Mihaela Cocea | University of Portsmouth (original) (raw)

Mihaela Cocea

Uploads

Papers by Mihaela Cocea

Research paper thumbnail of Validation Issues in Educational Data Mining: The Case of HTML-Tutor and iHelp

CRC Press eBooks, Oct 25, 2010

Research paper thumbnail of Decision tree learning based feature evaluation and selection for image classification

2017 International Conference on Machine Learning and Cybernetics (ICMLC), 2017

Research paper thumbnail of TorBot Stalker: Detecting Tor Botnets Through Intelligent Circuit Data Analysis

2018 IEEE 17th International Symposium on Network Computing and Applications (NCA), 2018

Research paper thumbnail of Towards gathering initial requirements of developing a mobile service to support informal learning at cultural heritage sites

Informal learning allows learners to be in charge of their own learning process instead of being ... more Informal learning allows learners to be in charge of their own learning process instead of being a content consumer. Harnessing mobile technology in informal learning field could help learners in taking a learning opportunity whenever they need either individually or in a group. This paper presents a small-scale study to investigate how people may use mobile technology for learning purposes in cultural heritage contexts. A focus group approach was used to capture preliminary results of user requirements. Based on these results, a scenario-based method was used to reflect a tangible picture regarding how people interact with mobile services. This study serves as an initial step of the series of gathering user requirements in developing a mobile location-based learning service.

Research paper thumbnail of Transformation of discriminative single-task classification into generative multi-task classification in machine learning context

2017 Ninth International Conference on Advanced Computational Intelligence (ICACI), 2017

Research paper thumbnail of Analysis of The Syntactical Structure Of Web Queries

2018 International Conference on Machine Learning and Cybernetics (ICMLC), 2018

Research paper thumbnail of Guidelines for designing a smart and ubiquitous learning environment with respect to cultural heritage

2017 11th International Conference on Research Challenges in Information Science (RCIS), May 1, 2017

Research paper thumbnail of Evaluating the topological quality of watermarked vector maps

Applied Soft Computing, 2018

Research paper thumbnail of Fuzzy rule based systems for interpretable sentiment analysis

2017 Ninth International Conference on Advanced Computational Intelligence (ICACI), 2017

Research paper thumbnail of Granular computing-based approach for classification towards reduction of bias in ensemble learning

Research paper thumbnail of Interpretability of Computational Models for Sentiment Analysis

Studies in Computational Intelligence, 2016

Research paper thumbnail of Collaborative rule generation: An ensemble learning approach

Journal of Intelligent & Fuzzy Systems, 2016

Research paper thumbnail of Advancements in GIS map copyright protection schemes - a critical review

Multimedia Tools and Applications, 2016

Research paper thumbnail of Exploiting Vector Map Properties for GIS Data Copyright Protection

2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), 2015

Research paper thumbnail of Network based rule representation for knowledge discovery and predictive modelling

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2015

Research paper thumbnail of Interpretability Analysis

Studies in Big Data, 2015

Research paper thumbnail of Case Studies

Studies in Big Data, 2015

Research paper thumbnail of Rule Based Systems for Big Data

Studies in Big Data, 2016

Research paper thumbnail of Detecting Sarcasm from Students’ Feedback in Twitter

Lecture Notes in Computer Science, 2015

Research paper thumbnail of Predicting learning-related emotions from students' textual classroom feedback via Twitter

Teachers/lecturers typically adapt their teaching to respond to students' emotions, e.g. prov... more Teachers/lecturers typically adapt their teaching to respond to students' emotions, e.g. provide more examples when they think the students are confused. While getting a feel of the students' emotions is easier in small settings, it is much more difficult in larger groups. In these larger settings tex-tual feedback from students could provide information about learning-related emotions that students experience. Prediction of emotions from text, however, is known to be a difficult problem due to language ambiguity. While prediction of general emotions from text has been reported in the literature , very little attention has been given to prediction of learning-related emotions. In this paper we report several experiments for predicting emotions related to learning using machine learning techniques and n-grams as features, and discuss their performance. The results indicate that some emotions can be distinguished more easily then others .

Research paper thumbnail of Validation Issues in Educational Data Mining: The Case of HTML-Tutor and iHelp

CRC Press eBooks, Oct 25, 2010

Research paper thumbnail of Decision tree learning based feature evaluation and selection for image classification

2017 International Conference on Machine Learning and Cybernetics (ICMLC), 2017

Research paper thumbnail of TorBot Stalker: Detecting Tor Botnets Through Intelligent Circuit Data Analysis

2018 IEEE 17th International Symposium on Network Computing and Applications (NCA), 2018

Research paper thumbnail of Towards gathering initial requirements of developing a mobile service to support informal learning at cultural heritage sites

Informal learning allows learners to be in charge of their own learning process instead of being ... more Informal learning allows learners to be in charge of their own learning process instead of being a content consumer. Harnessing mobile technology in informal learning field could help learners in taking a learning opportunity whenever they need either individually or in a group. This paper presents a small-scale study to investigate how people may use mobile technology for learning purposes in cultural heritage contexts. A focus group approach was used to capture preliminary results of user requirements. Based on these results, a scenario-based method was used to reflect a tangible picture regarding how people interact with mobile services. This study serves as an initial step of the series of gathering user requirements in developing a mobile location-based learning service.

Research paper thumbnail of Transformation of discriminative single-task classification into generative multi-task classification in machine learning context

2017 Ninth International Conference on Advanced Computational Intelligence (ICACI), 2017

Research paper thumbnail of Analysis of The Syntactical Structure Of Web Queries

2018 International Conference on Machine Learning and Cybernetics (ICMLC), 2018

Research paper thumbnail of Guidelines for designing a smart and ubiquitous learning environment with respect to cultural heritage

2017 11th International Conference on Research Challenges in Information Science (RCIS), May 1, 2017

Research paper thumbnail of Evaluating the topological quality of watermarked vector maps

Applied Soft Computing, 2018

Research paper thumbnail of Fuzzy rule based systems for interpretable sentiment analysis

2017 Ninth International Conference on Advanced Computational Intelligence (ICACI), 2017

Research paper thumbnail of Granular computing-based approach for classification towards reduction of bias in ensemble learning

Research paper thumbnail of Interpretability of Computational Models for Sentiment Analysis

Studies in Computational Intelligence, 2016

Research paper thumbnail of Collaborative rule generation: An ensemble learning approach

Journal of Intelligent & Fuzzy Systems, 2016

Research paper thumbnail of Advancements in GIS map copyright protection schemes - a critical review

Multimedia Tools and Applications, 2016

Research paper thumbnail of Exploiting Vector Map Properties for GIS Data Copyright Protection

2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), 2015

Research paper thumbnail of Network based rule representation for knowledge discovery and predictive modelling

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2015

Research paper thumbnail of Interpretability Analysis

Studies in Big Data, 2015

Research paper thumbnail of Case Studies

Studies in Big Data, 2015

Research paper thumbnail of Rule Based Systems for Big Data

Studies in Big Data, 2016

Research paper thumbnail of Detecting Sarcasm from Students’ Feedback in Twitter

Lecture Notes in Computer Science, 2015

Research paper thumbnail of Predicting learning-related emotions from students' textual classroom feedback via Twitter

Teachers/lecturers typically adapt their teaching to respond to students' emotions, e.g. prov... more Teachers/lecturers typically adapt their teaching to respond to students' emotions, e.g. provide more examples when they think the students are confused. While getting a feel of the students' emotions is easier in small settings, it is much more difficult in larger groups. In these larger settings tex-tual feedback from students could provide information about learning-related emotions that students experience. Prediction of emotions from text, however, is known to be a difficult problem due to language ambiguity. While prediction of general emotions from text has been reported in the literature , very little attention has been given to prediction of learning-related emotions. In this paper we report several experiments for predicting emotions related to learning using machine learning techniques and n-grams as features, and discuss their performance. The results indicate that some emotions can be distinguished more easily then others .

Log In