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- Multilingual sentiment analysis: from formal to informal and scarce resource languages192
This paper reviews the various current approaches and tools used for multilingual sentiment analysis, identifies challenges along this line of research, and provides several recommendations including a framework that is particularly applicable for dealing with scarce resource languages.
- Prediction of RNA-binding proteins from primary sequence by a support vector machine approach133
The potential of SVM as a useful tool for facilitating the prediction of protein-RNA interactions is suggested, and the SVM classification systems trained in this work were added to the Web-based protein functional classification software SVMProt.
- Effect of training datasets on support vector machine prediction of protein‐protein interactions92
The prediction results are consistent with observations, suggesting that real sequence is more practically useful in development of SVM classification system for facilitating protein‐protein interaction prediction.
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- Using Support Vector Machine Ensembles for Target Audience Classification on Twitter54
This paper investigates the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts and concludes that such an ensemble system can take advantage of data diversity, which enables real-world applications for differentiating prospective customers from the general audience.
- Proteome database of hepatocellular carcinoma51
This review presents an update of the two-dimensional electrophoresis proteome database of the cell line, HCC-M, which is also now freely accessible through the World Wide Web at http://proteome.btc.nus.edu.sg/hccm/.
- Ranking of high-value social audiences on Twitter44
A ranking mechanism capable of identifying the top-k social audience members on Twitter based on an index that has the potential to be adopted in real-world applications for differentiating prospective customers from the general audience and enabling market segmentation for better business decision making is presented.
- A multilingual semi-supervised approach in deriving Singlish sentic patterns for polarity detection39
A semi-supervised approach in constructing essential toolkits for analysing the polarity of a localised scarce-resource language, Singlish (Singaporean English), and suggests that this multilingual approach outshines polarity analysis using only the English language.
- An integrated approach in the discovery and characterization of a novel nuclear protein over‐expressed in liver and pancreatic tumors39
An integrated approach in protein discovery through the use of multidisciplinary tools was reported, and a novel protein, Hcc‐1, was identified by analysis of the hepatocellular carcinoma (HCC)‐M cell proteome.
- An unsupervised multilingual approach for online social media topic identification34
The research shows how an unsupervised online topic identification approach can be designed without much manual annotation effort, which may have great implications for future development of expert and intelligent systems.
- Hcc‐2, a novel mammalian ER thioredoxin that is differentially expressed in hepatocellular carcinoma33
This work demonstrates that an integrated proteomics and genomics approach can be a very powerful means of discovering potential diagnostic and therapeutic protein targets for cancer therapy.
- Identifying the High-Value Social Audience from Twitter through Text-Mining Methods21
This paper analyzes the Twitter content of an account owner and its list of followers through various text mining methods, which include fuzzy keyword matching, statistical topic modeling and machine learning approaches to identify a group of high-value social audience members.
- Automated doubt identification from informal reflections through hybrid sentic patterns and machine learning approach15
A hybrid approach is derived that leverages a novel Doubt Sentic Pattern Detection algorithm and a machine learning model to automate the identification of doubts from students’ informal reflections and shows that the hybrid approach has the potential to be adopted in the real-world doubt detection.
- Effects of Training Datasets on Both the Extreme Learning Machine and Support Vector Machine for Target Audience Identification on Twitter15
Analysis of various training datasets, which include Twitter contents of an account owner and its list of followers, using features generated in different ways for two machine learning approaches - the Extreme Learning Machine (ELM) and Support Vector Machine (SVM).
- PromptTutor: Effects of an LLM-Based Chatbot on Learning Outcomes and Motivation in Flipped Classrooms11
The integration of a Large Language Model (LLM) based chatbot, PromptTutor, into flipped classrooms (FC) for undergraduate Computer Science (CS) education demonstrates statistically significant improvements in students' quiz performance and motivation compared to traditional FC.
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- Use of a High-Value Social Audience Index for Target Audience Identification on Twitter9
This HVSA index enables a company or organisation to devise their marketing and engagement plan according to available resources, so that a high-value social audience can potentially be transformed to customers, and hence improve the return on investment.
- SPLASH: Systematic proteomics laboratory analysis and storage hub6
The systematic proteomics laboratory analysis and storage hub (SPLASH) database system is developed as an informatics infrastructure to support proteomics studies and consists of three modules and provides proteomics researchers a common platform to store, manage, search, analyze, and exchange their data.
- Evaluating ChatGPT to Answer Multi-Modal Exercises in Computer Science Education5
ChatGPT-4o answers better for those multi-modal exercises designed to assess students at the lower levels of Bloom's taxonomy than the higher levels, possibly due to ChatGPT-4o's lack of understanding underlying design concepts and limited ability to generate new multi-modal artifacts.
- Proteomics of Hepatocellular Carcinoma: Present Status and Future Prospects5
It is hoped that integration of proteome-based approaches with data from genomic profiling will lead to a better understanding of hepatocarcinogenesis which will contribute to the direct translation of the research findings into clinical practice.
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- Do my students understand? Automated identification of doubts from informal reflections3
An approach to automate the identification of doubts from students’ informal reflections through features analysis, word representation and machine learning is derived.
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- Detecting Doubt in Reflective Learning: A Learning Analytics Study with Large and Small Language Models–
These findings highlight both the promise and limitations of language models for doubt detection and the need to ensure that cautious or polite learners who may not express their doubts explicitly are recognized and supported in learning analytics systems.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar; position from the scholar’s ORCID record, last synced 2026-10-10. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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