Data Engineered Content Extraction Studies for Indian Web Pages

Prakash, Kolla Bhanu; Raman, Arun Raja

doi:10.1007/978-981-10-8055-5_45

Cited by 10 publications

(10 citation statements)

References 6 publications

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“…F. Lin et.al., [7] applied neural fuzzy method on the EEGs, as they are correlated to the behavioural attributes of the driver, to detect the fatigue in the driver. The self-organised [14] fuzzy system is then compared with the other neural network models developed.…”

Section: Related Workmentioning

confidence: 99%

“…For image data, deep learning architectures works well and gives good results compared to the standard machine learning algorithms. In our study, we applied ResNet50, which is a deep-residual based architecture and LSTM [18], a model of RNN architecture. CNN is widely applied for image classification tasks whereas RNN is preferred for sequential data.…”

Section: Modelingmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Modelingmentioning

confidence: 99%

Automated Road Safety Surveillance System using Hybrid CNN-LSTM Approach

Donepudi¹

2020

IJATCSE

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“…[12] Emotions are classified into two type's long term emotion and transient, this is another problem for the recognizer which does not get a clear picture of emotion. [13] The evaluation depends upon the level of intensity of the sound waves, which is an input to the system. [14] The speech input given to the system may be real world emotions or acted.…”

Section: A Speech Emotionmentioning

confidence: 99%

Emotion Recognition in Speech Processing using Fast Fourier Transform

Jammula¹,

gogireddy²,

Vege³

et al. 2019

IJRTE

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The idea of acquiring the state of human emotions from one’s speech, we have gathered required data that makes one to understand the concept behind this process. Human emotions can be predicted by his/her facial expressions or by the tone of their voice. Reading the facial expressions is one of the major tasks involved in image processing. Likewise, each emotion holds different tone in one's voice. It requires a various emotional tone frequency to calculate and analyse the emotions. We need to fetch approximate frequencies of emotions. It’s the challenging task as each speaker has various pitches while speaking and frequencies of the same person varies in his emotion. Another main issue is the noise in the input while a person is speaking, due to less quality recordings or surrounding environment. List of basic emotions are Happy, angry, sad, bored, surprised, disgust, fear. For this project the prior important concept is speech recognition. The machine must be capable of reading the input in form as speech and must be capable of analysing various contents. The input given is converted into wav format. At the same time machine must be also capable of fetching the frequencies. The calculation is performed using many methodologies that are defined.

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“…After reviewing the different literature showed that there have been several studies on the early papers like prediction of breast cancer using random forest, support vector machines [3] . Padma Priya and Sowmiya worked on the prediction of breast cancer using classification algorithms like Decision tree [4] .Shravya and pravalika implemented the models using logistic regression, K nearest neighbor.…”

Section: Literature Reviewmentioning

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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Data Engineered Content Extraction Studies for Indian Web Pages

Cited by 10 publications

References 6 publications

Automated Road Safety Surveillance System using Hybrid CNN-LSTM Approach

Automated Road Safety Surveillance System using Hybrid CNN-LSTM Approach

Emotion Recognition in Speech Processing using Fast Fourier Transform

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

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