Analyze of the Measuring Performance for Artificially Business Intelligent Systems

This paper analyzes the measuring performance of artificially business intelligent systems. Thousands of persons-years have been devoted to the research and development in the vari¬ous aspects of artificially intelligent systems. Much progress has been attained. However, there has been no means of evaluating the progress of the field. How can we assess the cur¬rent state of the science? Most of business intelligent systems are beginning to be deployed commercially. How can a commercial buyer evaluate the advantages and disadvantages of the intelligent candidate and decide which system will perform best for their business applica¬tion? If constructing a system from existing components, how does one select the one that is most appropriate within the desired business intelligent systems? The ability to measure the capabilities of business intelligent systems or components is more that an exercise in satisfy¬ing intellectual or philosophical curiosity. Without measurements and subsequent quantitative evaluation, it is difficult to gauge progress. It is both in a spirit of scientific enquiry and for pragmatic motivations that we embark on the quest for metrics for performance and intelli¬gence of business intelligent systems.artificially intelligent systems, analyze of the measuring performance, business intelligent systems, metrics for performance, meas¬urement performance

Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man

Classification of functional brain data for multimedia retrieval

This study introduces new signal processing methods for extracting meaningful information from brain signals (functional magnetic resonance imaging and single unit recording) and proposes a content-based retrieval system for functional brain data. First, a new method that combines maximal overlapped discrete wavelet transforms (MODWT) and dynamic time warping (DTW) is presented as a solution for dynamically detecting the hemodynamic response from fMRI data. Second, a new method for neuron spike sorting is presented that uses the maximal overlap discrete wavelet transform and rotated principal component analysis. Third, a procedure to characterize firing patterns of neuron spikes from the human brain, in both the temporal domain and the frequency domain, is presented. The combination of multitaper spectral estimation and a polynomial curve-fitting method is employed to transform the firing patterns to the frequency domain. To generate temporal shapes, eight local maxima are smoothly connected by a cubic spline interpolation. A rotated principal component analysis is used to extract common firing patterns as templates from a training set of 4100 neuron spike signals. Dynamic time warping is then used to assign each neuron firing to the closest template without shift error. These techniques are utilized in the development of a content-based retrieval system for human brain data

Consciosusness in Cognitive Architectures. A Principled Analysis of RCS, Soar and ACT-R

This report analyses the aplicability of the principles of consciousness developed in the ASys project to three of the most relevant cognitive architectures. This is done in relation to their aplicability to build integrated control systems and studying their support for general mechanisms of real-time consciousness.\ud To analyse these architectures the ASys Framework is employed. This is a conceptual framework based on an extension for cognitive autonomous systems of the General Systems Theory (GST).\ud A general qualitative evaluation criteria for cognitive architectures is established based upon: a) requirements for a cognitive architecture, b) the theoretical framework based on the GST and c) core design principles for integrated cognitive conscious control systems

Analyze of the Measuring Performance for Artificially Business Intelligent Systems

This paper analyzes the measuring performance of artificially business intelligent systems. Thousands of persons-years have been devoted to the research and development in the vari¬ous aspects of artificially intelligent systems. Much progress has been attained. However, there has been no means of evaluating the progress of the field. How can we assess the cur¬rent state of the science? Most of business intelligent systems are beginning to be deployed commercially. How can a commercial buyer evaluate the advantages and disadvantages of the intelligent candidate and decide which system will perform best for their business applica¬tion? If constructing a system from existing components, how does one select the one that is most appropriate within the desired business intelligent systems? The ability to measure the capabilities of business intelligent systems or components is more that an exercise in satisfy¬ing intellectual or philosophical curiosity. Without measurements and subsequent quantitative evaluation, it is difficult to gauge progress. It is both in a spirit of scientific enquiry and for pragmatic motivations that we embark on the quest for metrics for performance and intelli¬gence of business intelligent systems

Analyze of the Measuring Performance for Artificially Business Intelligent Systems

This paper analyzes the measuring performance of artificially business intelligent systems. Thousands of persons-years have been devoted to the research and development in the vari¬ous aspects of artificially intelligent systems. Much progress has been attained. However, there has been no means of evaluating the progress of the field. How can we assess the cur¬rent state of the science? Most of business intelligent systems are beginning to be deployed commercially. How can a commercial buyer evaluate the advantages and disadvantages of the intelligent candidate and decide which system will perform best for their business applica¬tion? If constructing a system from existing components, how does one select the one that is most appropriate within the desired business intelligent systems? The ability to measure the capabilities of business intelligent systems or components is more that an exercise in satisfy¬ing intellectual or philosophical curiosity. Without measurements and subsequent quantitative evaluation, it is difficult to gauge progress. It is both in a spirit of scientific enquiry and for pragmatic motivations that we embark on the quest for metrics for performance and intelli¬gence of business intelligent systems

An evolutionary approach to optimising neural network predictors for passive sonar target tracking

Object tracking is important in autonomous robotics, military applications, financial time-series forecasting, and mobile systems. In order to correctly track through clutter, algorithms which predict the next value in a time series are essential. The competence of standard machine learning techniques to create bearing prediction estimates was examined. The results show that the classification based algorithms produce more accurate estimates than the state-of-the-art statistical models. Artificial Neural Networks (ANNs) and K-Nearest Neighbour were used, demonstrating that this technique is not specific to a single classifier. [Continues.

The 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies

This publication comprises the papers presented at the 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland, on May 9-11, 1995. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed

Proceedings of the NASA Conference on Space Telerobotics, volume 2

These proceedings contain papers presented at the NASA Conference on Space Telerobotics held in Pasadena, January 31 to February 2, 1989. The theme of the Conference was man-machine collaboration in space. The Conference provided a forum for researchers and engineers to exchange ideas on the research and development required for application of telerobotics technology to the space systems planned for the 1990s and beyond. The Conference: (1) provided a view of current NASA telerobotic research and development; (2) stimulated technical exchange on man-machine systems, manipulator control, machine sensing, machine intelligence, concurrent computation, and system architectures; and (3) identified important unsolved problems of current interest which can be dealt with by future research

HEP-2 CELL FEATURE EXTRACTION USING WAVELET AND INDEPENDENT COMPONENT ANALYSIS

Human antibodies work to attack any diseases or bacteria that presented inside the body. However, there is an act when human antibodies tend to attack own body cells or tissues which is called as Anti-nuclear Antibodies (ANA). ANA consist of many different types that can be recognized by its nucleus size and shape. Common method of classifying ANA is by performing Indirect Immunofluorescences (IIF) with HEp-2 cell and observed the pattern under the microscope by naked eye which said to be inaccurate, takes time and subjective. Thus, this project will study on the technique to identify and classify the pattern of ANA automatically. Algorithms are created using MATLAB software and a Graphical User Interface (GUI) is generated for the algorithm to be easily used. This work will focus more on feature extraction using Wavelet and Independent Component Analysis (ICA). The type of Wavelet Transform that will be used is the 2D Discrete Wavelet Transform (2D DWT) and Fast ICA for Independent Component Analysis. Then Support Vector Machine (SVM) is used to perform the classifications parts using the features extracted from both methods. Different features obtained are tested in SVM and the performance of both methods is compared. From the result, it shows that by using the same classifier, Wavelet can provide better features for classification compared to ICA