Does the acceptance of hybrid learning affect learning approaches in France?

Purpose: Acceptance of a learning technology affects students’ intention to use that technology, but the influence of the acceptance of a learning technology on learning approaches has not been investigated in the literature. A deep learning approach is important in the field of health, where links must be created between skills, knowledge, and habits. Our hypothesis was that acceptance of a hybrid learning model would affect students’ way of learning.Methods: We analysed these concepts, and their correlations, in the context of a flipped classroom method using a local learning management system. In a sample of all students within a single year of study in the midwifery program (n= 38), we used 3 validated scales to evaluate these concepts (the Study Process Questionnaire, My Intellectual Work Tools, and the Hybrid E-Learning Acceptance Model: Learner Perceptions).Results: Our sample had a positive acceptance of the learning model, but a neutral intention to use it. Students reported that they were distractible during distance learning. They presented a better mean score for the deep approach than for the superficial approach (P< 0.001), which is consistent with their declared learning strategies (personal reorganization of information; search and use of examples). There was no correlation between poor acceptance of the learning model and inadequate learning approaches. The strategy of using deep learning techniques was moderately correlated with acceptance of the learning model (rs= 0.42, P= 0.03).Conclusion: Learning approaches were not affected by acceptance of a hybrid learning model, due to the flexibility of the tool. However, we identified problems in the students’ time utilization, which explains their neutral intention to use the system.

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An Efficient DenseNet-Based Deep Learning Model for Malware Detection

Entropy ◽

10.3390/e23030344 ◽

2021 ◽

Vol 23 (3) ◽

pp. 344

Author(s):

Jeyaprakash Hemalatha ◽

S. Abijah Roseline ◽

Subbiah Geetha ◽

Seifedine Kadry ◽

Robertas Damaševičius

Keyword(s):

Deep Learning ◽

Detection System ◽

Malware Detection ◽

Class Imbalance ◽

Learning Model ◽

Computational Time ◽

Learning Approaches ◽

Security Threat ◽

Static And Dynamic Analysis ◽

Deep Learning Model

Recently, there has been a huge rise in malware growth, which creates a significant security threat to organizations and individuals. Despite the incessant efforts of cybersecurity research to defend against malware threats, malware developers discover new ways to evade these defense techniques. Traditional static and dynamic analysis methods are ineffective in identifying new malware and pose high overhead in terms of memory and time. Typical machine learning approaches that train a classifier based on handcrafted features are also not sufficiently potent against these evasive techniques and require more efforts due to feature-engineering. Recent malware detectors indicate performance degradation due to class imbalance in malware datasets. To resolve these challenges, this work adopts a visualization-based method, where malware binaries are depicted as two-dimensional images and classified by a deep learning model. We propose an efficient malware detection system based on deep learning. The system uses a reweighted class-balanced loss function in the final classification layer of the DenseNet model to achieve significant performance improvements in classifying malware by handling imbalanced data issues. Comprehensive experiments performed on four benchmark malware datasets show that the proposed approach can detect new malware samples with higher accuracy (98.23% for the Malimg dataset, 98.46% for the BIG 2015 dataset, 98.21% for the MaleVis dataset, and 89.48% for the unseen Malicia dataset) and reduced false-positive rates when compared with conventional malware mitigation techniques while maintaining low computational time. The proposed malware detection solution is also reliable and effective against obfuscation attacks.

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PlotMI: visualization of pairwise interactions and positional preferences learned by a deep learning model from sequence data

10.1101/2021.03.14.435285 ◽

2021 ◽

Author(s):

Tuomo Hartonen ◽

Teemu Kivioja ◽

Jussi Taipale

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Sequence Data ◽

Predictive Performance ◽

Learning Model ◽

Biological Research ◽

Learning Approaches ◽

Learning Models ◽

Model Interpretation ◽

Pairwise Interactions

Deep learning models have in recent years gained success in various tasks related to understanding information coded in the DNA sequence. Rapidly developing genome-wide measurement technologies provide large quantities of data ideally suited for modeling using deep learning or other powerful machine learning approaches. Although offering state-of-the art predictive performance, the predictions made by deep learning models can be difficult to understand. In virtually all biological research, the understanding of how a predictive model works is as important as the raw predictive performance. Thus interpretation of deep learning models is an emerging hot topic especially in context of biological research. Here we describe plotMI, a mutual information based model interpretation strategy that can intuitively visualize positional preferences and pairwise interactions learned by any machine learning model trained on sequence data with a defined alphabet as input. PlotMI is freely available at https://github.com/hartonen/plotMI.

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Comparison between Deep Learning and Tree-Based Machine Learning Approaches for Landslide Susceptibility Mapping

Water ◽

10.3390/w13192664 ◽

2021 ◽

Vol 13 (19) ◽

pp. 2664

Author(s):

Sunil Saha ◽

Jagabandhu Roy ◽

Tusar Kanti Hembram ◽

Biswajeet Pradhan ◽

Abhirup Dikshit ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Landslide Susceptibility ◽

Learning Model ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Learning Approaches ◽

Statistical Measures ◽

Deep Learning Model

The efficiency of deep learning and tree-based machine learning approaches has gained immense popularity in various fields. One deep learning model viz. convolution neural network (CNN), artificial neural network (ANN) and four tree-based machine learning models, namely, alternative decision tree (ADTree), classification and regression tree (CART), functional tree and logistic model tree (LMT), were used for landslide susceptibility mapping in the East Sikkim Himalaya region of India, and the results were compared. Landslide areas were delimited and mapped as landslide inventory (LIM) after gathering information from historical records and periodic field investigations. In LIM, 91 landslides were plotted and classified into training (64 landslides) and testing (27 landslides) subsets randomly to train and validate the models. A total of 21 landslide conditioning factors (LCFs) were considered as model inputs, and the results of each model were categorised under five susceptibility classes. The receiver operating characteristics curve and 21 statistical measures were used to evaluate and prioritise the models. The CNN deep learning model achieved the priority rank 1 with area under the curve of 0.918 and 0.933 by using the training and testing data, quantifying 23.02% and 14.40% area as very high and highly susceptible followed by ANN, ADtree, CART, FTree and LMT models. This research might be useful in landslide studies, especially in locations with comparable geophysical and climatological characteristics, to aid in decision making for land use planning.

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Mixed Approaches to Learning in the Flipped Classroom: How Students Approach the Learning Environment

The Canadian Journal for the Scholarship of Teaching and Learning ◽

10.5206/cjsotl-rcacea.2020.1.8098 ◽

2020 ◽

Vol 11 (1) ◽

Author(s):

Margrét Sigrún Sigurðardóttir ◽

Thamar Melanie Heijstra

Keyword(s):

Deep Learning ◽

Active Learning ◽

Learning Environment ◽

Learning Process ◽

Flipped Classroom ◽

Approaches To Learning ◽

Male Students ◽

Learning Approach ◽

Learning Approaches ◽

Surface Approach

Flipped teaching is a trend within higher education. Through flipped teaching the learning environment can be altered by moving the lecture out of the classroom through online recordings, while in-classroom sessions focus on active learning and engaging students in their own learning process. In this paper, we used focus groups comprised of male students in a qualitative research course with the aim of understanding the ways in which we might improve active student engagement and motivation within the flipped classroom. The findings indicated that, within the flipped classroom, students mix surface and deep-learning approaches. The online recordings, which students interact with through a surface approach, can function as a stepping stone toward a deep-learning approach to in-class activities, but only if students come to class prepared. The findings therefore suggest that students must be made aware of the importance of preparation prior to flipped classroom in-class activities to ensure the active learning process is successful. By not listening to the recordings (e.g., due to technological failure, as was the case in this study), students can result in only employing a surface approach.

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A survey on Deep Learning Based Eye Gaze Estimation Methods

Journal of Innovative Image Processing - December 2019 ◽

10.36548/jiip.2021.3.003 ◽

2021 ◽

Vol 3 (3) ◽

pp. 190-207

Author(s):

S. K. B. Sangeetha

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Near Infrared ◽

Eye Gaze ◽

Infrared Image ◽

Estimation Methods ◽

Learning Technology ◽

Learning Approaches ◽

Gaze Tracking ◽

Inference Models

In recent years, deep-learning systems have made great progress, particularly in the disciplines of computer vision and pattern recognition. Deep-learning technology can be used to enable inference models to do real-time object detection and recognition. Using deep-learning-based designs, eye tracking systems could determine the position of eyes or pupils, regardless of whether visible-light or near-infrared image sensors were utilized. For growing electronic vehicle systems, such as driver monitoring systems and new touch screens, accurate and successful eye gaze estimates are critical. In demanding, unregulated, low-power situations, such systems must operate efficiently and at a reasonable cost. A thorough examination of the different deep learning approaches is required to take into consideration all of the limitations and opportunities of eye gaze tracking. The goal of this research is to learn more about the history of eye gaze tracking, as well as how deep learning contributed to computer vision-based tracking. Finally, this research presents a generalized system model for deep learning-driven eye gaze direction diagnostics, as well as a comparison of several approaches.

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Efficient Brain Tumor Detection Based on Deep Learning Models

Journal of Physics Conference Series ◽

10.1088/1742-6596/2128/1/012012 ◽

2021 ◽

Vol 2128 (1) ◽

pp. 012012

Author(s):

Mohamed R. Shoaib ◽

Mohamed R. Elshamy ◽

Taha E. Taha ◽

Adel S. El-Fishawy ◽

Fathi E. Abd El-Samie

Keyword(s):

Deep Learning ◽

Brain Tumor ◽

Transfer Learning ◽

False Positive Rate ◽

Disease Diagnosis ◽

Learning Model ◽

Practical Implementation ◽

Learning Technology ◽

Matthew’S Correlation Coefficient ◽

Sensitivity Specificity

Abstract Brain tumor is an acute cancerous disease that results from abnormal and uncontrollable cell division. Brain tumors are classified via biopsy, which is not normally done before the brain ultimate surgery. Recent advances and improvements in deep learning technology helped the health industry in getting accurate disease diagnosis. In this paper, a Convolutional Neural Network (CNN) is adopted with image pre-processing to classify brain Magnetic Resonance (MR) images into four classes: glioma tumor, meningioma tumor, pituitary tumor and normal patients, is provided. We use a transfer learning model, a CNN-based model that is designed from scratch, a pre-trained inceptionresnetv2 model and a pre-trained inceptionv3 model. The performance of the four proposed models is tested using evaluation metrics including accuracy, sensitivity, specificity, precision, F1_score, Matthew’s correlation coefficient, error, kappa and false positive rate. The obtained results show that the two proposed models are very effective in achieving accuracies of 93.15% and 91.24% for the transfer learning model and BRAIN-TUMOR-net based on CNN, respectively. The inceptionresnetv2 model achieves an accuracy of 86.80% and the inceptionv3 model achieves an accuracy of 85.34%. Practical implementation of the proposed models is presented.

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Using deep learning for trajectory classification in imbalanced dataset

The International FLAIRS Conference Proceedings ◽

10.32473/flairs.v34i1.128368 ◽

2021 ◽

Vol 34 (1) ◽

Author(s):

Nicksson Ckayo Arrais de Freitas ◽

Ticiana L. Coelho Da Silva ◽

José Antônio Fernandes De Macêdo ◽

Leopoldo Melo Júnioer

Keyword(s):

Deep Learning ◽

Classification Problem ◽

Learning Model ◽

Learning Approaches ◽

Imbalanced Dataset ◽

Imbalanced Datasets ◽

Learning Techniques ◽

Trajectory Classification ◽

Low Performance ◽

Deep Learning Model

Deep learning has gained much popularity in the past years due to GPU advancements, cloud computing improvements, and its supremacy, considering the accuracy results when trained on massive datasets. As with machine learning, deep learning models may experience low performance when handled with imbalanced datasets. In this paper, we focus on the trajectory classification problem, and we examine deep learning techniques for coping with imbalanced class data. We extend a deep learning model, called DeepeST (Deep Learning for Sub-Trajectory classification), to predict the class or label for sub-trajectories from imbalanced datasets. DeepeST is the first deep learning model for trajectory classification that provides approaches for coping with imbalanced dataset problems from the authors' knowledge. In this paper, we perform the experiments with three real datasets from LBSN (Location-Based Social Network) trajectories to identify who is the user of a sub-trajectory (similar to the Trajectory-User Linking problem). We show that DeepeST outperforms other deep learning approaches from state-of-the-art concerning the accuracy, precision, recall, and F1-score.

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Data Augmentation and Transfer Learning Strategies for Reaction Prediction in Low Chemical Data Regimes

10.26434/chemrxiv.13383275 ◽

2020 ◽

Author(s):

Yun Zhang ◽

Ling Wang ◽

Xinqiao Wang ◽

Chengyun Zhang ◽

Jiamin Ge ◽

...

Keyword(s):

Deep Learning ◽

Learning Strategies ◽

Transfer Learning ◽

Data Augmentation ◽

Learning Strategy ◽

Predictive Performance ◽

Learning Model ◽

Training Data ◽

Reaction Prediction ◽

Transformer Model

Abstract: Effective and rapid deep learning method to predict chemical reactions contributes to the research and development of organic chemistry and drug discovery. Despite the outstanding capability of deep learning in retrosynthesis and forward synthesis, predictions based on small chemical datasets generally result in low accuracy due to an insufficiency of reaction examples. Here, we introduce a new state art of method, which integrates transfer learning with transformer model to predict the outcomes of the Baeyer-Villiger reaction which is a representative small dataset reaction. The results demonstrate that introducing transfer learning strategy markedly improves the top-1 accuracy of the transformer-transfer learning model (81.8%) over that of the transformer-baseline model (58.4%). Moreover, we further introduce data augmentation to the input reaction SMILES, which allows for better performance and improves the accuracy of the transformer-transfer learning model (86.7%). In summary, both transfer learning and data augmentation methods significantly improve the predictive performance of transformer model, which are powerful methods used in chemistry field to eliminate the restriction of limited training data.

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Data Augmentation and Transfer Learning Strategies for Reaction Prediction in Low Chemical Data Regimes

10.26434/chemrxiv.13383275.v1 ◽

2020 ◽

Author(s):

Yun Zhang ◽

Ling Wang ◽

Xinqiao Wang ◽

Chengyun Zhang ◽

Jiamin Ge ◽

...

Keyword(s):

Deep Learning ◽

Learning Strategies ◽

Transfer Learning ◽

Data Augmentation ◽

Learning Strategy ◽

Predictive Performance ◽

Learning Model ◽

Training Data ◽

Reaction Prediction ◽

Transformer Model

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Transfer Learning Strategies for Deep Learning-based PHM Algorithms

Applied Sciences ◽

10.3390/app10072361 ◽

2020 ◽

Vol 10 (7) ◽

pp. 2361

Author(s):

Fan Yang ◽

Wenjin Zhang ◽

Laifa Tao ◽

Jian Ma

Keyword(s):

Big Data ◽

Deep Learning ◽

Learning Strategies ◽

Transfer Learning ◽

High Speed ◽

Health Management ◽

Learning Algorithm ◽

Learning Technology ◽

Deep Learning Algorithm ◽

Transfer Strategies

As we enter the era of big data, we have to face big data generated by industrial systems that are massive, diverse, high-speed, and variability. In order to effectively deal with big data possessing these characteristics, deep learning technology has been widely used. However, the existing methods require great human involvement that is heavily depend on domain expertise and may thus be non-representative and biased from task to similar task, so for a wide variety of prognostic and health management (PHM) tasks, how to apply the developed deep learning algorithms to similar tasks to reduce the amount of development and data collection costs has become an urgent problem. Based on the idea of transfer learning and the structures of deep learning PHM algorithms, this paper proposes two transfer strategies via transferring different elements of deep learning PHM algorithms, analyzes the possible transfer scenarios in practical application, and proposes transfer strategies applicable in each scenario. At the end of this paper, the deep learning algorithm of bearing fault diagnosis based on convolutional neural networks (CNN) is transferred based on the proposed method, which was carried out under different working conditions and for different objects, respectively. The experiments verify the value and effectiveness of the proposed method and give the best choice of transfer strategy.

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