Search Results for author:
Found 9 papers, 3 papers with code
Big Data and Deep Learning in Smart Cities: A Comprehensive Dataset for AI-Driven Traffic Accident Detection and Computer Vision Systems
This research aims to bridge existing research gaps by introducing benchmark datasets that leverage state-of-the-art algorithms tailored for traffic accident detection in smart cities.
MMST-ViT: Climate Change-aware Crop Yield Prediction via Multi-Modal Spatial-Temporal Vision Transformer
In this work, we develop a deep learning-based solution, namely Multi-Modal Spatial-Temporal Vision Transformer (MMST-ViT), for predicting crop yields at the county level across the United States, by considering the effects of short-term meteorological variations during the growing season and the long-term climate change on crops.
IoT Botnet Detection Using an Economic Deep Learning Model
The rapid growth of the Internet of Things (IoT) systems worldwide has increased network security challenges created by malicious third parties.
Zydeco-Style Spike Sorting Low Power VLSI Architecture for IoT BCI Implants
However, the current complex clustering neuron identification algorithms inside the implant chip consume a lot of power and bandwidth, causing higher heat dissipation issues and draining the implant's battery.
Speech Emotion Recognition using Supervised Deep Recurrent System for Mental Health Monitoring
Understanding human behavior and monitoring mental health are essential to maintaining the community and society's safety.
Review on Action Recognition for Accident Detection in Smart City Transportation Systems
This paper presents an intensive review focusing on action recognition in accident detection and autonomous transportation systems for a smart city.
Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification
Hybrid LSTM-fully convolutional networks (LSTM-FCN) for time series classification have produced state-of-the-art classification results on univariate time series.
Reduced-Gate Convolutional LSTM Using Predictive Coding for Spatiotemporal Prediction
Our reduced-gate model achieves equal or better next-frame(s) prediction accuracy than the original convolutional LSTM while using a smaller parameter budget, thereby reducing training time.
Reduced-Gate Convolutional LSTM Design Using Predictive Coding for Next-Frame Video Prediction
Spatiotemporal sequence prediction is an important problem in deep learning.
