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Telecom Big Data Assisted BS Resource Analysis for LTE/5G Systems

Telecom Big Data Assisted BS Resource Analysis for LTE/5G Sy...

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IEEE international symposium on Parallel and distributed Processing with applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Lexi Xu Xuefeng Chen Liang Zhao Xin Hu Nan Jiang Yuting Luan Xinzhou Cheng Jie Gao China Unicom Network Technology Research Institute Beijing China China Railway Engineering Consulting Group Corporation Beijing China Shenyang Aerospace University Shenyang China Beijing University of Posts and Telecommunications Beijing China Queen Mary University of London London United Kingdom

ISBN: (纸本)9781728152103

Due to the diverse services and high rate requirements of telecom users, mobile cellular systems envisage the challenges of high resource usage, especially the base station (BS) in the hot-spot area. This paper investigates on telecom big data of LTE/5G systems, and then we design a telecom big data assisted comprehensive BS resource analysis (CBSRA) algorithm. CBSRA algorithm considers six categories of resource data/indicators. Then, CBSRA algorithm judges the usage level of each indicator. Finally, CBSRA algorithm takes the comprehensive analysis and outputs the results. We implement the CBSRA algorithm in a realistic cellular system in China, and the proposed algorithm can assist operator to effectively detect BS with high resource usage.

关键词： 5G mobile communication Big Data cellular radio Long Term Evolution resource allocation telecommunication computing big data telecommunication computing Long Term Evolution cellular radio Resource utilization Telecommunications Bahamas

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An Improved Particle Swarm Optimization Algorithm Combined with Invasive Weed Optimization

An Improved Particle Swarm Optimization Algorithm Combined w...

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IEEE international symposium on Parallel and distributed Processing with applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Huan Zhao Xin Wang Zhongze Jiao Wen Zeng Jinxiao Dou Jianglong Yu Shenyang Aerospace University Institute of Thermal Energy Shenyang China University of Newcastle Chemical Engineering Callaghan Australia Key Laboratory of Advanced Coal and Coking Technology of Liaoning Province University of Science and Technology Liaoning Anshan China

ISBN: (纸本)9781728152103

This paper presents a hybrid algorithm based on the invasive weed optimization (IWO) and particle swarm optimization (PSO), named IW-PSO. By incorporating the reproduction and spatial dispersal of IWO into the traditional PSO, exploration and exploitation of the PSO can be enhanced and well balanced to achieve better performance. In a set of 15 test function problem, computational results, preceded by analysis and selection of IW-PSO parameters, show that IW-PSO can improve the search performance. In the other comparative experiment with fixed iteration, the IW-PSO algorithm is compared with various more up-to-date improved PSO procedures appearing in the literature. Comparative results demonstrate that IW-PSO can generate quite competitive quality solution in stability, accuracy and efficiency. As evidenced by the overall assessment based on two kinds of computational experience, IW-PSO can effectively obtain higher quality solutions so as to avoid being trapped in local optimum.

关键词： particle swarm optimisation Particle swarm optimization Invasive Weeds parametric subharmonic oscillator particle swarm algorithm local optimum algorithms

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Real-Time Traffic Congestion Analysis Based on Collected Tweets

Real-Time Traffic Congestion Analysis Based on Collected Twe...

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IEEE international symposium on Parallel and distributed Processing with applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Mohammed A. A. Al-qaness Mohamed Abd Elaziz Ammar Hawbani Aaqif Afzaal Abbasi Liang Zhao Sunghwan Kim Wuhan University Wuhan China Department of Mathematics Zagazig University Zagazig Eygept School of Computer Science and Technology University of Science and Technology of China Anhui China Department of Software Engineering Foundation University Islamabad Pakistan School of Computer Science and Technology Shenyang Aerospace University Shenyang China School of Electrical Engineering University of Ulsan Ulsan Korea

ISBN: (纸本)9781728152103

Due to the rapid increase in the use of and advancement of social media platforms, the amount of data available on the internet is increasing. The available information on the internet can be used to gain insights about searching trends and public interests. The advancements in machine learning and deep learning techniques drastically improved data analytics and processing solutions for social media and infotainment industry. It is no doubt that the majority of regular commuter encounters traffic congestion daily. There is a growing number of population in Metropolitans. This leads to higher population density rates and traditional methods for collecting traffic information using physical sensors are expensive, however, by using social media tools information regarding traffic jam, road and traffic congestion can be improved. In this paper, we analyze traffic congestion using Twitter data (tweets) in real-time. The proposed model extracts traffic-related tweets from Twitter and classifies the extracted information for traffic commute estimation road. In this study, tweets from Los Angeles, USA are taken into consideration as an analysis example. A machine learning classifier and a deep learning classier are used to classify traffic information. The model was trained to collect tweets containing the word 'traffic' in a real-time environment.

关键词： data analysis learning (artificial intelligence) social networking (online) traffic information systems Traffic information systems traffic congestion Social Media Data analysis Traffic information TWITTER Internets physical sensor SOLUTION PROCESSING real-time traffic Statistical analysis

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Research on Telecom Big Data Platform of LTE/5G Mobile Networks

Research on Telecom Big Data Platform of LTE/5G Mobile Netwo...

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IEEE international symposium on Parallel and distributed Processing with applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Lexi Xu Guanglu Shao Yue Cao Haochen Yang Chen Sun Tao Zhang Binyuan Wen Xinzhou Cheng Chuntao Song Xin He China United Network Communications Corporation Network Technology Research Institute Beijing China China United Network Communications Group Corporation School of Transportation Science and Engineering Beihang University Beijing China Beijing NO. 35 High School Beijing China School of Software Nanchang Hangkong University Nanchang China

ISBN: (纸本)9781728152103

In recent years, the LTE/5G mobile networks operation and telecom services lead to the explosion of telecom big data. Since telecom big data stores valuable information, this paper researches on telecom big data and further designs a telecom big data platform, in order to effectively utilizing telecom big data resources for the application. The designed platform consists of five layers, including telecom data sources layer, telecom data integration layer, telecom data storage and calculation layer, operation and management layer, telecom data application layer. Based on this telecom big data platform, we design both the cell judgment system and the telecom customer analysis system, which are further applied in the local telecom operators.

关键词： 5G mobile communication Big Data cellular radio Long Term Evolution telecommunication computing telecommunication network management telecommunication services big data telecommunication services Telecommunications data platform telecommunication computing Long Term Evolution Telecommunication network management cellular radio

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Meta-Learning Techniques to Analyze the Raman Data for Optical Diagnosis of Oral Cancer Detection

Meta-Learning Techniques to Analyze the Raman Data for Optic...

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IEEE international symposium on Parallel and distributed Processing with applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Mukta Sharma Lokesh Sharma Ming-Jer Jeng Liann-Be Chang Shiang-Fu Huang Shih-Lin Wu Department of Electronic Engineering Chang Gung University Taoyuan Taiwan AI Innovation Research Center Chang Gung University Taoyuan Department of Otolaryngology-Head and Neck Surgery Chang Gung Memorial Hospital Linkou Taiwan Department of Public Health Chang Gung University Taoyuan Taiwan Department of Cardiology Chang Gung Memorial Hospital Taoyuan Department of Electrical Engineering Ming Chi University of technology New Taipei City Taiwan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan

ISBN: (纸本)9781728152103

Recently, Machine Learning methods have shown great improvement while analyzing the biomedical data. Raman Spectroscopy (RS), a non-invasive technique, and widely used in screening to diagnose the oral cancer. In order to spot cancer in a smarter and faster way, we have employed Meta-Learning (ML) techniques to learn such as Bagging and Boosting on RS data. Further, we employed normal and tumor tissue class classification by Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Adaptive Boosting (AdaBoost) classifiers. The present study aims at examining the RS data with total 110 samples, including 57 tumor and 53 normal ones. To evaluate the performance, we have used the training samples to optimize, and testing samples to generalize the model parameters. The results show that the AdaBoost classifier with Bagging techniques showed the significant changes in accuracy.

关键词： biomedical optical imaging cancer image classification learning (artificial intelligence) medical image processing tumours Biomedical optical imaging Medical image processing Image classification Tumours Mouth Neoplasms Malignant Oral Cavity Tumor cancer test OPTICAL DIAGNOSTICS Boost bagging PURSE

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Leibniz international Proceedings in Informatics, LIPIcs

Leibniz International Proceedings in Informatics, LIPIcs

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32nd international symposium on distributed computing, DISC 2018

ISBN: (纸本)9783959770927

The proceedings contain 50 papers. The topics discussed include: selecting a leader in a network of finite state machines;the role of a-priori information in networks of rational agents;distributed approximate maximum matching in the CONGEST model;state machine replication is more expensive than consensus;allocate-on-use space complexity of shared-memory algorithms;integrated bounds for disintegrated storage;a tight lower bound for semi-synchronous collaborative grid exploration;multi-shot distributed transaction commit;deterministic blind radio networks;and TuringMobile: a turing machine of oblivious mobile robots with limited visibility and its applications.

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Thai Language Sentence Segmentation Based on N-Gram Context Model

Thai Language Sentence Segmentation Based on N-Gram Context ...

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IEEE international symposium on Parallel and distributed Processing with applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Hongbin Wang Zhiju Zhang Qiang Shen Yantuan Xian Yafei Zhang Cunli Mao Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming China

ISBN: (纸本)9781728152103

Segmentation of Thai language's sentences is the basic task of natural language processing of Thai language and has important significance on studying lexical analysis, syntactic analysis and machine translation relating to Thai language. Combined with statistical machine learning methods and features of Thai language, a segmentation method of Thai language's sentences based on n-gram context model was proposed. The Orchid1997 corpuses were expanded through this method based on the segmentation tasks of Thai language's sentences. And then, the symbols during Thai language's paragraphs (such as words and space, etc.) were marked using n-gram algorithm depend on the lexical features and syntactic features of Thai language. The segmentation of Thai language's sentences was implemented through identifying the space character at the end of the sentence. The experimental results show that this method can realize reliable segmentation of Thai language's sentences by utilized language characteristics effectively. The comprehensive segmentation performance of space and comprehensive performance F value of sentence segmentation is easy to reach 85.43% and 71.67%, respectively.

关键词： computational linguistics learning (artificial intelligence) natural language processing natural languages text analysis Natural Language Processing natural languages COMPUTATIONAL LINGUISTICS text processing Context modeling Tai languages

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Multi-Label Learning Based on Clustering Optimization

Multi-Label Learning Based on Clustering Optimization

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IEEE international symposium on Parallel and distributed Processing with applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Xiaoying Wang Jun Xie Xingliu Tao Xiaofeng Zhang Qi Lu Army Engineering University of PLA Nanjing China

ISBN: (纸本)9781728152103

Multi-label learning has been widely used in various fields. One problem in multi-label learning is that mining the correlation information between class labels in the entire label space is not only computationally unaffordable, but may be inappropriate (possibly inconsistent with the real world), which leads to the incorrect introduction of some label dependencies. So, the common solution is to divide the whole label space into several subspaces. However, the process of sub-space partitioning is usually independent of the classifier training process in the existing multi-label learning algorithms, which leads to the division is not optimal for the next classifiers training. In order to solve the above-mentioned problems, we propose a method whose basic idea is to optimize the label clustering process and the classifiers training process simultaneously. In addition, we apply group lasso constraints to the coefficient matrix to ensure that the output of classifiers can maintain the characteristics of consistency within each label cluster and mutually exclusive between label clusters. Experiments conducted on five benchmark multi-label datasets demonstrate the competitive performance of our proposed method compared with several state-of-the-art methods.

关键词： learning (artificial intelligence) pattern classification Pattern recognition label save area Classifiers Coefficient Matrix Learning Mutually exclusive Training Functional training

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Application of distributed Storage Technology for Financial Management and Control System in Electric Power System 16

Application of Distributed Storage Technology for Financial ...

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16th international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Yang, Yang Peng, Yang Xiao Feng, Gao Tian, Li Ye State Grid Henan Elect Power Informat & Telecommu Zhengzhou Henan Peoples R China Henan Elect Power Ind Grp Co Ltd Zhengzhou Henan Peoples R China China Int Telecommun Construct Grp Design Inst Co Zhengzhou Henan Peoples R China

ISBN: (纸本)9781538621622

distributed storage technology adopts an extensible system structure, shares storage load by many storage servers, locates stored information through location servers. Compared with a previous framework that financial management and control system of electric power companies adopted centralized storage servers to store all the data, storage servers are no longer bottleneck of information system performance. The servers not only improve reliability, availability and access efficiency of system, but also are apt to extend.

关键词： distributed storage cloud platform financial management and control system

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A Fast distributed Principal Component Analysis with Variance Reduction 16

A Fast Distributed Principal Component Analysis with Varianc...

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16th international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Shang-Guan, Shiyuan Yin, Jianping Natl Univ Def Technol Dept Comp Sci & Technol Changsha Hunan Peoples R China

ISBN: (纸本)9781538621622

Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) are widely used in dimension reduction, feature extraction, low-rank matrix approximation and so on. In large-scale applications, a common alternative is to use cluster which have multiple nodes and multiple cores to accelerate the time of solving problem. Most of the existing distributed PCA algorithm focus on reducing the communication, and there is little attention to the frequent waiting phenomenon caused by the synchronization mechanism. Meanwhile, most of these works are based on either the distributed memory of the processes-level parallelism or the shared memory of threads-level parallelism. In this paper, we propose a fast distributed PCA algorithm with variance reduced, which based on stochastic sampling and Stale Synchronous Parallel. Our algorithm contains the processes-level and threads-level parallelism. Experiments on the "Tianhe-2" super computer demonstrate that our algorithm has a good performance, speedup, and scalability.

关键词： distributed pca parameter server dimensionality reduction variance reduction

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