Browsing by Author "Pusatli, Tolga"

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Citation - WoS: 22
Citation - Scopus: 22
A discussion on the role of people in global software development
(Univ Osijek, Tech Fac, 2013) Misra, Sanjay; Colomo-Palacios, Ricardo; Pusatli, Tolga; Soto-Acosta, Pedro; 51704
Literature is producing a considerable amount of papers which focus on the risks, challenges and solutions of global software development (GSD). However, the influence of human factors on the success of GSD projects requires further study. The aim of our paper is twofold. First, to identify the challenges related to the human factors in GSD and, second, to propose the solution(s), which could help in solving or reducing the overall impact of these challenges. The main conclusions of this research can be valuable to organizations that are willing to achieve the quality objectives regarding GSD projects.
Citation - WoS: 60
Citation - Scopus: 70
Application of Machine Learning Techniques for the Estimation of the Safety Factor in Slope Stability Analysis
(Mdpi, 2022) Nanehkaran, Yaser Ahangari; Pusatli, Tolga; Jin Chengyong; Chen, Junde; Cemiloglu, Ahmed; Azarafza, Mohammad; Derakhshani, Reza; 51704
Slope stability is the most important stage in the stabilization process for different scale slopes, and it is dictated by the factor of safety (FS). The FS is a relationship between the geotechnical characteristics and the slope behavior under various loading conditions. Thus, the application of an accurate procedure to estimate the FS can lead to a fast and precise decision during the stabilization process. In this regard, using computational models that can be operated accurately is strongly needed. The performance of five different machine learning models to predict the slope safety factors was investigated in this study, which included multilayer perceptron (MLP), support vector machines (SVM), k-nearest neighbors (k-NN), decision tree (DT), and random forest (RF). The main objective of this article is to evaluate and optimize the various machine learning-based predictive models regarding FS calculations, which play a key role in conducting appropriate stabilization methods and stabilizing the slopes. As input to the predictive models, geo-engineering index parameters, such as slope height (H), total slope angle (beta), dry density (gamma(d)), cohesion (c), and internal friction angle (phi), which were estimated for 70 slopes in the South Pars region (southwest of Iran), were considered to predict the FS properly. To prepare the training and testing data sets from the main database, the primary set was randomly divided and applied to all predictive models. The predicted FS results were obtained for testing (30% of the primary data set) and training (70% of the primary data set) for all MLP, SVM, k-NN, DT, and RF models. The models were verified by using a confusion matrix and errors table to conclude the accuracy evaluation indexes (i.e., accuracy, precision, recall, and f1-score), mean squared error (MSE), mean absolute error (MAE), and root mean square error (RMSE). According to the results of this study, the MLP model had the highest evaluation with a precision of 0.938 and an accuracy of 0.90. In addition, the estimated error rate for the MLP model was MAE = 0.103367, MSE = 0.102566, and RMSE = 0.098470.
Citation - WoS: 10
Citation - Scopus: 9
Deep learning method for compressive strength prediction for lightweight concrete
(Techno-press, 2023) Nanehkaran, Yaser A.; Azarafza, Mohammad; Pusatli, Tolga; Bonab, Masoud Hajialilue; Irani, Arash Esmatkhah; Kouhdarag, Mehdi; Derakhshani, Reza; 51704
Concrete is the most widely used building material, with various types including high-and ultra-high-strength, reinforced, normal, and lightweight concretes. However, accurately predicting concrete properties is challenging due to the geotechnical design code's requirement for specific characteristics. To overcome this issue, researchers have turned to new technologies like machine learning to develop proper methodologies for concrete specification. In this study, we propose a highly accurate deep learning-based predictive model to investigate the compressive strength (UCS) of lightweight concrete with natural aggregates (pumice). Our model was implemented on a database containing 249 experimental records and revealed that water, cement, water-cement ratio, fine-coarse aggregate, aggregate substitution rate, fine aggregate replacement, and superplasticizer are the most influential covariates on UCS. To validate our model, we trained and tested it on random subsets of the database, and its performance was evaluated using a confusion matrix and receiver operating characteristic (ROC) overall accuracy. The proposed model was compared with widely known machine learning methods such as MLP, SVM, and DT classifiers to assess its capability. In addition, the model was tested on 25 laboratory UCS tests to evaluate its predictability. Our findings showed that the proposed model achieved the highest accuracy (accuracy=0.97, precision=0.97) and the lowest error rate with a high learning rate (R2=0.914), as confirmed by ROC (AUC=0.971), which is higher than other classifiers. Therefore, the proposed method demonstrates a high level of performance and capability for UCS predictions.
Citation - WoS: 1
Exploring Supportive and Deterrent Factors on Online Shopping in a Developing Country
(Igi Global, 2020) Pusatli, Tolga; Akman, Ibrahim; 51704
This study explores the influence of the reasons of individuals' supportive and deterrent behaviors against commercial usage of online media in the emerging market of Turkey. The supportive and deterrent factors are grouped in empirical categories as discouragement and encouragement, respectively. The impact of these factors on actual behavior were assessed via intermediary empirical category including the variables inefficiency, efficiency, intention, and subjective norm. A survey was conducted using a sample of 251 observations obtained from the visitors of three large/busy malls using purposive sampling. The multiple least-square regression was utilized to test the nature of the relationships between variables. Results indicated a significant discouraging impact of warranty, finance, habits, security on inefficiency perceptions, an encouraging impact of geography and convenience on efficiency perceptions. Significant behavioral impact of perceptions regarding inefficiency, efficiency, subjective norms, and intention on actual usage of online shopping was also observed.
Citation - WoS: 0
Citation - Scopus: 0
Innovative Stability Analysis of Complex Secondary Toppling Failures in Rock Slopes Using the Block Theory
(Springer Heidelberg, 2025) Mao, Yimin; Azarafza, Mohammad; Bonab, Masoud Hajialilue; Pusatli, Tolga; Nanehkaran, Yaser A.
We present the block theory-based secondary toppling stability analysis method (BTSTSA), an advanced and novel method specifically designed to assess secondary toppling failures in slopes. This innovative method comprehensively accounts for various failure mechanisms and computes the factor of safety (F.S) for rock slopes. Grounded in Block theory principles, particularly the key-block method, and supplemented by limit equilibrium techniques, BTSTSA offers a practical and reliable analytical framework. Our investigation focused on five discontinuous rock slopes in the South Pars region, southwest Iran, which are affected by composite toppling failure mechanisms. The stability analysis results were meticulously verified using the Aydan-Kawamoto method, a recognized benchmark in the field. Comparative analysis consistently demonstrated that the BTSTSA approach generates more conservative estimates of the F.S compared to the Aydan-Kawamoto method. This conservatism underscores the robustness and reliability of the BTSTSA framework and highlights its implications for practical engineering applications. The integration of this innovative analytical method with data from these investigations offers crucial insights for geotechnical engineers, equipping them to manage the complexities of secondary toppling failures in discontinuous rock slopes. These findings emphasize the importance of considering conservatism in engineering applications and provide a more accurate and reliable assessment of slope stability, particularly concerning secondary toppling failures, thereby benefiting geotechnical engineering practices.
Citation - WoS: 25
Citation - Scopus: 34
Security Awareness Level of Smartphone Users: An Exploratory Case Study
(Hindawi Ltd, 2019) Koyuncu, Murat; Koyuncu, Murat; Pusatli, Tolga; 51704; Uluslararası Ticaret ve Finansman
As smartphone technology becomes more and more mature, its usage extends beyond and covers also applications that require security. However, since smartphones can contain valuable information, they normally become the target of attackers. A physically lost or a hacked smartphone may cause catastrophic results for its owner. To prevent such undesired events, smartphone users should be aware of existing threats and countermeasures to be taken against them. Therefore, user awareness is a critical factor for smartphone security. This study investigates the awareness level of smartphone users for different security-related parameters and compares the awareness levels of different user groups categorized according to their demographic data. It is based on a survey study conducted on a population with a different range of age, education level, and IT security expertise. According to the obtained results, in general, the awareness level of participants is fairly low, which needs considerable improvement. In terms of age, the oldest group has the lowest level followed by the youngest group. Education level, in general, has a positive effect on the awareness level. Having knowledge about IT is another factor increasing the security awareness level of smartphone users.