Arun Kumar Ray

GA Based Winner Determination in Combinatorial Reverse Auction

Combinatorial auction provides efficient resource allocations than traditional auction mechanisms in multi-item auctions, where the valuation of the items are not additive. However, solving winner determination problem so as to minimize procurement cost in combinatorial auction is Incomplete. In this paper, we consider a procurement scenario where the buyer wants to procure single unit of multiple items from a set of suppliers using single round sealed bid auction. The suppliers provide XOR bids for all the combinations of items. We propose Genetic Algorithm for solving winner determination problem for combinatorial allocation. A concept of repairing infeasible solution is used during the solution. We show that the algorithm is able to solve the problem within reasonable time.

Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM

The segmentation, detection, and extraction of infected tumor area from magnetic resonance (MR) images are a primary concern but a tedious and time taking task performed by radiologists or clinical experts, and their accuracy depends on their experience only. So, the use of computer aided technology becomes very necessary to overcome these limitations. In this study, to improve the performance and reduce the complexity involves in the medical image segmentation process, we have investigated Berkeley wavelet transformation (BWT) based brain tumor segmentation. Furthermore, to improve the accuracy and quality rate of the support vector machine (SVM) based classifier, relevant features are extracted from each segmented tissue. The experimental results of proposed technique have been evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 96.51% accuracy, 94.2% specificity, and 97.72% sensitivity, demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 0.82 dice similarity index coefficient, which indicates better overlap between the automated (machines) extracted tumor region with manually extracted tumor region by radiologists. The simulation results prove the significance in terms of quality parameters and accuracy in comparison to state-of-the-art techniques.

Development of a New Algorithm Based on SVD for Image Watermarking

The research on watermarking has been increasing day-by-day since past decade. It has been largely driven by its important applications in digital copyrights management and protection. To provide more watermarks and to minimize the distortion of the watermarked image, a novel technique is presented in this paper. In this paper, the singular value decomposition (SVD)-based image watermarking scheme is proposed. The output result of SVD is more secure and robust. SVD is often used to develop robust watermarking algorithms. However, the SVD-based algorithms exhibit false-positive problem and pose security concern. In this work, we try to overcome this problem. In the proposed schemes, the host image is first decomposed into sub-bands by applying discrete wavelet transform (DWT). The watermark image is embedded in all the sub-bands by modifying the singular values of each sub-band. Next to it, we propose to encrypt and embed the singular values of the watermark image instead of original singular values. RSA algorithm has been used for the encryption process. Peak signal-to-noise ratio (PSNR) is used to measure the imperceptibility of the proposed schemes. The simulation result shows its efficacy.

Improving performance of K-SVD based image denoising using curvelet transform

Image denoising algorithm in transform domain which uses learning of dictionary has better PSNR performance than others. It is seen that the popular algorithms based on K-SVD proposed earlier has still in use. However, the texture part of the image could not be preserved during the process of denoising. It is also seen that the effect becomes more visible with increased value of standard deviation of the Gaussian noise. The proposed algorithm in this work uses curvelet transform along with K-SVD to retain the texture part of the image. The denoising with the proposed method shows better PSNR performance as compared to denoising with only K-SVD.

Bidding decision in multi-attribute reverse auction

We design a decision making model for solving the bidding problem in a sealed bid multi-attribute reverse auction. Bidders in reverse auctions face the problem of decrementing the bid in every round until the final round. Although the bidders receive information from the previous rounds regarding the winning bid, but the decision regarding the bid-decrement is purely subjective. Considering the fuzziness in the available information and the subjective parameters, we develop a new bidding algorithm for multi-attribute reverse auctions using multi-stage fuzzy game technique. In this work, we present the fuzzy decision making model and use this to develop the bidding algorithm for multi-attribute reverse auction. We illustrate the use of the algorithm through an example. The agent using the fuzzy bidding algorithm is able to decide the bid intelligently in every round of the auction. The algorithm may be used by the agents to take bidding decisions adaptively in repeated auction scenario.

Relationship preserving multi-attribute reverse auction: A web-based experimental analysis

Repeated use of reverse auction often degrades the buyer-supplier relationship. Theoretical studies show that providing incentive to the losing but competing suppliers can keep them interested to participate in future auctions thereby maintaining a healthy level of competition. We conduct web-based experiments to validate this theoretical observation in multi-attribute reverse auctions. We compare incentive-oriented and standard multi-attribute reverse auctions and demonstrate that the results in the laboratory setting corroborate the theoretical findings. Adopting incentive-oriented mechanism, the buyer is able to provide better utility to suppliers while protecting her own. We conclude that such a mechanism can reduce the negative perception of the suppliers and help build better buyer-supplier relationship in the long run.

Comparative Approach of MRI-Based Brain Tumor Segmentation and Classification Using Genetic Algorithm

The detection of a brain tumor and its classification from modern imaging modalities is a primary concern, but a time-consuming and tedious work was performed by radiologists or clinical supervisors. The accuracy of detection and classification of tumor stages performed by radiologists is depended on their experience only, so the computer-aided technology is very important to aid with the diagnosis accuracy. In this study, to improve the performance of tumor detection, we investigated comparative approach of different segmentation techniques and selected the best one by comparing their segmentation score. Further, to improve the classification accuracy, the genetic algorithm is employed for the automatic classification of tumor stage. The decision of classification stage is supported by extracting relevant features and area calculation. The experimental results of proposed technique are evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on segmentation score, accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 92.03% accuracy, 91.42% specificity, 92.36% sensitivity, and an average segmentation score between 0.82 and 0.93 demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 93.79% dice similarity index coefficient, which indicates better overlap between the automated extracted tumor regions with manually extracted tumor region by radiologists.

MRI Denoising Using Sparse Based Curvelet Transform with Variance Stabilizing Transformation Framework

Hilbert Vibration Decomposition (HVD) is introduced to the voltage flicker analysis. When voltage flicker accompanies with high order harmonics, the instantaneous frequency of its analytic signal in principle consists of two different parts, power frequency and a rapidly varying asymmetrical oscillating part. The important property of the instantaneous frequency offers a direct way to estimate the power frequency using a low-pass filter and remove the high order harmonics without pre-treatment procedures. Corresponding voltage flicker envelope is estimated using synchronous detection. The HVD method does not involves basic functions that the wavelet transform method needs. It can also adaptively estimate the frequency and amplitude of every modulation frequency component. Simulation results prove that the proposed method could accurately detect voltage flicker with high order harmonics. It has higher calculation efficiency and detection precision than wavelet transform method. Experimental results show that the new algorithm is feasible and efficient.

Analysis of various image feature extraction methods against noisy image: SIFT, SURF and HOG

We present the performance of three popular image feature extraction methods such as Scale Invariant Feature Transformation (SIFT), Speeded-Up Robust Features (SURF) and Histogram of Oriented Gradient (HOG). Specifically, we compare the performance of feature detection methods for images corrupted with different types of noise. The efficiency of three methods are measured by considering number of correct matches between original and noisy image found by the algorithm. In this study, we use images corrupted by three types of noise such as gaussian, salt & pepper and speckle. It is observed from the experimental results that for most of the noisy images, SIFT presents its stability but it is slow. SURF is the fastest one and its performance is close to SIFT. However, HOG show its advantages in detecting edge and texture information of image.

Modeling and analysis of data flow in MAC layer of WSN-MCN convergence network

With enormous growth of wireless communication technologies, mobile phone plays an important role in future development of ubiquitous network. New cellular devices are equipped with powerful computing, communicating and storage facilities which will improve scalability, energy efficiency and decrease packet delay, etc. The performance metrics like coverage, enhanced target tracking and superior channel capacity can be increased by using mobile sink over a static sink in Wireless Sensor Network (WSN). On the other hand Mobile Cellular Network (MCN) has technology of wide coverage area, powerful nodes and network robustness but its deployment is expensive. Therefore, integration of MCN and WSN is the need of the hour. This integrated type of communication network minimizes the data loss which is an important challenge that occurs at medium access control (MAC) layer. Therefore Quality of Service (QoS) metrics for data flow is emerging issues which need to be solved. In this paper, we study the issues in convergence of MCN and WSN with respect to MAC layer. An analytical model is designed to study performance metric for data traffic flow from sensor node to mobile phone. The QoS measures in terms of data packet delay, loss of data packets etc. are represented in the form of graphs.