This paper presents a hybrid classifier based on extended Self Organizing Map with Probabilistic Neural Network. In this approach, at first we use feature extraction technique of Self Organizing Map to achieve topolog...
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ISBN:
(纸本)9783319119328
This paper presents a hybrid classifier based on extended Self Organizing Map with Probabilistic Neural Network. In this approach, at first we use feature extraction technique of Self Organizing Map to achieve topological ordering in the input data pattern. Then, with the use of Gaussian function, we obtain a better representation of the input dataset. After that, Probabilistic Neural Network is used to classify the input data. We have tested the proposed scheme on Iris, Glass, Breast Cancer Wisconsin, Wine, Ionosphere, Liver (BUPA), Sonar, Thyroid, and Vehicle data sets. The experimental results show better recognition accuracy of the proposed model than that of traditional Probabilistic Neural Network based classifier.
Stress is recognized as a factor of predominant disease and in the future the costs for treatment will increase. The presented approach tries to detect stress in a very basic and easy to implement way, so that the cos...
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Machine learning methods are used today mostly for recognition problems. Convolutional Neural Networks (CNN) have time and again proved successful for many image processing tasks primarily for their architecture. In t...
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We present a method to simplify a formal context while retaining much of its information content. Although simple, our ICRA approach offers an effective way to reduce the complexity of a concept lattice and/or a knowl...
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The insufficient performance of statistical recognition of composite objects (images, speech signals) is explored in case of medium-sized database (thousands of classes). In contrast to heuristic approximate nearest-n...
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The proceedings contain 55 papers. The special focus in this conference is on patternrecognition and Machine Intelligence. The topics include: Recent Advances in Recommender Systems and Future Directions;On the Numbe...
The proceedings contain 55 papers. The special focus in this conference is on patternrecognition and Machine Intelligence. The topics include: Recent Advances in Recommender Systems and Future Directions;On the Number of Rules and Conditions in Mining Data with Attribute-Concept Values and "Do Not Care" Conditions;Simplifying Contextual Structures;Towards a Robust Scale Invariant Feature Correspondence;Hierarchical Agglomerative Method for Improving NPS;A New Linear Discriminant Analysis Method to Address the Over-Reducing Problem;Procedural Generation of Adjustable Terrain for Application in Computer Games Using 2D Maps;Fixed Point Learning Based 3D Conversion of 2D Videos;Fast and Accurate Foreground Background Separation for Video Surveillance;Enumeration of Shortest Isothetic Paths Inside a Digital Object;Modified Exemplar-Based Image Inpainting via Primal-Dual Optimization;A Novel Approach for Image Super Resolution Using Kernel Methods;Generation of Random Triangular Digital Curves Using Combinatorial Techniques;Tackling Curse of Dimensionality for Efficient Content Based Image Retrieval;Face Profile View Retrieval Using Time of Flight Camera Image Analysis;Context-Based Semantic Tagging of Multimedia Data;Improved Simulation of Holography Based on Stereoscopy and Face Tracking;Head Pose Tracking from RGBD Sensor Based on Direct Motion Estimation;A Novel Hybrid CNN-AIS Visual patternrecognition Engine;Modified Orthogonal Neighborhood Preserving Projection for Face recognition;An Optimal Greedy Approximate Nearest Neighbor Method in Statistical patternrecognition.
Identifying related offences in a criminal investigation is an important goal for crime analysts. This can deliver evidence that can assist in apprehension of suspects and better attribution of past crimes. The use of...
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Identifying related offences in a criminal investigation is an important goal for crime analysts. This can deliver evidence that can assist in apprehension of suspects and better attribution of past crimes. The use of pattern based approaches has the potential to assist crime experts in discovering new patterns of criminal activity. Hence, research in this area continues. This paper revisits frequent pattern growth models for crime pattern mining. Frequent pattern (FP) based approaches, such as the FP-Growth model, have been identified to be more effective than techniques proposed in the past, such as Apriori. Therefore, this research proposes a descriptive statistical approach, based on a quartile (floor-ceil) function, for the minimum support threshold (MST) choice selection, which is a major decision step in the pruning phase of the Traditional FP-Growth (TFPG) model. Our revised frequent pattern growth (RFPG) model further proposes a pattern-pattern (P-p) paradigm to identify tuples of subtle crime pattern(s) sequences or recurring trends in criminal activity. We present empirical results in order to guide intended audience about future decisions or research regarding this model. Results indicate that RFPG is more promising than TFPG and will always ensure the utilisation of a reasonable percentage of the crime dataset, in order to produce more reliable and sufficiently informative patterns or trends. (C) 2015 Published by Elsevier B.V.
One of the important problems in functional genomics is how to select the disease genes. In this regard, the paper presents a new similarity measure to compute the functional similarity between two genes. It is based ...
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The proceedings contain 58 papers. The topics discussed include: a crowdsourcing framework for toll plazas;personalization of news recommendation using genetic algorithm;a sparsification technique for faster hierarchi...
ISBN:
(纸本)9781479970025
The proceedings contain 58 papers. The topics discussed include: a crowdsourcing framework for toll plazas;personalization of news recommendation using genetic algorithm;a sparsification technique for faster hierarchical community detection in social networks;user interface for community detection in social networks;analyzing and classifying user search histories for web search engine optimization;a survey of storage remote replication software;a roughset based data labeling method for clustering categorical data;face recognition using local ternary pattern and booth's algorithm;complex event processing based remote health monitoring system;a robust and energy efficient dynamic ID based remote user authentication scheme for multi-server environment;and an energy efficient temporal credential based mutual authentication scheme for WSN.
Cloud Computing is now one of the most hyped information technology arenas and it has becoming one of the fastest rising sections of IT. Cloud computing permits us to scale our servers in greatness and availability in...
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ISBN:
(纸本)9781509001484
Cloud Computing is now one of the most hyped information technology arenas and it has becoming one of the fastest rising sections of IT. Cloud computing permits us to scale our servers in greatness and availability in order to provide services to a greater number of end users. Furthermore, adopters of the cloud service model are charged based on a pay-per-use basis of the cloud's server and network resources. Resources can easily be scaled up or down dynamically without much interaction between client and service provider. The function of this model is to reduce the effect of EDoS attack by some tactical enemy/s, set of enemies or zombie machine system (BOTNET) to curtail the accessibility of the target resources, which declines the profits and increases the cost of the different cloud workers in a direct or indirect way. In this paper, we proposed an approach, named pattern Attack recognition, to detect the Economical-Denial-Of-Sustainability (EDoS) attack from cloud platforms. We sketch a proposed model to assess Outcome of the results in terms of its response time and resource usage.
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