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Found 9 papers, 2 papers with code
Explainable Detection of Sarcasm in Social Media
Sarcasm is a linguistic expression often used to communicate the opposite of what is said, usually something that is very unpleasant with an intention to insult or ridicule.
Sentence Pair Embeddings Based Evaluation Metric for Abstractive and Extractive Summarization
As these sentence pair tasks involve capturing the semantic similarity between a pair of input texts, we leverage these models in our metric computation.
Efficient Track Anything
The high computation complexity of multistage image encoder and memory module has limited its applications in real-world tasks, e. g., video object segmentation on mobile devices.
Enhancing Cryptocurrency Market Forecasting: Advanced Machine Learning Techniques and Industrial Engineering Contributions
This chapter highlights the evolving landscape of cryptocurrency price prediction, the integration of emerging technologies, and the significant role of industrial engineers in refining predictive models.
Ethical AI for Social Good
The concept of AI for Social Good(AI4SG) is gaining momentum in both information societies and the AI community.
Interpretable Multi-Head Self-Attention model for Sarcasm Detection in social media
We show the effectiveness of our approach by achieving state-of-the-art results on multiple datasets from social networking platforms and online media.
Supervised Machine Learning based Ensemble Model for Accurate Prediction of Type 2 Diabetes
Thus, to test the accurate prediction of Type 2 diabetes, we use the patients' information from an electronic health records company called Practice Fusion, which has about 10, 000 patient records from 2009 to 2012.
Forecasting the Success of Television Series using Machine Learning
The models represent a baseline to understanding the success of a television show and how producers can increase the success of current television shows or utilize this data in the creation of future shows.
DeepFork: Supervised Prediction of Information Diffusion in GitHub
To understand human influence, information spread and evolution of transmitted information among assorted users in GitHub, we developed a deep neural network model: DeepFork, a supervised machine learning based approach that aims to predict information diffusion in complex social networks; considering node as well as topological features.
