Deep learning-based approximation of Goldbach partition function

2021 ◽  
pp. 2150152
Author(s):  
Eunmi Kim
Keyword(s):  
Number Theory ◽  
Deep Learning ◽  
Partition Function ◽  
Error Rate ◽  
Learning Model ◽  
Accurate Approximation ◽  
Deep Learning Model ◽  
Goldbach’S Conjecture ◽  
Goldbach's Conjecture

Goldbach’s conjecture is one of the oldest and famous unproved problems in number theory. Using a deep learning model, we obtain an approximation of the Goldbach partition function, which counts the number of ways of representing an even number greater than 4 as a sum of two primes. We use residues of number modulo first 25 primes as features and archive a more accurate approximation, which reduces error rate from [Formula: see text] to [Formula: see text].

Simple Extensible Deep Learning Model for Automatic Arabic Diacritization

2022 ◽  
Vol 21 (2) ◽  
pp. 1-16
Author(s):  
Hamza Abbad ◽  
Shengwu Xiong
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Error Rate ◽  
Short Term Memory ◽  
Confusion Matrix ◽  
Learning Model ◽  
Model Errors ◽  
Sequence Elements ◽  
Arabic Natural Language Processing ◽  
Deep Learning Model

Automatic diacritization is an Arabic natural language processing topic based on the sequence labeling task where the labels are the diacritics and the letters are the sequence elements. A letter can have from zero up to two diacritics. The dataset used was a subset of the preprocessed version of the Tashkeela corpus. We developed a deep learning model composed of a stack of four bidirectional long short-term memory hidden layers of the same size and an output layer at every level. The levels correspond to the groups that we classified the diacritics into (short vowels, double case-endings, Shadda, and Sukoon). Before training, the data were divided into input vectors containing letter indexes and outputs vectors containing the indexes of diacritics regarding their groups. Both input and output vectors are concatenated, then a sliding window operation with overlapping is performed to generate continuous and fixed-size data. Such data is used for both training and evaluation. Finally, we realize some tests using the standard metrics with all of their variations and compare our results with two recent state-of-the-art works. Our model achieved 3% diacritization error rate and 8.99% word error rate when including all letters. We have also generated the confusion matrix to show the performances per output and analyzed the mismatches of the first 500 lines to classify the model errors according to their linguistic nature.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

Very short-term forecasting of wind power generation using hybrid deep learning model

2021 ◽  
Vol 296 ◽  
pp. 126564
Author(s):  
Md Alamgir Hossain ◽  
Ripon K. Chakrabortty ◽  
Sondoss Elsawah ◽  
Michael J. Ryan
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Power ◽  
Learning Model ◽  
Wind Power Generation ◽  
Short Term ◽  
Short Term Forecasting ◽  
Deep Learning Model
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