Topic
- Computing and Processing
- Components, Circuits, Devices and Systems
- Communication, Networking and Broadcast Technologies
- Power, Energy and Industry Applications
- Signal Processing and Analysis
- Robotics and Control Systems
- General Topics for Engineers
- Fields, Waves and Electromagnetics
- Engineered Materials, Dielectrics and Plasmas
- Bioengineering
- Transportation
- Photonics and Electrooptics
- Engineering Profession
- Aerospace
- Geoscience
- Nuclear Engineering
- Career Development
- Emerging Technologies
- Telecommunications
- English for Technical Professionals
Moloud Abdar
Affiliation
Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Australia
Topic
Attention Mechanism,Convolutional Neural Network,Deep Neural Network,Neural Network,Uncertainty Quantification,Attention Module,Classification Accuracy,Convolutional Layers,Deep Learning,Image Dataset,Latent Space,Shallow Neural Network,Training Set,Transfer Learning,Visual Features,Visual Space,Activation Maps,Aleatory,Algorithm For High-dimensional Data,Algorithms For Datasets,Anatomical Regions,Angular Velocity,Attention Weights,Bayesian Neural Network,Breast Cancer,CIFAR-100 Dataset,CT Data,CT Features,Channel Attention Module,Chest X-ray,Class Assignment,Class Weights,Classification Datasets,Classification Model,Classification Of Samples,Closed And Open,Cockpit,Computed Tomography,Computed Tomography Datasets,Computer-aided Diagnosis,Concatenated Feature Map,Conditional Variational Autoencoder,Contextual Information,Coverage Probability,Cranial Part,Dataset Characteristics,Dataset Description,Decision Network,Decision-making Units,Decreasing Gradient,
Biography
Moloud Abdar ([email protected]) earned his Ph.D. degree in machine learning from Deakin University. He is with Deakin University, Geelong, Vic 3216, Australia. His research interests include data mining, machine learning, deep learning, computer vision, and sentiment analysis.