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6th International Conference on Graphic and Image Processing (ICGIP)

作者： Khalilinezhad, Mandieh Minaei, Behrooz Vernazza, Gianni Dellepiane, Silvana Univ Genoa Dept Naval Engn Elect & Telecommun Genoa Italy Iran Univ Sci & Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9781628415582

Data mining (DM) is the process of discovery knowledge from large databases. Applications of data mining in Blood Transfusion Organizations could be useful for improving the performance of blood donation service. The aim of this research is the prediction of healthiness of blood donors in Blood Transfusion Organization (BTO). For this goal, three famous algorithms such as Decision Tree C4.5, Naive Bayesian classifier, and Support Vector Machine have been chosen and applied to a real database made of 11006 donors. Seven fields such as sex, age, job, education, marital status, type of donor, results of blood tests (doctors' comments and lab results about healthy or unhealthy blood donors) have been selected as input to these algorithms. The results of the three algorithms have been compared and an error cost analysis has been performed. According to this research and the obtained results, the best algorithm with low error cost and high accuracy is SVM. This research helps BTO to realize a model from blood donors in each area in order to predict the healthy blood or unhealthy blood of donors. This research could be useful if used in parallel with laboratory tests to better separate unhealthy blood.

关键词： classification algorithm Blood donor Data mining

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Comparing Several Pixel-based classification Methods for Vegetation Structural Composition Mapping using Sentinel 2A Imagery in Salatiga Area, Central Java 7

Comparing Several Pixel-based Classification Methods for Veg...

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7th Geoinformation Science Symposium

作者： Hadi, Haeydar Anggara Danoedoro, Projo Univ Gadjah Mada Remote Sensing Lab Dept Geog Informat Sci Fac Geog Yogyakarta Indonesia

ISBN: (数字)9781510650411

ISBN: (纸本)9781510650411;9781510650404

Pixel-based classification is considered as a classic method of extracting land-cover related information from remotely sensed imagery, and has been used in various applications, including vegetation mapping. However, several recent studies also mentioned the weakness of the pixel-based approach, including the vegetation index transformation, in mapping the structural composition of vegetation. This study aimed to test several pixel-based classification algorithms for mapping the structural composition of vegetation using Sentinel-2A (10 meters) imagery in Salatiga and its surrounding, Central Java. In this study three classification algorithms, namely Maximum Likelihood, Minimum Distance to Mean, and Support Vector Machine were compared with respect to their accuracy results in mapping the vegetation structural composition. The authors evaluated the effects of additional data in the classification process by comparing two different datasets, i.e. (i) the one using original bands only, and (ii) the one containing original bands and additional data in the form of several vegetation indices and Leaf Area Index (LAI). We collected field samples using stratified random strategy, which were separated into two sub-datasets, as a basis for structural composition classification reference and accuracy assessment. In addition, comparison was also carried out using the original results and the one which was majority filtered. The results showed that the Maximum Likelihood algorithm performed the highest accuracies at a range of 74-86% using a combination of original bands and RVI (Ratio Vegetation Index). The result that was processed using a 5x5 majority filter showed the highest accuracy 86.29%. These results demonstrated that the pixel-based classification of Sentinel 2A imagery using the Maximum Likelihood algorithm could be used to map the structural composition of vegetation in the study area

关键词： Sentinel 2A pixel-based classification vegetation structural composition classification algorithm vegetation index

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Solving customer insurance coverage recommendation problem using a two-stage clustering-classification model

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INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT 2019年第1期14卷 9-19页

作者： Khalili-Damghani, Kaveh Abdi, Farshid Abolmakarem, Shaghayegh Islamic Azad Univ South Tehran Branch Dept Ind Engn Tehran Iran

Insurance coverage recommendation problem (ICRP) in which the most suitable coverage for customers is suggested is an essential issue for an insurance company. ICRP helps insurance companies to give suitable services to their customers. In ICRP, the insurance company tried to mine the features and records of data associated with the customers to suggest them the most economic and fitted insurance plan. The insurance companies have large databases which are considered as a proper infrastructure to analyze, model and predict the customer behavior. In this paper, a two-stage clustering-classification model is proposed to suggest suitable insurance coverage for customers. The first stage addresses a data pre-screening phase and clustering of customers based on the record of insurance coverage. Well-known clustering algorithms are employed. The superior clustering algorithm is selected based on Davies-Bouldin metric. In the second stage, several filter and wrapper methods are implemented to select proper features. The selected features are assumed as inputs of K-nearest neighbor classification algorithm. The proposed approach is applied in a real case study for clustering the customers and recommend insurance coverage. The results show that the model is capable of suggesting suitable insurance coverage based on customers' characteristics. [GRAPHICS] .

关键词： Knowledge engineering consumer behavior modeling data mining clustering algorithm classification algorithm insurance coverage recommendation

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Medical Document classification Based on MeSH

Medical Document Classification Based on MeSH

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47th Annual Hawaii International Conference on System Sciences

作者： Alghoson, Abdullah Muhammad Claremont Grad Univ Claremont CA 91711 USA

ISBN: (纸本)9781479925049

One of the most challenging projects in information systems is extracting information from unstructured texts, including medical document classification. I am developing a classification algorithm that classifies a medical document by analyzing its content and categorizing it under predefined topics from the Medical Subject Headings (MeSH). I collected a corpus of 50 full-text journal articles (N=50) from MEDLINE, which were already indexed by experts based on MeSH. Using natural language processing (NLP), my algorithm classifies the collected articles under MeSH subject headings. I evaluated the algorithm's outcome by measuring its precision and recall of resulting subject headings from the algorithm, comparing results to the actual documents' subject headings. The algorithm classified the articles correctly under 45% to 60% of the actual subject headings and got 40% to 53% of the total subject headings correct. This holds promising solutions for the global health arena to index and classify medical documents expeditiously.

关键词： classification medical information systems natural language processing text analysis MEDLINE MeSH subject heading NLP classification algorithm collected articles document subject heading full-text journal articles global health arena information systems medical document classification medical documents medical subject heading natural language processing unstructured text classification algorithms Indexing Libraries Measurement Medical services Natural language processing MeSH Medical Document classification based on MeSH using MeSH

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Chinese Text classification Review 9

Chinese Text Classification Review

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9th International Conference on Information Technology in Medicine and Education (ITME)

作者： Ma, Yajing Li, Yonghong Zhang, Xiang Wu, Xiaolong Northwest Minzu Univ Minist Educ Key Lab Chinas Ethn Languages & Informat Technol Lanzhou 730000 Gansu Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250300 Shandong Peoples R China

ISBN: (纸本)9781538677438

Text classification technology refers to the process of supervised learning of specified texts based on fixed rules. This paper describes the development process of text classification and its method techniques, including text representation, feature selection and classification algorithms, and draws the basic ideas, advantages and disadvantages of several current mainstream classification techniques.

关键词： Text classification Feature selection Weight calculation classification algorithm

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Short Range LIDAR for Bioagent Detection and classification

Short Range LIDAR for Bioagent Detection and Classification

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Conference on Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XI

作者： Ho, Nicolas Emond, Frederic Babin, Francois Healy, Dave Simard, Jean-Robert Buteau, Sylvie McFee, John E. INO 2740 Einstein St Quebec City PQ G1P 4S4 Canada MDA Richmond BC V6V 2J3 Canada Def Res & Dev Canada Valcartier Quebec City PQ G3J 1X5 Canada Def Res & Dev Canada Suffield Medicine Hat AB T1A 8K6 Canada

ISBN: (纸本)9780819481290

We have developed a small, relatively lightweight and efficient short range (<100 m) LIDAR instrument for remotely detecting harmful bioagents. The system is based on a pulsed, eye-safe, 355 nm laser exciting aerosols which then fluoresce with a typical spectrum. The system makes use of a novel technology for continuously monitoring for the presence of unusual concentrations of bioaerosols at a precise remote location within the monitored area, with response within seconds. Fluorescence is spectrally resolved over 32 channels capable of photon counting. Results show a sensitivity level of 40 ACPLA of Bacillus Globigii, an anthrax simulant, at a distance of 100 m (assumed worst case where 1 ppl = 1 ACPLA) considering particle sizes between 0.5 and 10 mu m, with a geometric mean at 1 um. The apparatus has been tested in the field during three test and evaluation campaigns with multiple bioagents and public security products. Preliminary results show that the system is able to distinguish between harmful bioagents and naturally occurring ones. A classification algorithm was successfully tested with a single type of bioagent;experiments for daytime measurements are discussed.

关键词： Standoff detection biological warfare agents lidar classification algorithm spectroscopy laser induced fluorescence

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Validation and Implementation of Customer classification System using Machine Learning

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MEASUREMENT-INTERDISCIPLINARY RESEARCH AND PERSPECTIVES 2024年第2期22卷 131-140页

作者： Yoon, Hyemin Kim, HyunJin Kim, Sangjin Dong A Univ Dept Management Informat Syst Busan South Korea Dong A Univ Grad Sch Business Adm Dept Digital Finance Busan South Korea

We have maintained the customer grade system that is being implemented to customers with excellent performance through customer segmentation for years. Currently, financial institutions that operate the customer grade system provide similar services based on the score calculation criteria, but the score calculation criteria vary from the financial institution to financial institution. In this study, we create a machine learning prediction model using items and added items that are based on the current customer grade of our bank,- and the purpose is an optimal model that considers the adequacy of existing variables and the validity of additional variables through comparison between models. Using Lasso, Elastic net and Multinomial Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine, we propose that the best model be found and gradually applied to customer grade calculation criteria.

关键词： Customer grade variable selection algorithm classification algorithm machine learning lasso elastic net multinomial logistic regression decision tree random forest support vector machine

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A Review of Crop classification Using Satellite-Based Polarimetric SAR Imagery 7

A Review of Crop Classification Using Satellite-Based Polari...

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7th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)

作者： Sun, Zheng Wang, Di Zhong, Geji Chinese Acad Agr Sci Inst Agr Resources & Reg Planning Beijing Peoples R China

ISBN: (纸本)9781538650394

Crop area information can provide decision support for agricultural production management, and it is an important basis for formulating grain policy and economic plan. Remote sensing has an irreplaceable role in large-scale agricultural monitoring, in which optical remote sensing is greatly influenced by cloud and rain weather. It is often unable to obtain clear and clear optical remote sensing images in the critical period of crop growth, which seriously images the accuracy and timeliness of the classification of crop remote sensing. Synthetic aperture radar (SAR) capable of monitoring ground objects throughout the day and all weather, Polarimetric SAR data also include scattering matrix, geometric details and permittivity information, it is sensitive to the geometry and height of vegetation, which can make up for the lack of optical remote sensing and has unique advantages in crop identification and monitoring. So full polarization SAR has wide application demand and great potential in crop remote sensing monitoring. The effective use of full polarimetric SAR data to classify crops is of great academic and practical value for promoting the greater role of radar technology in national agricultural remote sensing monitoring and agricultural supply side structural reform. This paper summarizes the different data types and polarization decomposition methods of polarization SAR used in crop identification. According to the current research, there are the following deficiencies: first, most of the research subjects are rice, and dry land crops are less studied. Secondly, the identification of dry land crops is not accurate enough, and the average recognition accuracy is less than 85%. Finally, there are few researches on the scattering mechanism, which leads to poor rationality and poor universality. First, determine the scattering mechanism of dry land crops, and use scattering mechanism to improve the accuracy and universality of remote sensing recognition of dryland

关键词： synthetic aperture radar crop classification full polarization classification algorithm

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Implementation of a Power Quality Signal classification System using Wavelet based Energy Distribution and Neural Network

Implementation of a Power Quality Signal Classification Syst...

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Proceedings of the 2015 IEEE International Conference On Power and Advanced Control Engineering ICPACE 2015

作者： Sebastian, Praveen Dsa, Pramod Antony Manipal Inst Technol Dept Elect Engn Manipal 576104 Karnataka India

ISBN: (纸本)9781479983711

This paper presents a classification system based on Wavelet Transform for normal Power Quality disturbances. The parameter used for the classification algorithm was energy distribution of detailed coefficient of Wavelet Transform up to 10 leveles of decomposition. Wavelet Transform parameters are effective in classification of disturbance waveforms. Artificial Neural Network was used for the classification purpose which gave satisfactory results. And the algorithm developed in MATIAB was interfaced with Data Acquisition devices to check its accuracy for online classification purpose.

关键词： Power Quality Wavelet Transform Wavelet Energy classification Artificial Neural Network Online classification classification algorithm

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A novel sensorless 60°-clamping vector classification pwm technique for switching losses reduction in three-phase PFC converters

A novel sensorless 60°-clamping vector classification pwm t...

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15th IEEE Canadian Conference on Electrical and Computer Engineering

作者： Yazdani, D Bakhshai, AR Jain, P Joos, G Isfahan Univ Technol Dept ECE Esfahan Iran

ISBN: (纸本)0780377818

Performance criteria of three-phase PFC converters improve significantly with increasing the switching frequency, and highly depend on the control strategy used This paper introduces a novel sensorless 60degrees-clamping vector classification PWM technique for three-phase PFC converters. The proposed scheme needs no current sensor and extra calculation for identification the highest current carrying phase that must not be switched. In addition, classification algorithm offers exact positioning of the switching instants. with less computational efforts and therefore shorter sampling period and higher switching frequency are possible, when compared with conventional SVM algorithms. In addition, to reduce the switching and conduction losses a novel switching pattern is presented in this paper. Simulation results on PSCAD/EMTDC software program, confirm the validity of the analytical work.

关键词： space vector modulation technique rectifiers classification algorithm

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