Browsing by Author "mehmet bozuyla"

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    Developing a fake news identification model with advanced deep language\rtransformers for Turkish COVID-19 misinformation data
    (2022) akın özçift; mehmet bozuyla
    The massive use of social media causes rapid information dissemination that amplifies harmful messages\rsuch as fake news. Fake-news is misleading information presented as factual news that is generally used to manipulate\rpublic opinion. In particular, fake news related to COVID-19 is defined as ‘infodemic’ by World Health Organization.\rAn infodemic is a misleading information that causes confusion which may harm health. There is a high volume\rof misinformation about COVID-19 that causes panic and high stress. Therefore, the importance of development of\rCOVID-19 related fake news identification model is clear and it is particularly important for Turkish language from\rCOVID-19 fake news identification point of view. In this article, we propose an advanced deep language transformer\rmodel to identify the truth of Turkish COVID-19 news from social media. For this aim, we first generated Turkish\rCOVID-19 news from various sources as a benchmark dataset. Then we utilized five conventional machine learning\ralgorithms (i.e. Naive Bayes, Random Forest, K-Nearest Neighbor, Support Vector Machine, Logistic Regression) on\rtop of several language preprocessing tasks. As a next step, we used novel deep learning algorithms such as Long ShortTerm Memory, Bi-directional Long-Short-Term-Memory, Convolutional Neural Networks, Gated Recurrent Unit and\rBi-directional Gated Recurrent Unit. For further evaluation, we made use of deep learning based language transformers,\ri.e. Bi-directional Encoder Representations from Transformers and its variations, to improve efficiency of the proposed\rapproach. From the obtained results, we observed that neural transformers, in particular Turkish dedicated transformer\rBerTURK, is able to identify COVID-19 fake news in 98.5% accuracy.
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    Majority vote decision fusion system to assist automated identification of vertebral column pathologies
    (2023) AKIN OZÇIFT; mehmet bozuyla
    This paper presents a majority vote decision fusion system called AIVCP (Automated Identification of Vertebral Column Pathologies). With this aim, we proposed a three-step decision fusion algorithm: In the first step, a pool of algorithms from different groups is obtained and the number of classifiers is decreased to 10 with the use of prediction accuracy and classifier diversity concept. As a second step, different majority vote combinations of 10 algorithms are searched with a grid search strategy guided on top of 10-fold cross validation evaluation and with prediction error analysis. In the second step, we obtained four base classifiers, i.e., Naïve Bayes (NB), Simple Logistics (SL), Learning Vector Quantization (LVQ) and Decision Stump (DS) whose majority vote decision fusion generate the most accurate diagnosis rate in Vertebral Column Pathologies domain. As the third step, we applied a Support Vector Machine based feature selection to increase prediction performance of the proposed system further. The experiments are evaluated with the use of 10-fold cross-validation, Sensitivity, Specificity and Confusion Matrices. The experimental results have shown that NB, SL, LVQ, and DS as single classifiers generate 82.58%, 87.09%, 82.90%, and 77.41% average diagnosis accuracies respectively. On the other hand, majority vote decision fusion of these single predictors produces 90.32% accuracy that is higher than each of the constituents. The resultant diagnosis accuracy of Vote algorithm for Vertebral column pathologies is quite promising.