KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Subject: search.filters.subject.Intelligent system

Search Results

Now showing 1 - 3 of 3
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    Intelligent approach to automated star-schema construction using a knowledge base
    (2021-11-15)
    Sanprasit, Non
    ;
    Jampachaisri, Katechan
    ;
    Titijaroonroj, Taravichet
    ;
    Kesorn, Kraisak
    Most data-warehouse construction processes are performed manually by experts, which is laborious, time-consuming, and prone to error. Furthermore, special knowledge is required to design complex multidimensional models, such as a star schema. This predicament has motivated computer scientists to propose automation techniques to generate such models. For this reason, we present a new strategy that incorporates knowledge-based models into a framework, named the Semantic-based Star-schema Designer, that assists the automation of star schema construction. Our models provide reasoning capabilities needed by star schema designs, including those that can disambiguate heterogeneous terms, detect appropriate data types and attribute sizes, and organize data hierarchies to support online analytical processes. We also propose strategies to overcome the uncertainty arising when attribute names are not available in the data source. The names of unknown attributes are thus predicted using an arithmetic coding technique to infer column names. Our system also generates star schema from semi-structured data (e.g., comma-separated-value files and spreadsheets), which do not provide primary keys, foreign keys, or relationship cardinalities between tables. Our framework facilitates star schema construction and their relationship information without human intervention using homegrown algorithms. Experiments demonstrate that our technique predicts column names and data types that enable the effective generation of star schema better than baseline approaches.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    A neural network PID-like controller using a hybrid of online Actor-Critic reinforcement algorithm with the square root cubature Kalman filter
    (2018-01-01)
    Sento, Adna
    ;
    Kitjaidure, Yuttana
    This paper presents a new model of the Neural Network PID-Like controller using an Actor-Critic reinforcement algorithm, called the Neural Network PID-Like controller using an Actor-Critic reinforcement algorithm (NNPID-AC). The proposed NNPID-AC controller is designed to develop the performances and the speed of calculation under the iterative learning algorithm. In the learning algorithm, the critic algorithm receives the reward value and control input to criticize the current state using the action-state value function approximation. Furthermore, instead of applying every available action to predict the local successor state, the algorithm only uses one-step estimation using the fifth degree spherical-radial cubature rule algorithm. To evaluate the proposed NNPID-AC controller, the robot arm MATLAB simulations have been implemented and provide the control system with the load and noise to prove the robustness and fault tolerance, respectively. From the results, the robot arm control system simulation under the control of the proposed NNPID-AC controller can potentially track the error and gives the best responses compared with the other conventional controller either with or without the load and the noise disturbance.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    A foundation of rough sets theoretical and computational hybrid intelligent system for survival analysis
    (2008-10-01)
    Pattaraintakorn, Puntip
    ;
    Cercone, Nick
    What do we (not) know about the association between diabetes and survival time? Our study offers an alternative mathematical framework based on rough sets to analyze medical data and provide epidemiology survival analysis with risk factor diabetes. We experiment on three data sets: geriatric, melanoma and Primary Biliary Cirrhosis. A case study reports from 8547 geriatric Canadian patients at the Dalhousie Medical School. Notification status (dead or alive) is treated as the censor attribute and the time lived is treated as the survival time. The analysis result illustrates diabetes is a very significant risk factor to survival time in our geriatric patients data. This paper offers both theoretical and practical guidelines in the construction of a rough sets hybrid intelligent system, for the analysis of real world data. Furthermore, we discuss the potential of rough sets, artificial neural networks (ANNs) and frailty index in predicting survival tendency. © 2008 Elsevier Ltd. All rights reserved.