Search Results for author:
Found 23 papers, 2 papers with code
DE-CGAN: Boosting rTMS Treatment Prediction with Diversity Enhancing Conditional Generative Adversarial Networks
This work shows that increasing the diversity of a training dataset can improve classification model performance.
CrimeAlarm: Towards Intensive Intent Dynamics in Fine-grained Crime Prediction
Granularity and accuracy are two crucial factors for crime event prediction.
MealRec$^+$: A Meal Recommendation Dataset with Meal-Course Affiliation for Personalization and Healthiness
Due to constraints related to user health privacy and meal scenario characteristics, the collection of data that includes both meal-course affiliation and two levels of interactions is impeded.
Primary and Secondary Factor Consistency as Domain Knowledge to Guide Happiness Computing in Online Assessment
We achieve this through: (1) proving that multiple prediction models with additive factor attributions will have the desirable property of primary and secondary relations consistency, and (2) showing that factor relations with quantity can be represented as an importance distribution for encoding domain knowledge.
Towards Causal Classification: A Comprehensive Study on Graph Neural Networks
The exploration of Graph Neural Networks (GNNs) for processing graph-structured data has expanded, particularly their potential for causal analysis due to their universal approximation capabilities.
Persona-centric Metamorphic Relation guided Robustness Evaluation for Multi-turn Dialogue Modelling
For that reason, this work evaluates several widely used training paradigms including learning from scratch, pretrain + fine-tune and prompt learning in personalized dialogue retrieval to know if they are more robust or if they have the same flaws as their predecessor.
Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment
The demands for fine-tuning PLMs, especially LLMs, have led to a surge in the development of PEFT methods, as depicted in Fig.
Exploring Causal Learning through Graph Neural Networks: An In-depth Review
In machine learning, exploring data correlations to predict outcomes is a fundamental task.
Clustered FedStack: Intermediate Global Models with Bayesian Information Criterion
In our study, we propose a novel Clustered FedStack framework based on the previously published Stacked Federated Learning (FedStack) framework.
Adaptive Multi-Agent Deep Reinforcement Learning for Timely Healthcare Interventions
Effective patient monitoring is vital for timely interventions and improved healthcare outcomes.
QXAI: Explainable AI Framework for Quantitative Analysis in Patient Monitoring Systems
The deep learning models achieved state-of-the-art results in both prediction and classification tasks.
PDRL: Multi-Agent based Reinforcement Learning for Predictive Monitoring
The PDRL framework is able to learn the future states of the traffic and weather forecasting and the cumulative rewards are gradually increasing over each episode.
FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning
These issues compromise both the accuracy and the computational efficiency of models in both Machine Learning and Unlearning.
Graph-enabled Reinforcement Learning for Time Series Forecasting with Adaptive Intelligence
In this study, we propose a novel approach for predicting time-series data using GNN and monitoring with Reinforcement Learning (RL).
A Survey of Multimodal Information Fusion for Smart Healthcare: Mapping the Journey from Data to Wisdom
The components of the comprehensive survey presented in this paper form the foundation for more successful implementation of multimodal fusion in smart healthcare.
Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy
Machine unlearning (MU) is gaining increasing attention due to the need to remove or modify predictions made by machine learning (ML) models.
Sentiment analysis and opinion mining on educational data: A survey
In the education sector, opinion mining is used to listen to student opinions and enhance their learning-teaching practices pedagogically.
AI enabled RPM for Mental Health Facility
To provide a therapeutic environment for both patients and staff, aggressive or agitated patients need to be monitored remotely and track their vital signs and physical activities continuously.
A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis
Research community approaches to extract the semantic meaning of emoticons and special characters in feedback which conveys user opinion and challenges in adopting NLP in education are explored.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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Remote patient monitoring using artificial intelligence: Current state, applications, and challenges
The adoption of artificial intelligence (AI) in healthcare is growing rapidly.
FedStack: Personalized activity monitoring using stacked federated learning
The federated learning architecture was applied to these models to build local and global models capable of state of the art performances.
MealRec: A Meal Recommendation Dataset
A publicly available dataset specialising in meal recommendation research for the research community is in urgent demand.
What is Next when Sequential Prediction Meets Implicitly Hard Interaction?
The experiences in the form of the unlikelihood of correct responses are drawn from each other by MED, which provides mutual exclusivity knowledge to figure out implicitly hard interactions.
