Neural network framework for multilingual Web documents

Prakash, Kolla Bhanu; Ananthan, T.; Rajavarman, V. N.

doi:10.1109/ic3i.2014.7019797

Cited by 4 publications

(5 citation statements)

References 10 publications

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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%

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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%

“…[8] The purpose of this research is to develop prediction models that detect the cancer is benign or malignant and take respective precautions to prevent it. [9]…”

Section: Literature Reviewmentioning

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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“…It performs fine-tuning for the algorithm by using a multi-task objective which incorporates a variety of unsupervised prediction procedures. Supervised learning, powered by deep learning, is largely responsible for the wave of AI applications that we have seen in the last few years [21]. Transfer learning will make these developments significantly greater.…”

Section: Transfer Learning In Chatbotmentioning

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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“…[21] Emotion recognition through speech is the topic of Human and Computer Interaction and Natural language processing. [22] Emotion Recognition through speech is extraction of the emotions and feature details of the human emotions. The linear prediction cepstrum-coefficients, Mel-Frequency spectral features, energy, pitch, format frequency many research companies are used, those features have the main objects for Emotion Recognition through speech.…”

Section: Emotion Recognitionmentioning

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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Neural network framework for multilingual Web documents

Cited by 4 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

Chatterbot implementation using Transfer Learning and LSTM Encoder-Decoder Architecture

Emotion Recognition in Speech Processing using Fast Fourier Transform

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