An efficient machine learning approach to nephrology through iris recognition

Abstract Iridology is a technique in science used to analyze color, patterns, and various other properties of the iris to assess an individual's general health. Few regions in the iris are connected by nerves coming from different organs of body, this shows some special unique qualities which i...
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Autor*in:	Divya, C. D. [verfasserIn] Gururaj, H. L. [verfasserIn] Rohan, R. [verfasserIn] Bhagyalakshmi, V. [verfasserIn] Rashmi, H. A. [verfasserIn] Domnick, A. [verfasserIn] Flammini, Francesco [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Iridology Digital image processing Edge detection

Anmerkung:	© The Author(s) 2021

Übergeordnetes Werk:	Enthalten in: Discover artificial intelligence - [Cham] : Springer International Publishing, 2021, 1(2021), 1 vom: 13. Okt.
Übergeordnetes Werk:	volume:1 ; year:2021 ; number:1 ; day:13 ; month:10

Links:	Volltext

DOI / URN:	10.1007/s44163-021-00010-4

Katalog-ID:	SPR04528377X

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allfields	10.1007/s44163-021-00010-4 doi (DE-627)SPR04528377X (SPR)s44163-021-00010-4-e DE-627 ger DE-627 rakwb eng Divya, C. D. verfasserin aut An efficient machine learning approach to nephrology through iris recognition 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract Iridology is a technique in science used to analyze color, patterns, and various other properties of the iris to assess an individual's general health. Few regions in the iris are connected by nerves coming from different organs of body, this shows some special unique qualities which is advantageous along with which assist in psychological condition, particular organ conditions and construction of the body. The structural and designed patterns present on specific part of iris represent the level of intensity of disorder caused by the organs. This method of approach can be employed as reasonable and logical guidelines for the detection and identification of disorders. Therefore, after scanning the image of iris advance study of disorder can be carried out for detecting the condition of organ. Initially by the service of an adaptive histogram, the image of eye should be separated from part of the image captured. Next the images of iris are classified and recognized using machine learning algorithm Support Vector machine or Support Vector Networks. The features are extracted from images of iris using white Gaussian filters which are then used as a feature descriptor. These descriptors count the occurrences of gradient orientation and magnitude in localized portions of an image. Then convert the image of iris to a gray scaled image, final image is standardized. Next is to convert it into rectangular shape and then assembling the HMM images of eyes related to the kidney. The final level is to diagnose the edge of image of iris HMM. By analysing end results, condition of the organ can be diagnosed and results can be obtained from the iris recognition system. Iridology (dpeaa)DE-He213 Digital image processing (dpeaa)DE-He213 Edge detection (dpeaa)DE-He213 Gururaj, H. L. verfasserin aut Rohan, R. verfasserin aut Bhagyalakshmi, V. verfasserin aut Rashmi, H. A. verfasserin aut Domnick, A. verfasserin aut Flammini, Francesco verfasserin aut Enthalten in Discover artificial intelligence [Cham] : Springer International Publishing, 2021 1(2021), 1 vom: 13. Okt. (DE-627)1774283107 (DE-600)3097625-X 2731-0809 nnns volume:1 year:2021 number:1 day:13 month:10 https://dx.doi.org/10.1007/s44163-021-00010-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 1 2021 1 13 10
spelling	10.1007/s44163-021-00010-4 doi (DE-627)SPR04528377X (SPR)s44163-021-00010-4-e DE-627 ger DE-627 rakwb eng Divya, C. D. verfasserin aut An efficient machine learning approach to nephrology through iris recognition 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract Iridology is a technique in science used to analyze color, patterns, and various other properties of the iris to assess an individual's general health. Few regions in the iris are connected by nerves coming from different organs of body, this shows some special unique qualities which is advantageous along with which assist in psychological condition, particular organ conditions and construction of the body. The structural and designed patterns present on specific part of iris represent the level of intensity of disorder caused by the organs. This method of approach can be employed as reasonable and logical guidelines for the detection and identification of disorders. Therefore, after scanning the image of iris advance study of disorder can be carried out for detecting the condition of organ. Initially by the service of an adaptive histogram, the image of eye should be separated from part of the image captured. Next the images of iris are classified and recognized using machine learning algorithm Support Vector machine or Support Vector Networks. The features are extracted from images of iris using white Gaussian filters which are then used as a feature descriptor. These descriptors count the occurrences of gradient orientation and magnitude in localized portions of an image. Then convert the image of iris to a gray scaled image, final image is standardized. Next is to convert it into rectangular shape and then assembling the HMM images of eyes related to the kidney. The final level is to diagnose the edge of image of iris HMM. By analysing end results, condition of the organ can be diagnosed and results can be obtained from the iris recognition system. Iridology (dpeaa)DE-He213 Digital image processing (dpeaa)DE-He213 Edge detection (dpeaa)DE-He213 Gururaj, H. L. verfasserin aut Rohan, R. verfasserin aut Bhagyalakshmi, V. verfasserin aut Rashmi, H. A. verfasserin aut Domnick, A. verfasserin aut Flammini, Francesco verfasserin aut Enthalten in Discover artificial intelligence [Cham] : Springer International Publishing, 2021 1(2021), 1 vom: 13. Okt. (DE-627)1774283107 (DE-600)3097625-X 2731-0809 nnns volume:1 year:2021 number:1 day:13 month:10 https://dx.doi.org/10.1007/s44163-021-00010-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 1 2021 1 13 10
allfields_unstemmed	10.1007/s44163-021-00010-4 doi (DE-627)SPR04528377X (SPR)s44163-021-00010-4-e DE-627 ger DE-627 rakwb eng Divya, C. D. verfasserin aut An efficient machine learning approach to nephrology through iris recognition 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract Iridology is a technique in science used to analyze color, patterns, and various other properties of the iris to assess an individual's general health. Few regions in the iris are connected by nerves coming from different organs of body, this shows some special unique qualities which is advantageous along with which assist in psychological condition, particular organ conditions and construction of the body. The structural and designed patterns present on specific part of iris represent the level of intensity of disorder caused by the organs. This method of approach can be employed as reasonable and logical guidelines for the detection and identification of disorders. Therefore, after scanning the image of iris advance study of disorder can be carried out for detecting the condition of organ. Initially by the service of an adaptive histogram, the image of eye should be separated from part of the image captured. Next the images of iris are classified and recognized using machine learning algorithm Support Vector machine or Support Vector Networks. The features are extracted from images of iris using white Gaussian filters which are then used as a feature descriptor. These descriptors count the occurrences of gradient orientation and magnitude in localized portions of an image. Then convert the image of iris to a gray scaled image, final image is standardized. Next is to convert it into rectangular shape and then assembling the HMM images of eyes related to the kidney. The final level is to diagnose the edge of image of iris HMM. By analysing end results, condition of the organ can be diagnosed and results can be obtained from the iris recognition system. Iridology (dpeaa)DE-He213 Digital image processing (dpeaa)DE-He213 Edge detection (dpeaa)DE-He213 Gururaj, H. L. verfasserin aut Rohan, R. verfasserin aut Bhagyalakshmi, V. verfasserin aut Rashmi, H. A. verfasserin aut Domnick, A. verfasserin aut Flammini, Francesco verfasserin aut Enthalten in Discover artificial intelligence [Cham] : Springer International Publishing, 2021 1(2021), 1 vom: 13. Okt. (DE-627)1774283107 (DE-600)3097625-X 2731-0809 nnns volume:1 year:2021 number:1 day:13 month:10 https://dx.doi.org/10.1007/s44163-021-00010-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 1 2021 1 13 10
allfieldsGer	10.1007/s44163-021-00010-4 doi (DE-627)SPR04528377X (SPR)s44163-021-00010-4-e DE-627 ger DE-627 rakwb eng Divya, C. D. verfasserin aut An efficient machine learning approach to nephrology through iris recognition 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract Iridology is a technique in science used to analyze color, patterns, and various other properties of the iris to assess an individual's general health. Few regions in the iris are connected by nerves coming from different organs of body, this shows some special unique qualities which is advantageous along with which assist in psychological condition, particular organ conditions and construction of the body. The structural and designed patterns present on specific part of iris represent the level of intensity of disorder caused by the organs. This method of approach can be employed as reasonable and logical guidelines for the detection and identification of disorders. Therefore, after scanning the image of iris advance study of disorder can be carried out for detecting the condition of organ. Initially by the service of an adaptive histogram, the image of eye should be separated from part of the image captured. Next the images of iris are classified and recognized using machine learning algorithm Support Vector machine or Support Vector Networks. The features are extracted from images of iris using white Gaussian filters which are then used as a feature descriptor. These descriptors count the occurrences of gradient orientation and magnitude in localized portions of an image. Then convert the image of iris to a gray scaled image, final image is standardized. Next is to convert it into rectangular shape and then assembling the HMM images of eyes related to the kidney. The final level is to diagnose the edge of image of iris HMM. By analysing end results, condition of the organ can be diagnosed and results can be obtained from the iris recognition system. Iridology (dpeaa)DE-He213 Digital image processing (dpeaa)DE-He213 Edge detection (dpeaa)DE-He213 Gururaj, H. L. verfasserin aut Rohan, R. verfasserin aut Bhagyalakshmi, V. verfasserin aut Rashmi, H. A. verfasserin aut Domnick, A. verfasserin aut Flammini, Francesco verfasserin aut Enthalten in Discover artificial intelligence [Cham] : Springer International Publishing, 2021 1(2021), 1 vom: 13. Okt. (DE-627)1774283107 (DE-600)3097625-X 2731-0809 nnns volume:1 year:2021 number:1 day:13 month:10 https://dx.doi.org/10.1007/s44163-021-00010-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 1 2021 1 13 10
allfieldsSound	10.1007/s44163-021-00010-4 doi (DE-627)SPR04528377X (SPR)s44163-021-00010-4-e DE-627 ger DE-627 rakwb eng Divya, C. D. verfasserin aut An efficient machine learning approach to nephrology through iris recognition 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract Iridology is a technique in science used to analyze color, patterns, and various other properties of the iris to assess an individual's general health. Few regions in the iris are connected by nerves coming from different organs of body, this shows some special unique qualities which is advantageous along with which assist in psychological condition, particular organ conditions and construction of the body. The structural and designed patterns present on specific part of iris represent the level of intensity of disorder caused by the organs. This method of approach can be employed as reasonable and logical guidelines for the detection and identification of disorders. Therefore, after scanning the image of iris advance study of disorder can be carried out for detecting the condition of organ. Initially by the service of an adaptive histogram, the image of eye should be separated from part of the image captured. Next the images of iris are classified and recognized using machine learning algorithm Support Vector machine or Support Vector Networks. The features are extracted from images of iris using white Gaussian filters which are then used as a feature descriptor. These descriptors count the occurrences of gradient orientation and magnitude in localized portions of an image. Then convert the image of iris to a gray scaled image, final image is standardized. Next is to convert it into rectangular shape and then assembling the HMM images of eyes related to the kidney. The final level is to diagnose the edge of image of iris HMM. By analysing end results, condition of the organ can be diagnosed and results can be obtained from the iris recognition system. Iridology (dpeaa)DE-He213 Digital image processing (dpeaa)DE-He213 Edge detection (dpeaa)DE-He213 Gururaj, H. L. verfasserin aut Rohan, R. verfasserin aut Bhagyalakshmi, V. verfasserin aut Rashmi, H. A. verfasserin aut Domnick, A. verfasserin aut Flammini, Francesco verfasserin aut Enthalten in Discover artificial intelligence [Cham] : Springer International Publishing, 2021 1(2021), 1 vom: 13. Okt. (DE-627)1774283107 (DE-600)3097625-X 2731-0809 nnns volume:1 year:2021 number:1 day:13 month:10 https://dx.doi.org/10.1007/s44163-021-00010-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 1 2021 1 13 10
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source	Enthalten in Discover artificial intelligence 1(2021), 1 vom: 13. Okt. volume:1 year:2021 number:1 day:13 month:10
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author	Divya, C. D.
spellingShingle	Divya, C. D. misc Iridology misc Digital image processing misc Edge detection An efficient machine learning approach to nephrology through iris recognition
authorStr	Divya, C. D.
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topic_title	An efficient machine learning approach to nephrology through iris recognition Iridology (dpeaa)DE-He213 Digital image processing (dpeaa)DE-He213 Edge detection (dpeaa)DE-He213
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title	An efficient machine learning approach to nephrology through iris recognition
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author_browse	Divya, C. D. Gururaj, H. L. Rohan, R. Bhagyalakshmi, V. Rashmi, H. A. Domnick, A. Flammini, Francesco
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title_sort	efficient machine learning approach to nephrology through iris recognition
title_auth	An efficient machine learning approach to nephrology through iris recognition
abstract	Abstract Iridology is a technique in science used to analyze color, patterns, and various other properties of the iris to assess an individual's general health. Few regions in the iris are connected by nerves coming from different organs of body, this shows some special unique qualities which is advantageous along with which assist in psychological condition, particular organ conditions and construction of the body. The structural and designed patterns present on specific part of iris represent the level of intensity of disorder caused by the organs. This method of approach can be employed as reasonable and logical guidelines for the detection and identification of disorders. Therefore, after scanning the image of iris advance study of disorder can be carried out for detecting the condition of organ. Initially by the service of an adaptive histogram, the image of eye should be separated from part of the image captured. Next the images of iris are classified and recognized using machine learning algorithm Support Vector machine or Support Vector Networks. The features are extracted from images of iris using white Gaussian filters which are then used as a feature descriptor. These descriptors count the occurrences of gradient orientation and magnitude in localized portions of an image. Then convert the image of iris to a gray scaled image, final image is standardized. Next is to convert it into rectangular shape and then assembling the HMM images of eyes related to the kidney. The final level is to diagnose the edge of image of iris HMM. By analysing end results, condition of the organ can be diagnosed and results can be obtained from the iris recognition system. © The Author(s) 2021
abstractGer	Abstract Iridology is a technique in science used to analyze color, patterns, and various other properties of the iris to assess an individual's general health. Few regions in the iris are connected by nerves coming from different organs of body, this shows some special unique qualities which is advantageous along with which assist in psychological condition, particular organ conditions and construction of the body. The structural and designed patterns present on specific part of iris represent the level of intensity of disorder caused by the organs. This method of approach can be employed as reasonable and logical guidelines for the detection and identification of disorders. Therefore, after scanning the image of iris advance study of disorder can be carried out for detecting the condition of organ. Initially by the service of an adaptive histogram, the image of eye should be separated from part of the image captured. Next the images of iris are classified and recognized using machine learning algorithm Support Vector machine or Support Vector Networks. The features are extracted from images of iris using white Gaussian filters which are then used as a feature descriptor. These descriptors count the occurrences of gradient orientation and magnitude in localized portions of an image. Then convert the image of iris to a gray scaled image, final image is standardized. Next is to convert it into rectangular shape and then assembling the HMM images of eyes related to the kidney. The final level is to diagnose the edge of image of iris HMM. By analysing end results, condition of the organ can be diagnosed and results can be obtained from the iris recognition system. © The Author(s) 2021
abstract_unstemmed	Abstract Iridology is a technique in science used to analyze color, patterns, and various other properties of the iris to assess an individual's general health. Few regions in the iris are connected by nerves coming from different organs of body, this shows some special unique qualities which is advantageous along with which assist in psychological condition, particular organ conditions and construction of the body. The structural and designed patterns present on specific part of iris represent the level of intensity of disorder caused by the organs. This method of approach can be employed as reasonable and logical guidelines for the detection and identification of disorders. Therefore, after scanning the image of iris advance study of disorder can be carried out for detecting the condition of organ. Initially by the service of an adaptive histogram, the image of eye should be separated from part of the image captured. Next the images of iris are classified and recognized using machine learning algorithm Support Vector machine or Support Vector Networks. The features are extracted from images of iris using white Gaussian filters which are then used as a feature descriptor. These descriptors count the occurrences of gradient orientation and magnitude in localized portions of an image. Then convert the image of iris to a gray scaled image, final image is standardized. Next is to convert it into rectangular shape and then assembling the HMM images of eyes related to the kidney. The final level is to diagnose the edge of image of iris HMM. By analysing end results, condition of the organ can be diagnosed and results can be obtained from the iris recognition system. © The Author(s) 2021
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title_short	An efficient machine learning approach to nephrology through iris recognition
url	https://dx.doi.org/10.1007/s44163-021-00010-4
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author2	Gururaj, H. L. Rohan, R. Bhagyalakshmi, V. Rashmi, H. A. Domnick, A. Flammini, Francesco
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