Repository logo
  • English
  • ไทย
  • Log In
    New user? Click here to register. Have you forgotten your password?
external-link-logo
  • Communities & Collections
  • All of AU-IR
  1. Home
  2. Browse by Author

Browsing by Author "Thitipong Tanprasert"

  • 0-9
  • A
  • B
  • C
  • D
  • E
  • F
  • G
  • H
  • I
  • J
  • K
  • L
  • M
  • N
  • O
  • P
  • Q
  • R
  • S
  • T
  • U
  • V
  • W
  • X
  • Y
  • Z

  • ก
  • ข
  • ฃ
  • ค
  • ฅ
  • ฆ
  • ง
  • จ
  • ฉ
  • ช
  • ซ
  • ฌ
  • ญ
  • ฎ
  • ฏ
  • ฐ
  • ฑ
  • ฒ
  • ณ
  • ด
  • ต
  • ถ
  • ท
  • ธ
  • น
  • บ
  • ป
  • ผ
  • ฝ
  • พ
  • ฟ
  • ภ
  • ม
  • ย
  • ร
  • ล
  • ว
  • ศ
  • ษ
  • ส
  • ห
  • ฬ
  • อ
  • ฮ
Results Per Page
Sort Options
  • Item
    An adaptive fuzzy control in policing mechanism schemes over high speed network
    (Bangkok : Assumption University, 2009) Somchai Lekcharoen ; Thitipong Tanprasert
  • Item
    An Adaptive Learning Algorithm for Supervised Neural Network with Contour Preserving Classification
    ( 2009-11) Piyabute Fuangkhon ; Thitipong Tanprasert
    A study of noise tolerance characteristics of an adaptive learning algorithm for supervised neural network is presented in this paper. The algorithm allows the existing knowledge to age out in slow rate as a supervised neural network is gradually retrained with consecutive sets of new samples, resembling the change of application locality under a consistent environment. The algorithm utilizes the contour preserving classification algorithm to pre-process the training data to improve the classification and the noise tolerance. The experimental results convincingly confirm the effectiveness of the algorithm and the improvement of noise tolerance.
  • Item
    An approach for Improving routability of 3-Dimensional maze routing
    (Bangkok : Assumption University, 1998) Pawut Satitsuksanoh ; Thitipong Tanprasert
  • Item
    Clustering Analysis on Alumni Data Using Abandoned and Reborn Particle Swarm Optimization
    ( 2016-02) Paulus Mudjihartono ; Thitipong Tanprasert ; Rachsuda Jiamthapthaksin
    Alumni data is one of the most important data that university management uses for developing the learning process decisions. This paper applies the idea of Abandoned and Reborn PSO (AR-PSO) to convert a clustering problem into the optimization form with an objective function to minimize the ugliness of the desired clusters. This algorithm of Clustering using AR-PSO (CAR-PSO) is slightly adapted to the cluster problem domain. The generated clusters need to be examined to decide if they are acceptable. There are three evaluations; the closeness, the separation and the purity. Finally, the experiment results show that the CAR-PSO is comparable with &-means in both types of alumni data while leaving the other two clustering algorithms.
  • Item
    Creatinine prediction from body composition a neural network approach
    ( 2011) Thitipong Tanprasert ; Chularat Tanprasert
    Creatinine, a naturally-produced chemical compound in blood, has been commonly used as a reliable indicator of kidney function. Creatinine level is typically obtained from blood-test. In this paper, a technique for predicting the criticality of creatinine level in blood is presented. The proposed technique takes only body size and mass parameters obtained from advanced weighing scale and body scanner, allowing the prediction to be done more casually. The technique applies a multi-layered feed-forward neural network for developing the prediction model. The achieved overall prediction accuracy is in the vicinity of 88% where the average false negative rate and the average false positive rate are 22.15% and 8.26%, respectively.
  • Item
    Diagnosing prostate cancer using backpropagation neural network and greedy decision procedure
    ( 2010-05) Gopalakrishnan, Anilkumar Kothalil ; Thitipong Tanprasert ; Faculty of Science and Technology
    A novel procedure for diagnosing prostate cancer (PC) based on Back propagation Neural Network (BPNN) is proposed. Elderly men with symptoms such as urinary retention, urinary hesitancy, urinary dribbling, burning urination, hematuria, etc. are considered as primary attributes. Prostate-specific antigen (PSA) level and Gleason score are the secondary attributes. Initial dataset is generated based on the clinical database. The BPNN assigns symptom levels of a set of patients based on their primary attributes. A greedy decision procedure predicts tumor stages of patients based on their strong symptom levels and secondary attributes. The simulation shows that the proposed procedure is an effective way for diagnosing prostate tumor stages.
  • Item
    Distance measurement with smartphone using acceleration model of hand movement
    ( 2016-02) Eakawat Tantamjarik ; Thitipong Tanprasert
    This paper proposes a novel method to obtain the displacement of a smartphone movement. The method utilizes genetic algorithm to synthesize a mathematical model of acceleration based on behavior of a person's hand movement from the raw acceleration data. Then, double integration is performed on the synthesized acceleration model, which is significantly less affected by the noise accumulation. The raw acceleration of the hand's movement is calibrated initially using acceleration-time graph analysis and a modified version of peak detection based on moving average is used to obtain the constraints for genetic algorithm. The obtained experiment results showed that the method is very effective at determining displacement with high accuracy.
  • Item
    Evaluation of human liver condition self-organizing map and feed forward backpropagation technique
    (Bangkok : Assumption University, 2013) Wisit Charoenwitsarutkun ; Thitipong Tanprasert
  • Item
    An incremental learning algorithm for supervised neural network with contour preserving Classification
    ( 2009-05) Piyabute Fuangkhon ; Thitipong Tanprasert
    This paper presents an alternative algorithm for integrating the existing knowledge of a supervised learning neural network with the new training data. The algorithm allows the existing knowledge to age out in slow rate as a neural network is gradually retrained with consecutive sets of new samples, resembling the change of application locality under a consistent environment. The algorithm also utilizes the contour preserving classification algorithm to increase the accuracy of classification. The experiment is performed on 2-dimension partition problem and the result convincingly confirms the effectiveness of the algorithm.
  • Item
    Local contour analysis by fuzzy neural network recognizers for fine handwritten Thai alphabets classification
    (Bangkok : Assumption University, 2001) Sarachai Taechotanon ; Thitipong Tanprasert
  • Item
    Multi-class contour preserving classification
    ( 2012-08) Piyabute Fuangkhon ; Thitipong Tanprasert
    The original contour preserving classification technique was proposed to improve the robustness and weight fault tolerance of a neu- ral network applied with a two-class linearly separable problem. It was recently found to be improving the level of accuracy of two-class classi- fication. This paper presents an augmentation of the original technique to improve the level of accuracy of multi-class classification by better preservation of the shape or distribution model of a multi-class problem. The test results on six real world multi-class datasets from UCI ma- chine learning repository present that the proposed technique supports multi-class data and can improve the level of accuracy of multi-class classification more effectively.
  • Item
    Multi-class contour preserving classification
    (Bangkok : Assumption University, 2013) Piyabute Fuangkhon ; Thitipong Tanprasert
  • Item
    Multi-class contour preserving classification
    ( 2012-08) Piyabute Fuangkhon ; Thitipong Tanprasert
    The original contour preserving classification technique was proposed to improve the robustness and weight fault tolerance of a neu- ral network applied with a two-class linearly separable problem. It was recently found to be improving the level of accuracy of two-class classi- fication. This paper presents an augmentation of the original technique to improve the level of accuracy of multi-class classification by better preservation of the shape or distribution model of a multi-class problem. The test results on six real world multi-class datasets from UCI ma- chine learning repository present that the proposed technique supports multi-class data and can improve the level of accuracy of multi-class classification more effectively.
  • Item
    Pass planning and decision making for distributed soccer agents in dynamic workspace
    (Bangkok : Assumption University, 2002) Worasing Rinsurongkawong ; Thitipong Tanprasert
  • Item
    Performance and caching issue in an integration of neural net and conventional PC
    (Bangkok : Assumption University, 2001) Veerachai Gosasang ; Thitipong Tanprasert
  • Item
    Preserving prior knowledge on supervised neural network
    (Bangkok : Assumption University, 2002) Thosaporn Kripruksawan ; Thitipong Tanprasert
  • Item
    Reduced Multi-class Contour Preserving Classification
    ( 2016) Piyabute Fuangkhon ; Thitipong Tanprasert ; Assumption University. Vincent Mary School of Science and Technology
    This research presents the augmentation of the original contour preserving classification technique to support multi-class data and to reduce the number of synthesized vectors, called multi-class outpost vectors (MCOVs). The technique has been proven to function on both synthetic-problem data sets and real-world data sets correctly. The technique also includes three methods to reduce the number of MCOVs by using minimum vector distance selection between fundamental multi-class outpost vectors and additional multi-class outpost vectors to select only MCOVs located at the decision boundary between consecutive classes of data. The three MCOV reduction methods include the FF-AA reduction method, the FA-AF reduction method, and the FAF-AFA reduction method. An evaluation has been conducted to show the reduction capability, the contour preservation capability, and the levels of classification accuracy of the three MCOV reduction methods on both non-overlapping and highly overlapping synthetic-problem data sets and highly overlapping real-world data sets. For non-overlapping problems, the experimental results present that the FA-AF reduction method can partially reduce the number of MCOVs while preserving the contour of the problem most accurately and obtaining similar levels of classification accuracy as when the whole set of MCOVs is used. For highly overlapping problems, the experimental results present that the FF-AA reduction method can partially reduce the number of MCOVs while preserving the contour of the problem most accurately and obtaining similar levels of classification accuracy as when the whole set of MCOVs is used.
  • Item
    Sample-synthesized simulated light sensitive model for structural categorization of handwritten Thai character
    (Bangkok : Assumption University, 2001) Kamolchol Jitwongtrakul ; Thitipong Tanprasert
  • Item
    A system for identifying writer from handwritten image
    (Bangkok : Assumption University, 2002) Jantra U-seng ; Thitipong Tanprasert

Contact Us

St. Gabriel's Library (Hua Mak Campus)
592/3 Soi Ramkhamhaeng 24, Ramkhamhaeng Rd., Hua Mak, Bang Kapi, Bangkok 10240, Thailand

(662) 3004543-62 Ext. 3403

library@au.edu

The Cathedral of Learning Library (Suvarnabhumi Campus)
88 Moo 8 Bang Na-Trad Km. 26 Bang Sao Thong, Samut Prakan 10570, Thailand

(662) 7232024

library@au.edu

Website:  www.library.au.edu