Bilgisayar Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940

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Institution Author: search.filters.institutionAuthor.Drias, YassineLanguage: search.filters.language.en

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  • Conference Object
    Citation - WoS: 2
    Citation - Scopus: 1
    Fuzzy Elephant Herding Optimization and DBSCAN for Emergency Transportation: A Case Study for the 2023Turkiye Earthquake
    (Springer international Publishing Ag, 2024) Drias, Yassine; Drias, Habiba; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF University
    In recent times, our planet has experienced numerous natural disasters across all continents. The damage caused by these disasters has been so extensive that Emergency Medical Services (EMS) proved incapable of handling the situation. In this article, we present a novel approach for urgent disaster transport with the aim of minimizing loss of life. In this context, we are investigating the Density-Based Spatial Clustering of Applications with Noise algorithm (DBSCAN) to cluster the large geographic zone affected by the 2023 earthquake in Turkiye. The clustering is done based on hospitals' capacity on one hand and damages on the other hand. The ambulance dispatching task is then tackled using a new fuzzy version of Elephant Herding Optimization called FEHO. This approach addresses the challenge of dispatching ambulances to cover emergency locations effectively and optimally in the clustered regions. Experiments conducted on real data demonstrate the effectiveness of our approach in managing emergency transportation and highlight its potential to minimize the number of casualties.
  • Conference Object
    Citation - Scopus: 1
    The Application of Two Bayesian Personalized Ranking Approaches Based on Item Recommendation From Implicit Feedback
    (Ieee, 2024) Tagtekin, Burak; Sahin, Zeynep; Çakar, Tuna; Drias, Yassine; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF University
    The present study has aimed to provide a different ranking approach that will be used actively in a sector-specific application regarding the optimization of item ranking presented to the users. The current online approach in several different applications still holds a manual ranking algorithm whose parameters are determined by the data specialists with adequate domain-knowledge. The obtained findings from the present study indicate that the optimized Bayesian Personalized Ranking models will be used for providing a suitable, data-driven input for the ranking system that would serve to be personalized. The outcomes of the present study also demonstrate that the model using LearnBPR optimized with a stochastic gradient descent algorithm outperform the other similar methods. The sample model outputs were also investigated by a user sample to ensure that the algorithm was working correctly. The next potential step is to provide a normalization process to include the extracted information to the current ranking system and observe the performance of this new algorithm with the A/B tests conducted.