Summarizing information from Web sites on distributed power generation and alternative energy development

The World Wide Web (WWW) has become a huge repository of information and knowledge, and an essential channel for information exchange. Many sites and thousands of pages of information on distributed power generation and alternate energy development are being added or modified constantly and the task of finding the most appropriate information is getting difficult. While search engines are capable to return a collection of links according to key terms and some forms of ranking mechanism, it is still necessary to access the Web page and navigate through the site in order to find the information. This paper proposes an interactive summarization framework called iWISE to facilitate the process by providing a summary of the information on the Web site. The proposed approach makes use of graphical visualization, tag clouds and text summarization. A number of cases are presented and compared in this paper with a discussion on future work

The application of user log for online business environment using content-based Image retrieval system

Over the past few years, inter-query learning has gained much attention in the research and development of content-based image retrieval (CBIR) systems. This is largely due to the capability of inter-query approach to enable learning from the retrieval patterns of previous query sessions. However, much of the research works in this field have been focusing on analyzing image retrieval patterns stored in the database. This is not suitable for a dynamic environment such as the World Wide Web (WWW) where images are constantly added or removed. A better alternative is to use an image's visual features to capture the knowledge gained from the previous query sessions. Based on the previous work (Chung et al., 2006), the aim of this paper is to propose a framework of inter-query learning for the WWW-CBIR systems. Such framework can be extremely useful for those online companies whose core business involves providing multimedia content-based services and products to their customers

A screening tool to quickly identify movement disorders in patients with inborn errors of metabolism

BackgroundMovement disorders are frequent in patients with inborn errors of metabolism (IEMs) but poorly recognized, particularly by nonmovement disorder specialists. We propose an easy-to-use clinical screening tool to help recognize movement disorders.ObjectiveThe aim is to develop a user-friendly rapid screening tool for nonmovement disorder specialists to detect moderate and severe movement disorders in patients aged ≥4 years with IEMs.MethodsVideos of 55 patients with different IEMs were scored by experienced movement disorder specialists (n = 12). Inter-rater agreements were determined on the presence and subtype of the movement disorder. Based on ranking and consensus, items were chosen to be incorporated into the screening tool.ResultsA movement disorder was rated as present in 80% of the patients, with a moderate inter-rater agreement (κ =0.420, P P ConclusionsWe designed a screening tool to recognize movement disorders in patients with IEMs. We propose that this screening tool can contribute to select patients who should be referred to a movement disorder specialist for further evaluation and, if necessary, treatment of the movement disorder. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.Neurological Motor Disorder

Towards liver-directed gene therapy: Retrovirus-mediated gene transfer into human hepatocytes

Liver-directed gene therapy is being considered in the treatment of inherited metabolic diseases. One approach we are considering is the transplantation of autologous hepatocytes that have been genetically modified with recombinant retroviruses ex vivo. We describe, in this report, techniques for isolating human hepatocytes and efficiently transducing recombinant genes into primary cultures. Hepatocytes were isolated from tissue of four different donors, plated in primary culture, and exposed to recombinant retroviruses expressing either the LacZ reporter gene or the cDNA for rabbit LDL receptor. The efficiency of gene transfer under optimal conditions, as determined by Southern blot analysis, varied from a maximum of one proviral copy per cell to a minimum of 0.1 proviral copy per cell. Cytochemical assays were used to detect expression of the recombinant derived proteins, E. coli β-galactosidase and rabbit LDL receptor. Hepatocytes transduced with the LDL receptor gene expressed levels of receptor protein that exceeded the normal endogenous levels. The ability to isolate and genetically modify human hepatocytes, as described in this report, is an important step towards the development of liver-directed gene therapies in humans.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45540/1/11188_2005_Article_BF01233625.pd

Relevance feedback and intelligent technologies in content-based image retrieval system for medical applications

Relevance feedback has gained much interest from researchers in the discipline of content-based image retrieval (CBIR). However, such approach is rarely used in the content-based medical image retrieval (CBMIR) systems. This paper reviews current CBMIR systems and discusses the possible applications of relevance feedback and intelligent technologies in the perspective areas of research for these systems. As a pilot study, this paper paves the ground work and provides a starting point of future research

A feature vector approach for inter-query learning for content-based image retrieval

Use of relevance feedback (RF) in the feature vector model has been one of the most widely used approaches to fine tuning queries for content-based image retrieval (CBIR). We propose a framework that extends RF to capturing the inter-query relationship between current and previous queries. Using the feature vector model, this avoids the need to memorize actual retrieval relationships between actual image indexes and the previous queries. This approach is suited to image database applications in which images are frequently added and removed. In the previous work, we developed a feature vector framework for inter-query learning using statistical discriminant analysis. One weakness of the previous framework is that the criteria for exploring and merging with an existing visual group are based on two constant thresholds, which are selected through trial and error. Another weakness is that it is not suited to mutually interrelated data clusters. Instead of using constant values, we have further extended the framework using positive feedback sample size as a factor for determining thresholds. Experiments demonstrated that our proposed framework outperforms the previous framework

Multiple layar kernel-based approach in relevance feedback content-based image retrieval system

Relevance feedback has drawn intense interest from many researchers in the field of content-based image retrieval (CBIR). In recent years, kernel-based approach has been a popular choice for the implementation of the relevance feedback based CBIR system. This is largely due to its ability to classify patterns with limited sample data. Since most of the kernel approaches reported have been treating the input as a long flat vector, such arrangement may increase the chances of polluting the feature element that uniquely identifies the selected image group. This paper proposes a two layer kernel configuration with an objective to improve the retrieval accuracy. While the performance of the two configurations is similar in certain conditions, the proposed configuration has shown to superior when dominant feature element exists that is capable to uniquely identify the selected image group

A hierarchical discriminant analysis framework for content-based image retrieval system for industrial applications

Content-based image retrieval (CBIR) systems have drawn interest from many researchers in recent years. One of the potential applications of CBIR is in industrial areas where the most relevant drawings or images can be retrieved speedily without the need to memorize any file name or specific key-words. To increase the retrieval speed, most of the systems pre-process the stored images by extracting a set of predefined features. Such scheme only works well for the server type database systems where the images have been stored previously. It is not feasible for systems that analyze images in real-time where the images are stored or added on an ongoing basis. For instance, personal image search engine for the World-Wide-Web is such an example. In this paper, the authors propose a multi-layer statistical discriminant framework which is able to select the most appropriate features to analyze newly received images thereby improving the retrieval accuracy and efficiency

A feature selection framework for small sampling data in content-based image retrieval system

Content-based image retrieval (CBIR) systems have drawn interest from many researchers in recent years. Over the last few years, kernel-based approach has been a popular choice for the implementation of the relevance feedback based CBIR system. This is largely due to its ability to classify patterns with limited sample data. A long flat vector has been a popular choice for the input configuration. The reasons are because it is relatively easy to implement and more importantly, because it preserve the information of identifying the target images via different combination of image features. However, one of the biggest weaknesses of such configuration is the curse of dimensionality. This paper introduces a relevance feedback framework via the use of statistical discriminant analysis method to select only relevant feature for next image retrieval cycle. Hence, minimize the dimensionality of the feature vector. This approach has been tested with four sets of images labelled with different themes. Each set contains 500 images, 50 labelled as positive while the rest are negative. The test showed an improvement from the previous flat input vector configuration when the training samples are relatively small

iWISE, an intelligent web interactive summarization engine

The Internet is expanding at a rapid rate and in particular the World Wide Web (WWW) has a huge number of users and providers of information. There are many web pages of information being added and the task of finding the most appropriate information is getting difficult. While search engines are capable to return a collection of links according to key terms and some form of ranking mechanism, the users are still need to access the Web page and navigate through the site in order to find the information. This paper proposes an interactive summarization framework called iWISE to facilitate the process. The details and background the research work are provided with discussion on future work