scholarly journals Recommendation Systems for Education: Systematic Review

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1611
Author(s):  
María Cora Urdaneta-Ponte ◽  
Amaia Mendez-Zorrilla ◽  
Ibon Oleagordia-Ruiz
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Formal Education ◽  
Research Work ◽  
Hybrid Approach ◽  
Recommendation Systems ◽  
Educational Resources ◽  
Future Research ◽  
Collaborative Approach ◽  
Developmental Approach

Recommendation systems have emerged as a response to overload in terms of increased amounts of information online, which has become a problem for users regarding the time spent on their search and the amount of information retrieved by it. In the field of recommendation systems in education, the relevance of recommended educational resources will improve the student’s learning process, and hence the importance of being able to suitably and reliably ensure relevant, useful information. The purpose of this systematic review is to analyze the work undertaken on recommendation systems that support educational practices with a view to acquiring information related to the type of education and areas dealt with, the developmental approach used, and the elements recommended, as well as being able to detect any gaps in this area for future research work. A systematic review was carried out that included 98 articles from a total of 2937 found in main databases (IEEE, ACM, Scopus and WoS), about which it was able to be established that most are geared towards recommending educational resources for users of formal education, in which the main approaches used in recommendation systems are the collaborative approach, the content-based approach, and the hybrid approach, with a tendency to use machine learning in the last two years. Finally, possible future areas of research and development in this field are presented.

scholarly journals Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Time Series

2021 ◽  
Author(s):  
Sidra Mehtab ◽  
Jaydip Sen
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Regression Models ◽  
Research Work ◽  
Hybrid Approach ◽  
Forecast Horizon ◽  
Stock Price Prediction ◽  
Price Prediction

Prediction of future movement of stock prices has been a subject matter of many research work. On one hand, we have proponents of the Efficient Market Hypothesis who claim that stock prices cannot be predicted, on the other hand, there are propositions illustrating that, if appropriately modelled, stock prices can be predicted with a high level of accuracy. There is also a gamut of literature on technical analysis of stock prices where the objective is to identify patterns in stock price movements and profit from it. In this work, we propose a hybrid approach for stock price prediction using machine learning and deep learning-based methods. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, over a period of four years: 2015 – 2018. Based on the NIFTY data during 2015 – 2018, we build various predictive models using machine learning approaches, and then use those models to predict the “Close” value of NIFTY 50 for the year 2019, with a forecast horizon of one week, i.e., five days. For predicting the NIFTY index movement patterns, we use a number of classification methods, while for forecasting the actual “Close” values of NIFTY index, various regression models are built. We, then, augment our predictive power of the models by building a deep learning-based regression model using Convolutional Neural Network (CNN) with a walk-forward validation. The CNN model is fine-tuned for its parameters so that the validation loss stabilizes with increasing number of iterations, and the training and validation accuracies converge. We exploit the power of CNN in forecasting the future NIFTY index values using three approaches which differ in number of variables used in forecasting, number of sub-models used in the overall models and, size of the input data for training the models. Extensive results are presented on various metrics for all classification and regression models. The results clearly indicate that CNN-based multivariate forecasting model is the most effective and accurate in predicting the movement of NIFTY index values with a weekly forecast horizon.

Learning Non-Taxonomic Relations of Ontologies

2021 ◽  
Vol 17 (1) ◽  
pp. 97-122
Author(s):  
Mohamed Hassan Mohamed Ali ◽  
Said Fathalla ◽  
Mohamed Kholief ◽  
Yasser Fouad Hassan
Keyword(s):  
Systematic Review ◽  
State Of The Art ◽  
Research Work ◽  
Semantic Knowledge ◽  
Future Research ◽  
Automatic Extraction ◽  
Ontology Learning ◽  
Comprehensive Understanding ◽  
Specific Domain ◽  
Taxonomic Relations

Ontologies, as semantic knowledge representation, have a crucial role in various information systems. The main pitfall of manually building ontologies is effort and time-consuming. Ontology learning is a key solution. Learning Non-Taxonomic Relationships of Ontologies (LNTRO) is the process of automatic/semi-automatic extraction of all possible relationships between concepts in a specific domain, except the hierarchal relations. Most of the research works focused on the extraction of concepts and taxonomic relations in the ontology learning process. This article presents the results of a systematic review of the state-of-the-art approaches for LNTRO. Sixteen approaches have been described and qualitatively analyzed. The solutions they provide are discussed along with their respective positive and negative aspects. The goal is to provide researchers in this area a comprehensive understanding of the drawbacks of the existing work, thereby encouraging further improvement of the research work in this area. Furthermore, this article proposes a set of recommendations for future research.

scholarly journals COPD phenotypes and machine learning cluster analysis: A systematic review and future research agenda

2020 ◽  
Vol 171 ◽  
pp. 106093 ◽  
Author(s):  
Vasilis Nikolaou ◽  
Sebastiano Massaro ◽  
Masoud Fakhimi ◽  
Lampros Stergioulas ◽  
David Price
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Cluster Analysis ◽  
Research Agenda ◽  
Future Research ◽  
Future Research Agenda

scholarly journals Hotel Recommender System based on Knowledge Graph and Collaborative Approach

2021 ◽  
pp. 63-71
Author(s):  
Yousef Abuzir ◽  
Mohamed Dwieb
Keyword(s):  
Recommendation System ◽  
Hybrid Approach ◽  
Recommendation Systems ◽  
Knowledge Graph ◽  
Similarity Function ◽  
Collaborative Approach ◽  
Analysis Techniques ◽  
Business Sectors ◽  
F Measure

With the rapid increase of Information technology, online services and social media, recommendation system becomes an important issue and a need for both the customer and business sectors. The main aim of traditional and online recommendation systems is to recommend the desired and the necessary services that are appropriate recommendations to users. Traditional recommendation systems often suffer from inefficient data analysis techniques, rating the different services without regard to the previous preferences of the users and do not meet the personal demands of the users. Therefore, in this paper we used a hybrid approach based on Knowledge graph and Machine Learning similarity function as a recommendation system. We used real datasets to conduct the experiment. We built the knowledge graph for the visitors, hotels and their ranks, and we used the knowledge graph and similarity scores to recommend a hotel or a set of hotels for the visitors based on former preferences and ratings of other visitors. The results show significant accuracy and good quality of service recommender systems with 93.5% for f-measure.

A systematic review: machine learning based recommendation systems for e-learning

2019 ◽  
Vol 25 (4) ◽  
pp. 2635-2664 ◽  
Author(s):  
Shristi Shakya Khanal ◽  
P.W.C. Prasad ◽  
Abeer Alsadoon ◽  
Angelika Maag
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Recommendation Systems ◽  
E Learning

scholarly journals Perspektiven für eine konzeptionelle Orientierung der Bildungsgeographie

2015 ◽  
Vol 70 (1) ◽  
pp. 75-88 ◽  
Author(s):  
T. Freytag ◽  
H. Jahnke
Keyword(s):  
Formal Education ◽  
Research Work ◽  
Human Geography ◽  
Reference Points ◽  
Future Research ◽  
International Studies ◽  
Geographical Education ◽  
Research Activities ◽  
Key Concepts ◽  
Suitable Reference

Abstract. Education has become central to the social and political debates in many countries. Under the influence of comparative international studies as well as national rankings of education institutions there is a growing awareness for social and regional inequalities of formal education infrastructure and processes. This article focuses on the geographical education research in Germany by reviewing existing research work on the one hand and opening perspectives for future studies in geography of education on the other. The main aim is to restructure the field of German Bildungsgeographie (geography of education) along key concepts and perspectives from human geography. After a brief introduction, the first part discusses the concept of Bildung (education) and its transformation. The following chapter sketches the major lines of research in geography of education in the German-speaking context. In the last and most extensive part six key concepts from human geography are pointed out as suitable reference points to situate existing and future research activities in the field of geography of education.

scholarly journals A Systematic Review of Predicting Elections Based on Social Media Data: Research Challenges and Future Directions

2021 ◽  
Vol 9 (10) ◽  
pp. 1410-1440
Author(s):  
T Heena Fayaz
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Social Networks ◽  
Social Media ◽  
Future Research ◽  
Learning Approaches ◽  
Electoral Campaigns ◽  
Regression Methods ◽  
New Research

Abstract: The way politicians communicate with the electorateand run electoral campaigns was reshaped by the emergence and popularization of contemporary social media (SM), such as Facebook, Twitter, and Instagram social networks (SN). Due to inherent capabilities of SM, such as the large amount of available data accessed in real time, a new research subject has emerged, focusing on using SM data to predict election outcomes. Despite many studies conducted in the last decade, results are very controversial, and many times challenged. In this context, this work aims to investigate and summarize how research on predicting elections based on SM data has evolved since its beginning, to outline the state of both the art and the practice,and to identify research opportunities within this field. In termsof method, we performed a systematic literature review analyzingthe quantity and quality of publications, the electoral context of studies, the main approaches to and characteristics of the successful studies, as well as their main strengths and challenges, and compared our results with previous reviews. We identified and analyzed 83 relevant studies, and the challenges were identified in many areas such as process, sampling, modeling, performance evaluation and scientific rigor. Main findings include the low success of the most-used approach, namely volume and sentiment analysis on Twitter, and the better results with new approaches, such as regression methods trained with traditional polls. Finally, a vision of future research on integrating advances on process definitions, modeling, and evaluation is also discussed, pointing out, among others, the need for better investigating the application of state-of-art machine learning approaches. Index Terms: Elections, Social Media, Social Networks, Machine Learning, Systematic Review

scholarly journals Tracking of Malicious Attacks on Data Online: A Systematic Review

2020 ◽  
Vol 8 (4) ◽  
pp. 31-44
Author(s):  
Abdulkadir Onivehu Isah ◽  
John Kolo Alhassan ◽  
Idris Ismaila ◽  
Olawale Surajudeen Adebayo
Keyword(s):  
Systematic Review ◽  
Research Work ◽  
Attack Detection ◽  
Future Research ◽  
Malicious Attacks ◽  
Research Papers ◽  
Research Attention ◽  
Encrypted Data ◽  
Keyword Guessing Attack ◽  
Guessing Attack

Tracking of computer network system attacks is a proactive measure to protect against attacks on data, that are basically encrypted for confidential security reasons, while in transit on the computer information channel. Cyber security threat continues to increase in direct proportion to the rate at which internet based services are deployed. In this systematic review, 53 research papers from reputable publishers were downloaded out of which 41 papers that are closely related to tracking of malicious attackers on encrypted data online were review under the consideration of attacks on encrypted data, and tracking malicious attacks; with respect to proposed technique, problem addressed, comparison to existing methodology, parameters used, major findings and then limitations and future knowledge. The authors then deduce the classification of four varying types of attacks (Keyword Guessing Attack, Selective opening attacks, Leakage-Abuse Attacks, and Key Reinstallation Attacks) from the review, to narrow down research into the future countermeasures for these attacks. 11 research papers actual discuss countermeasures for these classification types, with Keyword Guessing Attack being the focus of 6 research work, Selective Opening Attacks have 3 papers trying to solve vulnerabilities permitting such attacks, 2 papers aimed research solutions at Leakage-Abuse Attacks, and Key Reinstallation Attacks, has mention but none of the papers reviewed proffer mitigation techniques. The remaining 30 papers concentrated discussions on general attacks on encrypted data. Inclining future research attention to the four kinds of attacks against encrypted data will improve attack detection contrary to the commonly post-mortem approach.

NIMG-23. MACHINE LEARNING METHODS IN GLIOMA GRADE PREDICTION: A SYSTEMATIC REVIEW

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi133-vi133
Author(s):  
Ryan Bahar ◽  
Sara Merkaj ◽  
W R Brim ◽  
Harry Subramanian ◽  
Tal Zeevi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Deep Learning ◽  
Controlled Vocabulary ◽  
Future Research ◽  
Support Vector ◽  
Clinical Implementation ◽  
Grade Prediction ◽  
Full Text Review ◽  
Average Patient

Abstract PURPOSE Machine learning (ML) technologies have demonstrated highly accurate prediction of glioma grade, though it is unclear which methods and algorithms are superior. We have conducted a systematic review of the literature in order to identify the ML applications most promising for future research and clinical implementation. MATERIALS AND METHODS A literature review, in agreement with PRISMA, was conducted by a university librarian in October 2020 and verified by a second librarian in February 2021 using four databases: Cochrane trials (CENTRAL), Ovid Embase, Ovid MEDLINE, and Web of Science core-collection. Keywords and controlled vocabulary included artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and glioblastoma. Screening of publications was done in Covidence, and TRIPOD was used for bias assessment. RESULTS The search identified 11,727 candidate articles with 1,135 articles undergoing full text review. 86 articles published since 1995 met the criteria for our study. 79% of the articles were published between 2018 and 2020. The average glioma prediction accuracy of the highest performing model in each study was 90% (range: 53% to 100%). The most common algorithm used for cML studies was Support Vector Machine (SVM) and for DL studies was Convolutional Neural Network (CNN). BRATS and TCIA datasets were used in 47% of the studies, with the average patient number of study datasets being 186 (range: 23 to 662). The average number of features used in machine learning prediction was 55 (range: 2 to 580). Classical machine learning (cML) was the primary machine learning model in 68% of studies, with deep learning (DL) used in 32%. CONCLUSIONS Using multimodal sequences in ML methods delivers significantly higher grading accuracies than single sequences. Potential areas of improvement for ML glioma grade prediction studies include increasing sample size, incorporating molecular subtypes, and validating on external datasets.

Predicting the Future Research Gaps Using Hybrid Approach

2021 ◽  
pp. 157-175
Author(s):  
Premisha Premananthan ◽  
Banujan Kuhaneswaran ◽  
Banage T. G. S. Kumara ◽  
Enoka P. Kudavidanage
Keyword(s):  
Latent Dirichlet Allocation ◽  
Research Work ◽  
Hybrid Approach ◽  
Past Research ◽  
Future Research ◽  
Sri Lankan ◽  
Research Papers ◽  
Novel Approach ◽  
The Future ◽  
Artificial Neural Network Ann

Sri Lanka is one of the global biodiversity hotspots that contain a large variety of fauna and flora. But nowadays Sri Lankan wildlife has faced many issues because of poor management and policies to protect wildlife. The lack of technical and research support leads many researchers to retreat to select wildlife as their domain of study. This study demonstrates a novel approach to data mining to find hidden keywords and automated labeling for past research work in this area. Then use those results to predict the trending topics of researches in the field of biodiversity. To model topics and extract the main keywords, the authors used the latent dirichlet allocation (LDA) algorithms. Using the topic modeling performance, an ontology model was also developed to describe the relationships between each keyword. They classified the research papers using the artificial neural network (ANN) using ontology instances to predict the future gaps for wildlife research papers. The automatic classification and labeling will lead many researchers to find their desired research papers accurately and quickly.

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