Comparative Analysis of Deep Learning Models for Myanmar Text Classification

2020 ◽  
pp. 76-85
Author(s):  
Myat Sapal Phyu ◽  
Khin Thandar Nwet
Keyword(s):  
Deep Learning ◽  
Comparative Analysis ◽  
Text Classification ◽  
Learning Models

Comparative Analysis of Multivariable Deep Learning Models for Forecasting in Smart Grids

2020 ◽  
Author(s):  
E. Escobar Avalos ◽  
M. A. Rodriguez Licea ◽  
H. Rostro Gonzalez ◽  
A. Espinoza Calderon ◽  
A.I. Barranco Gutierrez ◽  
...  
Keyword(s):  
Deep Learning ◽  
Comparative Analysis ◽  
Smart Grids ◽  
Learning Models

Arabic text classification using deep learning models

2020 ◽  
Vol 57 (1) ◽  
pp. 102121 ◽  
Author(s):  
Ashraf Elnagar ◽  
Ridhwan Al-Debsi ◽  
Omar Einea
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Arabic Text ◽  
Learning Models ◽  
Arabic Text Classification

scholarly journals The Unreasonable Effectiveness of the Baseline: Discussing SVMs in Legal Text Classification

2021 ◽  
Author(s):  
Benjamin Clavié ◽  
Marc Alphonsus
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Text Classification ◽  
Traditional Approach ◽  
Error Reduction ◽  
Support Vector ◽  
Learning Models ◽  
Legal Text ◽  
Classification Tasks ◽  
Legal Domain

We aim to highlight an interesting trend to contribute to the ongoing debate around advances within legal Natural Language Processing. Recently, the focus for most legal text classification tasks has shifted towards large pre-trained deep learning models such as BERT. In this paper, we show that a more traditional approach based on Support Vector Machine classifiers reaches competitive performance with deep learning models. We also highlight that error reduction obtained by using specialised BERT-based models over baselines is noticeably smaller in the legal domain when compared to general language tasks. We discuss some hypotheses for these results to support future discussions.

scholarly journals A comparative review on deep learning models for text classification

2020 ◽  
Vol 19 (1) ◽  
pp. 325
Author(s):  
Muhammad Zulqarnain ◽  
Rozaida Ghazali ◽  
Yana Mazwin Mohmad Hassim ◽  
Muhammad Rehan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Language Processing ◽  
Text Classification ◽  
Question Answering ◽  
Learning Models ◽  
Semantic Classification ◽  
Analysis Question ◽  
Comparative Review ◽  
Classification Tasks

<p>Text classification is a fundamental task in several areas of natural language processing (NLP), including words semantic classification, sentiment analysis, question answering, or dialog management. This paper investigates three basic architectures of deep learning models for the tasks of text classification: Deep Belief Neural (DBN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), these three main types of deep learning architectures, are largely explored to handled various classification tasks. DBN have excellent learning capabilities to extracts highly distinguishable features and good for general purpose. CNN have supposed to be better at extracting the position of various related features while RNN is modeling in sequential of long-term dependencies. This paper work shows the systematic comparison of DBN, CNN, and RNN on text classification tasks. Finally, we show the results of deep models by research experiment. The aim of this paper to provides basic guidance about the deep learning models that which models are best for the task of text classification.</p>

Colorizing and Captioning Images Using Deep Learning Models and Deploying Them Via IoT Deployment Tools

2020 ◽  
Vol 10 (4) ◽  
pp. 35-50
Author(s):  
Rajalakshmi Krishnamurthi ◽  
Raghav Maheshwari ◽  
Rishabh Gulati
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Computer Science ◽  
Loss Rate ◽  
Learning Models

Neural networks and IoT are some top fields of research in computer science nowadays. Inspired by this, this article works on using and creating an efficient neural networks model for colorizing images and transports them to remote systems through IoT deployment tools. This article develops two models, Alpha and Beta, for the colorization of the greyscale images. Efficient models are developed to lessen the loss rate to around 0.005. Further, it also develops an efficient model for the captioning of an image. The paper then describes the use of tools like AWS Greengrass and Docker for the deployment of different neural networks models, providing a comparative analysis among them, combining neural networks with IoT deployment tools.

scholarly journals Spam-Detection with Comparative Analysis and Spamming Words Extractions

2021 ◽  
Author(s):  
Md Khairul Islam ◽  
Md Al Amin ◽  
Md Rakibul Islam ◽  
Md Nosin Ibna Mahbub ◽  
Md Imran Hossain Showrov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Spam Detection ◽  
Learning Models ◽  
Electronic Medium ◽  
Machine Learning Models

Communication through email plays an essential part especially in every sector of our day-to-day life. Considering its significance, it is important to filter spam emails from emails. Spam email, also known as junk email, is unwanted messages that are sent by the electronic medium in large quantities. Most of the spam emails are commercial in nature that is not only irritating but also harmful due to malicious scams or malware-hosting sites or use viruses attached to the message. In this paper, we identify spam emails and expose how spam emails can be distinguished from legitimate/normal emails. We deployed four machine learning models and two deep learning models over the datasets including the combined dataset. Besides, we also try to find the important keywords that are found repeatedly from spam emails repository. This type of knowledge will enable us to detect spam emails for our personnel and community security purpose.<br>

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