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Unsupervised classification of mixed data type of attributes using genetic algorithm (Numeric, categorical, ordinal, binary, ratio-scaled) 1

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3rd international conference on soft computing for Problem Solving, SocProS 2013

作者： Rastogi, Rohit Agarwal, Saumya Sharma, Palak Kaul, Uarvarshi Jain, Shilpi Computer Science and Engineering Department ABES Engineering College GhaziabadUttar Pradesh India

ISBN: (数字)9788132217718

ISBN: (纸本)9788132217701

data mining discloses hidden, previously unknown, and potentially useful information from large amounts of data. As comparison to the traditional statistical and machine learning data analysis techniques, data mining emphasizes to provide a convenient and complete environment for the data analysis. data mining has become a popular technology in analyzing complex data. Clustering is one of the data mining core techniques. In the field of data mining and data clustering, it is a highly desirable task to perform cluster analysis on large data sets with mixed numeric, categorical, ordinal, and ratio-scaled with binary and nominal values. However, most already available data merging and grouping through clustering algorithms are effective for the numeric data rather than the mixed data set. For this purpose, this paper makes efforts to present a new amalgamation algorithm for these mixed data sets by modifying the common cost function, trace of the within cluster dispersion matrix. The genetic algorithm (GA) is used to optimize the new cost function to obtain valid clustering result. We can compare and analyze that the GA-based clustering algorithm is feasible for the highdimensional data sets with mixed data values that are obtained in real life results. Core Idea of Our Paper: By this paper, we try to describe a technique for estimating the cost function metrics from mixed numeric, categorical and othertype databases by using an uncertain grade-of-membership clustering model with the efficiency of Genetic Algorithm. This technique can be applied to the problem of opportunity analysis for business decision-making. This general approach could be adapted to many other applications where a decision agent needs to assess the value of items from a set of opportunities with respect to a reference set representing its business. For processing numeric attributes, instead of generalizing them, a prototype may be developed for experiments with synthetic and real data sets, and co

关键词： data mining

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Domain specific search engine based on semantic web 3

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3rd international conference on soft computing for Problem Solving, SocProS 2013

作者： Kohli, Shruti Arora, Sonam Department of Computer Science BIT Noida Uttar Pradesh India Department of Computer Science ABES Engineering College Ghaziabad Uttar Pradesh India

ISBN: (纸本)9788132217671

Currently search engines are using keyword based approach which becomes problematic when the user is not aware about the way to write query such that desirable results only appear because for that he must know the semantic concepts that are used in that particular domain in which he is interested. To overcome this problem, we introduced the concept of Semantic Web which is an extension of the current Web that allows the meaning of information to be precisely described in terms of well-defined vocabularies that are understood by people and computers. Ontology is one of the fundamental ingredients used in the semantic web infrastructure. This paper focuses on the problem of internet users who most of the time looks for the good hotels available beforehand whenever they plan any trip. At times they go for image search option to have a view of hotels in a particular location. But problem is user gets confused whether search results displayed online by currently available search engines are reliable or not. This paper aims to implement a tool which is based on Semantic Web for searching the images of hotels and displaying the results. Semantic web refines the search in such a way that only relevant images are returned. The crux is to develop ontology for various hotels along with their locations and the facilities they provide. User enters his query from an interface and corresponding SPARQL query is generated. This query with the help of JENA API is searched in the data present in Ontology. © Springer India 2014.

关键词： Ontology

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An ontology and pattern clustering approach for activity recognition in smart environments 3

An ontology and pattern clustering approach for activity rec...

引用

3rd international conference on soft computing for Problem Solving, SocProS 2013

作者： Gayathri, K.S. Elias, Susan Shivashankar, S. Department of Computer Science and Engineering Sri Venkateswara College of Engineering Sriperumbudur602117 India Ericsson Research India Chennai India

ISBN: (纸本)9788132217701

Activity recognition aims at modeling the occupants’ behavior by analyzing the sensor data collected from the smart environment. Though most of the activity recognition systems use supervised learning techniques for building such models there is a shift towards the unsupervised learning paradigm as the process of annotating and labeling the data is prone to errors. This paper proposes an Event Pattern Activity Modeling Framework (EPAM) to identify the occupant activity pattern from the sensor data by using an unsupervised machine learning approach and further analysis is done with a knowledge driven approach. In the context of smart environments, an activity is considered as a sequence of events that are generated continuously from the sensor data. The segmentation algorithm proposed in EPAM is used to identify appropriate event patterns for an activity that are then grouped together using a pattern clustering algorithm that presents a hierarchy of activities. The set of activities of the occupant, observed in a smart environment is not always sequential but is highly interleaved and discontinuous. The proposed algorithm accommodates this valid factor by an innovative use of the Jaro Winkler similarity measure. The hierarchy of activity generated by the pattern clustering approach is used for activity modeling. Ontology based activity modeling is preferred over other modeling techniques because of its unified modeling, representation and semantically clear reasoning. The experimentalresults show that the proposed EPAM framework of segmentation, pattern clustering and ontological modeling is efficient and more effective than the existing approach of activity modeling. © Springer India 2014.

关键词： Supervised learning

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Strongly biased crossover operator for subgraph selection in edge-set based graph representation 3

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3rd international conference on soft computing for Problem Solving, SocProS 2013

作者： Arora, Sakshi Garg, M.L. School of Computer Science and Engineering S.M.V.D.U Katra India

ISBN: (纸本)9788132217671

The performance of crossover operator is a complex interplay of the various characteristics of the genetic algorithm (GA) and sometimes also of the problem under question. The fundamental design choices in a GA therefore, are its representation of candidate solutions and the operators that will act on that representation. This paper suggests a new strongly biased multiparent crossover operator that offers a strong locality and provides a strong heritability quotient but can still escape the local optima when the sample space is over represented by similar building blocks. The proposed operator uses a dynamic 2-D vector representation for the chromosomes and this data structure may evolve as the execution of the crossover operator proceeds. On a population which consists of solutions near to optimal and mostly lying in the basin of attraction of single optima, the bias towards the optima in the generated offsprings is proportional to the sample size from the search space. Based on this reasoning, the effect of arity of the proposed crossover operator is tested using a population in which all the candidates lie in close neighborhood of each other. To analyze the dynamic search behavior of the proposed crossover operator and the impact of the representation scheme on the locality, heritability and exploratory power of the operator, we suggest a no mutation, zero selection pressure GA model that generates bounded diameter minimum spanning trees from the underlying complete graphs on random and Euclidean instances. © Springer India 2014.

关键词： Genetic algorithms

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An efficient technique to control road traffic using Fuzzy Neural Network System

An efficient technique to control road traffic using Fuzzy N...

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international conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Apoorva Aggarwal Archana Purwar Shubham Gulati Department of Computer Science and Engineering Jaypee Institute of Information and Technology Noida India

Conventional road traffic controlling systems are dependent on human operators for most of the decisions. Such human operators have experienced a wide variety of incidents and traffic congestions. But even the most experienced operators fail to control traffic efficiently during non-recurrent situations. Non-recurrent situation is a situation which has not been seen earlier by a human operator prior to its occurrence. Controlling traffic flow in such a situation is a complex task as it demands quick reaction and expert knowledge. This paper proposes a novel and efficient approach to control traffic flow in non-recurrent traffic situations. The proposed approach uses multiple techniques, most of which are borrowed from soft computing such as, NN (Neural Network), FL (Fuzzy Logic) and GA (Genetic Algorithm). This approach involves clustering imprecise data, in the form of Gaussian mixtures, into fuzzy sets using Expectation Maximization algorithm. The approach also includes minimizing the initial population of chromosomes in Genetic algorithm using a novel algorithm. The proposed algorithm used in identification of valid rules for fuzzy system reduces space and time complexity of the process. The proposed approach has been validated using METANET.

关键词： Biological cells Traffic control Fuzzy neural networks Fuzzy control Fuzzy sets Roads Fuzzy logic

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Scheduling real-time workflow on MapReduce-based cloud

Scheduling real-time workflow on MapReduce-based cloud

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2013 3rd international conference on Innovative computing Technology, INTECH 2013

作者： Teng, Fei Yang, Hao Li, Tianrui Yang, Yan Li, Zhao School of Information Science and Technology Southwest Jiaotong University Chengdu 610031 China

ISBN: (纸本)9781479900473

As a popular programming model in cloud-based data processing environment, MapReduce and its open source implementation Hadoop, are widely applied both in industry and academic researches. A key challenge in MapReduce-based cloud is the ability to automatically control resource allocations to real-time workflows for achieving their custom-defined deadlines. Current researches on deadline-related MapReduce schedulers only support soft real-time scheduling, where the extension of the deadline is allowed. In this paper, the hard real-time scheduling problem with a strict deadline on MapReduce-based cloud is studied. We propose a SPS scheduler that can guarantee job completion time before the specified deadline for real-time workflows. SPS supports job preemption with low context-switch overhead so that it can make online scheduling decision when workflows randomly arrive in cloud. Experiments on Hadoop show that SPS effectively meets the deadline constraint even if the workflow demands exceed the cluster resources. © 2013 IEEE.

关键词： MapReduce

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Load and energy billing forecasting of an electricity utility

Load and energy billing forecasting of an electricity utilit...

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3rd international conference on soft computing Technology in Civil, Structural and Environmental Engineering, CSC 2013

作者： Figueiredo, K. Vellasco, M.M.B.R. Mattoso, J.C.A. Department of Applied Mathematics and Computational Science UEZO Rio de Janeiro Brazil Brazil Light S.A. Company Rio de Janeiro Brazil

ISBN: (纸本)9781905088584

This study presents a methodology for predicting electrical energy load and billing that considers climate data and economic-financial factors. Using the clustering method, the concession area of a distributing company was divided into 17 microclimates according to the climate characteristics of districts (of the city of Rio de Janeiro) and municipalities (of the state of Rio de Janeiro). From the microclimates, the billing and load series were separated by consumption class (residential, commercial, public power, public service and rural). Models based on multilayer perceptron (MLP) multistep neural networks and linear regression were developed for the monthly prediction with a maximum of 15 steps forward (multistep). Adequate performance was achieved for the majority of the consumption and load classes. ©Civil-Comp Press, 2013.

关键词： Forecasting

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Influence of data density on grade estimation in a limestone quarry using kriging

Influence of data density on grade estimation in a limestone...

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3rd international conference on soft computing Technology in Civil, Structural and Environmental Engineering, CSC 2013

作者： Chen, Y. Bessho, M. Ito, T. Graduate School of Energy Science Kyoto University Kyoto Japan International Center for Research and Education on Mineral and Energy Resources Akita University Akita Japan Faculty of Informatics Kansai University Osaka Japan

ISBN: (纸本)9781905088584

In order to construct an effective management of the product from a limestone quarry, the correlation between the precision of interpolation and data density when estimating the distribution of CaO grade in quarries by kriging was considered. The reference distribution map of which resolution is 1 meter was firstly generated for the square area of 160 × 160 meters using the grade measurement data of 1,472 cuttings from an operating limestone quarry. Then 8 testing distribution maps with the resolution of 2 to 9 meters (i.e., 25.6 to 1.4% data density compared with the reference distribution map) were prepared by thinning out the unnecessary data of the reference distribution map. These testing distribution maps were compared with the reference distribution map. As a result, reasonable CaO grade distribution was obtained for the testing distribution with 2 to 6 meter interval data (25.6 to 3.1% data density). It is consequently confirmed that the precision of interpolation decreases as the data density decreases, but the decrease tendency is not linear in the test conditions. In the point of error percentage, grade distribution estimated by the testing data set with 5 meter intervals (4.3% data density) shows good interpolation results with less than 1% error range for more than 75% of the analyzed area of the working face, while more than 50% of the area is beyond the 1% error range using the test data set with 9 meter intervals (1.4% data density). ©Civil-Comp Press, 2013.

关键词： Kriging

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Influence of exceptional data on bridge damage assessment based on one-class SVM pattern recognition

Influence of exceptional data on bridge damage assessment ba...

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3rd international conference on soft computing Technology in Civil, Structural and Environmental Engineering, CSC 2013

作者： Furuta, H. Nomura, Y. Nakatsu, K. Hattori, H. Yasuda, S. Faculty of Informatics Kansai University Japan Department of Science and Engineering Ritsumeikan University Japan Department of Modern Life Osaka Jonan Women's Junior College Japan Graduate School of Engineering Kyoto University Japan Graduate School of Informatics Kansai University Japan

ISBN: (纸本)9781905088584

Recently, it has become difficult to ensure the skill level of bridge maintenance engineers as a result of the retirement of experienced engineers. Hence, the daily inspection of bridges cannot be performed accurately and efficiently for the early detection of damage and to ensure appropriate measures. In previous research, damage assessment by introducing the technology of pattern recognition of digital images has been proposed. However, in the previous research, it was confirmed that there were some images which might be considered as exceptional data among the images of cracks of concrete decks. This paper describes the development of a support system for decision making in the damage assessment of bridges based on the pattern recognition by using the exception extraction. In this system, the exception extraction with one-class SVM removes some data that may cause the decrease of generalization capability of pattern recognition from the database. Thus, it may be expected that the accuracy and confidence of the results are improved. Numerical examples applying digital images of reinforced-concrete slabs of bridges with cracks are presented to demonstrate the practical use of the approach of this approach. ©Civil-Comp Press, 2013.

关键词： Pattern recognition

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A hybrid soft sensing approach of a cement mill using principal component analysis and artificial neural networks

A hybrid soft sensing approach of a cement mill using princi...

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IEEE international Advance computing conference, IACC

作者： Ajaya Kumar Pani Hare Krishna Mohanta Department of Chemical Engineering Birla Institute of Technology and Science Pilani India

ISBN: (纸本)9781467345279

soft sensors play an important role in predicting the values of unmeasured process variables from knowledge of easily measured process variables. Online estimation of particle size is vital for efficient control of a grinding circuit. Due to high energy consumption in cement grinding processes and unavailability of reliable hardware sensors for continuous monitoring, soft sensors have tremendous scope of application in cement mills. Modern cement plants are increasingly using vertical roller mills for clinker grinding. While there have been some works reported in the literature about modelling of ball mills, very few research work is available on vertical roller mill modelling. In the present work a PCA based neural network model of a cement mill is developed based on the actual plant data for estimation of cement fineness. Real time data for all process variables relevant to cement grinding process were collected from a cement plant having a clinker grinding capacity of 235 TPH. The collected raw industrial data were pre processed for outlier removal and missing value imputation. Principal component analysis of the input data was performed to transform the original variables to a less number of un correlated principal components. The selected principal component scores were divided to a training set and a validation set using Kennard-Stone subset selection algorithm. The training set was used to develop a back propagation neural network model which was subsequently tested with the validation set. Simulations results show satisfactory prediction capabilities of the developed model over that of linear regression and principal component regression models.

关键词： data models Sensors Training Principal component analysis Computational modeling Predictive models Neurons

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