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Found 19 papers, 2 papers with code
Towards Constraint-Based Adaptive Hypergraph Learning for Solving Vehicle Routing: An End-to-End Solution
The application of learning based methods to vehicle routing problems has emerged as a pivotal area of research in combinatorial optimization.
Dockformer: A transformer-based molecular docking paradigm for large-scale virtual screening
Molecular docking is a crucial step in drug development, which enables the virtual screening of compound libraries to identify potential ligands that target proteins of interest.
Pattern based learning and optimisation through pricing for bin packing problem
As a popular form of knowledge and experience, patterns and their identification have been critical tasks in most data mining applications.
GASE: Graph Attention Sampling with Edges Fusion for Solving Vehicle Routing Problems
Existing research studies have been focusing on novel encoding and decoding structures via various neural network models to enhance the node embedding representation.
Scale Optimization Using Evolutionary Reinforcement Learning for Object Detection on Drone Imagery
A set of rewards measuring the localization accuracy, the accuracy of predicted labels, and the scale consistency among nearby patches are designed in the agent to guide the scale optimization.
Multi-View Spectrogram Transformer for Respiratory Sound Classification
Deep neural networks have been applied to audio spectrograms for respiratory sound classification.
Elongated Physiological Structure Segmentation via Spatial and Scale Uncertainty-aware Network
Second, we extract the uncertainty under different scales and propose the multi-scale uncertainty-aware (MSUA) fusion module to integrate structure contexts from hierarchical predictions, strengthening the final prediction.
Mask Attack Detection Using Vascular-weighted Motion-robust rPPG Signals
Detecting 3D mask attacks to a face recognition system is challenging.
Boosting the Discriminant Power of Naive Bayes
In this paper, we propose a feature augmentation method employing a stack auto-encoder to reduce the noise in the data and boost the discriminant power of naive Bayes.
A Max-relevance-min-divergence Criterion for Data Discretization with Applications on Naive Bayes
Thus, we propose a Max-Dependency-Min-Divergence (MDmD) criterion that maximizes both the discriminant information and generalization ability of the discretized data.
Two-stage Rule-induction Visual Reasoning on RPMs with an Application to Video Prediction
Based on the two intrinsic natures of RPM problem, visual recognition and logical reasoning, we propose a Two-stage Rule-Induction Visual Reasoner (TRIVR), which consists of a perception module and a reasoning module, to tackle the challenges of real-world visual recognition and subsequent logical reasoning tasks, respectively.
A Semi-Supervised Adaptive Discriminative Discretization Method Improving Discrimination Power of Regularized Naive Bayes
Data discretization is important in naive Bayes.
Spatio-temporal Parking Behaviour Forecasting and Analysis Before and During COVID-19
Parking demand forecasting and behaviour analysis have received increasing attention in recent years because of their critical role in mitigating traffic congestion and understanding travel behaviours.
Data augmentation by morphological mixup for solving Raven's Progressive Matrices
Raven's Progressive Matrices (RPMs) are frequently used in testing human's visual reasoning ability.
Analytics and Machine Learning in Vehicle Routing Research
The Vehicle Routing Problem (VRP) is one of the most intensively studied combinatorial optimisation problems for which numerous models and algorithms have been proposed.
A Hybrid Pricing and Cutting Approach for the Multi-Shift Full Truckload Vehicle Routing Problem
This paper extends the previous work and presents a significantly more efficient approach by hybridising pricing and cutting strategies with metaheuristics (a variable neighbourhood search and a genetic algorithm).
Retrieving and ranking short medical questions with two stages neural matching model
Internet hospital is a rising business thanks to recent advances in mobile web technology and high demand of health care services.
Learning Regular Expressions for Interpretable Medical Text Classification Using a Pool-based Simulated Annealing and Word-vector Models
In this paper, we propose a rule-based engine composed of high quality and interpretable regular expressions for medical text classification.
Fuzzy C-means-based scenario bundling for stochastic service network design
Stochastic service network designs with uncertain demand represented by a set of scenarios can be modelled as a large-scale two-stage stochastic mixed-integer program (SMIP).
