Using bert to extract topic-independent sentiment features for social media bot detection
Millions of online posts about different topics and products are shared on popular social
media platforms. One use of this content is to provide crowd-sourced information about a …
media platforms. One use of this content is to provide crowd-sourced information about a …
Bidirectional transformer based on online text-based information to implement convolutional neural network model for secure business investment
M Heidari, S Rafatirad - 2020 IEEE International Symposium on …, 2020 - ieeexplore.ieee.org
Real estate investment decisions are critical for low-income people who have just one home
as their life-time investment option. So during the COVID-19 pandemic, unemployment …
as their life-time investment option. So during the COVID-19 pandemic, unemployment …
Ontology creation model based on attention mechanism for a specific business domain
Ontology can describe real-world objects and relationships. It can be used for searching,
web mining, and semantic analysis. However, creating the ontology for a different domain is …
web mining, and semantic analysis. However, creating the ontology for a different domain is …
EBNAS: Efficient binary network design for image classification via neural architecture search
C Shi, Y Hao, G Li, S Xu - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
Abstract To deploy Convolutional Neural Networks (CNNs) on resource-limited devices,
binary CNNs with 1-bit activations and weights prove to be a promising approach …
binary CNNs with 1-bit activations and weights prove to be a promising approach …
Tas: ternarized neural architecture search for resource-constrained edge devices
Ternary Neural Networks (TNNs) compress network weights and activation functions into 2-
bit representation resulting in remarkable network compression and energy efficiency …
bit representation resulting in remarkable network compression and energy efficiency …
Cloak & co-locate: Adversarial railroading of resource sharing-based attacks on the cloud
The heterogeneity of resources and the diversity of applications on the cloud motivated the
need for resource provisioning systems (RPSs) to meet the users' performance requirements …
need for resource provisioning systems (RPSs) to meet the users' performance requirements …
Adaptive performance modeling of data-intensive workloads for resource provisioning in virtualized environment
The processing of data-intensive workloads is a challenging and time-consuming task that
often requires massive infrastructure to ensure fast data analysis. The cloud platform is the …
often requires massive infrastructure to ensure fast data analysis. The cloud platform is the …
Novel casestudy and benchmarking of AlexNet for edge AI: From CPU and GPU to FPGA
F Al-Ali, TD Gamage… - 2020 IEEE Canadian …, 2020 - ieeexplore.ieee.org
Convolutional Neural Networks (CNNs) require massive parallelism due to the high-
precision floating-point arithmetic operations they perform. So, demand of processing power …
precision floating-point arithmetic operations they perform. So, demand of processing power …
ELC-ECG: Efficient LSTM cell for ECG classification based on quantized architecture
SA Mirsalari, N Nazari… - … on Circuits and …, 2021 - ieeexplore.ieee.org
Long Short-Term Memory (LSTM) is one of the most popular and effective Recurrent Neural
Network (RNN) models used for sequence learning in applications such as ECG signal …
Network (RNN) models used for sequence learning in applications such as ECG signal …
Multi-level binarized lstm in eeg classification for wearable devices
Long Short-Term Memory (LSTM) is widely used in various sequential applications.
Complex LSTMs could be hardly deployed on wearable and resourced-limited devices due …
Complex LSTMs could be hardly deployed on wearable and resourced-limited devices due …