New Generation Computing最新文献_第7页

Explainable AI: To Reveal the Logic of Black-Box Models 可解释的人工智能:揭示黑箱模型的逻辑

IF 2.6 4区计算机科学

New Generation Computing Pub Date : 2023-02-01 DOI: 10.1007/s00354-022-00201-2

Chinu, Urvashi Bansal

引用次数: 3

A Graph-Based Ontology Matching Framework 一种基于图的本体匹配框架

IF 2.6 4区计算机科学

New Generation Computing Pub Date : 2023-01-06 DOI: 10.1007/s00354-022-00200-3

F. Şentürk, V. Aytaç

引用次数: 1

Deep Learning Model for COVID-19 Sentiment Analysis on Twitter. 推特上新冠肺炎情绪分析的深度学习模型。

IF 2.6 4区计算机科学

New Generation Computing Pub Date : 2023-01-01 Epub Date: 2023-03-13 DOI: 10.1007/s00354-023-00209-2

Salvador Contreras Hernández, María Patricia Tzili Cruz, José Martín Espínola Sánchez, Angélica Pérez Tzili

{"title":"Deep Learning Model for COVID-19 Sentiment Analysis on Twitter.","authors":"Salvador Contreras Hernández, María Patricia Tzili Cruz, José Martín Espínola Sánchez, Angélica Pérez Tzili","doi":"10.1007/s00354-023-00209-2","DOIUrl":"10.1007/s00354-023-00209-2","url":null,"abstract":"The COVID-19 pandemic impacted the mood of the people, and this was evident on social networks. These common user publications are a source of information to measure the population's opinion on social phenomena. In particular, the Twitter network represents a resource of great value due to the amount of information, the geographical distribution of the publications and the openness to dispose of them. This work presents a study on the feelings of the population in Mexico during one of the waves that produced the most contagion and deaths in this country. A mixed, semi-supervised approach was used, with a lexical-based data labeling technique to later bring these data to a pre-trained model of Transformers completely in Spanish. Two Spanish-language models were trained by adding to the Transformers neural network the adjustment for the sentiment analysis task specifically on COVID-19. In addition, ten other multilanguage Transformer models including the Spanish language were trained with the same data set and parameters to compare their performance. In addition, other classifiers with the same data set were used for training and testing, such as Support Vector Machines, Naive Bayes, Logistic Regression, and Decision Trees. These performances were compared with the exclusive model in Spanish based on Transformers, which had higher precision. Finally, this model was used, developed exclusively based on the Spanish language, with new data, to measure the sentiment about COVID-19 of the Twitter community in Mexico.","PeriodicalId":54726,"journal":{"name":"New Generation Computing","volume":"41 2","pages":"189-212"},"PeriodicalIF":2.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10010651/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9880542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Variational Autoencoder Based Imbalanced COVID-19 Detection Using Chest X-Ray Images. 基于变分自编码器的胸部x线图像不平衡COVID-19检测

IF 2.6 4区计算机科学

New Generation Computing Pub Date : 2023-01-01 DOI: 10.1007/s00354-022-00194-y

Sankhadeep Chatterjee, Soumyajit Maity, Mayukh Bhattacharjee, Soumen Banerjee, Asit Kumar Das, Weiping Ding

{"title":"Variational Autoencoder Based Imbalanced COVID-19 Detection Using Chest X-Ray Images.","authors":"Sankhadeep Chatterjee, Soumyajit Maity, Mayukh Bhattacharjee, Soumen Banerjee, Asit Kumar Das, Weiping Ding","doi":"10.1007/s00354-022-00194-y","DOIUrl":"https://doi.org/10.1007/s00354-022-00194-y","url":null,"abstract":"Early and fast detection of disease is essential for the fight against COVID-19 pandemic. Researchers have focused on developing robust and cost-effective detection methods using Deep learning based chest X-Ray image processing. However, such prediction models are often not well suited to address the challenge of highly imabalanced datasets. The current work is an attempt to address the issue by utilizing unsupervised Variational Auto Encoders (VAEs). Firstly, chest X-Ray images are converted to a latent space by learning the most important features using VAEs. Secondly, a wide range of well established data resampling techniques are used to balance the preexisting imbalanced classes in the latent vector form of the dataset. Finally, the modified dataset in the new feature space is used to train well known classification models to classify chest X-Ray images into three different classes viz., \"COVID-19\", \"Pneumonia\", and \"Normal\". In order to capture the quality of resampling methods, 10-folds cross validation technique is applied on the dataset. Extensive experimental analysis have been carried out and results so obtained indicate significant improvement in COVID-19 detection using the proposed VAE based method. Furthermore, the ingenuity of the results have been established by performing Wilcoxon rank test with 95% level of significance.","PeriodicalId":54726,"journal":{"name":"New Generation Computing","volume":"41 1","pages":"25-60"},"PeriodicalIF":2.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9676807/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9333910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Application of Convolutional Neural Networks for COVID-19 Detection in X-ray Images Using InceptionV3 and U-Net. 卷积神经网络在InceptionV3和U-Net X射线图像中检测新冠肺炎的应用。

IF 2 4区计算机科学

New Generation Computing Pub Date : 2023-01-01 Epub Date: 2023-05-11 DOI: 10.1007/s00354-023-00217-2

Aman Gupta, Shashank Mishra, Sourav Chandan Sahu, Ulligaddala Srinivasarao, K Jairam Naik

{"title":"Application of Convolutional Neural Networks for COVID-19 Detection in X-ray Images Using InceptionV3 and U-Net.","authors":"Aman Gupta, Shashank Mishra, Sourav Chandan Sahu, Ulligaddala Srinivasarao, K Jairam Naik","doi":"10.1007/s00354-023-00217-2","DOIUrl":"10.1007/s00354-023-00217-2","url":null,"abstract":"COVID-19 has expanded overall across the globe after its initial cases were discovered in December 2019 in Wuhan-China. Because the virus has impacted people's health worldwide, its fast identification is essential for preventing disease spread and reducing mortality rates. The reverse transcription polymerase chain reaction (RT-PCR) is the primary leading method for detecting COVID-19 disease; it has high costs and long turnaround times. Hence, quick and easy-to-use innovative diagnostic instruments are required. According to a new study, COVID-19 is linked to discoveries in chest X-ray pictures. The suggested approach includes a stage of pre-processing with lung segmentation, removing the surroundings that do not provide information pertinent to the task and may result in biased results. The InceptionV3 and U-Net deep learning models used in this work process the X-ray photo and classifies them as COVID-19 negative or positive. The CNN model that uses a transfer learning approach was trained. Finally, the findings are analyzed and interpreted through different examples. The obtained COVID-19 detection accuracy is around 99% for the best models.","PeriodicalId":54726,"journal":{"name":"New Generation Computing","volume":"41 2","pages":"475-502"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10173914/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9527835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Systematic Literature Review and Future Perspectives for Handling Big Data Analytics in COVID-19 Diagnosis. 新冠肺炎诊断中处理大数据分析的系统文献综述和未来展望。

IF 2.1 4区计算机科学

New Generation Computing Pub Date : 2023-01-01 Epub Date: 2023-03-16 DOI: 10.1007/s00354-023-00211-8

Nagamani Tenali, Gatram Rama Mohan Babu

{"title":"A Systematic Literature Review and Future Perspectives for Handling Big Data Analytics in COVID-19 Diagnosis.","authors":"Nagamani Tenali, Gatram Rama Mohan Babu","doi":"10.1007/s00354-023-00211-8","DOIUrl":"10.1007/s00354-023-00211-8","url":null,"abstract":"In today's digital world, information is growing along with the expansion of Internet usage worldwide. As a consequence, bulk of data is generated constantly which is known to be \"Big Data\". One of the most evolving technologies in twenty-first century is Big Data analytics, it is promising field for extracting knowledge from very large datasets and enhancing benefits while lowering costs. Due to the enormous success of big data analytics, the healthcare sector is increasingly shifting toward adopting these approaches to diagnose diseases. Due to the recent boom in medical big data and the development of computational methods, researchers and practitioners have gained the ability to mine and visualize medical big data on a larger scale. Thus, with the aid of integration of big data analytics in healthcare sectors, precise medical data analysis is now feasible with early sickness detection, health status monitoring, patient treatment, and community services is now achievable. With all these improvements, a deadly disease COVID is considered in this comprehensive review with the intention of offering remedies utilizing big data analytics. The use of big data applications is vital to managing pandemic conditions, such as predicting outbreaks of COVID-19 and identifying cases and patterns of spread of COVID-19. Research is still being done on leveraging big data analytics to forecast COVID-19. But precise and early identification of COVID disease is still lacking due to the volume of medical records like dissimilar medical imaging modalities. Meanwhile, Digital imaging has now become essential to COVID diagnosis, but the main challenge is the storage of massive volumes of data. Taking these limitations into account, a comprehensive analysis is presented in the systematic literature review (SLR) to provide a deeper understanding of big data in the field of COVID-19.","PeriodicalId":54726,"journal":{"name":"New Generation Computing","volume":"41 2","pages":"243-280"},"PeriodicalIF":2.1,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10019802/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9880539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GUI Enabled Optimized Approach of CNN for Automatic Diagnosis of COVID-19 Using Radiograph Images. 基于GUI的CNN优化方法用于利用射线图像自动诊断新冠肺炎。

IF 2 4区计算机科学

New Generation Computing Pub Date : 2023-01-01 Epub Date: 2023-03-13 DOI: 10.1007/s00354-023-00212-7

Chalapathiraju Kanumuri, Renu Madhavi Chodavarapu

{"title":"GUI Enabled Optimized Approach of CNN for Automatic Diagnosis of COVID-19 Using Radiograph Images.","authors":"Chalapathiraju Kanumuri, Renu Madhavi Chodavarapu","doi":"10.1007/s00354-023-00212-7","DOIUrl":"10.1007/s00354-023-00212-7","url":null,"abstract":"World Health Organization (WHO) proclaimed the Corona virus (COVID-19) as a pandemic, since it contaminated billions of individuals and killed lakhs. The spread along with the severity of the disease plays a key role in early detection and classification to reduce the rapid spread as the variants are changing. COVID-19 could be categorized as a pneumonia infection. Bacterial pneumonia, fungal pneumonia, viral pneumonia, etc., are the classifications of several forms of pneumonia, which are subcategorized into more than 20 forms and COVID-19 will come under viral pneumonia. The wrong prediction of any of these can mislead humans into improper treatment, which leads to a matter of life. From the radiograph that is X-ray images, diagnosis of all these forms can be possible. For detecting these disease classes, the proposed method will employ a deep learning (DL) technique. Early detection of the COVID-19 is possible with this model; hence, the spread of the disease is minimized by isolating the patients. For execution, a graphical user interface (GUI) provides more flexibility. The proposed model, which is a GUI approach, is trained with 21 types of pneumonia radiographs by a convolutional neural network (CNN) trained on Image Net and adjusts them to act as feature extractors for the Radiograph images. Next, the CNNs are combined with united AI strategies. For the classification of COVID-19 detection, several approaches are proposed in which those approaches are concerned with COVID-19, pneumonia, and healthy patients only. In classifying more than 20 types of pneumonia infections, the proposed model attained an accuracy of 92%. Likewise, COVID-19 images are effectively distinguished from the other pneumonia images of radiographs.","PeriodicalId":54726,"journal":{"name":"New Generation Computing","volume":"41 2","pages":"213-224"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10010635/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9880538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey. 利用医学图像检测新冠肺炎的深度卷积神经网络：调查。

IF 2 4区计算机科学

New Generation Computing Pub Date : 2023-01-01 Epub Date: 2023-04-04 DOI: 10.1007/s00354-023-00213-6

Rana Khattab, Islam R Abdelmaksoud, Samir Abdelrazek

{"title":"Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey.","authors":"Rana Khattab, Islam R Abdelmaksoud, Samir Abdelrazek","doi":"10.1007/s00354-023-00213-6","DOIUrl":"10.1007/s00354-023-00213-6","url":null,"abstract":"Coronavirus Disease 2019 (COVID-19), which is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), surprised the world in December 2019 and has threatened the lives of millions of people. Countries all over the world closed worship places and shops, prevented gatherings, and implemented curfews to stand against the spread of COVID-19. Deep Learning (DL) and Artificial Intelligence (AI) can have a great role in detecting and fighting this disease. Deep learning can be used to detect COVID-19 symptoms and signs from different imaging modalities, such as X-Ray, Computed Tomography (CT), and Ultrasound Images (US). This could help in identifying COVID-19 cases as a first step to curing them. In this paper, we reviewed the research studies conducted from January 2020 to September 2022 about deep learning models that were used in COVID-19 detection. This paper clarified the three most common imaging modalities (X-Ray, CT, and US) in addition to the DL approaches that are used in this detection and compared these approaches. This paper also provided the future directions of this field to fight COVID-19 disease.","PeriodicalId":54726,"journal":{"name":"New Generation Computing","volume":"41 2","pages":"343-400"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10071474/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9579216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Optimized Deep Learning Approach for the Prediction of Social Distance Among Individuals in Public Places During Pandemic. 用于预测大流行病期间公共场所个体间社交距离的优化深度学习方法。

IF 2 4区计算机科学

New Generation Computing Pub Date : 2023-01-01 Epub Date: 2023-01-02 DOI: 10.1007/s00354-022-00202-1

Santosh Kumar Sahoo, G Palai, Baraa Riyadh Altahan, Sk Hasane Ahammad, P Poorna Priya, Md Amzad Hossain, Ahmed Nabih Zaki Rashed

{"title":"An Optimized Deep Learning Approach for the Prediction of Social Distance Among Individuals in Public Places During Pandemic.","authors":"Santosh Kumar Sahoo, G Palai, Baraa Riyadh Altahan, Sk Hasane Ahammad, P Poorna Priya, Md Amzad Hossain, Ahmed Nabih Zaki Rashed","doi":"10.1007/s00354-022-00202-1","DOIUrl":"10.1007/s00354-022-00202-1","url":null,"abstract":"Social distancing is considered as the most effective prevention techniques for combatting pandemic like Covid-19. It is observed in several places where these norms and conditions have been violated by most of the public though the same has been notified by the local government. Hence, till date, there has been no proper structure for monitoring the loyalty of the social-distancing norms by individuals. This research has proposed an optimized deep learning-based model for predicting social distancing at public places. The proposed research has implemented a customized model using detectron2 and intersection over union (IOU) on the input video objects and predicted the proper social-distancing norms continued by individuals. The extensive trials were conducted with popular state-of-the-art object detection model: regions with convolutional neural networks (RCNN) with detectron2 and fast RCNN, RCNN with TWILIO communication platform, YOLOv3 with TL, fast RCNN with YOLO v4, and fast RCNN with YOLO v2. Among all, the proposed (RCNN with detectron2 and fast RCNN) delivers the efficient performance with precision, mean average precision (mAP), total loss (TL) and training time (TT). The outcomes of the proposed model focused on faster R-CNN for social-distancing norms and detectron2 for identifying the human 'person class' towards estimating and evaluating the violation-threat criteria where the threshold (i.e., 0.75) is calculated. The model attained precision at 98% approximately (97.9%) with 87% recall score where intersection over union (IOU) was at 0.5.","PeriodicalId":54726,"journal":{"name":"New Generation Computing","volume":"41 1","pages":"135-154"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9807244/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9328325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comparative Analysis of Multidimensional COVID-19 Poverty Determinants: An Observational Machine Learning Approach. 多维COVID-19贫困决定因素的比较分析:一种观察机器学习方法。

IF 2.6 4区计算机科学

New Generation Computing Pub Date : 2023-01-01 DOI: 10.1007/s00354-023-00203-8

Sandeep Kumar Satapathy, Shreyaa Saravanan, Shruti Mishra, Sachi Nandan Mohanty

{"title":"A Comparative Analysis of Multidimensional COVID-19 Poverty Determinants: An Observational Machine Learning Approach.","authors":"Sandeep Kumar Satapathy, Shreyaa Saravanan, Shruti Mishra, Sachi Nandan Mohanty","doi":"10.1007/s00354-023-00203-8","DOIUrl":"https://doi.org/10.1007/s00354-023-00203-8","url":null,"abstract":"Poverty is a glaring issue in the twenty-first century, even after concerted efforts of organizations to eliminate the same. Predicting poverty using machine learning can offer practical models for facilitating the process of elimination of poverty. This paper uses Multidimensional Poverty Index Data from the Oxford Poverty and Human Development Initiative across the years 2019 and 2021 to make predictions of multidimensional poverty before and during the pandemic. Several poverty indicators under health, education and living standards are taken into consideration. The work implements several data analysis techniques like feature correlation and selection, and graphical visualizations to answer research questions about poverty. Various machine learning, such as Multiple Linear Regression, Decision Tree Regressor, Random Forest Regressor, XGBoost, AdaBoost, Gradient Boosting, Linear Support Vector Regressor (SVR), Ridge Regression, Lasso Regression, ElasticNet Regression, and K-Nearest Neighbor Regression algorithm, have been implemented to predict poverty across four datasets on a national and a subnational level. Regularization is used to increase the performance of the models, and cross-validation is used for estimation. Through a rigorous analysis and comparison of different models, this work identifies important poverty determinants and concludes that overall, Ridge Regression model performs the best with the highest R 2 score.","PeriodicalId":54726,"journal":{"name":"New Generation Computing","volume":"41 1","pages":"155-184"},"PeriodicalIF":2.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9889951/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9334470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2