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Found 28 papers, 5 papers with code
Dynamic AGV Task Allocation in Intelligent Warehouses
This paper explores the integration of Automated Guided Vehicles (AGVs) in warehouse order picking, a crucial and cost-intensive aspect of warehouse operations.
Neural Approximate Dynamic Programming for the Ultra-fast Order Dispatching Problem
Our work aims to enhance the operational efficiency of SDD by focusing on the ultra-fast Order Dispatching Problem (ODP), which involves matching and dispatching orders to couriers within a centralized warehouse setting, and completing the delivery within a strict timeline (e. g., within minutes).
Assessing the impact of emergency department short stay units using length-of-stay prediction and discrete event simulation
Accurately predicting hospital length-of-stay at the time a patient is admitted to hospital may help guide clinical decision making and resource allocation.
Data Augmentation for Conflict and Duplicate Detection in Software Engineering Sentence Pairs
This paper explores the use of text data augmentation techniques to enhance conflict and duplicate detection in software engineering tasks through sentence pair classification.
Explaining Exchange Rate Forecasts with Macroeconomic Fundamentals Using Interpretive Machine Learning
The changes in the sign and magnitude of the contributions of crude oil to the exchange rate are consistent with significant events in the commodity and energy markets and the evolution of the crude oil trend in Canada.
Transfer learning for conflict and duplicate detection in software requirement pairs
We formulate the conflict and duplicate detection problem as a requirement pair classification task.
DANLIP: Deep Autoregressive Networks for Locally Interpretable Probabilistic Forecasting
Despite the high performance of neural network-based time series forecasting methods, the inherent challenge in explaining their predictions has limited their applicability in certain application areas.
A Concurrent CNN-RNN Approach for Multi-Step Wind Power Forecasting
One approach to remedy this challenge is to utilize weather information from multiple points across a geographical grid to obtain a holistic view of the wind patterns, along with temporal information from the previous power outputs of the wind farms.
ADPTriage: Approximate Dynamic Programming for Bug Triage
In this study, we develop a Markov decision process (MDP) model for an online bug triage task.
Token Classification for Disambiguating Medical Abbreviations
Abbreviations are unavoidable yet critical parts of the medical text.
Interpretable Time Series Clustering Using Local Explanations
The explanations are used to obtain insights into the clustering models.
Improved $α$-GAN architecture for generating 3D connected volumes with an application to radiosurgery treatment planning
On a synthetic dataset of connected 3D spheres and ellipsoids, our model can generate fully connected 3D shapes with similar geometrical characteristics to that of training data.
Linear programming-based solution methods for constrained partially observable Markov decision processes
Finally, the flexibility of LP-based approaches is demonstrated by applying deterministic policy constraints, and a detailed investigation into their impact on rewards and CPU run time is provided.
A multi-objective constrained POMDP model for breast cancer screening
While most countries have large-scale screening programs, there is no consensus on a single globally accepted guideline for breast cancer screening.
Time Series Clustering for Grouping Products Based on Price and Sales Patterns
We compare our approach with traditional clustering algorithms, which typically rely on generic distance metrics such as Euclidean distance, and image clustering approaches that aim to group data by capturing its visual patterns.
S-DABT: Schedule and Dependency-Aware Bug Triage in Open-Source Bug Tracking Systems
We further incorporate the schedule of developers in our formulation to have a more comprehensive model for this multifaceted problem.
VARGAN: Variance Enforcing Network Enhanced GAN
Generative adversarial networks (GANs) are one of the most widely used generative models.
Text Classification for Predicting Multi-level Product Categories
In our experiments, we investigate the generalizability of the trained models to the products of other online retailers, the dynamic masking of infeasible subcategories for pretrained language models, and the benefits of incorporating product titles in multiple languages.
Word-level Text Highlighting of Medical Texts for Telehealth Services
The first method uses TF-IDF scores directly to highlight important parts of the text.
DABT: A Dependency-aware Bug Triaging Method
In software engineering practice, fixing a bug promptly reduces the associated costs.
Auto Response Generation in Online Medical Chat Services
These services have become gradually popular over the years with the advent of necessary technological infrastructure.
A Deep Reinforcement Learning Approach for the Meal Delivery Problem
We consider a meal delivery service fulfilling dynamic customer requests given a set of couriers over the course of a day.
Predicting the Number of Reported Bugs in a Software Repository
We observe that LSTM is effective when considering long-run predictions whereas Random Forest Regressor enriched by exogenous variables performs better for predicting the number of bugs in the short term.
Does chronology matter in JIT defect prediction? A Partial Replication Study
In this work, we aim to investigate the effect of code change properties on JIT models over time.
Moving from Cross-Project Defect Prediction to Heterogeneous Defect Prediction: A Partial Replication Study
Our main goal is to extend prior research and explore the feasibility of HDP and finally to compare its performance with that of its predecessor, Cross-Project Defect Prediction.
Explainable boosted linear regression for time series forecasting
We observe that EBLR substantially improves the base model performance through extracted features, and provide a comparable performance to other well established approaches.
Evaluation of Local Explanation Methods for Multivariate Time Series Forecasting
In this study, we propose two novel evaluation metrics for time series forecasting: Area Over the Perturbation Curve for Regression and Ablation Percentage Threshold.
BIG-bench Machine Learning
Multivariate Time Series Forecasting
+2
An empirical study on using CNNs for fast radio signal prediction
We compare deep learning-based prediction models including RadioUNET and four different variations of the UNET model for the power prediction task.
