Accuracy assessment of RFerns, NB, SVM, and kNN machine learning classifiers in aquaculture

Çakır, Mustafa; Yılmaz, Mesut; Oral, Mükerrem Atalay; KAZANCI, Hüseyin Özgür; Oral, Okan

doi:10.1016/j.jksus.2023.102754

Cited by 10 publications

(5 citation statements)

References 28 publications

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“…Five commonly used machine learning classification techniques are employed for comparison, including linear regression (LR), 64 support vector machine (SVM), 65 K nearest neighbors (KNN), 66 decision tree (DT), 67 and random forest (RF). 68 To ensure fairness, all features are utilized for the conventional techniques individually, and the result with the highest occurrence is selected as the final determination.…”

Section: Discussionmentioning

confidence: 99%

A low-cost machine learning framework for predicting drug–drug interactions based on fusion of multiple features and a parameter self-tuning strategy

Liang,

Lin,

Tan

et al. 2024

Phys. Chem. Chem. Phys.

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Section: Discussionmentioning

confidence: 99%

A low-cost machine learning framework for predicting drug–drug interactions based on fusion of multiple features and a parameter self-tuning strategy

Liang,

Lin,

Tan

et al. 2024

Phys. Chem. Chem. Phys.

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“…Metode K-Nearest Neighbors (K-NN) merupakan metode yang melakukan klasifikasi berdasarkan pada jarak antara data baru dengan k data yang sudah ada. Jarak antara kedua data ini dapat ditentukan dengan menggunakan fungsi jarak seperti jarak Euclidean, Manhattan dan Monkowski [19]. Metode K-NN mengasumsikan bahwa data yang memiliki jarak dekat memiliki kemiripan atau keterkaitan yang tinggi.…”

Section: K-nearest Neighborsunclassified

Eksplorasi dan Klasifikasi K-NN Terhadap Kejadian Luar Biasa Diare di Jawa Barat

Fulazzaky,

Waode,

Fitrianto

et al. 2023

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Tujuan dari penelitian ini adalah untuk mengkaji bagaimana kualitas air dan sanitasi mempengaruhi Kejadian Luar Biasa (KLB) Diare di Provinsi Jawa Barat, Indonesia, menggunakan data Pendataan Potensi Desa (PODES) tahun 2021. Diare merupakan permasalahan serius dalam kesehatan masyarakat Indonesia, terutama pada kelompok anak balita, dan salah satu faktor penyebab utamanya adalah rendahnya kualitas air dan sanitasi. Dalam konteks penelitian ini, kami menerapkan metode algoritma K-Nearest Neighbors (K-NN) untuk mengklasifikasikan wilayah-wilayah yang mengalami KLB Diare. Hasil eksplorasi data menunjukkan variasi yang signifikan dalam jumlah kasus diare di sejumlah kabupaten dan kota yang tersebar di wilayah Jawa Barat. Untuk menangani masalah ketidakseimbangan data, kami menerapkan teknik Pengurangan Acak (Random Under Sampling), Penambahan Acak (Random Over Sampling), dan Synthetic Minority Oversampling Technique (SMOTE).Hasil analisis menunjukkan bahwa model K-NN dengan penggunaan metode SMOTE menghasilkan tingkat akurasi tertinggi, yaitu sebesar 71.28%. Meskipun demikian, nilai F1 score untuk semua model cenderung rendah, yang mengindikasikan adanya tantangan dalam mengklasifikasikan wilayah-wilayah dengan KLB Diare. Penelitian ini memberikan wawasan yang penting mengenai korelasi antara kualitas air, sanitasi, dan KLB Diare di Jawa Barat, serta mengidentifikasi wilayah-wilayah yang memerlukan perhatian lebih dalam upaya pencegahan dan pengendalian penyakit diare. Hasil ini dapat digunakan sebagai dasar untuk merancang program-program kesehatan yang lebih efektif di daerah-daerah dengan tingkat insiden diare yang tinggi. Kata kunci: Algoritma K-Nearest Neighbors (K-NN), SMOTE, Ketidakseimbangan data dan teknik pengambilan sampel ulang, Kualitas air dan sanitasi, Program pencegahan dan pengendalian diare.

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“…The development of efficient fish health management systems is essential for the success of an aquaculture operation ( Boran et al, 2013 ). In recent years, management systems have been improved with the contribution of machine learning techniques ( Yilmaz et al, 2022 ; Yilmaz et al, 2023 ; Çakir et al., 2023 ). Despite the advances in developing new vaccines, drugs and computer-assisted techniques, diseases are still the most devastating problem in fish farms ( Kusuda & Kawai, 1998 ).…”

Section: Introductionmentioning

confidence: 99%

Antibiotic susceptibility and resistance genes profiles of Vagococcus salmoninarum in a rainbow trout (Oncorhyncus mykiss, Walbaum) farm

Yilmaz,

Arslan,

Atalay Oral

et al. 2024

PeerJ

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Disease outbreaks negatively affect fish production. Antimicrobial agents used in the treatment of diseases become ineffective over time because of antibiotic resistance developed by bacteria distributed in the aquaculture environment. This study was conducted for 4 months (cold period) in a fish farm to detect the fish disease, cold water streptococcosis. In the study, four brood stock showing disease signs were detected. Bacteria isolates were obtained and identified as Vagococcus salmoninarum. Antimicrobial susceptibility of V. salmoninarum was tested and antibiotic resistance gene profiles of V. salmoninarum isolates were screened. The phylogenetic relation of the isolates with the previously reported strains was evaluated. Antibiotic resistance developed by pathogenic bacteria is distributed in the aquaculture environment. The transfer of resistance genes from one bacterium to another is very common. This situation causes the antimicrobial agents used in the treatment of diseases to become ineffective over time. The disc diffusion test showed that all four isolates developed resistance to 13 (FFC30, AX25, C30, E15, CF30, L2, OX1, S10, T30, CRO30, CC2, PT15 and TY15) of the evaluated antibiotics and were about to develop resistance to six others (AM 10, FM 300, CFP75, SXT25, APR15 and TE30). Furthermore, antibiotic resistance genes tetA, sul1, sul2, sul3, dhfr1, ereB and floR were detected in the isolated strain. Moreover, the phylogenetic analysis showed that isolated V. salmoninarum strain (ESN1) was closely related to the bacterial strains isolated from USA and Jura.

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Accuracy assessment of RFerns, NB, SVM, and kNN machine learning classifiers in aquaculture

Cited by 10 publications

References 28 publications

A low-cost machine learning framework for predicting drug–drug interactions based on fusion of multiple features and a parameter self-tuning strategy

A low-cost machine learning framework for predicting drug–drug interactions based on fusion of multiple features and a parameter self-tuning strategy

Eksplorasi dan Klasifikasi K-NN Terhadap Kejadian Luar Biasa Diare di Jawa Barat

Antibiotic susceptibility and resistance genes profiles of Vagococcus salmoninarum in a rainbow trout (Oncorhyncus mykiss, Walbaum) farm

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