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Identification of Different Concentrations Pesticide Residues of Dimethoate on Spinach Leaves by Hyperspectral Image Technology

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IFAC-PapersOnLine 2018年第17期51卷 758-763页

作者： Zhan-qi, R.E.N. Zhen-hong, R.A.O. Hai-yan, J.I. Key Laboratory of Modern Precision Agriculture System Integration Research Ministry of Education China Agricultural University Beijing100083 China College of Science China Agricultural University Beijing100083 China

Taking spinach leaves with different concentrations of dimethoate as the research object, the feasibility of identifying spinach leaves containing different concentrations of dimethoate pesticide residues based on hyperspectral imaging and machine learning algorithms was discussed. The band range of hyperspectral images between 900 to 1700mm were scanned by hyperspectral imaging system. The region of interest (ROIs) of the leaves were selected by ENVI and corrected by multiple scatter correction (MSC). The principal component analysis (PCA) was used to analyze the spectral data, and the results showed that the PCA can effectively discriminate spinach samples with different concentrations at the visual level. In addition, the chi-square test feature selection algorithm was combined with support vectors classification (SVC), K nearest neighbor (KNN), random forest algorithm (RF), and linear discriminant analysis (LDA) respectively. The average and standard deviation of the prediction accuracy of the 10-fold cross-validation was chosen as evaluation methods. By comparison, chi-square test combined with LDA was the optimal model and the selected characteristic wavelengths were 1445.8, 1449, 1452.3, 1455.5, 1458.7, 1462, 1465.2 and 1468.4nm. The prediction accuracy and standard deviation of the model were 0.997, 0.008. The results showed that spinach leaves containing different concentrations of dimethoate pesticide residues could be accurately identified based on hyperspectral imaging. © 2018

关键词： Hyperspectral imaging Decision trees Discriminant analysis Image segmentation Independent component analysis Learning algorithms Learning systems Nearest neighbor search Pesticides Principal component analysis Spectroscopy Statistical tests Statistics 10 fold cross validation Dimethoate feature selection algorithm K nearest neighbor (KNN) Linear discriminant analyses (LDA) Pesticide residue Random forest algorithm Spinach leaves

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Data Mining Based Advanced algorithm for Intrusion Detections in Communication Networks 1

Data Mining Based Advanced Algorithm for Intrusion Detection...

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1st International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS)

作者： Bhosale, Karuna S. Nenova, Maria Iliev, Georgi Tech Univ Sofia Fac Telecommun Sofia Bulgaria

ISBN: (纸本)9781538677094

Nowadays the network security is the important topic to research. The Network Security from different types of attacks which is R2L, U2R, and DoS. It is very challenging tasks due to variety of research problems like noise, large data size, inefficient features selection method etc. Network Intrusion Detection System (IDS), as the basic security protection technique, is generally used limiting such malicious attacks. In this project, we are presenting the efficient IDS solution using filter based feature choice strategy. We are exhibiting the hybrid feature determination method. The Intrusion Detection System (IDS) examine the main part in distortion and ambushes in the framework. In this examination work, data mining methods unite with association rule features extraction and classifier. In this paper, we proposed filter based hybrid feature selection algorithm (HFSA), most relevant features are retained and used to construct classifiers for respective classes. In this system it is worked on the real time packets, which is captured using the Jpcap library. Along with HFSA method, first we are contributing approach of detecting the cyber-attack brute force by modifying the algorithms of HFSA and classification. For the classification we used the Naive bayes classifier. The performance of proposed method shows the efficiency compared to other methods.

关键词： Intrusion detection Data Mining Classifier feature selection algorithm communication networks

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Investigation of frame-to-frame back projection and feature selection algorithms for nonline-of-sight laser gated viewing 8

Investigation of frame-to-frame back projection and feature ...

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Conference on Electro-Optical Remote Sensing, Photonic Technologies, and Applications VIII and Military Applications in Hyperspectral Imaging and High Spatial Resolution Sensing II

作者： Laurenzis, Martin Velten, Andreas French German Res Inst St Louis St Louis France Univ Wisconsin Lab Opt & Computat Instrumentat Madison WI 53705 USA Morphol Inst Res Madison WI USA

ISBN: (纸本)9781628413137

In the present paper, we discuss new approaches to analyze laser gated viewing data for non-line-of-sight vision with a novel frame-to-frame back projection as well as feature selection algorithms. While first back projection approaches use time transients for each pixel, our new method has the ability to calculate the projection of imaging data on the obscured voxel space for each frame. Further, four different data analysis algorithms were studied with the aim to identify and select signals from different target positions. A slight modi fication of commonly used filters leads to powerful selection of local maximum values. It is demonstrated that the choice of the filter has impact on the selectivity i.e. multiple target detection as well as on the localization precision.

关键词： Non-line-of-sight vision laser gated viewing feature selection algorithm see around the corner

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Test on existence of histology subtype-specific prognostic signatures among early stage lung adenocarcinoma and squamous cell carcinoma patients using a Cox-model based filter

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BIOLOGY DIRECT 2015年第1期10卷 1-17页

作者： Tian, Suyan Wang, Chi An, Ming-Wen Jilin Univ Hosp 1 Div Clin Epidemiol Changchun 130021 Jilin Peoples R China Univ Kentucky Dept Biostat Lexington KY 40536 USA Univ Kentucky Markey Canc Ctr Lexington KY 40536 USA Vassar Coll Dept Math Poughkeepsie NY 12604 USA

Background: Non-small cell lung cancer (NSCLC) is the predominant histological type of lung cancer, accounting for up to 85% of cases. Disease stage is commonly used to determine adjuvant treatment eligibility of NSCLC patients, however, it is an imprecise predictor of the prognosis of an individual patient. Currently, many researchers resort to microarray technology for identifying relevant genetic prognostic markers, with particular attention on trimming or extending a Cox regression model. Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are two major histology subtypes of NSCLC. It has been demonstrated that fundamental differences exist in their underlying mechanisms, which motivated us to postulate the existence of specific genes related to the prognosis of each histology subtype. Results: In this article, we propose a simple filter feature selection algorithm with a Cox regression model as the base. Applying this method to real-world microarray data identifies a histology-specific prognostic gene signature. Furthermore, the resulting 32-gene (32/12 for AC/SCC) prognostic signature for early-stage AC and SCC samples has superior predictive ability relative to two relevant prognostic signatures, and has comparable performance with signatures obtained by applying two state-of-the art algorithms separately to AC and SCC samples. Conclusions: Our proposal is conceptually simple, and straightforward to implement. Furthermore, it can be easily adapted and applied to a range of other research settings.

关键词： Non-small cell lung cancer (NSCLC) Adenocarcinoma (AC) Squamous cell carcinoma (SCC) Cox model Prognosis Histology-subtype specific Gene expression barcode feature selection algorithm

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A Framework for Privacy Information Protection on Android

A Framework for Privacy Information Protection on Android

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International Conference on Computing, Networking and Communications (ICNC)

作者： Jia, Peng He, Xiang Liu, Liang Gu, Binjie Fang, Yong Sichuan Univ Coll Elect & Informat Engn Chengdu Peoples R China

ISBN: (纸本)9781479969593

Permissions-based security model of Android increasingly shows its vulnerability in protecting users' privacy information. According to the permissions-based security model, an application should have the appropriate permissions before gaining various resources (including data and hardware) in the phone. This model can only restrict an application to access system resources without appropriate permissions, but can not prevent malicious accesses to privacy data after the application having obtained permissions. During the installation of an application, the system will prompt what permissions the application is requesting. Users have no choice but to allow all the requested permissions if they want to use the application. Once an application is successfully installed, the system is unable to control its behavior dynamically, and at this time the application can obtain privacy information and send them out without the acknowledgements of users. Therefore, there is a great security risk of the permissions-based security model. This paper researches on different ways to access users` privacy information and proposes a framework named PriGuard for dynamically protecting users' privacy information based on Binder communication interception technology and feature selection algorithm. Applications customarily call system services remotely by using the Binder mechanism, then access the equipment and obtain information through system services. By redirecting the Binder interface function of Native layer, PriGuard intercepts Binder messages, as a result, intercepting the application's Remote Procedure Call (RPC) for system services, then it can dynamically monitor the application's behaviors that access privacy information. In this paper, we collect many different types of benign Application Package File (APK) samples, and get the Application Programming Interface (API) calls of each sample when it is running. Afterwards we transform these API calls of each sample into feature

关键词： privacy protection binder RPC intercept android feature selection algorithm

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Prediction Model of Human Body Composition Based on Physiological Information Entropy 8

Prediction Model of Human Body Composition Based on Physiolo...

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8th International Conference on BioMedical Engineering and Informatics (BMEI)

作者： Chen, Bo Gao, Xiu-e Zhang, Aiqiang Dalian Univ Coll Informat Engn Dalian Peoples R China

ISBN: (纸本)9781509000227

Body composition analysis can not only reflect the state of health, but also can play a role in disease prevention. Aiming at many influencing factors, complex modeling issues of the existing bioelectrical impedance analysis algorithms, this paper draws information entropy theory into modeling the human body composition for the first time, establishes entropy evaluation criteria of physiological characteristic parameters, puts forward feature selection algorithm based on physiological information entropy, selects a reasonable subset of features that can most effectively interpret body physiological information and have a minimal number of features to give the body composition prediction fitted model. Experimental results show that the algorithm can select the useful characteristic parameters and the fitted model improves the accuracy of body composition prediction.

关键词： body composition physiological information entropy feature selection algorithm fitted model

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feature selection and back-projection algorithms for nonline-of-sight laser-gated viewing

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JOURNAL OF ELECTRONIC IMAGING 2014年第6期23卷 63003.1-63003.6页

作者： Laurenzis, Martin Velten, Andreas French German Res Inst St Louis F-68301 St Louis France Univ Wisconsin Lab Opt & Computat Instrumentat Madison WI 53706 USA Morgridge Inst Res Madison WI 53715 USA

We discuss new approaches to analyze laser-gated viewing data for nonline-of-sight vision with a frame-to-frame back-projection as well as feature selection algorithms. Although first back-projection approaches use time transients for each pixel, our method has the ability to calculate the projection of imaging data on the voxel space for each frame. Further, different data analysis algorithms and their sequential application were studied with the aim of identifying and selecting signals from different target positions. A slight modification of commonly used filters leads to a powerful selection of local maximum values. It is demonstrated that the choice of the filter has an impact on the selectivity i.e., multiple target detection as well as on the localization precision. (C) 2014 SPIE and IS&T

关键词： nonline-of-sight vision laser-gated viewing feature selection algorithm see around the corner

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A Novel feature selection Method Based on an Integrated Data Envelopment Analysis and Entropy Model

A Novel Feature Selection Method Based on an Integrated Data...

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2nd International Conference on Information Technology and Quantitative Management (ITQM)

作者： Bamakan, Seyed Mojtaba Hosseini Gholami, Peyman Univ Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100090 Peoples R China Mellat Bank Iran TABM Dept Collect Co Tehran Iran

Data mining is a one of the growing sciences in the world that can play a competitive advantages rule in many firms. Data mining algorithms based on their functions can be divided in four categories;Classification, feature selection, Assassination rules and Clustering. One of the most important of these functions is feature selection which has been increasingly developed and many researchers provide variety of algorithms to deal with this function in recent years. feature selection algorithms mostly used for obtaining more precise and strong machine learning algorithms along with reducing the computation time. Another growing science is Multiple Criteria Decision Making techniques witch it also has variety of methods. In this paper, we use both Data Envelopment Analysis which is a useful technique for determining the efficiency of decision-making units and Entropy method which its function is weighting the criteria to selecting the appropriate features. Hence, our novel integrated method has been analyzed by implementing in a testing environment and we apply it on three datasets of UCI's datasets, so the result showed our innovated approach has comparable accuracy with the other feature selections algorithms. (C) 2014 Published by Elsevier B.V. Open access under CC BY-NC-ND license.

关键词： Data mining feature selection algorithm Entropy Data Envelopment Analysis Classification

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A Novel feature selection Method based on an Integrated Data Envelopment Analysis and Entropy Model

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Procedia Computer Science 2014年 31卷 632-638页

作者： Seyed Mojtaba Hosseini Bamakan Peyman Gholami Research Center of Fictitious Economy & Data Science University of Chinese Academy of Sciences Beijing 100090 China TABM (Dept Collection Company Affiliated to Mellat Bank of Iran)

Data mining is a one of the growing sciences in the world that can play a competitive advantages rule in many firms. Data mining algorithms based on their functions can be divided in four categories; Classification, feature selection, Assassination rules and Clustering. One of the most important of these functions is feature selection which has been increasingly developed and many researchers provide variety of algorithms to deal with this function in recent years. feature selection algorithms mostly used for obtaining more precise and strong machine learning algorithms along with reducing the computation time. Another growing science is Multiple Criteria Decision Making techniques witch it also has variety of methods. In this paper, we use both Data Envelopment Analysis which is a useful technique for determining the efficiency of decision-making units and Entropy method which its function is weighting the criteria to selecting the appropriate features. Hence, our novel integrated method has been analyzed by implementing in a testing environment and we apply it on three datasets of UCI's datasets, so the result showed our innovated approach has comparable accuracy with the other feature selections algorithms.

关键词： Data mining feature selection algorithm Entropy Data Envelopment Analysis Classification

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feature selection algorithm in Classification Learning Using Support Vector Machines

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COMPUTATIONAL MATHEMATICS AND MATHEMATICAL PHYSICS 2008年第7期48卷 1243-1260页

作者： Goncharov, Yu. V. Muchnik, I. B. Shvartser, L. V. Russian Acad Sci Dorodnicyn Comp Ctr Moscow 119333 Russia Rutgers State Univ New Brunswick NJ 08903 USA Ness Technol Atidim IL-61581 Tel Aviv Israel

An algorithm for selecting features in the classification learning problem is considered. The algorithm is based on a modification of the standard criterion used in the support vector machine method. The new criterion adds to the standard criterion a penalty function that depends on the selected features. The solution of the problem is reduced to finding the minimax of a convex-concave function. As a result, the initial set of features is decomposed into three classes-unconditionally selected, weighted selected, and eliminated features.

关键词： feature selection algorithm classification learning support vector machine saddle point searching algorithm

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