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Social Vehicle-To-Everything (V2X) communication model for Intelligent Transportation Systems based on 5G scenario 18

Social Vehicle-To-Everything (V2X) communication model for I...

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2nd international conference on Future Networks and Distributed Systems (ICFndS)

作者： Raza, Naeem Jabbar, Sohail Han, Jihun Han, Kijun Natl Text Univ Dept Comp Sci Faisalabad Pakistan Kyungpook Natl Univ Dept Comp Sci & Engn Daegu South Korea

ISBN: (纸本)9781450364287

Vehicular-to-Everything (V2X) communications are the emerging paradigm for the Intelligent Transportation System (ITS) used to enhance the traffic efficiency and reliability of timely data delivery by implementing a complete set of communication mechanism in all the devices and infrastructure involved in traffic control, monitoring, and management. This research work aims to propose a Social V2X Communication Model to improve the traffic efficiency in ITS environment by timely delivery of a required set of data with ultra-high speed integrated cellular 5G technologies. This is achieved by introducing the social behavior in V2X communication and with the provision of automated information, and surveillance. Along with this, triggering of required actions based on ultra-high speed and ultra-low latency 5G scenario of integrated networking technologies (Mobile Edge computing/Fog computing, software Defined Networking and Cloud computing) of integrated entities such as road users (Vehicles such as Cars, Buses, Trucks are parts of it. Motorcycles, and even the pedestrians), roadside infrastructure (Traffic lights, traffic signs, barriers and gates), Road Side Unit (RSU) and all the other network units (MEC /Fog Server, V2X Application Server (AS), SDN switches, and SDN controllers). The proposed model will support all kinds of communications such as a vehicle to vehicle (V2V) communications, vehicle to pedestrian communications (V2P), a vehicle to infrastructure (V2I) communications and vehicle to network (V2N) communications in a social internet of vehicles (SIOV) environment.

关键词： Intelligent Transportation Systems Vehicle-to-Everything Communications Social Internet of Things Social Internet of Vehicles 5G

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Human activity recognition by fusion of RGB, depth, and skeletal data 2nd

Human activity recognition by fusion of RGB, depth, and skel...

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2nd international conference on Computer Vision and Image Processing, CVIP 2017

作者： Khaire, Pushpajit Imran, Javed Kumar, Praveen Department of Computer Science and Engineering Visvesvaraya National Institute of Technology Nagpur India Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

ISBN: (纸本)9789811078941

A significant increase in research of human activity recognition can be seen in recent years due to availability of low-cost RGB-D sensors and advancement of deep learning algorithms. In this paper, we augmented our previous work on human activity recognition (Imran et al., IEEE international conference on advances in computing, communications, and informatics (ICACCI), 2016) [1] by incorporating skeletal data for fusion. Three main approaches are used to fuse skeletal data with RGB, depth data, and the results are compared with each other. A challenging UTD-MHAD activity recognition dataset with intraclass variations, comprising of twenty-seven activities, is used for testing and experimentation. Proposed fusion results in accuracy of 95.38% (nearly 4% improvement over previous work), and it also justifies the fact that recognition improves with an increase in number of evidences in support. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Musculoskeletal system

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Big data Analysis of TV Dramas Based on Machine Learning 1

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2nd international conference on Smart computing and Communication (SmartCom)

作者： Tan, Jiaqi Mao, Feiqiao Yang, Lianghai Wang, Jiahui Shenzhen Univ Nanhai Ave 3688 Shenzhen 518060 Guangdong Peoples R China

ISBN: (数字)9783319738307

ISBN: (纸本)9783319738307;9783319738291

Currently, large amount of TV dramas has overwhelmed the demand of TV station which had caused massive waste of resources. This article offers several practical solutions to tackle the above-mentioned problems through building model based on machine learning. Firstly, we build a TV score prediction model with regression and machine learning to rank the most welcomed TV drama. Moreover, we write an Internet worm to collect data from the internet, and build a Star popularity index prediction model by machine learning and regression. And list the much-acclaimed stars based on the popularity index. In conclusion, with the predict score of the TV drama predicted based on machine learning, it can provide a reference for TV station to manage TV programs and with the starring ranking it can help TV drama production team to produce TV dramas in a high quality.

关键词： Machine learning Big data TV dramas Predict score Staring ranking

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2nd international conference on Intelligent Information Technologies, ICIIT 2017

2nd International Conference on Intelligent Information Tech...

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2nd international conference on Intelligent Information Technologies, ICIIT 2017

ISBN: (纸本)9789811076343

The proceedings contain 26 papers. The special focus in this conference is on Intelligent Information Technologies. The topics include: Efficient and robust secure in-network aggregation in wireless sensor networks;context-aware conditional probabilistic hyper-exponential reputation technique for mitigating byzantine attacks;Illumination and communication using LED as light source in underground mines;leveraging social networks for smart cities: A case-study in mitigation of air pollution;smart garbage bin systems – A comprehensive survey;contextual pattern clustering for ontology based activity recognition in smart home;E-FPROMETHEE: An entropy based fuzzy multi criteria decision making service ranking approach for cloud service selection;Workflow scheduling using IOT enabled reputation of service providers in the cloud;SCICS: A soft computing based intelligent communication system in VANET;data consumption pattern of MQTT protocol for IoT applications;Classification and recommendation of competitive programming problems using CNN;a generic context-aware service discovery architecture for IoT services;understanding how adversarial noise affects single image classification;Top-k category search for an IP address-product network;booking based smart parking management system;VIBI: A braille inspired password entry model to assist person with visual impairments;a rehabilitation therapy for autism spectrum disorder using virtual reality;data access in heterogeneous data sources using object relational database;Optimization of UAV video frames for tracking;failure recovery using segment protection in software defined networks;spectrum sensing based heed routing performance enhancement strategy for cognitive radio sensor networks;Improved recommendation filtering component resilient to trust distortion attacks in a MANET.

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A new Bayesian-based method for privacy-preserving data mining 2nd

A new Bayesian-based method for privacy-preserving data mini...

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2nd international conference on Intelligent and Interactive Systems and Applications, IISA 2017

作者： Li, Guang School of Electronic and Control Engineering Chang’an University Xi’anShaanxi710064 China

ISBN: (纸本)9783319690957

Recently, data mining developed fast and attracted a lot of attention. When using data mining in real world, privacy protection is an important problem. Over the past ten years, many researchers study this problem and propose a lot of PPDM (privacy preserving data mining) methods. These methods can complete data mining task when protecting privacy. This paper gives a new Bayesian-based PPDM method, which is designed for classification. This method is a data perturbation method and is algorithm-independent, which means the perturbed data can be used by normal classification methods directly. Experiments show that comparing with existing methods, this new method perform better for protecting privacy, when they keeping data utility both. © 2018, Springer international Publishing AG.

关键词： data mining

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Multiclass classification of vertebral pathologies with limited data 2

Multiclass classification of vertebral pathologies with limi...

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2nd international conference on Biological Information and Biomedical Engineering, BIBE 2018

作者： Zhang, Liyuan Zhao, Jiashi Yang, Huamin Jiang, Zhengang Shi, Weili School of Computer Science and Technology Changchun University of Science and Technology No.7089 Weixing Road Changchun China

ISBN: (纸本)9783800747276

For intelligent diagnosis of vertebral pathologies, the available labeled data are usually limited and the discrimination of multiple lesions is not obvious. To deal with this issue, a novel multiclass classification method with limited data is proposed, which helps assist in the diagnosis of vertebral disease. The main idea of the proposed method is a feature augmentation strategy. Firstly, principal components analysis (PCA) is utilized to find out the principal features de-scribed pathologies. After that, discrete wavelet transform (DWT) is used to augment saliency features and remove noise data. Through computing the fisher criterion function of linear discriminant analysis (LDA) is to adaptively select the optimal wavelet decomposition level, thereby significantly increasing the inter-class separability. Finally, the sup-port vector machine (SVM) classifier based one-versus-one (OVO) decomposition strategy with nonlinear kernel is em-ployed to classify the augmented features. The proposed method is implemented and compared with other methods. The experimental results show that the proposed method can accurately classify vertebral pathologies and achieve better classification performance. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Pathology

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SCICS: A soft computing based intelligent communication system in VANET 2nd

SCICS: A soft computing based intelligent communication syst...

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2nd international conference on Intelligent Information Technologies, ICIIT 2017

作者： Rath, Mamata Pattanayak, Binod Kumar Department of IT C. V. Raman College of Engineering BhubaneswarOdisha India Department of Computer Science and Engineering SOA University BhubaneswarOdisha India

ISBN: (纸本)9789811076343

Delicate registering procedures are tolerant of imprecision, expected on guess, concentrates on vulnerability and in light of incomplete truth. Current real issue relating to congested activity in smart city is inescapably uncertain and in this manner plan of brilliant movement control framework is a challenging issue. Because of expanding rate of vehicles at movement focuses in keen urban communities, it makes startling deferrals amid travel, chance of mishaps are increasing, superfluous fuel utilization is an issue and unhygienic condition because of contamination additionally debases the wellbeing state of general individuals in an ordinary city situation. To stay away from such issues many keen urban communities are right now actualizing enhanced activity control frameworks that work on the guideline of movement robotization with avoidance of the above indicated issues. The fundamental test lies in utilization of ongoing examination performed with on-line movement data and effectively applying it to some activity stream. In this exploratory article, an upgraded activity administration framework called SCICS (soft computing based Intelligent Communication System) has been proposed utilizing swarm knowledge as a delicate registering procedure with astute correspondence between smart vehicles and movement of tower focuses. It utilizes an enhanced course redirection system with executed rationale in nano robots. Under a Vehicular Ad-hoc Network (VANET) situation, the correspondence between wise sensors happen through nano robots cooperatively. Reenactment results done utilizing Ns2 test system indicates empowering results in terms of better execution to control issue. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Vehicular ad hoc networks

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Application of Semantic computing in Cancer on Secondary data Analysis 2

Application of Semantic Computing in Cancer on Secondary Dat...

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2nd IEEE international conference on Robotic computing (IRC)

作者： Wang, Charles C. N. Chang, I-Seng Sheu, Phillip C. Y. Tsai, Jeffrey J. P. Asia Univ Dept Biomed Informat 500 Lioufeng Rd Taichung 41354 Taiwan Univ Calif Irvine Dept Elect Engn & Comp Sci 5200 Engn Hall Irvine CA 92697 USA

ISBN: (纸本)9781538646519

There have been an enormous number of publications on cancer research. These integrated but unstructured cancer-related articles are of great value for cancer diagnosis, treatment and prevention. In this study, we use the basic concepts underlying text mining and semantic computing to discuss the current state-of-the-art text mining applications in cancer research. Using the abstract of literature extracted from PubMed between 2008 and 2016, a total 925,648 articles are used for subsequent text mining. Among the 925,648 articles, the top 5 most studied cancer types were breast cancer (23.82%), lung cancer (10.54%), prostate cancer (9.90%), rectal cancer (8.44%), and ovarian cancer (4.44%). The top 3 most frequently occurred keywords in the abstracts of the 925,648 articles are patients, cancer, and cell where each appear 1,445,688, 1,284,140, and 676,924 times, respectively. Analysis of the key concepts indicate that the most common concepts are patients, cancer, cell and tumor. Our results suggest that while the risk factors of cancer, treatment of cancer, and survival of cancer patients were popular research topics, end-of-life cancer care issues are less studied. Further studies should explore these areas since they are as important as treatment of the disease itself for many patients.

关键词： cancer semantic computing text mining topic model GloVe

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Proceedings - 2016 IEEE 14th international conference on Dependable, Autonomic and Secure computing, DASC 2016, 2016 IEEE 14th international conference on Pervasive Intelligence and computing, PICom 2016, 2016 IEEE 2nd international conference on Big data Intelligence and computing, dataCom 2016 and 2016 IEEE Cyber science and Technology Congress, CyberSciTech 2016, DASC-PICom-dataCom-CyberSciTech 2016

Proceedings - 2016 IEEE 14th International Conference on Dep...

引用

14th IEEE international conference on Dependable, Autonomic and Secure computing, DASC 2016, 14th IEEE international conference on Pervasive Intelligence and computing, PICom 2016, 2nd IEEE international conference on Big data Intelligence and computing, dataCom 2016 and 2016 IEEE Cyber science and Technology Congress, CyberSciTech 2016, DASC-PICom-dataCom-CyberSciTech 2016

ISBN: (纸本)9781509040650

The proceedings contain 149 papers. The topics discussed include: a scheme for controlling functionality of mobile device using specialized codes;a cyber voice recognition with low SNR;evaluation of network risk using attack graph based security metrics;what influence the students'learning experience? the impact of peer learning in an online environment;the design and implementation of a high-efficiency distributed web crawler;ontology modeling and application of multi-agent for negotiation system;a preliminary study of plant domain ontology;an ontology pattern for emergency event modeling;intrusion detection based on IDBM;and a MapReduce-based framework for analyzing web logs in offline streams.

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Vertical structure analysis of ocean temperature and salinity using depth series EOF method with Argo data 18

Vertical structure analysis of ocean temperature and salinit...

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2018 international conference on Big data Technologies, ICBDT 2018 and its Workshop, 2018 2nd international conference on Business Information Systems, ICBIS 2018

作者： Qian, Chengcheng Chen, Ge Guo, Donglin Xu, Wenkun Ocean University of China No. 238 Songling Road Qingdao China North China Sea Marine Forecasting Center of State Oceanic Administration No. 27 Yunling Road Qingdao China Laboratory for Regional Oceanography and Numerical Modeling Qingdao National Laboratory for Marine Science and Technology No. 1 Wenhai Road Qingdao China Qingdao Geotechnical Investigation and Surveying Research Institute No. 189 Shandong Road Qingdao China

ISBN: (纸本)9781450364270

How to analyze, interpret and make use of marine big data has become a serious challenge to the geophysical community. A statistical technique vertical EOF (VEOF) is introduced to extract vertical pattern of the ocean. To explore the ocean environment with untraditional EOF method, which the depth series are used to replace time series in traditional analysis, allowing the vertical spatial patterns of oceanic variables to be revealed. Using the Argo dataset from 2004 to 2016, we analyze the vertical variations of temperature and salinity in the global ocean. The results show that the first VEOF modes exhibit coherent signatures with the vertical average of temperature and salinity which reveal that the first mode is climatology mode. We conduct the temperature seasonal VEOF analysis and consider that the second mode of VEOF is related with the thermocline and halocline. The comparison of the barrier layer thickness (BLT) pattern with the third mode of VEOF in the tropical Pacific Ocean shows that the third mode is associated with local heat transfer. The second mode possible results from the external heat transfer, while the third pattern is possible caused by the internal heat transfer according to phase change of the depth series. © 2018 Association for computing Machinery.

关键词： data mining

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