Search Results for author:
Found 48 papers, 10 papers with code
Enhancing Cognitive Workload Classification Using Integrated LSTM Layers and CNNs for fNIRS Data Analysis
Functional near-infrared spectroscopy (fNIRS) is employed as a non-invasive method to monitor functional brain activation by capturing changes in the concentrations of oxygenated haemoglobin (HbO) and deoxygenated haemo-globin (HbR).
Machine Learning Meets Advanced Robotic Manipulation
Automated industries lead to high quality production, lower manufacturing cost and better utilization of human resources.
A Review of Machine Learning-based Security in Cloud Computing
Cloud Computing (CC) is revolutionizing the way IT resources are delivered to users, allowing them to access and manage their systems with increased cost-effectiveness and simplified infrastructure.
A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges
Overall, the goal of the paper is to provide a comprehensive guide to the growing field of IL in robotics and AI.
Uncertainty Aware Neural Network from Similarity and Sensitivity
In the proposed NN training method for UQ, first, we train a shallow NN for the point prediction.
Survey on Leveraging Uncertainty Estimation Towards Trustworthy Deep Neural Networks: The Case of Reject Option and Post-training Processing
In this paper, we present a systematic review of the prediction with the reject option in the context of various neural networks.
A Brief Review of Explainable Artificial Intelligence in Healthcare
This paper presents a systematic review of XAI aspects and challenges in the healthcare domain.
CoV-TI-Net: Transferred Initialization with Modified End Layer for COVID-19 Diagnosis
In this research, the PyTorch pre-trained models (VGG19\_bn and WideResNet -101) are applied in the MNIST dataset for the first time as initialization and with modified fully connected layers.
Controlled Dropout for Uncertainty Estimation
In this study, we present a new version of the traditional dropout layer where we are able to fix the number of dropout configurations.
A Comprehensive Study on Torchvision Pre-trained Models for Fine-grained Inter-species Classification
We also apply their usual fully-connected layer and the Spinal fully-connected layer to investigate the effectiveness of SpinalNet.
Ranked #1 on
Fine-Grained Image Classification
on Bird-225
(using extra training data)
An Uncertainty-aware Loss Function for Training Neural Networks with Calibrated Predictions
Uncertainty quantification of machine learning and deep learning methods plays an important role in enhancing trust to the obtained result.
What happens in Face during a facial expression? Using data mining techniques to analyze facial expression motion vectors
Their positions and directions were exploited for automatic facial expression recognition using different data mining techniques.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models
In this step, the DL models were implemented and compared with different activation functions.
MCUa: Multi-level Context and Uncertainty aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification
It exploits the high sensitivity to the multi-level contextual information using an uncertainty quantification component to accomplish a novel dynamic ensemble model. MCUamodelhas achieved a high accuracy of 98. 11% on a breast cancer histology image dataset.
Breast Cancer Histology Image Classification
Classification
+2
Uncertainty-Aware Credit Card Fraud Detection Using Deep Learning
Countless research works of deep neural networks (DNNs) in the task of credit card fraud detection have focused on improving the accuracy of point predictions and mitigating unwanted biases by building different network architectures or learning models.
An Uncertainty-Aware Deep Learning Framework for Defect Detection in Casting Products
Secondly, to achieve a reliable classification and to measure epistemic uncertainty, we employ an uncertainty quantification (UQ) technique (ensemble of MLP models) using features extracted from four pre-trained CNNs.
Confidence Aware Neural Networks for Skin Cancer Detection
Deep learning (DL) models have received particular attention in medical imaging due to their promising pattern recognition capabilities.
An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works
In the discussion section, a comparison has been carried out between research on epileptic seizure detection and prediction.
UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection
Differently from most of existing studies, which used either CT scan or X-ray images in COVID-19-case classification, we present a simple but efficient deep learning feature fusion model, called UncertaintyFuseNet, which is able to classify accurately large datasets of both of these types of images.
Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review
In this paper, a complete review of automated MS diagnosis methods performed using DL techniques with MRI neuroimaging modalities are discussed.
Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods
This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series.
Combining a Convolutional Neural Network with Autoencoders to Predict the Survival Chance of COVID-19 Patients
To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images.
An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works
Future works in diagnosing SZ using AI techniques and MRI modalities are recommended in another section.
Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images
In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images.
Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data
Our system is capable of learning from a mixture of limited labeled and unlabeled data where supervised learners fail due to a lack of sufficient amount of labeled data.
Objective Evaluation of Deep Uncertainty Predictions for COVID-19 Detection
Accordingly, uncertainty quantification methods are capable of flagging risky predictions with high uncertainty estimates.
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes.
Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991-2020)
We have little knowledge about the optimal treatment methods as there are many sources of uncertainty in medical science.
An Uncertainty-aware Transfer Learning-based Framework for Covid-19 Diagnosis
The early and reliable detection of COVID-19 infected patients is essential to prevent and limit its outbreak.
Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review
Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL is presented.
SpinalNet: Deep Neural Network with Gradual Input
Traditional learning with ImageNet pre-trained initial weights and SpinalNet classification layers provided the SOTA performance on STL-10, Fruits 360, Bird225, and Caltech-101 datasets.
Ranked #1 on
Image Classification
on Kuzushiji-MNIST
Fine-Grained Image Classification
Satellite Image Classification
+1
Epileptic Seizures Detection Using Deep Learning Techniques: A Review
The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed.
Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review
Due to the intricate structure and function of the brain, proposing optimum procedures for ASD diagnosis with neuroimaging data without exploiting powerful AI techniques like DL may be challenging.
Material Recognition for Automated Progress Monitoring using Deep Learning Methods
Recent advancements in Artificial intelligence, especially deep learning, has changed many fields irreversibly by introducing state of the art methods for automation.
Review, Analysis and Design of a Comprehensive Deep Reinforcement Learning Framework
However, development of a deep RL-based system is challenging because of various issues such as the selection of a suitable deep RL algorithm, its network configuration, training time, training methods, and so on.
A Visual Communication Map for Multi-Agent Deep Reinforcement Learning
In this paper, we have proposed a more scalable approach that not only deals with a great number of agents but also enables collaboration between dissimilar functional agents and compatibly combined with any deep reinforcement learning methods.
Optimal Uncertainty-guided Neural Network Training
Most of the existing cost functions of uncertainty guided NN training are not customizable and the convergence of training is uncertain.
Deep Learning for Deepfakes Creation and Detection: A Survey
By reviewing the background of deepfakes and state-of-the-art deepfake detection methods, this study provides a comprehensive overview of deepfake techniques and facilitates the development of new and more robust methods to deal with the increasingly challenging deepfakes.
Real-time Intent Prediction of Pedestrians for Autonomous Ground Vehicles via Spatio-Temporal DenseNet
We trained and evaluated our framework based on real data collected from urban traffic environments.
Realistic Hair Simulation Using Image Blending
In this presented work, we propose a realistic hair simulator using image blending for dermoscopic images.
A New Tensioning Method using Deep Reinforcement Learning for Surgical Pattern Cutting
Surgeons normally need surgical scissors and tissue grippers to cut through a deformable surgical tissue.
Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications
Reinforcement learning (RL) algorithms have been around for decades and employed to solve various sequential decision-making problems.
3D Hand Pose Estimation using Simulation and Partial-Supervision with a Shared Latent Space
In this paper, we propose a novel method that seeks to predict the 3d position of the hand using both synthetic and partially-labeled real data.
SSIMLayer: Towards Robust Deep Representation Learning via Nonlinear Structural Similarity
Deeper convolutional neural networks provide more capacity to approximate complex mapping functions.
Multi-Agent Deep Reinforcement Learning with Human Strategies
In this paper, we introduce an approach that integrates human strategies to increase the exploration capacity of multiple deep reinforcement learning agents.
A Human Mixed Strategy Approach to Deep Reinforcement Learning
In 2015, Google's DeepMind announced an advancement in creating an autonomous agent based on deep reinforcement learning (DRL) that could beat a professional player in a series of 49 Atari games.
A Multi-Objective Deep Reinforcement Learning Framework
This paper introduces a new scalable multi-objective deep reinforcement learning (MODRL) framework based on deep Q-networks.
Deep Reinforcement Learning
Multi-Objective Reinforcement Learning
+1
Multi-Residual Networks: Improving the Speed and Accuracy of Residual Networks
We also demonstrate that our model outperforms very deep residual networks by 0. 22% (top-1 error) on the full ImageNet 2012 classification dataset.
