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Hurricane intensity prediction based on time series data mining

Hurricane intensity prediction based on time series data min...

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international workshop on the Analysis of Multi-temporal Remote Sensing Images (MultiTemp)

作者： Shuhan Yang Qingxiang Meng School of Remote Sensing and Information Engineering Whuhan University Wuhan China

this paper applied data mining methods to study the time-series hurricane data of 28 years (1982-2009) in Atlantic region to improve the accuracy of hurricane intensity prediction. According to the contrast test of base classifiers, IBk classifier and the LMT classifier were selected and integrated by using the Bagging method. the optimized Bagging base classifier sequence was trained using the optimal training data set after culling abnormal data. then the hurricane intensity prediction model: the Bagging-IBk&LMT model was formed. the testing result showed that the prediction model was better than single classification model.

关键词： Hurricanes Predictive models data models Bagging Classification algorithms Tropical cyclones Root mean square

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Skin cancer classification computer system development with deep learning 10

Skin cancer classification computer system development with ...

引用

workshop on Information Technology and Scientific Computing in the Framework of the 10th international Conference Information and Telecommunication Technologies and Mathematical Modeling of High-Tech Systems, ITTMM 2020

作者： Demidova, Anastasia V. Kulyabov, Dmitry S. Shchetinin, Eugeny Yu. Department of Applied Probability and Informatics Peoples' Friendship University of Russia 6 Miklukho-Maklaya St. Moscow117198 Russia Laboratory of Information Technologies Joint Institute for Nuclear Research 6 Joliot-Curie St. Dubna Moscow region141980 Russia Department of Data Analysis Decision Making and Financial Technologies Financial University under the Government of the Russian Federation 49 Leningradsky pr. Moscow111234 Russia

Melanoma is a deadly form of skin cancer that is often undiagnosed or misdiagnosed as a benign skin lesion. Its early detection is extremely important, since the life of patients with melanoma depends on accurate and early diagnosis of the disease. However, doctors often rely on personal experience and assess each patient's injuries based on a personal examination. Clinical studies allow us to get the accuracy of the diagnosis of melatoma from 65 to 80 percents, which was a good result for some time. However, modern research claims that the use of dermoscopic images in diagnosis significantly increases the accuracy of diagnosis of skin lesions. the visual differences between melanoma and benign skin lesions can be very small, making diagnosis difficult even for an expert doctor. Recent advances in the use of artificial intelligence methods in the analysis of medical images have made it possible to consider the development of intelligent medical diagnostic systems based on visualization as a very promising direction that will help the doctor in making more effective decisions about the health of patients and making a diagnosis at an early stage and in adverse conditions. In this paper, we propose an approach to solving the problem of classification of skin diseases, namely, melanoma at an early stage, based on deep learning. In particular, a solution to the problem of classification of a dermoscopic image containing either malignant or benign skin lesions is proposed. For this purpose, the deep neural network architecture was developed and applied to image processing. Computer experiments on the ISIC data set have shown that the proposed approach provides 92% accuracy on the test sample, which is significantly higher than other algorithms in this data set have shown. © 2020 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0). CEUR workshop Proceedings (***)

关键词： Image classification

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From Unilateral to Bilateral Learning: Detecting Mammogram Masses with Contrasted Bilateral Network 22nd

From Unilateral to Bilateral Learning: Detecting Mammogram M...

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10th international workshop on Machine Learning in Medical Imaging (MLMI) / 22nd international Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Liu, Yuhang Zhou, Zhen Zhang, Shu Luo, Ling Zhang, Qianyi Zhang, Fandong Li, Xiuli Wang, Yizhou Yu, Yizhou Deepwise AI Lab Beijing Peoples R China Peking Univ Comp Sci Dept Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China Peking Univ Ctr Data Sci Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9783030322267;9783030322250

the comparison of bilateral mammogram images is important for finding masses especially in dense breasts. However, most existing mammogram mass detection algorithms only considered unilateral image. In this paper, we propose a deep model called contrasted bilateral network (CBN) to take bilateral information into consideration. In CBN, Mask R-CNN is used as a basic framework, upon which two major modules are developed to exploit the bilateral information: distortion insensitive comparison module and logic guided bilateral module. the former one is designed to be robust to nonrigid distortion of bilateral registration, while the latter one integrates the bilateral domain knowledge of radiologist. Experimental results on DDSM dataset demonstrate that the proposed algorithm achieves the state-of-the-art performance.

关键词： Mammogram mass Object detection Domain knowledge

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Spectral CT Based Training dataset Generation and Augmentation for Conventional CT Vascular Segmentation 22nd

Spectral CT Based Training Dataset Generation and Augmentati...

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10th international workshop on Machine Learning in Medical Imaging (MLMI) / 22nd international Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Lartaud, Pierre-Jean Rouchaud, Aymeric Rouet, Jean-Michel Nempont, Olivier Boussel, Loic Philips Res France Suresnes France Univ Lyon CREATIS INSERM U1044INSAUMR5220 Lyon France Hosp Civils Lyon Radiol Dept Hop Croix Rousse Lyon France

ISBN: (纸本)9783030322458;9783030322441

Deep learning has proved to be a very efficient tool for organs automated segmentation in CT scans. However, variation of iodine contrast agent concentration within the vascular system or organs is a major source of variation in image contrast. this requires building large databases representative of the important differences in contrast enhancement across CT studies. Furthermore, creating a low- or non-enhanced annotated database is still a very laborious task as semi-automatic segmentation software and even expert eyes often fail to find structures' edges on low contrast images. In this study, we aim to develop a new deep-learning network training approach based on spectral data augmentation using dual energy spectral CT (Philips iQon) images as training dataset. Indeed this new generation of CT scanners allows generating, from a single scan, virtual non-contrast images (VNC), corresponding to unenhanced CT images on conventional CT scanners, and virtual mono-energetic (monoE) images at different kV, that mimics low (at high kV) to high (at low kV) iodine-based contrast-enhanced studies. An experienced radiologist can then segment the target structures on highly contrasted low kV monoE with a semi-automatic tool (ISP, Philips) yielding ground truth for both monoE and VNC images. As an illustration, we trained a 3D U-net convolutional neural network for aorta segmentation on conventional CT images. In addition to greatly facilitate the creation of an annotated non-contrast CT database, we demonstrate through multiple training experiments that using a variable proportion of VNC and randomly chosen monoE kV levels during the data augmentation process allows training networks that are able to segment target structures regardless of their level of contrast enhancement.

关键词： data augmentation Spectral CT dataset generation Deep learning Aorta Segmentation Non-contrast CT Convolutional neural network (CNN)

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Mathematical Morphology and Its Applications to Image and Signal Processing 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Pierre Soille Martino Pesaresi Georgios K. Ouzounis

ISBN: (数字)9783642215698

ISBN: (纸本)9783642215681

this book contains the refereed proceedings of the 10th international Symposium on Mathematical Morphology, ISMM 2011 held in Verbania-Intra, Italy in July 2011. It is a collection of 39 revised full papers, from which 27 were selected for oral and 12 for poster presentation, from a total of 49 submissions. Moreover, the book features two invited contributions in the fields of remote sensing, image analysis and scientific visualization. the papers are organized in thematic sections on theory, lattices and order, connectivity, image analysis, processing and segmentation, adaptive morphology, algorithms, remote sensing, visualization, and applications.

关键词： Image Processing and Computer Vision Math Applications in Computer Science Discrete Mathematics in Computer Science Computational Biology/Bioinformatics data structures Numeric Computing

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High Performance Computing for Computational Science - VECPAR 2012 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Michel Daydé Osni Marques Kengo Nakajima

ISBN: (数字)9783642387180

ISBN: (纸本)9783642387173

this book constitutes the thoroughly refereed post-conference proceedings of the 10th international Conference on High Performance Computing for Computational Science, VECPAR 2012, held in Kope, Japan, in July 2012. the 28 papers presented together with 7 invited talks were carefully selected during two rounds of reviewing and revision. the papers are organized in topical sections on CPU computing, applications, finite element method from various viewpoints, cloud and visualization performance, method and tools for advanced scientific computing, algorithms and data analysis, parallel iterative solvers on multicore architectures.

关键词： Algorithm Analysis and Problem Complexity System Performance and Evaluation Arithmetic and Logic structures Numeric Computing Computer Imaging, Vision, Pattern Recognition and Graphics

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theory and Applications of Models of Computation 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： T-H. Hubert Chan Lap Chi Lau Luca Trevisan

ISBN: (数字)9783642382369

ISBN: (纸本)9783642382352

this book constitutes the refereed proceedings of the 10th international Conference on theory and Applications of Models of Computation, TAMC 2013, held in Hong Kong, China, in May 2013. the 31 revised full papers presented were carefully reviewed and selected from 70 submissions. Bringing together a wide range of researchers with interests in computational theory and applications, the papers address the three main themes of the conference which were computability, complexity, and algorithms and present current research in these fields with aspects to theoretical computer science, algorithmic mathematics, and applications to the physical sciences.

关键词： theory of Computation Mathematics of Computing Algorithm Analysis and Problem Complexity Logics and Meanings of Programs data structures and Information theory Mathematical Logic and Formal Languages

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Content-aware global static compression for Web content delivery

Content-aware global static compression for Web content deli...

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international workshop on Web Content Caching and Distribution, WCW

作者： Junli Yuan Chi-Hung Chi Qibin Sun School of Computing Institute for Infocomm Research Singapore School of Software Tsinghua University Beijing China Institute for Infocomm Research Singapore

the effectiveness of real-time lossless compression on Web data is found to be limited by the relatively small size of its basic operation unit, Web object. However, we observe that while the repeated occurrence frequencies of strings within a single object might be low, the frequencies can be very high if they are considered over multiple objects. Furthermore, these strings with high occurrence frequencies are quite static, and the size of such string set is also quite small. In this paper, we propose a complementary compression mechanism on top of the existing HTTP supported algorithms, called the content-aware global static compression (CAGSC), to further improve Web data compression. Its basic idea is to include a static CAGSC table to perform compression on frequently, globally occurred strings. Our experimental result shows that CAGSC can obtain an average of 15% improvement over standard HTTP supported algorithms.

关键词： data compression Frequency Compression algorithms HTML Web pages Protocols Computer languages Sun Information retrieval Encoding

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Extracting semantics from random walks on Wikipedia: Comparing learning & counting methods 10

Extracting semantics from random walks on Wikipedia: Compari...

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10th international AAAI Conference on Web and Social Media, ICWSM 2016

作者： Dallmann, Alexander Lemmerich, Florian Niebler, thomas Hotho, Andreas Data Mining and Information Retrieval Group University of Würzburg Germany GESIS - Leibniz Institute for Social Sciences Germany Data Mining and Information Retrieval Group University of Würzburg and L3S Hannover Germany

ISBN: (纸本)9781577357681

Semantic relatedness between words has been extracted from a variety of sources. In this ongoing work, we explore and compare several options for determining if semantic relatedness can be extracted from navigation structures in Wikipedia. In that direction, we first investigate the potential of representation learning techniques such as DeepWalk in comparison to previously applied methods based on counting co-occurrences. Since both methods are based on (random) paths in the network, we also study different approaches to generate paths from Wikipedia link structure. For this task, we do not only consider the link structure of Wikipedia, but also actual navigation behavior of users. Finally, we analyze if semantics can also be extracted from smaller subsets of the Wikipedia link network. As a result we find that representation learning techniques mostly outperform the investigated co-occurrence counting methods on the Wikipedia network. However, we find that this is not the case for paths sampled from human navigation behavior. ©2016, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Semantics

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Validation of Long-Term data from FBG Temperature Sensors 10

Validation of Long-Term Data from FBG Temperature Sensors

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10th international workshop on Structural Health Monitoring (IWSHM)

作者： Abdel-Jaber, Hiba Glisic, Branko Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781605952758

Monitoring temperature in structures is essential for understanding their thermal behavior and performing thermal compensation for any other type of sensors used for structural health monitoring (e.g. strain). this is particularly important for long-term monitoring applications. Nevertheless, a universal procedure for the validation of temperature data from a monitoring system has not been created and implemented. In this paper, preliminary research on two methods that aim to validate temperature readings from temperature sensors is presented. the methods differ in the type of data available for validation: (1) data from two independent monitoring systems are available, and (2) data from a monitoring system and a nearby weather tower are available. the methods use linear regression and confidence intervals on the linear regression model parameters to quantify annual changes in readings, which are then used to characterize long-term performance of the sensor such as changes in sensitivity or stability, or other unusual behaviors. the methods were applied to data collected over five years from fiber optic sensors based on Fiber Bragg Gratings (FBG) installed on Streicker Bridge on the Princeton University campus, and similar qualitative conclusions were derived from the two methods at instrumented locations where two independent monitoring systems are installed. this validation of the two methods was used as a confirmation that the second method, for which data is generally available, can be used in lieu of the first in cases where two independent monitoring systems do not exist. Based on this validation, the second method was independently used to test data at other locations on the bridge, where only one type of sensors is installed. the validation procedure showed and quantified minor changes in sensitivity and stability in measurements from some instrumented locations after the first two years.

关键词： Temperature sensors

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