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1st international Informatics and Software Engineering conference, IISEC 2019

作者： Kutlu, Ozgur Demir, Onder Dogan, Baris Marmara Üniversitesi Fen Bilimleri Enstitüsü Istanbul Turkey Marmara Üniversitesi Teknoloji Fakültesi Bilgisayar Mühendisliǧi Istanbul Turkey Marmara Üniversitesi Teknoloji Fakültesi Mekatronik Mühendisliǧi Istanbul Turkey

ISBN: (纸本)9781728139920

In artificial intelligence applications, many sub-methods such as machine learning, artificial neural networks, classification, clustering algorithms are used. One of these methods is deep learning. Deep learning is an advanced machine learning class. Using the Deep learning method, video analysis, image classification, speech recognition and natural language processing are very successful. The data and experiences to be provided by the projects covering Deep learning and Unmanned Aerial Vehicles will increase the number of qualified studies on these issues and contribute to the development of high value-added products in these technologies. In this study, a control software that evaluates image data from unmanned aerial vehicles and makes various inferences (classification, positioning, marking) is created. By using the method of retraining the last layers of the pre-trained artificial neural network models with our data set, it has been tried to reduce the training time and increase the success. In these studies, 2 pre-educated models were used and as a result of training of these models, as a result of 190 thousand steps of training, 25.39 and 27.87 mAP values were reached. © 2019 IEEE.

关键词： Antennas

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Smart Garbage Segregation & Management System Using Internet of Things(IoT) & machine learning(ML) 1

Smart Garbage Segregation & Management System Using Internet...

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international conference on Innovation in Information Communication and Technology (ICIICT)

作者： Shamin, N. Fathimal, P. Mohamed Raghavendran, R. Prakash, Kamalesh SRM Inst Sci & Technol Dept Comp Sci & Engn Vadapalani Chennai Tamil Nadu India

ISBN: (纸本)9781728116044

The expansion in populace has prompted gigantic increment in the contamination also. It may peeve numerous relentless diseases for the people. For eliminating or alleviating the garbages and to keep up the cleanness, it requires a smart garbage managing architecture. But there is another severe problem, that to segregate the wastes that has been collected This paper proposes IoT stationed smart waste segregation and management device which detects the wastes in the dustbins with the aid of using Sensor devices and as soon as it is detected the waste substances in it will be segregated with the help of sensors and right away information is transferred to cloud database via IoT. Microcontroller is utilized as an association between the sensors and IoT module. Ultra-sonic sensor is utilized to distinguish the nearness of the waste material The moisture sensor is used to analyze and report the moisture content in the waste, and if there is moisture content available then the waste cannot be put in the dustbin. Metal sensor is used to separate the metal items and is separated to a section. image processing algorithm is used to identify the plastics and degradable items and is separated to another separate sections. The dustbin data are uploaded to the cloud using IoT in real time This helps in clearing the wastage from dustbin in an efficient and smartest way.

关键词： IoT device Sensor technology management system waste management plastic segregation waste management waste segregation system waste management system metal waste segregation IoT based system image processing machine learning

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Segmentation of skin lesions in dermoscopy images using fuzzy classification of pixels and histogram thresholding

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019年 168卷 11-19页

作者： Luis Garcia-Arroyo, Jose Garcia-Zapirain, Begonya Univ Deusto Deustotech LIFE Unit eVIDA Avda Univ 24 Bilbao 48007 Spain

Background and objective: To ensure proper functioning of a Computer Aided Diagnosis (CAD) system for melanoma detection in dermoscopy images, it is important to accurately detect the border of the lesion. This paper proposes a method developed by the authors to address this problem. Methods: The algorithm for segmentation of skin lesions in dermoscopy images is based on fuzzy classification of pixels and subsequent histogram thresholding. Results: This method participated in the 2016 and 2017 ISBI (international Symposium on Biomedical Imaging) Challenges, hosted by the ISIC (international Skin Imaging Collaboration). It was tested against two public databases containing 379 and 600 images respectively, and compared using the same defined metrics (Accuracy, Dice Coefficient, Jaccard Index, Sensitivity and Specificity) with the rest of participating state-of-the-art work, obtaining good results: (0.934, 0.869, 0.791, 0.870 and 0.978) and (0.884, 0.760, 0.665, 0.869 and 0.923) respectively, ranking 9th and 15th out of a total of 21 and 28 participants respectively using the Jaccard Index (which was the indicator used as a basis for ranking) and the 1st in the 2017 Challenge using the Sensitivity. Conclusion: The method has been proven to be robust and reliable. It's main contribution is the very design of the algorithm, highly innovative, which could also be used to deal with other segmentation problems of a similar nature. (C) 2018 Elsevier B.V. All rights reserved.

关键词： pattern recognition image processing machine learning Border detection

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Recent Progress in Computer-Vision-Based Human Activity recognition and Related Areas 14th

Recent Progress in Computer-Vision-Based Human Activity Reco...

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14th international conference on pattern recognition and Information processing (PRIP)

作者： Rigoll, Gerhard Tech Univ Munich Inst Human Machine Commun Munich Germany

ISBN: (纸本)9783030354305;9783030354299

This paper presents some more recent developments in the research area of human activity recognition. In many cases, human activity recognition can be achieved with the support of body sensors that deliver information about the motion of a person or about acceleration and position. Such data can contribute substantially to the accuracy of the results, however wearing extra equipment is not a very popular practice for most human users and therefore camera-based methods are mostly preferred in today's systems, which is also a result of the tremendous progress achieved in recent years in pattern recognition and machine learning. Therefore, also this paper concentrates on camera-based methods for human activity recognition. Activity recognition has a large variety of sub-research areas and has many exciting application areas, such as e.g. interaction in smart spaces, surveillance, human-robot interaction or automatic analysis of interest, emotions and human traits.

关键词： Computer Vision machine learning Activity recognition Gesture recognition Gait recognition Object tracking

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Multispectral Palmprint Biometric Verification System Using Deep CNN 2nd

Multispectral Palmprint Biometric Verification System Using ...

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2nd international conference on Recent Trends in image processing and pattern recognition, RTIP2R 2018

作者： Supreetha Gowda, H.D. Imran, Mohammad Kumar, G. Hemantha Department of Computer Science University of Mysore Mysore560 007 India Ejyle Technologies #125 & 126 1st Floor Brigade Arcade Brigade Metropolis Mahadevapura Post Garudacharpalya Bengaluru560048 India

ISBN: (纸本)9789811391835

In this paper, we are proposing deep biometric verification system based on multispectral palmprint images. We have built our own deep learning architecture, deployed on multispectral palmprint unimodal system, deep features and traditional texture namely log-gabor features are compared in this work. Multispectral palmprint biometric are captured under different illumination. images from multispectral database provides images obtained from varied wavelength in four different instance colours Red, Green, Blue and Near InfraRed. Deep learning is gaining extraordinary leaps by consolidating most of the machine learning algorithms essence and proved tremendous in computer vision and image recognition. We have evaluated our system by performing feature level fusion on traditional log gabor method and from the novel deep learning CNN (Convolution Neural Network) architecture proposed in our work. We have compared the performance of the system under different spectral wavelengths. Experimental results demonstrate the effectiveness of our proposed system by outer performing the traditional method results and shown reliable. © Springer Nature Singapore Pte Ltd. 2019.

关键词： image recognition

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Application of Data Mining Methods for pattern recognition in Negotiation Support Systems 19th

Application of Data Mining Methods for Pattern Recognition i...

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1st Joint Meeting of the 19th Annual international conference on Group Decision and Negotiation (GDN) / Behavioral OR conference

作者： Kaya, Muhammed-Fatih Schoop, Mareike Univ Hohenheim D-70599 Stuttgart Germany

ISBN: (纸本)9783030217112;9783030217105

Data mining methods have long been used to support organisational decision making by analysing organisational data from large databases. The present paper follows this tradition by discussing two different data mining techniques that are being implemented for pattern recognition in Negotiation Support Systems (NSSs), thereby providing process assistance to human negotiators. To this end, data from several international negotiation experiments via NSS Negoisst is used. Consequently, a suitable data representation of the underlying utility data and communication data has to be created for the applicability of data mining Each generated data type needs individual processing treatments and almost all data mining methods lose their feasibility without a correct data representation as consequence. Once a correct data representation is found, the potential for pattern recognition in electronic negotiation data can be evaluated using descriptive and predictive methods. Whilst Association Rule Discovery is used as a descriptive technique to generate essential sets of strategic association patterns, the Decision Tree is applied as a supervised learning technique for the prediction of classification patterns. The extent to which reliable as well as valuable patterns can be derived from the electronic negotiation data and valuable predictions can be generated is examined in this paper.

关键词： Negotiation Support Systems Data mining Text Mining Association Rule Discovery Decision Tree

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Why is the Winner the Best?

Why is the Winner the Best?

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conference on Computer Vision and pattern recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

international benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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A unified smart surveillance system incorporating adaptive foreground extraction and deep learning-based classification 1

A unified smart surveillance system incorporating adaptive f...

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1st international conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Muchtar, Kahlil Rahman, Faris Munggaran, Muhammad Rizky Dwiyantoro, Alvin Prayuda Juniarta Dharmadi, Richard Nugraha, Indra Nodeflux Jakarta Indonesia Syiah Kuala Univ Dept Elect & Comp Engn Banda Aceh Indonesia

ISBN: (纸本)9781538678220

The smart surveillance system is one of the most important services provided in a smart city. The smart surveillance applications are equipped with camera sensors to automatically detect and identify potential regions through automated object detection methods. Usually, such methods require high-complexity image processing techniques and algorithms. Hence, the design of low-complexity automated object detection algorithms becomes an important topic in this area. We consider a foreground extraction and deep learning to achieve these goals. A novel unified technique is proposed to detect a moving object from the surveillance videos based on CPU (central processing units). Unlike most of the existing methods that are relying on pixel information, we use a block-based texture and spatial-temporal information. We use this method to determine the area of the detected moving object(s) only. Furthermore, the area will be processed through a deep learning-based image classification in GPU (graphics processing units) in order to ensure high efficiency and accuracy. It cannot only help to detect object rapidly and accurately, but also can reduce big data volume needed to be stored in smart surveillance systems.

关键词： conferences Computer vision pattern recognition Surveillance Object detection Graphics processing units Big Data

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An Improved Pre-processing machine learning Approach for Cross-Sectional MR Imaging of Demented Older Adults 1

An Improved Pre-processing Machine Learning Approach for Cro...

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1st international conference of Intelligent Computing and Engineering, ICOICE 2019

作者： Khan, Afreen Zubair, Swaleha Al Sabri, Muaadhabdo Aligarh Muslim University Department of Computer Science Aligarh India

ISBN: (纸本)9781728144870

Data pre-processing is the foremost step employed in building any machine learning (ML) model. It has a significant effect on the generalization performance of the model. In the present study, we have attempted to present the data pre-processing techniques for analysis of cross-sectional Magnetic Resonance Imaging (MRI) data of demented and non-demented older adults. The MRI dataset consists of 434 MR sessions of 416 subjects, aged between 18 to 96 years. Before performing classification of MRI data and its pattern analysis, characteristics of the dataset, such as sampling, imbalanced dataset, missing values, outliers, incompleteness, and the presence of irrelevant features, had been addressed. We involved ten major steps to process dataset for the ML model building. Experimental results on the cross-sectional data indicated a significant relative improvement in the pattern analysis achieved by doing PCA (Principal Component Analysis). pattern analysis employing PCA resulted in noteworthy advancement in pattern recognition with an explained variance of 0.97377. © 2019 IEEE.

关键词： Magnetic resonance imaging

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The Epistle to Cangrande Through the Lens of Computational Authorship Verification 2nd

The Epistle to Cangrande Through the Lens of Computational A...

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2nd international Workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international Workshop on pattern recognition for Cultural Heritage, PatReCH 2019, 1st international Workshop eHealth in the Big Data and Deep learning Era, e-BADLE 2019, international Workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international conference on image Analysis and processing, ICIAP 2019

作者： Corbara, Silvia Moreo, Alejandro Sebastiani, Fabrizio Tavoni, Mirko Istituto di Scienza e Tecnologie dell’Informazione Consiglio Nazionale delle Ricerche Pisa56124 Italy Dipartimento di Filologia Letteratura e Linguistica Università di Pisa Pisa56126 Italy

ISBN: (纸本)9783030307530

The Epistle to Cangrande is one of the most controversial among the works of Italian poet Dante Alighieri. For more than a hundred years now, scholars have been debating over its real paternity, i.e., whether it should be considered a true work by Dante or a forgery by an unnamed author. In this work we address this philological problem through the methodologies of (supervised) Computational Authorship Verification, by training a classifier that predicts whether a given work is by Dante Alighieri or not. We discuss the system we have set up for this endeavour, the training set we have assembled, the experimental results we have obtained, and some issues that this work leaves open. © 2019, Springer Nature Switzerland AG.

关键词： machine learning

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