An Appraisal on Recurrent Pattern Analysis Algorithm from the Net Monitor Records

Nallamala, Sri Hari; Pathuri, Siva Kumar; Koneru, Suvarna Vani

doi:10.14419/ijet.v7i2.7.10879

Cited by 26 publications

(24 citation statements)

References 1 publication

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“…Patients lose time between having their eyes scanned, having their images examined by medical professionals, and setting up a follow-up visit. [7][8][9][10][11][12][13][14][15] By using automated systems, the proposed method can solve the above mentioned problems. Previous attempts have used image classification, pattern recognition, and machine learning to make quick progress in developing a thorough and automated approach of DR screening.…”

Section: Problem Identificationmentioning

confidence: 99%

Diagnosis and Grading of Diabetic Retinopathy using Deep Learning

2023

ijfans

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Diabetic retinopathy (DR), which causes tissue on the eye that damages visibility, is a common complication of type-2 diabetes. If it is not discovered in time, total blindness might occur. DR is irreversible. DR is primarily among adults who are of working age. More than 150 million people are affected by diabetic retinopathy (DR), which accounts for 2.6% of blindness worldwide. Different indications of DR are vision distortion, bulging of the eye, and formation of irregular blood vessels. The traditional way is to use Computer-aided Diagnosis (CAD) systems during treatment. The dataset used is the APTOS blindness detection dataset that is accessible in Kaggle. The Convolutional Neural Networks (CNN) is the most effective way for classifying images. In this paper, the MobileNet architecture, a deep learning technique is utilized to automate the diagnosis of the disease and estimate the severity of the eye into several stages through which the accuracy obtained for training is 95% and validation is 82%.

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Section: Problem Identificationmentioning

confidence: 99%

Diagnosis and Grading of Diabetic Retinopathy using Deep Learning

2023

ijfans

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“…There have been very limited advancements in the use of machine learning for detecting plant leaf diseases, and classification. [18][19][20][21][22][23][24][25][26]…”

Section: Problem Identificationmentioning

confidence: 99%

Paddy Crop Disease Detection Using Deep Learning

2023

ijfans

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Agriculture plays a crucial role in human life, with approximately 60% of the population directly or indirectly involved in agricultural activities. Paddy is one of the essential food crops globally and is particularly significant in the Asian subcontinent. As a result of excessive use of chemicals and unpredictable weather patterns, there has been a significant increase in crop diseases. Sometimes an expert may be unavailable to identify the disease.Due to mistaken conclusions of experts, there is an unnecessary use of pesticides which will affect the yield badly, hence, it is essential to know which disease has affected the Paddy crop. Early detection of these diseases is essential to minimize the losses . To address this issue, Deep Learning models, including Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and ResNet101, were employed to detect three types of paddy crop diseases including Leaf Blast (LB), Brown Spot (BS), and Hispa along with the healthy category. The dataset consisted of 6,061 images of three types of disease affected and healthy paddy crops, collected from Paddy Doctor Website and IRRI. ANN Model achieved an accuracy of 66.1%, CNN Model 94.3% and ResNet101 Model 98.2%.

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“…Sri Hari Nallamala, et al [3] describes an assortment of web use mining practice can propel exertion on various regions of logical, restorative and online networking applications to progress toward for the exploration and security joined zone.…”

Section: Related Workmentioning

confidence: 99%

Breast Cancer Detection using Machine Learning Way

2019

ijrte

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Affording in the direction of Breast Cancer Organization, Breast Cancer is solitary and one and only of the most perilous sorts of viruses that is located operative for females in the biosphere. By way of experimental professional distinguishing this cancer in her initial phase aids in abiding breathes. Based on cancer.net proposal individualized funnels for additional 120 kinds of cancer and correlated to genetic diseases. Aimed At discovering breast cancer fundamentally AI rehearses are utilized. We have foreseen adaptive ensemble voting scheme for broke down breast cancer with WBC (Wisconsin Breast Cancer) record. Intention of our effort is to associate & describe in what way CNN and logistic algorithm afford used for detecting breast cancer yet the variables are condensed. Here remain 2 categories of tumours be situated. Benign tumour and malignant tumours, where benign tumour is non-cancer and malignant is cancer tumour

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An Appraisal on Recurrent Pattern Analysis Algorithm from the Net Monitor Records

Cited by 26 publications

References 1 publication

Diagnosis and Grading of Diabetic Retinopathy using Deep Learning

Diagnosis and Grading of Diabetic Retinopathy using Deep Learning

Paddy Crop Disease Detection Using Deep Learning

Breast Cancer Detection using Machine Learning Way

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