Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
https://hdl.handle.net/20.500.12294/415
Bilgisayar Mühendisliği Bölümüne ait koleksiyonlar bu alt bölümde listelenir.
2024-03-28T15:26:51Z
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AutoFusion of feature pruning for decision making in operations research
https://hdl.handle.net/20.500.12294/4066
AutoFusion of feature pruning for decision making in operations research
Atas, Pinar Karadayi; Akyuz, Sureyya Ozogur
Recently, the fusion of algorithms in machine learning studies has taken a lot of attention, emphasizing the power of communal decision-making over-relying on a single decision-maker. One of the crucial questions in the aggregation of algorithms is which and how many models should be combined to achieve both the best accuracy and low complexity. It is already known in machine learning that as the complexity of the model increases too much, prediction accuracy decreases. There is a trade-off between these two features. In order to answer such questions, the diversity notion gets involved in overall consensus models. It is also shown that diversity alone does not determine the best ensemble (fusion), so accuracy and diversity together have been taken into account recently in such problems. We took into account those two notions simultaneously so that the number of algorithms and which algorithms should be in the ensemble is answered while solving the feature selection problems. The proposed method in this work is unique in that it includes an optimization model in the pruning phase, which finds the cardinality of the ensemble optimally. Using this optimization model, the size of the ensemble is found directly from the optimization model, instead of considered as a hyper-parameter. Our study shows a significant improvement in accuracy that achieves 0.702 on average among 8 datasets when compared to an unpruned case of 0.625. These results highlight the efficiency of our method both in model accuracy and in obtaining an optimal model complexity. We have validated our algorithm on different domains of data sets which shows better prediction accuracy values than existing ensemble-based feature selection methods. © 2024, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
2024-01-01T00:00:00Z
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Exploring the Molecular Interaction of PCOS and Endometrial Carcinoma through Novel Hyperparameter-Optimized Ensemble Clustering Approaches
https://hdl.handle.net/20.500.12294/4060
Exploring the Molecular Interaction of PCOS and Endometrial Carcinoma through Novel Hyperparameter-Optimized Ensemble Clustering Approaches
Atas, Pinar Karadayi
Polycystic ovary syndrome (PCOS) and endometrial carcinoma (EC) are gynecological conditions that have attracted significant attention due to the higher prevalence of EC in patients with PCOS. Even with this proven association, little is known about the complex molecular pathways that connect PCOS to an increased risk of EC. In order to address this, our study presents two main innovations. To provide a solid basis for our analysis, we have first created a dataset of genes linked to EC and PCOS. Second, we start by building fixed-size ensembles, and then we refine the configuration of a single clustering algorithm within the ensemble at each step of the hyperparameter optimization process. This optimization evaluates the potential performance of the ensemble as a whole, taking into consideration the interactions between each algorithm. All the models in the ensemble are individually optimized with the suitable hyperparameter optimization method, which allows us to tailor the strategy to the model's needs. Our approach aims to improve the ensemble's performance, significantly enhancing the accuracy and robustness of clustering outcomes. Through this approach, we aim to enhance our understanding of PCOS and EC, potentially leading to diagnostic and treatment breakthroughs.
2024-01-01T00:00:00Z
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Adaptation of n-out-of-n secret sharing scheme into IoT network
https://hdl.handle.net/20.500.12294/4037
Adaptation of n-out-of-n secret sharing scheme into IoT network
Kocatekin, Tugberk; Caliskan, Cafer
Internet of Things (IoT) has become an established part of our daily lives by interconnecting billions of devices in diverse areas such as healthcare, smart home technologies, agriculture, etc. However, these devices are limited in memory, energy and computational capabilities. This creates a great potential for security issues to arise as being constrained prevents them from applying complex cryptographic algorithms. In this study, we propose a novel method to provide a low-cost and secure communication for constrained IoT devices. The proposed method is based on a n-out-of-n secret sharing scheme and mimicks the idea of visual cryptography in a digital set-up.Generally, when an IoT device communicates with an outer party, it establishes the communication by itself or through a mediary such as a central hub or gateway; which leads to single point of failure. Our proposed method aims for a distributed environment in which devices collaborate with each other and therefore divide the responsibility of sending a message into multiple devices, instead of just one device. Therefore, when a device plans on sending a message, all its neighbors send it on the behalf of this device. © 2023 IEEE.
2023-01-01T00:00:00Z
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Early Software Defects Density Prediction: Training the International Software Benchmarking Cross Projects Data Using Supervised Learning
https://hdl.handle.net/20.500.12294/4034
Early Software Defects Density Prediction: Training the International Software Benchmarking Cross Projects Data Using Supervised Learning
Tahir, Touseef; Gencel, Cigdem; Rasool, Ghulam; Umer, Tariq; Rasheed, Jawad; Yeo, Sook Fern; Cevik, Taner
Recent reviews of the literature indicate the need for empirical studies on cross-project defect prediction (CPDP) that would allow aggregation of the evidence and improve predictive performance. Most empirical studies predict defects at granularity levels of method, class, file, and module/package during the coding phase, and thereby avoid external failure costs. The main goal of this study is to perform an empirical study on early defect prediction at the beginning of a project at the product level of granularity for using it as input in planning quality activities of the project. Hence, both internal and external failure costs could be avoided as much as possible through proper planning of quality. We first made a systematic mapping study (SMS) on secondary studies (literature reviews) on defect prediction to identify the most used datasets, the project attributes and metrics utilized as estimators, and the supervised learning methods employed for training the data. Then, we made an empirical study on defect density prediction using cross-project data. We collected 760 project data from the International Software Benchmarking (ISBSG) dataset version 11, which reported both defects and functional size attributes. We trained the prediction models using: i) the complete set of project attributes, ii) the individual attributes, and iii) multiple subsets of attributes. We employed classification and regression approaches of machine learning. The machine learning models are trained using original values of the dataset, and z-score and logged transformations of original values to explore the effects of data normalization on prediction. Most machine learning models trained on the z-score transformation of the dataset performed best for classifying defects. The Multilayer-Perceptron (Neural Network) model trained on the z-score transformation of complete dataset predicted defects with the highest F1-score of 0.89 using binary classification. The logged transformation and feature selection methods improved the results for multivariable regression. The multivariable regression predicted defects with the highest Root Mean Squared Error (RMSE) and R2 (r-squared) values of 0.4 and 0.9, respectively, with a subset of 11 features using logged transformation. The results of classification and regression approaches indicate that defects can be predicted with reasonable accuracy at the software product level using cross-project data. © 2013 IEEE.
2023-01-01T00:00:00Z