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22nd international Symposium on Methodologies for intelligent Systems, ISMIS 2015

22nd International Symposium on Methodologies for Intelligen...

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22nd international Symposium on Methodologies for intelligent Systems, ISMIS 2015

ISBN: (纸本)9783319252513

The proceedings contain 58 papers. The special focus in this conference is on data Mining Methods and databases, Information Retrieval, Recommender Systems. The topics include: data mining with histograms;discovering variability patterns for change detection in complex phenotype data;computation of approximate reducts with dynamically adjusted approximation threshold;a new formalism for evidential databases;ubiquitous city information platform powered by fuzzy based DSSs to meet multi criteria customer satisfaction;a framework supporting the analysis of process logs stored in either relational or NoSQL DBMSs;an approximate proximity graph incremental construction for large image collections indexing;experimenting analogical reasoning in recommendation;personalized meta-action mining for NPS improvement;on the qualitative calibration of bipolar queries;a scalable boosting learner using adaptive sampling;markov logic network-based statistical predicate invention;learning bayesian random cutset forests;classifier fusion within the belief function framework using dependent combination rules;on the effectiveness of evidence-based terminological decision trees;clustering classifiers learnt from local datasets based on cosine similarity;a hierarchical clustering approach to data editing;ontology-based topic labeling and quality prediction;exploring political sentiments on twitter for opinion mining;sentiment dictionary refinement using word embeddings;the cube of opposition and the complete appraisal of situations by means of sugeno integrals;model checking based query and retrieval in openstreetmap and granular rules and rule frames for compact knowledge representation.

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learning the Optimal Product Design Through History 22nd

Learning the Optimal Product Design Through History

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22nd international conference on Neural Information Processing (ICONIP)

作者： Parque, Victor Miyashita, Tomoyuki Waseda Univ Dept Modern Mech Engn Shinjuku Ku 3-4-1 Okubo Tokyo 1698555 Japan

ISBN: (纸本)9783319265322;9783319265315

The search for novel and high-performing product designs is a ubiquitous problem in science and engineering: aided by advances in optimization methods the conventional approaches usually optimize a (multi) objective function using simulations followed by experiments. However, in some scenarios such as vehicle layout design, simulations and experiments are restrictive, inaccurate and expensive. In this paper, we propose an alternative approach to search for novel and high-performing product designs by optimizing not only a proposed novelty metric, but also a performance function learned from historical data. Computational experiments using more than twenty thousand vehicle models over the last thirty years shows the usefulness and promising results for a wider set of design engineering problems.

关键词： Design Vehicle Optimization Genetic programming Particle swarm

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Heterogeneous Discriminant Analysis for Cross-View Action Recognition 22nd

Heterogeneous Discriminant Analysis for Cross-View Action Re...

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22nd international conference on Neural Information Processing (ICONIP)

作者： Sui, Wanchen Wu, Xinxiao Feng, Yang Liang, Wei Jia, Yunde Beijing Inst Technol Sch Comp Sci Beijing Lab Intelligent Informat Technol Beijing 100081 Peoples R China

ISBN: (纸本)9783319265612;9783319265605

We propose an approach of cross-view action recognition, in which the samples from different views are represented by heterogeneous features with different dimensions. Inspired by linear discriminant analysis (LDA), we introduce a discriminative common feature space to bridge the source and target views. Two different projection matrices are learned to respectively map the data from two different views into the common space by simultaneously maximizing the similarity of intra-class samples, minimizing the similarity of inter-class samples, and reducing the mismatch between data distributions of two views. Our method is neither restricted to the corresponding action instances in the two views nor restricted to a specific type of feature. We evaluate our approach on the IXMAS multi-view dataset and the experimental results demonstrate its effectiveness.

关键词： Cross-view action recognition Transfer learning Discriminant analysis Heterogeneous domain adaption

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Applied Machine learning Classifiers for Medical Applications: Clarifying the behavioural patterns using a variety of datasets 22

Applied Machine Learning Classifiers for Medical Application...

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22nd international conference on Systems, Signals and Image Processing (IWSSIP)

作者： Aljaaf, Aluned J. Al-Jumeily, Dhiya Hussain, Abir J. Fergus, Paul Al-Jumaily, Mohammed Radi, Naeem Liverpool John Moores Univ Appl Comp Res Grp Byrom St Liverpool L3 3AF Merseyside England Dr Sulaiman Al Habib Hosp Dept Neurosurg Dubai Healthcare City U Arab Emirates Al Khawarizmi Int Coll Abu Dhabi U Arab Emirates

ISBN: (纸本)9781467383530

Machine-learning (ML) techniques have grown to be among the leading research topics within the health care systems and particularly for clinical decision support systems (CDSS), which are commonly used in helping physicians to make more accurate diagnosis. However, applying these techniques for CDSS is most likely would face a lack of criteria for adequate use. Therefore, a range of recent studies have focused on evaluating different machine learning classifiers with the aim of identifying the most appropriate classifier to be used for particular decision making problem-domain. The majority of these studies have used a single dataset within a certain medical-related classification domain. Nevertheless, evaluating machine-learning classifiers with one sample of data appears to be unsatisfying, perhaps it is not reflecting the classifiers capabilities or their behavioral patterns under different circumstances. In this study, five well-known supervised machine-learning classifiers were examined using five different real-world datasets with a range of attributes. The main aim was to illustrate not only the impact of the datasets volume and attributes on the evaluation, but also and more importantly, present the classifiers capabilities and shortcomings under certain conditions, which potentially provide a guidance or instructions to help health analysts and researchers to determine the most suitable classifier to address a particular medical-related decision making problem.

关键词： machine learning clinical decision support system evaluation sensitivity classification accuracy time complexity

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Advance Flood Detection and Notification System based on Sensor Technology and Machine learning Algorithm 22

Advance Flood Detection and Notification System based on Sen...

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22nd international conference on Systems, Signals and Image Processing (IWSSIP)

作者： Khalaf, Mohammed Hussain, Abir Jaafar Al-Jumeily, Dhiva Fergus, Paul Idowu, Ibrahim Olatunji Liverpool John Moores Univ Appl Comp Res Grp Sch Comp & Math Sci Byrom St Liverpool L3 3AF Merseyside England

ISBN: (纸本)9781467383530

Floods are common natural disasters that cause severe devastation of any country. They are commonly caused by precipitation and runoff of rivers, particularly during periods of excessively high rainy season. Due to global warming issues and extreme environmental effects, flood has become a serious problem to the extent of bringing about negative impact to the mankind and infrastructure. To date, sensor network technology has been used in many areas including water level fluctuation. However, efficient flood monitoring and real time notification system still a crucial part because Information Technology enabled applications have not been employed in this sector in a broad way. This research presents a description of an alert generating system for flood detection with a focus on determining the current water level using sensors technology. The system then provides notification message about water level sensitivity via Global Communication and Mobile System modem to particular authorise person. Besides the Short Message Service, the system instantaneously uploads and broadcast information through web base public network. Machine-learning algorithms were conducted to perform the classification process. Four experiments were carried out to classify flood data from normal and at risk condition in which 99.5% classification accuracy was achieved using Random Forest algorithm. Classification using Bagging, Decision Tree and HyperPipes algorithms achieved accuracy of 97.7 %, 94.6% and 89.8 %, respectively.

关键词： Flood detection system Sensor network Short message service Global communication and mobile system Real-time data Machine learning algorithm

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A Hybrid Model of Fuzzy ARTMAP and the Genetic Algorithm for data Classification 22nd

A Hybrid Model of Fuzzy ARTMAP and the Genetic Algorithm for...

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22nd international conference on Neural Information Processing (ICONIP)

作者： Seera, Manjeevan Liew, Wei Shiung Loo, Chu Kiong Swinburne Univ Technol Fac Engn Comp & Sci Kuching Sarawak Malaysia Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur Malaysia

ISBN: (纸本)9783319265353;9783319265346

A framework for optimizing Fuzzy ARTMAP (FAM) neural networks using Genetic Algorithms (GAs) is proposed in this paper. A number of variables were identified for optimization, which include the presentation order of training data during the learning step, the feature subset selection of the training data, and the internal parameters of the FAM such as baseline vigilance and match tracking. A single configuration of all three variables were encoded as a chromosome string and evaluated by creating and training the FAM according to the variables. The fitness of the chromosome is determined by the final classification accuracy of the FAM. Evaluation on benchmark data sets are conducted with the results compared with literature. Experimental results indicate the effectiveness of the proposed framework in undertaking data classification tasks.

关键词： Chromosomes

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The Research of Music and Emotion Interaction with a Case Study of intelligent Music Selection System 22

The Research of Music and Emotion Interaction with a Case St...

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22nd ISPE-Inc international conference on Concurrent engineering

作者： Ko, Li-Wei Chuang, Kai-Hsiang Chiu, Ming-Chuan Natl Tsing Hua Univ Hsinchu 30013 Taiwan

ISBN: (纸本)9781614995449;9781614995432

Music plays an important role in human history. Since music could influence the emotion and performance of people, it has been widely applied to enhance the efficiency of some particular tasks, such as sports or medical applications. For example, a jogger can run longer distance with a proper music. However, people usually select the music according to some personal preference. In this manner, it might not be able to reach the anticipative goal of task. Moreover, manually music selection is a time-consuming activity. Therefore, to select the proper music from a huge amount of music database became a major issue. The aim of this study is to establish an intelligent music selection system. A method based on Music Information Retrieval (MIR) technique which can query and retrieve certain types of music was developed. This system was realized by application software. This system is not confined to appropriate music selection by tracing the user's heart rate variability. More importantly, it could keep users in a positive emotional state. This study expects to provide effective music patterns, contributing to the therapeutic music creation and the applications in the relevant field of music therapy.

关键词： data Mining Music information retrieval (MIR) Music therapy Heart rate variability (HRV)

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Applying text analyses and data mining to support process oriented multimodel approaches 1

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22nd European conference on Systems, Software and Services Process Improvement, EuroSPI 2015

作者： Karaffy, Zoltan Balla, Katalin Budapest University of Technology and Economics Budapest Hungary Robert Bosch Elektronika Ltd Hatvan Hungary SQI-Hungarian Software Quality Consulting Institute Ltd Budapest Hungary

ISBN: (数字)9783319246475

ISBN: (纸本)9783319246468

The target of this research is to develop an automatic and quantitative methodology and tool combination using text analyses, data mining and machine learning for the analyses of process oriented international quality approaches and documented quality systems of organizations in the field of software development. Such comparisons require at the moment lots of engineering work by experts thus resulting in inefficient human resource utilization. Our long-term goal is to have a tool that enables the auditors and other stakeholders in a software organization to perform quantitative and automatic pre-assessment about the conformance of the organizations’ documented quality systems compared to international quality approach(es) with efficient human resource utilization. This article is presenting the results of searching for the optimal methodology via comparing CMMI-DEV 1.3 and HiS Scope of Automotive Spice 2.5 standards and creating similarity maps. © Springer international Publishing Switzerland 2015.

关键词： data mining

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Towards Cloud Big data Services for intelligent Transport Systems 22

Towards Cloud Big Data Services for Intelligent Transport Sy...

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22nd ISPE-Inc international conference on Concurrent engineering

作者： Kemp, Gavin Vargas-Solar, Genoveva Ferreira Da Silva, Catarina Ghodous, Parisa Collet, Christine Lopez Amaya, Pedropablo Univ Lyon 1 LIRIS CNRS UMR5202 F-69621 Villeurbanne France LIG Grenoble Inst Technol St Martin Dheres France Ecole Natl Super Electrochim & Electrome Grenoble Thermodynam & Physicochim Met Lab CNRS LIG LAFMIA F-38402 St Martin Dheres France

ISBN: (纸本)9781614995449;9781614995432

In later years, the increase in computation power and data storage has opened new perspectives to data analysis. The possibility to analyse big data brings new insights into obscure and useful correlations in data providing undiscovered knowledge. Applying big data analytics to the transport data has brought better understanding to the transports network revealing unexpected choking points in cities. This technology is still largely inaccessible to small companies due to their limited computational resources and complex for large ones due to the time needed to develop a big data analytical system Using the high scalability of Cloud and the use of specialized services in a services oriented architecture, new perspective are to developing efficient and scalable big data infrastructure adapted to transport systems. This paper presents a big data infrastructure using service oriented architecture.

关键词： ITS Big data Cloud Services NoSQL

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Constrained Non-negative Matrix Factorization with Graph Laplacian 22nd

Constrained Non-negative Matrix Factorization with Graph Lap...

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22nd international conference on Neural Information Processing (ICONIP)

作者： Chen, Pan He, Yangcheng Lu, Hongtao Wu, Li Shanghai Jiao Tong Univ Dept Comp Sci & Engn Key Lab Shanghai Educ Commiss Intelligent Interac Shanghai 200240 Peoples R China

ISBN: (纸本)9783319265551;9783319265544

Non-negative Matrix Factorization (NMF) is proven to be a very effective decomposition method for dimensionality reduction in data analysis, and has been widely applied in computer vision, pattern recognition and information retrieval. However, NMF is virtually an unsupervised method since it is unable to utilize prior knowledge about data. In this paper, we present Constrained Non-negative Matrix Factorization with Graph Laplacian (CNMF-GL), which not only employs the geometrical information, but also properly uses the label information to enhance NMF. Specifically, we expect that a graph regularized term could preserve the local structure of original data, meanwhile data points both having the same label and possessing different labels will have corresponding constraint conditions. As a result, the learned representations will have more discriminating power. The experimental results on image clustering manifest the effectiveness of our algorithm.

关键词： Non-negative matrix factorization Dimensionality reduction Semi-supervised learning Graph Laplacian Clustering

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