Improved Arabic Characters Recognition by Combining Multiple Machine Learning Classifiers

In this paper, we investigate a range of strategies for combining multiple machine learning techniques for recognizing Arabic characters, where we are faced with imperfect and dimensionally variable input characters. Experimental results show that combined confidence-based backoff strategies can produce more accurate results than each technique produces by itself and even the ones exhibited by the majority voting combination

Improved Arabic characters recognition by combining multiple machine learning classifiers.

In this paper, we investigate a range of strategies for combining multiple machine learning techniques for recognizing Arabic characters, where we are faced with imperfect and dimensionally variable input characters. Experimental results show that combined confidence-based backoff strategies can produce more accurate results than each technique produces by itself and even the ones exhibited by the majority voting combination

Semantic Approach for Discovery and Visualization of Academic Information Structured with OAI-PMH

There are different channels to communicate the results of a scientific research; however, several research communities state that the Open Access (OA) is the future of acad emic publishing. These Open Ac cess Platforms have adopted OAI - PMH (Open Archives Initiative - the Protocol for Metadata Harvesting) as a standard for communication and interoperability. Nevertheless, it is significant to highlight that the open source know ledge discovery services based on an index of OA have not been developed. Therefore, it is necessary to address Knowledge Discovery (KD) within these platforms aiming at studen ts, teachers and/ or researchers , to recover both , the resources requested and th e resources that are not explicitly requested – which are also appropriate . This objective represents an important issue fo r structured resources under OAI - PMH. This fact is caused because interoperability with other developments carried out outside their implementation environment is generally not a priority (Level 1 "Shared term definitions"). It is here , where the Semantic Web (SW) beco mes a cornerstone of this work. Consequently, we propose OntoOAIV, a semantic approach for the selective knowledge disco very an d visu alization into structured information with OAI - PMH, focused on supporting the activities of scientific or academic research for a specific user. Because of the academic nature of the structured resources with OAI - PMH, the field of application chosen is the context information of a student. Finally, in order to validate the proposed approach, we use the RUDAR (Roskilde University Digital Archive) and REDALYC (Red de Revistas Científicas de América Latina y el Caribe, España y Portugal) repositor ies, which imple ment the OAI - PMH protocol , as well as one s tudent profile for carrying out KD

Extraction of web image information: semantic or visual cues?

Book Title : Artificial Intelligence Applications and Innovations : 8th IFIP WG 12.5 International Conference, AIAI 2012, Halkidiki, Greece, September 27-30, 2012, Proceedings, Part I, Pages 368-373. ISBN : 978-3-642-33408-5.Text based approaches for web image information retrieval have been exploited for many years, however the noisy textual content of the web pages makes their task challenging. Moreover, text based systems that retrieve information from textual sources such as image file names, anchor texts, existing keywords and, of course, surrounding text often share the inability to correctly assign all relevant text to an image and discard the irrelevant. A novel method for indexing web images is discussed in the present paper. The main concern of the proposed system is to overcome the obstacle of correctly assigning textual information to web images, while disregarding text that is unrelated to them. The proposed system uses visual cues in order to cluster a web page into several regions and compares this method to the use of semantic information and the realization of a k-means clustering. The evaluation reveals the advantages and disadvantages of the different clustering techniques and confirms the validity of the proposed method for web image indexin

An Integrated Decision Support System for the Planning, Analysis, Management and Rehabilitation of Pressurised Irrigation Distribution Systems

Water scarcity is a mounting problem in arid and semi-arid regions such as the Mediterranean. Therefore, smarter and more effective water management is required, especially in irrigated agriculture. Irrigation infrastructure such as pressurized irrigation distribution systems (PIDSs) play an important role for the intensification of agricultural production in the Mediterranean region. However, the operation and management of these systems can be complex as they involve several intertwined processes, which need to be considered simultaneously. For this reason, numerous decision support systems (DSSs) have been developed and are available to deal with these processes, but as independent components. To this end, a comprehensive DSS called DESIDS has been developed and tested in the framework of this research. This DSS has been developed bearing in mind the need of irrigation district managers for an integrated tool that can assist them in taking strategic decisions for managing and developing reliable, adequate and sustainable water distribution plans, which provide the best services to farmers. Hence, four modules were integrated in DESIDS: i) the irrigation demand and scheduling module; ii) the hydraulic analysis module; iii) the operation and management modules; and iv) the design and rehabilitation module. DESIDS was tested on different case studies located in the Apulia region, where it proved to be a valuable tool for irrigation district managers as it provides a wide range of decision options for proper operation and management of PIDSs. All this is obtained through a DSS that offers: i) high level of interactivity; ii) complete control of the irrigation managers; iii) adaptability and flexibility to the problems related to the operation of PIDSs; and iv) effectiveness in assisting irrigation managers with the decision making. The developed DSS can be used as a platform for future integrations and expansions to include other processes needed for better decision-making support