scholarly journals DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Sofia B. Dias ◽  
Sofia J. Hadjileontiadou ◽  
José Diniz ◽  
Leontios J. Hadjileontiadis
Keyword(s):  
Online Learning ◽  
Deep Learning ◽  
Predictive Model ◽  
Short Term Memory ◽  
Ground Truth ◽  
Face To Face ◽  
Learning Techniques ◽  
Learning Plans ◽  
First Time

AbstractCoronavirus (Covid-19) pandemic has imposed a complete shut-down of face-to-face teaching to universities and schools, forcing a crash course for online learning plans and technology for students and faculty. In the midst of this unprecedented crisis, video conferencing platforms (e.g., Zoom, WebEx, MS Teams) and learning management systems (LMSs), like Moodle, Blackboard and Google Classroom, are being adopted and heavily used as online learning environments (OLEs). However, as such media solely provide the platform for e-interaction, effective methods that can be used to predict the learner’s behavior in the OLEs, which should be available as supportive tools to educators and metacognitive triggers to learners. Here we show, for the first time, that Deep Learning techniques can be used to handle LMS users’ interaction data and form a novel predictive model, namely DeepLMS, that can forecast the quality of interaction (QoI) with LMS. Using Long Short-Term Memory (LSTM) networks, DeepLMS results in average testing Root Mean Square Error (RMSE) $$<0.009$$ < 0.009 , and average correlation coefficient between ground truth and predicted QoI values $$r\ge 0.97$$ r ≥ 0.97 $$(p<0.05)$$ ( p < 0.05 ) , when tested on QoI data from one database pre- and two ones during-Covid-19 pandemic. DeepLMS personalized QoI forecasting scaffolds user’s online learning engagement and provides educators with an evaluation path, additionally to the content-related assessment, enriching the overall view on the learners’ motivation and participation in the learning process.

Detection of Pavement Maintenance Treatments using Deep-Learning Network

2021 ◽  
pp. 036119812110078
Author(s):  
Lu Gao ◽  
Yao Yu ◽  
Yi Hao Ren ◽  
Pan Lu
Keyword(s):  
Deep Learning ◽  
Measurement Errors ◽  
Short Term Memory ◽  
Learning Networks ◽  
Pavement Maintenance ◽  
Time Period ◽  
Learning Techniques ◽  
Maintenance And Rehabilitation ◽  
History Of ◽  
Deep Learning Network

Pavement maintenance and rehabilitation (M&R) records are important as they provide documentation that M&R treatment is being performed and completed appropriately. Moreover, the development of pavement performance models relies heavily on the quality of the condition data collected and on the M&R records. However, the history of pavement M&R activities is often missing or unavailable to highway agencies for many reasons. Without accurate M&R records, it is difficult to determine if a condition change between two consecutive inspections is the result of M&R intervention, deterioration, or measurement errors. In this paper, we employed deep-learning networks of a convolutional neural network (CNN) model, a long short-term memory (LSTM) model, and a CNN-LSTM combination model to automatically detect if an M&R treatment was applied to a pavement section during a given time period. Unlike conventional analysis methods so far followed, deep-learning techniques do not require any feature extraction. The maximum accuracy obtained for test data is 87.5% using CNN-LSTM.

scholarly journals BO-LSTM: Classifying relations via long short-term memory networks along biomedical ontologies

2018 ◽  
Author(s):  
Andre Lamurias ◽  
Luka A. Clarke ◽  
Francisco M. Couto
Keyword(s):  
Deep Learning ◽  
Text Mining ◽  
Drug Interactions ◽  
Short Term Memory ◽  
Biomedical Ontologies ◽  
Short Term ◽  
Term Memory ◽  
Domain Specific ◽  
Learning Techniques ◽  
Long Short Term Memory

AbstractRecent studies have proposed deep learning techniques, namely recurrent neural networks, to improve biomedical text mining tasks. However, these techniques rarely take advantage of existing domain-specific resources, such as ontologies. In Life and Health Sciences there is a vast and valuable set of such resources publicly available, which are continuously being updated. Biomedical ontologies are nowadays a mainstream approach to formalize existing knowledge about entities, such as genes, chemicals, phenotypes, and disorders. These resources contain supplementary information that may not be yet encoded in training data, particularly in domains with limited labeled data.We propose a new model, BO-LSTM, that takes advantage of domain-specific ontologies, by representing each entity as the sequence of its ancestors in the ontology. We implemented BO-LSTM as a recurrent neural network with long short-term memory units and using an open biomedical ontology, which in our case-study was Chemical Entities of Biological Interest (ChEBI). We assessed the performance of BO-LSTM on detecting and classifying drug-drug interactions in a publicly available corpus from an international challenge, composed of 792 drug descriptions and 233 scientific abstracts. By using the domain-specific ontology in addition to word embeddings and WordNet, BO-LSTM improved both the F1-score of the detection and classification of drug-drug interactions, particularly in a document set with a limited number of annotations. Our findings demonstrate that besides the high performance of current deep learning techniques, domain-specific ontologies can still be useful to mitigate the lack of labeled data.Author summaryA high quantity of biomedical information is only available in documents such as scientific articles and patents. Due to the rate at which new documents are produced, we need automatic methods to extract useful information from them. Text mining is a subfield of information retrieval which aims at extracting relevant information from text. Scientific literature is a challenge to text mining because of the complexity and specificity of the topics approached. In recent years, deep learning has obtained promising results in various text mining tasks by exploring large datasets. On the other hand, ontologies provide a detailed and sound representation of a domain and have been developed to diverse biomedical domains. We propose a model that combines deep learning algorithms with biomedical ontologies to identify relations between concepts in text. We demonstrate the potential of this model to extract drug-drug interactions from abstracts and drug descriptions. This model can be applied to other biomedical domains using an annotated corpus of documents and an ontology related to that domain to train a new classifier.

scholarly journals Stock Pattern Classification from Charts using Deep Learning Algorithms

2020 ◽  
Vol 3 (1) ◽  
pp. 445-454
Author(s):  
Celal Buğra Kaya ◽  
Alperen Yılmaz ◽  
Gizem Nur Uzun ◽  
Zeynep Hilal Kilimci
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Pattern Classification ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Learning Techniques ◽  
Proposed Model ◽  
Istanbul Stock Exchange

Pattern classification is related with the automatic finding of regularities in dataset through the utilization of various learning techniques. Thus, the classification of the objects into a set of categories or classes is provided. This study is undertaken to evaluate deep learning methodologies to the classification of stock patterns. In order to classify patterns that are obtained from stock charts, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long-short term memory networks (LSTMs) are employed. To demonstrate the efficiency of proposed model in categorizing patterns, hand-crafted image dataset is constructed from stock charts in Istanbul Stock Exchange and NASDAQ Stock Exchange. Experimental results show that the usage of convolutional neural networks exhibits superior classification success in recognizing patterns compared to the other deep learning methodologies.

scholarly journals Characteristics of Online Learning in Higher Education During the COVID-19 Pandemic

2021 ◽  
Vol 66 (1) ◽  
pp. 135-155
Author(s):  
Diana-Crina Marin ◽  
Mușata Bocoș
Keyword(s):  
Higher Education ◽  
Online Learning ◽  
Time Management ◽  
Teaching And Learning ◽  
Educational Opportunities ◽  
Face To Face ◽  
Quality Of Teaching ◽  
Teaching Learning ◽  
Students Success

"In the context in which the teaching, learning, and assessment processes take place in the online environment, the question arises whether the currently organized learning situations are as effective as the learning situations carried out in the classroom, before the beginning of the pandemic. One of the disadvantages of online learning is related to the teacher’s low control over students’ activity. Factors such as initiative, creativity, efficient time management, intrinsic motivation, responsibility, and intellectual curiosity play an important role in students’ success in learning activities. Attendance at courses should not be formal and superficial and should be a process that involves the active and interactive participation of the students in the learning process. Providing high-quality educational opportunities to all students is a goal that is increasingly difficult to achieve in the context of the absence of face-to-face interactions. Also, applying a curriculum focused on the needs of the learner is becoming hard to achieve. Through this research, we aim to investigate issues related to how online learning takes place and to establish ways in which we can increase the efficiency of current teaching and learning processes. The study revealed that in the opinion of most of the students, the current epidemiological context has influenced in a negative way the quality of teaching and the student-teacher educational relationship. Keywords: Interactive learning, eLearning, independence in learning, higher education, efficient strategies "

scholarly journals Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Kazi Nabiul Alam ◽  
Md Shakib Khan ◽  
Abdur Rab Dhruba ◽  
Mohammad Monirujjaman Khan ◽  
Jehad F. Al-Amri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Performance Metrics ◽  
Short Term Memory ◽  
Confusion Matrix ◽  
Short Term ◽  
Learning Techniques ◽  
The World ◽  
Long Short Term Memory ◽  
Severe Anxiety

The COVID-19 pandemic has had a devastating effect on many people, creating severe anxiety, fear, and complicated feelings or emotions. After the initiation of vaccinations against coronavirus, people’s feelings have become more diverse and complex. Our aim is to understand and unravel their sentiments in this research using deep learning techniques. Social media is currently the best way to express feelings and emotions, and with the help of Twitter, one can have a better idea of what is trending and going on in people’s minds. Our motivation for this research was to understand the diverse sentiments of people regarding the vaccination process. In this research, the timeline of the collected tweets was from December 21 to July21. The tweets contained information about the most common vaccines available recently from across the world. The sentiments of people regarding vaccines of all sorts were assessed using the natural language processing (NLP) tool, Valence Aware Dictionary for sEntiment Reasoner (VADER). Initializing the polarities of the obtained sentiments into three groups (positive, negative, and neutral) helped us visualize the overall scenario; our findings included 33.96% positive, 17.55% negative, and 48.49% neutral responses. In addition, we included our analysis of the timeline of the tweets in this research, as sentiments fluctuated over time. A recurrent neural network- (RNN-) oriented architecture, including long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM), was used to assess the performance of the predictive models, with LSTM achieving an accuracy of 90.59% and Bi-LSTM achieving 90.83%. Other performance metrics such as precision,, F1-score, and a confusion matrix were also used to validate our models and findings more effectively. This study improves understanding of the public’s opinion on COVID-19 vaccines and supports the aim of eradicating coronavirus from the world.

Scale-dependent impacts of natural and anthropogenic drivers on groundwater level dynamics – analysis of shallow coastal aquifers using deep learning

2021 ◽  
Author(s):  
Annika Nolte ◽  
Steffen Bender ◽  
Jens Hartmann ◽  
Stefan Baltruschat
Keyword(s):  
Deep Learning ◽  
Groundwater Level ◽  
Coastal Aquifers ◽  
Short Term Memory ◽  
Spatial Scales ◽  
Global Scale ◽  
Meteorological Forcing ◽  
Inaccurate Information ◽  
Learning Techniques ◽  
Data Elements

&lt;p&gt;Groundwater level dynamics are very sensitive to groundwater withdrawal, but their effects and magnitude &amp;#8211; especially in combination with natural fluctuations &amp;#8211; must be often estimated due to missing or inaccurate information of all local pumping activities in an area. This study examines the potential of deep learning applications at large spatial scales to estimate the parts of local withdrawal activities and natural impacts &amp;#8211; meteorological and environmental &amp;#8211; on groundwater level dynamics. We will use big data elements from a newly constructed global groundwater database in a single long-term short-term memory (LSTM) model to examine scale-dependent impacts. The data used in the model consists of continuous groundwater level observations and catchment attributes &amp;#8211; spatially heterogeneous but temporally static catchment attributes (e.g. topography) and continuous observations of the meteorological forcing (e.g. precipitation) &amp;#8211; from several hundred catchments of shallow coastal aquifers of different continents. Our approach is to use only freely accessible data sources covering the global scale as catchment attributes. We will test how relationships between groundwater level dynamics and catchment attributes, at different scales, can improve interpretability of groundwater level simulations using deep learning techniques.&lt;/p&gt;

Arranging and Rearranging Practice in Digital Spaces

2018 ◽  
pp. 567-585
Author(s):  
Laurette S. M. Bristol ◽  
Merilyn Childs
Keyword(s):  
Online Learning ◽  
Professional Learning ◽  
Teacher Educators ◽  
Learning Program ◽  
Learning Space ◽  
Teaching Online ◽  
Learning And Teaching ◽  
Face To Face ◽  
Curriculum Decisions

The study that formed the basis of this chapter aimed to understand the practices mediating the quality of an online learning program from the perspective of educators in transition from face-to-face to online learning and teaching. A narrative community of enquiry was established for the period of the study, and seven academics from a single institution volunteered to participate in a six-month conversation about the sites for practice, challenges and curriculum decisions made while teaching online. A “practice architectures” perspective was adopted. The study found that “designing and redesigning” was not limited as supposed to a single transformation from face-to-face teaching to an online learning space. Rather, it was an ongoing professional practice, regardless of how novice or experienced and “tech savvy” the academic. The digital space is rapidly evolving, as are the professional learning demands of teacher educators. “Ambitious teacher practices” are permanently required.

Through the Eyes of Students and Faculty

2016 ◽  
pp. 557-584
Author(s):  
Maysaa Barakat ◽  
Debra N. Weiss-Randall
Keyword(s):  
Higher Education ◽  
Online Learning ◽  
Educational Leadership ◽  
Learning Environments ◽  
Social Exclusion ◽  
Research University ◽  
Attrition Rates ◽  
Face To Face

Online enrollments have been growing substantially faster than overall higher education enrollments. It is argued that online learning can help address issues of educational inequity, poverty, and social exclusion. The momentum is moving towards online learning, and universities are pressured to develop more online options for their students in order to stay relevant and provide needed flexibility. On average, courses that are delivered online have higher attrition rates than regular face-to-face courses. There are numerous challenges and difficulties in developing online learning environments without sacrificing the quality of learning. This case study examines the development, delivery, and evaluation of online learning through the eyes of students and faculty of an educational leadership department in a Southeast research university.

scholarly journals Multi-View Partial Multi-Label Learning with Graph-Based Disambiguation

2020 ◽  
Vol 34 (04) ◽  
pp. 3553-3560 ◽  
Author(s):  
Ze-Sen Chen ◽  
Xuan Wu ◽  
Qing-Guo Chen ◽  
Yao Hu ◽  
Min-Ling Zhang
Keyword(s):  
Predictive Model ◽  
Clustering Analysis ◽  
Experimental Studies ◽  
Ground Truth ◽  
Two Stage ◽  
Feature Vectors ◽  
Similarity Graph ◽  
Training Examples ◽  
Guided Clustering

In multi-view multi-label learning (MVML), each training example is represented by different feature vectors and associated with multiple labels simultaneously. Nonetheless, the labeling quality of training examples is tend to be affected by annotation noises. In this paper, the problem of multi-view partial multi-label learning (MVPML) is studied, where the set of associated labels are assumed to be candidate ones and only partially valid. To solve the MVPML problem, a two-stage graph-based disambiguation approach is proposed. Firstly, the ground-truth labels of each training example are estimated by disambiguating the candidate labels with fused similarity graph. After that, the predictive model for each label is learned from embedding features generated from disambiguation-guided clustering analysis. Extensive experimental studies clearly validate the effectiveness of the proposed approach in solving the MVPML problem.

Ground-truth uncertainty-aware metrics for machine learning applications on seismic image interpretation: Application to faults and horizon extraction

2020 ◽  
Vol 39 (10) ◽  
pp. 734-741
Author(s):  
Sébastien Guillon ◽  
Frédéric Joncour ◽  
Pierre-Emmanuel Barrallon ◽  
Laurent Castanié
Keyword(s):  
Deep Learning ◽  
Image Interpretation ◽  
Ground Truth ◽  
Learning Model ◽  
Seismic Interpretation ◽  
Data Set ◽  
Seismic Image ◽  
Machine Learning Applications ◽  
Deep Learning Model

We propose new metrics to measure the performance of a deep learning model applied to seismic interpretation tasks such as fault and horizon extraction. Faults and horizons are thin geologic boundaries (1 pixel thick on the image) for which a small prediction error could lead to inappropriately large variations in common metrics (precision, recall, and intersection over union). Through two examples, we show how classical metrics could fail to indicate the true quality of fault or horizon extraction. Measuring the accuracy of reconstruction of thin objects or boundaries requires introducing a tolerance distance between ground truth and prediction images to manage the uncertainties inherent in their delineation. We therefore adapt our metrics by introducing a tolerance function and illustrate their ability to manage uncertainties in seismic interpretation. We compare classical and new metrics through different examples and demonstrate the robustness of our metrics. Finally, we show on a 3D West African data set how our metrics are used to tune an optimal deep learning model.

Sign in / Sign up

Export Citation Format

Share Document