Deep learning-based appearance features extraction for automated carp species identification

Banan, Ashkan; Nasiri, Alireza; Taheri‐Garavand, Amin

doi:10.1016/j.aquaeng.2020.102053

Cited by 199 publications

(71 citation statements)

References 24 publications

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“…2019; Banan et al . 2020), indicating that these method perform well. Among the papers using precision and recall as evaluation indexes, the highest results to date are 99.68% and 99.45%, respectively, which illustrates the advantages of DL models.…”

Section: Technical Details and Overall Performancementioning

confidence: 86%

“…However, in general, most of the studies in which the accuracy is used as a performance evaluation index report values above 90%, some even reach almost 100% (Romero-Ferrero et al 2019;Banan et al 2020), indicating that these method perform well. Among the papers using precision and recall as evaluation indexes, the highest results to date are 99.68% and 99.45%, respectively, which illustrates the advantages of DL models.…”

Section: Performance Evaluation Indexes and Overall Performancementioning

confidence: 99%

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Deep learning for smart fish farming: applications, opportunities and challenges

Yang

Zhang

Liu

et al. 2020

Reviews in Aquaculture

194

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The rapid emergence of deep learning (DL) technology has resulted in its successful use in various fields, including aquaculture. DL creates both new opportunities and a series of challenges for information and data processing in smart fish farming. This paper focuses on applications of DL in aquaculture, including live fish identification, species classification, behavioural analysis, feeding decisions, size or biomass estimation, and water quality prediction. The technical details of DL methods applied to smart fish farming are also analysed, including data, algorithms and performance. The review results show that the most significant contribution of DL is its ability to automatically extract features. However, challenges still exist; DL is still in a weak artificial intelligence stage and requires large amounts of labelled data for training, which has become a bottleneck that restricts further DL applications in aquaculture. Nevertheless, DL still offers breakthroughs for addressing complex data in aquaculture. In brief, our purpose is to provide researchers and practitioners with a better understanding of the current state of the art of DL in aquaculture, which can provide strong support for implementing smart fish farming applications.

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Section: Technical Details and Overall Performancementioning

confidence: 86%

Section: Performance Evaluation Indexes and Overall Performancementioning

confidence: 99%

Deep learning for smart fish farming: applications, opportunities and challenges

Yang

Zhang

Liu

et al. 2020

Reviews in Aquaculture

194

View full text Add to dashboard Cite

show abstract

“…Practically limiting is also the fact that sophisticated estimators require long computing times. To achieve the task of finding a good and fast estimator for arbitrary domain dimensions and to make as little assumptions on the PDF as possible (low bias), we employ deep learning, which has recently been successfully applied to many reallife applications in different fields [3,12,33].…”

Section: Introductionmentioning

confidence: 99%

Data-driven deep density estimation

Puchert

Hermosilla

Ritschel

et al. 2021

Neural Comput & Applic

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Density estimation plays a crucial role in many data analysis tasks, as it infers a continuous probability density function (PDF) from discrete samples. Thus, it is used in tasks as diverse as analyzing population data, spatial locations in 2D sensor readings, or reconstructing scenes from 3D scans. In this paper, we introduce a learned, data-driven deep density estimation (DDE) to infer PDFs in an accurate and efficient manner, while being independent of domain dimensionality or sample size. Furthermore, we do not require access to the original PDF during estimation, neither in parametric form, nor as priors, or in the form of many samples. This is enabled by training an unstructured convolutional neural network on an infinite stream of synthetic PDFs, as unbound amounts of synthetic training data generalize better across a deck of natural PDFs than any natural finite training data will do. Thus, we hope that our publicly available DDE method will be beneficial in many areas of data analysis, where continuous models are to be estimated from discrete observations.

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“…[13][14][15] Demirezen et al proved the competence of artificial neural network (ANN) as a dependable and powerful predicting approach of outdoor temperature with minimum error in two different studies. [16,17] Banan et al utilized deep learning neural network as a smart and real-time approach to present an automate identification process of fish species [18]. Fan et al adopted the multilayer perceptron (MLP) together with spatiotemporal model and the long shortterm memory (LSTM) network to make an estimation of temperature distributions during the thermal process.…”

Section: Previous Studiesmentioning

confidence: 99%

Prediction and Analysis of the Severity and Number of Suburban Accidents Using Logit Model, Factor Analysis and Machine Learning: A case study in a developing country

2021

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The purpose of this study is to investigate and determine the factors affecting vehicle and pedestrian accidents taking place in the busiest suburban highway of Guilan Province located in the north of Iran and provide the most accurate prediction model. Therefore, the effective principal variables and the probability of occurrence of each category of crashes are analyzed and computed utilizing the factor analysis, logit, and Machine Learning approaches simultaneously. This method not only could contribute to achieving the most comprehensive and efficient model to specify the major contributing factor, but also it can provide officials with suggestions to take effective measures with higher precision to lessen accident impacts and improve road safety. Both the factor analysis and logit model show the significant roles of exceeding lawful speed, rainy weather and driver age (30–50) variables in the severity of vehicle accidents. On the other hand, the rainy weather and lighting condition variables as the most contributing factors in pedestrian accidents severity, underline the dominant role of environmental factors in the severity of all vehicle-pedestrian accidents. Moreover, considering both utilized methods, the machine-learning model has higher predictive power in all cases, especially in pedestrian accidents, with 41.6% increase in the predictive power of fatal accidents and 12.4% in whole accidents. Thus, the Artificial Neural Network model is chosen as the superior approach in predicting the number and severity of crashes. Besides, the good performance and validation of the machine learning is proved through performance and sensitivity analysis.

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Deep learning-based appearance features extraction for automated carp species identification

Cited by 199 publications

References 24 publications

Deep learning for smart fish farming: applications, opportunities and challenges

Deep learning for smart fish farming: applications, opportunities and challenges

Data-driven deep density estimation

Prediction and Analysis of the Severity and Number of Suburban Accidents Using Logit Model, Factor Analysis and Machine Learning: A case study in a developing country

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