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Article Radar Emitter Localization Based on Multipath Exploitation Using Machine Learning(Ieee-inst Electrical Electronics Engineers inc, 2024) Catak, Ferhat Ozgur; Al Imran, Md Abdullah; Dalveren, Yaser; Yildiz, Beytullah; Kara, AliIn this study, a Machine Learning (ML)-based approach is proposed to enhance the computational efficiency of a particular method that was previously proposed by the authors for passive localization of radar emitters based on multipath exploitation with a single receiver in Electronic Support Measures (ESM) systems. The idea is to utilize a ML model on a dataset consisting of useful features obtained from the priori-known operational environment. To verify the applicability and computational efficiency of the proposed approach, simulations are performed on the pseudo-realistic scenes to create the datasets. Well-known regression ML models are trained and tested on the created datasets. The performance of the proposed approach is then evaluated in terms of localization accuracy and computational speed. Based on the results, it is verified that the proposed approach is computationally efficient and implementable in radar detection applications on the condition that the operational environment is known prior to implementation.Article Citation - WoS: 9Citation - Scopus: 13Improving Word Embedding Quality With Innovative Automated Approaches To Hyperparameters(Wiley, 2021) Yildiz, Beytullah; Yıldız, Beytullah; Tezgider, Murat; Yıldız, BeytullahDeep learning practices have a great impact in many areas. Big data and significant hardware developments are the main reasons behind deep learning success. Recent advances in deep learning have led to significant improvements in text analysis and classification. Progress in the quality of word representation is an important factor among these improvements. In this study, we aimed to develop word2vec word representation, also called embedding, by automatically optimizing hyperparameters. Minimum word count, vector size, window size, negative sample, and iteration number were used to improve word embedding. We introduce two approaches for setting hyperparameters that are faster than grid search and random search. Word embeddings were created using documents of approximately 300 million words. We measured the quality of word embedding using a deep learning classification model on documents of 10 different classes. It was observed that the optimization of the values of hyperparameters alone increased classification success by 9%. In addition, we demonstrate the benefits of our approaches by comparing the semantic and syntactic relations between word embedding using default and optimized hyperparameters.Article Citation - WoS: 29Citation - Scopus: 43Text Classification Using Improved Bidirectional Transformer(Wiley, 2022) Tezgider, Murat; Yıldız, Beytullah; Yildiz, Beytullah; Aydin, Galip; Yıldız, BeytullahText data have an important place in our daily life. A huge amount of text data is generated everyday. As a result, automation becomes necessary to handle these large text data. Recently, we are witnessing important developments with the adaptation of new approaches in text processing. Attention mechanisms and transformers are emerging as methods with significant potential for text processing. In this study, we introduced a bidirectional transformer (BiTransformer) constructed using two transformer encoder blocks that utilize bidirectional position encoding to take into account the forward and backward position information of text data. We also created models to evaluate the contribution of attention mechanisms to the classification process. Four models, including long short term memory, attention, transformer, and BiTransformer, were used to conduct experiments on a large Turkish text dataset consisting of 30 categories. The effect of using pretrained embedding on models was also investigated. Experimental results show that the classification models using transformer and attention give promising results compared with classical deep learning models. We observed that the BiTransformer we proposed showed superior performance in text classification.
