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Visualizing risk factors of dementia from scholarly literature using knowledge maps and next-generation data models

Visual Computing for Industry,Biomedicine,and Art 2021年第1期4卷 165-182页

作者： Kiran Fahd Sitalakshmi Venkatraman Department of Information Technology Melbourne Polytechnic3181 PrahranVICAustralia

Scholarly communication of knowledge is predominantly document-based in digital repositories,and researchers find it tedious to automatically capture and process the semantics among related articles.Despite the present digital era of big data,there is a lack of visual representations of the knowledge present in scholarly articles,and a time-saving approach for a literature search and visual navigation is warranted.The majority of knowledge display tools cannot cope with current big data trends and pose limitations in meeting the requirements of automatic knowledge representation,storage,and dynamic visualization.To address this limitation,the main aim of this paper is to model the visualization of unstructured data and explore the feasibility of achieving visual navigation for researchers to gain insight into the knowledge hidden in scientific articles of digital repositories.Contemporary topics of research and practice,including modifiable risk factors leading to a dramatic increase in Alzheimer’s disease and other forms of dementia,warrant deeper insight into the evidence-based knowledge available in the literature.The goal is to provide researchers with a visual-based easy traversal through a digital repository of research articles.This paper takes the first step in proposing a novel integrated model using knowledge maps and next-generation graph datastores to achieve a semantic visualization with domain-specific knowledge,such as dementia risk factors.The model facilitates a deep conceptual understanding of the literature by automatically establishing visual relationships among the extracted knowledge from the big data resources of research articles.It also serves as an automated tool for a visual navigation through the knowledge repository for faster identification of dementia risk factors reported in scholarly articles.Further,it facilitates a semantic visualization and domain-specific knowledge discovery from a large digital repository and their associations.In this study,the implementation of the proposed

关键词： Big data data visualization Knowledge maps Dementia Non-relational database Graph database Neo4j Semantic visualization

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Machine Learning and Artificial Neural Network for Predicting Heart Failure Risk

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Computer Systems Science & Engineering 2023年第1期44卷 757-775页

作者： Polin Rahman Ahmed Rifat MD.IftehadAmjad Chy Mohammad Monirujjaman Khan Mehedi Masud Sultan Aljahdali Department of Electrical and Computer Engineering North South UniversityBashundharaDhaka1229Bangladesh Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

Heart failure is now widely spread throughout the world.Heart disease affects approximately 48%of the population.It is too expensive and also difficult to cure the disease.This research paper represents machine learning models to predict heart failure.The fundamental concept is to compare the correctness of various Machine Learning(ML)algorithms and boost algorithms to improve models’accuracy for prediction.Some supervised algorithms like K-Nearest Neighbor(KNN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF),Logistic Regression(LR)are considered to achieve the best results.Some boosting algorithms like Extreme Gradient Boosting(XGBoost)and Cat-Boost are also used to improve the prediction using Artificial Neural Networks(ANN).This research also focuses on data visualization to identify patterns,trends,and outliers in a massive data set.Python and Scikit-learns are used for ML.Tensor Flow and Keras,along with Python,are used for ANN model train-ing.The DT and RF algorithms achieved the highest accuracy of 95%among the classifiers.Meanwhile,KNN obtained a second height accuracy of 93.33%.XGBoost had a gratified accuracy of 91.67%,SVM,CATBoost,and ANN had an accuracy of 90%,and LR had 88.33%accuracy.

关键词： Heart failure prediction data visualization machine learning k-nearest neighbors support vector machine decision tree random forest logistic regression xgboost and catboost artificial neural network

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Research on sentiment terms extraction and visualization of character sentimental interactions in A Dream of Red Mansions

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The Journal of China Universities of Posts and Telecommunications 2022年第2期29卷 24-32页

作者： Huang Pei Zhang Meng Wan Liu Lei Xuanzheng School of Digital Media and Design Arts Beijing University of Posts and TelecommunicationsBeijing 100876China School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Beijing Key Laboratory of Network System and Network Culture Beijing University of Posts and TelecommunicationsBeijing 100876China School of Electronic Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China

In the context of interdisciplinary research,using computer technology to further mine keywords in cultural texts and carry out semantic analysis can deepen the understanding of texts,and provide quantitative support and evidence for humanistic studies.Based on the novel A Dream of Red Mansions,the automatic extraction and classification of those sentiment terms in it were realized,and detailed analysis of large-scale sentiment terms was carried out.Bidirectional encoder representation from transformers(BERT) pretraining and fine-tuning model was used to construct the sentiment classifier of A Dream of Red Mansions.Sentiment terms of A Dream of Red Mansions are divided into eight sentimental categories,and the relevant people in sentences are extracted according to specific rules.It also tries to visually display the sentimental interactions between Twelve Girls of Jinling and Jia Baoyu along with the development of the episode.The overall F_(1) score of BERT-based sentiment classifier reached 84.89%.The best single sentiment score reached 91.15%.Experimental results show that the classifier can satisfactorily classify the text of A Dream of Red Mansions,and the text classification and interactional analysis results can be mutually verified with the text interpretation of A dream of Red Mansions by literature experts.

关键词： A Dream of Red Mansions sentiment term extraction character sentiment interaction bidirectional encoder representation from transformers(BERT) data visualization

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Enhanced data narratives

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Journal of Management Analytics 2021年第2期8卷 171-194页

作者： Judd D.Bradbury Rosanna E.Guadagno Business Analytics Program Jindal School of ManagementUniversity of Texas at DallasRichardsonTXUSA The Center for International Security and Cooperation Stanford UniversityStanfordCAUSA

data narratives are an emerging form of communication that employs enhanced media for effective knowledge transfer of complex information.Researchers in the fields of data visualization and artificial intelligence have begun to pioneer new structures of communication to improve the efficiency of construction and the retention of information provided by the knowledge transfer experience.In this paper,we report the results of an empirical study conducted to compare the performance of various narrative communication techniques including frame based narrative visualization,documentary narrative visualization,computer generated text narratives and human generated text narratives.We assess the knowledge transfer performance for each of these data driven narrative structures.Across all conditions,an identical set of knowledge retention questions assessed participants’recall of details from their assigned narrative communication.Statistical analysis on group performance answering the knowledge retention questions revealed that some narrative communication techniques perform better with general audiences.

关键词： analytic communication data visualization algorithmic storytelling video analysis enhanced data narratives

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Identification of the clustering structure in microbiome data by density clustering on the Manhattan distance

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Science China(Information Sciences) 2016年第7期59卷 44-50页

作者： Xingpeng JIANG Xiaohua HU Tingting HE School of Computer Science Central China Normal University College of Computing & Informatics Drexel University

Clustering technology is a method for grouping data points into clusters containing a group of similar data points. In a real dataset such as microbiome data, the data points are presented as profiles or a probability distribution. These data points form the periphery of a cluster, making it difficult to identify the real clustering structure. In this study, we used density clustering on several distance measures to overcome this difficulty. Experiments using a real dataset indicated that the Manhattan distance is an appropriate distance measure for clustering analysis of microbiome data.

关键词： microbiome information distance data visualization density clustering microbial community

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Visual exploration of movement and event data with interactive time masks

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Visual Informatics 2017年第1期1卷 25-39页

作者： Natalia Andrienko Gennady Andrienko Elena Camossi Christophe Claramunt Jose Manuel Cordero Garcia Georg Fuchs Melita Hadzagic Anne-Laure Jousselme Cyril Ray David Scarlatti George Vouros Fraunhofer Institute IAIS Sankt AugustinGermany City University London LondonUK NATO Science and Technology Organization Centre for Maritime Research and ExperimentationItaly Naval Academy Research Institute France CRIDA-Reference Center for Research Development and Innovation in ATMMadridSpain Boeing Research&Technology Europe Spain Department of Digital System University of PiraeusGreece

We introduce the concept of time mask,which is a type of temporal filter suitable for selection of multiple disjoint time intervals in which some query conditions fulfil.Such a filter can be applied to time-referenced objects,such as events and trajectories,for selecting those objects or segments of trajectories that fit in one of the selected time intervals.The selected subsets of objects or segments are dynamically summarized in various ways,and the summaries are represented visually on maps and/or other displays to enable exploration.The time mask filtering can be especially helpful in analysis of disparate data(e.g.,event records,positions of moving objects,and time series of measurements),which may come from different sources.To detect relationships between such data,the analyst may set query conditions on the basis of one dataset and investigate the subsets of objects and values in the other datasets that co-occurred in time with these conditions.We describe the desired features of an interactive tool for time mask filtering and present a possible implementation of such a tool.By example of analysing two real world data collections related to aviation and maritime traffic,we show the way of using time masks in combination with other types of filters and demonstrate the utility of the time mask filtering.

关键词： data visualization Interactive visualization Interaction technique

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Image Post-Processing Method for Visual data Mining

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Wuhan University Journal of Natural Sciences 2006年第1期11卷 15-20页

作者： REN Yong-gong YU Ge School of Information Science and Engineering Northeastern University Shenyang 110004 Liaoning China School of Computer and Information Technology Liaoning Normal University Dalian 116029 Liaoning China

Visual data mining is one of important approach of data mining techniques. Most of them are based on computer graphic techniques but few of them exploit image-processing techniques. This paper proposes an image processing method, named RNAM （resemble neighborhood averaging method）, to facilitate visual data mining, which is used to post-process the data mining result-image and help users to discover significant features and useful patterns effectively. The experiments show that the method is intuitive, easily-understanding and effectiveness. It provides a new approach for visual data mining.

关键词： visual data mining data visualization image processing

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A pan-cancer integrative pathway analysis of multi-omics data

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Quantitative Biology 2020年第2期8卷 130-142页

作者： Henry Linder Yuping Zhang Department of Statistics University of ConnecticutStorrsCT 06269USA

Background:Multi-view-omics datasets offer rich opportunities for integrative analysis across genomic,transcriptomic,and epigenetic data platforms.Statistical methods are needed to rigorously implement current research on functional biology,matching the complex dynamics of systems genomic datasets.Methods:We apply imputation for missing data and a structural,graph-theoretic pathway model to a dataset of 22 cancers across 173 signaling pathways.Our pathway model integrates multiple data platforms,and we test for differential activation between cancerous tumor and healthy tissue populations.Results:Our pathway analysis reveals significant disturbance in signaling pathways that are known to relate to oncogenesis.We identify several pathways that suggest new research directions,including the Trk signaling and focal adhesion kinase activation pathways in sarcoma.Conclusions:Our integrative analysis confirms contemporary research findings,which supports the validity of our findings.We implement an interactive data visualization for exploration of the pathway analyses,which is available online for public access.

关键词： multi-platform data integration pathway analysis imputation cancer genomics data visualization

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A Transactive Energy Model for Smart Metering Systems Using Blockchain

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CSEE Journal of Power and Energy Systems 2021年第5期7卷 943-953页

作者： Juan C.Olivares-Rojas Enrique Reyes-Archundia Jose A.Gutierrez-Gnecchi Ismael Molina-Moreno Jaime Cerda-Jacobo Arturo Mendez-Patino National Technological Institute of Mexico/Technological Institute of Morelia-Division of Graduate Studies and Research MoreliaMichoacan 58120Mexico. School of Electrical Engineering Michoacan University of San Nicolas de HidalgoMorelia 58030.Mexico

The lowering cost of electricity generation technologies by renewable means has caused the role of end-users to change from being a simple consumer to a producer of their energy.This new role has been called a prosumer and has contributed to modifying electricity markets,by allowing prosumers to merchandize the energy surplus to electricity companies and other prosumers.The fundamental key to any economic transaction is trust between the parties.Recently,new technologies,such as blockchain,are transforming transaction security,and allowing decentralization of the smart power grid.This study presents the development of a transactional model of electrical energy using the infrastructure of smart metering and blockchain systems to allow the commercialization of electrical energy by prosumers.The results show that for the Mexican electricity market,they can he good alternatives hy reducing subsidies and being more profitable than current rates.

关键词： data visualization fault diagnosis IoT network photovoltaic system wireless remote monitoring

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Phase-selective fluorescence of doped Ge_2Sb_2Te_5 phase-change memory thin films

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Chinese Optics Letters 2015年第12期13卷 50-53页

作者：张科林金成王阳 Key Laboratory of High Power Laser Materials Shanghai Institute of Optics and Fine MechanicsChinese Academy of Sciences Department of Chemical Physics Fritz Haber Institute of the Max Planck Society

In this Letter, new concepts of fluorescence phase-change materials and fluorescence phase-change multilevel recording are proposed. High-contrast fluorescence between the amorphous and crystalline states is achieved in nickel- or bismuth-doped Ge;Sb;Te;phase-change memory thin films. Opposite phase-selective fluorescence effects are observed when different doping ions are used. The fluorescence intensity is sensitive to the crystallization degree of the films. This characteristic can be applied in reconfigurable multi-state memory and other logic devices. It also has likely applications in display and data visualization.

关键词： Amorphous films Crystalline materials data visualization Fluorescence Germanium Logic devices Phase change materials Semiconductor doping Thin films

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