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Effective Insect Recognition Using a Stacked Autoencoder with Maximum Correntropy Criterion

Effective Insect Recognition Using a Stacked Autoencoder wit...

引用

International Joint Conference on Neural Networks

作者： Yu Qi Goktug T. Cinar Vinicius M. A. Souza Gustavo E. A. P. A. Batista Yueming Wang Jose C. Principe Qiushi Academy for Advanced Studies Zhejiang University Hangzhou China

ISBN: (纸本)9781479919611

Throughout the history, insects had been intimately connected to humanity, in both positive and negative ways. Insects play an important part in crop pollination, on the other hand, some of them spread diseases that kill millions of people every year. Effective control of harmful insects while having little impact to beneficial insects and environment is extremely important. Recently, an intelligent trap that uses laser sensors was presented to control the population of target insects. The device could record and analyze sensor signals when an insect passes through the trap and make quick decisions whether to catch it or not. The effectiveness of the trap relies on the correct choice of classification algorithm to perform the insect detection. In this paper, we propose to use a deep neural network with maximum correntropy criterion (MCC) for reliable classification of insects in real-time. Experimental results show that, deep networks are effective for learning stable features from brief insect passage signals. By replacing the mean square error cost with MCC, the robustness of autoencoders against noise is improved significantly and robust features could be learned. The method is tested on five species of insects and a total of 5325 passages. High classification accuracy of 92.1 % is achieved. Compared with previously applied methods, better classification performance is obtained using only 10% of the computation time. Therefore, our method is efficient and reliable for online insect detection.

关键词： Effective Insect Recognition Stacked Autoencoder Maximum Correntropy Criterion Insecta Traps pest insects Laser sensors sorting algorithm Humanities beneficial insect MCC Genes computational time PASSAGE

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Research of Large-Scale and Complex Agricultural Data Classification algorithms Based on the Spatial Variability

Research of Large-Scale and Complex Agricultural Data Classi...

引用

The 9th International Conference on Computer and Computing Technologies in Agriculture (CCTA 2015)

作者： Hang Chen Guifen Chen Lixia Cai Yuqin Yang College of Information Technology Jilin Agricultural University Institute of Scientific and Technical Information of Jilin

ISBN: (纸本)9783319483566

In the actual classification problems, as a result of lack of clear boundary information between classification objects, that could lead to loss of classification accuracy easily. Therefore, this article from the spatial patterns of the sample properties to proceed, fuzzy clustering algorithm is proposed based on the sensitivity of attribute weights, through using the attribute weights to improve the classification capability between confusing samples, that is for researching and analysing soil nutrient spatial data with consecutive years to collect in Nongan town. Then through the analysis of the visualization technology to realize the visualization of the algorithm. Experimental results show that introducing weights portray attribute information could reduce the objective function value, and effectively alleviate the phenomenon of boundary data that cannot distinguish. Ultimately to improve the classification accuracy. Meanwhile, use of MATLAB to form visualization of three-dimensional image. The results provide a basis for to improve the accuracy of data classification and clustering analysis of large and complex agricultural data.

关键词： Large-scale and complex data Spatial variation law Fuzzy clustering Soil nutrients Sensitive attribute weights soil nutrients agricultural statistics Visualization sorting algorithm data classification large sizes MATLAB

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Improved waveform design for non-Gaussian target classification in cognitive radar

Improved waveform design for non-Gaussian target classificat...

引用

IET International Radar Conference 2015

作者： Zhou Rui Zhou Bo Liu Lin Zhang Xiaofen Jiao Bin Aviation University of Air Force Changchun 130022 China Luoyang Electronic Equipment Testing Center Luoyang 471003 China

Because the performance of traditional Gaussian cognitive radar(CR) system for target response varying from Gaussian will be degraded, this paper extended the Gaussian target response to arbitrary non-Gaussian target distribution. In this paper, the CR multiple hypothesis classification algorithm was used for non-Gaussian targets, and the sparse spectrum of correlated narrowband target responses was utilized. In addition, we simulated the multiple nonGaussian distribution targets, and the simulation results demonstrate the non-Gaussian target classification algorithm is very effective.

关键词： Cognitive Radar Non-Gaussian Target Waveform Design Non-Gaussian Targets Gauss Simulation results sorting algorithm waveforms

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Increasing Flexibility of Time Triggered Ethernet based Systems by Optimal Greedy Scheduling Approach

Increasing Flexibility of Time Triggered Ethernet based Syst...

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IEEE Conference on Emerging Technologies and Factory Automation

作者： Lukasz Wisniewski Markus Schumacher Juergen Jasperneite Christian Diedrich inIT - Institute Industrial IT Ostwestfalen-Lippe University of Applied Sciences Fraunhofer IOSB-INA Application Center Industrial Automation Institute for Automation Technology Otto-von-Guericke University Magdeburg

ISBN: (纸本)9781467379304

Many aspects have to be considered while achieving flexibility of production systems. One of them is flexible communication system that on one hand fulfils requirements of a particular industrial application and on the other hand allows integration of new network components optimally in a seamless way. In this paper, an approach to increase, rather static, Time Triggered Ethernet based systems is proposed. It is based on a greedy scheduling approach that can be implemented directly in a processing limited hardware such as IO Controller. This, together with the discovery and auto configuration mechanism proposed in [1] allows to shift the engineering process typically performed offline using engineering tool to the field level. Furthermore, it has been proven that the proposed scheduling approach is not only simple but also powerful. It is able to calculate an optimal schedule for any kind of tree graph and the complexity of the algorithm is defined by the complexity of the used sorting algorithm.

关键词： Tree graphs Ethernet networks greedy method sorting algorithm Engineering tools Network components field level IO Flexibility

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HYDROMETEOR CLASSIFICATION FOR X-BAND DUAL POLARIZATION RADAR ON-BOARD CIVIL AIRCRAFTS

HYDROMETEOR CLASSIFICATION FOR X-BAND DUAL POLARIZATION RADA...

引用

IEEE International Geoscience and Remote Sensing Symposium

作者： N. Roberto A. Adirosi L. Baldini L. Facheris F. Cuccoli A. Lupidi A. Garzelli CNIT - RASS

ISBN: (纸本)9781479979301

Polarimetric techniques applied to radars on-board civil aircraft can improve the estimation of risk zones due to dangerous weather during flight. Usually, X-band weather radars are installed at the nose of civil aircrafts. This band is affected by strong attenuation in case of intense precipitation (liquid or mixed phase). The current systems do not compensate backscattered power measurements for attenuation caused by propagation through precipitation, while dual-polarization radars are able to compensate effectively this source of error and to discriminate hydrometeors. In this work, a classification algorithm based on support vector machines (SVM) is proposed. Training is driven by the output of a Fuzzy Logic (FL) classification algorithm (typical classification approach used for ground-based weather radar). SVM high performance in terms of time processing and its flexibility of configuration using all type of inputs variables are important characteristics to be included in some avionic specific equipment, such as the Electronic Flight Bag (EFB). Two datasets have been used to test the SVM classification algorithm. The first dataset is composed of simulated radar polarimetric observations at X-band and the second one is composed of actual dual-polarization radar measurements collected during the Special Observation Period (SOP) 1.1 of HYdrological cycle in MEditerranean EXperiment (Hymex) campaign by the C-band Doppler dual-polarization weather radar (Polar 55C) installed at ISAC-CNR in Rome. Good performance are obtained for SVM classificator. The comparison with FL output shows a good agreement (up to 90%) both in qualitative comparison maps by maps and using a quantitative approach which metric is based on the confusion matrix.

关键词： Support Vector Machine Dual polarization radar Hydrometeor classification Avionic radar Fuzzy logic Support Vector Network sorting algorithm radar Fuzzy logic Superhigh frequency confusion matrix water circulation Radar measurement Power measurement Hydrometeors HIGH-PERFORMANCE meteorological radar mechanical heart valve

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A novel gender classification method based on MapReduce

A novel gender classification method based on MapReduce

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IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

作者： Tong Cui Haifeng Zhao Science and Technology on Information Systems Engineering Laboratory Nanjing China

ISBN: (纸本)9781479983544

A novel parallelize gender recognition method with MapReduce is presented, which successfully comprise several machine leaning algorithms which are employed for gender recognition. The mass of face sample images are gathered and separated as train dataset and test dataset, and Local Binary Pattern (LBP) features are extracted when those sample sets are pre-processed and made ready for following operations. And Principle Component Analysis (PCA) is applied to train dataset to extract the most distinguishing features. Three classification algorithms: Support Vector Machine(SVM), k-Nearest Neighborhood (k-NN) and Adaboost are implemented and compared to determine the most suitable and successful algorithm for gender parallelize machine learning (GPML). To achieve the shortest execution time, we propose to apply GPML with MapReduce to avoid parallelizing above three algorithms while also improving their scalability to big datasets. The results show that this method reduces the training computational complexity significantly when the number of computing nodes increases while gaining better speedup rates and extending performance than those on parallelize Adaboost.

关键词： face recognition feature extraction image classification learning (artificial intelligence) parallel processing principal component analysis support vector machines Adaboost GPML LBP features MapReduce PCA SVM classification algorithm face sample images gender classification method gender parallelize machine learning k-nearest neighborhood local binary pattern feature extraction machine leaning algorithms parallelized gender recognition method principle component analysis support vector machine test dataset train dataset training computational complexity gender recognition parallel meta-learning Support Vector Network principal components analysis sorting algorithm Image classification PARALLEL PROCESSING (COMPUTERS) Facial Recognition Passive Cutaneous Anaphylaxis Feature extraction Dataset Gender ADABOOST execution time Classification Methods

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SSVEP-based BCI: a "Plug & Play" Approach

SSVEP-based BCI: a "Plug & Play" Approach

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Niccolò Mora Ilaria De Munari Paolo Ciampolini the Department of Information Engineering Unversity of Parma Parco Area delle Scienze 181/A 43124 Parma Italy

ISBN: (纸本)9781424492695

Brain-Computer Interface (BCI) can provide users with an alternative/augmentative interaction path, based on the interpretation of their brain activity. Steady State Visual Evoked Potentials (SSVEP) paradigm has many appealing features, aiming at implementing BCI-enabled communication-control applications. In this paper, we present a complete signal processing chain for a self-paced, SSVEP-based BCI. The proposed approach mostly focuses at reducing the user effort in dealing with BCI, featuring no need of user-specific calibration or training. In this paper, the classification algorithm is introduced and first validated on offline waveforms, aiming at improving classification accuracy and minimizing the false positive rate. Then, implementation of an online, self-paced SSVEP BCI is illustrated. The scheme refers to a four-way choice and exploits discrimination between intentional control states and no-control ones. Good performance is achieved, both in terms of true positive rate (>94%), as well as low false positive rate (0.26 min~(-1)), even in experiments carried out outside lab-controlled conditions.

关键词： BCI SSVEP scheme refers False-positives sorting algorithm brain activity plugs four-way junction Brain-Computer Interfaces Visual Evoked Potentials

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ON THE ARCHITECTURE OF A BIG DATA CLASSIFICATION TOOL BASED ON A MAP REDUCE APPROACH FOR HYPERSPECTRAL IMAGE ANALYSIS

ON THE ARCHITECTURE OF A BIG DATA CLASSIFICATION TOOL BASED ...

引用

IEEE International Geoscience and Remote Sensing Symposium

作者： V. A. Ayma R. S. Ferreira P. N. Happ D. A. B. Oliveira G. A. O. P. Costa R. Q. Feitosa A. Plaza P. Gamba Dept. of Electrical Engineering Pontifical Catholic University of Rio de Janeiro Dept. of Technology of Computers and Communications University of Extremadura Dept. of Electronics University of Pavia

ISBN: (纸本)9781479979301

Advances in remote sensors are providing exceptional quantities of large-scale data with increasing spatial, spectral and temporal resolutions, raising new challenges in its analysis, e.g. those presents in classification processes. This work presents the architecture of the InterIMAGE Cloud Platform (ICP): Data Mining Package;a tool able to perform supervised classification procedures on huge amounts of data, on a distributed infrastructure. The architecture is implemented on top of the MapReduce framework. The tool has four classification algorithms implemented taken from WEKA's machine learning library, namely: Decision Trees, Naive Bayes, Random Forest and Support Vector Machines. The SVM classifier was applied on datasets of different sizes (2 GB, 4 GB and 10 GB) for different cluster configurations (5, 10, 20, 50 nodes). The results show the tool as a potential approach to parallelize classification processes on big data.

关键词： MapReduce Big Data Classification algorithms Cloud Computing big data ARCHITECTURE Cloud Computing Building construction sorting algorithm Support Vector Network image analysis Remote sensing equipment temporal resolution Decision Trees

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Design and Implementation of Targeting Advertising System based on C4.5 algorithm

Design and Implementation of Targeting Advertising System ba...

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International Conference on Computer Science and Network Technology

作者： Xuefei Wang Yan Shi The Open University of Harbin

ISBN: (纸本)9781467381741

With the development of Big data, cloud storage and data mining, targeting advertising technology has aroused wide attention. So many companies have invested in the target mobile advertising market in this area. Targeting advertising can effectively save resources, reduce capital investment. Aiming at this problem, this paper proposed a new method for targeting advertising based on C4.5 algorithm and cloud storage. The mess data about users, such as user information, user browsing behavior and so on, is stored in the cloud. With C4.5 algorithm, we do mess data processing for data classification. Then, with the results of classification, the server-push blocks push targeted advertising for users. This paper introduces the structure of the system and the detailed analysis of the classification algorithm. And the classification results obtained in this paper are compared with those by other algorithms. It is believed that this paper will be helpful in the development of targeting advertising.

关键词： target advertising Big data C4.5 algorithm big data Advertising as Topic targeting sorting algorithm data classification pushing Cloud Computing paper Consumers

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Abnormal objective recognition in video based on data mining of finance industry

Abnormal objective recognition in video based on data mining...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Zhi-Wang Jiang Hong-Xia Zhang Financial Intelligence Application Technology R&D- Center of Hebei Province Baoding China

ISBN: (纸本)9781467372220

In order to strengthen the financial industry security, this paper puts forward an intelligent monitoring system which has the function of abnormal automatic moving object recognition based on data mining technology. First of all, this paper elaborates the common classification of data mining methods, through the experiment and comparison, and selects associative classification algorithm for mining algorithm; Secondly, based on support and confidence level optimization, the associative classification algorithm is proposed, and simulation experiment shows that this method is effective to overcome the experience in accordance with the values given threshold caused adverse effect; Finally, the intelligent monitoring system consists of data mining system and the user's system. It is not only to achieve the purpose of the intelligent monitoring, but also to achieve a good human-computer interaction.

关键词： Associative classification Confidence level Data mining Object recognition Support data mining object recognition sorting algorithm Videotapes data mining system simulation test Support Associative

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