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Publication Index: WoSSubject: Artificial Neural Networks

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    Article
    Sustainable Stabilization of Expansive Soils Using Waste Marble Powder and Expanded Polystyrene Beads: Experimental Evaluation and Predictive Modelling
    (Elsevier, 2026) Akis, Ebru; Citak, Mete; Lotfi, Bahram
    Expansive soils exhibit considerable volume changes with moisture fluctuations leading to serious challenges for civil infrastructure, causing structural instability, pavement distortion, and foundation damage. While lime and cement remain widely used stabilizers, recent research has increasingly focused on waste-derived materials such as marble powder (MP) and expanded polystyrene beads (EPSb) as promising alternatives. These materials provide a practical approach to soil stabilization while contributing to the reuse of industrial by-products. In this study, the engineering behavior of high-plasticity clay was improved through the inclusion of MP and EPSb as additive materials. MP was added at 0%, 5%, 10%, 15%, and 20%, and EPSb at 0%, 0.3%, and 0.9% by dry weight of the high plasticity clay. Both additives were used alone and in combination. Laboratory tests, including Standard Proctor, free swell (FS), and unconfined compressive strength (UCS), were conducted. The results confirmed that the additives effectively reduced the liquid limit (LL) by 20.1% and the plasticity index (PI) by up to 22.4%. Results showed that EPSb effectively reduced FS and UCS, while MP decreased FS and increased UCS up to an optimal content. The most effective mixes achieved a maximum reduction of 54.7% in free swell (FS) (at 20% MP and 0.9% EPSb content) and a maximum increase of 13.1% in unconfined compressive strength (UCS) (at 5% MP content) compared to the untreated soil. The compaction tests further revealed a general decrease in optimum moisture content (OMC) and a slight increase in maximum dry density (MDD) with increasing MP content. Accordingly, the free swell (FS) and unconfined compressive strength (UCS) of the treated soils were predicted using multiple linear regression (MLR) and artificial neural network (ANN) models, developed from both the current experimental dataset and previously published studies. Input variables included untreated FS and UCS values, additive percentages, and one index property. The ANN model demonstrated superior predictive capability, achieving R2 values of 0.955 and 0.874 for FS and UCS, respectively, compared to 0.411 and 0.618 obtained with MLR. These results highlight the robustness of ANN in capturing nonlinear soil behavior and underscore its reliability and accuracy, particularly under limited data conditions.
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    Article
    Citation - WoS: 2
    Citation - Scopus: 4
    Exploiting Visual Features in Financial Time Series Prediction
    (Igi Global, 2020) Karacor, Adil Gursel; Erkan, Turan Erman
    The possibility to enhance prediction accuracy for foreign exchange rates was investigated in two ways: first applying an outside the box approach to modeling price graphs by exploiting their visual properties, and secondly employing the most efficient methods to detect patterns to classify the direction of movement. The approach that exploits the visual properties of price graphs which make use of density regions along with high and low values describing the shape; hence, the authors propose the name 'Finance Vision.' The data used in the predictive model consists of 1-hour past price values of 4 different currency pairs, between 2003 and 2016. Prediction performances of state-of-the-art methods; Extreme Gradient Boosting, Artificial Neural Network and Support Vector Machines are compared over the same data with the same sets of features. Results show that density based visual features contribute considerably to prediction performance.
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    Article
    Analyzing the Criteria Affecting Transition To Airplane by Comparing Different Methods
    (Mehmet Akif Ersoy Univ, 2022) Reyhanoglu, Izay; Tengilimoglu, Dilaver
    This study, using the multi-vehicle approach, discusses the criteria affecting the transition from alternative transportation modes (car, train, bus) to air transportation between city pairs that neither have a hub status nor non-stop flights between them. If these criteria change, the demand for air transportation will increase. For this purpose, a survey was conducted in the provinces of Kayseri and Bursa, which are among the important trade, industry, and tourism centers in Turkey, in the course of three months between January and March, 2018. Logistic regression, the artificial neural network model, and clustering analyses were applied to the data compiled from questionnaires responded to by 501 individuals in Kayseri and 453 individuals in Bursa. According to the empirical findings, it was concluded that the most significant criteria in the transition to air transportation according to all three methods are the cost of travel/ticket price and non-stop flight. Additionally, it was observed that the Artificial Neural Networks (ANN) model made more accurate predictions compared to others. This study is important since it compares three different methods for the purpose of criteria determination concerning the choice of transportation modes.