Journal of Big Data最新文献_第5页

Hate speech detection in the Bengali language: a comprehensive survey 孟加拉语中的仇恨言论检测：一项全面调查

IF 8.1 2区计算机科学

Journal of Big Data Pub Date : 2024-07-23 DOI: 10.1186/s40537-024-00956-z

Abdullah Al Maruf, Ahmad Jainul Abidin, Md. Mahmudul Haque, Zakaria Masud Jiyad, Aditi Golder, Raaid Alubady, Zeyar Aung

{"title":"Hate speech detection in the Bengali language: a comprehensive survey","authors":"Abdullah Al Maruf, Ahmad Jainul Abidin, Md. Mahmudul Haque, Zakaria Masud Jiyad, Aditi Golder, Raaid Alubady, Zeyar Aung","doi":"10.1186/s40537-024-00956-z","DOIUrl":"https://doi.org/10.1186/s40537-024-00956-z","url":null,"abstract":"The detection of hate speech (HS) in online platforms has become extremely important for maintaining a safe and inclusive environment. While significant progress has been made in English-language HS detection, methods for detecting HS in other languages, such as Bengali, have not been explored much like English. In this survey, we outlined the key challenges specific to HS detection in Bengali, including the scarcity of labeled datasets, linguistic nuances, and contextual variations. We also examined different approaches and methodologies employed by researchers to address these challenges, including classical machine learning techniques, ensemble approaches, and more recent deep learning advancements. Furthermore, we explored the performance metrics used for evaluation, including the accuracy, precision, recall, receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), sensitivity, specificity, and F1 score, providing insights into the effectiveness of the proposed models. Additionally, we identified the limitations and future directions of research in Bengali HS detection, highlighting the need for larger annotated datasets, cross-lingual transfer learning techniques, and the incorporation of contextual information to improve the detection accuracy. This survey provides a comprehensive overview of the current state-of-the-art HS detection methods used in Bengali text and serves as a valuable resource for researchers and practitioners interested in understanding the advancements, challenges, and opportunities in addressing HS in the Bengali language, ultimately assisting in the creation of reliable and effective online platform detection systems.","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"14 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141772928","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

Predictive modelling of MapReduce job performance in cloud environments using machine learning techniques 使用机器学习技术对云环境中的 MapReduce 作业性能进行预测建模

IF 8.1 2区计算机科学

Journal of Big Data Pub Date : 2024-07-23 DOI: 10.1186/s40537-024-00964-z

Mohammed Bergui, Soufiane Hourri, Said Najah, Nikola S. Nikolov

{"title":"Predictive modelling of MapReduce job performance in cloud environments using machine learning techniques","authors":"Mohammed Bergui, Soufiane Hourri, Said Najah, Nikola S. Nikolov","doi":"10.1186/s40537-024-00964-z","DOIUrl":"https://doi.org/10.1186/s40537-024-00964-z","url":null,"abstract":"Within the Hadoop ecosystem, MapReduce stands as a cornerstone for managing, processing, and mining large-scale datasets. Yet, the absence of efficient solutions for precise estimation of job execution times poses a persistent challenge, impacting task allocation and distribution within Hadoop clusters. In this study, we present a comprehensive machine learning approach for predicting the execution time of MapReduce jobs, encompassing data collection, preprocessing, feature engineering, and model evaluation. Leveraging a rich dataset derived from comprehensive Hadoop MapReduce job traces, we explore the intricate relationship between cluster parameters and job performance. Through a comparative analysis of machine learning models, including linear regression, decision tree, random forest, and gradient-boosted regression trees, we identify the random forest model as the most effective, demonstrating superior predictive accuracy and robustness. Our findings underscore the critical role of features such as data size and resource allocation in determining job performance. With this work, we aim to enhance resource management efficiency and enable more effective utilisation of cloud-based Hadoop clusters for large-scale data processing tasks.","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"48 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141772927","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

Introducing Mplots: scaling time series recurrence plots to massive datasets 介绍 Mplots：根据海量数据集缩放时间序列递推图

IF 8.1 2区计算机科学

Journal of Big Data Pub Date : 2024-07-20 DOI: 10.1186/s40537-024-00954-1

Maryam Shahcheraghi, Ryan Mercer, João Manuel de Almeida Rodrigues, Audrey Der, Hugo Filipe Silveira Gamboa, Zachary Zimmerman, Kerry Mauck, Eamonn Keogh

{"title":"Introducing Mplots: scaling time series recurrence plots to massive datasets","authors":"Maryam Shahcheraghi, Ryan Mercer, João Manuel de Almeida Rodrigues, Audrey Der, Hugo Filipe Silveira Gamboa, Zachary Zimmerman, Kerry Mauck, Eamonn Keogh","doi":"10.1186/s40537-024-00954-1","DOIUrl":"https://doi.org/10.1186/s40537-024-00954-1","url":null,"abstract":"Time series similarity matrices (informally, recurrence plots or dot-plots), are useful tools for time series data mining. They can be used to guide data exploration, and various useful features can be derived from them and then fed into downstream analytics. However, time series similarity matrices suffer from very poor scalability, taxing both time and memory requirements. In this work, we introduce novel ideas that allow us to scale the largest time series similarity matrices that can be examined by several orders of magnitude. The first idea is a novel algorithm to compute the matrices in a way that removes dependency on the subsequence length. This algorithm is so fast that it allows us to now address datasets where the memory limitations begin to dominate. Our second novel contribution is a multiscale algorithm that computes an approximation of the matrix appropriate for the limitations of the user’s memory/screen-resolution, then performs a local, just-in-time recomputation of any region that the user wishes to zoom-in on. Given that this largely removes time and space barriers, human visual attention then becomes the bottleneck. We further introduce algorithms that search massive matrices with quadrillions of cells and then prioritize regions for later examination by either humans or algorithms. We will demonstrate the utility of our ideas for data exploration, segmentation, and classification in domains as diverse as astronomy, bioinformatics, entomology, and wildlife monitoring.","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"47 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141742305","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

Emotion AWARE: an artificial intelligence framework for adaptable, robust, explainable, and multi-granular emotion analysis 情感 AWARE：用于适应性强、稳健、可解释和多粒度情感分析的人工智能框架

IF 8.1 2区计算机科学

Journal of Big Data Pub Date : 2024-07-10 DOI: 10.1186/s40537-024-00953-2

Gihan Gamage, Daswin De Silva, Nishan Mills, Damminda Alahakoon, Milos Manic

{"title":"Emotion AWARE: an artificial intelligence framework for adaptable, robust, explainable, and multi-granular emotion analysis","authors":"Gihan Gamage, Daswin De Silva, Nishan Mills, Damminda Alahakoon, Milos Manic","doi":"10.1186/s40537-024-00953-2","DOIUrl":"https://doi.org/10.1186/s40537-024-00953-2","url":null,"abstract":"Emotions are fundamental to human behaviour. How we feel, individually and collectively, determines how humanity evolves and advances into our shared future. The rapid digitalisation of our personal, social and professional lives means we are frequently using digital media to express, understand and respond to emotions. Although recent developments in Artificial Intelligence (AI) are able to analyse sentiment and detect emotions, they are not effective at comprehending the complexity and ambiguity of digital emotion expressions in knowledge-focused activities of customers, people, and organizations. In this paper, we address this challenge by proposing a novel AI framework for the adaptable, robust, and explainable detection of multi-granular assembles of emotions. This framework consolidates lexicon generation and finetuned Large Language Model (LLM) approaches to formulate multi-granular assembles of two, eight and fourteen emotions. The framework is robust to ambiguous emotion expressions that are implied in conversation, adaptable to domain-specific emotion semantics, and the assembles are explainable using constituent terms and intensity. We conducted nine empirical studies using datasets representing diverse human emotion behaviours. The results of these studies comprehensively demonstrate and evaluate the core capabilities of the framework, and consistently outperforms state-of-the-art approaches in adaptable, robust, and explainable multi-granular emotion detection.","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"153 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141587939","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

Examining ALS: reformed PCA and random forest for effective detection of ALS 检查 ALS：改革 PCA 和随机森林，有效检测 ALS

IF 8.1 2区计算机科学

Journal of Big Data Pub Date : 2024-07-10 DOI: 10.1186/s40537-024-00951-4

Abdullah Alqahtani, Shtwai Alsubai, Mohemmed Sha, Ashit Kumar Dutta

{"title":"Examining ALS: reformed PCA and random forest for effective detection of ALS","authors":"Abdullah Alqahtani, Shtwai Alsubai, Mohemmed Sha, Ashit Kumar Dutta","doi":"10.1186/s40537-024-00951-4","DOIUrl":"https://doi.org/10.1186/s40537-024-00951-4","url":null,"abstract":"ALS (Amyotrophic Lateral Sclerosis) is a fatal neurodegenerative disease of the human motor system. It is a group of progressive diseases that affects the nerve cells in the brain and spinal cord that control the muscle movement of the body hence, detection and classification of ALS at the right time is considered to be one of the vital aspects that can save the life of humans. Therefore, in various studies, different AI techniques are used for the detection of ALS, however, these methods are considered to be ineffectual in terms of identifying the disease due to the employment of ineffective algorithms. Hence, the proposed model utilizes Modified Principal Component Analysis (MPCA) and Modified Random Forest (MRF) for performing dimensionality reduction of all the potential features considered for effective classification of the ALS presence and absence of ALS causing mutation in the corresponding gene. The MPCA is adapted for capturing all the Low-Importance Data transformation. Furthermore, The MPCA is objected to performing three various approaches: Covariance Matrix Correlation, Eigen Vector- Eigenvalue decomposition, and selecting the desired principal components. This is done in aspects of implying the LI (Lower-Importance) Data Transformation. By choosing these potential components without any loss of features ensures better viability of selecting the attributes for ALS-causing gene classification. This is followed by the classification of the proposed model by using Modified RF by updating the clump detector technique. The clump detector is proceeded by clustering approach using K-means, and the data reduced by their dimension are grouped accordingly. These clustered data are analyzed either for ALS causing or devoid of causing ALS. Finally, the model’s performance is assessed using different evaluation metrics like accuracy, recall, F1 score, and precision, and the proposed model is further compared with the existing models to assess the efficacy of the proposed model.","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"21 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141587940","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

Exploring AI-driven approaches for unstructured document analysis and future horizons 探索人工智能驱动的非结构化文件分析方法及未来展望

IF 8.1 2区计算机科学

Journal of Big Data Pub Date : 2024-07-05 DOI: 10.1186/s40537-024-00948-z

Supriya V. Mahadevkar, Shruti Patil, Ketan Kotecha, Lim Way Soong, Tanupriya Choudhury

{"title":"Exploring AI-driven approaches for unstructured document analysis and future horizons","authors":"Supriya V. Mahadevkar, Shruti Patil, Ketan Kotecha, Lim Way Soong, Tanupriya Choudhury","doi":"10.1186/s40537-024-00948-z","DOIUrl":"https://doi.org/10.1186/s40537-024-00948-z","url":null,"abstract":"In the current industrial landscape, a significant number of sectors are grappling with the challenges posed by unstructured data, which incurs financial losses amounting to millions annually. If harnessed effectively, this data has the potential to substantially boost operational efficiency. Traditional methods for extracting information have their limitations; however, solutions powered by artificial intelligence (AI) could provide a more fitting alternative. There is an evident gap in scholarly research concerning a comprehensive evaluation of AI-driven techniques for the extraction of information from unstructured content. This systematic literature review aims to identify, assess, and deliberate on prospective research directions within the field of unstructured document information extraction. It has been observed that prevailing extraction methods primarily depend on static patterns or rules, often proving inadequate when faced with complex document structures typically encountered in real-world scenarios, such as medical records. Datasets currently available to the public suffer from low quality and are tailored for specific tasks only. This underscores an urgent need for developing new datasets that accurately reflect complex issues encountered in practical settings. The review reveals that AI-based techniques show promise in autonomously extracting information from diverse unstructured documents, encompassing both printed and handwritten text. Challenges arise, however, when dealing with varied document layouts. Proposing a framework through hybrid AI-based approaches, this review envisions processing a high-quality dataset for automatic information extraction from unstructured documents. Additionally, it emphasizes the importance of collaborative efforts between organizations and researchers to address the diverse challenges associated with unstructured data analysis.","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"31 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141576733","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

New custom rating for improving recommendation system performance 用于提高推荐系统性能的新自定义评级

IF 8.1 2区计算机科学

Journal of Big Data Pub Date : 2024-07-02 DOI: 10.1186/s40537-024-00952-3

Tora Fahrudin, Dedy Rahman Wijaya

{"title":"New custom rating for improving recommendation system performance","authors":"Tora Fahrudin, Dedy Rahman Wijaya","doi":"10.1186/s40537-024-00952-3","DOIUrl":"https://doi.org/10.1186/s40537-024-00952-3","url":null,"abstract":"Recommendation system is currently attracting the interest of many explorers. Various new businesses have surfaced with the rise of online marketing (E-Commerce) in response to Covid-19 pandemic. This phenomenon allows recommendation items through a system called Collaborative Filtering (CF), aiming to improve shopping experience of users. Typically, the effectiveness of CF relies on the precise identification of similar profile users by similarity algorithms. Traditional similarity measures are based on the user-item rating matrix. Approximately, four custom ratings (CR) were used along with a new rating formula, termed New Custom Rating (NCR), derived from the popularity of users and items in addition to the original rating. Specifically, NCR optimized recommendation system performance by using the popularity of users and items to determine new ratings value, rather than solely relying on the original rating. Additionally, the formulas improved the representativeness of the new rating values and the accuracy of similarity algorithm calculations. Consequently, the increased accuracy of recommendation system was achieved. The implementation of NCR across four CR algorithms and recommendation system using five public datasets was examined. Consequently, the experimental results showed that NCR significantly increased recommendation system accuracy, as evidenced by reductions in RMSE, MSE, and MAE as well as increasing FCP and Hit Rate. Moreover, by combining the popularity of users and items into rating calculations, NCR improved the accuracy of various recommendation system algorithms reducing RMSE, MSE, and MAE up to 62.10%, 53.62%, 65.97%, respectively, while also increasing FCP and Hit Rate up to 11.89% and 31.42%, respectively.","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"23 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141520245","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

Optimization-based convolutional neural model for the classification of white blood cells 基于优化的卷积神经模型用于白细胞分类

IF 8.1 2区计算机科学

Journal of Big Data Pub Date : 2024-06-26 DOI: 10.1186/s40537-024-00949-y

Tulasi Gayatri Devi, Nagamma Patil

{"title":"Optimization-based convolutional neural model for the classification of white blood cells","authors":"Tulasi Gayatri Devi, Nagamma Patil","doi":"10.1186/s40537-024-00949-y","DOIUrl":"https://doi.org/10.1186/s40537-024-00949-y","url":null,"abstract":"White blood cells (WBCs) are one of the most significant parts of the human immune system, and they play a crucial role in diagnosing the characteristics of pathologists and blood-related diseases. The characteristics of WBCs are well-defined based on the morphological behavior of their nuclei, and the number and types of WBCs can often determine the presence of diseases or illnesses. Generally, there are different types of WBCs, and the accurate classification of WBCs helps in proper diagnosis and treatment. Although various classification models were developed in the past, they face issues like less classification accuracy, high error rate, and large execution. Hence, a novel classification strategy named the African Buffalo-based Convolutional Neural Model (ABCNM) is proposed to classify the types of WBCs accurately. The proposed strategy commences with collecting WBC sample databases, which are preprocessed and trained into the system for classification. The preprocessing phase removes the noises and training flaws, which helps improve the dataset's quality and consistency. Further, feature extraction is performed to segment the WBCs, and African Buffalo fitness is updated in the classification layer for the correct classification of WBCs. The proposed framework is modeled in Python, and the experimental analysis depicts that it achieved 99.12% accuracy, 98.16% precision, 99% sensitivity, 99.04% specificity, and 99.02% f-measure. Furthermore, a comparative assessment with the existing techniques validated that the proposed strategy obtained better performances than the conventional models.","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"38 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503493","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

Feature reduction for hepatocellular carcinoma prediction using machine learning algorithms 利用机器学习算法减少预测肝细胞癌的特征

IF 8.1 2区计算机科学

Journal of Big Data Pub Date : 2024-06-18 DOI: 10.1186/s40537-024-00944-3

Ghada Mostafa, Hamdi Mahmoud, Tarek Abd El-Hafeez, Mohamed E. ElAraby

{"title":"Feature reduction for hepatocellular carcinoma prediction using machine learning algorithms","authors":"Ghada Mostafa, Hamdi Mahmoud, Tarek Abd El-Hafeez, Mohamed E. ElAraby","doi":"10.1186/s40537-024-00944-3","DOIUrl":"https://doi.org/10.1186/s40537-024-00944-3","url":null,"abstract":"Hepatocellular carcinoma (HCC) is a highly prevalent form of liver cancer that necessitates accurate prediction models for early diagnosis and effective treatment. Machine learning algorithms have demonstrated promising results in various medical domains, including cancer prediction. In this study, we propose a comprehensive approach for HCC prediction by comparing the performance of different machine learning algorithms before and after applying feature reduction methods. We employ popular feature reduction techniques, such as weighting features, hidden features correlation, feature selection, and optimized selection, to extract a reduced feature subset that captures the most relevant information related to HCC. Subsequently, we apply multiple algorithms, including Naive Bayes, support vector machines (SVM), Neural Networks, Decision Tree, and K nearest neighbors (KNN), to both the original high-dimensional dataset and the reduced feature set. By comparing the predictive accuracy, precision, F Score, recall, and execution time of each algorithm, we assess the effectiveness of feature reduction in enhancing the performance of HCC prediction models. Our experimental results, obtained using a comprehensive dataset comprising clinical features of HCC patients, demonstrate that feature reduction significantly improves the performance of all examined algorithms. Notably, the reduced feature set consistently outperforms the original high-dimensional dataset in terms of prediction accuracy and execution time. After applying feature reduction techniques, the employed algorithms, namely decision trees, Naive Bayes, KNN, neural networks, and SVM achieved accuracies of 96%, 97.33%, 94.67%, 96%, and 96.00%, respectively.","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"22 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503495","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

Advanced RIME architecture for global optimization and feature selection 用于全局优化和特征选择的先进 RIME 架构

IF 8.1 2区计算机科学

Journal of Big Data Pub Date : 2024-06-18 DOI: 10.1186/s40537-024-00931-8

Ruba Abu Khurma, Malik Braik, Abdullah Alzaqebah, Krishna Gopal Dhal, Robertas Damaševičius, Bilal Abu-Salih

引用次数: 0