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Found 9 papers, 2 papers with code
An explainable machine learning-based approach for analyzing customers' online data to identify the importance of product attributes
Online customer data provides valuable information for product design and marketing research, as it can reveal the preferences of customers.
Realtime Facial Expression Recognition: Neuromorphic Hardware vs. Edge AI Accelerators
The paper focuses on real-time facial expression recognition (FER) systems as an important component in various real-world applications such as social robotics.
Facial Expression Recognition
Facial Expression Recognition (FER)
Facial Expression Recognition at the Edge: CPU vs GPU vs VPU vs TPU
Facial Expression Recognition (FER) plays an important role in human-computer interactions and is used in a wide range of applications.
Facial Expression Recognition
Facial Expression Recognition (FER)
Energy-Efficient Deployment of Machine Learning Workloads on Neuromorphic Hardware
In recent years, several edge deep learning hardware accelerators have been released that specifically focus on reducing the power and area consumed by deep neural networks (DNNs).
Employing Deep Ensemble Learning for Improving the Security of Computer Networks against Adversarial Attacks
In this study, we present a novel architecture based on an ensemble classifier that combines the enhanced security of 1-Class classification (known as 1C) with the high performance of conventional 2-Class classification (known as 2C) in the absence of attacks. Our architecture is referred to as the 1. 5-Class (SPRITZ-1. 5C) classifier and constructed using a final dense classifier, one 2C classifier (i. e., CNNs), and two parallel 1C classifiers (i. e., auto-encoders).
Resisting Deep Learning Models Against Adversarial Attack Transferability via Feature Randomization
Our novel approach consists of changing the training strategy in the target network classifier and selecting random feature samples.
Static Hand Gesture Recognition for American Sign Language using Neuromorphic Hardware
The best DNN model achieves an accuracy of 99. 93% on the ASL Alphabet dataset, whereas the best performing SNN model has an accuracy of 99. 30%.
An Adversarial Attack Analysis on Malicious Advertisement URL Detection Framework
In this study, we extract a novel set of lexical and web-scrapped features and employ machine learning technique to set up system for fraudulent advertisement URLs detection.
MRAM-based Analog Sigmoid Function for In-memory Computing
We propose an analog implementation of the transcendental activation function leveraging two spin-orbit torque magnetoresistive random-access memory (SOT-MRAM) devices and a CMOS inverter.
