scholarly journals Genre Classification of Telugu and English Movie Based on the Hierarchical Attention Neural Network

2021 ◽  
Vol 14 (1) ◽  
pp. 54-62
Author(s):  
Kumar Govindaswamy ◽  
◽  
Shriram Ragunathan ◽  
Keyword(s):  
Neural Network ◽  
Recommendation System ◽  
Short Term Memory ◽  
Hierarchical Structures ◽  
Relevant Information ◽  
Twitter Data ◽  
Genre Classification ◽  
The Social ◽  
Audio Data

Genre Classification of movies is useful in the movie recommendation system for video streaming applications like Amazon, Netflix, etc. The existing methods used either video or audio data as input that requires more computation resources to process the data for the genre classification of movies. In this study, the Hierarchical Attention Neural Network (HANN) is proposed for genre classification of movies based on the social media called Twitter data as input. Twitter data related to the Telugu and English movies are collected and applied to HANN for movie’s genre classification. IMDB data are used to evaluate the performance of the proposed HANN method. The hierarchical structures of the twitter data is considered by the proposed HANN method and the most important words related to genre classification is identified by the attention mechanism, where the other neural networks such as Artificial Neural Network and Convolutional Neural Network (CNN) returns only the important weights resulting from previous words. The HANN method has the advantages of encoding the relevant information that helps to improve the performance of the recommendation system. The experimental results show that the HANN method achieve higher performance compared to other classifiers Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM). The HANN method achieves accuracy of 73.15% in classification, while the existing BiLSTM method achieve the accuracy of 68% in classification.

scholarly journals Correct Pronunciation Detection of the Arabic Alphabet Using Deep Learning

2021 ◽  
Vol 11 (6) ◽  
pp. 2508
Author(s):  
Nishmia Ziafat ◽  
Hafiz Farooq Ahmad ◽  
Iram Fatima ◽  
Muhammad Zia ◽  
Abdulaziz Alhumam ◽  
...  
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Research Attention ◽  
Correct Pronunciation ◽  
Deep Convolution Neural Network ◽  
Audio Data ◽  
Slow Progress ◽  
Long Short Term Memory

Automatic speech recognition for Arabic has its unique challenges and there has been relatively slow progress in this domain. Specifically, Classic Arabic has received even less research attention. The correct pronunciation of the Arabic alphabet has significant implications on the meaning of words. In this work, we have designed learning models for the Arabic alphabet classification based on the correct pronunciation of an alphabet. The correct pronunciation classification of the Arabic alphabet is a challenging task for the research community. We divide the problem into two steps, firstly we train the model to recognize an alphabet, namely Arabic alphabet classification. Secondly, we train the model to determine its quality of pronunciation, namely Arabic alphabet pronunciation classification. Due to the less availability of audio data of this kind, we had to collect audio data from the experts, and novices for our model’s training. To train these models, we extract pronunciation features from audio data of the Arabic alphabet using mel-spectrogram. We have employed a deep convolution neural network (DCNN), AlexNet with transfer learning, and bidirectional long short-term memory (BLSTM), a type of recurrent neural network (RNN), for the classification of the audio data. For alphabet classification, DCNN, AlexNet, and BLSTM achieve an accuracy of 95.95%, 98.41%, and 88.32%, respectively. For Arabic alphabet pronunciation classification, DCNN, AlexNet, and BLSTM achieve an accuracy of 97.88%, 99.14%, and 77.71%, respectively.

scholarly journals Sentiment classification of user's reviews on drugs based on global vectors for word representation and bidirectional long short-term memory recurrent neural network

2021 ◽  
Vol 23 (1) ◽  
pp. 345
Author(s):  
Hadab Khalid Obayed ◽  
Firas Sabah Al-Turaihi ◽  
Khaldoon H. Alhussayni
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Human Life ◽  
Health Sector ◽  
Ground Truth ◽  
Second Stage ◽  
The Social ◽  
Word Representation ◽  
Two Phases

<p>The process of product development in the health sector, especially pharmaceuticals, goes through a series of precise procedures due to its directrelevance to human life. The opinion of patients or users of a particular drugcan be relied upon in this development process, as the patients convey their experience with the drugs through their opinion. The social media field provides many datasets related to drugs through knowing the user's ratingand opinion on a drug after using it. In this work, a dataset is used that includes the user’s rating and review on the drug, for the purpose of classifying the user’s opinions (reviews) whether they are positive ornegative. The proposed method in this article includes two phases. The first phase is to use the Global vectors for word representation model for converting texts into the vector of embedded words. As for the second stage, the deep neural network (Bidirectional longshort-termmemory) is employedin the classification of reviews. The user's rating is used as a ground truth inevaluating the classification results. The proposed method present sencouraging results, as the classification results are evaluated through threecriteria, namely Precision, Recall and F-score, whose obtained values equal(0.9543, 0.9597and0.9558), respectively. The classification results of theproposed method are compared to a number of classifiers, and it was noticed that the results of the proposed method exceed those of the alternative classifiers.</p>

scholarly journals Memory in Linguistic Narrative vs. Postmodern Multitasked Multimedia Art Memory

2018 ◽  
Vol 1 (2) ◽  
pp. 101
Author(s):  
Vesna Srnic ◽  
Emina Berbic Kolar ◽  
Igor Ilic
Keyword(s):  
Short Term Memory ◽  
Social Role ◽  
Procedural Memory ◽  
Short Term ◽  
The Social ◽  
Multimedia Art ◽  
The Media

<p><em>In addition to the well-known classification of long-term and short-term memory, we are also interested in distinguishing episodic, semantic and procedural memory in the areas of linguistic narrative and multimedial semantic deconstruction in postmodernism. We compare the liveliness of memorization in literary tradition and literature art with postmodernist divisions and reverberations of traditional memorizations through human multitasking and performative multimedia art, as well as formulate the existence of creative, intuitive and superhuman paradigms.</em></p><em>Since the memory can be physical, psychological or spiritual, according to neurobiologist Dr. J. Bauer (Das Gedächtnis des Körpers, 2004), the greatest importance for memorizing has the social role of collaboration, and consequently the personal transformation and remodelling of genomic architecture, yet the media theorist Mark Hansen thinks technology brings different solutions of framing function (Hansen, 2000). We believe that postmodern deconstruction does not necessarily damage memory, especially in the field of human multitasking that utilizes multimedia performative art by means of anthropologization of technology, thereby enhancing artistic and affective pre&amp;post-linguistic experience while unifying technology and humans through intuitive empathy in society.</em>

Genre classification of compressed audio data

2008 ◽  
Author(s):  
Antonello Rizzi ◽  
Nicola Maurizio Buccino ◽  
Massimo Panella ◽  
Aurelio Uncini
Keyword(s):  
Genre Classification ◽  
Audio Data

scholarly journals Classification of Analyzed Text in Speech Recognition Using RNN-LSTM in Comparison with Convolutional Neural Network to Improve Precision for Identification of Keywords

2021 ◽  
Vol 11 (2) ◽  
pp. 1097-1108
Author(s):  
Bathaloori Reddy Prasad
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Convolutional Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Statistical Significance ◽  
Language Translation ◽  
Accuracy And Precision ◽  
Long Short Term Memory

Aim: Text classification is a method to classify the features from language translation in speech recognition from English to Telugu using a recurrent neural network- long short term memory (RNN-LSTM) comparison with convolutional neural network (CNN). Materials and Methods: Accuracy and precision are performed with dataset alexa and english-telugu of size 8166 sentences. Classification of language translation is performed by the recurrent neural network where a number of the samples (N=62) and convolutional neural network were a number of samples (N=62) techniques, the algorithm RNN implies speech recognition that can be compared with convolutional is the second technique. Results and Discussion: RNN-LSTM from the dataset speech recognition, feature Telugu_id produce accuracy 93% and precision 68.04% which can be comparatively higher than CNN accuracy 66.11%, precision 61.90%. It shows a statistical significance as 0.007 from Independent Sample T-test. Conclusion: The RNN-LSTM performs better in finding accuracy and precision when compared to CNN.

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