Search Results for author:
Found 25 papers, 5 papers with code
A New Approach to Extract Fetal Electrocardiogram Using Affine Combination of Adaptive Filters
Compared with the state-of-the-art MSF-ANC method, CRLS shows improved performance.
Automated Sex Classification of Children's Voices and Changes in Differentiating Factors with Age
For younger age groups, a broad range of acoustic factors contributed evenly to sex classification, while for older age groups, F0-related acoustic factors were found to be the most critical predictors generally.
End-to-End Lyrics Recognition with Self-supervised Learning
Lyrics recognition is an important task in music processing.
Spatio-Temporal Graph Representation Learning for Fraudster Group Detection
Then we use an RNN on the spatial relations to predict the spatio-temporal relations of reviewers in the group.
Social Fraud Detection Review: Methods, Challenges and Analysis
Many studies proposed approaches based on user behaviors and review text to address the challenges of fraud detection.
q-RBFNN:A Quantum Calculus-based RBF Neural Network
In this research a novel stochastic gradient descent based learning approach for the radial basis function neural networks (RBFNN) is proposed.
HIN-RNN: A Graph Representation Learning Neural Network for Fraudster Group Detection With No Handcrafted Features
Social reviews are indispensable resources for modern consumers' decision making.
Multi-Kernel Fusion for RBF Neural Networks
A simple yet effective architectural design of radial basis function neural networks (RBFNN) makes them amongst the most popular conventional neural networks.
ScoreGAN: A Fraud Review Detector based on Multi Task Learning of Regulated GAN with Data Augmentation
However, the lack of trusted labeled data has limited the performance of the current solutions in detecting fraud reviews.
DFraud3- Multi-Component Fraud Detection freeof Cold-start
In this research, instead of focusing only on one component, detecting either fraud reviews or fraud users (fraudsters), vector representations are learnt for each component, enabling multi-component classification.
Unconstrained Matching of 2D and 3D Descriptors for 6-DOF Pose Estimation
We use this concept to directly localize images in a 3D point cloud.
Direct Image to Point Cloud Descriptors Matching for 6-DOF Camera Localization in Dense 3D Point Cloud
We use this concept to localize the position and orientation (pose) of the camera of a query image in dense point clouds.
A Novel Adaptive Kernel for the RBF Neural Networks
In this paper, we propose a novel adaptive kernel for the radial basis function (RBF) neural networks.
RAFP-Pred: Robust Prediction of Antifreeze Proteins using Localized Analysis of n-Peptide Compositions
In extreme cold weather, living organisms produce Antifreeze Proteins (AFPs) to counter the otherwise lethal intracellular formation of ice.
DataDeps.jl: Repeatable Data Setup for Replicable Data Science
We present DataDeps. jl: a julia package for the reproducible handling of static datasets to enhance the repeatability of scripts used in the data and computational sciences.
Software Engineering
NovelPerspective: Identifying Point of View Characters
Our tool detects the main character that each section is from the POV of, and allows the user to generate a new ebook with only those sections.
Real Time Surveillance for Low Resolution and Limited-Data Scenarios: An Image Set Classification Approach
Class specific gallery subspaces are used to estimate regression models for each image of the test image set.
Learning of Colors from Color Names: Distribution and Point Estimation
Color names are often made up of multiple words.
Efficient Image Set Classification using Linear Regression based Image Reconstruction
We estimate regression models for each test image using the class specific gallery subspaces.
Learning deep structured network for weakly supervised change detection
We apply a constrained mean-field algorithm to estimate the pixel-level labels, and use the estimated labels to update the parameters of the CNN in an iterative EM framework.
Listening With Your Eyes: Towards a Practical Visual Speech Recognition System Using Deep Boltzmann Machines
This paper presents a novel feature learning method for visual speech recognition using Deep Boltzmann Machines (DBM).
Cost Sensitive Learning of Deep Feature Representations from Imbalanced Data
Class imbalance is a common problem in the case of real-world object detection and classification tasks.
A Discriminative Representation of Convolutional Features for Indoor Scene Recognition
To this end, we introduce a new large-scale dataset of 1300 object categories which are commonly present in indoor scenes.
Separating Objects and Clutter in Indoor Scenes
Objects' spatial layout estimation and clutter identification are two important tasks to understand indoor scenes.
Automatic Feature Learning for Robust Shadow Detection
We present a practical framework to automatically detect shadows in real world scenes from a single photograph.
