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6th international conference on Frontiers of Intelligent Computing: theory and Applications, FICTA-2017

6th International Conference on Frontiers of Intelligent Com...

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6th international conference on Frontiers of Intelligent Computing: theory and Applications, FICTA-2017

ISBN: (纸本)9789811075650

the proceedings contain 66 papers. the special focus in this conference is on . the topics include: AKM—Augmentation of K-means clustering algorithm for big data;face recognition using local binary pattern-blockwise method;Social group optimization (SGO) for clustering in wireless sensor networks;Implementation of high-performance floating point divider using pipeline architecture on FPGA;GA_DTNB: A hybrid classifier for medical data diagnosis;applications of spiking neural network to predict software reliability;An optimized rendering solution for ranking heterogeneous VM instances;gender prediction in author profiling using relieff feature selection algorithm;optimal coordination of overcurrent relays using simulated annealing and brute force algorithms;a new term weight measure for gender prediction in author profiling;the close-loop antenna array control system for automatic optimization in LTE and 3G network;Tumour classification in graph-cut segmented mammograms using GLCM features-fed SVM;Improving feature selection using elite breeding QPSO on gene data set for cancer classification;Epileptic seizure mining via novel empirical wavelet feature with J48 and KNN classifier;gallbladder shape estimation using tree-seed optimization tuned radial basis function network for assessment of acute cholecystitis;materialized view selection using backtracking search optimization algorithm;fractal coding for texture, satellite, and gray scale images to reduce searching time and complexity;Hybrid genetic algorithm–differential evolution approach for localization in WSN;local tetra pattern-based fruit grading using different classifiers;an integrated approach incorporating nonlinear dynamics and machine learning for predictive analytics and delving causal interaction;Image encryption based on arnold cat map and GA operator;Content-aware reversible data hiding: URHS.

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Computational Intelligence and Its Applications 1st ed. 2018

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丛书名： IFIP Advances in Information and Communication Technology

2018年

作者： Abdelmalek Amine Malek Mouhoub Otmane Ait Mohamed Bachir Djebbar

ISBN: (数字)9783319897431

ISBN: (纸本)9783319897424

this book constitutes the refereed proceedings of the 6th IFIP TC 5 international conference on Computational Intelligence and Its Applications, CIIA 2018, held in Oran, Algeria, in May 2018. the 56 full papers presented were carefully reviewed and selected from 202 submissions. they are organized in the following topical sections: data mining and information retrieval; evolutionary computation; machine learning; optimization; planning and scheduling; wireless communication and mobile computing; Internet of things (IoT) and decision support systems; pattern recognition and image processing; and semantic web services.

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Towards robust machine learning methods for the analysis of brain data

Towards robust machine learning methods for the analysis of ...

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international Winter Workshop on Brain-Computer Interface (BCI)

作者： K.-R Müller Machine Learning Group TU Berlin Berlin Germany Dept. of Brain and Cognitive Engineering Korea University Seoul Korea Max Planck Institute for Informatics Saarbrücken Germany

ISBN: (纸本)9781538625750;9781538625743

In this short abstract I will discuss recent directions that machine learning and BCI efforts of the BBCI team and coworkers have taken. It is the nature of this short text that many pointers to research are given all of which show a high overlap to prior own contributions; this is not only unavoidable but intentional. When analysing Brain data, it is challenging to combine data streams stemming from various modalities (see e.g. Biessmann et al., 2011, Sui et al., 2012, Fazli et al., 2015, Dähne et al., 2015). Hybrid BCIs are a successful example in this direction (Pfurtscheller et al., 2010, Müller-Putz et al. 2015, Dähne et al. 2015, Fazli et al. 2012, 2015). these techniques are firmly rooted in modern machine learning and signal processing that are now readily in use for analysing EEG, for decoding cognitive states etc. (Nikulin et al. 2007, and see Dornhege et al. 2004, Müller et al. 2008, Bünau et al. 2009, Tomioka and Müller, 2010, Blankertz et al., 2008, 2011, 2016, Lemm et al., 2011, for recent reviews and contributions to machine learning for BCI). Note that fusing information has also been a very common practice in the sciences and engineering (W altz and Llinas, 1990). the talk will discuss challenges for BCIs that are to be applied outside controlled lab spaces. Such complex and highly artifactual scenarios demand robust signal processing methods; see e.g. Samek et al. 2014, 2017b for recent reviews on robust methods for BCI. In addition I may expand on technical advances on the explanation framework for deep neural networks (Baehrens et al. 2010, Bach et al. 2015, Lapuschkin et al. 2016a and 2016b, Samek et al. 2017a, Montavon et al. 2017, 2018) to BCI data is given (Sturm et al. 2016). Furthermore, time permitting, I will revisit co-adaptive BCI systems (Vidaurre et al. 2011, Müller et al. 2017) and report on an upcoming study connecting fMRI and EEG data for co-adaptive training (Nierhaus et al. 2017). this abstract is based on joint work with Wojcie

关键词： Electroencephalography Biological neural networks Robustness Brain-computer interfaces Neuroscience learning systems pattern recognition

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Engineering Applications of Neural Networks 1st ed. 2017

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丛书名： Communications in Computer and Information Science

2017年

作者： Giacomo Boracchi Lazaros Iliadis Chrisina Jayne Aristidis Likas

ISBN: (数字)9783319651729

ISBN: (纸本)9783319651712

this book constitutes the refereed proceedings of the 18th international conference on Engineering Applications of Neural Networks, EANN 2017, held in Athens, Greece, in August 2017. the 40 revised full papers and 5 revised short papers presented were carefully reviewed and selected from 83 submissions. the papers cover the topics of deep learning, convolutional neural networks, image processing, pattern recognition, recommendation systems, machine learning, and applications of Artificial Neural Networks (ANN) applications in engineering, 5G telecommunication networks, and audio signal processing. the volume also includes papers presented at the 6th mining Humanistic data Workshop (MHDW 2017) and the2nd Workshop on 5G-Putting Intelligence to the Network Edge (5G-PINE).

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ivhd: A robust linear-time and memory efficient method for visual exploratory data analysis 13th

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13th international conference on machine learning and data mining in pattern recognition, mldm 2017

作者： Dzwinel, Witold Wcislo, Rafal AGH University of Science and Technology Kraków Poland

ISBN: (纸本)9783319624150

data embedding (DE) and graph visualization (GV) methods are very compatible tools used in Exploratory data Analysis for visualization of complex data such as high-dimensional data and complex networks. However, high computational complexity and memory load of existing DE and GV algorithms, considerably hinders visualization of truly large and big data consisting of as many as M~106+ data objects and N~103+ dimensions. Recently, we have shown that by employing only a small fraction of distances between data objects one can obtain very satisfactory reconstruction of topology of a complex data in 2D in a linear-time O(M). In this paper, we demonstrate the high robustness of our approach. We show that even poor approximations of the nn-nearst neighbor graph, representing high-dimensional data, can yield acceptable data embeddings. Furthermore, some incorrectness in the nearest neighbor list can often be useful to improve the quality of data visualization. this robustness of our DE method, together with its high memory and time efficiency, meets perfectly the requirements of big and distributed data visualization, when finding the accurate nearest neighbor list represents a great computational challenge. © Springer international Publishing AG 2017.

关键词： data visualization

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Generalized Hand Gesture recognition for Wearable Devices in IoT: Application and Implementation Challenges 12th

Generalized Hand Gesture Recognition for Wearable Devices in...

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12th international conference on machine learning and data mining (mldm)

作者： Iyer, Darshan Mohammad, Fahim Guo, Yuan Al Safadi, Ebrahim Smiley, Benjamin J. Liang, Zhiqiang Jain, Nilesh K. Intel Corp Hillsboro OR 97124 USA

ISBN: (纸本)9783319419206;9783319419190

the proliferation of low power and low cost continuous sensing technology is enabling new and innovative applications in wearables and Internet of things (IoT). At the same time, new applications are creating challenges to maintain real-time response in a resource-constrained device, while maintaining an acceptable performance. In this paper, we describe an IMU (Inertial Measurement Unit) sensor-based generalized hand gesture recognition system, its applications, and the challenges involved in implementing the algorithm in a resource-constrained device. We have implemented a simple algorithm for gesture spotting that substantially reduces the false positives. the gesture recognition model was built using the data collected from 52 unique subjects. the model was mapped onto Intel (R) Quark (TM) SE pattern Matching Engine, and field-tested using 8 additional subjects achieving 92% performance.

关键词： Gesture recognition machine learning pattern recognition Feature engineering Wearable device Internet of things

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A Time Series Model of the Writing Process 1

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12th international conference on machine learning and data mining (mldm)

作者： Volkovich, Zeev ORT Braude Coll Engn Software Engn Dept IL-21982 Karmiel Israel

ISBN: (数字)9783319419206

ISBN: (纸本)9783319419206;9783319419190

the necessity to operate with the huge number of anonymous documents abounding on the Internet is initiating the study of new methods for authorship recognition. the principal weakness of the methods used in this area is that they assess the similarity of text styles without any regard to their surroundings. this paper proposes a novel mathematical model of the writing process striving to quantify this dependency. A text is divided into a series of sequential sub-documents, which are represented via term histograms. the histograms proximity is estimated through a simple probability distance. Intending to typify the text writing style, a new characteristic representing the mean distance between a current sub-document and numerous earlier ones is advanced. An empirical distribution over the whole document of this feature specifies the writing style. So, dissimilarity of such distributions indicates a difference in the writing styles, and their coincidence implies the styles' identity. Numerical experiments demonstrate high potential ability of the proposed approach.

关键词： learning of internal representations and models mining text documents Text mining Time series and sequential pattern mining Authorship recognition

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Using Glocal Event Alignment for Comparing Sequences of Significantly Different Lengths 1

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12th international conference on machine learning and data mining (mldm)

作者： Vinh-Trung Luu Ripken, Mathis Forestier, Germain Fondement, Frederic Muller, Pierre-Alain Univ Haute Alsace MIPS 12 Rue Freres Lumiere F-68093 Mulhouse France

ISBN: (数字)9783319419206

ISBN: (纸本)9783319419206;9783319419190

this work takes place in the context of conversion rate optimization by enhancing the user experience during navigation on e-commerce web sites. the requirement is to be able to segment visitors into meaningful clusters, which can then be targeted with specific call-to-actions, in order to increase the web site turnover. this paper presents an original approach, which equally combines global-and local-alignment techniques (Needleman-Wunsch and Smith-Waterman) in order to automatically segment visitors according to the sequence of visited pages. Experimental results on synthetic datasets show that our approach out-performs other typically used alignment metrics, such as hybrid approaches or Dynamic Time Warping.

关键词： Web mining Sequential pattern mining Clustering

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Building FP-Tree on the Fly: Single-Pass Frequent Itemset mining 12th

Building FP-Tree on the Fly: Single-Pass Frequent Itemset Mi...

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12th international conference on machine learning and data mining (mldm)

作者： Shahbazi, Nima Soltani, Rohollah Gryz, Jarek An, Aijun York Univ Dept Comp Sci & Engn Toronto ON Canada

ISBN: (纸本)9783319419206;9783319419190

the FP-Growth algorithm has been studied extensively in the field of frequent pattern mining. the algorithm offers the advantage of avoiding costly database scans in comparison with Apriori-based algorithms. However, since it still requires two database scans, it cannot be used on streaming data. Also, the algorithm is designed for static datasets, where the input transactions are fixed and thus cannot be used for incremental or interactive mining. Existing incremental mining algorithms are not easily adoptable for on-the-fly, fast, and memory efficient FP-tree mining. In this paper we propose a novel SPFP-tree (single pass frequent pattern tree) algorithm that scans the database only once and provides the same tree as FP-Growth. Our algorithm changes the tree structure dynamically to create a highly compact frequency-ordered tree on the fly. With the insertion of each new transaction our algorithm dynamically maintains a tree identical to an FP-tree. Experimental results show the efficiency of the SPFP-tree algorithm in both incremental and interactive mining of frequent patterns.

关键词： data mining Frequent pattern Incremental mining Association rule Interactive mining

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A Clustering Approach for Discovering Intrinsic Clusters in Multivariate Geostatistical data 12th

A Clustering Approach for Discovering Intrinsic Clusters in ...

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12th international conference on machine learning and data mining (mldm)

作者： Fouedjio, Francky CSIRO Mineral Resources Perth WA Australia

ISBN: (纸本)9783319419206;9783319419190

Multivariate georeferenced data have become omnipresent in the many scientific fields and pose substantial analysis challenges. One of them is the grouping of data locations into spatially contiguous clusters so that data locations within the same cluster are more similar while clusters are different from each other, in terms of a concept of dissimilarity. In this work, we develop an agglomerative hierarchical clustering approach that takes into account the spatial dependency between observations. It relies on a dissimilarity matrix built from a non-parametric kernel estimator of the multivariate spatial dependence structure of data. It integrates existing methods to find the optimal cluster number. the capability of the proposed approach to provide spatially compact, connected and meaningful clusters is illustrated to the National Geochemical Survey of Australia data.

关键词： Clustering Geostatistics Multivariate data Non-parametric

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