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检索条件"任意字段=3rd International Conference on Soft Computing in Data Science, SCDS 2017"

共 694 条记录，以下是151-160 订阅

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Deep reinforcement learning based offloading scheme for mobile edge computing 3

Deep reinforcement learning based offloading scheme for mobi...

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3rd IEEE international conference on Smart Internet of Things, SmartIoT 2019

作者： Yao, Pengfei Chen, Xin Chen, Ying Li, Zhuo School of Computer Science Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9781728134888

In the period of rapid development of mobile communication, mobile edge computing is an effective method to meet the increasing computing demands. When local computing capacity is insufficient, we can offload a enormous number of computing resources to the edge server for computing. Nevertheless, the design of computation offloading policies for a virtual MEC system remains challenging. In particular, when decisions are made to offload tasks to the edge server for execution. Specifically, under a time-varying condition, it is decided whether to execute the task locally or offload the task to the edge server for calculation. This article considers data forms with different data sizes, and the data produced by each user has different priority degree of execution. In order to solve the scheduling problem of tasks in the queue, we use Deep Q Network (DQN) to solve the problem. The simulation results are given at the end of the paper and compared with the existing excellent algorithms. © 2019 IEEE.

关键词： Mobile edge computing

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Utilization of machine learning in vibrodiagnostics 23rd

Utilization of machine learning in vibrodiagnostics

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23rd international conference on soft computing, MENDEL 2017

作者： Zuth, Daniel Marada, Tomáš Faculty of Mechanical Engineering Institute of Automation and Computer Science Brno University of Technology Brno Czech Republic

ISBN: (纸本)9783319978871

The article deals with possibilities of use machine learning in vibrodiagnostics to determine a fault type of the rotary machine. Sample data are simulated according to the expected vibration velocity waveform signal at a specific fault. Then the data are pre-processed and reduced for using Matlab Classification Learner which creates a model for identifying faults in the new data samples. The model is finally tested on a new sample data. The article serves to verify the possibility of this method for later use on a real machine. In this phase is tested data preprocessing and a suitable classification method. © Springer Nature Switzerland AG 2019.

关键词： Machine learning

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data Quality Issues in Big data: A Review 3rd

Data Quality Issues in Big Data: A Review

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3rd international conference of Reliable Information and Communication Technology (IRICT)

作者： Salih, Fathi Ibrahim Ismail, Saiful Adli Hamed, Mosaab M. Yusop, Othman Mohd Azmi, Azri Azmi, Nurulhuda Firdaus Mohd Univ Teknol Malaysia Adv Informat Sch Jalan Sultan Yahya Petra Kuala Lumpur 54100 Malaysia

ISBN: (纸本)9783319990071;9783319990064

data with good quality has precedence when analyzing and using big data to deduce value from such tremendous volume of data in today's business environments. Decisions and insights derived from poor data has a negative and unpredictable consequences to organizations. At present, due to the lack of comprehensive and intensive research in the field of data quality, especially large data, there is an urgent need to address this issue by researchers to reach the optimal way to estimate and evaluate the quality of large data. Thus, enabling institutions to make rational decisions based on evaluation outputs. In this paper, the current research on the quality of large data was reviewed and summarized by exploring the basic characteristics of large data. The main challenges facing the quality of information were also discussed in the context of large data. Some of the initiatives suggested by the researchers to evaluate the quality of the data have been highlighted. Finally, we believe that the results of these reviews will enhance the conceptual measurements of the large data quality and produce a concrete groundwork for the future by creating an integrated data quality assessment and evaluation models using the suitable algorithms.

关键词： Big data data quality Quality assessment Big data quality dimensions DQ evaluation

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Realizing the Value of Big data in Process Monitoring and Control: Current Issues and Opportunities 3rd

Realizing the Value of Big Data in Process Monitoring and Co...

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3rd international conference of Reliable Information and Communication Technology (IRICT)

作者： Rubab, Saddaf Taqvi, Syed A. Hassan, Mohd Fadzil Univ Teknol PETRONAS Dept Comp & Informat Sci Seri Iskandar 32610 Perak Darul Rid Malaysia Univ Teknol PETRONAS Chem Engn Dept Seri Iskandar 32610 Perak Darul Rid Malaysia NED Univ Engn & Technol Dept Chem Engn Karachi 75270 Pakistan Natl Univ Sci & Technol NUST Islamabad Pakistan

ISBN: (纸本)9783319990071;9783319990064

With the advancement of Big data, data analytics have benefited many organizations and industries. The continuous improvement of process industries is a challenging task that requires insights for efficient process monitoring with minimum downtime and safer operations. These industries generate a large amount of data i.e. "Big data" every second that contains useful information. For better control, different analysis can be executed using statistical methods, data mining or machine learning. This data can be used for daily process operations and decision making. However, the past literature on Big data have put limited focus on realizing its implication in process monitoring and control. The paper emphasizes on the issues and opportunities faced by process industries in terms of Big data collection, storage and analysis for process monitoring and control.

关键词： Advanced process control Big data Analytics Decision making data mining Machine learning Process monitoring and control

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Characterizing label errors: Confident learning for noisy-labeled image segmentation 23rd

Characterizing label errors: Confident learning for noisy-la...

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23rd international conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2020

作者： Zhang, Minqing Gao, Jiantao Lyu, Zhen Zhao, Weibing Wang, Qin Ding, Weizhen Wang, Sheng Li, Zhen Cui, Shuguang Shenzhen Research Institute of Big Data The Chinese University of Hong Kong ShenzhenGuangdong China Warshel Institute for Computational Biology The Chinese University of Hong Kong ShenzhenGuangdong China School of Mechatronic Engineering and Automation Shanghai University Shanghai China Tencent AI Lab Bellevue United States

ISBN: (纸本)9783030597092

Convolutional neural networks (CNNs) have achieved remarkable performance in image processing for its mighty capability to fit huge amount of data. However, if the training data are corrupted by noisy labels, the resulting performance might be deteriorated. In the domain of medical image analysis, this dilemma becomes extremely severe. This is because the medical image annotation always requires medical expertise and clinical experience, which would inevitably introduce subjectivity. In this paper, we design a novel algorithm based on the teacher-student architecture for noisy-labeled medical image segmentation. Creatively, We introduce confident learning (CL) method to identify the corrupted labels and endow CNN an anti-interference ability to the noises. Specifically, the CL technique is introduced to the teacher model to characterize the suspected wrong-labeled pixels. Since the noise identification maps are a little away from sufficient precision, the spatial label smoothing regularization technique is utilized to generate soft-corrected masks for training the student model. Since our method identifies and revises the noisy labels of the training data in a pixel-level rather than simply assigns lower weights to the noisy masks, it outperforms the state-of-the-art method in the noisy-labeled image segmentation task on the JSRT dataset, especially when the training data are severely corrupted by noises. © Springer Nature Switzerland AG 2020.

关键词： Image segmentation

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A data-driven model based on support vector machine to identify chronic hypertensive and diabetic patients 1

引用

3rd international conference on Physiological computing Systems, PhyCS 2016, 4th international conference on Physiological computing Systems, PhyCS 2017 and 5th international conference on Physiological computing Systems, PhyCS 2018

作者： Soguero-Ruiz, Cristina de Miguel-Bohoyo, Pablo Mora-Jiménez, Inmaculada Department of Signal Theory and Communications Telematics and Computing Universidad Rey Juan Carlos Madrid Spain University Hospital of Fuenlabrada FuenlabradaMadrid Spain

ISBN: (数字)9783030279509

ISBN: (纸本)9783030279493

Hypertension and diabetes are chronic conditions that have a considerable prevalence in the elderly. It is estimated that both hypertensive patients and people with diagnosed diabetes double cost of normotensive individuals and those in the absence of diabetes, respectively. It is therefore important to pay attention to these chronic conditions, both from a health and economical point of view, especially in scenarios with budget limitations. Clinical identification of chronic patients can be performed by feeding data of the patient encounters with the healthcare system to population classification systems such as Clinical Risk Groups (CRGs). CRGs classify individuals in unique and excluding health status categories taking both demographic and clinical information during certain period of time. In this work, we characterize healthy and chronic hypertensive and diabetic population at different chronic statuses (CRG) according to gender, age, diagnoses and drugs. After this characterization, we propose to use a supervised machine learning approach, in particular Support Vector Machines, to construct a data-driven model identifying the patient health status. We conclude that drugs and diagnoses are quite informative to discriminate patients with hypertension and diabetes, achieving promising results with the use of data-driven models. © Springer Nature Switzerland AG 2019.

关键词： Diagnosis

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Parallel coordinates based visualization for high-dimensional data 3

Parallel coordinates based visualization for high-dimensiona...

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3rd international conference on Big data Research, ICBDR 2019

作者： Li, Weiyu Lang, Jiying Zhang, He Yang, Fei Zhang, Lei Pan, Jingchang School of Mechanical Electrical and Information Engineering Shandong University Weihai China School of Art and Design Harbin University Harbin China

ISBN: (纸本)9781450372015

High-dimensional data is generated in a wide range of scientific, social and economic life. Identifying and analyzing these high-dimensional data efficiently can improve the scientificity of decisions. Visualization can transform abstract data or large scale digital representation into an intuitive visible form, which will greatly enhance the insight of latent schema information for decision makers, and thus provide the basis for in-depth analysis and processing of high-dimensional data. This paper will explore a visualization framework to visualize the high-dimensional data through both visual angle and the intrinsic meaning of data. The rules behind data and information can thus clearly and intuitively be seen through data visualization. © 2019 Association for computing Machinery.

关键词： Visualization

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Comparing the Performance of FCBF, Chi-Square and Relief-F Filter Feature Selection Algorithms in Educational data Mining 3rd

Comparing the Performance of FCBF, Chi-Square and Relief-F F...

引用

3rd international conference of Reliable Information and Communication Technology (IRICT)

作者： Zaffar, Maryam Hashmani, Manzoor Ahmed Savita, K. S. Univ Teknol PETRONAS Dept Comp & Informat Sci Seri Iskander 32610 Malaysia Univ Teknol PETRONAS High Performance Cloud Comp Ctr Seri Iskander 32610 Malaysia

ISBN: (纸本)9783319990071;9783319990064

Educational data Mining (EDM) is a very vital area of data Mining, and it is helpful in predicting the performance of students. This paper is a step towards identifying the factors affecting the academic performance of the students. It is very necessary to increase the quality of dataset as to get better prediction results. There are many feature selection algorithms, however three filter feature selection algorithms FCBF, Chi-Square, and ReliefF are selected due their better performance, and applied on three different student's data sets. The results of three filter feature selection algorithms are evaluated. The result of the paper extracted that Chi-Square and ReliefF perform better than FCBF on a dataset with larger number of features, however the performance of three selected algorithms is found worst on a student dataset with less number of instances. The analysis of the results of algorithms shows that, Home to Institution (School/College) travel time is one of the important feature affecting the performance of the students. Student's previous academic background and Socio-economic factors also appeared to be the important factors for predicting the academic performance of the students.

关键词： Educational data Mining (EDM) Filter feature selection algorithms FCBF Chi-Square ReliefF

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Geospatial data Management in Apache Spark: A Tutorial 35

Geospatial Data Management in Apache Spark: A Tutorial

引用

IEEE 35th international conference on data Engineering (ICDE)

作者： Yu, Jia Sarwat, Mohamed Arizona State Univ Sch Comp Infomat & Decis Syst Engn 699 S Mill Ave Tempe AZ 85281 USA

ISBN: (纸本)9781538674741

The volume of spatial data increases at a staggering rate. This tutorial comprehensively studies how existing works extend Apache Spark to uphold massive-scale spatial data. During this 1.5 hour tutorial, we first provide a background introduction of the characteristics of spatial data and the history of distributed data management systems. A follow-up section presents the common approaches used by the practitioners to extend Spark and introduces the vital components in a generic spatial data management system. The third, fourth and fifth sections then discuss the ongoing efforts and experience in spatial temporal data, spatial data analytics and streaming spatial data, respectively. The sixth part finally concludes this tutorial to help the audience better grasp the overall content and points out future research directions.

关键词： Spatial databases Sparks Tutorials Distributed databases Indexes Geospatial analysis Trajectory

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Schema Proposition Model for NoSQL Applications 3rd

Schema Proposition Model for NoSQL Applications

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3rd international conference of Reliable Information and Communication Technology (IRICT)

作者： Imam, Abdullahi Abubakar Basri, Shuib Ahmad, Rohiza Gonzalez-Aparicio, Maria T. Univ Teknol PETRONAS Comp & Informat Sci Dept Seri Iskandar 32610 Perak Malaysia Univ Teknol PETRONAS SQ2E Res Cluster Seri Iskandar 32610 Perak Malaysia Ahmadu Bello Univ Zaria Nigeria Univ Oviedo Comp Dept Gijon Spain

ISBN: (纸本)9783319990071;9783319990064

This paper proposes a schema proposition model for physical and logical modeling of NoSQL (Not Only SQL) document-store databases. Unlike the traditional databases which are rigid in design, the NoSQL databases are flexible, scalable, and low-latent making them to house big data efficiently. However, issues such as dataset complexity coupled with programmers' low competency are making database modeling and designing very challenging, especially in balancing the significant parameters like availability, consistency and scalability. The proposed model aims to address this balancing by abstractly proposing schemas to programmers with respect to user defined system requirements, Create, Read, Update and Delete (CRUD) operations, etc. Exploratory and experimental approaches were adopted in this research. System requirement formulas were introduced to compute the expected performance. A mini system was developed to validate the proposed model by comparing the manually generated schema to the schema generated through the proposed model. Results obtained indicated a significant improvement in the schema generated by the proposed model.

关键词： NoSQL databases database modeling Big data Schema proposition Document-store databases

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