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A Combined Deep-Learning and Transfer-Learning Approach for Supporting Social Influence Prediction

Alfredo Cuzzocrea, Carson K. Leung, Deyu Deng, Jiaxing Jason Mai, Fan Jiang, Edoardo Fadda
2020 Procedia Computer Science  
Such a combined deep-learning and transfer-learning approach well supports the social influence prediction.  ...  Such a combined deep-learning and transfer-learning approach well supports the social influence prediction.  ...  This project is partially supported by NSERC (Canada) and University of Manitoba.  ... 
doi:10.1016/j.procs.2020.10.025 fatcat:hpukbkxor5eadic6rwlpvsmifq

Prediction of Stress and Mood using Neural Network, LSTM and Transfer learning

Neethu V T
2021 International Journal for Research in Applied Science and Engineering Technology  
For second task prediction, the model created for first task is reused as pretrained model where we make use of transfer learning.  ...  Neural Network and LSTM is used to predict the stress and mood. Predicting the stress is considered as first task and as mood prediction as second task.  ...  The approach is meant during a two-stream manner, combined with jointtuning layers for depression prediction.  ... 
doi:10.22214/ijraset.2021.35347 fatcat:uar6ueo4kbeyvbif5ptw5a6pvq

Cross-Cultural Differences in Adopting Social Cognitive Career Theory at Student Employability in PLS-SEM: The Mediating Roles of Self-Efficacy and Deep Approach to Learning

Wen-Xuan Zhao, Michael Yao-Ping Peng, Fang Liu
2021 Frontiers in Psychology  
The results indicate that teacher knowledge transfer has significant positive correlations with self-efficacy and a deep approach to learning and student employability, and the self-efficacy and a deep  ...  Malaysian university students and examines the relationship between teacher knowledge transfer and student employability from the perspective of a social cognitive career theory.  ...  W-XZ contributed to writing, data analysis, and design of research methods and tables. FL participated in developing a research design, proof writing, and interpreting the analysis.  ... 
doi:10.3389/fpsyg.2021.586839 fatcat:muiecdfm3vffjddb6h56yjryqq

Stock Market Analysis with Text Data: A Review [article]

Kamaladdin Fataliyev, Aneesh Chivukula, Mukesh Prasad, Wei Liu
2021 arXiv   pre-print
Stock market movements are influenced by public and private information shared through news articles, company reports, and social media discussions.  ...  The aim of this study is to survey the main stock market analysis models, text representation techniques for financial market prediction, shortcomings of existing techniques, and propose promising directions  ...  Pai and Lin [108] propose a hybrid model by combining ARIMA with support vector machines and apply it for multiple stock prediction.  ... 
arXiv:2106.12985v2 fatcat:prfo5c6bmfd3piwpl5yevfycje

Deep Learning Applications for COVID-19 Analysis: A State-of-the-Art Survey

Wenqian Li, Xing Deng, Haijian Shao, Xia Wang
2021 CMES - Computer Modeling in Engineering & Sciences  
The deep learning methods combined with transfer learning are familiar with classification-detection approaches based on chest X-ray and CT images are presented in detail.  ...  In the absence of a vaccine, the machine learning-related approaches are applied to analyze vaccine candidates in the realm of biology and medicine.  ...  Acknowledgement: The authors wish to express their appreciation to the reviewers for their helpful suggestions which greatly improved the presentation of this paper.  ... 
doi:10.32604/cmes.2021.016981 fatcat:4puoo5jsifhh7k3k3qrwyrdx2y

IEEE Access Special Section Editorial: Advanced Data Mining Methods for Social Computing

Yongqiang Zhao, Shirui Pan, Jia Wu, Huaiyu Wan, Huizhi Liang, Haishuai Wang, Huawei Shen
2020 IEEE Access  
YONGQIANG ZHAO (Member, IEEE) received the B.S. degree in automation and the M.S. and Ph.D. degrees in control theory and control engineering from Northwestern  ...  The article by Mandaglio and Tagarelli, ''Generalized preference learning for trust network inference,'' proposes a principled approach based on a preference learning paradigm, under a preference-based  ...  The article by Wang et al., ''Failure-aware mobile crowd sensing: A social relationship-based transfer approach,'' proposes and studies a problem, namely failure-aware mobile crowdsensing.  ... 
doi:10.1109/access.2020.3043060 fatcat:qbqk5f4ojvadlazhk2mc343sra

An Efficient Supervised Method for Fake News Detection using Machine and Deep Learning Classifiers

2020 International journal of recent technology and engineering  
This paper comes up with the applications of Machine learning and deep learning algorithms for police work the 'fake news', that is, dishonorable news stories that come from the unauthorized article writers  ...  It's required to create a model which is able to differentiate between "Real" news and "Fake" news. The model was created exploitation numerous deep and machine learning strategies.  ...  MACHINE LEARNING AND DEEP LEARNING METHODS USED a.  ... 
doi:10.35940/ijrte.f8930.038620 fatcat:qmdj5rzhqbbzhcs6m7ucuosr5y

Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning [article]

Iqra Ameer, Muhammad Arif, Grigori Sidorov, Helena Gòmez-Adorno, Alexander Gelbukh
2022 arXiv   pre-print
We trained traditional machine learning, deep learning, and transfer learning multi-class models to detect mental disorders of individuals.  ...  Given the current social distance restrictions across the world, most individuals now use social media as their major medium of communication.  ...  Acknowledgements The work was done with support from the Mexican Government through the grant A1-S-47854 of the CONACYT, Mexico and grants 20211784, 20211884, 20211178 of the Secretaría de Investigación  ... 
arXiv:2207.01012v1 fatcat:ckr5w4n72veb7lwxllt2h2y47q

Cloud Sentiment Accuracy Comparison using RNN, LSTM and GRU

Muhammad Raheel Raza, Walayat Hussain, Jose Maria Merigo
2021 2021 Innovations in Intelligent Systems and Applications Conference (ASYU)  
Therefore, it is very important to choose the right method to predict consumer's sentiment for a greatest result.  ...  Cloud computing has become a de facto choice of many individuals and enterprises for computing solutions.  ...  0.977 Comparing the results of prediction accuracy of the deep learning models, GRU outperforms the LSTM and RNN approaches.  ... 
doi:10.1109/asyu52992.2021.9599044 fatcat:c23bvi4bvvetdd67wvd75ymboa

Achievement Goals and Learning Approaches in the Context of Social Studies Teaching: Comparative Analysis of 3x2 and 2x2 Models

2020 Participatory Educational Research  
Also, performance-goal orientation negatively predicts deep learning approaches but positively predicts surface learning approaches.  ...  The study concludes that learning achievement goals positively predict deep learning approaches but negatively predict surface learning approaches.  ...  Similarly, students who adopt a deep learning approach make their learning meaningful not through memorizing and are very good at transferring their newlyacquired knowledge and skills to novel situations  ... 
doi:10.17275/per. fatcat:pqwnhs2amfe4heoul64bggge4y

A Novel Approach to Pre-Impact Measurement from Impact Investing Using Random Forest and Deep Neural Networks

Emmanuel Kwesi Baah, James Ben Hayfron-Acquah, Dominic Asamoah
2021 International Journal of Computer Applications  
In this study, a combination of machine learning and deep learning models is used on the intended community to measure the preimpact factors suitable to generating confidence for the full granting of funds  ...  A deep neural network is then used to predict the various classes chosen for the classification problem.  ...  CONCLUSION In this study, a new approach to pre-impact measurement is designed and tested, which to the authors' knowledge, is the first approach combining machine learning and deep learning aside from  ... 
doi:10.5120/ijca2021921554 fatcat:yx2uuj56ybd4dhrmktg7pljpce

The state-of-the-art on Intellectual Property Analytics (IPA): A literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data

Leonidas Aristodemou, Frank Tietze
2018 World Patent Information  
In this paper, we contribute to the ongoing discussion on the use of intellectual property analytics methods, i.e artificial intelligence methods, machine learning and deep learning approaches, to analyse  ...  This literature review follows a narrative approach with search strategy, where we present the state-of-the-art in intellectual property analytics by reviewing 57 recent articles.  ...  Acknowledgement The authors would like to acknowledge support of the Engineering and Physical Sciences Research Council (EPSRC).  ... 
doi:10.1016/j.wpi.2018.07.002 fatcat:cawnmevwcna2zep7z6ikixwzgu

Role of Social Sentiment Analysis in Stock Trends Forecasting

2019 International journal of recent technology and engineering  
In the past few years several researches have been carried out for predicting the future trends of stock market through sentiment analysis on social media comments.  ...  This paper gives the survey on the various techniques, tools and methodologies adopted by several researchers on Stock Market Prediction based on sentiment analysis of Social networks  ...  deep learning models etc., A.  ... 
doi:10.35940/ijrte.b1030.0782s519 fatcat:farswv57gbclhkim7p6rqg7u3q

Exploring the combined relationships of student and teacher factors on learning approaches and self‐directed learning readiness at a Malaysian university

Megan Kek, Henk Huijser
2011 Studies in Higher Education  
; and teachers' individual characteristics, teaching efficacies, university and classroom learning environments, teacher outcomes and approaches to teaching on approaches to learning (deep and surface  ...  The analyses, through hierarchical linear modelling (HLM), revealed what and how personal, family, learning environment and teacher factors directly influenced approaches to learning and self-directed  ...  deep approaches to learning and their scores for a deep approach to learning are likely to be high.  ... 
doi:10.1080/03075070903519210 fatcat:3q25zngajrdujni5y3w7gnnnkq

A Novel Deep Reinforcement Learning Based Stock Direction Prediction using Knowledge Graph and Community Aware Sentiments [article]

Anil Berk Altuner, Zeynep Hilal Kilimci
2021 arXiv   pre-print
In this study, we propose a novel method that is based on deep reinforcement learning methodologies for the direction prediction of stocks using sentiments of community and knowledge graph.  ...  Stock market prediction has been an important topic for investors, researchers, and analysts. Because it is affected by too many factors, stock market prediction is a difficult task to handle.  ...  Deep reinforcement learning is the combination of reinforcement learning and deep learning that is being able to solve a wide range of complex decision-making tasks that were previously out of reach for  ... 
arXiv:2107.00931v1 fatcat:c6oaw3y7nbayremuw2cxeekmmu
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