Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed
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Publication Metadata only A Variational Joint Segmentation and Registration Framework for Multimodal Images(SPRINGER INTERNATIONAL PUBLISHING AG, 2020) Ademaj, Adela; Rada, Lavdie; Ibrahim, Mazlinda; Chen, Ke; Zheng, Y; Williams, BM; Chen, K; Ruprecht Karls University Heidelberg; Bahcesehir University; Universiti Pertahanan Nasional Malaysia; University of LiverpoolImage segmentation and registration are closely related image processing techniques and often required as simultaneous tasks. In this work, we introduce an optimization-based approach to a joint registration and segmentation model for multimodal images deformation. The model combines an active contour variational term with a mutual information smoothing fitting term and solves in this way the difficulties of simultaneously performed segmentation and registration models for multimodal images. This combination takes into account the image structure boundaries and the movement of the objects, leading in this way to a robust dynamic scheme that links the object boundaries information that changes over time. Comparison of our model with state of art shows that our method leads to more consistent registrations and accurate results.Publication Metadata only Machine Vision-Based Expert System for Automated Skin Cancer Detection(SPRINGER INTERNATIONAL PUBLISHING AG, 2022) Junayed, Masum Shah; Jeny, Afsana Ahsan; Rada, Lavdie; Islam, Md Baharul; BritoLoeza, C; MartinGonzalez, A; CastanedaZeman, V; Safi, A; Bahcesehir University; Daffodil International UniversitySkin cancer is the most frequently occurring kind of cancer, accounting for about one-third of all cases. Automatic early detection without expert intervention for a visual inspection would be of great help for society. The image processing and machine learning methods have significantly contributed to medical and biomedical research, resulting in fast and exact inspection in different problems. One of such problems is accurate cancer detection and classification. In this study, we introduce an expert system based on image processing and machine learning for skin cancer detection and classification. The proposed approach consists of three significant steps: pre-processing, feature extraction, and classification. The pre-processing step uses the grayscale conversion, Gaussian filter, segmentation, and morphological operation to represent skin lesion images better. We employ two feature extractors, i.e., the ABCD scoring method (asymmetry, border, color, diameter) and gray level co-occurrence matrix (GLCM), to extract cancer-affected areas. Finally, five different machine learning classifiers such as logistic regression (LR), decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) used to detect and classify skin cancer. Experimental results show that random forest exceeds all other classifiers achieving an accuracy of 97.62% and 0.97 Area Under Curve (AUC), which is state-of-the-art on the experimented open-source dataset PH2.Publication Metadata only Toward Automatic Water Pollution Analysis: A Machine Learning Approach for Water-Quality Monitoring Through Pattern Classification of Water Crystallization(SPRINGER INTERNATIONAL PUBLISHING AG, 2022) Rada, Lavdie; Tanriverdi, Yusuf Baran; Kara, Omer Ekmel; Hemond, Elizabeth M.; Tezel, Ulas; BritoLoeza, C; MartinGonzalez, A; CastanedaZeman, V; Safi, A; Bahcesehir University; Bogazici UniversityHeavy metal contamination in drinking water and water resources is one of the problems generated by increasing water demand and growing industrialization. Heavy metals can be toxic to humans and other living beings when their intake surpasses a certain threshold. Generally, heavy metal contamination analysis of water resources requires qualified experts with specialized equipment. In this paper, we introduce a method for citizen-based water-quality monitoring through simple pattern classification of water crystallization using a smartphone and portable microscope. This work is a first step toward the development of a Water Expert System smartphone application that will provide the ability to analyze water resource contamination remotely by sending images to the database and receiving an automatic analysis of the sample via machine learning software. In this study, we show the ability of the method to detect Fe 2 mg/1 L, 5 mg/L,10 mg/L polluted distilled water compared with other heavy metals (Al, Pb) pollution. The experimental results show that the classification used method has an accuracy greater than 90%.Publication Metadata only Insult Detection in the Turkish Language Through Different Machine Learning Algorithms(IEEE, 2023) Ozgen, Kerem; Rada, Lavdie; Bahcesehir UniversityIn this research paper, we propose to use the Turkish Court of Cassation- Yargitay- cases to build a dataset for insult detection tasks and compare machine learning models trained on this dataset. We accumulated studies available in the literature compiling Court of Cassation cases and generated a train and test set for testing machine learning algorithms for insult detection. Although machine learning is not capable of understanding the legal context, cultural background, and the nature of insults or non-insults, it can help identify insults with proper training data created by experts. As far as for the authors knowledge this is the first study to use machine learning for the purpose of automatically distinguishing between insult and non-insult cases within the Turkish justice system. Our research, though its is in its first steps, represents a significant contribution to the field, as it addresses a gap in the existing literature and provides a machine learning approach to improving the efficiency and accuracy of legal decision-making.