Integrated Retinal Information System for Analyzing Kidney Condition

Integrated Retinal Information System for Analyzing Kidney Condition

Iridology is a science and practice that can express body state based on the analysis of iris structure. The changes or disturbances of disease on body network will be informed by neuron nerve fiber to brain. This energy wave information spread to eye by brain, recorded and fixed by pupil.Then, thes...

Full description

Saved in:
Bibliographic Details
Main Author: Perdana, Hatta
Format: Thesis
Language:eng
eng
Published: 2009
Subjects:
QA76.76 Fuzzy System.
Online Access:https://etd.uum.edu.my/1571/1/Hatta_Perdana_2009.pdf
https://etd.uum.edu.my/1571/2/1.Hatta_Perdana_2009.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
id my-uum-etd.1571
record_format uketd_dc
institution Universiti Utara Malaysia
collection UUM ETD
language eng
eng
topic QA76.76 Fuzzy System.
spellingShingle QA76.76 Fuzzy System.
Perdana, Hatta
Integrated Retinal Information System for Analyzing Kidney Condition
description Iridology is a science and practice that can express body state based on the analysis of iris structure. The changes or disturbances of disease on body network will be informed by neuron nerve fiber to brain. This energy wave information spread to eye by brain, recorded and fixed by pupil.Then, these recorded fixation become data trails which can be detected by disturbance/disease that is filed by body organ. The research about iridology to analyzing kidney condition has been conducted before using Learning Vector Quantization (LVQ) method. The accuracy is not 100%. In this research, the researcher implements Support Vector Machine(SVM) in classifying the kidney condition to replace LVQ using Matlab R2007b. The accuracy in classifying the kidney condition for right eyes is 100% and for the left eyes is 100% in training set data. If we compared to the accuracy of classification using LVQ, implementing SVM is much better because by implementing LVQ, the accuracy is only 96% for right eyes and only 92% for left eyes.
format Thesis
qualification_name masters
qualification_level Master's degree
author Perdana, Hatta
author_facet Perdana, Hatta
author_sort Perdana, Hatta
title Integrated Retinal Information System for Analyzing Kidney Condition
title_short Integrated Retinal Information System for Analyzing Kidney Condition
title_full Integrated Retinal Information System for Analyzing Kidney Condition
title_fullStr Integrated Retinal Information System for Analyzing Kidney Condition
title_full_unstemmed Integrated Retinal Information System for Analyzing Kidney Condition
title_sort integrated retinal information system for analyzing kidney condition
granting_institution Universiti Utara Malaysia
granting_department College of Arts and Sciences (CAS)
publishDate 2009
url https://etd.uum.edu.my/1571/1/Hatta_Perdana_2009.pdf
https://etd.uum.edu.my/1571/2/1.Hatta_Perdana_2009.pdf
_version_ 1747827168600129536
spelling my-uum-etd.15712013-07-24T12:12:22Z Integrated Retinal Information System for Analyzing Kidney Condition 2009 Perdana, Hatta College of Arts and Sciences (CAS) College of Art and Sciences QA76.76 Fuzzy System. Iridology is a science and practice that can express body state based on the analysis of iris structure. The changes or disturbances of disease on body network will be informed by neuron nerve fiber to brain. This energy wave information spread to eye by brain, recorded and fixed by pupil.Then, these recorded fixation become data trails which can be detected by disturbance/disease that is filed by body organ. The research about iridology to analyzing kidney condition has been conducted before using Learning Vector Quantization (LVQ) method. The accuracy is not 100%. In this research, the researcher implements Support Vector Machine(SVM) in classifying the kidney condition to replace LVQ using Matlab R2007b. The accuracy in classifying the kidney condition for right eyes is 100% and for the left eyes is 100% in training set data. If we compared to the accuracy of classification using LVQ, implementing SVM is much better because by implementing LVQ, the accuracy is only 96% for right eyes and only 92% for left eyes. 2009 Thesis https://etd.uum.edu.my/1571/ https://etd.uum.edu.my/1571/1/Hatta_Perdana_2009.pdf application/pdf eng validuser https://etd.uum.edu.my/1571/2/1.Hatta_Perdana_2009.pdf application/pdf eng public masters masters Universiti Utara Malaysia Agus Faisal Karim, T. M. (2009). Salah Diagnosis, Pasien Keracunan.Retrieved 15 June, 2009, from http://berita.liputan6.com/daerah/200906/232418/Salah.Diagnosis.Pasien.Keracunan Ahmad, U. (2007). Pengolahan Citra Digital dan Teknik Pemrogramannya:Graha Ilmu.Aniati Murni, D. C. Segmentasi Citra. Retrieved 21 June, 2009, from http://www.cs.ui.ac.id/WebKuliah/citra/2005/cit24.ppt Anto Satriyo Nugroho, A. B. W., Dwi Handoko. (2003). Support Vector Machine : Teori dan Aplikasinya dalam Bioinformatika. Retrieved 21 June, 2009, from http://asnugroho.net/papers/ikcsvm.pdf Ardisasmita, S. (2002). Segmentasi Dan Rekonstruksi Citra Organ Dalam Tiga Dimensi Menggunakan Matematika Morfologi Dan Triangulasi Delauney.Arisandy, Y. (2007). Komputerisasi Iridologi untuk mendeteksi Kondisi Ginjal Berbasis Jaringan Saraf Tiruan Learning Vector Quantization.ASHEER KASAR BACHOO, J.-R. T. (2005). Texture Detection for Segmentation of Iris Images. Proceedings of SAICSIT 2005.Bartman. (2008). Dokter Indonesia bekerja demi kemanusiaan atau bisnis? Retrieved 15 June, 2009, from http://forum.detik.com/showthread.php?t=48980 C. R. Prashanth, S. P. G., T. D. Mahalakshmi, K. B. Raja, K. R. Venugopal, L.M. Patnaik. (2009). Iris Feature Extraction using Directional Filter Bank for Personal Identification. Compute2009(ACM). Carlos Soares, P. B. B. (2006). Selecting Parameters of SVM Using Meta-Learning and Kernel Matrix-based Meta-Features. SAC’06.Chirag Gupta , S. M. K. (21 June 2009). Implementation of Canny Edge Detector of color images on CELL/B.E.Architecture. Retrieved 21 June, 2009, from http://www.ces.clemson.edu/~stb/ece847/fall2007/projects/canny_cell.pdf Duraiswami, Y. M. L. a. R. Canny Edge Detection on NVIDIA CUDA.Retrieved 21 June, 2009, from http://www.umiacs.umd.edu/~ramani/pubs/luo_gpu_canny_fin_2008.pdf George Varbanov, P. B. (2007). An Improving Model Watermarking with Iris Biometric Code. International Conference on Computer Systems and Technologies.Gustavo Camps-Valls, J. G. e., Gabriel G´omez-P´erez, Jes ´us Malo. (2008).On the Suitable Domain for SVM Training in Image Coding.Han, J., & Kamber, M. (2006). Data Mining: Concepts and Techniques: Elsevier Inc.Herlina, B. (2007). Meramal Kesehatan Tubuh Dengan Iridologi. Retrieved 21 June, 2009, from www.klinik.netHiru, D. (2005). Iridologi: Mendeteksi Penyakit Hanya dengan Mengintip Mata Image Processing Algorithm. Retrieved 21 June, 2009, from http://www.ii.metu.edu.tr/~ion528/demo/lectures/6/4/index.html Jingzhong Xu, Y. W., Xubing Zhang. (2006). A method of edge detection based on improved canny algorithm for the lidar depth image.Geoinformatics Retrieved 21 June, 2009, from http://spie.org/x648.html?product_id=712923 Kausari, M. (2008). Komputerisasi Iridologi Untuk Mendeteksi Kondisi Ginjal Menggunakan Principal Component Analysis (PCA) dan K-Nearest Neighbors (KNN).King-Shy Goh, E. C., Kwang-Ting Cheng. (2001). SVM Binary Classifier Ensembles for Image Classification. CIKM’O1.Lakin Wecker, F. S., Marina Gavrilova. (2005). Iris Synthesis: A Reverse Subdivision Application.Navratil, F. (2001). Iridology Flashcards : Advanced: Return to Health Books.Nazrul Effendy, R. I., Ayodya. Deteksi Pornografi Pada Citra Digital Menggunakan Pengolahan Citra Dan Jaringan Saraf Tiruan.RITENDRA DATTA, D. J., JIA LI, and JAMES Z. WANG. (2008). Image Retrieval: Ideas, Influences, and Trends of the New Age.Runxuan Zhang, G.-B. H., Narasimhan Sundararajan, and P. Saratchandran. (2007). Multicategory Classification Using an Extreme Learning Machine for Microarray Gene Expression Cancer Diagnosis.COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,. Shubhangi D C, P. S. H. (2009). Multi-Class SVM Classifier for English Handwritten Digit Recognition using Manual Class Segmentation.Shubhangi D C, P. S. H. (2009). Support Vector Machine (SVM) Classifier for Brain Tumor Detection. ICAC3’09.Sinaga, J. (2007). Retrieved 21 June, 2009, from http://www.indomedia.com/bernas/042001/13/UTAMA/13pel3.htm T. H. Manjula Devi, P. P. S., Swathi Saigali, Harsha Mathew, K. B. Raja,Venugopal K. R. and L. M. Patnaik. (2009). Extracting hidden image using Histogram, DFT and SVM. Compute 2009,.Tan, S., Kumar. (2004). Introduction to Data Mining.Wynne Hsu, M. L. L., Kheng Guan Goh. (2000). Image Mining in IRIS:Integrated Retinal Information System. (ACM SIGMOD). Xiaogang Wang, X. T. (2003). Bayesian Face Recognition Using GaborFeatures. WBMA’03.

Similar Items