A Semantic Approach for Fashion Recommendation Using Logistic Regression and Ontologies

Yethindra, D. Naga; Deepak, Gerard

doi:10.1109/icses52305.2021.9633891

Cited by 24 publications

(1 citation statement)

References 7 publications

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“…The Table II presents a comparative analysis of different methods based on their performance metrics, including accuracy, precision, recall, and F1-score. Each row corresponds to a specific method, such as SNN & CNN [17] , Logistic Regression [18], CBRNN [19], and the proposed GRU-BERT model. Accuracy refers to the overall correctness of the model's predictions, while precision measures the ratio of correctly predicted positive cases to the total predicted positive cases.…”

Section: A Performance Evaluationmentioning

confidence: 99%

Advancing Automated and Adaptive Educational Resources Through Semantic Analysis with BERT and GRU in English Language Learning

Jayakumar,

Rajakumari,

Sarwar

et al. 2024

IJACSA

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Semantics describe how language and its constituent parts are understood or interpreted. Semantic analysis is the computer analysis of language to derive connections, meaning, and context from words and sentences. In English language learning, dynamic content generation entails developing instructional materials that adjust to the specific requirements of each student, delivering individualized and contextually appropriate information to boost understanding and engagement. To tailor instructional materials to the various requirements of students, dynamic content creation is essential in English language learning (ELL). This work is a unique method for automatic and adaptive content production in ELL that uses Gated Recurrent Unit (GRU) and Bidirectional Encoder Representations from Transformers (BERT) together. The suggested approach uses BERT enabling content selection, adaption, and adaptive educational content production, and GRU for semantic extraction of features and contextual information captured from textual input. The article presents a novel approach to creating automated and adaptable educational tools for ELL that uses GRU for semantic feature extraction. Using persuasive essays collected in the PERSUADE 2.0 corpus annotated with discourse components and competency scores, this is an extensive dataset. After extensive testing, this approach shows outstanding outcomes, with high accuracy reaching 97% when compared to the current Spiking Neural Network (SNN) &Convolutional Neural Network (CNN), Logistic Regression (LR), and Convolutional Bidirectional Recurrent Neural Network (CBRNN). Python is used to implement the suggested work. The suggested strategy improves ELL engagement and understanding by providing individualized, contextually appropriate learning resources to each student. In addition, the flexibility of the system allows for real-time modifications to suit the changing needs and preferences of the learners. By providing instructors and students in a variety of educational contexts with a scalable and effective approach, this study advances automated content development in ELL. The model architecture will be improved in the future, along with the application's expansion into other domains outside of ELL and the investigation of new language aspects.

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Section: A Performance Evaluationmentioning

confidence: 99%