Relational Benefits, Customer Satisfaction, And Customer Loyalty In Chain Store Restaurants

[[abstract]]This study aims to investigate the structural relationships among relational benefits, customer satisfaction, and customer loyalty in the chain store restaurants. Based on a theoretical background literature review, three types of customer relational benefits were determined: psychological, social, and special treatment benefits. Theoretical relationships among relational benefits, customer satisfaction, and customer loyalty were derived from the review of literature, and a theoretical model was proposed. The proposed model was then tested employing data collected from 267 customers of chain store restaurants. The results of subsequent analysis of the data indicated that relational benefits influence customer loyalty, and customer satisfaction with employees influence customer loyalty. In addition, the impact of which is partially mediated by satisfaction with employees. The managerial implications of these findings are discussed in the latter part of this article.[[notice]]補正完畢[[incitationindex]]EI[[booktype]]電子

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Computer Architecture in Industrial, Biomechanical and Biomedical Engineering

This book aims to provide state-of-the-art information on computer architecture and simulation in industry, engineering, and clinical scenarios. Accepted submissions are high in scientific value and provide a significant contribution to computer architecture. Each submission expands upon novel and innovative research where the methods, analysis, and conclusions are robust and of the highest standard. This book is a valuable resource for researchers, students, non-governmental organizations, and key decision-makers involved in earthquake disaster management systems at the national, regional, and local levels

An image size unconstrained ownership identification scheme for gray-level and color ownership statements based on sampling methods

[[abstract]]This paper describes an ownership identification method with gray-level or color ownership statements. The proposed scheme uses the theories and properties of sampling distribution of means to achieve the requirements of robustness and security. Besides, the sampling method also provides that the ownership statements can be of any size regardless of the size of the original image. Since our method does not really insert the ownership statements into the host image, the original image will not be altered and the rightful ownership can be identified without resorting to the original image. Moreover, our method also allows plural ownership statements to be registered for a single host image without causing any damage to other hidden ownership statements. Finally, experimental results will show the robustness of our scheme for gray-level and color ownership statements against several common attacks.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SCI[[incitationindex]]E

Radial Basis Function Neural Network in Identifying The Types of Mangoes

Mango (Mangifera Indica L) is part of a fruit plant species that have different color and texture characteristics to indicate its type. The identification of the types of mangoes uses the manual method through direct visual observation of mangoes to be classified. At the same time, the more subjective way humans work causes differences in their determination. Therefore in the use of information technology, it is possible to classify mangoes based on their texture using a computerized system. In its completion, the acquisition process is using the camera as an image processing instrument of the recorded images. To determine the pattern of mango data taken from several samples of texture features using Gabor filters from various types of mangoes and the value of the feature extraction results through artificial neural networks (ANN). Using the Radial Base Function method, which produces weight values, is then used as a process for classifying types of mangoes. The accuracy of the test results obtained from the use of extraction methods and existing learning methods is 100%

Advanced Information Systems and Technologies

This book comprises the proceedings of the V International Scientific Conference "Advanced Information Systems and Technologies, AIST-2017". The proceeding papers cover issues related to system analysis and modeling, project management, information system engineering, intelligent data processing computer networking and telecomunications. They will be useful for students, graduate students, researchers who interested in computer science

Advanced Information Systems and Technologies

This book comprises the proceedings of the V International Scientific Conference "Advanced Information Systems and Technologies, AIST-2017". The proceeding papers cover issues related to system analysis and modeling, project management, information system engineering, intelligent data processing computer networking and telecomunications. They will be useful for students, graduate students, researchers who interested in computer science

Deep Learning Detected Nutrient Deficiency in Chili Plant

Chili is a staple commodity that also affects the Indonesian economy due to high market demand. Proven in June 2019, chili is a contributor to Indonesia's inflation of 0.20% from 0.55%. One factor is crop failure due to malnutrition. In this study, the aim is to explore Deep Learning Technology in agriculture to help farmers be able to diagnose their plants, so that their plants are not malnourished. Using the RCNN algorithm as the architecture of this system. Use 270 datasets in 4 categories. The dataset used is primary data with chili samples in Boyolali Regency, Indonesia. The chili we use are curly chili. The results of this study are computers that can recognize nutrient deficiencies in chili plants based on image input received with the greatest testing accuracy of 82.61% and has the best mAP value of 15.57%