An evolutionary deep neural network for predicting morbidity of gastrointestinal infections by food contamination

Song, Qin; Zheng, Yu-Jun; Xue, Yu; Sheng, Weiguo; Zhao, Meirong

doi:10.1016/j.neucom.2016.11.018

Cited by 70 publications

(34 citation statements)

References 56 publications

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“…Deep networks have the potential to model the influence of environmental variables on living species even though they have not yet been applied in this way. Studies in the medical field managed to predict gastrointestinal morbidity in humans from pollutants in the environment (Song, Zheng, Xue, Sheng, & Zhao, ), a method that could easily be transferable to wild animals. Recurrent networks have also been shown to successfully predict abundance and community dynamics based on environmental variables for phytoplankton (Jeong, Joo, Kim, Ha, & Recknagel, ) and benthic communities (Chon, Kwak, Park, Kim, & Kim, ).…”

Section: Overview Of Applications In Ecologymentioning

confidence: 99%

Applications for deep learning in ecology

2019

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A lot of hype has recently been generated around deep learning, a novel group of artificial intelligence approaches able to break accuracy records in pattern recognition. Over the course of just a few years, deep learning has revolutionized several research fields such as bioinformatics and medicine with its flexibility and ability to process large and complex datasets. As ecological datasets are becoming larger and more complex, we believe these methods can be useful to ecologists as well. In this paper, we review existing implementations and show that deep learning has been used successfully to identify species, classify animal behaviour and estimate biodiversity in large datasets like camera‐trap images, audio recordings and videos. We demonstrate that deep learning can be beneficial to most ecological disciplines, including applied contexts, such as management and conservation. We also identify common questions about how and when to use deep learning, such as what are the steps required to create a deep learning network, which tools are available to help, and what are the requirements in terms of data and computer power. We provide guidelines, recommendations and useful resources, including a reference flowchart to help ecologists get started with deep learning. We argue that at a time when automatic monitoring of populations and ecosystems generates a vast amount of data that cannot be effectively processed by humans anymore, deep learning could become a powerful reference tool for ecologists.

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Section: Overview Of Applications In Ecologymentioning

confidence: 99%

Applications for deep learning in ecology

2019

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“…Food contamination can lead to gastrointestinal infectious diseases and is harm to human health, and thus attracts increasing attention all over the world. Song, Zheng, Xue, Sheng, and Zhao (2017) researched an evolution method for predicting morbidity of gastrointestinal infections by food contamination using DNN. The research was designed for the morbidity prediction of gastrointestinal infectious diseases using a large number of contaminant-related information (from 227 types of contaminants in different concentrations and 119 types of food widely consumed in the investigated region) acquired in the current week as well as the previously recorded morbidity information.…”

Section: Food Contaminationmentioning

confidence: 99%

Application of Deep Learning in Food: A Review

Zhou

Zhang

Liu

et al. 2019

Comp Rev Food Sci Food Safe

361

166

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Deep learning has been proved to be an advanced technology for big data analysis with a large number of successful cases in image processing, speech recognition, object detection, and so on. Recently, it has also been introduced in food science and engineering. To our knowledge, this review is the first in the food domain. In this paper, we provided a brief introduction of deep learning and detailedly described the structure of some popular architectures of deep neural networks and the approaches for training a model. We surveyed dozens of articles that used deep learning as the data analysis tool to solve the problems and challenges in food domain, including food recognition, calories estimation, quality detection of fruits, vegetables, meat and aquatic products, food supply chain, and food contamination. The specific problems, the datasets, the preprocessing methods, the networks and frameworks used, the performance achieved, and the comparison with other popular solutions of each research were investigated. We also analyzed the potential of deep learning to be used as an advanced data mining tool in food sensory and consume researches. The result of our survey indicates that deep learning outperforms other methods such as manual feature extractors, conventional machine learning algorithms, and deep learning as a promising tool in food quality and safety inspection. The encouraging results in classification and regression problems achieved by deep learning will attract more research efforts to apply deep learning into the field of food in the future.

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“…In this regard, investigating and comparing performance of different techniques for forecasting help to select better prediction model in studies with forecasting purposes in practical applications. Among them, those methods that work based on statistical learning theory have been proved to be more efficient in different surveillance systems (Kane et al, ; Song, Zheng, Xue, Sheng, & Zhao, ; Zhang et al, ). These methods are naturally useful endemic time series forecasting methods because of their powerful non‐linear modelling ability and handling of complexity in data (Zhang et al, ).…”

Section: Resultsmentioning

confidence: 99%

A comparison of three data mining time series models in prediction of monthly brucellosis surveillance data

Shirmohammadi‐Khorram

Tapak

Hamidi

et al. 2019

Zoonoses and Public Health

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The early and accurately detection of brucellosis incidence change is of great importance for implementing brucellosis prevention strategic health planning. The present study investigated and compared the performance of the three data mining techniques, random forest (RF), support vector machine (SVM) and multivariate adaptive regression splines (MARSs), in time series modelling and predicting of monthly brucellosis data from 2005 (March/April) to 2017 (February/March) extracted from a national public health surveillance system in Hamadan located in west of Iran. The performances were compared based on the root mean square errors, mean absolute errors, determination coefficient (R2) and intraclass correlation coefficient criteria. Results indicated that the RF model outperformed the SVM and MARS models in modeling used data and it can be utilized successfully utilized to diagnose the behaviour of brucellosis over time. Further research with application of such novel time series models in practice provides the most appropriate method in the control and prevention of future outbreaks for the epidemiologist.

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An evolutionary deep neural network for predicting morbidity of gastrointestinal infections by food contamination

Cited by 70 publications

References 56 publications

Applications for deep learning in ecology

Applications for deep learning in ecology

Application of Deep Learning in Food: A Review

A comparison of three data mining time series models in prediction of monthly brucellosis surveillance data

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