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Found 15 papers, 2 papers with code
Dynamic Modality and View Selection for Multimodal Emotion Recognition with Missing Modalities
The study of human emotions, traditionally a cornerstone in fields like psychology and neuroscience, has been profoundly impacted by the advent of artificial intelligence (AI).
Alleviating Catastrophic Forgetting in Facial Expression Recognition with Emotion-Centered Models
Facial expression recognition is a pivotal component in machine learning, facilitating various applications.
Improving Sampling Methods for Fine-tuning SentenceBERT in Text Streams
The proliferation of textual data on the Internet presents a unique opportunity for institutions and companies to monitor public opinion about their services and products.
Methods for Generating Drift in Text Streams
To learn from textual data over time, the machine learning system must account for concept drift.
Temporal Analysis of Drifting Hashtags in Textual Data Streams: A Graph-Based Application
People use the internet to express opinions about anything, making social media platforms a social sensor.
Concept Drift Adaptation in Text Stream Mining Settings: A Comprehensive Review
Due to the advent and increase in the popularity of the Internet, people have been producing and disseminating textual data in several ways, such as reviews, social media posts, and news articles.
Random Forest Kernel for High-Dimension Low Sample Size Classification
High dimension, low sample size (HDLSS) problems are numerous among real-world applications of machine learning.
Deep Single Models vs. Ensembles: Insights for a Fast Deployment of Parking Monitoring Systems
Searching for available parking spots in high-density urban centers is a stressful task for drivers that can be mitigated by systems that know in advance the nearest parking space available.
Detecting Relevant Information in High-Volume Chat Logs: Keyphrase Extraction for Grooming and Drug Dealing Forensic Analysis
The growing use of digital communication platforms has given rise to various criminal activities, such as grooming and drug dealing, which pose significant challenges to law enforcement and forensic experts.
Advances on Concept Drift Detection in Regression Tasks using Social Networks Theory
Mining data streams is one of the main studies in machine learning area due to its application in many knowledge areas.
Evaluating k-NN in the Classification of Data Streams with Concept Drift
Moreover, these data are likely subject to changes in data distribution, known as concept drift.
Evaluation of Self-taught Learning-based Representations for Facial Emotion Recognition
This work describes different strategies to generate unsupervised representations obtained through the concept of self-taught learning for facial emotion recognition (FER).
A Systematic Review on Computer Vision-Based Parking Lot Management Applied on Public Datasets
Computer vision-based parking lot management methods have been extensively researched upon owing to their flexibility and cost-effectiveness.
A Survey on Concept Drift in Process Mining
Concept drift in process mining (PM) is a challenge as classical methods assume processes are in a steady-state, i. e., events share the same process version.
scikit-dyn2sel -- A Dynamic Selection Framework for Data Streams
Mining data streams is a challenge per se.
