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Minimum Penalty Perturbation Heuristics for Curriculum-Based Timetables Subject to Multiple Disruptions
(Elsevier, 2021)
Course timetables are often rendered infeasible due to unexpected changes in requirements and must be
repaired. Given an initial timetable, planners prefer a repaired timetable whose quality is worsened as little as
possible ...
Multi-Objective Simulated Annealing for Hyper-Parameter Optimization in Convolutional Neural Networks
(PeerJ, Inc., 2021)
In this study, we model a CNN hyper-parameter optimization problem as a
bi-criteria optimization problem, where the first objective being the classification
accuracy and the second objective being the computational ...
Hyper-Parameter Selection in Convolutional Neural Networks Using Microcanonical Optimization Algorithm
(The Institute of Electrical and Electronics Engineers, 2020)
The success of Convolutional Neural Networks is highly dependent on the selected architecture
and the hyper-parameters. The need for the automatic design of the networks is especially important
for complex architectures ...
Search Space Sampling by Simulated Annealing for Identifying Robust Solutions in Course Timetabling
(Institute of Electrical and Electronics Engineers Inc., 2020)
For many combinatorial optimization problems, it
is important to identify solutions that can be repaired without
degrading solution quality in case changes in the data associated
with the constraints make the initial ...
Bi-criteria Simulated Annealing for the Curriculum-based Course Timetabling Problem With Robustness Approximation
(Springer, 2022)
In the process of developing a university’s weekly course timetable, changes in the data, such as the available time periods
of professors or rooms, render the timetable infeasible, requiring the administrators to repair ...
Görüntülerdeki Gauss Dağılımlı Gürültülerin Derin Sinir Ağı Modelleri ile Giderilmesi
(IEEE, 2022)
Mikroskobik görüntülemede çevresel faktörler dolayısıyla elde edilen görüntülerde olu¸san gürültülerin giderilmesi,
tıbbi görüntüleme alanında önemli bir ara¸stırma konusu haline
gelmi¸stir. Herhangi bir dijital mikroskopi ...
Novel Surrogate Measures Based on a Similarity Network for Neural Architecture Search
(IEEE, 2023)
We propose two novel surrogate measures to predict the validation accuracy of the classification
produced by a given neural architecture, thus eliminating the need to train it, in order to speed up neural
architecture ...
BaDENAS: Retina Damar Segmentasyonu için Bayes Tabanlı Sinir Mimarisi Arama
(IEEE, 2023)
Retinal damar segmentasyonu, retinal görüntülerin analizi için önemli bir görevdir ve göz hastalıklarının teşhisinde ve tedavisinde kullanılan etkili bir araçtır. Damar segmentasyonunu otomatik hale getiren U-Net gibi derin ...
Hücre Canlılığı Tespitinde Yapay Öğrenme Yaklaşımları
(IEEE, 2023)
Hücre canlılığı, kök hücre tedavileri, kanser tedavileri, estetik ve kozmetik gibi klinik araştırmalarda önemli yer tutmaktadır. Doğru tedavi ve yaklaşımın uygulanabilmesi için alınan örnekteki toplam hücre canlılık oranı ...
Triplet MAML for Few-shot Classification Problems
(Springer, 2023)
In this study, we propose a TripletMAML algorithm as an extension to Model-Agnostic Meta-Learning (MAML) which is the most widely-used optimization-based meta-learning algorithm. We approach MAML from a metric-learning ...
