A content recommendation system for effective e-learning using embedded feature selection and fuzzy DT based CNN

Rosewelt, L. Antony; Renjit, J. Arokia

doi:10.3233/jifs-191721

Cited by 43 publications

(11 citation statements)

References 33 publications

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“…Table 1 demonstrates that the performance of the proposed product recommendation system is proved as better in terms of prediction or recommendation accuracy than the existing systems such as the Fuzzy recommendation system (6) , Recommendation system (21) , Fuzzy Recommendation System (9) , Irem and Sule (11) and Abolfazl et al (10) . The reason for the performance is the use of weighted semantic optimiser, and fuzzy temporal rules incorporated deep classifier.…”

Section: Resultsmentioning

confidence: 96%

“…Among them, the proposed system considers the precision value. Figure 2 shows the precision value analysis between the proposed product recommendation system and the existing recommendation systems such as Fuzzy recommendation system (6) , Recommendation system (21) , Fuzzy Recommendation System (9) , Irem and Sule (11) and Abolfazl et al (10) . Here, the five different sets of reviews such as 1000, 2000, 3000, 4000 and 5000 have been considered in the experiments E1, E2, E3, E4 and E5.…”

Section: Resultsmentioning

confidence: 99%

“…The reason for the performance is the use of weighted https://www.indjst.org/ semantic optimiser, and fuzzy temporal rules incorporated deep classifier. Table 1 shows the comparative analysis by considering the prediction or recommendation accuracy between the proposed product recommendation system and the existing systems such as Fuzzy recommendation system (6) , Recommendation system (21) , Fuzzy Recommendation System (9) , Irem and Sule (11) and Abolfazl et al (10) . Here, the five different sets of reviews such as 1000, 2000, 3000, 4000 and 5000 have been considered in the experiments E1, E2, E3, E4 and E5.…”

Section: Resultsmentioning

confidence: 99%

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Sentiment and Fuzzy Aware Product Recommendation System Using HOA and FT-DBN in E- Commerce

Manikandan¹,

Rämä²,

S³

2023

IJST

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Objectives: To identify and select the customers' liked products by introducing a new product recommendation system. Methods: This work proposes a new product recommendation system that incorporates a new feature optimization method called Sentiment weighted Horse herd Optimization Algorithm (SHOA) to identify the most suitable words that help perform effective prediction. This work's prediction process is carried out by applying a newly proposed Deep Belief Network incorporating fuzzy temporal features. This work uses two different Amazon datasets. The first dataset contains 51, 00,000 review comments about various products, including books and movies. The second dataset is built with 82,00,000 review comments on Toys and Games. These data sets consider the product id and review rate important features and are used to compare with all other available works through experimental results. Findings:The experiments have been conducted using the Amazon dataset and proved better than other recommendation systems in terms of effectiveness and efficiency through Precision, Recall, Serendipity and nDCG value. Novelty: The introduction of a new DBN with Fuzzy Temporal rules and the newly developed SHOA is novel in this work to recommend suitable products to the customer.

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Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Sentiment and Fuzzy Aware Product Recommendation System Using HOA and FT-DBN in E- Commerce

Manikandan¹,

Rämä²,

S³

2023

IJST

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“…Many scholars (Essadek & Rabeyron, 2020; Ghazawy et al, 2021; Nishimura et al, 2021; Rodríguez‐Rey et al, 2020; Villani et al, 2021; Wang, Hegde et al, 2020; Zhang et al, 2020) have used questionnaires to analyze the psychological impact of college students. Some scholars (Ansari et al, 2016; Antony Rosewelt et al, 2020; Dahdouh et al, 2019; Medio et al, 2020; Pariserum Perumal et al, 2019) have used artificial intelligence methods to study students' mental health, academic performance, and other performance and to analyze students' browsing behavior, reading characteristics, and hobbies. We believe that the integration of these two methods will produce better results.…”

Section: Discussionmentioning

confidence: 99%

“…As seen in Table 2, we have summarized five papers with the highest relevance to our study topic (Ansari et al, 2016;Antony Rosewelt et al, 2020;Dahdouh et al, 2019;Medio et al, 2020;Pariserum Perumal et al, 2019), indicating that the use of recommender systems in education has gradually gained popularity. The MoodleREC recommender system model was developed by Medio et al, 2020. To determine students' learning interests by the length of their stops in each module (Pariserum Perumal et al, 2019).…”

Section: Research Of Ai Recommendation System Applied In Education Fieldmentioning

confidence: 99%

Exploration of college students' psychological problems based on online education under COVID‐19

Cai

Wang

Zou

2023

Psychology in the Schools

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As a result of the COVID‐19 pandemic, many college students have been isolated at home and unable to walk into class as usual. This series of protective measures to avoid the spread of the disease may have an additional psychological impact on the lives of college students. The purpose of this study was to propose a strategy for using an intelligent online learning system based on content recommendations and electronic questionnaires in the educational domain. We invited 3000 isolated university students (47.6% male and 52.4% female) to an online trial. It proved to be effective in helping us intervene in students' psychological problems quickly, objectively, efficiently, and in real‐time. In addition, our analysis of the data collected from the intelligent online learning system showed that the degree to which college students' psychological problems were affected by isolation was closely related to students' grade level, family background, major category, and computer proficiency. The current study suggests that the mental health of college students should be well monitored during segregation. Targeted psychological counseling is more necessary for students in upper grades, low‐income families, liberal arts majors, and those with weak computer proficiency to reduce the emotional impact of segregation on students.

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An efficient framework for the similarity prediction with query recommendation in E‐learning system

Prasad

Muthappa²

2022

Concurrency and Computation

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Summary A novel model of data similarity estimation and clustering method is proposed in this article to retrieve the relevant data with the best matching in big data processing. An advanced model of graph distance pattern (GDP) method with lexical subgroup (LS) system is used to estimate the similarity between the query data and the entire database. With the help of neural network, the relevancy of feature attributes in the database are predicted and matching index is sorted to provide the recommended data for given query data. This was achieved by using the correlated sim‐neural network (CSNN). This is an enhanced model of neural network technology to find the relevancy based on the correlation factor of feature set. The training process of CSNN classifier is carried by estimating the correlation factor of the attributes of dataset. These are forms as the clusters and paged with proper indexing based on the LS parameter of similarity metric. The results obtained by the proposed system for recall, precision, accuracy, error rate, F‐measure, kappa coefficient, specificity, and MCC are 0.98, 0.98, 0.97, 0.03, 0.99, 0.991, 0.986, and 0.984, respectively.

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A content recommendation system for effective e-learning using embedded feature selection and fuzzy DT based CNN

Cited by 43 publications

References 33 publications

Sentiment and Fuzzy Aware Product Recommendation System Using HOA and FT-DBN in E- Commerce

Sentiment and Fuzzy Aware Product Recommendation System Using HOA and FT-DBN in E- Commerce

Exploration of college students' psychological problems based on online education under COVID‐19

An efficient framework for the similarity prediction with query recommendation in E‐learning system

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