TR-Dizin İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/396

Browse

Now showing 1 - 3 of 3

Broadband Low Reflection Surfaces With Silicon Nano-Pillar Square Arrays for Energy Harvesting
(2022) Tut, Turgut; 01. Abdullah Gül University; 02.07. Malzeme Bilimi ve Nanoteknoloji Mühendisliği; 02. Mühendislik Fakültesi
In this work, optimization of the nanopillar arrays and thin films coated on silicon substrate has been investigated in order to minimize the optical reflection loss from the silicon substrate surface. Nano-pillars's height, incline angle, array properties are systematically optimized. Full field Finite Difference Time Domain method is used to simulate EM fields and calculate the reflection from the modified nanostructured substrate surfaces in 400nm-1100nm spectral range. Optimization recipe is clearly presented and it is not only useful for square arrays but for regular arrays of nano-pillars in general.
Document Classification With Contextually Enriched Word Embeddings
(2024) Akbaş, Ayhan; Mahmood, Raad; Bakal, Mehmet; 01. Abdullah Gül University; 02. 04. Bilgisayar Mühendisliği; 02. Mühendislik Fakültesi
The text classification task has a wide range of application domains for distinct purposes, such as the classification of articles, social media posts, and sentiments. As a natural language processing application, machine learning and deep learning techniques are intensively utilized in solving such challenges. One common approach is employing the discriminative word features comprising Bag-of-Words and n-grams to conduct text classification experiments. The other powerful approach is exploiting neural network-based (specifically deep learning models) through either sentence, word, or character levels. In this study, we proposed a novel approach to classify documents with contextually enriched word embeddings powered by the neighbor words accessible through the trigram word series. In the experiments, a well-known web of science dataset is exploited to demonstrate the novelty of the models. Consequently, we built various models constructed with and without the proposed approach to monitor the models' performances. The experimental models showed that the proposed neighborhood-based word embedding enrichment has decent potential to use in further studies.
Machine Learning Based Early Prediction of Type 2 Diabetes: A New Hybrid Feature Selection Approach Using Correlation Matrix With Heatmap and SFS
(2022) Buyrukoglu, Selim; Akbaş, Ayhan; 01. Abdullah Gül University
A new hybrid machine learning method for the prediction of type 2 diabetes is introduced and explained in detail. Also, outcomes are compared with similar researches. Early prediction of diabetes is crucial to take necessary measures (i.e. changing eating habits, patient weight control etc.), to defer the emergence of diabetes and to reduce the death rate to some extent and ease medical care professionals’ decision-making in preventing and managing diabetes mellitus. The purpose of this study is the creation of a new hybrid feature selection approach combination of Correlation Matrix with Heatmap and Sequential forward selection (SFS) to reveal the most effective features in the detection of diabetes. A diabetes data set with 520 instances and seven features were studied with the application of the proposed hybrid feature selection approach. The evaluation of the selected optimal features was measured by applying Support Vector Machines(SVM), Random Forest(RF), and Artificial Neural Networks(ANN) classifiers. Five evaluation metrics, namely, Accuracy, F-measure, Precision, Recall, and AUC showed the best performance with ANN (99.1%), F-measure (99.1%), Precision (99.3%), Recall (99.1%), and AUC (99.2%). Our proposed hybrid feature selection model provided a more promising performance with ANN compared to other machine learning algorithms.

Browse

Browsing TR-Dizin İndeksli Yayınlar Koleksiyonu by Journal "Balkan Journal of Electrical and Computer Engineering"