Medical Image Fusion Using Deep Learning Mechanism

Sparse representation(SR) model named convolutional sparsity based morphological component analysis is introduced for pixel-level medical image fusion. The CS-MCA model can achieve multicomponent and global SRs of source images, by integrating MCA and convolutional sparse representation(CSR) into a unified optimization framework. In the existing method, the CSRs of its gradient and texture components are obtained by the CSMCA model using pre-learned dictionaries. Then for each image component, sparse coefficients of all the source images are merged and then fused component is reconstructed using the corresponding dictionary. In the extension mechanism, we are using deep learning based pyramid decomposition. Now a days deep learning is a very demanding technology. Deep learning is used for image classification, object detection, image segmentation, image restoration. Keywords: CNN, CT, MRI, MCA, CS-MCA.

An Empirical Study of Personality Values and Their Influence on Personalitys Virtual Image: Component of Basic Beliefs

The article provides a critical analysis of the literature on the researches of the sphere of personality values in the Internet space. Also, in the course of the study, it was determined that the sphere of personality values is a complex dynamic construct of the personality, which determines its inner world and outlines the vector of its activity. It was also revealed that the virtual personality of an Internet user is a complex formation that is formed during the transition of a real person to the socio-cultural space of the Internet, with subsequent integration into it and the internalization of the values and meanings of the virtual society. The virtual personality of an Internet user as a psychological phenomenon was analyzed; such properties of a virtual personality as virtuality (the degree of acceptance of virtual reality as a social environment), involvement (the level of information and computer technologies awareness and a sense of belonging to a virtual society) and orientation (the presence or absence of ideas about socially approved behavior in Internet society) are described. The article analyzes the results of an empirical study of the influence of the component of basic beliefs in the value-semantic sphere of a person on the properties of their virtual image. It was revealed that there is a correlation between the indicator of the basic belief in the benevolence of the world and the level of virtual personality (people with the belief in the benevolence of the world in the Internet space have developed motivation for creative activity), as well as a correlation between the indicator of the basic belief in the controllability of the world and the level of involvement of the virtual personality (people with a conviction of the controllability of the world in the Internet space tend to perceive themselves as a significant part of it).

The relationship between orthorexia nervosa symptomatology and body image attitudes and distortion

Orthorexia Nervosa (ON), a condition characterized by a fixation on healthy eating, still does not conform to any consensus concerning diagnostic criteria, notably in regard to a possible body image component. This study investigated the relationship between ON symptomatology, measured with the Eating Habit Questionnaire, and body image attitudes and body image distortion in a non-clinical sample. Explicit body image attitudes and distortion were measured using the Multidimensional Body-Self Relations Questionnaire. Implicit body image attitudes and distortion were assessed using the reverse correlation technique. Correlational analyses showed that ON is associated with both explicit and implicit attitudes and distortion toward body image. More precisely, multivariate analyses combining various body image components showed that ON is mostly associated with explicit overweight preoccupation, explicit investment in physical health and leading a healthy lifestyle, and implicit muscularity distortion. These findings suggest that ON symptomatology is positively associated with body image attitudes and distortion in a non-clinical sample. However, further studies should be conducted to better understand how ON symptomatology relates to body image, especially among clinical samples.

Prominence of organic image in tourist destinations: Indian leisure tourism narrative

Purpose The process of understanding a tourist begins with an extensive evaluation of tourist’s perceptions about a destination. Though destination image has been widely discussed in tourism literature since the 1970s, little attention has been given to organic image despite its relative significance. First, this study aims to clear the existing ambiguity in the cognitive component of destination image by organizing and structuring the extant literature. Design/methodology/approach This exploratory study then seeks to identify the salient organic image attributes in tourists’ consideration for travel options through the application of repertory test. Respondents’ statements on 25 competing destinations in Madhya Pradesh, India were transcribed verbatim for analysis. A measure of frequencies and commonality among 12 construct themes was then carried out. Findings Along with history, heritage and culture, this study found tourists to have considered organic image attributes associated with destination stereotypes as important discriminators between competing leisure tourist destinations. Moreover, the elicitation of context-specific attributes along with a note in the pattern of tourist responses highlighted the merits of repertory test when presented with different category triads. Originality/value This study differs from other organic image studies as it evaluates the prominence of organic image in the context of leisure tourism. Being one of the few studies to have extensively discussed the organic image component, this study contributes to the progression of organic image literature.

THE STRUCTURAL MODEL OF THE CONCEPT ‘FRIENDSHIP’ IN THE CONSCIOUSNESS OF THE UKRAINIANS

The main results of the experimental research of the concept FRIENDSHIP in the Ukrainian language are given in the article. On the base of the received data, the concept’s model was built and the main cognitive signs were analyzed. The description of the structure of the concept is also proposed. The method of semantic and cognitive analysis of concepts was chosen. The latter was proposed and multiply used by Russian linguists Z. Popova and J. Sternin. The free, directed, receptive, and symbolic experimental techniques were used as main ones. The first stage of building the concept’s model is the description of its macrostructure. The found cognitive signs of the given concept are distributed according to its main structural components - image component, informational sense and interpretation field. Thus, the informational sense of the concept includes cognitive signs that characterize its essence and defining components. The interpretation field is usually very large and includes many cognitive signs, which characterize the attitude of the folk to the given concept and various encyclopedic knowledge about its features, functions, and they are usually gained from the individual’s experience. The phraseological zone of the interpretation field is analyzed separately. It describes the understanding of the concept by the folk’s consciousness mainly in historic perspective. The results of researching proverbs and sayings are included into the concept’s model. The structure of the concept’s model is build in a way of different fields and it depicts the hierarchy of separate cognitive signs in the structure. The division of separate cognitive sign in to the fields (nuclear, periphery) was made on the basis of their "brightness " (the quantity of association proposed during the experiments). It is worth noting that dictionaries give only small part of such sighs, which does not depict the whole structure of the concept. Proposed experimental research of the structure of a separate concept gives the opportunity to describe them more effectively.

Wavelet Based Image Coding via Image Component Prediction Using Neural Networks

In the process of wavelet based image coding, it is possible to enhance the performance by applying prediction. However, it is difficult to apply the prediction using a decoded image to the 2D DWT which is used in JPEG2000 because the decoded pixels are apart from pixels which should be predicted. Therefore, not images but DWT coefficients have been predicted. To solve this problem, predictive coding is applied for one-dimensional transform part in 2D DWT. Zhou and Yamashita proposed to use half-pixel line segment matching for the prediction of wavelet based image coding with prediction. In this research, convolutional neural networks are used as the predictor which estimates a pair of target pixels from the values of pixels which have already been decoded and adjacent to the target row. It helps to reduce the redundancy by sending the error between the real value and its predicted value. We also show its advantage by experimental results.

Models Of Forecasting Of Enterprise’s Behavior In Non-Stationary External Environment

In conditions of a high level of market economy instability, an enterprise's behavior is a complex phenomenon that depends on the impact of numerous external and internal factors. This determines the need to develop specific tools for modeling the behavior of the production system to support its competitiveness. The article is devoted to the construction of forecasting models of an enterprise's behavior, namely: 1) a predicting model of the production system's activities that combines the resource capabilities of the enterprise with the state and prospects of branch development; 2) a model of determining the sustainability of the enterprise's development trajectory. The first model contains the production component in the form of the Cobb-Douglas function; financial, labor, innovative and enterprise’s image components, represented by autoregressive functions. The specifically feature of the constructed model is to use capital and labor resources of the enterprise as factors that ensure the transfer and interaction of internal and external fluctuations of the production system. To using factors of the export volume and the gross value added of the branch in the enterprise’s image component allowed to have regard to the development opportunities of the enterprise in the national economy. To determine the stability of the enterprise's development trajectory, the study uses Lyapunov's theory of stability. The general integral factor proposes to use as a factor that reflects the trend of the enterprise’s behavior. Models tested on the data of two industrial enterprises in Morocco.

Flame Image Processing and Classification Using a Pre-Trained VGG16 Model in Combustion Diagnosis

Nowadays, despite a negative impact on the natural environment, coal combustion is still a significant energy source. One way to minimize the adverse side effects is sophisticated combustion technologies, such as, e.g., staged combustion, co-combustion with biomass, and oxy-combustion. Maintaining the combustion process at its optimal state, considering the emission of harmful substances, safe operation, and costs requires immediate information about the process. Flame image is a primary source of data which proper processing make keeping the combustion at desired conditions, possible. The paper presents a method combining flame image processing with a deep convolutional neural network (DCNN) that ensures high accuracy of identifying undesired combustion states. The method is based on the adaptive selection of the gamma correction coefficient (G) in the flame segmentation process. It uses the empirically determined relationship between the G coefficient and the average intensity of the R image component. The pre-trained VGG16 model for classification was used. It provided accuracy in detecting particular combustion states on the ranging from 82 to 98%. High accuracy and fast processing time make the proposed method possible to apply in the real systems.