scholarly journals Polytechnic Students’ Academic Performance Prediction Based On Using Deep Neural Network

2021 ◽  
pp. 1-11
Author(s):  
S. M. Abdullah Al Shuaeb ◽  
Shamsul Alam ◽  
Md. Mizanur Rahman ◽  
Md. Abdul Matin
Keyword(s):  
Neural Network ◽  
Academic Performance ◽  
Performance Prediction ◽  
Deep Neural Network ◽  
Huge Amount ◽  
Novel Technique ◽  
Student Academic Performance ◽  
Academic Activities ◽  
Proposed Model ◽  
Prediction Systems

Students’ academic achievement plays a significant role in the polytechnic institute. It is an important task for the technical student to achieve good results. It becomes more challenging by virtue of the huge amount of data in the polytechnic student databases. Recently, the lack of monitoring of academic activities and their performance has not been harnessed. This is not a good way to evaluate the academic performance of polytechnic students in Bangladesh at present. The study on existing academic prediction systems is still not enough for the polytechnic institutions. Consequently, we have proposed a novel technique to improve student academic performance. In this study, we have used the deep neural network for predicting students' academic final marks. The main objective of this paper is to improve students' results. This paper also explains how the prediction deep neural network model can be used to recognize the most vital attributes in a student's academic data namely midterm_marks, class_ test, attendance, assignment, and target_ marks. By using the proposed model, we can more effectively improve polytechnic student achievement and success.

scholarly journals Deep Neural Network Model for Identification of Predictive Variables and Evaluation of Student’s Academic Performance

2021 ◽  
Vol 35 (5) ◽  
pp. 409-415
Author(s):  
Kandula Neha ◽  
Jahangeer Sidiq ◽  
Majid Zaman
Keyword(s):  
Neural Network ◽  
Academic Performance ◽  
Academic Success ◽  
Student Performance ◽  
Deep Neural Network ◽  
Individual Performance ◽  
Predictive Variables ◽  
Important Concern ◽  
Proposed Model ◽  
Final Exams

An important concern for students at all levels, from universities to colleges to junior high and high school, is predicting academic achievement and individual performance. Class tests, homework, lab exams, general tests, and final exams all have an impact on a student's academic success or failure. Students' progress can be assessed by looking at their grades in core subjects and electives. The majority of research, on the other hand, says that a student's achievement is best measured by graduation. Researchers set out to develop mathematical models that may be utilized to forecast student academic performance evaluations based on internal and external type predictive indicators. Multiple predictive variables are taken into account for the assessment of student performance while modelling an efficient template for student performance assessment. The proposed model uses Deep Neural Network (DNN) in the process of considering the predictive variables and evaluating student performance using the variables. The proposed model is compared with the traditional models and the results represent that the proposed model accuracy levels are high contrasted to existing models.

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.

scholarly journals Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1620 ◽  
Author(s):  
Ganjar Alfian ◽  
Muhammad Syafrudin ◽  
Norma Latif Fitriyani ◽  
Muhammad Anshari ◽  
Pavel Stasa ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Risk Factors ◽  
Diabetic Retinopathy ◽  
Prediction Model ◽  
Deep Neural Network ◽  
Early Stage ◽  
Individual Risk ◽  
Proposed Model ◽  
Individual Risk Factors

Extracting information from individual risk factors provides an effective way to identify diabetes risk and associated complications, such as retinopathy, at an early stage. Deep learning and machine learning algorithms are being utilized to extract information from individual risk factors to improve early-stage diagnosis. This study proposes a deep neural network (DNN) combined with recursive feature elimination (RFE) to provide early prediction of diabetic retinopathy (DR) based on individual risk factors. The proposed model uses RFE to remove irrelevant features and DNN to classify the diseases. A publicly available dataset was utilized to predict DR during initial stages, for the proposed and several current best-practice models. The proposed model achieved 82.033% prediction accuracy, which was a significantly better performance than the current models. Thus, important risk factors for retinopathy can be successfully extracted using RFE. In addition, to evaluate the proposed prediction model robustness and generalization, we compared it with other machine learning models and datasets (nephropathy and hypertension–diabetes). The proposed prediction model will help improve early-stage retinopathy diagnosis based on individual risk factors.

Challenging the Recognition of Facial Expression via Deep Learning

2014 ◽  
Vol 571-572 ◽  
pp. 717-720
Author(s):  
De Kun Hu ◽  
Yong Hong Liu ◽  
Li Zhang ◽  
Gui Duo Duan
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Gradient Descent ◽  
Deep Neural Network ◽  
Back Propagation ◽  
Stochastic Gradient Descent ◽  
Excellent Performance ◽  
Proposed Model ◽  
Input Layer ◽  
Fully Connected

A deep Neural Network model was trained to classify the facial expression in unconstrained images, which comprises nine layers, including input layer, convolutional layer, pooling layer, fully connected layers and output layer. In order to optimize the model, rectified linear units for the nonlinear transformation, weights sharing for reducing the complexity, “mean” and “max” pooling for subsample, “dropout” for sparsity are applied in the forward processing. With large amounts of hard training faces, the model was trained via back propagation method with stochastic gradient descent. The results of shows the proposed model achieves excellent performance.

scholarly journals Student’s Academic Performance Prediction Using Factor Analysis Based Neural Network

2019 ◽  
Vol 5 (4) ◽  
pp. 61
Author(s):  
Shamsuddeen Suleiman ◽  
Ahmad Lawal ◽  
Umar Usman ◽  
Shehu Usman Gulumbe ◽  
Aminu Bui Muhammad
Keyword(s):  
Neural Network ◽  
Factor Analysis ◽  
Academic Performance ◽  
Performance Prediction

scholarly journals Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 672 ◽  
Author(s):  
Lin Chen ◽  
Jianting Fu ◽  
Yuheng Wu ◽  
Haochen Li ◽  
Bin Zheng
Keyword(s):  
Neural Network ◽  
Gesture Recognition ◽  
Surface Electromyography ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Hand Gesture ◽  
Proposed Model ◽  
Electromyography Signals ◽  
Motion Intention ◽  
Semg Signals

By training the deep neural network model, the hidden features in Surface Electromyography(sEMG) signals can be extracted. The motion intention of the human can be predicted by analysis of sEMG. However, the models recently proposed by researchers often have a large number of parameters. Therefore, we designed a compact Convolution Neural Network (CNN) model, which not only improves the classification accuracy but also reduces the number of parameters in the model. Our proposed model was validated on the Ninapro DB5 Dataset and the Myo Dataset. The classification accuracy of gesture recognition achieved good results.

scholarly journals Filtering Reordering Table Using a Novel Recursive Autoencoder Model for Statistical Machine Translation

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Jinying Kong ◽  
Yating Yang ◽  
Lei Wang ◽  
Xi Zhou ◽  
Tonghai Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Machine Translation ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Statistical Machine Translation ◽  
Learning Problem ◽  
Proposed Model ◽  
Discriminative Model

In phrase-based machine translation (PBMT) systems, the reordering table and phrase table are very large and redundant. Unlike most previous works which aim to filter phrase table, this paper proposes a novel deep neural network model to prune reordering table. We cast the task as a deep learning problem where we jointly train two models: a generative model to implement rule embedding and a discriminative model to classify rules. The main contribution of this paper is that we optimize the reordering model in PBMT by filtering reordering table using a recursive autoencoder model. To evaluate the performance of the proposed model, we performed it on public corpus to measure its reordering ability. The experimental results show that our approach obtains high improvement in BLEU score with less scale of reordering table on two language pairs: English-Chinese (+0.28) and Uyghur-Chinese (+0.33) MT.

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