Search Results for author:
Found 6 papers, 2 papers with code
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.
Salt Detection Using Segmentation of Seismic Image
In this project, a state-of-the-art deep convolution neural network (DCNN) is presented to segment seismic images for salt detection below the earth's surface.
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 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.
Location Forensics Analysis Using ENF Sequences Extracted from Power and Audio Recordings
These ENF variations are inherently located in a multimedia signal which is recorded close to the grid or directly from the mains power line.
