A New Feature Selection Method based on Intuitionistic Fuzzy Entropy to Categorize Text Documents

Selection of highly discriminative feature in text document plays a major challenging role in categorization. Feature selection is an important task that involves dimensionality reduction of feature matrix, which in turn enhances the performance of categorization. This article presents a new feature selection method based on Intuitionistic Fuzzy Entropy (IFE) for Text Categorization. Firstly, Intuitionistic Fuzzy C-Means (IFCM) clustering method is employed to compute the intuitionistic membership values. The computed intuitionistic membership values are used to estimate intuitionistic fuzzy entropy via Match degree. Further, features with lower entropy values are selected to categorize the text documents. To find the efficacy of the proposed method, experiments are conducted on three standard benchmark datasets using three classifiers. F-measure is used to assess the performance of the classifiers. The proposed method shows impressive results as compared to other well known feature selection methods. Moreover, Intuitionistic Fuzzy Set (IFS) property addresses the uncertainty limitations of traditional fuzzy set

HHT Based Analysis of Non Stationary Signals and Metal Structures

Hilbert-Huang Transform (HHT) is an innovative data-processing technique for analyzing non stationary and nonlinear signals. The analysis of these signals is to transform the time-domain data to frequency versus time data instead of the amplitude versus frequency.This paper investigates techniques to apply HHT for locating the instantaneous frequency in a signal. This signal processing technique helps in identifying several frequency components which are the indicators of the problems present in the system under test. This can be applied for analyzing aircrafts’ body structure, biomedical signals and seismic signals. Monitoring of civil structures such as bridges and buildings is critical for long-term operational cost and safety of aging structures. Applying HHT to these signals obtained from the various sensors placed in the vicinity of event or entity, it is possible to identify the problems

Automorphic properties of low energy string amplitudes in various dimensions

This paper explores the moduli-dependent coefficients of higher derivative interactions that appear in the low-energy expansion of the four-graviton amplitude of maximally supersymmetric string theory compactified on a d-torus. These automorphic functions are determined for terms up to order D^6R^4 and various values of d by imposing a variety of consistency conditions. They satisfy Laplace eigenvalue equations with or without source terms, whose solutions are given in terms of Eisenstein series, or more general automorphic functions, for certain parabolic subgroups of the relevant U-duality groups. The ultraviolet divergences of the corresponding supergravity field theory limits are encoded in various logarithms, although the string theory expressions are finite. This analysis includes intriguing representations of SL(d) and SO(d,d) Eisenstein series in terms of toroidally compactified one and two-loop string and supergravity amplitudes.Comment: 80 pages. 1 figure. v2:Typos corrected, footnotes amended and small clarifications. v3: minor corrections. Version to appear in Phys Rev

A Convolution Neural Network Engine for Sclera Recognition

The world is shifting to the digital era in an enormous pace. This rise in the digital technology has created plenty of applications in the digital space, which demands a secured environment for transacting and authenticating the genuineness of end users. Biometric systems and its applications has seen great potentials in its usability in the tech industries. Among various biometric traits, sclera trait is attracting researchers from experimenting and exploring its characteristics for recognition systems. This paper, which is first of its kind, explores the power of Convolution Neural Network (CNN) for sclera recognition by developing a neural model that trains its neural engine for a recognition system. To do so, the proposed work uses the standard benchmark dataset called Sclera Segmentation and Recognition Benchmarking Competition (SSRBC 2015) dataset, which comprises of 734 images which are captured at different viewing angles from 30 different classes. The proposed methodology results showcases the potential of neural learning towards sclera recognition system

Dissection of genetic diversity present in eggplant populations using simple sequence repeat markers

Eggplant (Solanum melongena L.) is the third most important solanaceous vegetable and most diversified within species spread across the world-geographical area. A study was conducted to assess the genetic diversity among the selected fifty-four eggplant genotypes (sub-categorized into five sub-population) using twenty-three SSR markers. The Analysis of Molecular Variance among the five sub-population of eggplant revealed the existence of 90.67% variation within populations and 9.34% variation among populations. The SSR markers analysis revealed important locus-wise information like mean Observed-Heterozygosity (0.216), mean Expected-Heterozygosity (0.496), Shannon’s Information Index (0.879), mean number of different alleles (3.209), mean number of effective alleles (2.535), Fixation-Index (0.649). Further, Phylogenetic-analysis clearly categorize genetically distinct individuals in which the most diversified clusters was cluster-1 (C1) out of total of five clusters and especially, wild cultivars were grouped into cluster-5 (C5). The obtained results can be used in eggplant breeding and germplasm conservation in a resourceful manner

DOES “CASH MANAGEMENT” INFLUENCE COMPANY TO MANAGE LIQUIDITY AND SPENDING

Successful cash management is essential to both corporate and personal financial success. A review of the main ideas, tactics, and advantages of cash management is given in this abstract. In order to maintain economic security, liquidity, and development, effective cash management which includes everything from transaction volume to long-term investments is essential. This abstract examines a number of aspects of cash management, such as anticipating cash flows, managing liquidity, and streamlining the process with technology and financial instruments. It drives into detail about how important it is to keep a sufficient cash balance to cover current expenses while optimising return on surplus funds. In addition to helping individuals and organizations take advantage of financial opportunities like investments, purchases, and debt

Perceptions and practices of self-medication among undergraduate medical students at Gulbarga Institute of Medical Sciences, Kalaburagi, India

Background: Self-medication practice is widespread in many countries and the irrational use of drugs is a cause of concern. It assumes a special significance among medical students as they are exposed to knowledge about diseases and drugs. To assess practice and perception of self-medication among undergraduate medical students.Methods: It is a descriptive study in which study population was comprised of 2nd year undergraduate medical students of Gulbarga Institute of Medical Sciences, Kalaburagi, Karnataka, India. Total 134 students were included in the study. Written informed consent was obtained from each volunteer prior to the study. Students were given a questionnaire that included both open and close ended questions about self-medication practice. Data was analysed and presented as counts and percentages.Results: It was found that 72 (54%) students practiced self-medication. The principal morbidity for seeking self-medication was cold and cough as reported by 70 (53%) students. Antibiotics were most commonly self-medicated as reported by 55 (36%) students. Only 34 (25%) students opined that self-medication is part of self-care.Conclusions: Self-medication is widely practiced among under­ graduate medical students. In this situation, we should educate the students about advantages and disadvantages of self-medication