Abstract: Case based reasoning (CBR) methodology presents a foundation for a new technology of building intelligent computeraided diagnoses systems. This Technology directly addresses the problems found in the traditional Artificial Intelligence (AI) techniques, e.g. the problems of knowledge acquisition, remembering, robust and maintenance. This paper discusses the CBR methodology, the research issues and technical aspects of implementing intelligent medical diagnoses systems. Successful applications in cancer and heart diseases developed by Medical Informatics Research Group at Ain Shams University are also discussed.
Abstract: This work concerns the evolution and the maintenance
of an ontological resource in relation with the evolution of the corpus
of texts from which it had been built.
The knowledge forming a text corpus, especially in dynamic domains,
is in continuous evolution. When a change in the corpus occurs, the
domain ontology must evolve accordingly. Most methods manage
ontology evolution independently from the corpus from which it is
built; in addition, they treat evolution just as a process of knowledge
addition, not considering other knowledge changes. We propose a
methodology for managing an evolving ontology from a text corpus
that evolves over time, while preserving the consistency and the
persistence of this ontology.
Our methodology is based on the changes made on the corpus to
reflect the evolution of the considered domain - augmented surgery
in our case. In this context, the results of text mining techniques,
as well as the ARCHONTE method slightly modified, are used to
support the evolution process.
Abstract: In this paper, the implementation of a rule-based
intuitive reasoner is presented. The implementation included two
parts: the rule induction module and the intuitive reasoner. A large
weather database was acquired as the data source. Twelve weather
variables from those data were chosen as the “target variables"
whose values were predicted by the intuitive reasoner. A “complex"
situation was simulated by making only subsets of the data available
to the rule induction module. As a result, the rules induced were
based on incomplete information with variable levels of certainty.
The certainty level was modeled by a metric called "Strength of
Belief", which was assigned to each rule or datum as ancillary
information about the confidence in its accuracy. Two techniques
were employed to induce rules from the data subsets: decision tree
and multi-polynomial regression, respectively for the discrete and the
continuous type of target variables. The intuitive reasoner was tested
for its ability to use the induced rules to predict the classes of the
discrete target variables and the values of the continuous target
variables. The intuitive reasoner implemented two types of
reasoning: fast and broad where, by analogy to human thought, the
former corresponds to fast decision making and the latter to deeper
contemplation. . For reference, a weather data analysis approach
which had been applied on similar tasks was adopted to analyze the
complete database and create predictive models for the same 12
target variables. The values predicted by the intuitive reasoner and
the reference approach were compared with actual data. The intuitive
reasoner reached near-100% accuracy for two continuous target
variables. For the discrete target variables, the intuitive reasoner
predicted at least 70% as accurately as the reference reasoner. Since
the intuitive reasoner operated on rules derived from only about 10%
of the total data, it demonstrated the potential advantages in dealing
with sparse data sets as compared with conventional methods.
Abstract: Preparation and negotiation of innovative and future
projects can be characterized as a strategic-type decision situation,
involving many uncertainties and an unpredictable environment.
We will focus in this paper on the bidding process. It includes cooperative
and strategic decisions.
Our approach for bidding process knowledge capitalization is
aimed at information management in project-oriented organizations,
based on the MUSIC (Management and Use of Co-operative
Information Systems) model.
We will show how to capitalize the company strategic knowledge
and also how to organize the corporate memory. The result of the
adopted approach is improvement of corporate memory quality.
Abstract: In our current political climate of assessment and
accountability initiatives we are failing to prepare our children for a
participatory role in the creative economy. The field of education is
increasingly falling prey to didactic methodologies which train a
nation of competent test takers, foregoing the opportunity to educate
students to find problems and develop multiple solutions. No where is
this more evident than in the area of art education. Due to a myriad of
issues including budgetary shortfalls, time constraints and a general
misconception that anyone who enjoys the arts is capable of teaching
the arts, our students are not developing the skills they require to
become fully literate in critical thinking and creative processing.
Although art integrated curriculum is increasingly being viewed as a
reform strategy for motivating students by offering alternative
presentation of concepts and representation of knowledge acquisition,
misinformed administrators are often excluding the art teacher from
the integration equation. The paper to follow addresses the problem
of the need for divergent thinking and conceptualization in our
schools. Furthermore, this paper explores the role of education, and
specifically, art education in the development of a creatively literate
citizenry.
Abstract: This study investigates the roles of knowledge
acquisition, absorptive capacity, and innovation capability in finance
and manufacturing industries. With 362 valid questionnaires from
manufactures and financial industries in Taiwan, we examine the
relationships between absorptive capacity, knowledge acquisition and
innovation capability using a structural equation model. The results
indicate that absorptive capacity is the mediator between knowledge
acquisition and innovation capability, and that knowledge acquisition
has a positive effect on absorptive capacity.
Abstract: This article discusses the concept of student ownership of knowledge and seeks to determine how to move students from knowledge acquisition to knowledge application and ultimately to knowledge generation in a virtual setting. Instructional strategies for fostering student engagement in a virtual environment are critical to the learner-s strategic ownership of the knowledge. A number of relevant theories that focus on learning, affect, needs and adult concerns are presented to provide a basis for exploring the transfer of knowledge from teacher to learner. A model under development is presented that combines the dimensions of knowledge approach, the teacher-student relationship with regards to knowledge authority and teaching approach to demonstrate the recursive and scaffolded design for creation of virtual learning environments.
Abstract: The Programmable Logic Controller (PLC) plays a
vital role in automation and process control. Grafcet is used for
representing the control logic, and traditional programming
languages are used for describing the pure algorithms. Grafcet is used
for dividing the process to be automated in elementary sequences that
can be easily implemented. Each sequence represent a step that has
associated actions programmed using textual or graphical languages
after case. The programming task is simplified by using a set of
subroutines that are used in several steps. The paper presents an
example of implementation for a punching machine for sheets and
plates. The use the graphical languages the programming of a
complex sequential process is a necessary solution. The state of
Grafcet can be used for debugging and malfunction determination.
The use of the method combined with a set of knowledge acquisition
for process application reduces the downtime of the machine and
improve the productivity.
Abstract: Data mining and knowledge engineering have become a tough task due to the availability of large amount of data in the web nowadays. Validity and reliability of data also become a main debate in knowledge acquisition. Besides, acquiring knowledge from different languages has become another concern. There are many language translators and corpora developed but the function of these translators and corpora are usually limited to certain languages and domains. Furthermore, search results from engines with traditional 'keyword' approach are no longer satisfying. More intelligent knowledge engineering agents are needed. To address to these problems, a system known as Multilingual Word Semantic Network is proposed. This system adapted semantic network to organize words according to concepts and relations. The system also uses open source as the development philosophy to enable the native language speakers and experts to contribute their knowledge to the system. The contributed words are then defined and linked using lexical and semantic relations. Thus, related words and derivatives can be identified and linked. From the outcome of the system implementation, it contributes to the development of semantic web and knowledge engineering.