scholarly journals Deep Learning and Collaborative Filtering-Based Methods for Students’ Performance Prediction and Course Recommendation

2021 ◽  
Vol 2021 ◽  
pp. 1-13 ◽  
Author(s):  
Jinyang Liu ◽  
Chuantao Yin ◽  
Yuhang Li ◽  
Honglu Sun ◽  
Hong Zhou
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Collaborative Filtering ◽  
Performance Prediction ◽  
Good Effect ◽  
Course Selection ◽  
Hybrid Prediction ◽  
Proposed Model ◽  
Student’S Performance ◽  
Vocational Learning

At the beginning of a new semester, due to the limited understanding of the new courses, it is difficult for students to make predictive choices about the courses of the current semester. In order to help students solve this problem, this paper proposed a hybrid prediction model based on deep learning and collaborative filtering. The proposed model can automatically generate personalized suggestions about courses in the next semester to assist students in course selection. The two important tasks of this study are course recommendation and student ranking prediction. First, we use a user-based collaborative filtering model to give a list of recommended courses by calculating the similarity between users. Then, for the courses in the list, we use a hybrid prediction model to predict the student’s performance in each course, that is, ranking prediction. Finally, we will give a list of courses that the student is good at or not good at according to the predicted ranking of the courses. Our method is evaluated on students’ data from two departments of our university. Through experiments, we compared the hybrid prediction model with other nonhybrid models and confirmed the good effect of our model. By using our model, students can refer to the different recommendation lists given and choose courses that they may be interested in and good at. The proposed method can be widely applied in Internet of Things and industrial vocational learning systems.

ACO_NB-Based Hybrid Prediction Model for Medical Disease Diagnosis

2021 ◽  
pp. 526-536
Author(s):  
Amit Kumar ◽  
Manish Kumar ◽  
Nidhya R.
Keyword(s):  
Prediction Model ◽  
Disease Diagnosis ◽  
Second Phase ◽  
Data Sets ◽  
Feature Subset ◽  
Hybrid Prediction ◽  
Related Data ◽  
Medical Disease ◽  
Proposed Model ◽  
Two Phases

In recent years, a huge increase in the demand of medically related data is reported. Due to this, research in medical disease diagnosis has emerged as one of the most demanding research domains. The research reported in this chapter is based on developing an ACO (ant colony optimization)-based Bayesian hybrid prediction model for medical disease diagnosis. The proposed model is presented in two phases. In the first phase, the authors deal with feature selection by using the application of a nature-inspired algorithm known as ACO. In the second phase, they use the obtained feature subset as input for the naïve Bayes (NB) classifier for enhancing the classification performances over medical domain data sets. They have considered 12 datasets from different organizations for experimental purpose. The experimental analysis advocates the superiority of the presented model in dealing with medical data for disease prediction and diagnosis.

scholarly journals A Deep Learning Prediction Model Based on Extreme-Point Symmetric Mode Decomposition and Cluster Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Guohui Li ◽  
Songling Zhang ◽  
Hong Yang
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Extreme Point ◽  
Prediction Models ◽  
Original Data ◽  
Symmetric Mode ◽  
Intrinsic Mode Functions ◽  
Model Based ◽  
Mode Decomposition ◽  
Proposed Model

Aiming at the irregularity of nonlinear signal and its predicting difficulty, a deep learning prediction model based on extreme-point symmetric mode decomposition (ESMD) and clustering analysis is proposed. Firstly, the original data is decomposed by ESMD to obtain the finite number of intrinsic mode functions (IMFs) and residuals. Secondly, the fuzzy c-means is used to cluster the decomposed components, and then the deep belief network (DBN) is used to predict it. Finally, the reconstructed IMFs and residuals are the final prediction results. Six kinds of prediction models are compared, which are DBN prediction model, EMD-DBN prediction model, EEMD-DBN prediction model, CEEMD-DBN prediction model, ESMD-DBN prediction model, and the proposed model in this paper. The same sunspots time series are predicted with six kinds of prediction models. The experimental results show that the proposed model has better prediction accuracy and smaller error.

scholarly journals Multivariate Grey Prediction Model with Priority Accumulation of New Information

2021 ◽  
Vol 248 ◽  
pp. 02009
Author(s):  
Lanxi Zhang
Keyword(s):  
Prediction Model ◽  
Absolute Error ◽  
Good Effect ◽  
Grey Prediction ◽  
Grey System ◽  
Engineering Project ◽  
Average Percentage ◽  
Grey Prediction Model ◽  
New Information ◽  
Proposed Model

According to the new information priority principle of grey system, this paper tries to optimize the traditional multivariate grey prediction model. Firstly, the basic theory of the traditional grey prediction model is put forward. Based on this, the background value is improved by using the new information priority principle, and the cumulative generation with parameters is defined. Taking the settlement trend of A4# building of an engineering project in Anhui province as an example, the model is applied to the settlement analysis, and the proposed model is compared with the existing grey prediction model, the average percentage absolute error between the predicted value and the observed value is calculated, and the regression graphs of each model are drawn. Through the analysis, we can see that the established model has achieved a good effect, and then verified the practicability and reliability of the proposed model.

Enhancing Resiliency Feature in Smart Grids through a Deep Learning Based Prediction Model

2020 ◽  
Vol 13 (3) ◽  
pp. 508-518
Author(s):  
Abderrazak Khediri ◽  
Mohamed Ridda Laouar ◽  
Sean B. Eom
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Smart Grids ◽  
Deep Belief Network ◽  
Power Grids ◽  
Economic Losses ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Belief Network ◽  
Proposed Model

Background: Enhancing the resiliency of electric power grids is becoming a crucial issue due to the outages that have recently occurred. One solution could be the prediction of imminent failure that is engendered by line contingency or grid disturbances. Therefore, a number of researchers have initiated investigations to generate techniques for predicting outages. However, extended blackouts can still occur due to the frailty of distribution power grids. Objective: This paper implements a proactive prediction model based on deep-belief networks to predict the imminent outages using previous historical blackouts, trigger alarms, and suggest solutions for blackouts. These actions can prevent outages, stop cascading failures and diminish the resulting economic losses. Methods: The proposed model is divided into three phases: A, B and C. The first phase (A) represents the initial segment that collects and extracts data and trains the deep belief network using the collected data. Phase B defines the Power outage threshold and determines whether the grid is in a normal state. Phase C involves detecting potential unsafe events, triggering alarms and proposing emergency action plans for restoration. Results: Different machine learning and deep learning algorithms are used in our experiments to validate our proposition, such as Random forest, Bayesian nets and others. Deep belief Networks can achieve 97.30% accuracy and 97.06% precision. Conclusion: The obtained findings demonstrate that the proposed model would be convenient for blackouts’ prediction and that the deep-belief network represents a powerful deep learning tool that can offer plausible results.

scholarly journals Agricultural Product Recommendation Model based on BMF

2020 ◽  
Vol 5 (2) ◽  
pp. 415-424
Author(s):  
Fucheng Wan ◽  
Dengyun Zhu ◽  
Xiangzhen He ◽  
Qi Guo ◽  
Dongjiao Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Collaborative Filtering ◽  
Language Model ◽  
Agricultural Product ◽  
Vector Construction ◽  
Proposed Model ◽  
Memory Network ◽  
Scoring Matrix ◽  
Short Time ◽  
Matrix Factorisation

AbstractIn this article, based on the collaborative deep learning (CDL) and convolutional matrix factorisation (ConvMF), the language model BERT is used to replace the traditional word vector construction method, and the bidirectional long–short time memory network Bi-LSTM is used to construct an improved collaborative filtering model BMF, which not only solves the phenomenon of ‘polysemy’, but also alleviates the problem of sparse scoring matrix data. Experiments show that the proposed model is effective and superior to CDL and ConvMF. The trained MSE value is 1.031, which is 9.7% lower than ConvMF.

scholarly journals An Improved Early Student’s Academic Performance Prediction Using Deep Learning

2021 ◽  
Vol 16 (12) ◽  
pp. 108
Author(s):  
Nida Muhammad Aslam ◽  
Irfan Ullah Khan ◽  
Leena H. Alamri ◽  
Ranim S. Almuslim
Keyword(s):  
Academic Performance ◽  
Deep Learning ◽  
Performance Prediction ◽  
Early Prediction ◽  
Huge Amount ◽  
Data Set ◽  
Evaluation Measures ◽  
Learning Techniques ◽  
Proposed Model ◽  
Deep Learning Model

Nowadays due to technological revolution huge amount of data is generated in every fields including education as well. Extracting the useful insights from consequential data is a very critical task. Moreover, advancement in the deep learning techniques resulted in the effective prediction and analysis of data. In our proposed study deep learning model is be used for predicting the student’s academic performance. Experiments were performed using the two courses da-ta i.e., mathematics and Portuguese course. The data set contains demograph-ic, social, educational and students course grade data. The data set suffers from the imbalance, SMOTE (synthetic minority oversampling technique) is used. We evaluate the performance of the proposed model using several fea-ture sets and evaluation measures such as precision, recall, F-score, and ac-curacy. The result showed the significance of the proposed deep learning mod-el in early prediction of the students’ academic performance. The model achieved an accuracy of 0.964 for Portuguese course data set and 0.932 using mathematics course data set. Similarly, the precision of 0.99 for Portuguese and 0.94 for mathematics.

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