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Improving Tuberculosis Diagnostics Using Deep Learning and Mobile Health Technologies among Resource-Poor and Marginalized Communities

Improving Tuberculosis Diagnostics Using Deep Learning and M...

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ieee international conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)

作者： Yu Cao Chang Liu Benyuan Liu Maria J. Brunette Ning Zhang Tong Sun Peifeng Zhang Jesus Peinado Epifanio Sanchez Garavito Leonid Lecca Garcia Walter H. Curioso Department of Computer Science University of Massachusetts-Lowell Lowell MA USA Department of Work Environment University of Massachusetts-Lowell Lowell MA USA Partners in Health Perú University of Washington Seattle Washington DC USA National Hospital Sergio E. Bernales Perú University of Washington Seattle Washington DC USA Biomedical and Health Informatics University of Washington Seattle Washington DC USA

ISBN: (纸本)9781509009442

Tuberculosis (TB) is a chronic infectious disease worldwide and remains a major cause of death globally. Of the estimated 9 million people who developed TB in 2013, over 80% were in South-East Asia, Western Pacific, and African. The majority of the infected populations was from resource-poor and marginalized communities with weak healthcare infrastructure. Reducing TB diagnosis delay is critical in mitigating disease transmission and minimizing the reproductive rate of the tuberculosis epidemic. The combination of machine learning and mobile computing techniques offers a unique opportunity to accelerate the TB diagnosis among these communities. The ultimate goal of our research is to reduce patient wait times for being diagnosed with this infectious disease by developing new machine learning and mobile health techniques to the TB diagnosis problem. In this paper, we first introduce major technique barriers and proposed system architecture. Then we report two major progresses we recently made. The first activity aims to develop large-scale, real-world and well-annotated X-ray image database dedicated for automated TB screening. The second research activity focus on developing effective and efficient computational models (in particularly, deep convolutional neural networks (CNN)-based models) to classify the image into different category of TB manifestations. Experimental results have demonstrated the effectiveness of our approach. Our future work includes: (1) to further improve the performance of the algorithms, and (2) to deploy our system in the city of Carabayllo in Perú, a densely occupied urban community and high-burden TB.

关键词： X-ray imaging Mobile communication Diagnostic radiography Image databases Mobile handsets Mobile computing

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A First Look at an Automated Pipeline for NGS-Based Breast-Cancer Diagnosis: The CArDIGAN Approach

A First Look at an Automated Pipeline for NGS-Based Breast-C...

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international ieee conference on Signal-Image Technologies and Internet-Based System

作者： Giuseppe Aceto Antonio Montieri Valerio Persico Antonio Pescapé Valeria D'Argenio Francesco Salvatore Lucio Pastore Dipartimento di Ingegneria Elettrica e Tecnologie dellʼInformazione Università di Napoli “Federico II” NM2 s.r.l Italy Dipartimento di Biochimica e Biotecnologie Mediche Università di Napoli “Federico II” CEINGE-Biotecnologie Avanzate and SEMM (Italy)

Continuous improvements on Next-Generation Sequencing approaches are providing a wealth of data for life sciences, and massive application of ICT (Information and Communication Technologies) to molecular research has become essential for the progress of medical research. While several software tools have been developed to assist in analysis, there is still a lack of a focused and integrated solution for several of specific research goals. One such goal is molecular diagnostics of Breast Cancer (BC), the most common malignancy in females. In this paper we present, describe, and evaluate experimentally- on real data-part of a software pipeline we have designed, and are implementing, specifically aimed at assisting in BC diagnostics. The results show that the pipeline is effective in assisting and enhancing BC diagnostics, and encourage towards further automation.

关键词： Pipelines Bioinformatics Sequential analysis Genomics Cancer DNA Cloud computing

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Towards Factor-oriented Understanding of Video Game Genres using Exploratory Factor Analysis on Steam Game Tags

Towards Factor-oriented Understanding of Video Game Genres u...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Xiaozhou Li Tampere University Tampere Finland

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

Context. Genre classification, as a tool of information managing, facilitates greatly the users' needs in identifying and retrieving information items of interest within mutually exclusive divisions of collection. Different from that of other products, current video game genre classifications suffer from providing such exclusive divisions. In the domain of game studies, game genre has long been an essential topic, when consensus has not yet been reached. On the other hand, the user-generated tags have been widely adopted enabling end users to annotate and interact with freely. It provides a unique set of crowd-sourced metadata that facilitates the description and understanding of the target products. Such feature is also adopted for video game distribution platforms. Objective. Thus, the goal of this work is to investigate the factor-oriented understanding of video game genre classification based on the analysis of large volume of game tag data from Steam platform. It shall contribute largely to the research on game genres within the game studies domain. Method. For such purpose, exploratory factor analysis is used herein towards the exploration of latent grouping of game tags seeking the underlying patterns. Results. As a result, 29 factors are extracted based on the over 20,000 video game data with 77 gameplay-based tags. It shows that the majority of video games on Steam can be rather seen as a combination of several factors to clear-cut classification.

关键词： Industries Games Tools Metadata Information management Data mining informatics

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Research on Logistics Carbon Footprint Analysis System

Research on Logistics Carbon Footprint Analysis System

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The 2010 ieee international conference on progress in informatics and computing

作者： Zhiyong Tian,Bingwu Liu Information School Beijing Wuzi University Beijing,China Feng Zheng School of Economics and Management North China University of Technology Beijing,China

Developing low-carbon economy is a consensus of all governments in the world,and low-carbon economy has been one of the emerging research focuses *** has special position in low-carbon *** most basic work of researching low-carbon logistics is to measure the carbon emission and low-carbon degree of logistics in ***,the paper introduces the concept and analysis method of carbon footprint into logistics area to evaluate low-carbon degree of logistics and analyze where to be ***,by taking advantage of information technology theory and application,it studies the system framework of Logistics Carbon Footprint Analysis System based on information system *** it designs the system structure,analyzes its function modular and sets forth the key *** research of this paper can lay the foundation for the further study for promoting low-carbon logistics development by informationization.

关键词： low-carbon economy low-carbon logistics carbon footprint decision support system internet of things

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Stock Trends Prediction based on Hypergraph Modeling Clustering Algorithm

Stock Trends Prediction based on Hypergraph Modeling Cluster...

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2014 ieee international conference on progress in informatics and computing

作者： Yongen Luo Jicheng Hu Xiaofeng Wei Dongjian Fang Heng Shao State key lab of software engineering Wuhan University School of Computer Science Wuhan University

In this paper, we use hypergraph model topredict the stock trends. Sometimes, itis hard toknow what a certain stock will dotomorrow. But here we make each stock as a vertex of a hypergraph and the relations, going up or down of stocks, are abstracted as hyperedges. If some stocks have the same actions, going up or down together, during k days, we can build a hyperedge among these stocks. According tothe actions of stocks in the last several months, the hypergraph for these stocks is established. Furthermore, the problem of stocks trends prediction is translated intothe clustering of the hypergraph. The stocks in the same cluster set always have the same trends. Then the trend of one stock can be predicted by the rest stocks in the same set. And, of course, what the stock did during the last several days should be alsotaken intoaccount for the prediction. In order toget an accurate prediction, we set a new optimization index for the segmentation of a hypergraph, which is called modularity. Through the definition of modularity, itis not necessary for us topreset the partitioning number any more. When the modularity is immediately below the critical value, the partitioning is stopped automatically. Experiment result shows that our proposed scheme achieves fine stock trend prediction.

关键词： stock trends prediction hypergraph model clustring set modularity

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Comparison of Immune and Genetic Algorithms for Parameter Optimization of Plate Color Recognition

Comparison of Immune and Genetic Algorithms for Parameter Op...

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The 2010 ieee international conference on progress in informatics and computing

作者： Feng Wang,Dexian Zhang College of Information Science and Engineering Henan University of Technology Zhengzhou,450001,P.R.China Lichun Man Department of Information Henan Provincial Bureau for Letters and Calls Zhengzhou,450003,P.R.China

To address the parameter optimization problem of plate color recognition,two approaches based on IA(immune algorithm) and GA(genetic algorithm) are proposed respectively. Theoretical comparison of IA and GA is first *** experimental comparison of the two algorithms is given by using them to perform the parameter optimization task for color recognition of license *** plate color recognition algorithm,color features are extracted in the HSV(hue, saturation,and value) color space and weighted fusion of the fuzzy maps on three components is utilized to perform color *** improve the adaptability of recognition algorithm,weights of color feature components and thresholds of classification functions are optimized by immune and genetic algorithms *** experiments were conducted on three data *** the experimental results show that the immune-based approach achieves higher accuracy and smaller mean square *** the theoretical and experimental comparisons,it is shown that many immune mechanisms,such as clonal explosion,immune supplementation, concentration adjustment,*** be used to solve the parameter optimization problem effectively and efficiently.

关键词： immune algorithm(IA) genetic algorithm(GA) intelligent optimization

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Research on the Reengineering of Warehousing Process Based on Internet of Things

Research on the Reengineering of Warehousing Process Based o...

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2014 ieee international conference on progress in informatics and computing

作者： Jun Liu Jiahui Li Tian Jiang Information Institute Beijing Wuzi University Graduate Department Beijing Wuzi University

Warehousing is an important guarantee for well running materials supply and logistics. And warehousing process directly affects the normal operation of the business. Therefore, how toguarantee the optimal warehousing process and complete it on-demand has become a topic which is worth studying. There are many problems existing in the warehousing business. According tothe analysis of the current situation of traditional warehousing business process, the paper proposes a basic idea of reengineering warehousing business process based on the Internet of things technology. The Internet of Things technology can achieve timely and accurate information collection and sharing soit takes great advantages in the warehousing business process reengineering. From the analysis of storage, stock and release these three big chains of operations, the paper studies the business process reengineering solutions based on the Internet of things technology and makes a before and after comparison of the warehousing business process reengineering in the form of flow charts, then formulates the warehousing system framework based on the Internet of things environment, and then further analyzes, integrates and optimizes the warehousing business, finally reengineers the operation process.

关键词： warehousing Internet of things Business process reengineering

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An Efficient Multi-Keyword Search Mechanism in Structured P2P Networks

An Efficient Multi-Keyword Search Mechanism in Structured P2...

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The 2010 ieee international conference on progress in informatics and computing

作者： Mo Hai,Shuhang Guo School of Information Central University of Finance and Economics Beijing,China

Structured P2P networks are efficient for searching by a single ***,it poses a challenge to support multiple-keyword *** of a multiple-keyword search in structured P2P networks needs a large number of hops and generates a large number of messages when using traditional method-distributing inverted indices among *** researches on multiple-keyword search focus on the decrease of number of hops and number of messages,but no good solution is put forward by *** this paper we propose a multiple-keyword search mechanism in structured P2P *** mechanism groups peers with similar searches *** formation and maintenance of groups are in a self-organizing *** in the same group share their cached *** results show this mechanism outperforms current multiple-keyword search mechanisms in both the number of hops and the number of messages.

关键词： structured P2P networks multiple keywords search number of hops number of messages

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Research on the Construction of a Breast Cancer Assistant Diagnosis and Treatment Knowledge Graph Based on Case-Based Reasoning Methods

Research on the Construction of a Breast Cancer Assistant Di...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Shiwen Wang Jiahao Chen Meiling Wang Xiao Liu Xuchen Cao Tianjin Normal University Tianjin China Tianjin Medical University Cancer Institute & Hospital Tianjin China

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

By constructing a knowledge graph in the field of breast cancer and combining the knowledge graph with case-based reasoning methods, a conceptual model of adjuvant diagnosis and treatment of breast cancer is constructed, which provides a theoretical basis and implementation strategy for breast cancer diagnosis and treatment, Q&A and other related fields. Firstly, the data layer was constructed by combining the diagnosis and treatment data of breast diseases in specialized hospitals, related medical websites, and data in the field of breast cancer in the “Breast Cancer Diagnosis and Treatment Guidelines”. Secondly, combined with the ontology of breast cancer and empirical data ontology, the diagnosis and treatment pathway of breast cancer and the idea of evidence-based medicine were integrated to construct a model layer. Thirdly, through the OggDB database, the knowledge graph in the field of breast cancer is constructed, stored, displayed, and applied. Finally, the completed knowledge graph was combined with the case-based reasoning method to construct a conceptual model of adjuvant diagnosis and treatment of breast cancer. The knowledge graph aims to be applied to the conceptual model of assisted diagnosis and treatment of breast cancer based on case reasoning method, and serve the diagnosis and treatment process of breast diseases, so as to improve the efficiency of diagnosis and treatment of breast diseases.

关键词： Hospitals Databases Knowledge graphs Ontologies Breast cancer Cognition Data models informatics Medical diagnostic imaging Guidelines

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Comparison Study Of Several Kinds Of Inertia Weights For PSO

Comparison Study Of Several Kinds Of Inertia Weights For PSO

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The 2010 ieee international conference on progress in informatics and computing

作者： Wenhua Han Dept.of Information and Controlling Engineering Shanghai University of Electric Power Shanghai,China Ping Yang,Haixia Ren,Jianpeng Sun Dept.of Information and Controlling Engineering Shanghai University of Electric Power Shanghai,China

Particle swarm optimization(PSO) is one of the modern heuristic *** has attracted great attention due to its features of easy implementation,robustness to control parameters and computation efficiency compared with other existing heuristic *** performance of a PSO can depend on its parameters such as the inertia weight factors and two acceleration *** value of inertia weight can treat the balance between exploration and exploitation,so a proper control of inertia weight is very important to find the optimum solution *** paper compared several kinds of inertia *** results demonstrate that simulated annealing inertia weight and linearly decreasing inertia weight have better convergence performance in the searching procedure. The dimension of the solution has no effect on the performance of inertia *** performance of the two kinds of inertia weight will change when the benchmark functions and acceleration coefficients change.

关键词： Particle swarm optimization Inertia weight Linearly decreasing Chaotic sequence Simulated annealing

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