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Advances in Digital Technologies: Proceedings of the 8th international conference on Applications of Digital Information and Web Technologies ICADIWT 2017

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2017年

作者： Jolanta Mizera-Pietraszko R Rodriguez Jorge D M Almazo Prez Pit Pichappan

ISBN: (纸本)9781614997726

Web technology is ubiquitous in modern life, enabling various forms of communication in real time between the users and computers, as well as between network devices, by means of artificial (markup) languages and cascading style sheets (CSS). Multimedia packages implemented in the WWW can also further expand the user groups to include, for example, the amblyopic or the hearing-impaired. According to Microsoft, Web technology also encompasses Web servers and programming languages for building Web applications. But such a breathtaking development that meets dynamically changing new emerging networking standards demands a large-scale infrastructure that will enable us to access digital information in its every form, whatever its purpose. this book presents 20 papers and 3 keynote speeches from the 8th international conference on Applications of Digital Information and Web Technologies (ICADIWT 2017), held at the Universidad Autnoma de Ciudad Jurez, Jurez City, Chihuahua, Mexico, in March 2017. Over the years, the ICADIWT conference has created its own research community of participants from many countries, who attend the event each year to demonstrate and discuss their research findings. the community is growing every year. the scope of the ICADIWT 2017 conference covers a wide range of research areas, and the papers in the book are divided into 7 subject areas: pattern recognition; distributed computing; mobile technologies; digital technologies for aerospace; medical systems applications; system engineering; and control systems.

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Segmentation of microscopic images for pollen grains detection

Segmentation of microscopic images for pollen grains detecti...

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8th international conference of pattern recognition Systems (ICPRS 2017)

作者： E.M.D.S. Santos A.R.S. Marcal Universidade do Porto Porto Porto PT

ISBN: (纸本)9781785616525

A method for the identification of pollen grains in bright-field microscopic images is presented. the method is based on the segmentation of raw microscopic images for the identification of 4 types of pollen grains. the segmentation method developed tackles a major difficulty of the problem - the existence of clustered pollen grains. the method was tested using 44 images with a total of 2266 pollen grains. It is able to provide a good detection for Olea Europea pollen, with a True Detection Rate (TDR) of 0.989, Quercus (TDR of 0.961) and Castanea (TDR of 0.90). For Corylus pollen, the results were not as good, with a TDR of 0.747.

关键词： biology computing botany image recognition image segmentation microscopy Pollen Image recognition biology computing Botany image segmentation time domain reflectometry Microscopy images microprojection

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Automatic classification of seismic signals of the chilean Llaima Volcano using cartesian genetic programming based artificial neural network

Automatic classification of seismic signals of the chilean L...

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8th international conference of pattern recognition Systems (ICPRS 2017)

作者： G. Khattak M. S. Khan G. M. Khan F. Huenupan M. Curilem University of Engineering and Technology Lahore PK Universidad de La Frontera Temuco CL

ISBN: (纸本)9781785616525

Volcanoes are ruptures in the earth's crust unleashing the dormant forces lying buried deep beneath the crust. the present work is an endeavour towards the quest of automatic volcanic event classification. We propose a volcanic event classification system based on Cartesian Genetic Programming based Artificial Neural Network (CGPANN). CGPANN is a technique for generation of ANN networks without any constraints on network size and topology. Two types of volcanic events for the Chilean Llaima volcano, long period (LP), related to pressure in the volcanic ducts occurring at discrete periods, and volcano tectonic (VT), that is due to the rock fracture, are classified in the present work. the system shows over 80% correct classification for unseen events. the current work also attempts to explore the networks generated and features selected in order to gain an insight into the underlying processes.

关键词： Earth genetic algorithms geophysics computing neural nets pattern classification seismic waves seismology topology volcanology Genetic Algorithm (GA) geophysics computing volcanology seismology seismic waves Neural network pattern recognition volcanoes Automatic classification topology topsoil Earth Rock fractures Artificial neural networks volcanic

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Motorcycle classification in urban scenarios using convolutional neural networks for feature extraction

Motorcycle classification in urban scenarios using convoluti...

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8th international conference of pattern recognition Systems (ICPRS 2017)

作者： Jorge E. Espinosa Sergio A. Velastin John W. Branch Facultad de Ingenierías Politécnico Colombiano Jaime Isaza Cadavid - Medellín -Colombia Universidad Carlos III de Madrid Madrid Madrid ES Universidad Nacional de Colombia Bogota CO

ISBN: (纸本)9781785616525

this paper presents a motorcycle classification system for urban scenarios using Convolutional Neural Network (CNN). Significant results on image classification has been achieved using CNNs at the expense of a high computational cost for training with thousands or even millions of examples. Nevertheless, features can be extracted from CNNs already trained. In this work AlexNet, included in the framework CaffeNet, is used to extract features from frames taken on a real urban scenario. the extracted features from the CNN are used to train a support vector machine (SVM) classifier to discriminate motorcycles from other road users. the obtained results show a mean accuracy of 99.40% and 99.29% on a classification task of three and five classes respectively. Further experiments are performed on a validation set of images showing a satisfactory classification.

关键词： feature extraction image classification motorcycles neural nets support vector machines traffic engineering computing Feature extraction Motorcycles Neural network Image classification Support Vector Network engineering computing Taxonomy Road users

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Fast case-based reasoning for large-scale streaming classification

Fast case-based reasoning for large-scale streaming classifi...

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8th international conference of pattern recognition Systems (ICPRS 2017)

作者： S. Ramírez-Gallego B. Krawczyk S. García M. Woźniak J.M. Benítez F. Herrera Universidad de Granada Granada AndalucÃa ES Politechnika Wroclawska Wroclaw PL

ISBN: (纸本)9781785616525

Processing high-speed big data streams is one of the most challenging tasks in machine learning nowadays. To deal with these unbounded and massive amounts of data, highly efficient methods that continuously update their structure are required. this paper aims at presenting a new incremental and distributed lazy classifier, and a distributed instance selection technique that efficiently process real-world data streams. Both algorithms have been implemented in a single system by using the Apache Spark platform. thanks to this original design, the high computational requirements of standard lazy classifiers have been alleviated. A thorough experimental framework has been conducted on a set of big datasets, both artificial and real. Our study show the usefulness of our solutions and show that casebased reasoning can perform as a competitive option in largescale streaming environments.

关键词： Machine learning Data streams Big data In-stance reduction Nearest neighbor Distributed computing Apache Spark big data Distributed computing Nearest neighbor data flow Case Based Reasoning Artificial Intelligence Machine Learning Streaming

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New deep convolutional neural network architecture for pedestrian detection

New deep convolutional neural network architecture for pedes...

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8th international conference of pattern recognition Systems (ICPRS 2017)

作者： Carlos Ismael Orozco María Elena Buemi Julio Jacobo Berlles Universidad Nacional de Salta Salta AR Universidad de Buenos Aires Buenos Aires AR

ISBN: (纸本)9781785616525

Pedestrian detection is currently a topic of interest in computer vision due to its applications such as driver assistance systems and surveillance in public spaces, among others. the good results obtained using deep convolutional networks in vision tasks make them an attractive tool to improve the capacities of pedestrian detection systems. In this work we propose a deep convolutional network architecture to classify as pedestrian or non-pedestrian the candidate regions previously generated using a simple pyramidal sliding window approach. A distintive characteristic of the CNN in this system is that it separates pedestrian from non-pedestrian images without the aid of a pre-classification stage, and without the need of special tuning steps or initials conditions, making it more straightforward than other CNN-based *** data used for training and testing come from the Caltech-USA Pedestrian dataset [8, 9]. We have evaluated the classification results on the proposed architecture and have obtained an average of ~ 98% success for the validation set. We have also carried out another evaluation of our system using the Benchmark proposed by Dollάr ***. [9], and obtained results that are competitive with those mentioned in the bibliography.

关键词： computer vision driver information systems image classification neural nets object detection pedestrians traffic engineering computing Pedestrians driver information systems images computer vision Neural network Object detection Image classification NETWORK STRUCTURE engineering computing neural net architecture sort phase Driver Assistance Systems

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A blood orange computer vision sorting system

A blood orange computer vision sorting system

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8th international conference of pattern recognition Systems (ICPRS 2017)

作者： Sajad Sabzi Yousef Abbaspour-Gilandeh Juan Ignacio Arribas University of Mohaghegh Ardabili Ardabil Ardabil IR Universidad de Valladolid Valladolid Castilla y LeÃ³n ES

ISBN: (纸本)9781785616525

To have a proper sorting system with a low error rate can be very useful in automatic packaging of products. Furthermore, physical dimensions and shape are important in sorting and sizing of fruits and vegetables. In this paper Iranian orange (blood orange) are considered to present an automatic mass sorting system with low error rate using image processing coupled with an adaptive neurofuzzy inference system (ANFIS). Linear regression analysis was used to compare results and an efficient algorithm was designed and implemented in MatLab. this algorithm is able to measure area, eccentricity, perimeter, length/area, red, green, and blue RGB components, width, contrast, texture, width/area, width/length, roughness and length. In ANFIS model, samples were divided into two sets: 70% for training and 30% for testing. Best ANFIS, linear and nonlinear regression models, yielded values of the coefficient of determination (R2), sum squared error (SSE), and mean squared error (MSE) of 0.989, 21.46, 1.65 (ANFIS), 0.91, 1156.69, 12.05 (linear) and 0.88, 1538.10, 15.86 (nonlinear), respectively. Based on results, ANFIS model showed clearly better capability for mass prediction compared to both linear and nonlinear regression. A prototype for an automatic non-intrusive orange mass sorting system is depicted to conclude.

关键词： computer vision food products fuzzy reasoning image colour analysis materials handling neural nets production engineering computing regression analysis production engineering computing materials handling image colour analysis images computer vision Neural network fuzzy reasoning Error analysis sorting system Food regression analysis Nonlinear Regression

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Semantic Aware Online Detection of Resource Anomalies on the Cloud 8

Semantic Aware Online Detection of Resource Anomalies on the...

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8th IEEE international conference on Cloud computing Technology and Science (CloudCom)

作者： Bhattacharyya, Arnamoy Jandaghi, Seyed Ali Jokar Sotiriadis, Stelios Amza, Cristiana Univ Toronto Dept Elect & Comp Engn Toronto ON Canada

ISBN: (纸本)9781509014453

As cloud based platforms become more popular, it becomes an essential task for the cloud administrator to efficiently manage the costly hardware resources in the cloud environment. Prompt action should be taken whenever hardware resources are faulty, or configured and utilized in a way that causes application performance degradation, hence poor quality of service. In this paper, we propose a semantic aware technique based on neural network learning and pattern recognition in order to provide automated, real-time support for resource anomaly detection. We incorporate application semantics to narrow down the scope of the learning and detection phase, thus enabling our machine learning technique to work at a very low overhead when executed online. As our method runs "life-long" on monitored resource usage on the cloud, in case of wrong prediction, we can leverage administrator feedback to improve prediction on future runs. this feedback directed scheme with the attached context helps us to achieve an anomaly detection accuracy of as high as 98.3% in our experimental evaluation, and can be easily used in conjunction with other anomaly detection techniques for the cloud.

关键词： pattern recognition

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Intelligent Decision Technologies 2016 1st ed. 2016

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丛书名： Smart Innovation, Systems and Technologies

2016年

作者： Ireneusz Czarnowski Alfonso Mateos Caballero Robert J. Howlett Lakhmi C. Jain

ISBN: (数字)9783319396279

ISBN: (纸本)9783319396262

the KES-IDT-2016 proceedings give an excellent insight into recent research, both theoretical and applied, in the field of intelligent decision making. the range of topics explored is wide, and covers methods of grouping, classification, prediction, decision support, modelling and many more in such areas as finance, linguistics, medicine, management and transportation. this proceedings contain several sections devoted to specific topics, such as: Specialized Decision Techniques for Data Mining, Transportation and Project Management pattern recognition for Decision Making Systems New Advances of Soft computing in Industrial and Management Engineering Recent Advances in Fuzzy Systems Intelligent Data Analysis and Applications Reasoning-based Intelligent Systems Intelligent Methods for Eye Movement Data Processing and Analysis Intelligent Decision Technologies for Water Resources Management Intelligent Decision Making for Uncertain Unstructured Big Data Decision Making theory for Economics Interdisciplinary Approaches in Business Intelligence Research and Practice pattern recognition in Audio and Speech Processing the KES-IDT conference is a well-established international annual conference, interdisciplinary in nature. these two volumes of proceedings form an excellent account of the latest results and outcomes of recent research in this leading-edge area.

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Intelligent Decision Technologies 2016 1st ed. 2016

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丛书名： Smart Innovation, Systems and Technologies

2016年

作者： Ireneusz Czarnowski Alfonso Mateos Caballero Robert J. Howlett Lakhmi C. Jain

ISBN: (数字)9783319396309

ISBN: (纸本)9783319396293

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