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This paper proposes a knowledge management system that enables supporting and supplementing higher learning education particularly, in the Department of Industrial Engineering and Operations Research. DennIE's goal is to assist the department in enabling in and out of class knowledge be accessible to its community. Knowledge such as technological, procedural, and conceptual can be submitted, reviewed, approved, stored and shared. This helps the whole department in supplementing student education by further enriching and widening educational resources. A group of users were asked to test and use DennIE. A customer satisfaction survey was then administered and yielded promising results in Denny's effectiveness and usefulness. [ABSTRACT FROM AUTHOR]

An Expert System Shell is an interface for strengthening, refining and maintaining the knowledgebase of an Expert System by directly interacting with it. The expert system shell is a complete development environment for developing and maintaining Knowledge-Based Applications and Expert Systems. It provides a step-by-step methodology for a knowledge engineer that allows the domain experts themselves to be directly involved in structuring and encoding the knowledge through an expert interface. Agriculture being a multidisciplinary science there is a scope of developing multiple expert systems for various crops, disease, insects, irrigation scheduling, fertilizer management and various other issues needed for a sustainable agriculture. There has been no such system or software that supports information management in this area. Developing such a platform for developing multidisciplinary and multi crop expert systems is a new approach that can provide computational convenience to replicate it for multiple crops. The shell described in this paper deals with the development procedure of multi crop expert system using the same inference engine used for "Expert System on Wheat Crop Management" developed by Division of Computer Applications, IASRI, New Delhi. [ABSTRACT FROM PUBLISHER]

Conventional approaches of image indexing and retrieval from digital libraries include content-based, metadata-based, and keyword-based approaches. This paper addresses a different way of image retrieval motivated by real-life applications for an intelligent system that can automatically select appropriate background images from textual passages. We explored techniques for developing automatic image-retrieval systems based on essential contextual information of a textual passage. We propose a framework that applies semantic role labeling techniques and a common sense knowledge base, ConceptNet. The primitive results indicate that the proposed methodology has a potential on applications with textual passages that describe things and events that are regularly seen in every day life. However, for fantasy tales that describe truly fictitious things and events, the use of ConceptNet does not allow to obtain accurate results. [ABSTRACT FROM AUTHOR]

This research paper presents an intelligent system to predict the risk assessment of hypertension in three main related areas like diabetes, cardiovascular problems, and kidney disorders. The system is targeted on patients in Sultanate of Oman. Currently there is no specific system in the domain of hypertension or its associated diseases in the Sultanate. Also currently available medical systems in Oman do not employ an intelligent approach; they are just using database-oriented methodologies. They are not flexible and adaptable to complex requirements and processes and lack intelligence. We propose a system with ontologies as knowledge base (medical knowledge base), patient medical profile to be stored in a semantic way and an inference mechanism to extract data in the decision making process. Ontology is among the most powerful tools to encode medical knowledge formally. Since the knowledge base is constructed through ontology, it can be easily reused and extended in a variety of different problems. The proposed system which is an interactive decision support system (DSS) is a partial replacement of traditional database oriented system which is not capable of finding out patient risk analysis in an intelligent way. [ABSTRACT FROM PUBLISHER]