Recognizing Customer Knowledge Level towards Products for Recommendation in Electronic Commerce

A powerful online recommendation system in Electronic Commerce (EC) must know its targeted customers well and employ effective marketing strategies. Market research is a very important way to know the customers well. For high-tech products with great variety such as computers, cellular phones, and digital cameras, customers’ knowledge level towards products may have a decisive influence on their purchase decision. While many online recommendation systems focus on utilizing data mining techniques in user profile and transaction data, this paper presents a method for recognizing customer knowledge level as a preprocess for more effective online recommendation in EC. The method consists of two Back Propagation Networks (BPN) and predicts based on customer characteristics and online navigation behaviors. A simple simulated digital camera EC store case study was conducted and the good preliminary result implies the good potential of the proposed method

A Framework for Enterprise Knowledge Discovery from Databases

Knowledge discovery from large databases has become an emerging research topic and application area in recent years primarily because of the successful introduction of large business information systems to enterprises in the electronic business era. However, transferring subjects/problems from managerial perspective to data mining tasks from information technology perspective requires multidisciplinary domain knowledge. This paper proposes a practical framework for enterprise knowledge discovery in a systematical manner. The six-step framework employs the cause-andeffect diagram to model enterprise processes, tasks and attributes corresponding diagram to define data mining tasks, and multi-criteria method to assess the mined results in the form of association rules. This research also applied the proposed framework to a real case study of knowledge discovery from service records. The mining results have been proven useful in product design and quality improvement and the framework has demonstrated its applicability of guiding an enterprise to discover knowledge from historical data to tackle existing problems

Contrast-enhanced Ultrasonography in Small Liver Tumors (< 3 cm)

Ultrasonography is a safe, convenient, low cost and noninvasive diagnostic modality for liver tumors. Power Doppler sonography may demonstrate fine tumor vessels in small lesions and hypovascular lesions. However, it has limitations including motion artifacts, less sensitivity to slow vascular flow, poor demonstration of deep-seated lesions (> 7 cm in depth), and high sensitivity to tissue motion (heart beat or aortic pulsation). Owing to improvements in contrast agents and new technologies such as harmonic and pulse inversion imaging, contrast-enhanced ultrasound (CEUS) has improved the detection rate compared with Doppler ultrasound in studies of liver lesions. The enhanced vascular patterns have been proved to correlate well with the findings from dynamic computed tomography or magnetic resonance imaging. CEUS provides the ability to detect small focal liver lesions and even metastatic liver tumors of less than 1 cm in diameter. This review attempts to determine ways to allow the diagnosis of small hepatocellular carcinomas (HCCs), especially in cirrhotic patients, using CEUS. Because HCCs are small, the feeding arteries are fine and the arterial blood flow to the tumor is slow, CEUS used in the diagnosis of nodules of 1–2 cm in cirrhotic patients is not satisfactory. The portal and late phases in pulse inversion imaging may provide more information to detect small lesions in the cirrhotic liver and improve the diagnostic sensitivity and specificity. Contrast-enhanced flash echo with subtraction mode is another way of detecting this type of small tumor. In the arterial phase, some tumors are hard to identify, owing to the isoechoic status of the tumors with respect to the surrounding liver parenchyma. However, these small lesions may be shown by flash echo subtraction imaging. Concurrent delayed phase imaging is useful in the diagnosis of small hypovascular HCCs. In conclusion, CEUS improves the diagnostic accuracy of focal liver lesions, even in tumors as small as 1–2 cm. This safe, convenient, low cost and noninvasive diagnostic modality should be promoted in routine clinical practice

The impact of enterprise application integration on information system lifecycles

Information systems (IS) have become the organisational fabric for intra-and inter-organisational collaboration in business. As a result, there is mounting pressure from customers and suppliers for a direct move away from disparate systems operating in parallel towards a more common shared architecture. In part, this has been achieved through the emergence of new technology that is being packaged into a portfolio of technologies known as enterprise application integration (EAI). Its emergence however, is presenting investment decision-makers charged with the evaluation of IS with an interesting challenge. The integration of IS in-line with the needs of the business is extending their identity and lifecycle, making it difficult to evaluate the full impact of the system as it has no definitive start and/or end. Indeed, the argument presented in this paper is that traditional life cycle models are changing as a result of technologies that support their integration with other systems. In this paper, the need for a better understanding of EAI and its impact on IS lifecycles are discussed and a classification framework proposed.Engineering and Physical Sciences Research Council (EPSRC) Grant Ref: (GR/R08025) and Australian Research Council (DP0344682)

Use Data Mining to Improve Genetic Algorithm Efficiency for a Job Shop Scheduling Problem

This paper proposes a new improved Genetic Algorithm (GA) by utilizing a Data Mining technique, and demonstrates how it is superior to traditional GA on a popular job shop scheduling problem. GA has long been widely applied to solve complex optimization problems in a good variety of areas. It has advantages of adaptive capability, efficient search, potential to avoid local optimum, etc. In recent literature, researchers have proposed a good number of new GAs by combining basic GA with other techniques, such as heuristic rules, simulated annealing, neural networks, fuzzy sets, and so on, in order to improve the efficiency for various optimization problems. Data mining is a new evolving technology for knowledge extraction, classification, clustering, estimation, etc. The capability of finding frequent patterns in large data set is the key reason why it is integrated with GA in this research. Due to the fundamental concept of GA’s randomness during evolution, a traditional GA may become less efficient in search for optimum. By embedding the frequent schemata into the GA evolution process, the new improved GA could reduce the search time by preserving segments of good solutions without accidentally being lost due to random crossover or mutation. The proposed new GA was experimented on a popular 6x6 job shop scheduling problem. The results have shown its better efficiency than traditional GAs and potential for further research works

Federal Transit Administration\u27s Impact on Public Transportation in the United States

The Federal Transit Administration invests in building the capacity and improving the quality of public transportation throughout the United States of America. Under FTA\u27s leadership, public rail, bus, trolley, ferry, and other transit services have reached greater levels of safety, reliability, availability, and accessibility. Come hear the highlights of FTA\u27s impacts and participate in an interactive question/answer session and discussion on career options in public transportation.https://pdxscholar.library.pdx.edu/trec_seminar/1035/thumbnail.jp

An On-Line Personalized Promotion Decision Support System for Electronic Commerce

With the development of the Internet and Electronic Commerce (EC), enterprises have overcome the space and time barriers and are now capable of serving customers electronically. However, it is a great challenge to attract and retain the customers over Internet. One approach is to provide the responsive personalized service to satisfy the customer demand and promote sales at the first time. Hence, in this paper, we propose a decision support system which develops best promotion products based on combinations of different marketing strategies, pricing strategies, and customer behaviors evaluated in terms of multiple criteria. Data mining techniques are utilized to help the business discover patterns to develop on-line sales promotion products for each customer for enhancing customer satisfaction and loyalty. The proposed system consists of four components: (1) establishing marketing strategies, (2) promotion pattern model, (3) personalized promotion products, and (4) on-line transaction model. A simple example is given to illustrate the implementation and application of proposed decision support system

Poorly differentiated synovial sarcoma is associated with high expression of enhancer of zeste homologue 2 (EZH2)

Background: Enhancer of zeste homologue 2 (EZH2) is a polycomb group (PcG) family protein. Acting as a histone methyltransferase it plays crucial roles in maintaining epigenetic stem cell signature, while its deregulation leads to tumor development. EZH2 overexpression is commonly associated with poor prognosis in a variety of tumor types including carcinomas, lymphomas and soft tissue sarcomas. However, although the synovial sarcoma fusion proteins SYT-SSX1/2/4 are known to interact with PcG members, the diagnostic and prognostic significance of EZH2 expression in synovial sarcoma has not yet been investigated. Also, literature data are equivocal on the correlation between EZH2 expression and the abundance of trimethylated histone 3 lysine 27 (H3K27me3) motifs in tumors. Methods: Immunohistochemical stains of EZH2, H3K27me3, and Ki-67 were performed on tissue microarrays containing cores from 6 poorly differentiated, 39 monophasic and 10 biphasic synovial sarcomas, and evaluated by pre-established scoring criteria. Results of the three immunostainings were compared, and differences were sought between the histological subtypes as well as patient groups defined by gender, age, tumor location, the presence of distant metastasis, and the type of fusion gene. The relationship between EZH2 expression and survival was plotted on a Kaplan-Meier curve. Results: High expression of EZH2 mRNA and protein was specifically detected in the poorly differentiated subtype. EZH2 scores were found to correlate with those of Ki-67 and H3K27me3. Cases with high EZH2 score were characterized by larger tumor size (≥5cm), distant metastasis, and poor prognosis. Even in the monophasic and biphasic subtypes, higher expression of EZH2 was associated with higher proliferation rate, larger tumor size, and the risk of developing distant metastasis. In these histological groups, EZH2 was superior to Ki-67 in predicting metastatic disease. Conclusions: High expression of EZH2 helps to distinguish poorly differentiated synovial sarcoma from the monophasic and biphasic subtypes, and it is associated with unfavorable clinical outcome. Importantly, high EZH2 expression is predictive of developing distant metastasis even in the better-differentiated subtypes. EZH2 overexpression in synovial sarcoma is correlated with high H3K27 trimethylation. Thus, along with other epigenetic regulators, EZH2 may be a future therapeutic target