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Predicting process behaviour using deep learning

Joerg Evermann, Jana-Rebecca Rehse, Peter Fettke
2017 Decision Support Systems  
Predicting business process behaviour is an important aspect of business process management.  ...  This is both a novel method in process prediction, which has largely relied on explicit process models, and also a novel application of deep learning methods.  ...  We present a novel approach to predicting the next process event using deep learning.  ... 
doi:10.1016/j.dss.2017.04.003 fatcat:gtjjkvxzvjgllp5s2eyicky3rq

Insights into Data through Model Behaviour: An Explainability-driven Strategy for Data Auditing for Responsible Computer Vision Applications [article]

Alexander Wong, Adam Dorfman, Paul McInnis, Hayden Gunraj
2021 arXiv   pre-print
appropriate prediction behaviour.  ...  We demonstrate this strategy by auditing two popular medical benchmark datasets, and discover hidden data quality issues that lead deep learning models to make predictions for the wrong reasons.  ...  can be used on deep learning regression models.  ... 
arXiv:2106.09177v1 fatcat:jv5ar76krzgx7leaciwnnuomli

Analysis, Prediction and Maintenance of Teaching learning process based on empathize Students' View of attending Online/Regular Class

Vithya Ganesan, V. Govindarajan, Pachipala Yellamma, Naren. J
2021 EAI Endorsed Transactions on e-Learning  
Learning models have been widely used in predicting diseases, disorders, behaviour aspects in human beings etc.  ...  The paper also encompasses comparison with various Machine Learning approaches in student behavioural prediction.  ...  Machine Learning in Behavioural Analysis Machine Learning is widely used in the study of students' behavioural analysis.  ... 
doi:10.4108/eai.12-1-2021.168090 fatcat:zm4wmefgsva5zeq5ofzdtdguvq

Learning behaviour and learning outcomes: the roles for social influence and field of study

Lillian Smyth, Kenneth I. Mavor, Michael J. Platow
2017 Social Psychology of Education  
The current RUNNING HEAD: LEARNING BEHAVIOUR AND OUTCOMES: THE ROLE FOR SOCIAL INFLUENCE 4 study situates this advance in the context of Biggs' broader 3P model of the learning process (Presage, Process  ...  Strongly identified students, students who perceived deep learning norms and students taking a deep learning approach all reported more positive outcomes.  ...  These two theoretical foundations are then used as a base from which models of the process of tertiary learning may be understood and developed.  ... 
doi:10.1007/s11218-016-9365-7 fatcat:5vy2r3upira2xaftm4mqllgjyy

Deep Video Anomaly Detection: Opportunities and Challenges [article]

Jing Ren, Feng Xia, Yemeng Liu, Ivan Lee
2021 arXiv   pre-print
Deep learning has shown its capacity in a number of domains, ranging from acoustics, images, to natural language processing.  ...  Recently, many studies on extending deep learning models for solving anomaly detection problems have emerged, resulting in beneficial advances in deep video anomaly detection techniques.  ...  This unsupervised deep learning model could identify anomalous human behaviours by learning normal behaviours. Similarly, Morais et al.  ... 
arXiv:2110.05086v1 fatcat:5tpj4bqdd5csbp6efvvcqvufeq

The Role of Machine Learning in Internet-of-Things (IoT) Research: A Review

Aneri M., Rutvij H.
2018 International Journal of Computer Applications  
In recent year, machine learning technique have been used widely because of its technologies such that identification, extraction, classification, regression and forecasting.  ...  Intelligent processing and analysis of big data is the key to developing smart IoT applications. Such applications are logistic, transportation, agriculture, healthcare, and environment.  ...  Machine learning is process of making new facts and through using analysis and development with learning process [4] . increased with time.  ... 
doi:10.5120/ijca2018916609 fatcat:ia3lxjerd5a4lczw4yqwtfc43i

Explainable Artificial Intelligence (XAI) towards Model Personality in NLP task

Dimas Adi, Nadhila Nurdin
2021 IPTEK Journal of Engineering  
Keywords⎯ Deep learning, Explainable artificial intelligence, Natural language processing, Sentiment analysis I.  ...  Abstract⎯ In recent years, the development of Deep Learning in the field of Natural Language Processing, especially in sentiment analysis, has achieved significant progress and success.  ...  In recent times, Deep Learning techniques for solving various Natural Language Processing tasks (e.g.  ... 
doi:10.12962/j23378557.v7i1.a8989 fatcat:ewjw2opqknec3oohzcwq5abfgu

Deep Learning for Discussion-Based Cross-Domain Performance Prediction of MOOC Learners Grouped by Language on FutureLearn

Ismail Duru, Ayse Saliha Sunar, Su White, Banu Diri
2021 Arabian Journal for Science and Engineering  
Analysing learners' behaviours in MOOCs has been used to identify predictive features associated with positive outcomes in engagement and learning success.  ...  One of the deep learning architecture, Bidirectional LSTM, trained with discussions on the language learning 73% successfully predicted learners' performance on a different MOOC.  ...  The dataset used in this paper is provided by the University of Southampton for the ethically approved collaborative study (ID: 23593).  ... 
doi:10.1007/s13369-020-05117-x pmid:33425646 pmcid:PMC7786318 fatcat:ve3bf4vxwrcjhoi453n3uvo4ai

The Impacts of Learning Analytics on Primary Level Mathematics Curriculum

Izzat Syahir Mohd Ramli, Siti Mistima Maat, Fariza Khalid
2020 Universal Journal of Educational Research  
LA is a fast growing area in academia and helps teacher on collecting, analysis, processing and visualization of data to help them understand students in deep.  ...  The advancement of technology has triggered a revolution in data generation by the devices used. Hence, learning analytics (LA) is very useful and significant in today's teaching and learning.  ...  The theory of planned behaviour [1] should be insert because this theory will help teacher to visualise student behaviour and understand students in deep when they use GBL.  ... 
doi:10.13189/ujer.2020.081914 fatcat:rly3qukfbrfitfzggnmq3dn4fa

EDM – survey of performance factors and algorithms applied

Deepali R Vora, Kamatchi Iyer
2018 International Journal of Engineering & Technology  
It mainly focuses in mining useful patterns and discovering useful knowledge from the educational information systems from schools, to colleges and universities.  ...  The paper presents a survey of various tasks performed in EDM and algorithms (methods) used for the same.  ...  •Objective3: Deep Learning is effective in EDM applications Deep Learning is the new field of machine learning applied in various areas like natural language processing, image and video processing etc.  ... 
doi:10.14419/ijet.v7i2.6.10074 fatcat:aocn5okhrretfomrx5emt42bz4

Application of the ANFIS model in deflection prediction of concrete deep beam

Mohammad Mohammadhassani, Hossein Nezamabadi-Pour, MohdZamin Jumaat, Mohammed Jameel, S.J.S. Hakim, Majid Zargar
2013 Structural Engineering and Mechanics  
of the modelling tools to predict deflection for high strength self compacting concrete (HSSCC) deep beams.  ...  In this study, about 3668measured data on eight HSSCC deep beams are considered.  ...  This involves the use of classical and /or modern models for prediction of deep beam deflection with emphasize on behaviour and non-linear strain distribution.  ... 
doi:10.12989/sem.2013.45.3.323 fatcat:ktytxu6xvbe3hlmaltksvz7rsm

Preferences for deep-surface learning: A vocational education case study using a multimedia assessment activity

Simon Hamm, Ian Robertson
2010 Australasian Journal of Educational Technology  
Firstly, learners' preferences for deep or surface learning were evaluated using the revised two-factor Study Process Questionnaire.  ...  Using these two data sets, learners preferred and implemented learning approaches were compared.  ...  is also consistent with deep learning behaviours.  ... 
doi:10.14742/ajet.1027 fatcat:cfeavkfxljcrzfntjzqic4vhwy

Fundamentals of deep data science

Naveena M
2020 South asian journal of engineering and technology  
The tools and softwares designed using big data science are creating huge impact on the society.  ...  The present study reports the fundamentals of deep data sciences and their emerging roles across the globe. The influence of deep data science plays important roles in informationscience.  ...  Predictive modelling is a process that uses data and statistics to predict outcomes with data models.  ... 
doi:10.26524/sajet.2020.2.4 fatcat:rntt37hwf5curegpf46h2di3lm

Prediction of E-Learning Efficiency by Deep Learning in EKhool Online Portal Networks

K.Srinivas
2020 Multimedia Research  
In this paper, Deep Belief Network (DBN) is used for predicting e-learning efficiency in e-khool online portal network.  ...  Once the performance indicators are extracted, the deep learning model is applied for predicting the student performance using E-khool model.  ...  Fig. 1.Architecture of E-khool learning management system Deep Learning Model for Predicting the Student Performance Using E-Khool Model The DBN [17] is used to predict the e-learning performance of  ... 
doi:10.46253/j.mr.v3i4.a2 fatcat:aj5fwob4a5fytgpk7lmxx7dnie

An Investigation on Online Versus Batch Learning in Predicting User Behaviour [chapter]

Nikolay Burlutskiy, Miltos Petridis, Andrew Fish, Alexey Chernov, Nour Ali
2016 Research and Development in Intelligent Systems XXXIII  
The proposed method for comparison of online and offline algorithms as well as the provided experimental evidence can be used for choosing a machine learning set-up for predicting user behaviour on the  ...  It is demonstrated that a simple online learning algorithm outperforms state-of-the-art batch algorithms and performs as well as a deep learning algorithm, Deep Belief Networks.  ...  Acknowledgments The authors are grateful for illuminating discussions to Dr Yuri Kalnishkan's team in the project "On-line Self-Tuning Learning Algorithms for Handling Historical Information" (funded by  ... 
doi:10.1007/978-3-319-47175-4_9 dblp:conf/sgai/BurlutskiyPFCA16 fatcat:3o2rojzq2zd3thplqgvfgb6ype
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