MAÜ GCRIS Standart veritabanının içerik oluşturulması ve kurulumu Research Ecosystems (https://www.researchecosystems.com) tarafından devam etmektedir. Bu süreçte gördüğünüz verilerde eksikler olabilir.

Bilgisayar Programcılığı Bölümü

Permanent URI for this communityhttps://hdl.handle.net/20.500.12514/187

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Cataract 1
CICIDS2017; Cyber security; deep learning; feature selection; intrusion detection system 1
Diabetic retinopathy 1
Doğal dil işleme, Veri kazıma, Duygu analizi, Yinelenen yapay sinir ağı, Word2Vec, Kelime Gömme 1
Etkileşimli Tahta, Öz Yeterlilik, Meslek Lisesi, FATİH Projesi 1
Interactive whiteboard, self-efficacy, vocational high school, FATIH Project. 1
Merging systems, enhancing inter-disciplinary relations and increasing needs require multi objectives rather than a single objective in the optimization problems nowadays. However, the objectives are frequently conflicting. When an objective is improved, the other objective(s) may deteriorate. In the multi-objective optimization problems (MOOPs), the aim is to come up with the best solutions that can be an alternative for each other in terms of objective function values under the constraints caused by various reasons. During the last two decades, MOOPs and solution methods have been studied with great interest. It is possible to come across a MOOP in almost every discipline in the literature. MOOPs have been modelled and solved not only in the fields with more applications such as production, management, business administration, marketing, transportation and finance but also in the basic sciences such as chemistry, maths and statistics. Solution of MOOPs requires the simultaneous optimization of conflicting multi objectives. In MOOPs, an optimal solution set on which a compromise is reached among the conflicting objectives is obtained. In this study, the articles on multi-objective optimization written in 2015 and later are analysed and 61 articles are chosen among them. Classical and heuristic methods implemented for the solution of MOOPs presented in these articles are mentioned. The articles are classified according to their subject areas. The methodology used in each article is identified. According to their implementation areas, the multi-objective optimization methods and the areas they are implemented the most are discussed. The areas to be focused on in the future studies to obtain more robust results in the optimization are identified. 1
Natural language processing, Data scraping, Sentiment analysis, Recurrent neural networks, Word2Vec, Word embeddings 1
Retinal diseases 1
Siber güvenlik, saldırı tespit sistemi, derin öğrenme, öznitelik seçimi, CICIDS2017 1