Purpose




The purpose of this paper is to examine if certain board characteristics have an impact on the financial performance of manufacturing firms in India.




Design/methodology/approach




The study draws on data from 275 firms listed in NSE during from 2011 to 2015, using a multiple regression model. The present study examines the effect of board characteristics such as board size, CEO duality, independence and board activity devoted to the effectiveness of firms performance regarding market and accounting based financial performance measures.




Findings




The finding supports an inverse association between the extent of board characteristics and the firms’ performance indicators. The study also finds a statistically significant negative relationship between board size and Tobins Q, ROA and ROE. The evidence also shows that the board independence and meeting frequency moderate the relationship between return on equity and return on assets by enhancing these measures among corporate governance mechanisms.




Research limitations/implications




The present study does not include all possible board characteristics, i.e., large shareholders dominance on the board and promoter’s and institutional shareholding, to support firm’s performance. Further research might include the ownership structure of the board to improve firm’s performance.




Originality/value




The study focuses on the corporate governance issues such as size, duality, independence and activity of the boards and their influence on firm performance. The subject analyzes the possible impact of board characteristics and firm-related features that have received much attention from academic research, which has largely focused on studying the publications of corporate governance in India and Asian context.

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Determinants of corporate financial performance relating to board characteristics of corporate governance in Indian manufacturing industry: An empirical study

Currently the corporate governance reforms in India are at cross roads where though the intention behind the reforms is good yet there is a need to look for a complete solution addressing country specific challenges in Indian context. Keeping pace with developments at international level, India also introduced reforms for improving corporate, social and environment disclosures. This paper explores the effectiveness of these corporate governance reforms by analyzing the corporate governance practices followed by Indian companies in two reform periods (FY 2012–13 as Period 1) and (FY 2015–16 as Period 2). Considering mandatory regulations as per clause 49 of Listing agreement with Securities exchange board of India and the governance norms in the new Company Act, 2013, a corporate governance performance (CGP) index is developed to measure corporate governance score of Indian companies. Though there is a significant improvement in corporate governance structures implied by Indian companies but the number of independent directors inducted in the board decreases after the reforms in period 2. All the sectors under study show a significant improvement in following corporate governance practices after the reforms. The study reported a significant relationship between integrated framework of total corporate social performance and financial performance only in period 1. Corporate governance reforms do not impact financial linkages in Indian market in period 2.

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Implications of corporate governance on financial performance: an analytical review of governance and social reporting reforms in India

Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segmentation method (both hard and soft segmentation) to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using prior anatomical knowledge). Results have been successfully validated on human T2-weighted (T2) brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described.

/pdf/colorization-and-automated-segmentation-of-human-t2-mr-brain-588dy6ssch.pdf

Colorization and automated segmentation of human T2 MR brain images for characterization of soft tissues.

Signature identification and verification are some of the biometric systems used for personal identification. Signatures can be considered as authentication of an individual by the analysis of handwriting style, subjected to inter-personal and intra-personal variations. This paper presents an extensive systematic overview of online and offline signature identification and verification techniques. In offline signature verification, surveys related to two approaches, namely, writer-dependent, and writer-independent approaches are presented. Moreover, the compiled study of feature extraction and classification techniques used for signature identification and verification process has also been incorporated. Several databases introduced in the literature are considered to evaluate different signature identification and verification techniques and corresponding results are reported in this article. The entire survey is further summarized in the form of a table for comparisons. In order to reveal the superiority of the present survey, the comparison of the present survey with the existing recent survey works has also been presented. Finally, future directions are provided for further research.

Signature identification and verification techniques: state-of-the-art work

With the development of the Internet, people are interested to share their views and opinions about the product on the web, forums, blogs etc. These online reviews are important for individual users and organization. Recently, it is a common tendency to the user to read the reviews or comments before purchasing some products or services. The online reviews are helpful for the business organizations in order to promote their product. However, in practice, these online reviews may be fake in order to promote or devalue the product. These fake reviews are called as opinion spam. Objective of the research paper is that, to select the best feature subset for detecting the fake review. To select a small subset of features out of the thousands of feature is important for accurate classification of review spam detection. Therefore, a good feature selection method is needed in order to speed up the processing rate, predictive accuracy. In this paper hybrid improved Binary Particle Swarm optimization (iBPSO) and cuckoo search (CS) is used for feature selection and Naive Bayes and k Nearest Neighbor classifier is used for classifying the review as spam and ham. Experimental results have shown that the proposed algorithm has yielded the best performance compared with the swarm intelligence techniques Binary Particle Swarm Optimization (BPSO) and Shuffled Frog Leaping (SFL).

Hybrid Optimization Algorithm of Improved Binary Particle Swarm Optimization (iBPSO) And Cuckoo Search for Review Spam Detection

In this paper, we are proposing a method for adding colors to grayscale images without human interference. In contrast to many previous computer-aided colorizing methods, which require intensive and perfect human interference, this method needs only the user to provide a target gray level image for the process of ‘colorization’, a colorful image of the similar content as the grayscale image is automatically retrieve from the database of images, as an input source image. Then, the best matching source pixel is determined using luminance and texture matching procedure, for each pixel of the target image into a perceptually de-correlated color space. Once a best matching source pixel is found, its chromaticity values are assigned to the target pixel while the original luminance value of the target pixel is retained.

/pdf/colorization-of-gray-scale-images-using-fully-automated-sndq7lhbhf.pdf

Colorization of Gray Scale Images using Fully Automated Approach

This paper presents an artificial neural network approach to word analysis to generate the 3 dimension scene or image from the textual description. We start with the recognition of characters and then form the words from these characters. The words used in natural language will have some special meaning and gives some information. Each word represents some inherited properties of some of the objects. The properties of each word will depend on the object being used in the sentence. Therefore the word itself gives lots of information about the objects. The neural network approach to lexical classifications is the first step to find the objects and its properties. The next step is neural network based approach to word classification is extracting words attribute and then relating it with the other words using artificial neural network. The multilayer feedforward neural network will be used. Here we will analyze the different parts of the speech with their inherit properties which the word have in the sentence.

Natural Language Words Analysis for Affective Scene Generation from Written Text using Artificial Neural Network

Various techniques are already introduced for personal identification and verification based on different types of biometrics which can be physiological or behavioral. Signatures lies in the category of behavioral biometric which can distort or changed with course of time. Signatures are considered to be most promising authentication method in all legal and financial documents. It is necessary to verify signers and their respective signatures. This paper presents an Offline Signature recognition and verification system(SRVS). In this system signature database of signature images is created, followed by image preprocessing, feature extraction, neural network design and training, and classification of signature as genuine or counterfeit.

/pdf/an-offline-signature-recognition-and-verification-system-239ea8et0z.pdf

An offline signature recognition and verification system based on neural network

Image processing is a mechanism to convert an image into digital form and perform various operations on it, in order to get an enhanced image or to extract some useful information from it. In Image processing system, it treats images as two dimensional signals while applying number of image processing methods to them. This leads to an increasing number of generated digital images. Therefore it is required automatic systems to recognize the objects from the images. These systems may collect the number of features of a image and specification of image and consequently the different features of an object will identify the object from the image. Image processing is among rapidly growing technologies today, with its applications in various aspects of a business. Image Processing forms core research area within engineering and computer science disciplines too. Its most common and effective method is retrieve the textual features from various methods. But most of the methods do not yield the more accurate features form the image. So there is a requirement of an effective and efficient method for features extraction from the image. Moreover, images are rich in content, so some approaches are proposed based on various features derived directly from the content of the image: these are the grid-based-color-moments (GBCM) approaches. They allow users to search the desired object from image by specifying visual features (e.g., colour, texture and shape). Once the features have been defined and extracted, the retrieval becomes a task of measuring similarity between image features. In this paper, we have proposed a number of existing methods for GBCM applications.

/pdf/object-recognition-from-image-using-grid-based-color-moments-1xmz9gj841.pdf

Object recognition from image using grid based color moments feature extraction method

In this paper, we are proposing a fully automatic method for adding colors to grayscale images. In contrast to many previous computer-aided colorizing methods, which require intensive and accurate human intervention, this method needs only the user to provide a target gray level image for the process of ‘colorization’, a colorful image of the similar content as the grayscale image is automatically retrieved from the database of images, as an input source image. Then, the source and target image are both transformed into a perceptually de-correlated color space. In this color space a best matching source pixel is determined for each pixel of the target image. The matching criterion uses the first order statistics of the luminance distribution in a small window around the source and target pixels. Once a best matching source pixel is found, its chromaticity values are assigned (transferred) to the target pixel while the original luminance value of the target pixel is retained. The only requirement of the method is that the compositions of the source and target scenes resemble each other.

Avinash Dhole

Papers

Colorization of Gray Scale Images using Fully Automated Approach

Natural Language Words Analysis for Affective Scene Generation from Written Text using Artificial Neural Network

An offline signature recognition and verification system based on neural network

Object recognition from image using grid based color moments feature extraction method

An Effective Method for Transferring Color to Gray Scale Image Using Luminance Matching Without Human Intervention