AIMC Topic: Travel
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MTSA-SC: A multi-task learning approach for individual trip destination prediction with multi-trajectory subsequence alignment and space-aware loss functions.
PloS one
Jun 6, 2025
Individual Trip Destination Prediction aims to accurately forecast an individual's future travel destinations by analyzing their historical trajectory data, holding significant application value in intelligent navigation, personalized recommendations...
Point-of-interest recommender model using geo-tagged photos in accordance with imperialist Fuzzy C-means clustering.
PloS one
Mar 7, 2025
Although recommender systems (RSs) strive to provide recommendations based on individuals' histories and preferences, most recommendations made by these systems do not utilize location and time-based information. This paper presents a travel recommen...
Machine learning analysis of the effects of COVID-19 on migration patterns.
Scientific reports
Nov 30, 2024
This study investigates the impact of the COVID-19 pandemic on European tourist mobility patterns from 2019 to 2021 by conceptualizing countries as monomers emitting radiation to model and analyze their patterns through the lens of socio-economics an...
Gender disparities in rural motorcycle accidents: A neural network analysis of travel behavior impact.
Accident; analysis and prevention
Nov 22, 2024
Rural road accidents involving motorcycle riders present a formidable challenge to road safety globally. This study offers a comprehensive gender-based comparative analysis of rural road accidents among motorcycle riders, aimed at illuminating factor...
Construction of an aerolysin-based multi-epitope vaccine against an machine learning and artificial intelligence-supported approach.
Frontiers in immunology
Mar 1, 2024
, a gram-negative coccobacillus bacterium, can cause various infections in humans, including septic arthritis, diarrhea (traveler's diarrhea), gastroenteritis, skin and wound infections, meningitis, fulminating septicemia, enterocolitis, peritonitis,...
A new era is emerging at scientific user facilities.
IUCrJ
Jan 1, 2024
Global scientific exchange has been profoundly perturbed by the COVID-19 pandemic, altering user travel behaviours and accelerating the use of remote access. Combined with the advent of artificial intelligence (AI), these trends together can change h...
Deep learning hybrid model for analyzing and predicting the impact of imported malaria cases from Africa on the rise of Plasmodium falciparum in China before and during the COVID-19 pandemic.
PloS one
Dec 6, 2023
BACKGROUND: Plasmodium falciparum cases are rising in China due to the imported malaria cases from African countries. The main goal of this study is to examine the impact of imported malaria cases in African countries on the rise of P. falciparum cas...
Stochastic scheduling of autonomous mobile robots at hospitals.
PloS one
Oct 5, 2023
This paper studies the scheduling of autonomous mobile robots (AMRs) at hospitals where the stochastic travel times and service times of AMRs are affected by the surrounding environment. The routes of AMRs are planned to minimize the daily cost of th...
TransCode: Uncovering COVID-19 transmission patterns via deep learning.
Infectious diseases of poverty
Feb 28, 2023
BACKGROUND: The heterogeneity of COVID-19 spread dynamics is determined by complex spatiotemporal transmission patterns at a fine scale, especially in densely populated regions. In this study, we aim to discover such fine-scale transmission patterns ...
Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters.
International journal of environmental research and public health
Dec 15, 2022
Travel mode choice (TMC) prediction is crucial for transportation planning. Most previous studies have focused on TMC in adults, whereas predicting TMC in children has received less attention. On the other hand, previous children's TMC prediction stu...
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