Electrical Systems are designed in the amalgamation of various types of electrical equipment at the generation, transmission, and distribution verge to furnish uninterrupted power to the consumers. To communicate this...
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Process models are an important tool for software engineers to produce reliable software within schedule and budget. Especially technically challenging domains like machinelearning need a supportive process model to ...
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ISBN:
(纸本)9781450357401
Process models are an important tool for software engineers to produce reliable software within schedule and budget. Especially technically challenging domains like machinelearning need a supportive process model to guide the developers and stakeholders during the development process. One major problem type of machinelearning is anomaly detection. Its goal is to identify anomalous data points (outlier) between the normal data instances. Anomaly detection has a wide scope of applications in industrial and scientific areas. Detecting intruders in computer networks, distinguishing between cancerous and healthy tissue in medical images, cleaning data from disturbing outliers for further evaluation and many more. The cross-industry standard process for datamining (CRISP-DM) has been developed to support developers with all kinds of datamining applications. It describes a generic model of six phases that covers the whole development cycle. The generality of the CRISP-DM model is as much a strength as it is a weakness, since the particularities of different problem types like anomaly detection can not be addressed without making the model overly complex. There is a need for a more practical, specialised process model for anomaly detection applications. We demonstrate this issue and outline an approach towards a practical process model tailored to the development of anomaly detection systems.
Extracting loosely structured data records (DRs) has wide applications in many domains, such as forum patternrecognition, blog data analysis, and books and news review analysis. Currently existing methods work well f...
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ISBN:
(纸本)9781424418367
Extracting loosely structured data records (DRs) has wide applications in many domains, such as forum patternrecognition, blog data analysis, and books and news review analysis. Currently existing methods work well for strongly structured DRs only. In this paper, we address the problem of extracting loosely structured DRs through miningstrict patterns. In our method, we utilize both content feature and tag tree feature to recognize the loosely structured DRs, and propose a new approach to extract the DRs automatically. Through experimental study we demonstrate that this method is both effective and robust in practice.
As the wave of data-driven informatization sweeps the world, society is moving from the information age (IT) to the data age (DT). GIS technology has become an important tool for complex regional development and predi...
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ISBN:
(纸本)9798400707032
As the wave of data-driven informatization sweeps the world, society is moving from the information age (IT) to the data age (DT). GIS technology has become an important tool for complex regional development and prediction. However, the existing power grid design methods cannot meet the needs of improving production efficiency and controlling engineering costs. This paper proposes an intelligent line selection and optimization scheme for transmission lines based on big data analysis and artificial intelligence. The map model is designed using GIS multi-band raster maps, and datamining technology is used to maximize the application value of multi-factor geographic information data. The reverse dynamic programming algorithm is used for intelligent line selection, and the particle swarm optimization-convolutional neural network (PSO-CNN) model is used for engineering quantity prediction to improve the prediction accuracy and obtain more accurate optimization scheme ranking results. The scheme aims to reduce the design workload, reduce the industry design cost, improve the intelligence and standardization level of transmission and transformation engineering design, and provide strong support for the construction of smart grids.
This paper develops a supervised discriminant technique, called marginal and nonlocal discriminant embedding (MNDE), for dimensionality reduction of high-dimensional data in small sample size problems. MNDE can be see...
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ISBN:
(纸本)9781424441990
This paper develops a supervised discriminant technique, called marginal and nonlocal discriminant embedding (MNDE), for dimensionality reduction of high-dimensional data in small sample size problems. MNDE can be seen as a linear approximation of a multimanifold-based learning framework in which nonlocal property is taken into account besides the marginal property and local property. MNDE seeks to find a set of perfect projections that not only can impact the samples of intraclass and maximize the margin of interclass, but also can simultaneously maximize the nonlocal scatter that characterizes the sum scatter of any pair of data out of local K-neighborhood. This characteristic makes MNDE more intuitive and more powerful than LDA and Marginal Fisher Analysis (MFA). The proposed method is applied to face recognition and is examined on the Yale and AR face image databases.
In this paper, we propose an automatic method for manuscript author verification based on an analysis of consecutive patches extracted from an image. The classification algorithm uses a deep convolutional network with...
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ISBN:
(纸本)9781509066285
In this paper, we propose an automatic method for manuscript author verification based on an analysis of consecutive patches extracted from an image. The classification algorithm uses a deep convolutional network with two types of patch extraction: one based on connected components and the other based on usage of a fixed-size sliding window. We apply this method to verify the authorship of the Arabic manuscript entitled al-Khitat attributed to the hand of the renowned medieval Arab historian al-Maqrizi. Using appropriately collected ground-truth labeled data for convolutional network training purpose, our method has demonstrated promising results when applied to previously unseen manuscripts.
The proceedings contain 202 papers. The topics discussed include: local search algorithm for K-means clustering based on minimum sub-cluster size;a discretization algorithm of continuous attributes based on supervised...
ISBN:
(纸本)9781424441990
The proceedings contain 202 papers. The topics discussed include: local search algorithm for K-means clustering based on minimum sub-cluster size;a discretization algorithm of continuous attributes based on supervised clustering;a modified differential evolution algorithm for multi-objective optimization problems;collaborative filtering in personalized recommendation based on users pattern subspace clustering;the generic object classification based on MIML machinelearning;cluster based multi-populations genetic algorithm in noisy environment;feature selection for classifying datastream based on maximum entropy;kernel-plural discriminant analysis based on Fourier transform and its application to face recognition;local graph embedding discriminant analysis for face recognition with single training sample per person;two-dimensional local graph embedding discriminant analysis(F2DLGEDA) with its application to face and palm biometrics;and gait recognition based on multi-resolution regional shape context.
The proceedings contain 145 papers. The topics discussed include: advancing knowledge discovery and datamining;knowledge management in the ubiquitous software development;a novel network intrusion detection system (N...
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ISBN:
(纸本)0769530907
The proceedings contain 145 papers. The topics discussed include: advancing knowledge discovery and datamining;knowledge management in the ubiquitous software development;a novel network intrusion detection system (NIDS) based on signatures search of datamining;mining high utility itemsets in large high dimensional data;effective pruning strategies for sequential patternmining;cooperation forensic computing research;grasping related words of unknown word for automatic extension of lexical dictionary;a novel website structure optimization model for more effective web navigation;average fuzzy direction based handwritten Chinese characters recognition approach;the datamining technology based on CIMS and its application on automotive remanufacturing;an empirical study on improving the manufacturing informatization index system of China;and centrality research on the traditional Chinese medicine network.
The proceedings contain 7 papers. The topics discussed include: interactive trace clustering to enhance incident completion time prediction in process mining;pattern-based reconstruction of anomalous traces in busines...
The proceedings contain 7 papers. The topics discussed include: interactive trace clustering to enhance incident completion time prediction in process mining;pattern-based reconstruction of anomalous traces in business process event logs;impact of non-fitting cases for remaining time prediction in a multi-attribute process-aware method;continual-learning-as-a-service (CLaaS): on-demand efficient adaptation of predictive models;efficient anomaly detection on temporal data via echo state networks and dynamic thresholding;making FreeRTOS pervasive systems learn to select energy saving technique for mixed taskset;and a cloud-based continual learning system for road sign classification in autonomous driving.
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