8 citations · 16 across the 7 of their papers we have counts for
7 papers
Enhancing Organ at Risk Segmentation with Improved Deep Neural Networks
Ilkin Isler, Curtis Lisle, Justin Rineer +4
Organ at risk (OAR) segmentation is a crucial step for treatment planning and outcome determination in radiotherapy treatments of cancer patients. Several deep learning based segme…
Variational Autoencoder Generative Adversarial Network for Synthetic Data Generation in Smart Home
Mina Razghandi, Hao Zhou, Melike Erol-Kantarci +1
Data is the fuel of data science and machine learning techniques for smart grid applications, similar to many other fields. However, the availability of data can be an issue due to…
Smart Home Energy Management: Sequence-to-Sequence Load Forecasting and Q-Learning
Mina Razghandi, Hao Zhou, Melike Erol-Kantarci +1
A smart home energy management system (HEMS) can contribute towards reducing the energy costs of customers; however, HEMS suffers from uncertainty in both energy generation and con…
Short-Term Load Forecasting for Smart HomeAppliances with Sequence to Sequence Learning
Mina Razghandi, Hao Zhou, Melike Erol-Kantarci +1
Appliance-level load forecasting plays a critical role in residential energy management, besides having significant importance for ancillary services performed by the utilities. In…
Privacy-Preserving Learning of Human Activity Predictors in Smart Environments
Sharare Zehtabian, Siavash Khodadadeh, Ladislau Bölöni +1
The daily activities performed by a disabled or elderly person can be monitored by a smart environment, and the acquired data can be used to learn a predictive model of user behavi…
Cluster Aware Mobility Encounter Dataset Enlargement
Rajarshi Haldar, Salih Safa Bacanli, Moayad Aloqaily +2
The recent emerging fields in data processing and manipulation has facilitated the need for synthetic data generation. This is also valid for mobility encounter dataset generation.…