Supervised Learning in image enhance from electron microscopy inversion results

In Nondestructive testing there is a variety of applications in Material Science, where the specimen is imaged by an Electron Microscope and then by image inversion, informationis extracted for the material interior. This type of information might contain noise either by the imaging procedure or by the numerical part of the inversion. We present a method that can improve the interior density results of an inversed material from a series of Scanning Electron Microscope (SEM) images. For this method, the material density can contain some discontinuity, such as regions where it is dense and regions where there are voids.The proposed method directly stands on the Bayesian learning framework, adopting Gaussian Stochastic Processes (GSPs). Two test sample cases that contain some discontinuities in the density are tested. We also provide a comparison between two different GSP modelling approaches; one is a typical GSP and the other accounts for discontinuity, by introducing hyperparameters. The GSP method gives reconstructed data in reasonable agreement with the known original density distribution, giving confidence that the method can be applied to experimentally obtained SEM images.

Effect of corporate social responsibility on consumer retention for FMCG industries: a deep learning analysis of professional students of Odisha

Corporate social responsibility (CSR) represents a form of international private business self-regulation that contributes to the social aims of a charitable, activist, or philanthropic nature through helping ethically based practices. The benefits from CSR like generating better public image, enhance in community support, enhance in market share, enhance in the satisfaction of the consumer, enhance in the exports, offering better quality services and products, and enhance in the productivity is not considered as the perception level consumers. This paper analyzes the effect of CSR activities on the fast moving consumer goods (FMCG) industries on the basis of the retention of the consumers of FMCG products. Initially, a questionnaire is prepared that consists of five categories. Each category is composed of more than ten questions. The information related to the questionnaire is obtained in the view of the professional students from Odisha. The customers are requested to fill the exact information as much as possible. Next, a higher-order statistical analysis is considered for realizing the behaviour of the categories. Additionally, the usage of deep learning called Optimized RNN (O-RNN) is used for predicting the customer retention in maintaining the CSR on FMCG industries. The improvement in the traditional RNN is done by optimizing the hidden neurons by the improved form of Sea Lion Optimization Algorithm (slno) called Modified slno (M-slno) with the intention of minimizing the error among the actual and the predicted outcome, hence it is called as O-RNN. The retention of the customers regarding the effect of CSR activities on FMCG companies and the benefits from CSR is investigated using the O-RNN prediction analysis against several traditional deep learning models as well as different statistical measures.

Algorithms for Vein Image Enhancement and Matching in the Cloud IoT-Based M-Health Environment

In this chapter, the authors have described the methodologies to achieve the objectives of veins image enhancement, feature extractions, and matching with other veins images in the cloud IoT-based m-health environment. The initial steps to propose the algorithms for veins image enhance and feature extractions will have five parts. Once the proposed algorithm is written, the hardware architecture designs of the proposed veins image enhancements and feature extraction algorithm will be described by the authors. The hardware designs are presented in subsequent sections of this chapter. Further, the hardware designs are elaborated in detail for each of the techniques. The presented algorithms are implemented in MATLAB 11.0 software, and these algorithms are simulated and integrated with different veins sample images. The hardware designs of veins image enhancements and feature extractions are implemented using Verilog Hardware Language Description (VHLD), and these implemented results are simulated using MSA (Model-Sim-Altera) for sample images of different types of veins.

A Biomimetic Model of Adaptive Contrast Vision Enhancement from Mantis Shrimp

Mantis shrimp have complex visual sensors, and thus, they have both color vision and polarization vision, and are adept at using polarization information for visual tasks, such as finding prey. In addition, mantis shrimp, almost unique among animals, can perform three-axis eye movements, such as pitch, yaw, and roll. With this behavior, polarization contrast in their field of view can be adjusted in real time. Inspired by this, we propose a bionic model that can adaptively enhance contrast vision. In this model, a pixel array is used to simulate a compound eye array, and the angle of polarization (AoP) is used as an adjustment mechanism. The polarization information is pre-processed by adjusting the direction of the photosensitive axis point-to-point. Experiments were performed around scenes where the color of the target and the background were similar, or the visibility of the target was low. The influence of the pre-processing model on traditional feature components of polarized light was analyzed. The results show that the model can effectively improve the contrast between the object and the background in the AoP image, enhance the significance of the object, and have important research significance for applications, such as contrast-based object detection.

Corporate Sustainability Initiatives in Gender Equality: Organizational Practices Fostering Inclusiveness at Work in an Emerging-Market Context

The objective of this paper is to explore through in-depth interviews the reasons why diversity management initiatives targeting women is an important strategic management issue for companies. Interviewed are the human resource managers of two of Turkey’s leading business groups: a manager from the HR department of one of Turkey’s largest banks and the deputy general manager of a non-governmental organization. The evidence suggests that these firms exercise carefully honed policies aimed at fostering gender equality and to that end engage in a variety of activities targeting various human resource functions. Their motives appear to be to reinforce their corporate image, enhance employee satisfaction, and reap such anticipated rewards as greater productivity and increased innovation capability. The importance of company leadership is underlined as an antecedent to the adoption of gender-equality focussed diversity management. The study may have important implications for building sustainable corporate brands, especially in the emerging-market context.

The Influence of Corporate Culture on the Core Competitiveness in China’s Small and Medium-Sized Commercial Banks

The small and medium-sized commercial Banks in the financial system reform in our country plays a very important role, enterprise culture construction is one of the important factors that influence its core competitiveness. To strengthen the construction of enterprise culture, enhancing core competitiveness of small and medium-sized commercial banks focus to strengthen the cohesion, innovation of commercial Banks,to control and influence.Small and medium-sized commercial banks can continue to strengthen enterprise innovation power,quick to adapt to changes in industry environment;Establish the advanced mechanism of choose and employ persons, the "people-oriented" enterprise values; Establish a good brand image, enhance the social influence of establishing effective enhance the core competitiveness of enterprise culture cultivation.

DCT-Based Digital Image Steganography

In order to solve the security problem of the multimedia in the communication process, steganography plays a vital role. In this paper, the DCT domain of digital image steganography is to study the impact of image quality with different embedding positions, the secret information in the DCT domain of an image, different carrier images and the amount of information. Studies show that: embedding the information in the low frequency part of the image, selecting the image with the rich distribution pixel value, is conducive to improve the steganography effects of the information in the image, enhance the concealment of the secret information.