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Lattice Computing is the class of algorithms built on the basis of Lattice Theory. They either perform operations in the ring of the real valued spaces endowed with some (inf, sup) lattice operators, or use lattice theory to produce generalizations or fusions of conventional approaches. Lattice Computing has produced a variety of algorithms for data processing, classification, signal filtering over...
Information granules allow us to abstract real world objects using their relevant attributes. Level of abstraction allows us to create a network of information granules that are connected to each other through a real-world relationship. For example, the phone calls are connected to the origin and destination phone numbers. This paper describes the relationship between clustering schemes by propagating...
We use generalized resistor-inductor-capacitor (RLC) circuits to model bases, nucleosides and nucleotides at the atomic and molecular levels and analyze the responses of these circuits to different types of input signals. Based on simple recurrence equations that characterize the impedances of such circuits, we develop compositions of these basic circuits to model arbitrarily long nucleotide strings...
Feature recognition is a complex process consists of several steps. This process mostly requires the extraction of geometric features like normal vectors and curvatures, as well as segmentation of points cloud. There are two fundamental approaches can be used to calculate normal vectors and curvatures: analytical and numerical. The first approach is practically more complex. Nevertheless, the second...
Image compression has received tremendous research input in recent times; however, only few works have been done with respect to face recognition regardless of the fact that, usually, face images need to be transmitted across networks for recognition purposes as seen in surveillance systems. This work analytically investigates the effects of image compression on recognition accuracies of three selected...
This paper presents a comparison of competitive learning algorithms for Self Organizing Map (SOM). The competitive learning algorithms showed to self organizing map algorithm are winner-takes-all, Frequency Sensitive Competitive Learning and Rival Penalized Competitive Learning. The result shows the performance in classification of partial discharge on power cables using SOM.
It is possible to go and physically observe any situation under the area of our reach, but this is not so for the areas beyond our physical boundaries. For the purpose, a methodology inspired from nature is proposed using remote sensing inputs based on swarm intelligence for the anticipatory computation of the regions beyond our borders. The paper presents a nature inspired anticipatory computing...
Fuzzy logic deals with partial truth. A fuzzy based approach to blog analysis, on the basis of various feature words, allows us to determine the degree to which a blogger's style belongs to a particular age or gender group. Each blog was represented by a set of normalized word frequencies of selected feature words in it. Using membership values obtained from applying Fuzzy C-Means (FCM) algorithm...
In the present article, an efficient method for object tracking is proposed using Radial Basis Function Neural Networks and K-means. This proposed method starts with K-means algorithm to do the segmentation of the object and background in the frame. The Pixel-based color features are used for identifying and tracking the object. The remaining background is also considered. These classified features...
In this paper we proposed a novel multimodal biometric approach using iris and periocular biometrics to improve the performance of iris recognition in case of non-ideal iris images. Though iris recognition has the highest accuracy among all the available biometrics, still the noises at the image acquisition stage degrade the recognition accuracy. The periocular region can act as a supporting biometric,...
Recently, content based image retrieval (CBIR) has gained active research focus due to wide applications such as crime prevention, medicine, historical research and digital libraries. With digital explosion, image collections in databases in distributed locations over the Internet pose a challenge to retrieve images that are relevant to user queries efficiently and accurately. It becomes increasingly...
In this paper we have evaluated a new approach of Q-learning based on knowledge update in more extended environment. After learning at a fixed goal position, it is convenient for a robot to reach to the fixed destination from where it has started learning. With the new approach we can change the destination even after learning. The above process is evaluated with the concept of state-action pair values...
Multiple description (MD) video coding is a promising method to solve real-time video transmission over unreliable network. In the conventional MD video coding, the original video sequence can be split directly into two subsequences by odd and even means. Then the two sub-sequences can be compressed as two descriptions by the standard video encoder. The conventional MD scheme is simple to realize...
The scope of this research is to propose a method for determining pairs of corresponding points between medical images, the method is based on the implementation of Particle Swarm Optimization (PSO) used as a function optimizer and Mutual Information used as a similarity measure. Firstly, Landmarks (LMs) were chosen manually specific to Braces Pty Ltd cephalometric analysis and used Thin Plate Spline...
The proactive recommender system automatically delivers (i.e. pushes) recommendations to the user, without explicit request from him. The push model seems to be very effective in the applications where the availability of items changes often and rapidly, as it helps users timely receive their interested information. However, if the system pushes uninterested information to the user, or even pushes...
This paper presents a hybrid intelligent watermarking scheme for securing fingerprint images in the wavelet domain. The proposed method uses an NN-PSO based hybrid approach to secure a person's fingerprint image by watermarking it with its corresponding face image and demographic information. The input fingerprint image is divided into blocks in the proposed approach and a feed forward neural network...
The objective of this paper is to evaluate a new combined approach intended for reliable CT liver image segmentation, to separate the liver from other organs, and segment the liver into a set of regions of interest (ROIs). The approach combines the level set with watershed approach used as post segmentation step to produce a reliable segmentation result. Features of first order statistics and grey-level...
This paper presents an automatic image annotation approach for region labeling. The proposed approach is based on multi-class k-nearest neighbor, K-means, and particle swarm optimization algorithms for feature weighting, in conjunction with normalized cuts based image segmentation technique. This hybrid approach refines the output of multi-class classification that is based on the usage of k-nearest...
This paper presents and evaluates the performance of two well-known segmentation approaches that were applied on liver computed tomography (CT) images. The two approaches are K-means and normalized cuts. An experiment was applied on ten liver CT scan images, with reference segmentations, in order to test the performance of the two approaches. Experimental results were compared using an evaluation...
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