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DLRT: Deep learning approach for reliable diabetic treatment
(
Institute of Electrical and Electronics Engineers Inc.
, 2017 , Conference Paper)
Diabetic therapy or insulin treatment enables patients to control the blood glucose level. Today, instead of physically utilizing syringes for infusing insulin, a patient can utilize a gadget, for example, a Wireless Insulin ...
A Deep Learning Based Automatic Severity Detector for Diabetic Retinopathy
(
Springer Verlag
, 2018 , Conference Paper)
Automated Diabetic Retinopathy (DR) screening methods with high accuracy have the strong potential to assist doctors in evaluating more patients and quickly routing those who need help to a specialist. In this work, we ...
Fault and performance management in multi-cloud based NFV using shallow and deep predictive structures
(
Institute of Electrical and Electronics Engineers Inc.
, 2017 , Conference Paper)
Deployment of Network Function Virtualization (NFV) over multiple clouds accentuates its advantages like flexibility of virtualization, proximity to customers and lower total cost of operation. However, NFV over multiple ...
DistPrivacy: Privacy-Aware Distributed Deep Neural Networks in IoT surveillance systems
(
Institute of Electrical and Electronics Engineers Inc.
, 2020 , Conference Paper)
With the emergence of smart cities, Internet of Things (IoT) devices as well as deep learning technologies have witnessed an increasing adoption. To support the requirements of such paradigm in terms of memory and computation, ...
Deep Reinforcement Learning Algorithm for Smart Data Compression under NOMA-Uplink Protocol
(
Institute of Electrical and Electronics Engineers Inc.
, 2020 , Conference Paper)
One of the highly promising radio access strategies for enhancing performance in the next generation cellular communications is non-orthogonal multiple access (NOMA). NOMA offers a number of advantages including better ...
Multimodal deep learning approach for Joint EEG-EMG Data compression and classification
(
Institute of Electrical and Electronics Engineers Inc.
, 2017 , Conference Paper)
In this paper, we present a joint compression and classification approach of EEG and EMG signals using a deep learning approach. Specifically, we build our system based on the deep autoencoder architecture which is designed ...
Deep Learning for RF-Based Drone Detection and Identification: A Multi-Channel 1-D Convolutional Neural Networks Approach
(
Institute of Electrical and Electronics Engineers Inc.
, 2020 , Conference Paper)
Commercial unmanned aerial vehicles, or drones, are getting increasingly popular in the last few years. The fact that these drones are highly accessible to public may bring a range of security and technical issues to ...
Energy-efficient networks selection based deep reinforcement learning for heterogeneous health systems
(
Institute of Electrical and Electronics Engineers Inc.
, 2021 , Conference Paper)
Smart health systems improve the existing health services by integrating information and technology into health and medical practices. However, smart healthcare systems are facing major challenges including limited network ...
Deep learning and low rank dictionary model for mHealth data classification
(
Institute of Electrical and Electronics Engineers Inc.
, 2018 , Conference Paper)
In the context of mobile Health (mHealth) applications, data are prone to several sources of contamination which would lead to false interpretation and misleading classification results. In this paper, a robust deep learning ...
EEG-based Analysis Study for Patients Receiving Intravenous Antibiotic Medication
(
Institute of Electrical and Electronics Engineers Inc.
, 2020 , Conference Paper)
In this paper, we conduct a biological data collection and analysis study for patients undergoing routine planned intravenous antibiotic treatment. The acquired data (i.e., Electroencephalogram (EEG), temperature and blood ...