Development of algorithm for identification of maligant growth in cancer using artificial neural network

The precise identification and characterization of small pulmonary nodules at low-dose CT is a necessary requirement for the completion of valuable lung cancer screening. It is compulsory to develop some automated tool, in order to detect pulmonary nodules at low dose ct at the beginning stage itself. The numerous algorithms had been proposed earlier by many researchers in the past, but, the accuracy of prediction is always a challenging task. In this work, an artificial neural network based methodology is proposed to find the irregular growth of lung tissues. Higher probability of detection is taken as a goal to get an automated tool, with great accuracy. The finest feature sets derived from Haralick Gray level co occurrence Matrix and used as the dimension reduction way for feeding neural network. In this work, a binary Binary classifier neural network has been proposed to identify the normal images out of all the images. The capability of the proposed neural network has been quantitatively computed using confusion matrix and found in terms of classification accuracy

Analgorithmic Framework for Automatic Detection and Tracking Moving Point Targets in IR Image Sequences

Imaging sensors operating in infrared (IR) region of electromagnetic spectrum are gaining importance in airborne automatic target recognition (ATR) applications due to their passive nature of operation. IR imaging sensors exploit the unintended IR radiation emitted by the targets of interest for detection. The ATR systems based on the passive IR imaging sensors employ a set of signal processing algorithms for processing the image information in real-time. The real-time execution of signal processing algorithms provides the sufficient reaction time to the platform carrying ATR system to react upon the target of interest. These set of algorithms include detection, tracking, and classification of low-contrast, small sized-targets. Paper explained a signal processing framework developed to detect and track moving point targets from the acquired IR image sequences in real-time.Defence Science Journal, Vol. 65, No. 3, May 2015, pp.208-213, DOI: http://dx.doi.org/10.14429/dsj.65.816

Comparative Analysis of common Edge Detection Algorithms using Pre-processing Technique

Edge detection is the process of segmenting an image by detecting discontinuities in brightness. So far, several standard segmentation methods have been widely used for edge detection. However, due to inherent quality of images, these methods prove ineffective if they are applied without any preprocessing. In this paper, an image preprocessing approach has been adopted in order to get certain parameters that are useful to perform better edge detection with the standard edge detection methods. The proposed preprocessing approach involves median filtering to reduce the noise in image and then Edge Detection technique is carried out. And atlast Standard edge detection methods can be applied to the resultant preprocessing image and its Simulation results are show that our preprocessed approach when used with a standard edge detection method enhances its performance

The passivity of adaptive output regulation of nonlinear exosystem with application of aircraft motions

This paper deals with passivity of adaptive output regulation of nonlinear exosystem. It is shown that factorisable low-high frequency gains and harmonic uncertainties are estimated to the exogenous signals with adaptive nonlinear system. The design methodology guarantees asymptotic regulation in the case where the dimension of the regulator is sufficiently large in relation, which affects the number of harmonics acting on the system. On the other hand, harmonics of uncertain amplitude, phase, and frequency are the major sources, and the bounded steady-state regulation error ensures that adaptive nonlinear system is globally asymptotically stable via passivity theory. Kalman–Yacubovitch–Popov property provides that the uncertain adaptive nonlinear system is passive. Finally, specific examples are shown in order to demonstrate the applicability of the result

AWARENESS OF MUTUAL FUND INVESTMENT AMONG THE INVESTORS – AN EMPIRICAL STUDY

Capital market has been strengthened due to because of increase in investment in mutual funds by small and medium investors. Most of the investors are having awareness about mutual funds and its benefits like tax benefits, less risk, cost etc. The mutual fund industry in India has undergone a most successful phase in the last 15 years. The growth in number of schemes offered by Indian mutual funds from 403 schemes in 2002-03 to 1294 schemes in 2011-12 has shown the inclination of investors towards mutual funds. The resources mobilized by public sector funds is Rs. 314706 crore in 2002-03 and reached to a high of Rs.10, 019,023 crore in 2009-10 of which the share of public sector mutual fund is around 80 percent of the total fund mobilized. The present study is an endeavour to know the awareness of investors about mutual funds

(E)-3-[2-(4-Chloro­phenyl­sulfon­yl)vin­yl]-6-methyl-4H-chromen-4-one

In the title compound, C18H13ClO4S, the mean planes of the chloro­phenyl ring and the S—C=C—C chain are oriented at angles of 52.7 (2) and 51.3 (2)°, respectively, with respect to the sulfonyl (O=S=O) plane. The dihedral angle between the mean planes of the chloro­phenyl group and the benzopyran ring is 80.7 (1)°. The crystal structure is stabilized by two inter­molecular C—H⋯O inter­actions, forming centrosymmetrc dimers, which are linked via a second C—H⋯O inter­action into a chain structure