To Review and Compare Evolutionary Algorithms in Optimization of Distributed Database Query

Abdalla, Mohammed Hussein; Karabatak, Murat

doi:10.1109/isdfs49300.2020.9116418

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…One is obtained by calculating the evolutionary moment. This algorithm is used for deep learning [38]. Adam combines the benefits of two other random descending slope extensions with the AdaGrad Algorithm, which maintains the speed of learning in each parameter, improving performance on scattered slope problems [39].…”

Section: Fig 4 Shows Zero Padding In the Proposed Methodsmentioning

confidence: 99%

A New Model for Emotions Analysis in Social Network Text Using Ensemble Learning and Deep learning

et al. 2022

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Recently, emotion analysis has become widely used. Therefore, increasing the accuracy of existing methods has become a challenge for researchers. The proposed method in this paper is a hybrid model to improve the accuracy of emotion analysis; Which uses a combination of convolutional neural network and ensemble learning. In the proposed method, after receiving the dataset, the data is pre-processed and converted into process able samples. Then the new dataset is split into two categories of training and test. The proposed model is a structure for machine learning in the form of ensemble learning. It contains blocks consisting of a combination of convolutional networks and basic classification algorithms. In each convolutional network, the base classification algorithms replace the fully connected layer. Evaluate the proposed method, in IMDB, PL04 and SemEval dataset with accuracy, precision, recall and F1 criteria, shows that, on average, for all three datasets, the precision of polarity detection is 90%, the recall of polarity detection is 93%, the F1 of polarity detection is 91% and finally the accuracy of polarity detection is 92%.

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Section: Fig 4 Shows Zero Padding In the Proposed Methodsmentioning

confidence: 99%

A New Model for Emotions Analysis in Social Network Text Using Ensemble Learning and Deep learning

et al. 2022

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“…Methods based on machine learning and deep learning algorithms have been used in these endeavors. It's been found that deep learning outperforms these two methods even if they have yielded promising results [13]. Based on emotion, monkeypox tweets have been classified and displayed [14].…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis Based on Hybrid Neural Network Techniques Using Binary Coordinate Ascent Algorithm

Abdalla,

Majidpour,

Rasul

et al. 2023

IEEE Access

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Sentiment analysis is a technique for determining whether data is positive, negative, or neutral using Natural Language Processing (NLP). The particular challenge in classifying huge amounts of data is that it takes a long time and requires the employment of specialist human resources. Various deep learning techniques have been employed by different researchers to train and classify different datasets with varying outcomes. However, the results are not satisfactory. To address this challenge, this paper proposes a novel Sentiment Analysis approach based on Hybrid Neural Network Techniques. The preprocessing step is first applied to the Amazon Fine Food Reviews dataset in our architecture, which includes a number of data cleaning and text normalization techniques. The word embedding technique is then used to capture the semantics of the input by clustering semantically related inputs in the embedding space on the cleaned dataset. Finally, generated features were classified using three different deep learning techniques, including Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Hybrid CNN-RNN models, in two different ways, with each technique as follows: classification on the original feature set and classification on the reduced feature set based on Binary Coordinate Ascent (BCA) and Optimal Coordinate Ascent (OCA). The experimental results show that a hybrid CNN-RNN with the BCA and OCA algorithms outperforms state-of-the-art methods with 97.91% accuracy.

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A Genetic Algorithm Model for Join Order Query Optimization in Hadoop-Hive Framework

Jain,

Tiwari

2024

Lecture Notes in Networks and Systems

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To Review and Compare Evolutionary Algorithms in Optimization of Distributed Database Query

Cited by 4 publications

References 12 publications

A New Model for Emotions Analysis in Social Network Text Using Ensemble Learning and Deep learning

A New Model for Emotions Analysis in Social Network Text Using Ensemble Learning and Deep learning

Sentiment Analysis Based on Hybrid Neural Network Techniques Using Binary Coordinate Ascent Algorithm

A Genetic Algorithm Model for Join Order Query Optimization in Hadoop-Hive Framework

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