Impact of image organizations on multimedia document retrieval

In this paper we compare ranking effectiveness of heterogeneous multimedia document retrieval when different image organizations are used for formulating queries. The quality of image queries depends on the organization of images used to make queries which in turn significantly impacts retrieval precision. CBIR (content based information retrieval) needs an effective and efficient organization of images including user interface which must be part of the configuration parameters of image retrieval research. <br /

The size of national delegations and the need for attendance regulation at climate conferences

With the growing significance of the United Nations climate conferences the number of attendees has grown considerably over the last years. But more participants do not necessarily ensure better decisions. And sometimes attending a United Nations climate conference is only a way to raise the profile of the attendee

Listen to your Data: Model-Based Sonification for Data Analysis

Hermann T, Ritter H. Listen to your Data: Model-Based Sonification for Data Analysis. In: Lasker GE, Syed MR, eds. Advances in intelligent computing and multimedia systems. Windsor, Ontario: Int. Inst. for Advanced Studies in System research and cybernetics; 1999: 189-194.Sonification is the use of non-speech audio to convey information. We are developing tools for interactive data exploration, which make use of sonification for data presentation. In this paper, model-based sonification is presented as a concept to design auditory displays. Two designs are described: (1) particle trajectories in a "data potential" is a sonification model to reveal information about the clustering of vectorial data and (2) "data-sonograms" is a sonification for data from a classification problem to reveal information about the mixing of distinct classes

Association of maternal obesity with fetal and neonatal death: evidence from South and South-East Asian countries

Background Obesity prevalence is increasing in many countries in the world, including Asia. Maternal obesity is highly associated with fetal and neonatal deaths. This study investigated whether maternal obesity is a risk factor of fetal death (measured in terms of miscarriage and stillbirth) and neonatal mortality in South and South-East Asian countries. Methods This cross-sectional study pooled the most recent Demographic and Health Surveys (DHS) from eight South and South-East Asian countries (2014–2018). Multivariate logistic regression was deployed to check the relationships between maternal obesity with fetal and neonatal deaths. Finally, multilevel logistic regression model was employed since the DHS data has a hierarchical structure. Results The pooled logistic regression model illustrated that maternal obesity is associated with higher odds of miscarriage (adjusted odds ratio [aOR]: 1.26, 95% CI: 1.20–1.33) and stillbirths (aOR: 1.46, 95% CI: 1.27–1.67) after adjustment of confounders. Children of obese mothers were at 1.18 (aOR: 1.18, 95% CI: 1.08–1.28) times greater risk of dying during the early neonatal period than mothers with a healthy weight. However, whether maternal obesity is statistically a significant risk factor for the offspring’s late neonatal deaths was not confirmed. The significant association between maternal obesity with miscarriage, stillbirth and early neonatal mortality was further confirmed by multilevel logistic regression results. Conclusion Maternal obesity in South and South-East Asian countries is associated with a greater risk of fetal and early neonatal deaths. This finding has substantial public health implications. Strategies to prevent and reduce obesity should be developed before planning pregnancy to reduce the fetal and neonatal death burden. Obese women need to deliver at the institutional facility centre that can offer obstetrics and early neonatal care

Breast cancer detection based on simplified deep learning technique with histopathological image using BreaKHis database

Presented here are the results of an investigation conducted to determine the effectiveness of deep learning (DL)-based systems utilizing the power of transfer learning for detecting breast cancer in histopathological images. It is shown that DL models that are not specifically developed for breast cancer detection can be trained using transfer learning to effectively detect breast cancer in histopathological images. The outcome of the analysis enables the selection of the best DL architecture for detecting cancer with high accuracy. This should facilitate pathologists to achieve early diagnoses of breast cancer and administer appropriate treatment to the patient. The experimental work here used the BreaKHis database consisting of 7909 histopathological pictures from 82 clinical breast cancer patients. The strategy presented for DL training uses various image processing techniques for extracting various feature patterns. This is followed by applying transfer learning techniques in the deep convolutional networks like ResNet, ResNeXt, SENet, Dual Path Net, DenseNet, NASNet, and Wide ResNet. Comparison with recent literature shows that ResNext-50, ResNext-101, DPN131, DenseNet-169 and NASNet-A provide an accuracy of 99.8%, 99.5%, 99.675%, 99.725%, and 99.4%, respectively, and outperform previous studies

気候変動への持続可能な適応を可能にするコベネフィット:バングラデシュの事例

京都大学0048新制・論文博士博士(地球環境学)乙第13389号論地環博第15号新制||地環||40(附属図書館)(主査)准教授 森 晶寿, 教授 諸富 徹, 准教授 SINGER JANE学位規則第4条第2項該当Doctor of Global Environmental StudiesKyoto UniversityDFA

Strategic Applications of Distance Learning Technologies

https://cornerstone.lib.mnsu.edu/university-archives-msu-authors/1358/thumbnail.jp