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Found 13 papers, 1 papers with code
Review for Handling Missing Data with special missing mechanism
Understanding what missing data is, how it occurs, and why it is crucial to handle it appropriately is paramount when working with real-world data, especially in tabular data, one of the most commonly used data types in the real world.
The Pursuit of Fairness in Artificial Intelligence Models: A Survey
This survey offers a synopsis of the different ways researchers have promoted fairness in AI systems.
Hierarchical Classification for Intrusion Detection System: Effective Design and Empirical Analysis
With the increased use of network technologies like Internet of Things (IoT) in many real-world applications, new types of cyberattacks have been emerging.
A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats
Within this second tier, we also embed a multi-classification mechanism coupled with a clustering algorithm.
usfAD Based Effective Unknown Attack Detection Focused IDS Framework
To address this challenge, we put forth two strategies for semi-supervised learning based IDS where training samples of attacks are not required: 1) training a supervised machine learning model using randomly and uniformly dispersed synthetic attack samples; 2) building a One Class Classification (OCC) model that is trained exclusively on benign network traffic.
Training Machine Learning models at the Edge: A Survey
This survey delves into Edge Learning (EL), specifically the optimization of ML model training at the edge.
HUMS2023 Data Challenge Result Submission
We implemented a simple method for early detection in this research.
SHINE: Deep Learning-Based Accessible Parking Management System
However, this gradual increment in the number of vehicles has inevitably led to parking-related issues, including the abuse of disabled parking spaces (hereafter referred to as accessible parking spaces) designated for individuals with disabilities.
Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey
Indeed, Adversarial Artificial Intelligence (AI) which refers to a set of techniques that attempt to fool machine learning models with deceptive data, is a growing threat in the AI and machine learning research community, in particular for machine-critical applications.
Unintended Bias in Language Model-driven Conversational Recommendation
Conversational Recommendation Systems (CRSs) have recently started to leverage pretrained language models (LM) such as BERT for their ability to semantically interpret a wide range of preference statement variations.
Linked Data Triples Enhance Document Relevance Classification
These use an external corpus as a knowledge base that conditions the model to help predict what a document is about.
Feature Extraction Functions for Neural Logic Rule Learning
Combining symbolic human knowledge with neural networks provides a rule-based ante-hoc explanation of the output.
A Distributed Collaborative Filtering Algorithm Using Multiple Data Sources
Collaborative Filtering (CF) is one of the most commonly used recommendation methods.
