A. Paschke","doi":"10.1007/s41060-023-00442-4","DOIUrl":"https://doi.org/10.1007/s41060-023-00442-4","url":null,"abstract":"","PeriodicalId":45667,"journal":{"name":"International Journal of Data Science and Analytics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91137197","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
Context-adaptive intelligent agents behaviors: multivariate LSTM-based decision making on the cryptocurrency market 上下文自适应智能代理行为:基于多变量lstm的加密货币市场决策
IF 2.4
International Journal of Data Science and Analytics Pub Date : 2023-08-19 DOI: 10.1007/s41060-023-00435-3
D. Kanzari
{"title":"Context-adaptive intelligent agents behaviors: multivariate LSTM-based decision making on the cryptocurrency market","authors":"D. Kanzari","doi":"10.1007/s41060-023-00435-3","DOIUrl":"https://doi.org/10.1007/s41060-023-00435-3","url":null,"abstract":"","PeriodicalId":45667,"journal":{"name":"International Journal of Data Science and Analytics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75545569","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
CIAMS: clustering indices-based automatic classification model selection CIAMS:基于聚类指标的自动分类模型选择
International Journal of Data Science and Analytics Pub Date : 2023-08-19 DOI: 10.1007/s41060-023-00441-5
Sudarsun Santhiappan, Nitin Shravan, Balaraman Ravindran
{"title":"CIAMS: clustering indices-based automatic classification model selection","authors":"Sudarsun Santhiappan, Nitin Shravan, Balaraman Ravindran","doi":"10.1007/s41060-023-00441-5","DOIUrl":"https://doi.org/10.1007/s41060-023-00441-5","url":null,"abstract":"","PeriodicalId":45667,"journal":{"name":"International Journal of Data Science and Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135937636","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
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