Visual Exploration of Neural Document Embedding in Information Retrieval: Semantics and Feature Selection

Application of the artificial intelligence system based on graphics and vision in ethnic tourism of subtropical grasslands

This study aims to optimize the evaluation and decision-making of ethnic tourism resources through the utilization of deep learning algorithms and Internet of Things (IoT) technology. Specifically, emphasis is placed on the recognition and feature extraction of Mongolian decorative patterns, providing new insights for the deep application of cultural heritage and visual design. In this study, the existing DL algorithm is improved, integrating the feature extraction algorithm of ResNet + Canny + Local Binary Pattern (LBP), and utilizing an intelligent decision method to analyze the intelligent development of indigenous tourism resources. Simultaneously, the DL algorithm and IoT technology are combined with visual design and convolutional neural networks to perform feature extraction and technology recognition. Visual design offers an intuitive representation of tourism resources, while fuzzy decision-making provides a more accurate evaluation in the face of uncertainty. By implementing an intelligent decision-making system, this study achieves a multiplier effect. The integration of intelligent methods not only enhances the accuracy of tourism resource evaluation and decision-making but also elevates the quality and efficiency of the tourism experience. This multiplier effect is evident in the system's capacity to manage substantial datasets and deliver prompt, precise decision support, thus playing a pivotal role in tourism resource management and planning. The findings of this study demonstrate that optimizing intelligent development technology for rural tourism through IoT can enhance the efficacy of intelligent solutions. In terms of pattern recognition accuracy, AlexNet, VGGNet, and ResNet achieve accuracies of 90.8 %, 94.5 %, and 96.9 %, respectively, while the proposed fusion algorithm attains an accuracy of 98.8 %. These results offer practical insights for rural tourism brand strategy and underscore the utility of applying fuzzy decision systems in urban tourism and visual design. Moreover, the research outcomes hold significant practical implications for the advancement of Mongolian cultural tourism and provide valuable lessons for exploring novel paradigms in image analysis and pattern recognition. This study contributes beneficial insights for future research endeavors in related domains.

A Study of Healthcare Team Communication Networks using Visual Analytics

Cooperation among teams or individuals of healthcare professionals (HCPs) is one of the crucial factors towards patients' survival outcome. However, it is challenging to uncover and understand such factors in the complex Multiteam System (MTS) communication networks representing daily HCP cooperation. In this paper, we present a study on MTS communication networks constructed with real-world cancer patients' Electronic Health Record (EHR) access logs. We adopt a visual analytics workflow to extract associations between semantic characteristics of MTS communication networks and the patients' survival outcomes. The workflow consists of a neural network learning phase to classify the data based on the chosen input and output attributes, a dimensionality reduction and optimization phase to produce a simplified set of results for examination, and finally an interpreting phase conducted by the user through an interactive visualization interface. We provide the insights found using this workflow with two case studies and an expert interview.

A Predictive Visual Analytics System for Studying Neurodegenerative Disease Based on DTI Fiber Tracts

Diffusion tensor imaging (DTI) has been used to study the effects of neurodegenerative diseases on neural pathways, which may lead to more reliable and early diagnosis of these diseases as well as a better understanding of how they affect the brain. We introduce a predictive visual analytics system for studying patient groups based on their labeled DTI fiber tract data and corresponding statistics. The system's machine-learning-augmented interface guides the user through an organized and holistic analysis space, including the statistical feature space, the physical space, and the space of patients over different groups. We use a custom machine learning pipeline to help narrow down this large analysis space and then explore it pragmatically through a range of linked visualizations. We conduct several case studies using DTI and T1-weighted images from the research database of Parkinson's Progression Markers Initiative.

A Critical Reflection on Visualization Research: Where Do Decision Making Tasks Hide?

It has been widely suggested that a key goal of visualization systems is to assist decision making, but is this true? We conduct a critical investigation on whether the activity of decision making is indeed central to the visualization domain. By approaching decision making as a user task, we explore the degree to which decision tasks are evident in visualization research and user studies. Our analysis suggests that decision tasks are not commonly found in current visualization task taxonomies and that the visualization field has yet to leverage guidance from decision theory domains on how to study such tasks. We further found that the majority of visualizations addressing decision making were not evaluated based on their ability to assist decision tasks. Finally, to help expand the impact of visual analytics in organizational as well as casual decision making activities, we initiate a research agenda on how decision making assistance could be elevated throughout visualization research.

Cluster-based information retrieval using pattern mining

This paper addresses the problem of responding to user queries by fetching the most relevant object from a clustered set of objects. It addresses the common drawbacks of cluster-based approaches and targets fast, high-quality information retrieval. For this purpose, a novel cluster-based information retrieval approach is proposed, named Cluster-based Retrieval using Pattern Mining (CRPM). This approach integrates various clustering and pattern mining algorithms. First, it generates clusters of objects that contain similar objects. Three clustering algorithms based on k-means, DBSCAN (Density-based spatial clustering of applications with noise), and Spectral are suggested to minimize the number of shared terms among the clusters of objects. Second, frequent and high-utility pattern mining algorithms are performed on each cluster to extract the pattern bases. Third, the clusters of objects are ranked for every query. In this context, two ranking strategies are proposed: i) Score Pattern Computing (SPC), which calculates a score representing the similarity between a user query and a cluster; and ii) Weighted Terms in Clusters (WTC), which calculates a weight for every term and uses the relevant terms to compute the score between a user query and each cluster. Irrelevant information derived from the pattern bases is also used to deal with unexpected user queries. To evaluate the proposed approach, extensive experiments were carried out on two use cases: the documents and tweets corpus. The results showed that the designed approach outperformed traditional and cluster-based information retrieval approaches in terms of the quality of the returned objects while being very competitive in terms of runtime.

Disease surveillance using online news: Dengue and zika in tropical countries

BACKGROUND

Around the world in tropical areas, certain vector-borne diseases have become endemic and hyperendemic. Among the developing nations, there are common difficulties in establishing the incidences of various diseases, especially vector-borne diseases with complex etiologies and a broad spectrum of presentations. One alternative approach to characterization of the disease outbreaks examines the possibilities of developing proxy information from online news articles. Such sources are being evaluated for applications to disease surveillance, early outbreak detection, and epidemiology research. Our study here looks to examine the potential of news articles in elucidating outbreaks of dengue in India and zika disease in Brazil.

OBJECTIVE

This study is designed to assess the potential usefulness of news articles in tracking case numbers of dengue and zika through an improved understanding of how news outlets report on disease. We specifically examine the possibilities of providing near real-time reporting on the development of outbreaks of dengue and zika.

METHODS

Newspaper articles related to dengue fever and zika disease in India and Brazil, respectively were extracted from the LexisNexis database. We targeted news articles available from five popular international news sources and two local newspapers in each country. The news articles were processed to provide yearly and weekly time series in the number of articles concerned with dengue and zika to test their potential suitability as proxies for disease prevalence. The collections of articles were analyzed using a text mining tool-kit that subdivides a collections of news articles into smaller clusters to study the topical focus of articles and their relevance to tracking diseases.

RESULTS

For dengue fever in India, the local newspapers provide a better source of information than international newspapers. The multi-year analysis (2010-2016) suggests that the numbers of dengue cases are strongly correlated with the numbers of news reports, with an R² value of 0.88. For zika disease in Brazil, the news reports provided useful information on the timing of the zika outbreak. Reporting increase sharply at the beginning of 2016, peaked in weeks 5 to 8, and decreased sharply. The numbers of articles remained low for the remainder of 2016 and 2017. Comparisons with reported case again show article numbers to be a useful proxy of prevalence of zika in Brazil.

CONCLUSIONS

The paper describes a strategy that applies newspaper as proxies to monitor outbreaks of infectious diseases and to study the epidemiology. It has potential applicability in some developing countries and regions with relatively poor medical infrastructures and records. Clearly, large national newspapers in India provide a better source of information on diseases than international outlets. This approach has potential with selected diseases in a few selected countries. Article numbers internationally appear to vary in proportion to the perceived health impact.

Disease surveillance using online news: an extended study of dengue fever in India

BACKGROUND

The study demonstrates the potential in using newspaper information as a proxy for monitoring dengue fever outbreaks in India. Online newspapers are being considered as sources of information on disease surveillance, early outbreak detection, and epidemiology research. Our objective is to understand the complex dengue epidemiology and discover inter-relationships between dengue fever and local social-environmental factors by mining information from local Indian news articles.

RESULTS

We search and extract articles from the newspaper database, LexisNexis. News articles related to dengue fever in India are analyzed together with local environmental, climate, and population data in both temporally and spatially to study disease epidemiology. We also examine the influence of newsworthiness for constructing a disease surveillance system. In terms of temporal aspects, dengue outbreaks follow consistent patterns every year. However, for many areas, this application is frustrated by the relatively small numbers of news articles.

CONCLUSIONS

The study has advanced capabilities in producing approaches that provide for richer interpretations of textual information provided in newspaper articles. Such approaches appear particularly well suited for developing countries with relatively poor medical infrastructures and records.