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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201-210 订阅

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An Improved Collaborative Filtering Recommendation Algorithm Based on Reliability 3

An Improved Collaborative Filtering Recommendation Algorithm...

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3rd IEEE international conference on Cloud computing and Big data Analysis (ICCCBDA)

作者： Fan, Shiping Yu, Hao Huang, Haihui Chongqing Univ Posts & Telecommun Software Engn Chongqing Peoples R China Chongqing Univ Posts & Telecommun Commun & Informat Engn Chongqing Peoples R China

ISBN: (纸本)9781538643013

The similarity of the user-based collaborative filtering algorithm is based on the user-item rating matrix. However, the accuracy of predicting the users' rating was affected by the data sparseness problem. To alleviate the data sparseness problem of the collaborative filtering and the variant ratings on the common items. In this paper, we propose a similarity calculation measure that based on reliability. Firstly, the measure makes use of the users' ratings on the common items to obtain the credibility of rating between users, and then introduces the credibility into the adjusted cosine similarity to alleviate the affect of variant ratings on common items. Secondly, the punishment function was introduced to the adjusted cosine similarity, and alleviate the influence of the popular items on the similarity calculation. Finally, the former two kinds of similarity were measured comprehensively to predict the user's rating more accurate and to improve the reliability of the similarity calculation. Experimental results show that compared with the Pearson similarity and the adjusted cosine similarity, the improved method proposed in this paper could get a lower value of MAE, which means that it could improve the accuracy of predicting users' rating and the personalized recommendation efficiency.

关键词： collaborating filtering data sparsity reliable user MAE

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Identification of Heavy Hitters for Network data Streams with Probabilistic Sketch 3

Identification of Heavy Hitters for Network Data Streams wit...

引用

3rd IEEE international conference on Cloud computing and Big data Analysis (ICCCBDA)

作者： Zhou, Aiping Zhu, Huisheng Liu, Lijun Zhu, Chengang Taizhou Univ Sch Comp Sci & Technol Taizhou Peoples R China Southeast Univ Minist Educ Key Lab Comp Network & Informat Integrat Nanjing Jiangsu Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538643013

Traffic measurement is critical for many network applications, such as traffic accounting, anomaly detection. One central problem is to identify heavy hitters, flow size of which is more than the given threshold or the certain percentage of total network traffic in the measurement interval. According to low accuracy and memory utilization for traditional methods, we propose an identification algorithm of heavy hitters with probabilistic sketch in high-speed networks (for short HHS) Since the probabilistic sketch consists of a few bit vectors, data updating only needs a small amount of calculation. It can estimate flow size by probabilistic counting approach approximately. Moreover, in order to improve the identification accuracy of heavy hitters, we introduce confidence interval to estimate flow size. We use real network traffic to evaluate the performance of the proposed method. The experimental results illustrate that our method can estimate flow size accurately and identify heavy hitters effectively.

关键词： heavy hitter identificaation network data stream probablistic sketch

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An Approach of Pilot Contamination Reduction Based on Power Control and Orthogonal Identification 3

An Approach of Pilot Contamination Reduction Based on Power ...

引用

3rd IEEE international conference on Cloud computing and Big data Analysis (ICCCBDA)

作者： Xu, Yaohua Wu, Yong Anhui Univ Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei Anhui Peoples R China Chinese Acad Sci Shanghai Inst Micro Syst & Informat Technol Key Lab Wireless Sensor Network & Commun Shanghai Peoples R China

ISBN: (纸本)9781538643013

Massive multiple input multiple output (MIMO) cellular cells with small radius can satisfy the communication requirements where communication traffic is high. Since the radius of the cell is small, the large-scale fading interference of the second-circle cells outside the central cell still exists. However, it is not realistic that the traditional pilot power control method ignores the interference from the second-circle cells outside the central cell. In fact there is still a large pilot contamination problem. To solve the problem of pilot contamination in the traditional pilot power control method, this paper combines the uplink pilot power control and the orthogonal cell identification. All cells are classified by pilot power control, the cells of each class are grouped again by assigning orthogonal cell identification and the corresponding identifications are added to the head of pilot sequence which is transmitted by the mobile terminals of the local cell. Making sure that the BS identifies the pilots from different groups of cells. The experimental results show that the method of this paper is not only simple, but also can reduce the pilot contamination in multi-cell system effectively.

关键词： massive MIMO multi-cell cellular network pilot contamination power control orthogonal identification

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A Discussion on Fall Detection Issues and Its Deployment: When cloud meets battery 3

A Discussion on Fall Detection Issues and Its Deployment: Wh...

引用

3rd IEEE international conference on Cloud computing and Big data Analysis, ICCCBDA 2018

作者： Khojasteh, Samad Barri Villar, Jose R. De La Cal, Enrique Gonzalez, Victor M. Tan, Qing Kiadi, Morteza Computer Science Department University of Oviedo Oviedo Spain School of Computing and Information Systems Athabasca University Edmonton Canada

ISBN: (纸本)9781538643006

Fall detection for elder people is a very challenging task that has not been solved yet. In this population, fall detection devices must not introduce extra constraints, like carrying with a belt fixed device or a mobile phone. This paper describes one fall detection approach accomplishing with these constraints. Afterwards, a discussion on some model decisions concerning the computational restrictions according to where the data processing and classification are performed is also included. © 2018 IEEE.

关键词： Fall detection

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Real-Time Object Recognition Algorithm Based on Deep Convolutional Neural Network 3

Real-Time Object Recognition Algorithm Based on Deep Convolu...

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3rd IEEE international conference on Cloud computing and Big data Analysis (ICCCBDA)

作者： Yang, Lihong Wang, Liewei Wu, Shuo Hefei Res Inst Publ Safety Technol Res Inst 38 China Elect Technol Grp Corp Hefei Anhui Peoples R China

ISBN: (纸本)9781538643013

Radar detection of moving objects is vulnerable to external environment. By introducing the object confirmation algorithm, the intelligent radar perimeter security system's false alarm rate can be reduced significantly. The object confirmation algorithm is essentially an object detection algorithm. Due to the poor generalization of artificial feature extraction algorithms, we use the deep convolution neural network to extract deep features automatically for object confirmation. In order to meet the real-time requirement in engineering practices, our algorithm use the YOLOv2 system as a basis, and selects anchor boxes which meet object scales of our training data set by k-means++ clustering. To improve the YOLOv2 network structure, low-layer deep features which denote the texture information and high-layer deep features which denote the semantic information are combined layer by layer to make object detection more accurate. The experimental results show that the false alarm rate of the intelligent radar perimeter security system is further reduced by introducing the object confirmation algorithm. Especially for extreme weather, false alarms of radar detection greatly increase. But most of them are eliminated after running object confirmation algorithm. Therefore the warning accuracy of the entire system can be guaranteed. The detection speed of the object confirmation algorithm is 33FPS, which meets the real-time requirement of engineering practices.

关键词： YOLOv2 deep convolutional neural network feature fusion perimeter security

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Collaborative Filtering Algorithm Based on Rating Difference and User Interest 3

Collaborative Filtering Algorithm Based on Rating Difference...

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3rd IEEE international conference on Cloud computing and Big data Analysis (ICCCBDA)

作者： Huang, Haihui Yu, Guo Wang, Xin Chongqing Univ Posts & Telecommun Software Engn Chongqing Peoples R China Chongqing Univ Posts & Telecommun Commun & Informat Engn Chongqing Peoples R China

ISBN: (纸本)9781538643013

Collaborative filtering algorithm is one of widely used approaches in daily life, so how to improve the quality and efficiency of collaborative filtering algorithm is an essential problem. Usually, some traditional algorithm focuses on the user rating, while they don't take the user rating differences and user interest into account. However, users who have little rating difference or have a similar interest may be highly similar. In this paper, a collaborative filtering algorithm based on scoring difference and user interest is proposed. Firstly, a rating difference factor is added to the traditional collaborative filtering algorithm, where the most appropriate factor can be obtained by experiments. Secondly, calculate the user's interest by combining the attributes of the items, then further calculate the similarity of personal interest between users. Finally, the user rating differences and interest similarity are weighted to get final item recommendation and score forecast. The experimental results on data set shows that the proposed algorithm decreases both Mean Absolute Error and Root Mean Squared Error, and improves the accuracy of the proposed algorithm.

关键词： collaborative filtering user similarity rating differences user interest

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Efficient computer-aided diagnosis of alzheimer’s disease and Parkinson’s disease—a survey 3rd

Efficient computer-aided diagnosis of alzheimer’s disease a...

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3rd international conference on Nanoelectronics, Circuits and Communication Systems, NCCS 2017

作者： Nancy Noella, R.S. Priyadarshini, J. School of Computing Science and Engineering VIT University Chennai600127 India

ISBN: (纸本)9789811307751

Dementia is a broad category of brain-related diseases that continues for a long term and severely affects thinking and daily functioning of a human being. Among different types of dementia the fatal type of brain problems are Alzheimer’s Disease (AD) and Parkinson’s Disease (PD). More than 70% of cases are reported as dementia is in the Alzheimer’s category. In AD, the patient’s brain gets severely damaged, especially the outer part of the brain like cerebral cortex, hippocampus, ventricles, etc. The AD patients have enlarged ventricles, shrinkage in hippocampus and cortex. PD is also a common dementia after AD. In PD, the patient’s mid-brain gets damaged, i.e., substantia nigra. The proposed work presents an efficient automation for the detection of the AD and PD with Machine Learning Techniques (MLT). To detect the presence of PD and AD, two different types of brain image databases have to be selected: Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT) database images, both of them contain data for AD and PD patients in comparison with the healthy brain images. From the input image, different features have to be extracted like statistical moments, geometrical moments, texture features, etc. Then Region of Interest (ROI) has to be selected to differentiate disease-affected areas. The results have to be generated automatically by comparing input image with the trained samples in the database. The proposed system concentrates on applying the MLT for segregating the outer part of brain with central part of brain for diagnosing the AD and PD in comparison with the healthy brain data. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Computer aided diagnosis

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Multi-language Person Social Relation Extraction Model Based on Distant Supervision 3

Multi-language Person Social Relation Extraction Model Based...

引用

3rd IEEE international conference on Cloud computing and Big data Analysis (ICCCBDA)

作者： Huang, Yangchen Jia, Yan Huang, Jiuming He, Zhonghe Natl Univ Def Technol Sch Comp Changsha 410073 Hunan Peoples R China Hunan Singhand Intelligent Data Technol Co Ltd KB R&D Dept Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781538643013

Relation extraction refers to a method of efficiently identifying entities from the text and extracting semantic relations between entities. The person social relation extraction is one of the most important fields in relation extraction. A large number of techniques have been proposed on relation extraction thus far, and supervised machine learning methods are the most widely used. However, the disadvantages of supervised machine learning methods are that manually annotating training data set is costly and time-consuming, which block the improvement of the supervised relation extraction model. Aiming at the limitation, we propose a novel person social relation extraction model on both Chinese and English corpus with distant supervision. Distant supervision method can make full use of the information in the knowledge base and provide training data without manual effort. In particular, it is an effective method in the very large corpora which contains thousands of relations. In this model, we use distant supervision method to get the weak-labeled data set. Then, a supervised method is used to train a classifier, which is expect to distinguish the relation between the person entities in the input sentence. Experiment results on real-world datasets show that, our model can take advantage of all informative sentences in knowledge base and outperforms several competitive analogous methods, what's more, it does not need any human-labeled training data.

关键词： relation extraction person social relation distant supervision machine learning

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Research on Meteorological data Simulation 3rd

引用

3rd international conference on Human Centered computing (HCC)

作者： Wu, Zhenglun Liu, Shufen Bao, Tie Jilin Univ Changchun Jilin Peoples R China

ISBN: (纸本)9783319745213;9783319745206

This paper proceeded with simulating the meteorological data. Based on the meteorological data that has been collected, this paper puts forward the simulation method where the meteorological data can be classified into discrete data and continuous data which can be simulated respectively. By establishing the mathematical model of meteorological variables and the employment of the relevant knowledge, the simulation of meteorological data can be achieved. The simulated relationship among meteorological data accord with the relationship among the real data. In other words, the simulated result approach the real meteorological data to a certain extent. Moreover, this result guarantees the application of the unmanned aerial vehicle in logistics, disaster relief, medical care etc.

关键词： Meteorological data Simulation Mathematical model Discrete

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Power Waveforms Analysis for Sea State Determination Using Beidou MEO Satellite Delay Signals 3

Power Waveforms Analysis for Sea State Determination Using B...

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3rd IEEE international conference on Cloud computing and Big data Analysis (ICCCBDA)

作者： Islam, Mohammad Shohidul Yang, Dongkai Masum, S. M. Injamamul Haque Santillan, Rodolfo D. Moreno Siddika, Sultana Easmin Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Beihang Univ Int Sch Beijing 100191 Peoples R China Beihang Univ Sch Econ & Management Beijing 100191 Peoples R China

ISBN: (纸本)9781538643013

In this paper, remote sensing of global navigation satellite system (GNSS) is proposed for determination of sea state condition. The approach is already proved as an effective tool for measurement of sea state, especially for sea wind estimation, sea wave height measurement, sea roughness measurement and so on. Firstly, this technique is utilized to generate the power waveforms of GNSS-R delay signals from Beidou MEO satellite. Secondly, these waveforms are analyzed to retrieve information from sea surface. Two models are used to generate power waveforms from Beidou MEO satellite reflected signals. The wave spectrum model is used to find sea surface up and cross variances. The Z-V scattering model is used to generate power waveforms using the outputs of the wave spectrum model. The generated power waveforms are analyzed with three parameters, i.e. coherent integration time, wind speed & satellite elevation angle. From the analysis, it's observed that amplitude and shape of these power waveforms are clearly different for different situations of sea surface. It indicates that there is a sharp relationship between amplitude of the waveforms and sea surface state conditions. This relation is used to determine the sea state determination. The obtained result of sea state is compared to real surface situation i.e. wind speed & sea wave height which are collected from data campaign site. It is found that the determined sea states are closely matched with real situations of sea surface.

关键词： Beidou MEO satellite power waveforms sea surface scattering model wave spectral model sea state

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