Vassilis Loumos

Dropout prediction in e-learning courses through the combination of machine learning techniques

In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to accurately classify some e-learning students, whereas another may succeed, three decision schemes, which combine in different ways the results of the three machine learning techniques, were also tested. The method was examined in terms of overall accuracy, sensitivity and precision and its results were found to be significantly better than those reported in relevant literature.

CorpWiki: A self-regulating wiki to promote corporate collective intelligence through expert peer matching

One of the main challenges that organizations face nowadays, is the efficient use of individual employee intelligence, through machine-facilitated understanding of the collected corporate knowledge, to develop their collective intelligence. Web 2.0 technologies, like wikis, can be used to address the above issue. Nevertheless, their application in corporate environments is limited, mainly due to their inability to ensure knowledge creation and assessment in a timely and reliable manner. In this study we propose CorpWiki, a self-regulating wiki system for effective acquisition of high-quality knowledge content. Inserted articles undergo a quality assessment control by a large number of corporate peer employees. In case the quality is inadequate, CorpWiki uses a novel expert peer matching algorithm (EPM), based on feed-forward neural networks, that searches the human network of the organization to select the most appropriate peer employee who will improve the quality of the article. Performance evaluation results, obtained through simulation modeling, indicate that CorpWiki improves the final quality levels of the inserted articles as well as the time and effort required to reach them. The proposed system, combining machine-learning intelligence with the individual intelligence of peer employees, aims to create new inferences regarding corporate issues, thus promoting the collective organizational intelligence.

Operator context scanning to support high segmentation rates for real time license plate recognition

Introducing high definition videos and images in object recognition has provided new possibilities in the field of intelligent image processing and pattern recognition. However, due to the large amount of information that needs to be processed, the computational costs are high, making the HD systems slow. To this end, a novel algorithm applied to sliding window analysis, namely Operator Context Scanning (OCS), is proposed and tested on the license plate detection module of a License Plate Recognition (LPR) system. In the LPR system, the OCS algorithm is applied on the Sliding Concentric Windows pixel operator and has been found to improve the LPR systems performance in terms of speed by rapidly scanning input images focusing only on regions of interest, while at the same time it does not reduce the system effectiveness. Additionally, a novel characteristic is presented, namely, the context of the image based on a sliding windows operator. This characteristic helps to quickly categorize the environmental conditions upon which the input image was taken. The algorithm is tested on a data set that includes images of various resolutions, acquired under a variety of environmental conditions.

Classifying Web pages employing a probabilistic neural network

The paper proposes a system capable of identifying and categorising Web pages on the basis of information filtering. The system is a three-layer probabilistic neural network (PNN) with biases and radial basis neurons in the middle layer and competitive neurons in the output layer. The domain of study involves the e-commerce area. Thus, the PNN scopes to identify e-commerce Web pages and classify them to the respective type according to a framework which describes the fundamental phases of commercial transactions in the Web. The system was tested with many types of Web pages, demonstrating the robustness of the method, since no restrictions were imposed except for the language of the content, which is English. The probabilistic classifier was used for estimating the population of specific e-commerce Web pages. Potential applications involve surveying Web activity in commercial servers, as well as Web page classification in largely expanding information areas like e-government or news and media.

A computer vision approach for textile quality control

Textile manufacturers have to monitor the quality of their products in order to maintain the high-quality standards established for the clothing industry. Thus, textile quality control is a key factor for the increase of competitiveness of their companies. Textile faults have traditionally been detected by human visual inspection. However, human inspection is time consuming and does not achieve a high level of accuracy. Therefore, industrial vision units are of strategic interest for the textile industry as they could form the basis of a system achieving a high degree of accuracy on textile inspection. This work describes the software core of a system designed for fabric inspection on the basis of simple image-processing operations as well as its efficiency on detection of usual textile defects. The prerequisites of the overall system are then discussed analytically, as well as the limitations and the restrictions imposed due to the nature of the problem. The software algorithm and the evaluation of the first results are also presented in details. Copyright

Collaborative e-learning environments enhanced by wiki technologies

E-learning environments have met rapid technological advancements in the previous years. Nevertheless, current e-learning techniques do not adequately support student interaction and collaboration, resulting in decreased student progress and motivation. In this paper, a blended technique combining collaborative forums and wiki technologies is proposed. Through collaborative forums, students discuss course related topics assigned by the tutors to produce new educational material. This material is then stored in the wiki platform for further use. The proposed technique was applied on an e-learning course provided by the National Technical University of Athens and its effectiveness was evaluated using student activity data and questionnaire analysis. Results showed that the technique adequately supported teamwork, increasing student motivation and progress while simultaneously producing satisfactory level educational material.

Web-based decision-support system methodology for smart provision of adaptive digital energy services over cloud technologies

Energy information systems, which manage energy consumptions over internet, have been evolving over the past decade and can be considered as a part of a specialised sequential decision process, regarding the provision of personalised energy services to the community. The aim of this study is to develop and present an innovative decision-support system and cloud computing software methodology that brings together energy consultants, consumers, energy services procedures and modern web interoperable technologies. The authors propose a web-based knowledge system, using distributed cloud architecture and metering grids over ADSL broadband connections. By using some clustering algorithms and a web middleware, energy profiles over time are analysed and observed. The resulting clusters and centroids are projected and statistically analysed over time, producing a centroid-locus. Hypercube topology was used for efficient data management and software agent-based parallel analysis. The system operates efficiently on a multi-tier cloud-based middleware that generates in real-time using various service software components to the end consumers. The case study on real Greek energy measurements, for the first time in Greece, indicated a compact and efficient distributed procedure that could analyse and produce adaptive personalised information services.

A template-guided approach to vehicle surveillance and access control

Video-based surveillance techniques have extended their scope to the field of undervehicle inspection. However, their role is currently restricted to the visualization of the inspection results, while the evaluation of their outcome depends solely on the judgement of the operator. This paper presents an intelligent template-guided approach, which addresses the task of computer-assisted undervehicle inspection and access control. It combines a license plate identification stage which authenticates incoming vehicles and a template-guided undervehicle inspection stage which alerts the operator in case of questionable content alterations.

Intelligent traffic management through MPEG-7 vehicle flow surveillance

Recent development in the aspects of low-level image processing for feature extraction, as well as in the standardization of description schemes for video description, provide means for the development of intelligent transportation systems. ITS may support a wide range of abilities, including vehicle identification and reidentification, event detection and optimum resource management. This paper analyzes the structure operation principles and the structure of an integrated highway management system. Its operation is based on the combination of sensor networks, low-level image processing algorithms and high-level description schemes. Low-level algorithms perform the tasks of license plate recognition for vehicle identification, as well as feature extraction through change detection, for event detection purposes. High level schemes are based on the recently developed MPEG-7 description schemes and are utilized as an information processing framework, which formulates identification and event representation procedures

Information fusion meta-search interface for precise photo acquisition on the Web

Nowadays most Web pages contain both text and images. Nevertheless, search engines index documents based on their disseminated content or their meta-tags only. Although many search engines offer image search, this service is based over textual information filtering and retrieval. Thus, in order to facilitate effective search for images on the Web, text analysis and image processing must work in complement. We present an enhanced information fusion version of the meta-search engine proposed in [Anagnostopoulos .I et al., (2002)], which utilizes up to 9 known search engines simultaneously for content information retrieval while 3 of them can be used for image processing in parallel. In particular this proposed meta-search engine is combined with fuzzy logic rules and a neural network in order to provide an additional search service for human photos in the Web.