Toward a prediction approach based on deep learning in Big Data analytics

Haddad, Omar; Fkih, Fethi; Omri, Mohamed Nazih

doi:10.1007/s00521-022-07986-9

Cited by 6 publications

(2 citation statements)

References 23 publications

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“…Normally, expressions of opinions written naturally by humans are texts that contain a lot of vagueness and noise [51][52][53]. The opinion orientation of personal text differs depending on the context or domain in which it is expressed.…”

Section: Discussionmentioning

confidence: 99%

Sentiment Analysis of Students’ Feedback on E-Learning Using a Hybrid Fuzzy Model

Alzaid,

Fkih

2023

Applied Sciences

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It is crucial to analyze opinions about the significant shift in education systems around the world, because of the widespread use of e-learning, to gain insight into the state of education today. A particular focus should be placed on the feedback from students regarding the profound changes they experience when using e-learning. In this paper, we propose a model that combines fuzzy logic with bidirectional long short-term memory (BiLSTM) for the sentiment analysis of students’ textual feedback on e-learning. We obtained this feedback from students’ tweets expressing their opinions about e-learning. There were some ambiguous characteristics in terms of the writing style and language used in the collected feedback. It was written informally and not in adherence to standardized Arabic language writing rules by using the Saudi dialects. The proposed model benefits from the capabilities of the deep neural network BiLSTM to learn and also from the ability of fuzzy logic to handle uncertainties. The proposed models were evaluated using the appropriate evaluation metrics: accuracy, F1-score, precision, and recall. The results showed the effectiveness of our proposed model and that it worked well for analyzing opinions obtained from Arabic texts written in Saudi dialects. The proposed model outperformed the compared models by obtaining an accuracy of 86% and an F1-score of 85%.

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Section: Discussionmentioning

confidence: 99%

Sentiment Analysis of Students’ Feedback on E-Learning Using a Hybrid Fuzzy Model

Alzaid,

Fkih

2023

Applied Sciences

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“…Tis is because typical data processing technology cannot handle enormous amounts of data. Te data analysis technique [14] and its frameworks for storing and processing huge data assist in overcoming the problems of analyzing diverse and complicated data to obtain business-relevant information [15].…”

Section: Introductionmentioning

confidence: 99%

A Novel Technique for Facial Recognition Based on the GSO-CNN Deep Learning Algorithm

Al-Abboodi,

Al-Ani

2024

Journal of Electrical and Computer Engineering

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Face recognition is one of the important elements that can be used for securing the facilities, emotion recognition, sentiment exploration, fraud analysis, and traffic pattern analysis. Intelligent face recognition has yielded excellent accuracy in a controlled environment whereas vice versa in an uncontrolled environment. However, conventional methods can no longer satisfy the demand at present due to their low recognition accuracy and restrictions on many occasions. This study proposed an optimal deep learning-based face recognition system that improves the security of the model developed in the IoT cloud environment. Initially, the dataset of images was gathered from the public repository. The captured images are explored using image processing techniques like image preprocessing employing the Gaussian filter technique for removing the noise and smoothing the image. The histogram of oriented gradients (HOGs) is used for the image segmentation. The processed images are preserved at the cloud service layer. Extract features were linked to facial activities using the spatial-temporal interest point (STIP). On the other hand, the extracted feature vectors are investigated using galactic swarm optimization (GSO) techniques that give optimized feature vectors. The necessary features are selected using the gray level co-occurrence matrix (GLCM), which separates the statistical texture features. The GSO output is fed into the deep convolutional neural network (DCNN) that effectively trains the captured face images. This will allow the effectiveness of the GSO-CNN technique to be assessed in terms of recognition accuracy, recall, precision, and error rate.

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