Abstract:
In this article we first provide an overview of AI techniques and then present a machine learning based document retrieval system we developed. GANNET (Genetic Algorithms and Neural Nets System) performed concept (keyword) optimization for user-selected documents during document retrieval using the genetic algorithms. It then used the optimized concepts to perform concept exploration in a large network of related concepts through the Hopfield net parallel relaxation procedure. Our preliminary experiment showed that GANNET helped improve search recall by identifying the underlying concepts (keywords) which best describe the user-selected documents.
Abstract:
Affect represents a person's emotions toward objects, issues or other persons. Recent years have witnessed a surge in studies of users' affect in social media, as marketing literature has shown that users' affect influences decision making. The current literature in this area, however, has largely focused on the message level, using text-based features and various classification approaches. Such analyses not only overlook valuable information about the user who posts the messages, but also fail to consider that users' affect may change over time. To overcome these limitations, we propose a new research design for social media affect analysis by specifically incorporating users' characteristics and the time dimension. We illustrate our research design by applying it to a major Dark Web forum of international Jihadists. Empirical results show that our research design allows us to draw on theories from other disciplines, such as social psychology, to provide useful insights on the dynamic change of users' affect in social media. © 2011 IEEE.
Abstract:
An experiment with an Al-based software agent shows that it can help users organize and consolidate ideas from electronic brainstorming. The agent recalled concepts as effectively as experienced human meeting facilitators and in a fifth of the time.
Abstract:
Digital libraries of geo-spatial multimedia content are currently deficient in providing fuzzy, concept-based retrieval mechanisms to users. The main challenge is that indexing and thesaurus creation are extremely laborintensive processes for text documents and especially for images. Recently, 800,000 declassified satellite photographs were made available by the United States Geological Survey. Additionally, millions of satellite and aerial photographs are archived in national and local map libraries. Such enormous collections make human indexing and thesaurus generation methods impossible to utilize. In this article we propose a scalable method to automatically generate visual thesauri of large collections of geo-spatial media using fuzzy, unsupervised machine-learning techniques. © 1999 John Wiley & Sons, Inc.
