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检索条件"任意字段=19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018"

共 134 条记录，以下是31-40 订阅

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Deep learning-Based Approach for the Semantic Segmentation of Bright Retinal Damage 1

引用

19th international conference on intelligent data engineering and automated learning (ideal)

作者： Silva, Cristiana Colomer, Adrian Naranjo, Valery Univ Minho Campus Gualtar P-4710 Braga Portugal Univ Politecn Valencia Inst Invest & Innovac Bioingn I3B Camino Vera S-N Valencia 46022 Spain

ISBN: (数字)9783030034931

ISBN: (纸本)9783030034931;9783030034924

Regular screening for the development of diabetic retinopathy is imperative for an early diagnosis and a timely treatment, thus preventing further progression of the disease. the conventional screening techniques based on manual observation by qualified physicians can be very time consuming and prone to error. In this paper, a novel automated screening model based on deep learning for the semantic segmentation of exudates in color fundus images is proposed with the implementation of an end-to-end convolutional neural network built upon U-Net architecture. this encoder-decoder network is characterized by the combination of a contracting path and a symmetrical expansive path to obtain precise localization with the use of context information. the proposed method was validated on E-OPHthA and DIARETDB1 public databases achieving promising results compared to current state-of-the-art methods.

关键词： Semantic segmentation Deep learning Fundus images Exudates U-Net

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Different Approaches of data and Attribute Selection on Headache Disorder 19th

Different Approaches of Data and Attribute Selection on Head...

引用

19th international conference on intelligent data engineering and automated learning (ideal)

作者： Simic, Svetlana Bankovic, Zorana Simic, Dragan Simic, Svetislav D. Univ Novi Sad Fac Med Hajduk Veljkova 1-9 Novi Sad 21000 Serbia Frontiers Media SA Pozuelo Alarcon Sn Madrid Spain Univ Novi Sad Fac Tech Sci Trg Dositeja Obradovica 6 Novi Sad 21000 Serbia

ISBN: (纸本)9783030034962;9783030034955

Half of the general population experiences a headache during any given year. Medical data and information in turn provide knowledge on which physicians base their decisions and actions but, in general, it is not easy to manage them. It becomes increasingly necessary to extract useful knowledge and make scientific decisions for diagnosis and treatment of this disease from the database. this paper presents comparison of data and attribute selected features by automatic machine learning methods and algorithms, and by diagnostic tools and expert physicians, almost all from the last decade.

关键词： data selection Attribute selection Headache Diagnosis Machine learning Decision support

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Categorical Big data Processing 19th

Categorical Big Data Processing

引用

19th international conference on intelligent data engineering and automated learning (ideal)

作者： Salvador-Meneses, Jaime Ruiz-Chavez, Zoila Garcia-Rodriguez, Jose Univ Cent Ecuador Ciudadela Univ Quito Ecuador Univ Alicante Ap 99 Alicante 03080 Spain

ISBN: (纸本)9783030034931;9783030034924

Prior to the application of a machine learning algorithm, the information has to be stored in memory which may consumes big memory amounts. If we reduce the amount of memory used to represent datasets, we can reduce the number of operations required to process it. All the libraries used to represent the information make a traditional representation (vector, matrix for example), which force to iterate over the whole dataset to obtain a result. In this paper we present a technique to process categorical data that was previously encoded in blocks of arbitrary size, the method process the data block by block which can reduces the number of iterations over the original dataset, and at the same time, the performance is similar to the traditional processing of the data. this method also requires the data to be stored in memory but in an encoded way that optimize the memory size consumed for the representation as well as the operations required to process it. the results of the experiments carried out show a slightly lower time processing than the obtained with traditional implementations, which allows us to obtain a good performance.

关键词： Big data Compression Processing Categorical data BLAS

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data Set Partitioning in Evolutionary Instance Selection 19th

Data Set Partitioning in Evolutionary Instance Selection

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Kordos, Miroslaw Czepielik, Lukasz Blachnik, Marcin Univ Bielsko Biala Dept Comp Sci & Automat Willowa 2 Bielsko Biala Poland Silesian Tech Univ Dept Appl Informat Krasinskiego 8 Katowice Poland

ISBN: (纸本)9783030034931;9783030034924

Evolutionary instance selection outperforms in most cases non-evolutionary methods, also for function approximation tasks considered in this work. However, as the number of instances encoded into the chromosome grows, finding the optimal subset becomes more difficult, especially that running the optimization too long leads to over-fitting. A solution to that problem, which we evaluate in this work is to reduce the search space by clustering the dataset, run the instance selection algorithm for each cluster and combine the results. We also address the issue of properly processing the instances close to the cluster boundaries, as this is where the drop of accuracy can appear. the method is experimentally verified on several regression datasets with thousands of instances.

关键词： Chromosomes

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A Study of Fuzzy Clustering to Archetypal Analysis 19th

A Study of Fuzzy Clustering to Archetypal Analysis

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Mendes, Goncalo Sousa Nascimento, Susana Univ Nova Lisboa Dept Comp Sci Fac Ciencias & Tecnol Lisbon Portugal Univ Nova Lisboa NOVA LINCS Fac Ciencias & Tecnol Lisbon Portugal

ISBN: (纸本)9783030034962;9783030034955

this paper presents a comparative study between a method for fuzzy clustering which retrieves pure individual types from data, the fuzzy clustering with proportional membership (FCPM), and an archetypal analysis algorithm based on Furthest-Sum approach (FS-AA). A simulation study comprising 82 data sets is conducted with a proper data generator, FCPM-DG, whose goal is twofold: first, to analyse the ability of archetypal clustering algorithm to recover Archetypes from data of distinct dimensionality;second, to analyse robustness of FCPM and FS-AA algorithms to outliers. the effectiveness of these algorithms are yet compared on clustering 12 diverse benchmark data sets from machine learning. the evaluation conducted with five primer unsupervised validation indices shows the good quality of the clustering solutions.

关键词： Archetypal analysis Fuzzy clustering Synthetic multidimensional data Fuzzy validation indices

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On the Opportunities for Using Mobile Devices for Activity Monitoring and Understanding in Mining Applications 19th

On the Opportunities for Using Mobile Devices for Activity M...

引用

19th international conference on intelligent data engineering and automated learning (ideal)

作者： Nalepa, Grzegorz J. Brzychczy, Edyta Bobek, Szymon AGH Univ Sci & Technol Krakow Poland

ISBN: (纸本)9783030034962;9783030034955

Over the last decades, number of embedded and portable computer systems for monitoring of activities of miners and underground environmental conditions that have been developed has increased. However, their potential in terms of computing power and analytic capabilities is still underestimated. In this paper we elaborate on the recent examples of the use of wearable devices in mining industry. We identify challenges for high level monitoring of mining personnel with the use of mobile and wearable devices. To address some of them, we propose solutions based on our recent works, including context-aware data acquisition framework, physiological data acquisition from wearables, methods for incomplete and imprecise data handling, intelligent data processing and reasoning module, hybrid localization using semantic maps, and adaptive power management. We provide a basic use case to demonstrate the usefulness of this approach.

关键词： Mobile devices Activity monitoring data understanding

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Intrusion Detection Using Transfer learning in Machine learning Classifiers Between Non-cloud and Cloud datasets 19th

Intrusion Detection Using Transfer Learning in Machine Learn...

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Ahmadi, Roja Macredie, Robert D. Tucker, Allan Brunel Univ London Uxbridge Middx England

ISBN: (纸本)9783030034931;9783030034924

One of the critical issues in developing intrusion detection systems (IDS) in cloud-computing environments is the lack of publicly available cloud intrusion detection datasets, which hinders research into IDS in this area. there are, however, many non-cloud intrusion detection datasets. this paper seeks to leverage one of the well-established non-cloud datasets and analyze it in relation to one of the few available cloud datasets to develop a detection model using a machine learning technique. A complication is that these datasets often have different structures, contain different features and contain different, though overlapping, types of attack. the aim of this paper is to explore whether a simple machine learning classifier containing a small common feature set trained using a non-cloud dataset that has a packet-based structure can be usefully applied to detect specific attacks in the cloud dataset, which contains time-based traffic. through this, the differences and similarities between attacks in the cloud and non-cloud datasets are analyzed and suggestions for future work are presented.

关键词： Cloud intrusion detection model Transfer learning Machine learning DDoS attack

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Compound Local Binary Pattern and Enhanced Jaya Optimized Extreme learning Machine for Digital Mammogram Classification 1

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Mohanty, Figlu Rup, Suvendu Dash, Bodhisattva Int Inst Informat Technol Image & Video Proc Lab Bhubaneswar Odisha India

ISBN: (数字)9783030034931

ISBN: (纸本)9783030034931;9783030034924

the fatality rate due to breast cancer still continues to remain high across the world and women are the frequent sufferers of this cancer. Mammography is one of the powerful imaging modalities to detect and diagnose cancer at its early stage effectively. A computer-aided diagnosis (CAD) system is a potential tool which analyses the mammographic images to reach a correct decision. the present work aims at developing a CAD framework which can classify the mammograms accurately. this work has primarily four stages. First, contrast limited adaptive histogram equalization (CLAHE) is used for pre-processing. Second, feature extraction is realized using compound local binary pattern (CLBP) followed by principal component analysis (PCA) for feature reduction. Finally, an enhanced Jaya-based extreme learning machine is utilized to classify the mammograms as normal or abnormal, and further, benign or malignant. the success rate in terms of classification accuracy achieves 100% and 99.48% for MIAS and DDSM datasets, respectively.

关键词： Mammography Computer-aided diagnosis Compound local binary pattern Jaya algorithm Extreme learning machine

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On Detecting Online Radicalization Using Natural Language Processing 19th

On Detecting Online Radicalization Using Natural Language Pr...

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Oussalah, Mourad Faroughian, F. Kostakos, Panos Univ Oulu Ctr Ubiquitous Comp Oulu Finland Aston Univ Aston England

ISBN: (纸本)9783030034962;9783030034955

this paper suggests a new approach for radicalization detection using natural language processing techniques. Although, intuitively speaking, detection of radicalization from only language cues is not trivial and very debatable, the advances in computational linguistics together with the availability of large corpus that allows application of machine learning techniques opens us new horizons in the field. this paper advocates a two stage detection approach where in the first phase a radicalization score is obtained by analyzing mainly inherent characteristics of negative sentiment. In the second phase, a machine learning approach based on hybrid KNN-SVM and a variety of features, which include 1, 2 and 3-g, personality traits, emotions, as well as other linguistic and network related features were employed. the approach is validated using both Twitter and Tumblr dataset.

关键词： Natural language processing Radicalization Machine learning

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Weighted Voting and Meta-learning for Combining Authorship Attribution Methods 19th

Weighted Voting and Meta-Learning for Combining Authorship A...

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Petrovic, Smiljana Petrovic, Ivan Palesi, Ileana Calise, Anthony Iona Coll New Rochelle NY 10801 USA CUNY Bronx Community Coll Bronx NY 10453 USA

ISBN: (纸本)9783030034931;9783030034924

Our research concentrates on ways to combine machine learning techniques for authorship attribution. Traditionally, research in authorship attribution is focused on the development of new base-classifiers (combinations of stylometric features and learning methods). A large number of base-classifiers developed for authorship attribution vary in accuracy, often proposing different authors for a disputed document. In this research, we use predictions of multiple base-classifiers as a knowledge base for learning the true author. We introduce and compare two novel methods that utilize multiple base-classifiers. In the Weighted Voting approach, each base-classifier supports an author in proportion to its accuracy in leave-one-out classification. In our Meta-learning approach, each base-classifier is treated as a feature and methods' predictions in leave-one-out cross-validation are used as training data from which machine learning methods produce an aggregated decision. We illustrate our results through a collection of 18th century political writings. Anonymously written essays were common during this period, leading to frequent disagreements between scholars over their attribution.

关键词： Authorship attribution Combining classifiers Meta-learning

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