Optical tomography: Image improvement using mixed projection of parallel and fan beam modes

Mixed parallel and fan beam projection is a technique used to increase the quality images. This research focuses on enhancing the image quality in optical tomography. Image quality can be defined by measuring the Peak Signal to Noise Ratio (PSNR) and Normalized Mean Square Error (NMSE) parameters. The findings of this research prove that by combining parallel and fan beam projection, the image quality can be increased by more than 10%in terms of its PSNR value and more than 100% in terms of its NMSE value compared to a single parallel beam

Evaluating online customer data helpfulness to set targets: a QFD perspective

Retrieving knowledge and useful information from customers is crucial to develop customer-focused products and maintain the market share. With the rapid growth of the Internet, the ability of users to create and publish content has generated a wealth of product information from customers’ point of view. Given the abundance of large scale, publicly available data social media can enable novel social ways of providing and receiving feedback from new products and concepts. In order to avoid information overload, identifying and analyzing helpful reviews has become a critical challenge. Identifying helpful online reviews and learning how to extract valuable data from product design perspective has become a crucial task due to the existing information overload –identifying what is relevant to analyze is a key task for companies. Existing studies have focused on identifying variables that affect the perceived helpfulness of an online comment. To the best author’s knowledge, actual studies about helpfulness do not consider the Quality Function Deployment perspective on evaluating to what extend the customer data from social media is helpful to set objective targets. The thesis aims to evaluate social media data helpfulness from the designer’s perspective taking as basis QFD. Evaluating this, the work hypothesis is that the helpfulness definition has to move beyond, taking into consideration what is needed to build The House of Quality, a key tool in product design. To do so, an exploratory analysis of real public data from Twitter, Facebook and iMore forum is taken as basis. The purpose of undertaking exploratory research is primarily to investigate and to identify if the proposed variables for defining review’s helpfulness currently existing in the literature review can help designers in target setting within a QFD perspective The presented thesis shows that to go further within target setting is needed to have the QFD perspective: not all current exposed variables do not help to explain online reviews helpfulness.Outgoin

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

Evaluating online customer data helpfulness to set targets: a QFD perspective

Retrieving knowledge and useful information from customers is crucial to develop customer-focused products and maintain the market share. With the rapid growth of the Internet, the ability of users to create and publish content has generated a wealth of product information from customers’ point of view. Given the abundance of large scale, publicly available data social media can enable novel social ways of providing and receiving feedback from new products and concepts. In order to avoid information overload, identifying and analyzing helpful reviews has become a critical challenge. Identifying helpful online reviews and learning how to extract valuable data from product design perspective has become a crucial task due to the existing information overload –identifying what is relevant to analyze is a key task for companies. Existing studies have focused on identifying variables that affect the perceived helpfulness of an online comment. To the best author’s knowledge, actual studies about helpfulness do not consider the Quality Function Deployment perspective on evaluating to what extend the customer data from social media is helpful to set objective targets. The thesis aims to evaluate social media data helpfulness from the designer’s perspective taking as basis QFD. Evaluating this, the work hypothesis is that the helpfulness definition has to move beyond, taking into consideration what is needed to build The House of Quality, a key tool in product design. To do so, an exploratory analysis of real public data from Twitter, Facebook and iMore forum is taken as basis. The purpose of undertaking exploratory research is primarily to investigate and to identify if the proposed variables for defining review’s helpfulness currently existing in the literature review can help designers in target setting within a QFD perspective The presented thesis shows that to go further within target setting is needed to have the QFD perspective: not all current exposed variables do not help to explain online reviews helpfulness.Outgoin

Building an Expert System for Evaluation of Commercial Cloud Services

Commercial Cloud services have been increasingly supplied to customers in industry. To facilitate customers' decision makings like cost-benefit analysis or Cloud provider selection, evaluation of those Cloud services are becoming more and more crucial. However, compared with evaluation of traditional computing systems, more challenges will inevitably appear when evaluating rapidly-changing and user-uncontrollable commercial Cloud services. This paper proposes an expert system for Cloud evaluation that addresses emerging evaluation challenges in the context of Cloud Computing. Based on the knowledge and data accumulated by exploring the existing evaluation work, this expert system has been conceptually validated to be able to give suggestions and guidelines for implementing new evaluation experiments. As such, users can conveniently obtain evaluation experiences by using this expert system, which is essentially able to make existing efforts in Cloud services evaluation reusable and sustainable.Comment: 8 page, Proceedings of the 2012 International Conference on Cloud and Service Computing (CSC 2012), pp. 168-175, Shanghai, China, November 22-24, 201