Search Results for author:
Found 23 papers, 2 papers with code
Predictive Analysis for Optimizing Port Operations
This research addresses a significant gap in port analysis models for vessel Stay and Delay times, offering a valuable contribution to the field of maritime logistics.
Agent based modelling for continuously varying supply chains
Academic/practical: However, learning in continuously varying environments remains a challenge in the reinforcement learning literature. Methodology: This paper therefore seeks to address whether agents can control varying supply chain problems, transferring learning between environments that require different strategies and avoiding catastrophic forgetting of tasks that have not been seen in a while.
A novel feature selection method based on quantum support vector machine
We apply QSVMF for feature selection on a breast cancer dataset, comparing the performance of QSVMF against classical approaches with the selected features.
Several fitness functions and entanglement gates in quantum kernel generation
Furthermore, we demonstrate that the separability indexes of data can be leveraged to estimate the number of non-local gates required for the quantum support vector machine's feature maps.
A Functional approach for Two Way Dimension Reduction in Time Series
The rise in data has led to the need for dimension reduction techniques, especially in the area of non-scalar variables, including time series, natural language processing, and computer vision.
ISA-Net: Improved spatial attention network for PET-CT tumor segmentation
In this paper, we propose a deep learning segmentation method based on multimodal positron emission tomography-computed tomography (PET-CT), which combines the high sensitivity of PET and the precise anatomical information of CT. We design an improved spatial attention network(ISA-Net) to increase the accuracy of PET or CT in detecting tumors, which uses multi-scale convolution operation to extract feature information and can highlight the tumor region location information and suppress the non-tumor region location information.
Distributional Actor-Critic Ensemble for Uncertainty-Aware Continuous Control
Uncertainty quantification is one of the central challenges for machine learning in real-world applications.
Sequential Point Clouds: A Survey
Point cloud has drawn more and more research attention as well as real-world applications.
Optimal service resource management strategy for IoT-based health information system considering value co-creation of users
This paper explores optimal service resource management strategy, a continuous challenge for health information service to enhance service performance, optimise service resource utilisation and deliver interactive health information service.
Enhancing Digital Health Services: A Machine Learning Approach to Personalized Exercise Goal Setting
This study aims to fill this gap by developing a machine learning algorithm that dynamically updates auto-suggestion exercise goals using retrospective data and realistic behavior trajectory.
PSMNet: Position-aware Stereo Merging Network for Room Layout Estimation
In this paper, we propose a new deep learning-based method for estimating room layout given a pair of 360 panoramas.
Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth
Conventional self-supervised monocular depth prediction methods are based on a static environment assumption, which leads to accuracy degradation in dynamic scenes due to the mismatch and occlusion problems introduced by object motions.
K-nearest Multi-agent Deep Reinforcement Learning for Collaborative Tasks with a Variable Number of Agents
Traditionally, the performance of multi-agent deep reinforcement learning algorithms are demonstrated and validated in gaming environments where we often have a fixed number of agents.
Deep Reinforcement Learning with Adjustments
Our method can learn complex policies to achieve long-term goals and at the same time it can be easily adjusted to address short-term requirements without retraining.
An Offline Deep Reinforcement Learning for Maintenance Decision-Making
Several machine learning and deep learning frameworks have been proposed to solve remaining useful life estimation and failure prediction problems in recent years.
FESTA: Flow Estimation via Spatial-Temporal Attention for Scene Point Clouds
Scene flow depicts the dynamics of a 3D scene, which is critical for various applications such as autonomous driving, robot navigation, AR/VR, etc.
Interactions of Linguistic and Domain Overhypotheses in Category Learning
For humans learning to categorize and distinguish parts of the world, the set of assumptions (overhypotheses) they hold about potential category structures is directly related to their learning process.
Computational Engineering, Finance, and Science
A Non-linear Function-on-Function Model for Regression with Time Series Data
Specifically, we aim to learn mathematical mappings from multiple chronologically measured numerical variables within a certain time interval S to multiple numerical variables of interest over time interval T. Prior arts, including the multivariate regression model, the Seq2Seq model, and the functional linear models, suffer from several limitations.
Challenges of Applying Deep Reinforcement Learning in Dynamic Dispatching
In this paper, we review the main challenges in using deep RL to address the dynamic dispatching problem in the mining industry.
Assess the impacts of human mobility change on COVID-19 dynamics in Arizona, U.S.: a modeling study incorporating Google Community Mobility Reports
In June 2020, Arizona, U. S., emerged as one of the world's worst coronavirus disease 2019(COVID-19) spots after the stay-at-home order was lifted in the middle of May.
Populations and Evolution Physics and Society
GPR-based Subsurface Object Detection and Reconstruction Using Random Motion and DepthNet
This system is composed of three modules: 1) visual inertial fusion (VIF) module to generate the pose information of GPR device, 2) deep neural network module (i. e., DepthNet) which detects B-scan of GPR image, extracts hyperbola features to remove the noise in B-scan data and predicts dielectric to determine the depth of the objects, 3) 3D GPR migration module which synchronizes the pose information with GPR scan data processed by DepthNet to reconstruct and visualize the 3D underground targets.
Using Networks and Partial Differential Equations to Predict Bitcoin Price
The paper is the first attempt to apply a PDE model to the bitcoin transaction network for predicting bitcoin price.
FHEDN: A based on context modeling Feature Hierarchy Encoder-Decoder Network for face detection
The proposed network is consist of multiple context modeling and prediction modules, which are in order to detect small, blur, occluded and diverse pose faces.
