Enhancing microalgae classification accuracy in marine ecosystems through convolutional neural networks and support vector machines
Eker Develi, Elif
Accurately classifying microalgae species is vital for monitoring marine ecosystems and managing the emergence of marine mucilage, which is crucial for monitoring mucilage phenomena in marine environments. Traditional methods have been inadequate due to time-consuming processes and the need for expert knowledge. The purpose of this article is to employ convolutional neural networks (CNNs) and support vector machines (SVMs) to improve classification accuracy and efficiency. By employing advanced computational techniques, including MobileNet and GoogleNet models, alongside SVM classification, the study demonstrates significant advancements over conventional identification methods. In the classification of a dataset consisting of 7820 images using four different SVM kernel functions, the line...
Evaluating IMO 2030 strategies for environmental sustainability in maritime transportation using ANP method
Kök, Gökçe Tuğdemir
The maritime sector significantly contributes to global climate change by emitting pollutants and greenhouse gases (GHGs). In response to these challenges, the International Maritime Organization (IMO) has implemented the 2030 Strategic Vision to promote environmental sustainability in the industry. This study employs the Analytic Network Process (ANP), a sophisticated multi-criteria decision-making instrument, to quantitatively assess and rank eight strategies outlined during the 81st session of the Marine Environment Protection Committee (MEPC 81). The analysis indicates that Ballast Water Management (normalized weight: 0.269) is identified as the highest priority, succeeded by proposals concerning Ship Recycling (0.179) and Emission Control Areas (ECAs) (0.142). The study’s approach, su...