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检索条件"任意字段=International Conference of Soft Computing and Pattern Recognition"

共 7589 条记录，以下是1451-1460 订阅

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Comparison of pattern recognition Techniques for Classification of the Acoustics of Loose Gravel

Comparison of Pattern Recognition Techniques for Classificat...

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international conference on soft computing and Machine Intelligence (ISCMI)

作者： Nausheen Saeed Moudud Alam Roger G. Nyberg Mark Dougherty Diala Jomaa Pascal Rebreyend Faculty of Data & Information Sciences Dalarna University Borlänge Sweden School of Information Technology Halmstad University Halmstad Sweden

ISBN: (数字)9781728175591

ISBN: (纸本)9781728175607

Road condition evaluation is a critical part of gravel road maintenance. One of the parameters that are assessed is loose Gravel. An expert does this evaluation by subjectively looking at images taken and written text for deciding on the road condition. This method is labor-intensive and subjected to an error of judgment; therefore, it is not reliable. Road management agencies are looking for more efficient and automated objective measurement methods. In this study, acoustic data of gravel hitting the bottom of the car is used, and the relation between these acoustics and the condition of loose gravel on gravel roads is seen. A novel acoustic classification method based on Ensemble bagged tree (EBT) algorithm is proposed in this study for the classification of loose gravel sounds. The accuracy of the EBT algorithm for Gravel and Nongravel sound classification is found to be 97.5. The detection of the negative classes, i.e., non- gravel detection, is preeminent, which is considerably higher than Boosted Trees, RUSBoosted Tree, Support vector machines (SVM), and decision trees.

关键词： Roads Vegetation Classification algorithms Cameras Maintenance engineering Data collection Standards

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pattern recognition Technique for Synthesis Fractal Petri Nets 6

Pattern Recognition Technique for Synthesis Fractal Petri Ne...

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6th international conference on Control, Decision and Information Technologies (CoDIT)

作者： Semenov, Alexander MAI Moscow Aviat Inst Moscow Russia

ISBN: (纸本)9781728105215

The elasticity principles have become one of the most important features of contemporary distributed systems. This novel paradigm has major consequences on Petri nets modeling. The problem is to decide whether an arbitrary Petri net satisfies the elasticity and how to make it elastic. The problem is solved by synthesis of Fractal Petri nets. Fractal Petri nets based on Iterated Algebraic System which makes possible elastic synthesis by incrementing or decrementing of original Petri net. One of the important tasks is recognition of a subnet which used as a pattern for elastic synthesis. For this purpose evolutionary classification of Petri nets based on subnets synthesis is introduced. And furthermore, algebraic operations replication and composition make elastic synthesis object-oriented. Control of the elasticity over the resources and system behavior is achieved with the use of patterns overlapping technique.

关键词： Petri nets Fractals Elasticity Object oriented modeling Concurrent computing pattern recognition Artificial neural networks

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CoCoNet: A Collaborative Convolutional Network applied to fine-grained bird species classification

CoCoNet: A Collaborative Convolutional Network applied to fi...

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international conference on Image and Vision computing New Zealand, IVCNZ

作者： Tapabrata Chakraborti Brendan McCane Steven Mills Umapada Pal University of Otago Computer Vision and Pattern recognition Unit Indian Statistical Institute

The following topics are dealt with: convolutional neural nets; learning (artificial intelligence); image classification; computer vision; feature extraction; video signal processing; deep learning (artificial intelli... 详细信息

ISBN: (数字)9781728185798

ISBN: (纸本)9781728185804

关键词： Training Visualization Image recognition Collaboration Computer architecture Birds Task analysis

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Automatic Medicinal Plants Classification using Multi-channel Modified Local Gradient pattern with SVM Classifier 8

Automatic Medicinal Plants Classification using Multi-channe...

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Joint 8th international conference on Informatics, Electronics and Vision (ICIEV) / 3rd international conference on Imaging, Vision and pattern recognition (icIVPR) / international conference on Activity and Behavior computing (ABC)

作者： Habiba, Umme Howlader, Md Rasel Islam, Md Aminul Faisal, Rahat Hossain Rahman, Md Mostafijur Univ Barisal Dept Comp Sci & Engn Barisal Bangladesh

ISBN: (纸本)9781728107882

Most of the people in the world rely on traditional medicine which is made from medicinal plants. However, very few works concentrate on automatic classification. Therefore, the automatic classification of medicinal plants demands more investigation which is an important issue for conservation, authentication, and production of medicines. In this paper, for automatically classifying medicinal plants, we present a Multi-channel Modified Local Gradient pattern (MCMLGP), a new texture-based feature descriptor that uses different channels of color images for extracting more significant features to improve the performance of classification. We have trained our proposed approach using SVM classifier with various kernels such as linear, polynomial and HI. In addition, we have used different feature descriptors for comparative experimental analysis with MCMLGP by conducting the rigorous experiment on our own medicinal plants dataset. The proposed approach gain higher accuracy (96.11%) than other techniques, and significantly valuable for exploration and evolution of medicinal plants classification.

关键词： Image Classification Medicinal plant LGP Adaptive Threshold SVM

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Performance evaluation of sensor information fusion system based on cloud theory and fuzzy pattern recognition

Performance evaluation of sensor information fusion system b...

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Information Technology,Big Data and Artificial Intelligence (ICIBA), international conference on IEEE international conference on

作者： Wenming Song Weilu Wen Qiang Guo Hao Chen Jiaqi Zhao AVIC aeronautical radio electronics research Institute Shanghai China College of Information and Communication Engineering Harbin Engineering University Harbin China College of Physics and Optoelectronic Engineering Harbin Engineering University Harbin China

ISBN: (数字)9781728152240

ISBN: (纸本)9781728152257

The sensor information fusion system was composed of complex factors, and there was no unified quantitative standard for each factor. During the system evaluation, the existing evaluation method cannot be applied to all influencing factors, and the result of the evaluation cannot represent the true performance of the system. This paper proposed an evaluation method combining cloud theory and fuzzy pattern recognition model. Based on the cloud theory, the reasonable conversion of qualitative indicators was realized, and the uncertainty of attribute factors was evaluated indefinitely. Combined with the fuzzy pattern recognition model, an evaluation model was established for quantitative indicators, and it was comprehensively processed by fuzzy evaluation method. Evaluation examples show that the method can more accurately and objectively evaluate the performance of the system.

关键词： Indexes Target tracking pattern recognition Cloud computing Radar tracking Eigenvalues and eigenfunctions Target recognition

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9th international conference on Big Data, BigData 2020, held as part of the Services conference Federation, SCF 2020

9th International Conference on Big Data, BigData 2020, held...

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9th international conference on Big Data, BigData 2020, held as part of the Services conference Federation, SCF 2020

ISBN: (纸本)9783030596118

The proceedings contain 18 papers. The special focus in this conference is on Big Data. The topics include: Validating Goal-Oriented Hypotheses of Business Problems Using Machine Learning: An Exploratory Study of Customer Churn;The Collaborative Influence of Multiple Interactions on Successive POI Recommendation;Chemical XAI to Discover Probable Compounds Spaces Based on Mixture of Multiple Mutated Exemplars and Bioassay Existence Ratio;clinical Trials Data Management in the Big Data Era;cross-Cancer Genome Analysis onCancer Classification Using Both Unsupervised and Supervised Approaches;heavy Vehicle Classification Through Deep Learning;spatial Association pattern Mining Using In-Memory Computational Framework;Dissecting Biological Functions for BRCA Genes and Their Targeting MicroRNAs Within Eight Clusters;a Performance Prediction Model for Spark Applications;Predicting the DJIA with News Headlines and Historic Data Using Hybrid Genetic Algorithm/Support Vector Regression and BERT;processing Big Data Across Infrastructures;fake News Classification of Social Media Through Sentiment Analysis;scalable Reference Genome Assembly from Compressed Pan-Genome Index with Spark;a Web Application for Feral Cat recognition Through Deep Learning;MCF: Towards Window-Based Multiple Cuckoo Filter in Stream computing;A Data-Driven Method for Dynamic OD Passenger Flow Matrix Estimation in Urban Metro Systems.

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Evaluation of Neural Network Output Results Reliability in pattern recognition 3rd

Evaluation of Neural Network Output Results Reliability in P...

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3rd international Scientific conference on Intelligent Information Technologies for Industry (IITI)

作者： Marshakov, Daniil V. Galushka, Vasily V. Fathi, Vladimir A. Fathi, Denis V. Don State Tech Univ Rostov Na Donu 344000 Russia

ISBN: (纸本)9783030018184

Artificial neural networks (ANN) are well-known effective parallel systems which have successfully approved themselves in solving of complicated artificial intelligence problems. The practice of the widespread ANN application due to their high efficiency in solving non-formalized or hard-formalized problems associated with the need for ANN training on experimental material particularly in pattern recognition. In solving problems of pattern recognition the feedforward neural network is usually used due to the simplicity of algorithmic implementation, the availability of advanced training methods, the possibilities of multi-parallel computations. When neural network classifier implementing within decision support systems, it is necessary to assess the ANN results reliability based upon interpretation of the output signals to establish trust between users and the neural network algorithm. In this paper the ANN output results reliability evaluation method in terms of the degree of belonging of the recognized patterns to the originally specified classes is considered. The proposed method is based on the computation of Euclidean distance between the actual ANN output vector characterizing the class of the object recognition and a set of sample vectors defining a priori known classes at the training stage followed by its evaluation by the curve construction of the normal probability distribution law coinciding with the Gaussian function. A feature of this method is the construction of an individual probability distribution curve computed for each ANN output vector. An experimental research of the proposed method in MATLAB is presented on the example of solving the known Fisher's Iris Database classification for input data without noise and with noise. The obtained results confirm the adequacy of the proposed method which can be used both in independent neural network pattern recognition systems and within decision support complexes.

关键词： Artificial neural network pattern recognition Classification Reliability

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Fuzzy demand forecast classification and fuzzy pattern recognition for distributed production 18th

Fuzzy demand forecast classification and fuzzy pattern recog...

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18th Online World conference on soft computing in Industrial Applications, WSC 2018

作者： Roller, Dieter Engesser, Erik Institute of Computer-Aided Product Development Systems Universität Stuttgart Universitätsstraße 38 Stuttgart70569 Germany

ISBN: (纸本)9783030006105

Global Corporations have to manage distributed production over the whole world. Therefore global supply chains are needed. This paper discusses the problem how global production plants and their supply chains can be classified. The classification focuses on demand and supply of production and supply chain. The problem of forecasting the demand of a global supply chain is introduced. Objective of the paper is to show solutions of the mentioned problems by using fuzzy classification and fuzzy pattern recognition methods. The approach is to use the classification methods fuzzy c-means (FCM) and Improved Fuzzy Clustering (IFC). Supply and demand patterns can be found with fuzzy pattern recognition. Therefore the methods Multi Feature pattern recognition and Fuzzy Inference System Type-2 (FIS 2) with neural network methods are introduced. The solution of the mentioned approach is realized by the application PROCAS (Process Optimization, Control, Analysis and Simulation). PROCAS uses a data warehouse database for multidimensional fuzzy classification data and Business Intelligence (BI) functionalities. The key result is that fuzzy classification and fuzzy pattern recognition applications improve the planning and operating of supply and demand in a distributed production and a global supply chain. © Springer Nature Switzerland AG 2019.

关键词： Supply chains

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4th international conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems, ICAIECES 2019

4th International Conference on Artificial Intelligence and ...

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4th international conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems, ICAIECES 2019

ISBN: (纸本)9789811501982

The proceedings contain 67 papers. The special focus in this conference is on Artificial Intelligence and Evolutionary Computations in Engineering Systems. The topics include: Development of a Novel Embedded Board: CANduino;Sales Demand Forecasting Using LSTM Network;privacy-Enhanced Emotion recognition Approach for Remote Health Advisory System;an Effective Approach for Plant Monitoring, Classification and Prediction Using IoT and Machine Learning;encrypted Transfer of Confidential Information Using Steganography and Identity Verification Using Face Data;intelligent Wireless Sensor Networks for Precision Agriculture;mining of Removable Closed patterns in Goods Dataset;Comparative Analysis and Performance of DSTATCOM Device Using PI and Second-Order Sliding Mode Control;stock Market Prediction Using Long Short-Term Memory;Dragonfly Algorithm for Optimal Allocation of D-STATCOM in Distribution Systems;plant Disease Identification and Detection Using Support Vector Machines and Artificial Neural Networks;SRF Control Algorithm for Five-Level Cascaded H-Bridge D-STATCOM in Single-Phase Distribution System;a Robust Multi-unit Feature-Level Fusion Framework for Iris Biometrics;providing Data Security in Deep Learning by Using Genomic Procedure;fault Identification in Deadline Sensitive Applications in Cloud computing;web Service Selection Mechanism in Service-Oriented Architecture Based on Publish–Subscribe pattern in Fog Environment;heterogeneous Defect Prediction Using Ensemble Learning Technique;fuzzy Lattice-Based Orthogonal Image Transformation Technique for Natural Image Analysis;DC Smart Grid System for EV Charging Station;PVW: An Efficient Dynamic Symmetric Cipher to Enable End-to-End Secure Communication;smart Monitoring System for Football Players;efficiency Comparison and Analysis of Pseudo-random Generators in Network Security;artificial Intelligence and Cybersecurity: Past, Presence, and Future.

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Multiple Speech Source Separation by Using MVDR for B-Format Recordings 2020

Multiple Speech Source Separation by Using MVDR for B-Format...

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Proceedings of the 2020 9th international conference on computing and pattern recognition

作者： Yitian Jia Maoshen Jia Lu Li Ju Gao Shaoxin Yang Beijing University of Technology Beijing China Beijing-Dublin International College Beijing University of Technology Beijing China

ISBN: (纸本)9781450387835

This paper presents a multiple speech source separation method that adopts the minimum variance distortionless response (MVDR) beamformer to separate speech sources based on the direction-of-arrival (DOA) estimated from the B-format microphone recordings. Unlike conventional microphone arrays, the B-format microphone provides complete spatial information of the pressure gradient and acoustic pressure which is useful to multiple sources localization. Therefore, this paper adopts the single-source points detection of the B-format microphone recordings, where only the dominant source is present in the time-frequency domain. A higher accuracy of source localization is achieved by identifying and utilizing only single-source points which contain correct direction cues. According to the DOA estimates, MVDR beamformer is then adopted for recovering the sources separately. The objective evaluation shows that the proposed method outperforms the existing BSS algorithm.

关键词： Blind source separation B-format microphone minimum variance distortionless response direction of arrival

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