Altınbaş Üniversitesi Kurumsal Akademik Arşivi
DSpace@Altınbaş, Altınbaş Üniversitesi tarafından doğrudan ve dolaylı olarak yayınlanan; kitap, makale, tez, bildiri, rapor, araştırma verisi gibi tüm akademik kaynakları uluslararası standartlarda dijital ortamda depolar, Üniversitenin akademik performansını izlemeye aracılık eder, kaynakları uzun süreli saklar ve telif haklarına uygun olarak Açık Erişime sunar.
Güncel Gönderiler
Machine learning techniques for differentiating psychrophilic and non-psychrophilic bacterial α/β hydrolase enzymes
(Springer Science and Business Media Deutschland GmbH, 2025) Vardar-Yel, Nurcan
Psychrophilic enzymes represent a category of macromolecules that have acquired specific properties that enable these enzymes to perform their catalytic activity at low temperatures with high efficiency. One of the factors contributing to their adaptation is increased active site flexibility. Psychrophilic enzymes are of significant industrial interest due to their applications in food production, environmental remediation, pharmaceuticals, textiles, and detergents. Despite growing interest, the molecular mechanisms underlying the adaptation of psychrophilic enzymes to low temperatures remain largely unexplored. This study aims to investigate the differences between psychrophilic and non-psychrophilic bacterial α/β hydrolase enzymes. 464 psychrophilic and 562 non-psychrophilic α/β hydrolase enzymes were retrieved from the UniProt database. Further classification of these enzymes based on amino acid composition was performed using a set of machine learning algorithms such as Decision Tree (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM). Ten variables, including the contents of Ala, Gly, Ser, Thr, charged, aliphatic, aromatic, and hydrophobic amino acids, as well as the aliphatic index and the grand average of hydropathy (GRAVY), were analyzed. The Random Forest algorithm achieved the highest classification rate with an accuracy of 77%. Further analyses showed that the amino acid threonine and serine played the most important role in determining psychrophilic traits. This suggests that these amino acids play a significant role in enhancing the enzyme's hydrogen-bonding capacity, thereby contributing to its structural flexibility and stability under cold conditions. This study confirms that some amino acids, especially serine and threonine, are generally involved in the cold adaptation of psychrophilic α/β hydrolase enzymes and may provide an interesting platform from a biotechnological point of view.
Harnessing AI for Leadership Development: Predictive Model for Leadership Assessment
(Sakarya University, 2025) Aloamiri, Adel; Abdu Ibrahim, Abdullahi
The present paper has been devoted to the study conducted with the purpose of examining the possibility of applying Machine Learning techniques in classifying leadership based on structured survey data. The objective was to create a predictive model that would allow classifying leadership into three groups – Low, Medium, and High – based on behavior scores. The model was expected to offer a reliable tool for improving leadership development programs and recruitment processes by providing a precise and scalable leadership classification, The study illustrates the potential of advanced ML techniques for rethinking the traditional approaches to the assessment of leadership. Due to the use of advanced ensemble modeling, it was possible to ensure the high accuracy of 93.3% in leadership predicting. Such outcomes can generate considerable advantages for organizational development strategies. The use of ensemble machine learning in the domain of organizational behavior studies can be considered as a valuable academic contribution as it has demonstrated the capacity of determining the application of ensemble techniques for enhancing leadership studies. at the same time, it offers a useful instrument to develop more sophisticated and data-driven practices for leadership development.
Machine learning prediction of effective radiation doses in various computed tomography applications: a virtual human phantom study
(2025) Tanyıldızı-Kökkülünk, Handan
Objectives: In this work, it was aimed to employ machine learning (ML) algorithms to accurately forecast the radiation doses for phantoms while accounting for the most popular CT protocols.
Methods: A cloud-based software was utilized to calculate the effective doses from different CT protocols. To simulate a range of adult patients with different weights, eight entire body mesh-based computational phantom sets were used. The head, neck, and chest-abdomen-pelvis CT scan characteristics were combined to create a dataset with 33 rows for each phantom and 792 rows total. At the ML stage, linear (LR), random forest (RF) and support vector regression (SVR) were used. Mean absolute error, mean squared error and accuracy were used to evaluate the performances.
Results: The female phantoms received higher doses (7.8 %) than males. Furthermore, an average of 11 % more dose was taken to the normal weight phantom than to the overweight, the overweight in comparison to the obese I, and the obese I in comparison to the obese II. Among the ML algorithms, the LR showed 0 error rate and 100 % accuracy in predicting CT doses.
Conclusions: The LR was shown to be the best approach out of those used in the ML estimation of CT-induced doses.
Enhancing Radiotherapy Tolerance With Papaya Seed-Derived Nanoemulsions
(Malden, 2025) Siddiqui, Muhammad Tariq; Ölçeroğlu, Bilge; Gümüş, Zinar Pınar; Şenışık, Ahmet Murat; Barlas, Fırat Barış
Flavonoid-rich plant materials have gained attention for their potential to reduce radiotherapy side effects. Carica papaya (CP) seeds, known for high flavonoid content, hold promise for therapeutic applications. This study explored the extraction and evaluation of two oils-sunflower oil-based papaya oil (SPO) and pure papaya oil (PPO)-and their nano emulsions (SPOE and PPOE), derived from CP seeds, for radioprotective effects. Chemical analysis using QTOF-MS revealed antioxidants and phytochemicals in the oils and emulsions. Size analysis and zeta potential measurements using dynamic light scattering (DLS) showed particle sizes of 140 ± 26.06 nm for PPOE and 293.7 ± 49.42 nm for SPOE. Post-radiation, both SPOE and PPOE significantly enhanced cell viability, with values of 72.24 ± 3.92 (p ≤ 0.001) and 75.85 ± 2.62 (p ≤ 0.001), respectively. These nanoemulsions show potential as topical agents for reducing radiation-induced tissue damage in radiotherapy. Despite the promising in vitro findings, further in vivo studies are needed to confirm the clinical relevance of these nanoemulsions. Additionally, their incorporation into sunscreen formulations could provide further protection against radiation-induced skin damage, broadening their potential applications.
Wind farm sites selection using a machine learning approach and geographical information systems in Türkiye
(Springer Science and Business Media B.V., 2025) Khalaf, Oras Fadhil; Uçan, Osman Nuri; Alsamarai, Naseem Adnan
This research highlights the importance of integrating machine learning algorithms with Geographical Information Systems (GIS) applications in the field of renewable energy by finding a suitable site for wind farms due to their importance in preserving the environment to achieve efficiency and cost-effectiveness and reduce the environmental impact of fossil fuel energy sources. Using GIS various factors affecting wind energy localization were processed and analyzed including natural, socio-economic and environmental criteria. Ensemble learning of four supervised machine learning algorithms (Random Forest, K-Nearest Neighbor, Support Vector Machines, Naive Bayes) was used to classify suitable and unsuitable data representing geo-referenced points on the ground with three criteria for each site (wind speed, elevation and slope). The results of the algorithms varied in terms of accuracy and variance, then the results were collected, and the intersection between them was found so that the location classification would be agreed upon in the results of the algorithms used. The aim of using this technique is to reduce the error, increase the accuracy and avoid the bias or variance present in individual models. Accuracy of the algorithms result was respectively (K-Nearest Neighbor, Random Forest, Support Vector Machines, Naive Bayes) (93.022%, 93.018%, 95.095%, 89.553%). The final result is a map using GIS showing the suitable and unsuitable sites of wind farms in the study area (Türkiye) has been chosen as a study area in the research due to several factors that make it suitable for wind energy projects, including its geographical location, which gives it great climatic and terrain diversity, as it is surrounded by seas (Black Sea, Aegean Sea, and Mediterranean Sea), which leads to the activity of seasonal and continuous winds, which contributes to the activity of seasonal and permanent winds. Its drive to develop investment in renewable energy due to economic and population growth has increased the demand for energy and consequently the development of renewable and sustainable energy sources. This research contributes to supporting the global transition to sustainable energy by providing a new methodology for integrating multiple technologies to support a sustainable energy future.
