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Found 24 papers, 2 papers with code
TemporalAugmenter: An Ensemble Recurrent Based Deep Learning Approach for Signal Classification
Ensemble modeling has been widely used to solve complex problems as it helps to improve overall performance and generalization.
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.
CautionSuicide: A Deep Learning Based Approach for Detecting Suicidal Ideation in Real Time Chatbot Conversation
Early detection of suicidal ideations can help to prevent suicide occurrence by providing the victim with the required professional support, especially when the victim does not recognize the danger of having suicidal ideations.
Smart City Transportation: Deep Learning Ensemble Approach for Traffic Accident Detection
The dynamic and unpredictable nature of road traffic necessitates effective accident detection methods for enhancing safety and streamlining traffic management in smart cities.
AI on the Road: A Comprehensive Analysis of Traffic Accidents and Accident Detection System in Smart Cities
Accident detection and traffic analysis is a critical component of smart city and autonomous transportation systems that can reduce accident frequency, severity and improve overall traffic management.
Machine Learning Based IoT Adaptive Architecture for Epilepsy Seizure Detection: Anatomy and Analysis
A seizure tracking system is crucial for monitoring and evaluating epilepsy treatments.
Deep Learning Approach for Early Stage Lung Cancer Detection
Lung cancer is the leading cause of death among different types of cancers.
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.
A Convolutional-based Model for Early Prediction of Alzheimer's based on the Dementia Stage in the MRI Brain Images
Alzheimer's disease is a degenerative brain disease.
LiteLSTM Architecture Based on Weights Sharing for Recurrent Neural Networks
Long short-term memory (LSTM) is one of the robust recurrent neural network architectures for learning sequential data.
Baby Physical Safety Monitoring in Smart Home Using Action Recognition System
In this study, we present a novel lightweight framework combining transfer learning techniques with a Conv2D LSTM layer to extract features from the pre-trained I3D model on the Kinetics dataset for a new AR task (Smart Baby Care) that requires a smaller dataset and less computational resources.
A Transfer Learning Based Approach for Classification of COVID-19 and Pneumonia in CT Scan Imaging
This paper used deep transfer learning to classify the data via Inception-ResNet-V2 neural network architecture.
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.
Vision-Based American Sign Language Classification Approach via Deep Learning
Hearing-impaired is the disability of partial or total hearing loss that causes a significant problem for communication with other people in society.
Early Stage Diabetes Prediction via Extreme Learning Machine
Diabetes is one of the chronic diseases that has been discovered for decades.
Deep Learning Algorithm for Threat Detection in Hackers Forum (Deep Web)
Developing tools that can be deployed for threat detection is integral in securing digital communication in cyberspace.
LiteLSTM Architecture for Deep Recurrent Neural Networks
Long short-term memory (LSTM) is a robust recurrent neural network architecture for learning spatiotemporal sequential data.
Intrusion Detection System in Smart Home Network Using Bidirectional LSTM and Convolutional Neural Networks Hybrid Model
In this paper, we proposed an intrusion detection system (IDS) to detect anomalies in a smart home network using a bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN) hybrid model.
A Rule-Based Model for Victim Prediction
Social network analysis is employed to measure the influence of peers on the outcome variable.
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.
