Image Retrieval with Relational Semantic Indexing Color and Gray Images

Due to the development of digital technology large number of image is available in web and personal database and it take more time to classify and organize them. In AIA assigns label to image content with this image is automatically classified and desired image can be retrieved. Image retrieval is the one of the growing research area. To retrieve image Text and content based methods used. In recent research focus on annotation based retrieval. Image annotation represents assigning keywords to image based on its contents and it use machine learning techniques. Using image content with more relevant keywords leads fast indexing and retrieval of image from large collection of image database. Many techniques have been proposed for the last decades and it gives some improvement in retrieval performance. In this proposed work Relational Semantic Indexing (RSI) based LQT technique reduces the search time and increase the retrieval performance. This proposed method includes segmentation, feature extraction, classification, and RSI based annotation steps. This proposed method compared against IAIA, and LSH algorithm

The Art of Data Science

To flourish in the new data-intensive environment of 21st century science, we need to evolve new skills. These can be expressed in terms of the systemized framework that formed the basis of mediaeval education - the trivium (logic, grammar, and rhetoric) and quadrivium (arithmetic, geometry, music, and astronomy). However, rather than focusing on number, data is the new keystone. We need to understand what rules it obeys, how it is symbolized and communicated and what its relationship to physical space and time is. In this paper, we will review this understanding in terms of the technologies and processes that it requires. We contend that, at least, an appreciation of all these aspects is crucial to enable us to extract scientific information and knowledge from the data sets which threaten to engulf and overwhelm us.Comment: 12 pages, invited talk at Astrostatistics and Data Mining in Large Astronomical Databases workshop, La Palma, Spain, 30 May - 3 June 2011, to appear in Springer Series on Astrostatistic

Analyzing behavior patterns of internet consumers through database integration

A question facing contemporary entrepreneurs is: How can Electronic Commerce be used to exploit business opportunities now available through highly efficient electronic markets? This study aims to answer that question. Although the Internet is increasingly being adopted to market products and services, little academic attention has been paid to its linkages with database technology. This research used focus groups and convergent interviews to develop a framework illustrating how firms use the Internet and database technology for strategic advantage. Results revealed that integration of Internet and database marketing enhances the effectiveness of EC\u27s potential, offering such benefits as increased accuracy and speed, cost saving, and more importantly greater interaction and better relationships with customers; thus providing more effective application of existing and emerging technologies. The extent, degree and speed of communication enabled by the Internet makes it a synergistic component of an effectual database marketing strategy. Findings from this research have implications for both theory and practice providing strategies to achieve optimal results through the integration of Internet and database technologies

Document-level sentiment analysis of email data

Sisi Liu investigated machine learning methods for Email document sentiment analysis. She developed a systematic framework that has been qualitatively and quantitatively proved to be effective and efficient in identifying sentiment from massive amount of Email data. Analytical results obtained from the document-level Email sentiment analysis framework are beneficial for better decision making in various business settings

Ethics and taxation : a cross-national comparison of UK and Turkish firms

This paper investigates responses to tax related ethical issues facing busines

Intellectual evolution of social innovation: a bibliometric analysis and avenues for future research trends

Despite the fact that the concept of social innovation is extensively employed by scholars and practitioners, yet the conceptualisation and the research structure remained fragmented and scattered, because no rigorous attempt has been made to understand the core concept of social innovation. The notion of social innovation is multi-faceted and multi-disciplinary fluctuating from public-policy to environmental sustainability; which makes an investigation of the concept essential for business-to-business practitioners and scholars. By processing 370 publications from a sample of 125 journals and books with a total of 2941 citations, the authors unpack/unfold the intellectual foundation of social innovation in business and management domains by performing four bibliometric analyses and they evaluate the research domain qualitatively (1970-2019). By using co-citation, network visualisation through co-occurrence data, multi-dimensional scaling, and hierarchical cluster analysis, this research sheds light to the intellectual structure of social innovation including social value, economic value, societal impact, and bifocal innovations. This research reveals the key research clusters embodied by social innovation foundation. The present study identifies four important components for the future avenues of social innovation (i.e. opportunity, innovation practice, opportunity exploiter, value), and proposes a potential research framework to the researchers and practitioners, hoping to provide insights on social innovation

A Medical Records Managing and Securing Blockchain Based System Supported by a Genetic Algorithm and Discrete Wavelet Transform

The privacy of patients is jeopardised when medical records and data are spread or shared beyond the protected cloud of institutions. This is because breaches force them to the brink that they start abstaining from full disclosure of their condition. This type of condition has a negative effect on scientific research, patients and all stakeholders. A blockchain-based data sharing system is proposed to tackle this issue, which employs immutability and autonomy properties of the blockchain to sufficiently resolve challenges associated with access control and handle sensitive data. Our proposed system is supported by a Discrete Wavelet Transform to enhance the overall security, and a Genetic Algorithm technique to optimise the queuing optimization technique as well. Introducing this cryptographic key generator enhances the immunity and system access control, which allows verifying users securely in a fast way. This design allows further accountability since all users involved are already known and the blockchain records a log of their actions. Only when the users' cryptographic keys and identities are confirmed, the system allows requesting data from the shared queuing requests. The achieved execution time per node, confirmation time per node and robust index for block number of 0.19 s, 0.17 s and 20 respectively that based on system evaluation illustrates that our system is robust, efficient, immune and scalable