Search Results for author:
Found 10 papers, 4 papers with code
Enabling Smart Retrofitting and Performance Anomaly Detection for a Sensorized Vessel: A Maritime Industry Experience
This study presents a deep learning-driven anomaly detection system augmented with interpretable machine learning models for identifying performance anomalies in an industrial sensorized vessel, called TUCANA.
Anomaly Detection Dataset for Industrial Control Systems
Using Machine Learning (ML) for Intrusion Detection Systems (IDS) is a promising approach for ICS cyber protection, but the lack of suitable datasets for evaluating ML algorithms is a challenge.
Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake System
We initiated a research project with the goal to demonstrate a complete safety case for an ML component in an open automotive system.
Machine Learning Testing in an ADAS Case Study Using Simulation-Integrated Bio-Inspired Search-Based Testing
This paper presents an extended version of Deeper, a search-based simulation-integrated test solution that generates failure-revealing test scenarios for testing a deep neural network-based lane-keeping system.
HMS-OS: Improving the Human Mental Search Optimisation Algorithm by Grouping in both Search and Objective Space
The human mental search (HMS) algorithm is a relatively recent population-based metaheuristic algorithm, which has shown competitive performance in solving complex optimisation problems.
An LSTM-based Plagiarism Detection via Attention Mechanism and a Population-based Approach for Pre-Training Parameters with imbalanced Classes
To this end, this paper employs a PBMH algorithm, artificial bee colony (ABC), to moderate the problem.
An Enhanced Differential Evolution Algorithm Using a Novel Clustering-based Mutation Operator
Differential evolution (DE) is an effective population-based metaheuristic algorithm for solving complex optimisation problems.
Efficient and Effective Generation of Test Cases for Pedestrian Detection -- Search-based Software Testing of Baidu Apollo in SVL
This paper presents the results of our proposed test generation technique in the 2021 IEEE Autonomous Driving AI Test Challenge.
Automated Performance Testing Based on Active Deep Learning
On the other hand, we have a limited test budget to execute tests.
An Autonomous Performance Testing Framework using Self-Adaptive Fuzzy Reinforcement Learning
On the other hand, if the optimal performance testing policy for the intended objective in a testing process instead could be learned by the testing system, then test automation without advanced performance models could be possible.
