Search Results for author:
Found 22 papers, 4 papers with code
Robustness Evaluation of Machine Learning Models for Robot Arm Action Recognition in Noisy Environments
This paper studies robot arm action recognition in noisy environments using machine learning techniques.
Hypertension Detection From High-Dimensional Representation of Photoplethysmogram Signals
Although some studies suggest a relationship between blood pressure and certain vital signals, such as Photoplethysmogram (PPG), reliable generalization of the proposed blood pressure estimation methods is not yet guaranteed.
Ranked #1 on
Hypertension detection
on MIMIC-III
S-Rocket: Selective Random Convolution Kernels for Time Series Classification
A population-based optimization algorithm evolves the population in order to find a best state vector which minimizes the number of active kernels while maximizing the accuracy of the classifier.
A Framework For Pruning Deep Neural Networks Using Energy-Based Models
A typical deep neural network (DNN) has a large number of trainable parameters.
Pruning of Convolutional Neural Networks Using Ising Energy Model
Pruning is one of the major methods to compress deep neural networks.
A Real-World Demonstration of Machine Learning Generalizability: Intracranial Hemorrhage Detection on Head CT
An ML model was trained using 21, 784 scans from the RSNA Intracranial Hemorrhage CT dataset while generalizability was evaluated using an external validation dataset obtained from our busy trauma and neurosurgical center.
Deep Sequential Learning for Cervical Spine Fracture Detection on Computed Tomography Imaging
Fractures of the cervical spine are a medical emergency and may lead to permanent paralysis and even death.
EDropout: Energy-Based Dropout and Pruning of Deep Neural Networks
The energy-based model stochastically evolves the population to find states with lower energy loss.
Survey of Dropout Methods for Deep Neural Networks
Dropout methods are a family of stochastic techniques used in neural network training or inference that have generated significant research interest and are widely used in practice.
Ising-Dropout: A Regularization Method for Training and Compression of Deep Neural Networks
In this paper, we propose an adaptive technique to wisely drop the visible and hidden units in a deep neural network using Ising energy of the network.
Cylindrical Transform: 3D Semantic Segmentation of Kidneys With Limited Annotated Images
The images generated from a cylindrical transform augment a limited annotated set of images in three dimensions.
Recent Advances in Recurrent Neural Networks
Recurrent neural networks (RNNs) are capable of learning features and long term dependencies from sequential and time-series data.
Generalization of Deep Neural Networks for Chest Pathology Classification in X-Rays Using Generative Adversarial Networks
Medical datasets are often highly imbalanced with over-representation of common medical problems and a paucity of data from rare conditions.
Opposition based Ensemble Micro Differential Evolution
Differential evolution (DE) algorithm with a small population size is called Micro-DE (MDE).
Interpretation of Mammogram and Chest X-Ray Reports Using Deep Neural Networks - Preliminary Results
Radiology reports are an important means of communication between radiologists and other physicians.
Image Augmentation using Radial Transform for Training Deep Neural Networks
We propose a sampling method based on the radial transform in a polar coordinate system for image augmentation to facilitate the training of deep learning models from limited source data.
A Convolutional Neural Network for Search Term Detection
Pathfinding in hospitals is challenging for patients, visitors, and even employees.
Learning Over Long Time Lags
This paper provides a fundamental review on RNNs and long short term memory (LSTM) model.
Diversity Enhancement for Micro-Differential Evolution
Furthermore, comprehensive comparative simulations and analysis on performance of the MDE algorithms over various mutation schemes, population sizes, problem types (i. e. uni-modal, multi-modal, and composite), problem dimensionalities, and mutation factor ranges are conducted by considering population diversity analysis for stagnation and trapping in local optimum situations.
Deep Recurrent Neural Networks for Sequential Phenotype Prediction in Genomics
In analyzing of modern biological data, we are often dealing with ill-posed problems and missing data, mostly due to high dimensionality and multicollinearity of the dataset.
Toward Smart Power Grids: Communication Network Design for Power Grids Synchronization
In smart power grids, keeping the synchronicity of generators and the corresponding controls is of great importance.
Combined A*-Ants Algorithm: A New Multi-Parameter Vehicle Navigation Scheme
In this paper a multi-parameter A*(A- star)-ants based algorithm is proposed in order to find the best optimized multi-parameter path between two desired points in regions.
