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3rd international conference on data Management, Analytics and Innovation, ICDMAI 2019

3rd International Conference on Data Management, Analytics a...

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3rd international conference on data Management, Analytics and Innovation, ICDMAI 2019

ISBN: (纸本)9789811393631

The proceedings contain 35 papers. The special focus in this conference is on data Management, Analytics and Innovation. The topics include: Enhanced Prediction of Heart Disease Using Particle Swarm Optimization and Rough Sets with Transductive Support Vector Machines Classifier;dataCan: Robust Approach for Genome Cancer data Analysis;dataAutism: An Early Detection Framework of Autism in Infants using data Science;anaBus: A Proposed Sampling Retrieval Model for Business and Historical data Analytics;abrupt Scene Change Detection Using Block Based Local Directional Pattern;a Framework for Web Archiving and Guaranteed Retrieval;business Intelligence Through Big data Analytics, data mining and Machine Learning;the Effect of Big data on the Quality of Decision-Making in Abu Dhabi Government Organisations;The Impact of Technology Readiness on the Big data Adoption Among UAE Organisations;identifying Phishing Through Web Content and Addressed Bar-Based Features;sports data Analytics: A Case Study of off-Field Behavior of Players;phrase Based Information Retrieval Analysis in Various Search Engines Using Machine Learning Algorithms;The Politics of Artificial Intelligence Behaviour and Human Rights Violation Issues in the 2016 US Presidential Elections: An Appraisal;crop Prediction Using Artificial Neural Network and Support Vector Machine;a Hybrid and Adaptive Approach for Classification of Indian Stock Market-Related Tweets;generative Adversarial Networks as an Advancement in 2D to 3D Reconstruction Techniques;impact of Artificial Intelligence on Human Resources;role of Activation Functions and Order of Input Sequences in Question Answering;gestTalk—Real-Time Gesture to Speech Conversion Glove;a Unified Framework for Outfit Design and Advice.

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Improving the Identification of Co-occurring Driver Pathways by Integrating Multi-Omics data 22

Improving the Identification of Co-occurring Driver Pathways...

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22nd IEEE international conference on High Performance computing and Communications, 18th IEEE international conference on Smart City and 6th IEEE international conference on data Science and Systems, HPCC-SmartCity-DSS 2020

作者： Cai, Qirong Wu, Jingli Du, Rui Zhang, Zhen Pan, Ke Beijing Institute of Technology College of Computer Science Zhuhai China Guangxi Normal University Guangxi Key Lab of Multi-source Information Mining Security Guilin China Guangxi Normal University College of Computer Science and Information Technology Guilin China

ISBN: (纸本)9781728176499

It is widely accepted that driver pathways offer important information for precision and personalized medicine of cancer treatment, hence the problems of driver pathways identification have become a research hotspot in bioinformatics. In this paper, an improved collaborative mutation driver pathways model ICMDP is proposed by integrating the somatic mutation, copy number variation and gene expression data. The model has two characteristics:(1) each individual pathway has moderate mutual exclusion and high coverage;(2) collaborative driver pathways exhibit significant common mutations in cancer samples, and the genes in collaborative driver pathways are related. Meanwhile, a parthenogenetic algorithm PA-ICMDP is proposed for solving the ICMDP model. Experiments were performed to compare algorithms PA-ICMDP, CoMDP and GAMTOC by using real biological data sets, i.e., the samples of glioblastoma and ovarian. The experimental results indicate that the PA-ICMDP algorithm can not only identify important collaborative driver pathways with higher co-occurrence mutation rates, but also detect more important driver genes such as MET, MDM2, GAB2, TERT, TBX3 and so on. In addition, the EICMDP model and the PA-EICMDP algorithm are put forward by extending the ICMDP model and the PAICMDP algorithm respectively. They can effectively identify other important pathways that collaborate with known driver pathways. The experimental results indicate that the methods presented in this paper may become suitable tools for mining driver genes and driver pathways related to cancer development. © 2020 IEEE.

关键词： Bioinformatics

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Early Detection of COVID-19 from CT Scans Using Deep Learning Techniques 4th

Early Detection of COVID-19 from CT Scans Using Deep Learnin...

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4th international conference on computing and Network Communications, CoCoNet 2020

作者： Limna Das, P. Manoj, A. Sai Sharma, Sachin Jayaraj, P.B. Department of Computer Science and Engineering National Institute of Technology Calicut Kozhikode India Institute of Advanced Research Gandhinagar India

ISBN: (纸本)9789813369863

The novel coronavirus 2019 (COVID-2019) which began from China, further spread to all over the planet and was announced as a pandemic by WHO. It has blocked our daily lives and world economy to a large extent. In the lack of any particular vaccine for present pandemic COVID-19, it is necessary to recognize the disease at an early stage and quarantine these infected patients to stop the further spread. The popular diagnosis of COVID-19 is being done using polymerase chain reaction (PCR), but there are some cases of false interpretation. Rapid antibody test also has faulty/wrong implications. Till now, we have witnessed the global deficit of testing labs and testing kits for COVID-19. There is an urgent requirement for developing quick and reliable devices that can help doctors in diagnosing COVID-19. Developing a computer-based COVID detection tool will be very useful as it can screen the positive cases from a mass collection. Radiological imaging like computed tomography (CT) scans can be used for the early diagnosis. With the invention of AI algorithms, we can apply learning algorithms for early detection of COVID-19. 2016 on wards, deep learning, a deep neural network-based learning technique is widely applied in biomedical problems. In this article, we suggest a fast and reliable diagnostic tool using deep learning algorithms for identifying this pandemic. We have built two models for this purpose;one with an EfficientNet architecture using focal loss and a GradCam heatmap for testing its reliability in practical use. We also built a model using ResNet by custom vision AI of Microsoft Azure. data was collected from different sources and the highly scaled EfficientNet architecture outperformed the Resnet architecture of MS Azure for classifying the COVID CT scans by an increase in accuracy of 10%. We are planning to deploy this software in the form of a chatbot. Also, our model continuously learns from data regularly and would attain better accuracy in future. © 2

关键词： COVID-19

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Spatial-Temporal Convolutional Graph Attention Networks for Citywide Traffic Flow Forecasting 20

Spatial-Temporal Convolutional Graph Attention Networks for ...

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29th ACM international conference on Information and Knowledge Management (CIKM)

作者： Zhang, Xiyue Huang, Chao Xu, Yong Xia, Lianghao South China Univ Technol Guangzhou Peoples R China JD Finance Amer Corp Mountain View CA USA Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781450368599

Traffic flow prediction plays an important role in many spatial-temporal data applications, e.g., traffic management and urban planning. Various deep learning techniques are developed to model the traffic dynamic patterns with different neural network architectures, such as attention mechanism, recurrent neural network. However, two important challenges have yet to be well addressed: (i) Most of these methods solely focus on local spatial dependencies and ignore the global inter-region dependencies in terms of traffic distributions;(ii) It is important to capture channel-aware semantics when performing spatial-temporal information aggregation. To address these challenges, we propose a new traffic prediction framework-Spatial-Temporal Convolutional Graph Attention Network (ST-CGA), to enable the traffic prediction with the modeling of region dependencies, from locally to globally in a comprehensive manner. In our ST-CGA framework, we first develop a hierarchical attention networks with a graph-based neural architecture, to capture both the multi-level temporal relations and cross-region traffic dependencies. Furthermore, a region-wise spatial relation encoder is proposed to supercharge ST-CGA mapping spatial and temporal signals into different representation subspaces, with channel-aware recalibration residual network. Extensive experiments on four real-world datasets demonstrate that ST-CGA achieve substantial gains over many state-of-the-art baselines.

关键词： Spatial-temporal data mining Graph Attention Networks Traffic flow Forecasting Urban computing

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Big RDF data Storage, Computation, and Analysis: A Strawman's Arguments 39

Big RDF Data Storage, Computation, and Analysis: A Strawman'...

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39th IEEE international conference on Distributed computing Systems (ICDCS)

作者： Yuan, Pingpeng Lin, Longlong Kou, Zhijuan Jin, Hai Liu, Ling Huazhong Univ Sci & Technol Sch Comp Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Serv Comp Technol & Syst LabCluster & Grid Comp Wuhan Peoples R China Georgia Inst Technol Coll Comp Sch Comp Sci Distributed Data Intens Syst Lab Atlanta GA 30332 USA

ISBN: (纸本)9781728125190

RDF data is big and continues to grow rapidly. RDF data sets are typically viewed as heterogeneous graph data sets with complex correlations and multifaceted heterogeneity. Although there have been flurry of research on processing and analyzing RDF data, efficient storage, computation, analysis of big and growing RDF data continue to challenge multiple computer science disciplines, ranging from systems, network computing, data management to data analytics. In this paper, we make use of strawman arguments on big RDF data challenges with respect to storage, computation, analysis, focusing on identifying the grand challenges in developing high performance RDF storage, parallel computation, efficient distribution, and smart RDF analytics. We attempt to answer a number of important and frequently asked questions: (1) Do we really need RDF-specific storage techniques and algorithms for building efficient and high performance RDF stores? (2) How hard can it be to effectively parallelize big RDF data for high performance storage, computation, and mining? (3) What type of distribution models can we employ to scale RDF processing models and algorithms for real-time querying (subgraph pattern matching) and mining of big RDF data? (4) Can machine learning algorithms be leveraged to effectively mine and dive into RDF data? We attempt to answer these questions by sharing some of our results, our unique experiences, and our lessons learned from a collection of research projects in RDF systems research and development. We will also discuss our ongoing research endeavors, potential applications and avenues for future work.

关键词： Storage Graph Partitioning Computation Framework Query Processing Graph mining

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miningBreastCancer: Selection of Candidate Gene Associated with Breast Cancer via Comparison between data mining of TCGA and Text mining of PubMed 20

MiningBreastCancer: Selection of Candidate Gene Associated w...

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Proceedings of the 2020 ACM international conference on Intelligent computing and its Emerging Applications

作者： Chou-Cheng Chen Yao-Lung Kuo Chi-Hui Chiang Intelligent Center for Health Industry Technology Development and Professional Cultivation Chia Nan University of Pharmacy & Science Tainan Taiwan Department of Surgery National Cheng Kung University College of Medicine National Cheng Kung University Hospital Tainan Douliu Taiwan Department of Information Management Chia Nan University of Pharmacy & Science Tainan Taiwan

ISBN: (纸本)9781450383042

In 2016, 12,676 new cases of breast cancer were diagnosed among Taiwan women. In 2018 the standardized death rate of breast cancer was 12.5 per 100,000 persons. Previous studies have integrated data and text mining to yield fusion genes, identify genetic factors for breast cancer and select single-gene feature sets for colon cancer discrimination. However, our study is the first to select significantly different expression between breast normal tissue and cancer using TCGA data and biostatistics, excluding know genes using abstracts from PubMed and natural language processing. The top twenty genes for research potential from the selection of mining-BreastCancer are EML3, ABCB9, GRASP, KANK3, GPR146, ZNF623, CCDC9, ADCY4, DLL1, ADAM33, GRRP1, LRRN4CL, C14orf180, ABCD4, ABCC6P1, PEAR1, FAM43A, C20orf160, KIF21A and PP-FIA3. Few studies for these genes exist, but they hold significantly different expressions between breast cancer and normal tissue, each pathologic tumor and lymph node, or between each pathologic metastasis. These results show that miningBreastCancer can help scientists select genes for research potential. miningBreastCancer is available through http://***/***.

关键词： research potential TCGA data mining natural language processing (NLP) text mining PubMed biostatistics

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EEG Pattern Recognition Based on Self-adjusting Dynamic Time Dependency Method 6th

EEG Pattern Recognition Based on Self-adjusting Dynamic Time...

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6th international conference on data Science, ICDS 2019

作者： Zhang, Hao Lan Xue, Yun Zhang, Bailing Li, Xingsen Lu, Xinzhe Center for SCDM NIT Zhejiang University Ningbo China Southern China Normal University Guangzhou China Guangdong University of Technology Guangzhou China School of Management Zhejiang University Hangzhou China

ISBN: (纸本)9789811528095

The application of biometric identification technology has been applied extensively in modern society. EEG pattern recognition method is one of the key biometric identification technologies for advanced secure and reliable identification technology. This paper introduces a novel EEG pattern recognition method based on Segmented EEG Graph using PLA (SEGPA) model, which incorporates the novel self-adjusting time series dependency method. In such a model, the dynamic time-dependency method has been applied in the recognition process. The preliminary experimental results indicate that the proposed method can produce a reasonable recognition outcome. © Springer Nature Singapore Pte Ltd 2020.

关键词： data mining

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Predicting Football Match Result Using Fusion-based Classification Models

Predicting Football Match Result Using Fusion-based Classifi...

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international Joint conference on Computer Science and Software Engineering (JCSSE)

作者： Chananyu Pipatchatchawal Suphakant Phimoltares Advanced Virtual and Intelligent Computing (AVIC) Research Center Faculty of Science Chulalongkorn University Pathumwan Bangkok Thailand

In recent decades, many researchers attempted to predict football match outcome. To forecast future match results, most papers relied on using in-game match statistics, such as number of shots on target, yellow cards, red cards, etc. In this paper, fusion-based classification model was constructed for future matches, using none of in-game statistics. The model used video games’ ratings of players and teams to help in prediction. Two types of fusion-based models, which are hierarchical model and ensemble model, were proposed in this paper. In the experiment, the proposed models were compared with different simple classification models in terms of accuracy using a dataset of English Premier League (EPL) season 2010/2011 to 2014/2015. Additionally, each model was also tested on the whole 2015/2016 EPL season as the selected season contains several unexpected results. Both proposed models yielded the accurate rates at 56.5332% and 56.8002%, which are higher than those of the other models.

关键词： Training Computer science Analytical models Computational modeling Current measurement Predictive models data models

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Prediction of learning behavior based on improved random forest algorithm

Prediction of learning behavior based on improved random for...

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2020 international conference on Big data mining and Information Processing, BDMIP 2020

作者： Xia, Xiaona Chinese Academy of Education Big Data Qufu Normal University Qufu Shandong273165 China School of Information Science and Engineering Qufu Normal University Rizhao Shandong276826 China Faculty of Education Qufu Normal University Qufu Shandong273165 China

The research of learning behaviors is the premise of improving the learning process and results. The scale, composition and quality of the corresponding dataset will have the key impacts on research methods. Based on big dataset of learning behaviors, this study demonstrates the rules of learning behaviors, uses intelligent learning algorithms to analyze data and predict trends. Based on data characteristics, this paper evolves random forest algorithm and constructs the data structure. Taking the assessment results of learners as the test targets, the data samples are divided into two parts: train set and test set. Through the train set, we design the data model. With the help of the test set, the optimal model is finally selected, that is, the learning interaction activities are predicted and estimated. The research shows that learning behaviors have predictability and guidance, transferability and adjustability, groupness and personality. Based on this conclusion, the data analysis scheme of learning platforms is designed, which will help to improve the platform construction and learning behaviors. © Published under licence by IOP Publishing Ltd.

关键词： Learning algorithms

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Simulation in Real Conditions of Navigation and Obstacle Avoidance with PX4/Gazebo Platform

Simulation in Real Conditions of Navigation and Obstacle Avo...

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IEEE international conference on Pervasive computing and Communications (PerCom)

作者： Garcia, Jesus Molina, Jose M. Univ Carlos III Appl Artificial Intelligence Grp Madrid Spain

ISBN: (纸本)9781538691519

Design of UAV systems requires from advanced tools to analyse the system components and their interaction in real operational conditions. In this work, the authors present an approach to evaluate a LIDAR sensor and the capacity for navigation and obstacle avoidance in simulated situations using a real UAV platform. It uses available software for mission definition and execution in UAVs based on PixHawk flight controller and peripherals. The solution shows physical integration of the main types of sensors in UAV domain both for navigation and collision avoidance, and at the same time the use of powerful simulation models developed with Gazebo. Some illustrative results show the performance of this navigation and obstacle avoidance function using the simulated sensors and the control of the real UAV in realistic conditions.

关键词： UAVs sensor processing data Analysis System Design Gazebo simulation

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