This paper focuses on using fuzzy neural network data mining techniques to analyze nonlinear relations among chemical factors. Through standardizing and rescaling the raw data, we processed the data into fuzzy neural network not only to learn chemical knowledge from large amounts of experimental data, but also predict future chemical parameters for further experimental verification. The results show that the most relative chemical factor can be obtained by analyzing the experimental errors using fuzzy rules.
The three dimensional structure of a protein affects the structural, functional and biological characteristics of the various cells and genes significantly. Therefore, prediction of the structure of a protein is the key to understanding the biological functions of proteins. In this paper, a new hybrid neural network has been proposed for predicting protein secondary structure. A new encoding scheme for representing amino acid protein sequences has also been designed which greatly reduces the convergence time of the prediction process.
In this paper, we propose an Avatar-based intelligent agent technique for 3D Web based Virtual Communities based on distributed artificial intelligence, intelligent agent techniques, and databases and knowledge bases in a digital library. One of the goals of this joint NSF (IIS-9980130) and ACM SIGGRAPH Education Committee (ASEC) project is to create a virtual community of educators and students who have a common interest in comptuer graphics, visualization, and interactive techniqeus. In this virtual community (ASEC World) Avatars will represent the educators, students, and other visitors to the world. Intelligent agents represented as specially dressed Avatars will be available to assist the visitors to ASEC World. The basic Web client-server architecture of the intelligent knowledge-based avatars is given. Importantly, the intelligent Web agent software system for the 3D virtual community is implemented successfully.
Techniques based on neural networks can provide efficient solutions to a wide variety of problems in computer science. Routing in computer networks is to schedule messages and select communication links so that messages can be transferred efficiently between source and destination processors. Finding an optimal solution to many routing problems usually reqrueis exponential time and is impractical in reality. Hence, many heuristic algorithms have been designed to find sub-optimal solutions. In this research we use neural networks with a set of constraints to capture various collisions in multistage interconnection networks (MINs). Our simulation results have indicated that the Hopfield neural network can be used to routing to avoid link collisions in electronic MINs and crosstalks in optical MINs.
In this paper, a smart agent is proposed to search image/graphics based database for students and an instructor. The seach procedure is based on content-based image retrieval (CBIR). In addition, the smart agent can also give help in seaching image/graphics data, answering simple questions and optimizing educational activities graphically.
Intelligent data mining techniques have useful e-Business applications. Because an e-Commerce application is related to multiple domains such as statistical analysis, market competition, price comparison, profit improvement and personal preferences, this paper presents a hybrid knowledge-based e-Commerce system fusing intelligent techniques, statistical data mining, and personal information to enhance QoS (Quality of Service) of e-Commerce. A Web-based e-Commerce application software system, eDVD Web Shopping Center, is successfully implemented uisng Java servlets and an Oracle81 database server. Simulation results have shown that the hybrid intelligent e-Commerce system is able to make smart decisions for different customers.
When a user logs in to a website, behind the scenes the user leaves his/her impressions, usage patterns and also access patterns in the web servers log file. A web usage mining agent can analyze these web logs to help web developers to improve the organization and presentation of their websites. They can help system administrators in improving the system performance. Web logs provide invaluable help in creating adaptive web sites and also in analyzing the network traffic analysis. This paper presents the design and implementation of a Web usage mining agent for digging in to the web log files.
A web data mining system using granular computing and ASP programming is proposed. This is a web based application, which allows web users to submit survey data for many different companies. This survey is a collection of questions that will help these companies develop and improve their business and customer service with their clients by analyzing survey data. This web application allows users to submit data anywhere. All the survey data is collected into a database for further analysis. An administrator of this web application can login to the system and view all the data submitted. This web application resides on a web server, and the database resides on the MS SQL server.
A medical data mining algorithm is proposed based on data mining techniques and relevant intelligent methods such as granular computing, neural computing and soft computing. It could lead to increased understanding of the causes of various diseases leading to better diagnosis.
KEYWORDS: Fuzzy logic, Logic, Data mining, Fuzzy systems, Intelligence systems, Neural networks, Systems modeling, Artificial intelligence, System integration, Computing systems
In today's business world there is an abundance of available data and a great need to make good use of it. Many businesses would benefit from examining customer habits and trends and making marketing and product decisions based on that analysis. However, the process of manually examining data and making sound decisions based on that data is time consuming and often impractical. Intelligent systems that can make judgments similar to human judgments are sorely needed. Thus, systems based on fuzzy logic present themselves as an option to be seriously considered. The work described in this paper attempts to make an initial comparison between fuzzy logic and more traditional hard or crisp logic to see which would make a better substitute for human intervention. In this particular case study, customers are classified into categories that indicate how desirable the customer would be as a prospect for marketing. This classification is based on a small set of customer data. The results from these investigations make it clear that fuzzy logic is more able to think for itself and make decisions that more closely match human decision and is therefore significantly closer to human logic than crisp logic.
KEYWORDS: Fuzzy logic, Databases, Neural networks, Knowledge discovery, Data processing, Data fusion, Data conversion, Feature extraction, Multimedia, Data mining
In this paper, a granular-neural-network-based Knowledge Discovery and Data Mining (KDDM) method based on granular computing, neural computing, fuzzy computing, linguistic computing and pattern recognition is presented. The major issues include (1) how to use neural networks to discover granular knowledge from numerical-linguistic databases, and (2) how to use discovered granular knowledge to predict missing data. A Granular Neural Network (GNN) is designed to deal with numerical-linguistic data fusion and granular knowledge discovery in numerical-linguistic databases. From a data granulation point of view, the GNN can process granular data in a database. From a data fusion point of view, the GNN makes decisions based on different kinds of granular data. From a KDDM point of view, the GNN is able to learn internal granular relations between numerical-linguistic inputs and outputs, and predict new relations in a database.
By overcoming weaknesses of the linear fuzzy system, our new piecewise nonlinear constructive method can effectively construct a reasonable fuzzy system with the near-optimal number of fuzzy rules. In addition, the new approach is capable of generating a commonly used fuzzy rule base with both meaningful input and output membership functions. The normal-fuzzy-reasoning-based nonlinear constructive approach provides us with a powerful tool to model a normal fuzzy system piece by piece (i.e. interval by interval) for both given data and any required accuracy. Additionally, the piecewise construct approach is a useful tool to discover meaningful fuzzy knowledge from raw data.
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