Bilgisayar Mühendisliği Bölümü Koleksiyonu

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

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Citation - WoS: 1
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
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.
Neural Decoding of Brand Perception and Preferences: Understanding Consumer Behavior Through Fnirs and Machine Learning
(Ieee, 2024) Çakar, Tuna; Girisken, Yener; Tuna, Esin; Filiz, Gozde; Drias, Yassine
This research examines the link between consumer brand perceptions and neural activity by employing Functional Near-Infrared Spectroscopy (fNIRS) and machine learning techniques. The study analyzes the neural projections of participants' reactions to brand-associated adjectives, processing data collected from 168 individuals through machine learning algorithms. The findings underscore the significance of the lateral regions of the prefrontal cortex in the decision- making process related to brand perceptions. The aim is to understand how brands are perceived when associated with various adjectives and to develop this understanding through neural patterns using machine learning models. This study demonstrates the potential of integrating neural data with machine learning methods in the field of applied neuroscience.
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
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.

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