Foundations to frontiers of big data analytics

Prakash, K. B.; Rajaraman, A.; Perumal, Thingaran; Kolla, Padma

doi:10.1109/ic3i.2016.7917968

Cited by 14 publications

(5 citation statements)

References 1 publication

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“…[24] They are trained set, validation set, test set. [25] Training set is used to train the network, validation set is used to verify the quality of the training and test set used to test the network with new related data [9] .…”

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

Self Cite

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“…The Retrieval-based models select an answer based on the question from a set of responses. It produces no new words, so We don't have to owe grammar a look [20]. The Generative Models are really wise.…”

Section: Proposed Algorithmsmentioning

confidence: 99%

Chatterbot implementation using Transfer Learning and LSTM Encoder-Decoder Architecture

Prakash¹

2020

IJETER

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The goal of this project is to develop a chatbot using deep learning models. Chatterbot is an existing research area whose main goal is to appear as human as possible and most of the current models(which use RNN and related sequential learning models) are unable to achieve this task to relate over long dependencies. Adding onto that , NLP tasks require a lot of data which can be hard to collect for smaller projects/tasks which inspired to try out sequence to sequence learning model using LSTM. For that we have used a movie dialog corpus of 220,579 conversation exchanges among which about 50,000 conversational exchanges are only used as a training corpus to our model since training on more conversation exchanges requires high computation power than we have.

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“…The kind of CNN architecture used in both the models is ResNet101. It is applied with two and three groups classification respectively [17]. Ordinal regression is implemented for estimating the age in both the models from the images of the people's face.…”

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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Foundations to frontiers of big data analytics

Cited by 14 publications

References 1 publication

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

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

Chatterbot implementation using Transfer Learning and LSTM Encoder-Decoder Architecture

Automated Road Safety Surveillance System using Hybrid CNN-LSTM Approach

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