Story Scrambler - Automatic Text Generation Using Word Level RNN-LSTM

DIPTI, PAWADE; Sakhapara, Avani; MANSI, JAIN; Jain, Neha; Gada, Krushi

doi:10.5815/ijitcs.2018.06.05

Cited by 49 publications

(23 citation statements)

References 13 publications

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“…All web pages can also be categorized as web spam which are only displaying the catalogs of some products but in reality, they are redirecting the users to other traders without giving the additional value. Generating the text automatically is a tough job and there is no satisfactory and good technique available for this yet [37]. There are multiple levels of consistency in natural text, therefore, it is very difficult to follow all at once [38].…”

Section: Understanding the Content Spammentioning

confidence: 99%

An Improved Framework for Content-based Spamdexing Detection

Shahzad¹,

Mahdin²,

Mohd³

2020

IJACSA

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To the modern Search Engines (SEs), one of the biggest threats to be considered is spamdexing. Nowadays spammers are using a wide range of techniques for content generation, they are using content spam to fill the Search Engine Result Pages (SERPs) with low-quality web pages. Generally, spam web pages are insufficient, irrelevant and improper results for users. Many researchers from academia and industry are working on spamdexing to identify the spam web pages. However, so far not even a single universally efficient method is developed for identification of all spam web pages. We believe that for tackling the content spam there must be improved methods. This article is an attempt in that direction, where a framework has been proposed for spam web pages identification. The framework uses Stop words, Keywords Density, Spam Keywords Database, Part of Speech (POS) ratio, and Copied Content algorithms. For conducting the experiments and obtaining threshold values WEBSPAM-UK2006 and WEBSPAM-UK2007 datasets have been used. An excellent and promising F-measure of 77.38% illustrates the effectiveness and applicability of proposed method.

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Section: Understanding the Content Spammentioning

confidence: 99%

An Improved Framework for Content-based Spamdexing Detection

Shahzad¹,

Mahdin²,

Mohd³

2020

IJACSA

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“…Dipti et al, 2018 [23] worked with creating a new story based on the input from the previously written stories. First, they have taken the character and storyline as an input.…”

Section: Sramya Cskanimozhi Selvimentioning

confidence: 99%

Recurrent Neural Network based Models for Word Prediction

Ramya*¹,

Selvi²

2019

IJRTE

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Globally, people are spending a cumulative amount of time on their mobile device, laptop, tab, desktop, etc,. for messaging, sending emails, banking, interaction through social media, and all other activities. It is necessary to cut down the time spend on typing through these devices. It can be achieved when the device can provide the user more options for what the next word might be for the current typed word. It also increases the speed of typing. In this paper, we suggest and presented a comparative study on various models like Recurrent Neural Network, Stacked Recurrent Neural Network, Long Short Term Memory network (LSTM) and Bi-directional LSTM that gives solution for the above said problem. Our primary goal is to suggest the best model among the four models to predict the next word for the given current word in English Language. Our survey says that for predicting next word RNN provide accuracy 60% and loss 40%, Stacked RNN provide accuracy 62% and loss 38%, LSTM provide accuracy 64% and loss 36% and Bidirectional LSTM provide accuracy 72% and loss 28%.

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“…We utilized the puzzle concept that assumes that all patients from a national dataset are pieces of a puzzling problem to reconstruct the population's survival history. Our concept relies on the recurrent artificial neural network that supports solving the N-puzzle problem and is frequently applied to natural language processing (e.g., automated generation of texts) 10 .…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-based Personalized and Risk-adapted Surveillance Management for Urologic Cancer: A SEER-based Study

Eminaga¹,

Breil²,

Semjonow³

et al. 2020

Preprint

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Identification of prognosis changing events is essential during cancer surveillance. Current clinical practice is limited by “one-size-fits-all” time management or subjective assessment that is not necessarily appropriate for individual patients’ risk profiles. A risk-adapted follow-up is more efficient and optimal from the clinical perspective. Herein we introduce an artificial intelligence-based data-driven solution for risk-adapted follow-up schedule that devises the number of follow-up visits per year and assesses the cancer-specific risk profile over a long interval depending on the age at diagnosis. We utilized Surveillance, Epidemiology, and End Results (SEER), a cancer registry database and a resource for cancer-specific survival estimation in the United States that includes more than 2 million patients diagnosed with urologic cancers. We tested different machine learning algorithms on their definition for the feature importance and selected clinically relevant parameters regularly used in clinical routine to develop a survival modeling. As a result, the underlying recurrent neural network algorithm for follow-up modeling was fitted on the unseen test set with an overall concordance index score of 0.80. A controlled access to the online tool is available for physicians.

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Story Scrambler - Automatic Text Generation Using Word Level RNN-LSTM

Cited by 49 publications

References 13 publications

An Improved Framework for Content-based Spamdexing Detection

An Improved Framework for Content-based Spamdexing Detection

Recurrent Neural Network based Models for Word Prediction

Artificial Intelligence-based Personalized and Risk-adapted Surveillance Management for Urologic Cancer: A SEER-based Study

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