AIMC Topic: Foodborne Diseases
Clear Filters Showing 11 to 20 of 34 articles
Frontiers of machine learning in smart food safety.
Advances in food and nutrition research
Jun 22, 2024
Integration of machine learning (ML) technologies into the realm of smart food safety represents a rapidly evolving field with significant potential to transform the management and assurance of food quality and safety. This chapter will discuss the c...
Machine learning approach as an early warning system to prevent foodborne Salmonella outbreaks in northwestern Italy.
Veterinary research
Jun 5, 2024
Salmonellosis, one of the most common foodborne infections in Europe, is monitored by food safety surveillance programmes, resulting in the generation of extensive databases. By leveraging tree-based machine learning (ML) algorithms, we exploited dat...
Random forest models of food safety behavior during the COVID-19 pandemic.
International journal of environmental health research
May 17, 2024
Machine learning approaches are increasingly being adopted as data analysis tools in scientific behavioral predictions. This paper utilizes a machine learning approach, Random Forest Model, to determine the top prediction variables of food safety beh...
Multiplex Detection of Foodborne Pathogens using 3D Nanostructure Swab and Deep Learning-Based Classification of Raman Spectra.
Small (Weinheim an der Bergstrasse, Germany)
Apr 2, 2024
Proactive management of foodborne illness requires routine surveillance of foodborne pathogens, which requires developing simple, rapid, and sensitive detection methods. Here, a strategy is presented that enables the detection of multiple foodborne b...
Application of machine learning to the monitoring and prediction of food safety: A review.
Comprehensive reviews in food science and food safety
Dec 14, 2021
Machine learning (ML) has proven to be a useful technology for data analysis and modeling in a wide variety of domains, including food science and engineering. The use of ML models for the monitoring and prediction of food safety is growing in recent...
High-Efficiency Machine Learning Method for Identifying Foodborne Disease Outbreaks and Confounding Factors.
Foodborne pathogens and disease
Apr 26, 2021
The China National Center for Food Safety Risk Assessment (CFSA) uses the Foodborne Disease Monitoring and Reporting System (FDMRS) to monitor outbreaks of foodborne diseases across the country. However, there are problems of underreporting or errone...
Classification of foodborne bacteria using hyperspectral microscope imaging technology coupled with convolutional neural networks.
Applied microbiology and biotechnology
Feb 12, 2020
Foodborne pathogens have become ongoing threats in the food industry, whereas their rapid detection and classification at an early stage are still challenging. To address early and rapid detection, hyperspectral microscope imaging (HMI) technology co...
A colorimetric Loop-mediated isothermal amplification (LAMP) assay based on HRP-mimicking molecular beacon for the rapid detection of Vibrio parahaemolyticus.
Biosensors & bioelectronics
Dec 16, 2019
In the world wide, food poisoning accidents related to Vibrio spp. are on the rise, even numbers of food poisoning by other foodborne pathogens are decreasing. Therefore, the requirement of the rapid, sensitive and convenient detection method for V. ...
Identifying non-O157 Shiga toxin-producing Escherichia coli (STEC) using deep learning methods with hyperspectral microscope images.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Jul 16, 2019
Non-O157 Shiga toxin-producing Escherichia coli (STEC) serogroups such as O26, O45, O103, O111, O121 and O145 often cause illness to people in the United States and the conventional identification of these "Big-Six" are complex. The label-free hypers...
Using Machine Learning To Predict Antimicrobial MICs and Associated Genomic Features for Nontyphoidal .
Journal of clinical microbiology
Jan 30, 2019
Nontyphoidal species are the leading bacterial cause of foodborne disease in the United States. Whole-genome sequences and paired antimicrobial susceptibility data are available for strains because of surveillance efforts from public health agencie...
Don't Miss the Future of Medicine
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 30978
- Neural Networks, Computer 29696
- Algorithms 26813
- Deep Learning 26324
- Artificial Intelligence 24267
- Robotics 13307
- Image Processing, Computer-Assisted 9512
- Retrospective Studies 9097
- Magnetic Resonance Imaging 6173
- Reproducibility of Results 5589
- Tomography, X-Ray Computed 4765
- Support Vector Machine 4678
- Computational Biology 4229
- Brain 4045
- Computer Simulation 3877
Recent Journals
- Scientific reports 5865
- Sensors (Basel, Switzerland) 4794
- PloS one 4011
- Neural networks : the official journal of the International Neural Network Society 2928
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1839
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1278
- IEEE journal of biomedical and health informatics 1129
- Journal of chemical information and modeling 994
- Nature communications 938
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 861
- Briefings in bioinformatics 850
- IEEE transactions on neural networks and learning systems 823