9 results
Search Results
Now showing 1 - 9 of 9
Conference Object Citation - Scopus: 6A Mini-Review on Radio Frequency Fingerprinting Localization in Outdoor Environments: Recent Advances and Challenges(Institute of Electrical and Electronics Engineers Inc., 2022) Dogan,D.; Dalveren,Y.; Kara,A.A considerable growth in demand for locating the source of emissions in outdoor environments has led to the rapid development of various localization methods. Among these, RF fingerprinting (RFF) localization has become one of the most promising method due to its unique advantages resulted from the recent developments in machine learning techniques. In this short review, it is aimed to assess the existing RFF methods in the literature for outdoor localization. For this purpose, firstly, the current state of RFF localization methods in outdoor environments are overviewed. Then, the main research challenges in the development of RFF localization are highlighted. This is followed by a brief discussion on the open issues in order to give future research directions. Furthermore, the research efforts currently undertaken by the authors are briefly addressed. © 2022 IEEE.Conference Object An Empirical Comparison of Customer Behavior Modeling Approaches for Shopping List Prediction(Ieee, 2018) Peker, Serhat; Kocyigit, Altan; Eren, P. ErhanShopping list prediction is a crucial task for companies as it can enable to provide a specific customer a personalized list of products and improve customer satisfaction and loyalty as well. To predict customer behaviors, many studies in the literature have employed customer behavior modeling approaches which are individual-level and segment-based. However, previous efforts to predict customers' shopping lists have rarely employed these state-of-the-art approaches. In this manner, this paper introduces the segment based approach into the shopping list prediction and then presents an empirical comparison of the individual-level and the segment-based approaches in this problem. For this purpose, well-known machine learning classifiers and customers' purchase history are employed, and the comparison is performed on a real-life dataset by conducting a series of experiments. The results suggest that there is no clear winner in this comparison and the performances of customer behavior modeling approaches depend on the machine learning algorithm employed. The study can help researchers and practitioners to understand different aspects of using customer behavior modeling approaches in the shopping list prediction.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.Article Citation - WoS: 38Citation - Scopus: 48Focus Variation Measurement and Prediction of Surface Texture Parameters Using Machine Learning in Laser Powder Bed Fusion(Asme, 2020) Ozel, Tugrul; Altay, Ayca; Kaftanoglu, Bilgin; Leach, Richard; Senin, Nicola; Donmez, AlkanThe powder bed fusion-based additive manufacturing process uses a laser to melt and fuse powder metal material together and creates parts with intricate surface topography that are often influenced by laser path, layer-to-layer scanning strategies, and energy density. Surface topography investigations of as-built, nickel alloy (625) surfaces were performed by obtaining areal height maps using focus variation microscopy for samples produced at various energy density settings and two different scan strategies. Surface areal height maps and measured surface texture parameters revealed the highly irregular nature of surface topography created by laser powder bed fusion (LPBF). Effects of process parameters and energy density on the areal surface texture have been identified. Machine learning methods were applied to measured data to establish input and output relationships between process parameters and measured surface texture parameters with predictive capabilities. The advantages of utilizing such predictive models for process planning purposes are highlighted.Article Citation - WoS: 1Citation - Scopus: 1Machine Vs. Deep Learning Comparision for Developing an International Sign Language Translator(Taylor & Francis Ltd, 2022) Eryilmaz, Meltem; Balkaya, Ecem; Ucan, Eylul; Turan, Gizem; Oral, Seden GulayThis study aims to enable deaf and hard-of-hearing people to communicate with other individuals who know and do not know sign language. The mobile application was developed for video classification by using MediaPipe Library in the study. While doing this, considering the problems that deaf and hearing loss individuals face in Turkey and abroad modelling and training stages were carried out with the English language option. With the real-time translation feature added to the study individuals were provided with instant communication. In this way, communication problems experienced by hearing-impaired individuals will be greatly reduced. Machine learning and Deep learning concepts were investigated in the study. Model creation and training stages were carried out using VGG16, OpenCV, Pandas, Keras, and Os libraries. Due to the low success rate in the model created using VGG16, the MediaPipe library was used in the formation and training stages of the model. The reason for this is that, thanks to the solutions available in the MediaPipe library, it can normalise the coordinates in 3D by marking the regions to be detected in the human body. Being able to extract the coordinates independently of the background and body type in the videos in the dataset increases the success rate of the model in the formation and training stages. As a result of an experiment, the accuracy rate of the deep learning model is 85% and the application can be easily integrated with different languages. It is concluded that deep learning model is more accure than machine learning one and the communication problem faced by hearing-impaired individuals in many countries can be reduced easily.Conference Object Citation - Scopus: 8An Empirical Comparison of Customer Behavior Modeling Approaches for Shopping List Prediction(Institute of Electrical and Electronics Engineers Inc., 2018) Peker,S.; Kocyigit,A.; Erhan Eren,P.Shopping list prediction is a crucial task for companies as it can enable to provide a specific customer a personalized list of products and improve customer satisfaction and loyalty as well. To predict customer behaviors, many studies in the literature have employed customer behavior modeling approaches which are individual-level and segment-based. However, previous efforts to predict customers' shopping lists have rarely employed these state-of-the-art approaches. In this manner, this paper introduces the segment based approach into the shopping list prediction and then presents an empirical comparison of the individual-level and the segment-based approaches in this problem. For this purpose, well-known machine learning classifiers and customers' purchase history are employed, and the comparison is performed on a real-life dataset by conducting a series of experiments. The results suggest that there is no clear winner in this comparison and the performances of customer behavior modeling approaches depend on the machine learning algorithm employed. The study can help researchers and practitioners to understand different aspects of using customer behavior modeling approaches in the shopping list prediction. © 2018 Croatian Society MIPRO.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.Conference Object Using Intelligent Support Systems for Endoscopic Surgery Training: Analysis of Hand Motion(Iated-int Assoc Technology Education & development, 2017) Topalli, D.; Cagiltay, N. E.The use of simulation techniques in medical education is an emerging topic in surgical training process, and there are limited number of studies found in this field in our country and the world. Recently, using the machine learning techniques in surgical training constitutes a new area of research. By using these techniques, cost-efficient educational tools will be developed in order to improve education efficiency and patient safety. In this scope, it is aimed to develop an intelligent support system by examining the hand movements of the experienced surgeons during a surgical education process and guide less-experienced surgeons. In order to develop this system, previously developed surgical simulation system infrastructure in ECE Project supported by Tubitak-1001 program will be used. The hand movements' data of experts obtained by special tactile devices (haptics) are analyzed with an experimental study. The results of this study aimed to improve the surgical simulation training process with the machine learning algorithm developed and therefore, provide a significant contribution to the surgical training process.Review Citation - WoS: 1Citation - Scopus: 2Bias in human data: A feedback from social sciences(Wiley Periodicals, inc, 2023) Takan, Savas; Ergun, Duygu; Yaman, Sinem Getir; Kilincceker, OnurThe fairness of human-related software has become critical with its widespread use in our daily lives, where life-changing decisions are made. However, with the use of these systems, many erroneous results emerged. Technologies have started to be developed to tackle unexpected results. As for the solution to the issue, companies generally focus on algorithm-oriented errors. The utilized solutions usually only work in some algorithms. Because the cause of the problem is not just the algorithm; it is also the data itself. For instance, deep learning cannot establish the cause-effect relationship quickly. In addition, the boundaries between statistical or heuristic algorithms are unclear. The algorithm's fairness may vary depending on the data related to context. From this point of view, our article focuses on how the data should be, which is not a matter of statistics. In this direction, the picture in question has been revealed through a scenario specific to "vulnerable and disadvantaged" groups, which is one of the most fundamental problems today. With the joint contribution of computer science and social sciences, it aims to predict the possible social dangers that may arise from artificial intelligence algorithms using the clues obtained in this study. To highlight the potential social and mass problems caused by data, Gerbner's "cultivation theory" is reinterpreted. To this end, we conduct an experimental evaluation on popular algorithms and their data sets, such as Word2Vec, GloVe, and ELMO. The article stresses the importance of a holistic approach combining the algorithm, data, and an interdisciplinary assessment.This article is categorized under:Algorithmic Development > Statistics
