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Berber, Selçuk; Kale, Semih

doi:10.4194/1303-2712-v18_2_12

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…Length measurements are based on the total length from rostrum point to telson point. In the measurement of the weight of narrow-clawed crayfish, the weight of the broodstock was measured with a 0.01 g precision weighing, while the weight of the juvenile individuals was measured with a 0.0001 g precision scale (Berber and Kale, 2018).…”

Section: Study Area and Data Collectionmentioning

confidence: 99%

Modeling and predicting meat yield and growth performance using morphological features of narrow-clawed crayfish with machine learning techniques

Gültepe,

Berber,

Gültepe

2024

Preprint

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In this study, predictions of length-weight relationships and meat productivity were generated by machine learning models using measurement data of male and female crayfish in the narrow-clawed crayfish population living in Apolyont Lake. The data set was created using the growth performance and morphometric characters obtained from 1416 crayfish in different years to determine the length-weight relationship and length-meat yield. Statistical methods, artificial intelligence, and machine learning are used due to the difficulty of constructing mathematical models in multi-parameter and multivariate problems. In recent studies, artificial intelligence and machine learning methods give higher accuracy than other prediction methods in large data sets with complex structures. No previous studies have been conducted on such population parameters. The analysis results show that most of the models designed as an alternative to traditional estimation methods in future planning studies in sustainable fisheries, aquaculture, and natural sources management are valid for machine learning and artificial intelligence. Seven different machine learning algorithms were applied to the data set and the length-weight relationships and length-meat yields were evaluated for both male and female individuals. Support Vector Regression (SVR) has achieved the best prediction performance accuracy with 0.996 and 0.992 values for the length-weight of males and females, with 0.996 and 0.995 values for the length-meat yield of males and females. The results showed that the SVR outperforms the others for all scenarios regarding the accuracy, sensitivity, and specificity metrics.

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Section: Study Area and Data Collectionmentioning

confidence: 99%

Modeling and predicting meat yield and growth performance using morphological features of narrow-clawed crayfish with machine learning techniques

Gültepe,

Berber,

Gültepe

2024

Preprint

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“…Moreover, it is also known to have been grafted through lakes and canals in Czechia, Poland, Germany, Finland, Denmark, the Netherlands, Britain, Lithuania, Latvia, Spain and Italy (Skurdal & Taugbøl 2002). The species is distributed across Thrace, north, central and west Anatolia in Türkiye (Holthius 1961;Geldiay & Kocataş 1970;Berber et al 2012Berber et al , 2019Berber et al , 2020Berber & Kale 2018;Berber 2020;Kale et al , 2021. Turkish records on crayfish production date back to 1965, mentioned as 270 tons in total, while the highest crayfish harvest was 7937 tons in 1984.…”

Section: Introductionmentioning

confidence: 99%

Antioxidant radical scavenging capacity and total carotenoid content of narrow-clawed crayfish (Pontastacus leptodactylus, Eschscholtz, 1823) in Atikhisar Reservoir (Çanakkale, Türkiye)

Acarlı,

Kızılkaya,

Vural

et al. 2023

OandHS

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In this study, antioxidant radical scavenging capacity and total carotenoid content in the meat and shells of Pontastacus leptodactylus were investigated. Concerning the antioxidant scavenging effect, the highest IC50 values were found to be 388.77 mg g-1 and 155.53 mg g-1 for females and males in July and March, respectively. The mean IC50 values of the meat were calculated as 239.83 mg g-1 and 105.21 mg g-1 for females and males, respectively. The mean total carotenoid content in the meat was found to be 14.35 and 12.78 µg g-1 for females and males, respectively. The results indicated that crayfish meat had antioxidant radical scavenging capacity and was rich in carotenoid content.

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Modeling and predicting meat yield and growth performance using morphological features of narrow-clawed crayfish with machine learning techniques

Gültepe,

Berber,

Gültepe

2024

Sci Rep

View full text Add to dashboard Cite

In recent studies, artificial intelligence and machine learning methods give higher accuracy than other prediction methods in large data sets with complex structures. Instead of statistical methods, artificial intelligence, and machine learning are used due to the difficulty of constructing mathematical models in multi-parameter and multivariate problems. In this study, predictions of length–weight relationships and meat productivity were generated by machine learning models using measurement data of male and female crayfish in the narrow-clawed crayfish population living in Apolyont Lake. The data set was created using the growth performance and morphometric characters from 1416 crayfish in different years to determine the length–weight relationship and length-meat yield. Statistical methods, artificial intelligence, and machine learning are used due to the difficulty of constructing mathematical models in multi-parameter and multivariate problems. The analysis results show that most models designed as an alternative to traditional estimation methods in future planning studies in sustainable fisheries, aquaculture, and natural sources management are valid for machine learning and artificial intelligence. Seven different machine learning algorithms were applied to the data set and the length–weight relationships and length-meat yields were evaluated for both male and female individuals. Support vector regression (SVR) has achieved the best prediction performance accuracy with 0.996 and 0.992 values for the length–weight of males and females, with 0.996 and 0.995 values for the length-meat yield of males and females. The results showed that the SVR outperforms the others for all scenarios regarding the accuracy, sensitivity, and specificity metrics.

show abstract

Untitled

Cited by 7 publications

References 11 publications

Modeling and predicting meat yield and growth performance using morphological features of narrow-clawed crayfish with machine learning techniques

Modeling and predicting meat yield and growth performance using morphological features of narrow-clawed crayfish with machine learning techniques

Antioxidant radical scavenging capacity and total carotenoid content of narrow-clawed crayfish (Pontastacus leptodactylus, Eschscholtz, 1823) in Atikhisar Reservoir (Çanakkale, Türkiye)

Modeling and predicting meat yield and growth performance using morphological features of narrow-clawed crayfish with machine learning techniques

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