Search Results for author:
Found 10 papers, 3 papers with code
Negative Selection Approach to support Formal Verification and Validation of BlackBox Models' Input Constraints
This paper investigates a meta-heuristic approach to search for unsafe candidate sub-requirements in partitioned input space.
Mitigating shortage of labeled data using clustering-based active learning with diversity exploration
In this paper, we proposed a new clustering-based active learning framework, namely Active Learning using a Clustering-based Sampling (ALCS), to address the shortage of labeled data.
A Robust Completed Local Binary Pattern (RCLBP) for Surface Defect Detection
Our approach uses a combination of Non-Local (NL) means filter with wavelet thresholding and Completed Local Binary Pattern (CLBP) to extract robust features which are fed into classifiers for surface defects detection.
DA$^{\textbf{2}}$-Net : Diverse & Adaptive Attention Convolutional Neural Network
Standard Convolutional Neural Network (CNN) designs rarely focus on the importance of explicitly capturing diverse features to enhance the network's performance.
A Software Tool for Evaluating Unmanned Autonomous Systems
The North Carolina Agriculture and Technical State University (NC A&T) in collaboration with Georgia Tech Research Institute (GTRI) has developed methodologies for creating simulation-based technology tools that are capable of inferring the perceptions and behavioral states of autonomous systems.
A Supervised Feature Selection Method For Mixed-Type Data using Density-based Feature Clustering
In this paper, a supervised feature selection method using density-based feature clustering (SFSDFC) is proposed to obtain an appropriate final feature subset for mixed-type data.
A Clustering-based Framework for Classifying Data Streams
The overlap among classes and the labeling of data streams constitute other major challenges for classifying data streams.
Evolving Multi-label Classification Rules by Exploiting High-order Label Correlation
The correlation between labels can be exploited at different levels such as capturing the pair-wise correlation or exploiting the higher-order correlations.
Deep Learning with Attention Mechanism for Predicting Driver Intention at Intersection
As intersection is considered to be as one of the major source of road accidents, predicting a driver's intention at an intersection is very crucial.
Driver Identification Based on Vehicle Telematics Data using LSTM-Recurrent Neural Network
Results show that the proposed model prediction accuracy remains satisfactory and outperforms the other approaches despite the extent of anomalies and noise-induced in the data.
