Earticle

Color image processing is very useful because color is a strong marker that also simplifies the recognition and extraction of objects from a scene and humans can distinguish thousands of shades of color and intensity compared to just around two dozen shades of grey. The processing of color images can be classified into two main areas as full-color and pseudo-colour. The images are first acquired with a full color sensor such as color TV camera, color scanner and second category has the question of assigning a color to a different monochrome intensity and intensity ranges. The aim of a color model is to promote color specification in a widely agreed way to some standard. A color model is a coordinate system specification and a subspace within that system where a single point represents each color. Some particular conversions can convert different color models to one another. This paper specify overview of different types of images, color models, their conversion and applications.

This study develops a mobile banking (hereafter M-banking) research model by adopting the concept of ‘perceived value’ which mediates the relationship between M-banking quality and M-banking attractiveness and usage. A questionnaire-based field survey was administered to M-banking users (n=273). Our findings revealed that the perceived value of M-banking services depends heavily on information quality, whereas system and service quality exert at least some influence on the M-banking value. We also found that monetary value and emotional value significantly affected M-banking attractiveness, while monetary value, functional value and M-banking attractiveness significantly influenced M-banking usage. Further, interesting differences between early and late adopters were found.

Social media websites have emerged as one of the platforms to raise users’ opinions and influence the way any business is commercialized. Opinion of people matters a lot to analyze how the propagation of information impacts the lives in a large-scale network like Twitter Sentiment analysis of the tweets determine the polarity and inclination of vast population towards specific topic, item or entity. These days, the applications of such analysis can be easily observed during public elections, movie promotions, brand end. In this project, we exploited the fast and in memory computation framework 'Apache Spark' to extract live tweets and perform sentiment analysis. The primary aim is to provide a method for analyzing sentiment score in noisy twitter streams. This paper reports on the design of a sentiment analysis, extracting vast number of tweets. Results classify user's perception via tweets into positive and negative. Secondly, we discuss various techniques to carryout sentiment analysis on twitter data in detail.

The service quality of mobile health apps is a crucial factor affecting users’ satisfaction. For analyzing the influence of service quality of mobile health apps on users’ satisfaction in China, a hypothetical model with three dimensions and eight variables was established. Then this hypothetical model was combined with the model of mobile health service quality and hypotheses were proposed based on the actual situations according to the updated D&M information system success (UMDISS) theory. Questionnaires were issued to collect data and the SEM-PLS method was used to valid the hypothetical model and hypotheses. The reliability and security of the system quality of mobile health apps positively impacted the users’ satisfaction. The cooperation and personalization of service quality had a significant impact on the users’ willingness to use mobile health apps. Through multi-group analyses, it was found that the relationship between mobile health app system quality, users’ satisfaction, and the use of mobile health apps differed under different familiarities with mobile health apps. This study is expected to provide theoretical guidance for the marketing of mobile health apps.

The vision of the future Internet of Things is posing new challenges and opportunities for data management and analysis technology. Gigabytes of data are generated everyday by millions of sensors, actuators, RFID tags, and other devices. As the volume of data is growing dramatically, so is the demand for performance enhancement. When it comes to this Big Data problem, much attention has been paid to cloud computing and virtualization for their unlimited resource capacity, exible resource allocation and management, and distributed processing ability that promise high scalability and availability. On the other hand, the types and nature of data are getting more and more various. Data can come in any format, structured or unstructured, ranging from text and number to audio, picture, or even video. Data are generated, stored, and transferred across multiple nodes. Data can be updated and queried in real time or on demand. Hence, the traditional and dominant relational database systems have been questioned whether they can still be the best choice for current systems with all the new requirements. It has been realized that the emphasis on data consistency and the constraint of using relational data model cannot well with the variety of modern data and their distributed trend. This led to the emergence of NoSQL databases with their support for a schema-less data model and horizontal scaling on clusters of nodes. NoSQL databases have gained much attention from the community and are increasingly considered as a viable alternative to traditional databases.