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Found 19 papers, 1 papers with code
Enhancing Traffic Prediction with Learnable Filter Module
Our approach focuses on enhancing the quality of the input data for traffic prediction models, which is a critical yet often overlooked aspect in the field.
Meta Attentive Graph Convolutional Recurrent Network for Traffic Forecasting
Firstly, most approaches are primarily designed to model the local shared patterns, which makes them insufficient to capture the specific patterns associated with each node globally.
Adaptive Modeling of Uncertainties for Traffic Forecasting
We evaluated QuanTraffic by applying it to five representative DNN models for traffic forecasting across seven public datasets.
Traffic Prediction with Transfer Learning: A Mutual Information-based Approach
In modern traffic management, one of the most essential yet challenging tasks is accurately and timely predicting traffic.
Uncertainty Quantification for Traffic Forecasting: A Unified Approach
To achieve this, we develop Deep Spatio-Temporal Uncertainty Quantification (DeepSTUQ), which can estimate both aleatoric and epistemic uncertainty.
Social-DualCVAE: Multimodal Trajectory Forecasting Based on Social Interactions Pattern Aware and Dual Conditional Variational Auto-Encoder
Pedestrian trajectory forecasting is a fundamental task in multiple utility areas, such as self-driving, autonomous robots, and surveillance systems.
Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysis
Similarly, due to the heterogeneity of the connected remote devices, FEEL faces the challenge of heterogeneous data and non-IID (Independent and Identically Distributed) data.
Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach
Through extensive case studies on a real-world dataset, it is shown that FedGRU's prediction accuracy is 90. 96% higher than the advanced deep learning models, which confirm that FedGRU can achieve accurate and timely traffic prediction without compromising the privacy and security of raw data.
PPGAN: Privacy-preserving Generative Adversarial Network
To address this issue, we propose a Privacy-preserving Generative Adversarial Network (PPGAN) model, in which we achieve differential privacy in GANs by adding well-designed noise to the gradient during the model learning procedure.
A Social Spider Algorithm for Solving the Non-convex Economic Load Dispatch Problem
Economic Load Dispatch (ELD) is one of the essential components in power system control and operation.
Parameter Sensitivity Analysis of Social Spider Algorithm
Social Spider Algorithm (SSA) is a recently proposed general-purpose real-parameter metaheuristic designed to solve global numerical optimization problems.
Adaptive Chemical Reaction Optimization for Global Numerical Optimization
A newly proposed chemical-reaction-inspired metaheurisic, Chemical Reaction Optimization (CRO), has been applied to many optimization problems in both discrete and continuous domains.
A Social Spider Algorithm for Global Optimization
Inspired by the social spiders, we propose a novel Social Spider Algorithm to solve global optimization problems.
Sensor Deployment for Air Pollution Monitoring Using Public Transportation System
Air pollution monitoring is a very popular research topic and many monitoring systems have been developed.
Optimal V2G Scheduling of Electric Vehicles and Unit Commitment using Chemical Reaction Optimization
An electric vehicle (EV) may be used as energy storage which allows the bi-directional electricity flow between the vehicle's battery and the electric power grid.
Real-Coded Chemical Reaction Optimization with Different Perturbation Functions
The distributions are tested by a set of well-known benchmark functions and simulation results show that problems with different characteristics have different preference on the distribution function.
Evolutionary Artificial Neural Network Based on Chemical Reaction Optimization
Evolutionary algorithms (EAs) are very popular tools to design and evolve artificial neural networks (ANNs), especially to train them.
Chemical Reaction Optimization for the Set Covering Problem
The set covering problem (SCP) is one of the representative combinatorial optimization problems, having many practical applications.
An Inter-molecular Adaptive Collision Scheme for Chemical Reaction Optimization
However, the functionality of the inter-molecular ineffective collision operator in the canonical CRO design overlaps that of the on-wall ineffective collision operator, which can potential impair the overall performance.
