Khaled Shaalan

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    A Multiagent Approach for Diagnostic Expert Systems via the Internet
    (Elsevier, 2004) Shaalan, Khaled F.; Rafea, Ahmed A.; El-Badry, Mona
    In recent years there has been considerable interest in the possibility of building complex problem solving systems as groups of co-operating experts. This has led us to develop a multiagent expert systems capable to run on servers that can support a large group of users (clients) who communicate with the system over the network. The system provides an architecture to coordinate the behavior of several specific agent types. Two types of agents are involved. One type works on the server computer and the other type works on the client computers. The society of agents in our system consists of expert systems agents (diagnosis agents, and a treatment agent) working on the server side, each of which contains an autonomous knowledge-based system. Typically, agents will have expertise in distinct but related domains. The whole system is capable of solving problems, which require the cumulative expertise of the agent community. Besides to the user interface agent who employs an intelligent data collector, so-called communication model in KADS, working on the client sides. We took the advantage of a successful pre-existing expert systems—developed at CLAES (Central Laboratory for Agricultural Expert Systems, Egypt)—for constructing an architecture of a community of cooperating agents. This paper describes our experience with decomposing the diagnosis expert systems into a multi-agent system. Experiments on a set of test cases from real agricultural expert systems were preformed. The expert systems agents are implemented in Knowledge Representation Object Language (KROL) and JAVA languages using KADS knowledge engineering methodology on the WWW platform.
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    Machine Translation of English Noun Phrases into Arabic
    (2004) Shaalan, Khaled F.; Rafea, Ahmed A.; MONEIM, AZZA ABDEL; BARAKA, HODA
    The present work reports our attempt in automating the translation of English noun phrase (NP) into Arabic. Translating NP is a very important task toward sentence translation since NPs form the majority of textual content of the scientific and technical documents. The system is implemented in Prolog and the parser is written in DCG formalism. The paper also describes our experience with the developed MT system and reports results of its application on real titles of theses from the computer science domain.
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    An expert system for the best weight distribution on ferryboats
    (2004) Shaalan, Khaled F.; Rizk, Mohammed; Abdelhamid, Yasser; Bahgat, Reem
    There are some problems that need expertise in order to get a satisfactory solution. Ferryboat carries goods, fresh water, diesel oil, luggage and storing rooms up to its permissible draft in order to maintain safety according to the international safety regulations. The best weight distribution on ferryboat needs human expertise to handle many variables, such as the amount of the bunker and fresh water that allow us to use more rooms for charging in order to maximize the profit. This sort of problems can be classified under Configuration Problem. In this paper, we address the development of a ferryboat expert systems (WDFB) using CommonKADS knowledge engineering methodology. We propose a reusable problem-solving approach, which is an enhancement of the structure-oriented approach, capable of solving the ferryboat configuration problem. The proposed model includes heuristics that make the search of suitable configuration more efficient, taking into consideration the transformation knowledge and the optimality criteria. The results of testing the system on a real-world data from National Navigation Company, Suez, Egypt, were satisfactory.
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    A Framework for Information Extraction, Storage and Retrieval
    (2004) El-Beltagy, Samhaa R.; Said, Mohammed; Shaalan, Khaled F.
    This paper presents a set of tools that were developed in order to facilitate and speed up the process of building information extraction and retrieval systems for documents that exhibit a set of predefined characteristics. Specifically, the work presents a simple framework for extracting information found in publications or documents that are issued in large volumes and which cover similar concepts or issues within a given domain. The paper presents a simple model for defining background knowledge and for using that to automatically augment segments of input documents with metadata in order to assist users in easily locating information within these documents through a structured front end. The model presented makes use of both document structure as well as dynamically acquired background knowledge to achieve its goals.
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    Towards Resolving Ambiguity in Understanding Arabic Sentence
    (2004) Rafea, Ahmed A.; Shaalan, Khaled F.
    Ambiguity is a major reason why computers do not yet understand natural language. We have made great deal strides towards developing tools for morphological and syntactic analyzers for Arabic in recent years. The absence of diacritics, which represent most vowels, in the written text creates ambiguity which hinders the development of Arabic natural language processing applications. Thus, ambiguity increases the range of possible interpretations of natural language. In this paper, we give a road map of solutions to common ambiguity problems inherent in parsing of Arabic sentence.
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    An Interactive System for Association Rule Discovery for Life Assurance
    (2004) Rafea, Ahmed A.; Shafik, Suzanne S.; Shaalan, Khaled F.
    This paper uses prior domain knowledge to guide the mining of association rules in life assurance business environment. This approach is used in order to overcome the drawbacks of data mining using rule induction such as loss of information, discover too many obvious patterns, and mining of overwhelmed association rules. A data mining interactive rule induction algorithm is introduced to mine rules at micro levels. The mined rules describe the impact of different insurance policies attributes, customer profiles, and market channels on company portfolio growth. A system was built based on this algorithm and was tested and verified on real data set in Misr Insurance company, which is the leading insurance company in Egypt.