Deep neural network based m-learning model for predicting mobile learners’ performance

Adnan, Muhammad; Habib, Asad; Ashraf, Jawad; Mussadiq, Shafaq; Raza, Arsalan Ali

doi:10.3906/elk-1907-8

Cited by 6 publications

(8 citation statements)

References 34 publications

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“…The activation function used in the last dense layer is sigmoid, which outputs probabilities of both the classes. Dropout is used between convolution 1D layer and max pooling layer and then between last two dense layers [37]. Its step-wise description is given in Algorithm 1. end for 15: end procedure…”

Section: ) Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Stacked Deep Dense Neural Network Model to Predict Alzheimer’s Dementia Using Audio Transcript Data

et al. 2022

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Alzheimer's disease (AD) is caused by cortical degeneration leading to memory loss and dementia. A possible criterion for the early identification of Alzheimer's dementia is to identify the difference between positive and negative linguistic and cognitive abilities of the patients. This study involves the use of Convolutional Neural Network (CNN), designed a hybrid model with CNN & Bidirectional Long-Short Term Memory (Bidirectional LSTM), and proposed a Stacked Deep Dense Neural Network (SDDNN) model for text classification and prediction of Alzheimer's dementia. These models were trained end-to-end using DementiaBank clinical transcript dataset. The transcripts consisted of recorded interviews of Alzheimer's patients with clinical experts. The models were investigated under two settings: Randomly initialized and Glove embedding. Further, hyperparameter optimization was accomplished using GridSearch, which yielded optimal parameters for the design of suitable learning models for most accurate predictions. Other parameters were computed and compared based on AUC, accuracy, specificity, precision, F1 score, and recall. To ensure performance generalization, the classification accuracy was tested using 10-fold cross-validation approach. The performance and classification accuracy of the proposed model was significantly improved to 93.31% when applied with Glove embedding and hyperparameter tuning. This research work will considerably help the clinical experts in early detection and diagnosis of AD.

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Section: ) Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Stacked Deep Dense Neural Network Model to Predict Alzheimer’s Dementia Using Audio Transcript Data

et al. 2022

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“…Mobile devices can help students in learning a foreign language in those situations where the several challenges and students are not aware of the new language [25]. M-learning can provide optimal conditions for students to enhance their engagement while learning the target language.…”

Section: Literature Reviewmentioning

confidence: 99%

Memory Load and Performance-based Adaptive Smartphone E-learning Framework for E-commerce Applications in Online Learning

Alzahrani¹,

Alzahrani²,

Adnan³

et al. 2022

Journal of Internet Technology

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<p>The term e-commerce is not confined to the purchase and sale of goods only. There are several occasions where students are not able to comprehend any idea on their own. With the availability of online learning platforms such as Massive Open Online Courses (MOOCs) and Virtual Learning Environments (VLEs), students may visit online Internet sites, where various articles are made available with a single click. With mobile devices in their hands, they can also listen to the presentations of different academics from anywhere and at any time. In an online learning environment, it is challenging to provide tailored learning content to students that fulfill their needs and requirement. Therefore, we in this research paper propose an adaptive smartphone learning framework. The framework first considers students’ academic performance, preferred learning content, and memory load i.e., cognitive characteristics. Based on students’ academic performance, preferred learning content, and memory load, appropriate adaptive and motivational triggers are sent on students’ smartphones to guide and motivate them in their learning process. We have introduced ten types of adaptive triggers that in different scenarios will be presented to students to help them in remembering important learning points. Furthermore, performance-based motivational triggers in connection with adaptive triggers are also introduced. These motivational triggers are sent on students’ smartphones for giving them a suggestion, hope, appreciation, and warnings during their studies. In this study, we also show how different students’ learning scenarios are created and how our framework deals with those scenarios in students’ learning process.</p> <p> </p>

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“…As stated previously, RF algorithm suffers from determination of the optimal hyper-parameter and in this study the RF and PR are integrated with ICO for improving the results and develop robust algorithms. It is already reported that each tree in a RF model can grow incorrectly and reduced the prediction accuracy of the model (Adnan et al 2019) node are two operators from these hyper-parameter which significantly affect the RF prediction power. To this end, ICO algorithm was implemented to determine the best subset of features in RF model to enhance the result.…”

Section: Iterative Classifier Optimizermentioning

confidence: 99%

Iterative classifier optimizer-based pace regression and random forest hybrid models for suspended sediment load prediction

Meshram

Safari

Khosravi

et al. 2020

Environ Sci Pollut Res

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Suspended sediment load is a substantial portion of the total sediment load in rivers and plays a vital role in determination of the service life of the downstream dam. To this end, estimation models are needed to compute suspended sediment load in rivers. The application of artificial intelligence (AI) techniques has become popular in water resources engineering for solving complex problems such as sediment transport modeling. In this study, novel integrative intelligence models coupled with iterative classifier optimizer (ICO) are proposed to compute suspended sediment load in Simga station in Seonath river basin, Chhattisgarh State, India. The proposed models are hybridization of the random forest (RF) and pace regression (PR) models with the iterative classifier optimizer (ICO) algorithm to develop ICO-RF and ICO-PR hybrid models. The recommended models are established using the discharge and sediment daily data spanning a 35-year period . The accuracy of the developed models is examined in terms of error; by root mean square error (RMSE) and mean absolute error (MAE); and based on a correlation index of determination coefficient (R 2 ). The proposed novel hybrid models of ICO-RF and ICO-PR have been found to be more precise than their stand-alone counterparts of RF and PR. Overall, ICO-RF models delivered better accuracy than their alternatives. The results of this analysis tend to claim the appropriateness of the implemented methodology for precise modeling of the suspended sediment load in rivers.

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Deep neural network based m-learning model for predicting mobile learners’ performance

Cited by 6 publications

References 34 publications

Stacked Deep Dense Neural Network Model to Predict Alzheimer’s Dementia Using Audio Transcript Data

Stacked Deep Dense Neural Network Model to Predict Alzheimer’s Dementia Using Audio Transcript Data

Memory Load and Performance-based Adaptive Smartphone E-learning Framework for E-commerce Applications in Online Learning

Iterative classifier optimizer-based pace regression and random forest hybrid models for suspended sediment load prediction

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