Abstract: Cluster analysis is an unsupervised learning method that constitutes a cornerstone of an intelligent data analysis process. Clustering categorical data is an important research area data mining. In this paper we propose a novel algorithm to cluster categorical data. Based on the minimum dissimilarity value objects are grouped into cluster. In the merging process, the objects are relocated using silhouette coefficient. Experimental results show that the proposed method is efficient.

Abstract: Due to the abundantly available imaging technologies, manipulation of digital images has become a serious problem nowadays, in various fields like medical imaging, digital forensics, journalism, scientific publications, etc. In this paper, we concentrate on detection of a specific category of digital image forgery known as region duplication forgery or copy-move forgery, which is done by copying a block of an image and pasting it on to some other block of the same image. We present a novel approach based on the application of wavelet transform that detects and localizes such forgeries. Our technique works by first applying wavelet transform to the input image to yield a reduced dimension representation. We then perform exhaustive search to identify the similar blocks in the image by mapping them to log-polar coordinates and using phase correlation as the similarity criterion. This is done only once at the lowest resolution of the wavelet transform. Only the matched blocks are carried for comparison to the next level. This drastically reduces the time needed for the detection process. This approach works even if the pasted region has undergone transformations like translation and rotation.

Abstract: In this paper we propose an approach to recognize handwritten Tamil characters using a multilayer perceptron with one hidden layer. The feature extracted from the handwritten character is Fourier descriptors. Also an analysis was carried out to determine the number of hidden layer nodes to achieve high performance of backpropagation network in the recognition of handwritten Tamil characters. The system was trained using several different forms of handwriting provided by both male and female participants of different age groups. Test results indicate that Fourier descriptors combined with backpropagation network provide good recognition accuracy of 97% for handwritten Tamil characters.

Abstract: Claims typically found at the end of a patent document are one of the key elements of a patent and define the boundaries or scope of protection conferred by a patent. Claims of related patents also need to be read and reviewed carefully by an inventor or a patent attorney at the time of drafting a patent application. We present a method and a tool to do a claim similarity analysis between two different patents based on natural language processing techniques. The technique proposed in this paper relies on computing similarity between two claims based on syntactic and semantic matching of the natural language text describing the claims. We present results of experiments performed on patent claim data obtained from patents published on Google patents website. The motivation behind the research presented in this paper is to build patent processing tools to increase the overall productivity of a patent analyst or a patent attorney while doing claims infringement, validity and quality analysis.

Abstract: Genetic complementary learning (GCL) is a biological brain-inspired learning system based on human pattern recognition, and genes selection process. It is a confluence of the hippocampal complementary learning and the evolutionary genetic algorithm. With genetic algorithm providing the possibility of optimal solution, and complementary learning providing the efficient pattern recognition, GCL may offer superior performance. In contrast to other computational finance tools such as neural network and statistical methods, GCL provides greater interpretability and it does not rely on the assumption of the underlying data distribution. It is an evolving and autonomous system that avoids the time-consuming process of manual rule construction or modeling. This is highly favorable especially in financial world where data is ever changing, and requires frequent update. The feasibility of GCL as stock market predictor, and bank failure early warning system is investigated. The experimental results show that GCL is a competent computational finance tools for stock market prediction and bank failure early warning system.

Abstract: The aim of this article is to bring forth the issue of integrating the services provided by intelligent artifacts in Ambient Intelligence applications. Specifically, we propose a Distributed Constraint Optimization procedure for achieving a functional integration of intelligent artifacts in a smart home. To this end, we employ Adopt-N, a state-of-the-art algorithm for solving Distributed Constraint Optimization Problems (DCOP). This article attempts to state the smart home coordination problem in general terms, and provides the details of a DCOP-based approach by describing a case study taken from the RoboCare project. More specifically, we show how (1) DCOP is a convenient metaphor for casting smart home coordination problems, and (2) the specific features which distinguish Adopt-N from other algorithms for DCOP represent a strong asset in the smart home domain.

Abstract: Visibility in an underwater and satellite images is poor, also light is strongly attenuated in water, producing images of low contrast and little color variation. Image preprocessing, smoothing, contrast stretching and restoration technology is concerned with producing and re-establishing an actual array of pixels for object representation to enhance the slow moving raw images. In this paper, an image processing method has been proposed for enhancing various slow motion underwater, ground, and satellite images, taken from underwater submarines and celestial sites. In the suggested method after noise smoothing & contrast stretching, image is equalized for better contrast using histogram equalization (HE), however, it tends to change the mean brightness of the image. So this paper proposes a novel extension of histogram equalization, actually histogram specification, to overcome such drawback as HE, named brightness preserving histogram equalization with maximum entropy (BPHEME), Experimental results show that BPHEME can not only enhance the image effectively, but also preserve the original brightness quite well.

Abstract: In document images, we often find printed lines over-lapping with hand written elements especially in case of signatures. Typical examples of such images are bank cheques and payment slips. Although the detection and removal of the horizontal lines has been addressed, the restoration of the handwritten area after removal of lines, persists to be a problem of interest. In this paper, we propose a method for line removal and restoration of the erased areas of the handwritten elements. Subjective evaluation of the results have been conducted to analyze the effectiveness of the proposed method. The results are promising with an accuracy of 86.33%. The entire process takes less than half a second for completion on a 2.4 GHz 512 MB RAM Pentium IV PC for a document image.

Abstract: We study a waste collection vehicle routing problem with time windows (VRPTW) complicated by multiple disposal trips and driver's lunch breaks. Recently Kim et al. [1] introduced and addressed this problem using an extension of the well-known Solomon's insertion approach, and a clustering-based algorithm. We propose and present the results of an initial study of a multi-objective genetic algorithm for the waste collection VRPTW using a set of benchmark data from real-world problems obtained by Kim et al.

Abstract: In digital signal processing applications, the convolution with a very long sequence is often required. In order to compute convolution of long sequence, overlap-add method (OLA) and overlap-save method (OLS) can be considered. The OLA and OLS are well known efficient schemes for high-order filtering. The most commonly used implementation for digital filtering algorithms are digital signal processors, special purpose digital filtering chips and application specific integrated circuits (ASICs) for large volumes. In this paper, a high performance, high throughput and area efficient architecture for the field programmable gate array (FPGAs) implementation of block convolution process is proposed. The most significant aspect of the proposed method is the development of a multiplier architecture based on vertical and crosswise structure of ancient Indian Vedic mathematics and embedding it in OLA and OLS methods for improved efficiency. The coding is done in VHDL (very high speed integrated circuits hardware description language) and the FPGA synthesis is done using Xilinx Spartan library. The results shows that OLA and OLS method of block convolution implemented using Vedic multiplication is efficient in terms of area/speed compared to its implementation using conventional multiplier architectures.

Abstract: The paper presents a two stage classification approach for handwritten Devanagari characters. The first stage is using structural properties like shirorekha, spine in character and second stage exploits some intersection features of characters which are fed to a feedforward neural network. Simple histogram based method does not work for finding shirorekha, vertical bar (spine) in handwritten Devnagari characters. So we designed a differential distance based technique to find a near straight line for shirorekha and spine. This approach has been tested for 50000 samples and we got 89.12% success.

Abstract: For stenography we have used the least significant bit (4LSB) substitution method [2]. The 4LSB method is implemented for color bitmap images (24 bit and 8 bit i.e. 256 color palette images) and wave files as the carrier media. "The goal of stegnography is to hide messages inside other harmless messages in a way that does not allow any enemy to even detect that there is a second secret message present. " By using this proposed algorithm, we can hide our file of any format in an image and audio file. We can then send the image via e-mail attachment or post it on the web site and anyone with knowledge that it contains secret information, and who is in possession of the encryption password, will be able to open the file, extract the secret information and decrypt it.

Abstract: Presence of multiple objectives in a problem, in principle, gives rise to a set of optimal solution (largely known as Pareto-optimal solution), instead of single optimal solution. This type of problem is known as multi-objective optimization problem (MOP). In general a MOP has been solved using weighted sums or decision-making schemes. An alternative way is to look for the Pareto-optimal front. Many evolutionary algorithms (EAs) like genetic algorithm (GA) have been suggested to solve a MOP, hence termed as multi-objective evolutionary algorithms (MOEAs). Nondominated sorting genetic algorithm (NSGA-II) is one such MOEA which demonstrates the ability to indentify a Pareto-optimal front efficiently. Thus, it provides the decision maker (DM) a complete picture of the optimal solution space. This paper presents an application of NSGA-II in order to solve a multi-objective series system reliability optimization problem. Here, conflicting objectives such as maximization of system reliability and minimization of the system cost have been considered. Supremacy of the approach over the existing approach have been depicted and discussed through the results obtained.

Abstract: Stealing cell phone has become the eye catching business for culprits. There should be a reliable method of protecting the instrument from burglars. Our paper proposes a new innovative, reliable, fool proof security measure for cell phones using voice recognition and fingerprint recognition. Our proposal demands neither external hardware nor extra cost. The above methods possess no hardship for the user unlike the present security systems. This method can be adopted universally in all models of Cell phones for protection.

Abstract: Local binary patterns LBP, is one of the features which has been used for texture classification. In this paper, a method based on using these features is proposed for detecting defects in patterned fabrics. In the training stage, at first step LBP operator is applied to all rows (columns) of a defect free fabric sample, pixel by pixel, and the reference feature vector is computed. Then this image is divided into windows and LBP operator is applied to each row (column) of these windows. Based on comparison with the reference feature vector a suitable threshold for defect free windows is found. In the detection stage, a test image is divided into windows and using the threshold, defective windows can be detected. The proposed method is simple and gray scale invariant. Because of its simplicity, online implementation is possible as well.

Abstract: This work gives an overview of the broad field of computational swarm intelligence and its applications in swarm robotics. Computational swarm intelligence is modelled on the social behavior of animals and its principle application is as an optimization technique. Swarm robotics is a relatively new and rapidly developing field which draws inspiration from swarm intelligence. It is an interesting alternative to classical approaches to robotics because of some properties of problem solving present in social insects, which is flexible, robust, decentralized and self-organized. This work highlights the possibilities for further research.

Abstract: A new adaptive single pixel based lossless intra coding technique employs pixel based DPCM(Differential Pulse Code Modulation) is presented as an enhancement of H.264/MPEG-4 AVC(Advanced Video Coding) standard.. In this paper, we have addressed a technique to trace a single significant pixel in the source block adaptively based on perceptual considerations, applied pixel wise DP CM for spatial prediction for residual transform coding. However, the block style ofH.264/AVC is not troubled for the transform encoding and decoding process. From the experiments, it follows that the new adaptive single pixel based lossless intra coding technique offers a better image quality and good compression ratio as compared with the current standard. If the visual significance of source block is considered for spatial prediction, it offers better results. In this paper, we highlight the exclusion of excess visual data present in both Luma and Chromo components of a video frame for lossless Intra coding of H.264/AVC

Abstract: Ant colony optimization (ACO) is a meta-heuristic based on colony of artificial ants which work cooperatively, building solutions by moving on the problem graph and by communicating through artificial pheromone trails mimicking real ants. One of the active research directions is the application of ACO algorithms to solve dynamic shortest path problems. Solving traffic jams is one such problem where the cost i.e. time to travel increases during rush hours resulting in tremendous strain on daily commuters and chaos. This paper describes a new approach-DSATJ (Dynamic System for Avoiding Traffic Jam) which aims at choosing an alternative optimum path to avoid traffic jam and then resuming that same path again when the traffic is regulated. The approach is inspired by variants of ACO algorithms. Traffic jam is detected through pheromone values on edges which are updated according to goodness of solution on the optimal tours only. Randomness is introduced in the probability function to ensure maximum exploration by ants. Experiments were carried out with the partial road map of North-West region of Delhi, India, to observe the performance of our approach.

Abstract: More and more digital images and video are being captured and stored. In order to use this information, an efficient retrieval technique is required. One major development in this area is content based image retrieval techniques which use image features for image indexing and retrieval. The main features used for image retrieval are color, texture and shape. This Paper looks into the image retrieval technique based on color. This paper proposes the method to retrieve images based on dominant colors in the foreground image. The foreground of the image only gives semantics compared to the background of the image. This is the central theme of the proposed work. Experimental results show that this technique achieves higher Recall and Precision rates compared to existing technique in dominant color based image retrieval.

Abstract: Machining optimization has an inevitable role in contemporary operation of CNC and non-conventional machining processes. As there is a financial restraint involved in all the machines, the effective operation of these machines take a center stage to get required payback. The machining variables are detrimental for the incurred cost due to machining which in turn necessitates finding optimum values before a part is put into production. The operating parameters in this context are cutting speed, feed rate, depth of cut etc. that do not violate any of the constraints that may apply on the process and satisfy the objective function such as minimizing the total production time or production cost or maximizing the production rate or combination of several objective functions. Turning is one of the important operations in industries. The turning centres are so versatile that they find wide applications in all the manufacturing industries. Hence it is desired to optimize the operating parameters of the turning process. In this work a component is considered with turning, facing and undercutting. The objective function is to minimize the machining time with constraints such as cutting power, cutting force, tool life, surface finish of the product and the range of the operating parameters. For solving the above problems optimization procedure was developed using Genetic Algorithm. The optimization problem was solved very efficiently using the above procedure. The result is comparable with the current practice.

Abstract: Watermarking algorithms have a basic requirement that the watermark amplitude should be as high as possible for robustness and at the same time the watermark should not introduce any perceptible artifacts. Thus, the design of watermarking algorithms involves a tradeoff between imperceptibility and robustness. This paper proposes a novel state of the art algorithm, which is based on wavelet and fuzzy logic, to determine an optimal value for the watermark amplitude to be inserted in a 3D model. The system being adaptive to the local geometry of the mesh inserts an 8 bit grey scale image as watermark as compared to inserting a binary image in existing algorithms. Simulation results prove it to be robust against smoothing, cropping, affine operations and noise attacks.

Abstract: The traffic sign detection and recognition system is an essential module of the driver warning and assistance system Smart vehicles are the order of the day Such vehicles have the ability to warn drivers of pending situations, remind them of speed limits and even automatically take evasive action Due to the visual nature of existing infrastructure, signs and line markings, image processing will play a large part in these systems In this paper we present an automated Traffic sign recognition system allowing an invariance localization to changes in position, scale, rotation, weather conditions, partial occlusion, and the presence of other objects of the same color The reliability demonstrated by the proposed method suggests that this system could be a part of an integrated driver warning and assistance system based on computer vision technology

Abstract: In this paper we present a technique for recognizing the lung nodules for different diagnosis of lung cancer based on CT images. Nodule detection is carried in the following steps: preprocessing using wavelet technique, biorthogonal wavelet is used for image enhancement. The enhanced image is subjected to Bi-Histogram equalization. The resultant image is more accurate and sharp. The enhanced image is binarised using the thresholding. Then the binarised image is subjected to Morphological transform. The filtered image is segmented and features are extracted. The extracted features are given to the fuzzy inference systems (FIS). The fuzzy system finds the severity of the lung nodules based on the IF-THEN rules.

Abstract: Software testing plays a vital role in quality software development. Usually, the number of test cases required to develop error-free software, will be very high. Since, exhaustive testing is not possible; the test cases that we need to generate should be optimal and also should cover the entire software and reveal as many errors as possible. In the proposed approach, the intelligent search agent (ISA) will take the decision of optimized test sequences by searching through the SUT, which is represented as a graph in which each node is associated with a heuristic value and each edge is associated with an edge weight. The intelligent agent will find the best sequence by following the nodes that satisfy the fitness criteria and generates the optimized test sequences from the set of all test paths of the SUT. Finally, we compared ISA with ACO and proved that ISA is taking less time and cost in generating optimal test sequences.

Abstract: Today there are many Internet sites which require only the entry by human users but unfortunately some computer softwares called bots are designed by some hackers to enter these sites and use their resources through false registration. As a result some systems named CAPTCHA have been introduced to tell apart human users and computer software. This paper introduces a new CAPTCHA method. In this method a simple mathematical problem is generated according to a predefined pattern but instead of some object's name, we put their images. Then the whole problem is saved and shown to the user inform of an image to be answered by him. But since answering this problem requires four abilities of understanding text of question, detection of question images, understanding the problem, and solving the problem, only a human user can answer this question and present computer programs are unable to solve it. This project has been implemented by PHP language.

Abstract: The evaluation of conversational agents or chatterbots question answering systems is a major research area that needs much attention. Before the rise of domain-oriented conversational agents based on natural language understanding and reasoning, evaluation is never a problem as information retrieval-based metrics are readily available for use. However, when chatterbots began to become more domain specific, evaluation becomes a real issue. This is especially true when understanding and reasoning is required to cater for a wider variety of questions and at the same time to achieve high quality responses. This paper discusses the inappropriateness of the existing measures for response quality evaluation and the call for new standard measures and related considerations are brought forward. As a short-term solution for evaluating response quality of conversational agents, and to demonstrate the challenges in evaluating systems of different nature, this research proposes a blackbox approach using observation, classification scheme and a scoring mechanism to assess and rank three example systems, AnswerBus,START and AINI.

Abstract: This paper presents a comprehensive study of the pixel-based skin color detection techniques. Two main issues of the skin detection are the selection of the best color space and skin color pixel classification algorithm. A large set of XM2VTS face database is used to examine whether the selection of color space can enhance the compactness of the skin class and discriminability between skin and non-skin class in thirteen color spaces and six different skin color pixel classification algorithms. The results show that 1) the selection of the color space can improve the skin classification performance 2) the segmentation performance degrades only when chrominance information is used for classification 3) Bayesian classifier is found to perform better as compared to other classification algorithms. Piecewise linear decision boundary classifier algorithm outperforms all the other skin classification algorithms when it is used for images with good illumination conditions.