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检索条件"任意字段=6th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2014"

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6th international conference on soft computing and pattern recognition, socpar 2014

6th International Conference on Soft Computing and Pattern R...

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6th international conference on soft computing and pattern recognition, socpar 2014

ISBN: (纸本)9781479959341

the proceedings contain 81 papers. the topics discussed include: variable hierarchical dependencies in feature selection;bi-spectrum based-EMD applied to the non- stationary vibration signals for bearing faults diagnosis;wireless landmines tracking system based on GPS and GPRS;performance of curvelets, dual-tree complex wavelet and discrete wavelet transform in handwritten word classification;possibility theory for supervised classification of remotely sensed images: a study case in an urban area in Algeria;proposition of a classification system beta-LS-SVM and its application to medical data sets;topological and textural features for off-line signature verification based on artificial immune algorithm;supervised wavelet-network based fuzzy-logic classifier performance on the UCI databases;scintigraphic image segmentation based on grammatical inference and spiral matrix;and microcalcifications detection in mammograms based on ant colony optimization and Markov random field.

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Proceedings of the 13th international conference on soft computing and pattern recognition (socpar 2021) 1st ed. 2022

Proceedings of the 13th International Conference on Soft Com...

引用

丛书名： Lecture Notes in Networks and Systems

2022年

作者： Ajith Abraham Andries Engelbrecht Fabio Scotti Niketa Gandhi Pooja Manghirmalani Mishra Giancarlo Fortino Virgilijus Sakalauskas Sabri Pllana

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Hybrid soft computing Methods for Prediction of Oil Prices 6

Hybrid Soft Computing Methods for Prediction of Oil Prices

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6th international conference on soft computing and pattern recognition (socpar)

作者： Gabralla, Lubna A. Abraham, Ajith Sudan Univ Sci & Technol Fac Comp Sci & Informat Technol Khartoum Sudan Machine Intelligence Res Labs Auburn WA USA VSB Tech Univ Ostrava IT4Innovat Ostrava Czech Republic

ISBN: (纸本)9781479959341

this paper aims to provide combination of multiple prediction models using different strategies including ensemble selection, voting, stacking and multi-schemes to design a model capable of predicting oil prices accurately. Daily data from 1999 to 2012 with 14 variables were used, which were further divided into 10 sub-datasets according to various attribute selection methods. Four groups of training and testing were examined. Experimental results conclude that performance of the combination model works better than author's previous work and ensemble selection outperforms other combination methods.

关键词： ensemble selection vote stacking multi-scheme prediction oil prices

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An automated method for breast mass segmentation 6

An automated method for breast mass segmentation

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6th international conference on soft computing and pattern recognition (socpar)

作者： Khouaja, Sourour Jlassi, Hajer Hamrouni, Kamel Natl Engn Sch Tunis Res Unit Signal Image & Pattern Recognit Tunis Tunisia

ISBN: (纸本)9781479959341

Breast cancer continues to be a significant health problem in the world. the most familiar breast anomalies types are mass and microcalcification. However Automatic methods for detecting these abnormalities can identify breast cancer at an early stage. In this paper, we propose a marker-controlled watershed algorithm to locate breast masses. the preprocessing step has been introduced to remove all undesirable areas from mammogram. Foreground and background markers are then selected in order to apply a watershed segmentation algorithm that identifies the location of tumor region in mammogram. the proposed method was successful to segment mass anomalies. It has been tested on publicly available Mammographic Image Analysis Society (MIAS) database and it has achieved an overall mass detection rate of 90.83% and an area Az of 0.913 under the receiver operating characteristic curve ROC for mass segmentation.

关键词： Mammogram mass segmentation pectoral muscle watershed

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NEW HUMAN BODY SHAPE DESCRIPTOR BASED ON ANthROPOMETRICS POINTS 6

NEW HUMAN BODY SHAPE DESCRIPTOR BASED ON ANTHROPOMETRICS POI...

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6th international conference on soft computing and pattern recognition (socpar)

作者： Hamad, Moez thomassey, Sebastien Bruniaux, Pascal Univ Lille North France GEMTEX ENSAIT Lille France

ISBN: (纸本)9781479959341

this paper will focus on the issue of human body dissimilarity detection from 3D bodyscan. A new 3D human body shape descriptor is proposed as well as a global geometric shape analysis of body shape surfaces coupled with anthropometrics points. the aim of this research is then to establish a new methodology of human body morphology shapes detection in order to define the morphotypes of a given population. A computation of the geodesic distributions based on anthropometrics feature points for human torso provides quantitative information about their similarities. the Euclidean distance is the metric used for the comparison of the shape descriptors. the k-means clustering technique is then implemented to define the most relevant morphologies. Our methodology is then evaluated on 3D scan database of 53 female. the study may be attracted for further researchers from several research communities including pattern recognition, computer graphics, computer vision, anthropometry, human morphology, data analysis and mass customization.

关键词： 3D shape descriptor body scanner 3D CAD system pattern recognition geodesic distributions

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A Composite Methodology for Supporting Collaboration pattern Discovery via Semantic Enrichment and Multidimensional Analysis 6

A Composite Methodology for Supporting Collaboration Pattern...

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6th international conference on soft computing and pattern recognition (socpar)

作者： Cuzzocrea, Alfredo Diamantini, Claudia Genga, Laura Potena, Domenico Storti, Emanuele CNR ICAR Via P Bucci 41C I-87036 Arcavacata Di Rende Italy Univ Calabria I-87036 Arcavacata Di Rende Italy Univ Politecn Marche Dipartimento Ingn Informaz I-60131 Ancona Italy

ISBN: (纸本)9781479959341

Classical process discovery approaches usually investigate logs generated by processes in order to mine and discovery corresponding process schemas. When the collaboration processes case is addressed, such approaches turn to be poorly effective, due to the fact that: (i) logs of collaboration processes are usually stored in heterogenous data storages which also expose different data types;(ii) it is not easy and direct to derive a common analysis model from such logs. As a consequence, classical methodologies usually fail. In order to fulfill this gap, in this paper we describe a composite methodology that combines semantics-based techniques and multidimensional analysis paradigms to support effective and efficient collaboration process discovery from log data.

关键词： data mining pattern recognition semantic networks collaboration pattern discovery collaboration process log storage common analysis model composite methodology data types heterogeneous data storage log generation multidimensional analysis process discovery approach process schema discovery process schema mining semantic enrichment semantic-based techniques Buildings Collaboration Context Data mining Electronic mail Semantics Taxonomy multi-dimensional analysis data mining data types semantic networks electronic mail Semantics Buildings pattern recognition logs

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Hardware-Oriented Succinct-Data-Structure based on Block-Size-Constrained Compression 7

Hardware-Oriented Succinct-Data-Structure based on Block-Siz...

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7th international conference of soft computing and pattern recognition (socpar)

作者： Waidyasooriya, Hasitha Muthumala Ono, Daisuke Hariyama, Masanori Tohoku Univ Grad Sch Informat Sci Aoba Ku Aoba 6-6-05 Sendai Miyagi 9808579 Japan

ISBN: (纸本)9781467393607

Succinct data structures are introduced to efficiently solve a given problem while representing the data using as little space as possible. However, the full potential of the succinct data structures have not been utilized in software-based implementations due to the large storage size and the memory access bottleneck. this paper proposes a hardware-oriented data compression method to reduce the storage space without increasing the processing time. We use a parallel processing architecture to reduce the decompression overhead. According to the evaluation, we can compress the data by 37.5% and still have fast data access with small decompression overhead.

关键词： Succinct data structures data compression text-search FPGA big-data

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Image encryption scheme for secure digital images based on 3D cat map and Turing machine 7

Image encryption scheme for secure digital images based on 3...

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7th international conference of soft computing and pattern recognition, socpar 2015

作者： Mohamed, Nehal A. El-Azeim, Mostafa A. Zaghloul, Alaa El-Latif, Ahmed A. Abd Dept. of Computer Science Faculty of Information Technology MUST University 6th October Egypt Dept. of Computer Science Faculty of Information Technology AASTMT Heliopolis Egypt Dept. of Mathematics Faculty of Science Menoufia University Shebin El-Koom Egypt

ISBN: (纸本)9781467393607

In present time, in order to provide security of multimedia data while transmission and storage processes, the protection of image data can be accomplished using encryption. this paper presents a new image encryption scheme relaying on a chaotic 3D cat map and Turing machine in the form of dynamic random growth technique. the proposed technique composed of two processes of pixels' locations confusion using a chaotic 3D cat map, which is performed concurrently with substituting values swapping pixels' locations using Turing machine. the generated key is dependent on the plain image, to resist the chosen plaintext attack. Both experimental and security analysis show that the presented technique can achieve a large key space and resist the common against cipher attacks. these good cryptographic advantages make it suitable for image transmission over network. © 2015 IEEE.

关键词： Pixels

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Hybrid soft computing methods for prediction of oil prices

Hybrid soft computing methods for prediction of oil prices

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international conference of soft computing and pattern recognition (socpar)

作者： Lubna A. Gabrall Ajith Abraham Sudan University of Science and Technology Khartoum Khartoum SD Machine Intelligence Research Labs WA USA

关键词： Predictive models Biological system modeling Stacking Data models Indexes Computational modeling Training

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Test System for Defect Detection in Construction Materials with Ultrasonic Waves by Support Vector Machine and Neural Network

Test System for Defect Detection in Construction Materials w...

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6th international conference on soft computing and Intelligent Systems (SCIS) / 13th international Symposium on Advanced Intelligence Systems (ISIS)

作者： Saechai, Saowanee Kongprawechnon, Waree Sahamitmongkol, Raktipong Thammasat Univ Sirindhorn Int Inst Technol Sch Informat Comp & Commun Technol Pathum Thani Thailand Thammasat Univ Sirindhorn Int Inst Technol Construct & Maintenance Technol Res Ctr Pathum Thani Thailand

ISBN: (纸本)9781467327435;9781467327428

this paper introduces a newly developed test system for structure inspection and evaluation of cement-based products by applying ultrasonic test with support vector machine (SVM) classifier. In other words, this paper represents a novel method based on SVM for defect detection, classification of number of defects, and identification of defect materials. With the system, pattern of ultrasonic waves for each case of specimen can be obtained from direct and indirect measurements. Machine learning algorithm called support vector machine and artificial neural network (ANN) are employed for classification and verification of the wave patterns obtained from different samples. By applying the system, the presence or absence of a defect in mortar can be identified. Moreover, the system can also classify the number of defects and identify the defect materials being inside the mortar. For classification, input features are extracted in different ways and the numbers of training sets are varied. Base on the results from SVM, the signals extracted in frequency domain gives better performance than time domain. Using a larger training set can give more satisfactory results. In this article, the methodology is explained and the classification results are discussed. the effectiveness of the developed test system is evaluated. Comparison of the classification results that obtained by between SVM and ANN classifiers is also demonstrated. this study shows that this technique based on pattern recognition has a high potential for practical inspection of concrete structure.

关键词： ultrasonic pulse velocity defect detection pattern recognition support vector machine neural network

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