Browsing by Author "Benzer, Semra"

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Effect of Polynomial, Radial Basis, and Pearson VII Function Kernels in Support Vector Machine Algorithm for Classification of Crayfish
(2022) Garabaghi, Farid Hassanbaki; Benzer, Recep; Benzer, Semra; Gunal, Aysel Caglan; 0000-0002-5339-0554; A-5050-2014
Freshwater crayfish are one of the most important aquatic organisms that play a pivotal role in the aquatic food chain as well as serving as bioindicators for the aquatic ecosystem health assessment. Hemocytes, the blood cells of crustaceans, can be considered stress and health indicators in crayfish, and are used to evaluate the health response. Therefore, total hemocyte cell numbers (THCs) are useful parameters to show the health of crustaceans and serve as stress indicators to decide the quality of the habitat. Since, catching the fish and the other aquatic organisms, and collecting the data for further assessments are time-consuming and frustrating, today, scientists tend to use swift, more sophisticated, and more reliable methods for modeling the ecosystem stressors based on bioindicators. One tool which has attracted the attention of science communities in the last decades is machine learning algorithms that are reliable and accurate methods to solve classification and regression problems. In this study, a support vector machine is carried out as a machine learning algorithm to classify healthy and unhealthy crayfish based on physiological characteristics. To solve the non-linearity problem of the data by transporting data to high-dimensional space, different kernel functions including polynomial (PK), Pearson VII function-based universal (PUK), and radial basis function (RBF) kernels are used and their effect on the performance of the SVM model was evaluated. Both PK and PUK functions performed well in classifying the crayfish. RBF, however, had an adverse impact on the performance of the model. PUK kernel exhibited an outstanding performance (Accu-racy = 100%) for the classification of the healthy and unhealthy crayfish.
Investigation of some machine learning algorithms in fish age classification
(2021) Benzer, Semra; Garabaghi, Farid Hassanbaki; Benzer, Recep; Mehr, Homay Danaei; 0000-0002-5339-0554; A-5050-2014
Marine and freshwater scientists use fish scales, vertebrae, otoliths and length-weights values to estimate fish age because reliable fish age estimation plays a very important role in fish stock management. The advances in technology and the widespread use of artificial intelligence have revealed the use of traditional observations and techniques in the fishing industry. The aim of this study was to evaluate the effectiveness of three disesteemed machine learning algorithms (NB, J48 DT, RF) in comparison with ANNs which has been widely used in such studies in the literature. In culmination, all three algorithms outperformed ANNs and can be considered as alternatives in case of coming across noisy and non-linear datasets. Moreover, among these three algorithms J48 DT and RF showed exceptional performance where the data for specific fish age groups weren't abundant.
Morphometric analysis of Crayfish - traditional and artificial intelligent approach
(2022) Benzer, Semra; Benzer, Recep; 0000-0002-5339-0554; A-5050-2014
Crayfish are crustaceans of cultural importance in most countries, traditionally consumed on important occasions for centuries, and of high economic value in the market. This study was carried out to analyze some morphological characteristics, the total length of total weight, the carapace length total length, the chela length total length, the abdomen length total length relationships, properties, and ratios of freshwater crayfish (Astacus leptodactylus Eschscholtz 1823) in Iznik Lake. Length-weight and length-length relationships with traditional methods and artificial neural networks, which are the most important subfields of artificial intelligence, have been evaluated. The total length-weight relationships for males, females and all individuals were found to be: W = 0.08197221 L (2.61) (R-2 = 0.941), W = 0.08252047 L (2.53) (R-2 = 0.948) and W = 0.06874014 L (2.65) (R-2 = 0.927), respectively. As a result, the morphometric relationships in Astacus leptodactylus examined in this study will provide information for future studies and monitoring management plans with traditional and artificial intelligence approaches.