Deep learning in mobile and wireless networking: A survey

C Zhang, P Patras, H Haddadi - IEEE Communications surveys …, 2019 - ieeexplore.ieee.org
The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …

A survey of deep learning on mobile devices: Applications, optimizations, challenges, and research opportunities

T Zhao, Y Xie, Y Wang, J Cheng, X Guo… - Proceedings of the …, 2022 - ieeexplore.ieee.org
Deep learning (DL) has demonstrated great performance in various applications on
powerful computers and servers. Recently, with the advancement of more powerful mobile …

[PDF][PDF] Challenges in Deep Learning.

P Angelov, A Sperduti - ESANN, 2016 - esann.org
In recent years, Deep Learning methods and architectures have reached impressive results,
allowing quantum-leap improvements in performance in many difficult tasks, such as speech …

Hu-fu: Hardware and software collaborative attack framework against neural networks

W Li, J Yu, X Ning, P Wang, Q Wei… - 2018 IEEE Computer …, 2018 - ieeexplore.ieee.org
Recently, Deep Learning (DL), especially Convolutional Neural Network (CNN), develops
rapidly and is applied to many tasks, such as image classification, face recognition, image …

Online robust policy learning in the presence of unknown adversaries

A Havens, Z Jiang, S Sarkar - Advances in neural …, 2018 - proceedings.neurips.cc
The growing prospect of deep reinforcement learning (DRL) being used in cyber-physical
systems has raised concerns around safety and robustness of autonomous agents. Recent …

Deep multi-layer perceptron-based obstacle classification method from partial visual information: application to the assistance of visually impaired people

SK Jarraya, WS Al-Shehri, MS Ali - IEEE Access, 2020 - ieeexplore.ieee.org
How to navigate safely, recognize encountered obstacles, and move independently from
one location to another in unknown environments are some of the challenges that face …

Human violence detection using deep learning techniques

SAA Akash, RSS Moorthy, K Esha… - Journal of Physics …, 2022 - iopscience.iop.org
The world's average annual fatality rate from human violence is 7.9 per 10,000 people. Most
of this human violence takes place in an isolated area or of sudden. The information delay …

[PDF][PDF] Review of violence detection system using deep learning

V Dandage, H Gautam, A Ghavale… - Int. Research Journal …, 2019 - academia.edu
Nowadays, tremendous growth is observed in research of surveillance system. The
surveillance cameras installed at various public places like offices, hospitals, schools …

Improving classification accuracy of feedforward neural networks for spiking neuromorphic chips

AJ Yepes, J Tang, BS Mashford - arXiv preprint arXiv:1705.07755, 2017 - arxiv.org
Deep Neural Networks (DNN) achieve human level performance in many image analytics
tasks but DNNs are mostly deployed to GPU platforms that consume a considerable amount …

Wearable Sensors and Real-Time System for Detecting violence using Artificial Intelligence

B Arthi, K PoornaPushkala, A Arya… - 2022 International …, 2022 - ieeexplore.ieee.org
Aggressive activity in public spaces is a significant threat to personal safety and social
cohesion. Cameras and other security devices have been mounted in various locations for …