Content Extraction Studies using Neural Network and Attribute Generation

Prakash, Kolla Bhanu; Rangaswamy, Madhavi

doi:10.17485/ijst/2016/v9i22/95165

Cited by 13 publications

(11 citation statements)

References 10 publications

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“…[10] Neural network helps to predict malignancy in cancer tumors very efficiently. [11] In order to perform the model we use a real data-set connected to the detection of breast cancer tumors. [12] After studying case studies, extensive literature reviews shows that there are lots of factors influencing cancer.…”

Section: Methodsmentioning

confidence: 99%

Deep Neural Network Design and Implementation for Predicting Malignancy of Cancer Tumors

Priya*,

Satyanjani,

Amarendra

et al. 2019

IJRTE

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Breast cancer is the most frequent cancer among women after lung cancer. It is second popular cause of death in the world. Breast cancer cells usually form a tumor (abnormal tissue).There are two types of breast cancer tumors: those that are non-cancerous, or ‘benign’, and those that are cancerous, which are ‘malignant’. Early detection of cancer tumors can prevent its mortality and can take respective precautions according to the type of cancer tumor (either it is benign or malignant). The main objective of this paper is to early diagnosis of the malignancy of cancer tumors which helps to decrease the death rate and helps to give more lives. The selection of suitable deep learning technique is a challenge for the diagnosis of breast cancer. So we created model for prediction of malignancy of cancer tumors using neural network to analyze risk levels that helps in prognosis. It is useful for a doctor to predict the stage of cancer and take respective precautions

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Section: Methodsmentioning

confidence: 99%

Deep Neural Network Design and Implementation for Predicting Malignancy of Cancer Tumors

Priya*,

Satyanjani,

Amarendra

et al. 2019

IJRTE

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“…Information regarding each feature, such as the local structure and ordinal information, is collected from the facial images. Then correlation [15] among the features is minimized and the semi-supervised prediction algorithm is implemented and the results are compared with the state-of-the-art algorithms.…”

Section: Related Workmentioning

confidence: 99%

Automated Road Safety Surveillance System using Hybrid CNN-LSTM Approach

Donepudi¹

2020

IJATCSE

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Among all the reasons for the occurrence of road accidents, the human state of drowsiness and Underage driving contributes a major share. With the rigid implementation of traffic rules and national schemes, it does not result in decreasing the accidents. Hence, there is a need for automation of surveillance which strictly restricts the teen driving and fatigue driving. In this paper, we introduce a face image descriptor-based combination of deep learning model i.e., convolution neural network (CNN) with ResNet50 architecture to predict age and a recurrent neural network (RNN) with LSTM architecture to detect the drowsiness in driver and alert them when they are drowsy. The algorithm is based on face recognition for age prediction and the blink frequency for detecting the fatigue. Image processing techniques are utilized to obtain the feature-based extraction for prediction. The proposed model developed could give a validation accuracy of 96% thus providing the promising results. This automation model thus could help the road safety authorities in their work and also decreases the occurrence of road accidents.

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“…We are using Hyperparameter Optimization in this, which helps in creating to create models allows us to do a grid search. Here we used mtry, ntree, nodesize for best accuracy [15][16][17][18]. In R.F, not all the attributes are equally used in amount for classification and prediction [19][20][21].…”

Section: Gini Impurity = Entropy =mentioning

confidence: 99%

Predicting Breast Cancer using Modern Data Science Methodology

Botcha¹,

Prakash²

2019

IJITEE

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Breast Cancer is the mass occurring cancer in women according to the World Health Organization(WHO), But the early prediction of breast cancer helps in the recovery for the effected one's. Reasons for breast cancer were Hormone replacement therapy or getting explore to harmful radioactive rays and due to late childbearing. The aim is to diagnose cancer by using a machine learning technique, Random Forest, for accurate solutions. The dataset we used is the Wisconsin Breast Cancer dataset. The output which the error rate was only about "0.0177"

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Content Extraction Studies using Neural Network and Attribute Generation

Cited by 13 publications

References 10 publications

Deep Neural Network Design and Implementation for Predicting Malignancy of Cancer Tumors

Deep Neural Network Design and Implementation for Predicting Malignancy of Cancer Tumors

Automated Road Safety Surveillance System using Hybrid CNN-LSTM Approach

Predicting Breast Cancer using Modern Data Science Methodology

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