Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Gul, Muhammet(3)
- Doğan, Gürkan(2)
- Ergen, Burhan(2)
- Yucesan, Melih(2)
- Agarwal, P(1)
- Ak, M. Fatih(1)
- Aslan, Naim(1)
- Baskonus, Haci Mehmet(1)
- Benzer, Fulya(1)
- Camas, Mustafa(1)
- Cattani, Carlo(1)
- Celik, Erkan(1)
- Celik, Fatih(1)
- Cirik, Semra(1)
- Dragomir, SS(1)
- Erdem, Ebubekir(1)
- Erişir, Mine(1)
- Garipcan, Ali Murat(1)
- Jleli, M(1)
- Kaner, Seyfullah(1)
- Karaaslan, Nagihan M.(1)
- Kocabaş, Mehmet(1)
- Koru, Edis(1)
- Koç, Mümin Mehmet(1)
- Kutluyer, Filiz(1)
- Kyeremeh, Kwaku(1)
- Lo, Huai-Wei(1)
- Samet, B(1)
- Sazak Camas, Anil(1)
- Seyhaneyildiz Can, Safak(1)
Yayınlar
- MakaleAçık Erişim2025Scopus SJRQ1A novel deep learning-based statistical randomness evaluation test methodology for cryptographic applications
Kaner, Seyfullah; Garipcan, Ali Murat; Erdem, Ebubekir
The security of cryptographic systems is directly linked to the statistical randomness properties of the random numbers used. Traditional statistical randomness tests can be limited in evaluating the properties of these numbers and can require long processing times. While widely used test suites such as the National Institute of Standards and Technology (NIST) Special Publication (SP) 800–22 play a crucial role in assessing data randomness, they are slow on large data sets, can only evaluate certain statistical properties, and fail to detect complex data patterns and dependency structures. In this paper, we propose a new deep learning (DL) based method to overcome these limitations. In the study, bit sequences produced by an FPGA-based true random number generator (TRNG) and different pseudo-random number generators (PRNGs) were converted into image format, and classification experiments were carried out on the AlexNet, ResNet50, and EfficientNetB0 architectures. The results showed that AlexNet, with 87% accuracy and 99% recall, was by far the most successful method compared to the other models. Furthermore, the ablation analysis revealed that the components of data augmentation, early stopping, and cross-validation play a critical role in the model’s stability and generalizability. Providing a reliable randomness evaluation with high accuracy, this new method, as an alternative to traditional statistical test suites, demonstrates that AI-based solutions can enhance the effectiveness and accuracy of randomness assessment in cryptographic applications. The success of DL methods in complex data structures offers significant potential for improving the security of modern cryptographic systems.
AtıfScopus9WoS9OpenAlex11
İncele - MakaleAçık Erişim2023A new approach based on convolutional neural network and feature selection for recognizing vehicle types
Doğan, Gürkan; Ergen, Burhan
The number of vehicles used in traffic life has reached enormous dimensions today. The increase in the number of vehicles day by day causes some traffic problems along with it; such as traffic congestion, accidents, pollution, and safety. To overcome all these problems, convolutional neural networks (CNN) methods are one of the trend methods used in recent years due to their success. In this study, a new approach is proposed to use this power of CNN in low-power devices. First of all, MobileNetv1, MobileNetv2, and NASNetMobile models were optimized to increase accuracy performance. Then, an approach is proposed in which these optimized mobile CNN approaches are used only as feature extractors, and methods such as combining, selecting, and classifying the obtained features are used together. As a result of the classification made with this approach, the classification accuracy has increased by approximately 5%.
AtıfScopus10OpenAlex12İncele - DüzeltmeAçık Erişim2023Correction to: A new approach based on convolutional neural network and feature selection for recognizing vehicle types(Iran Journal of Computer Science, 10.1007/s42044-022-00125-6)
Doğan, Gürkan; Ergen, Burhan
The sentence beginning 'Bu nedenle bu …' in this article should have read 'Therefore, in this study, a combination of OMobileNetv1, OMobileNetv2, ONASNetMobile CNN methods for feature extraction, LSVC method for feature selection and SVM method for classification is proposed.' The original article has been corrected.
- Kitap BölümüYalnız künye2022X-Ray Computed Tomography and Magnetic Resonance Imaging Applications of Magnetic Nanoalloys and Nanocomposites
Aslan, Naim; Koç, Mümin Mehmet
Metallic nanoparticles were used in different medical and biological applications. Such applications can cover targeted drug delivery, biomarking, MRI imaging, CT imaging, magnetic hyperthermia, etc. Metallic nanoparticles show outstanding properties with high electron affinity and good electrical, electronic, and optoelectronic properties with possible magnetic characteristics. In this regard, magnetic nanoparticles and magnetic nanoalloys are found to be a suitable candidate to be used in imaging applications. In this chapter, concepts of X-ray computed tomography imaging and magnetic resonance imaging are briefly discussed. Magnetic nanoparticles, nanoalloys, and nanocomposites used in imaging, diagnostics, and theranostic applications were illustrated.
AtıfScopus2OpenAlex1İncele - MakaleAçık Erişim2021Fermatean fuzzy TOPSIS-based approach for occupational risk assessment in manufacturing
Gul, Muhammet; Lo, Huai-Wei; Yucesan, Melih
The importance of risk assessment in the context of occupational health and safety by manufacturing operators strengthens their hands in solving the problems they may encounter in business processes related to health and safety. One of the most important phases of conducting an exhaustive occupational risk assessment is to analyze potential hazards and associated risks quantitatively. Since manufacturing is one of the industries that require workers to be highly exposed to work, creating a safer environment to reduce occupational injuries is an important task. This study proposes a novel fuzzy risk assessment approach developed by integrating Fermatean fuzzy sets (FFSs) and technique for order preference by similarity to ideal solution (TOPSIS) method for ranking potential hazards in manufacturing. FFSs are a new version of fuzzy set theory that covers the intuitionistic fuzzy sets and Pythagorean fuzzy sets. This version of the fuzzy set is crucial in the decision-making process to handle uncertain information more easily and reflect uncertainty better. A linguistic scale under Fermatean fuzzy documentation has also been developed for experts/decision makers to disclose their judgments easily. Occupational risk analysts can benefit from this approach since FFSs are used for the first time in occupational risk assessment, and the approach is presented in integration with TOPSIS. The proposed approach is applied in the aluminum plate-manufacturing process risk assessment. In the conclusion of the implementation, risks arising in the production are prioritized. In addition, this study made comparisons with other fuzzy methods to demonstrate the proposed approach’s difference and practicality. This study’s results can support practitioners and risk analysts in formulating the improvement measures to increase the safety of the work environment further.
AtıfScopus105WoS86OpenAlex108İncele - MakaleAçık Erişim2021A holistic FMEA approach by fuzzy-based Bayesian network and best–worst method
Yucesan, Melih; Gul, Muhammet; Celik, Erkan
Failure mode and effect analysis (FMEA) is a risk analysis tool widely used in the manufacturing industry. However, traditional FMEA has limitations such as the inability to deal with uncertain failure data including subjective evaluations of experts, the absence of weight values of risk parameters, and not considering the conditionality between failure events. In this paper, we propose a holistic FMEA to overcome these limitations. The proposed approach uses the fuzzy best–worst (FBWM) method in weighting three risk parameters of FMEA, which are severity (S), occurrence (O), and detection (D), and to find the preference values of the failure modes according to parameters S and D. On the other side, it uses the fuzzy Bayesian network (FBN) to determine occurrence probabilities of the failure modes. Experts use a procedure using linguistic variables whose corresponding values are expressed in trapezoidal fuzzy numbers, and determine the preference values of the failure modes according to parameter O in the constructed BN. Thus, the FBN including expert judgments and fuzzy set theory addresses uncertainty in failure data and includes a robust probabilistic risk analysis logic to capture the dependence between failure events. As a demonstration of the approach, a case study was conducted in an industrial kitchen equipment manufacturing facility. The results of the approach have also been compared with existed methods demonstrating its robustness.
AtıfScopus105WoS93İncele - MakaleAçık Erişim2019AHP–TOPSIS integration extended with Pythagorean fuzzy sets for information security risk analysis
Ak, M. Fatih; Gul, Muhammet
Risk analysis (RA) contains several methodologies that object to ensure the protection and safety of occupational stakeholders. Multi attribute decision-making (MADM) is one of the most important RA methodologies that is applied to several areas from manufacturing to information technology. With the widespread use of computer networks and the Internet, information security has become very important. Information security is vital as institutions are mostly dependent on information, technology, and systems. This requires a comprehensive and effective implementation of information security RA. Analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) are commonly used MADM methods and recently used for RA. In this study, a new RA methodology is proposed based on AHP–TOPSIS integration extended with Pythagorean fuzzy sets. AHP strengthened by interval-valued Pythagorean fuzzy numbers is used to weigh risk parameters with expert judgment. Then, TOPSIS with Pythagorean fuzzy numbers is used to prioritize previously identified risks. A comparison of the proposed approach with three approaches (classical RA method, Pythagorean fuzzy VIKOR and Pythagorean fuzzy MOORA) is also provided. To illustrate the feasibility and practicality of the proposed approach, a case study for information security RA in corrugated cardboard sector is executed.
AtıfScopus152WoS128OpenAlex167İncele - MakaleYalnız künye2019Scopus SJRQ2A comprehensive study about Hibiscus sabdariffa leaves: antioxidant activity, polyphenol profile and macro- and micro-element content
Karaaslan, Nagihan M.
Hibiscus sabdariffa leaves are generally consumed by people in cold drinks and herbal tea. The purpose of this study is to examine in terms of antioxidant activity, polyphenol profile and macro- and micro-element content of H. sabdariffa leaves. Firstly, antioxidant activity studies were carried out by applying antioxidant activity assays to different extracts of the plant leaves. It has been observed that the antioxidant activity results vary depending on the extraction solvent and that the result obtained for one extract in some tests could not be obtained for the same extract in the other tests when solvents are changed. Secondly, the polyphenol profile of this plant was identified with a high-performance liquid chromatography–diode array detector (HPLC–DAD). For this purpose, thirteen polyphenols were examined in leaves of this plant, but only caffeic acid, chlorogenic acid, ellagic acid, p-coumaric acid and quercetin were detected among the studied polyphenols. Finally, macro-element including calcium (Ca), potassium (K), magnesium (Mg), sodium (Na) and micro-element including copper (Cu), iron (Fe), zinc (Zn) analysis was carried out using flame atomic absorption spectrometry (FAAS).
AtıfScopus9WoS8OpenAlex10İncele - MakaleAçık Erişim2019Scopus SJRQ2l-Arginine Improves Sperm Motility in Threatened Coruh Trout Salmo coruhensis (Teleostei: Salmonidae)
Kocabaş, Mehmet; Kutluyer, Filiz; Erişir, Mine; Benzer, Fulya
l-arginine is a versatile amino acid and precursor of nitric oxide that provides protection for preventing free radical damage. Herein, experiments were designed to clarify the effect of l-arginine supplementation on the sperm motility of a threatened trout, Coruh trout Salmo coruhensis. Activation solution was supplemented with levels of 0 mM (Control), 2 mM, 4 mM, and 8 mM l-arginine, and motility and survival of sperm cells were assessed. The selected concentrations were determined according to results of our preliminary experiments. The sperm motility rates at l-arginine concentrations of 0, 2, 4, and 8 mM were 73.33 ± 5.77%, 76.67 ± 5.75%, 80.01 ± 0.01%, and 90.00 ± 0.05%, respectively; notility durations were 21.67 ± 2.08, 32.33 ± 3.79, 32.67 ± 1.15, and 37.00 ± 6.38 s, respectively. Significant effect of l-arginine supplementation was determined on the percentage and duration of motile spermatozoa (p < 0.05). The maximum increment was evoked at the greatest concentration (8 mM). Here, we showed that l-arginine supplement can improve sperm motility of S. coruhensis.
AtıfScopus1WoS1OpenAlex2İncele - Kitap BölümüYalnız künye2018Scopus SJRQ4Nonlinear dynamical model for DNA
Baskonus, Haci Mehmet; Cattani, Carlo; Agarwal, P; Dragomir, SS; Jleli, M; Samet, B
This chapter deals with a nonlinear dynamical system arising in the analysis of the double-chain model in deoxyribonucleic acid (DNA). Bernoulli sub-equation function method and modified exp (-Ω(ξ))-expansion function method to obtain some novel dynamical structures to the nonlinear dynamical system are used. We construct some new exponential, hyperbolic and complex periodic wave solutions to this model. Under some suitable values of parameters, we plot the 2D and 3D graphics of the solutions obtained in this study. All the solutions found in this study satisfy the nonlinear dynamical system. Moreover, these solutions can be used to explain some new significant physical meanings of the nonlinear dynamical model for DNA.
AtıfScopus10WoS9OpenAlex8İncele - MakaleYalnız künye2018Scopus SJRQ2Microbial Sorption of Uranium Using Amycolatopsis sp. K47 Isolated from Uranium Deposits
Celik, Fatih; Camas, Mustafa; Kyeremeh, Kwaku; Sazak Camas, Anil
The increasing contamination of soils, sediments, and water with heavy metals through natural and industrial processes is a worldwide problem. Mining processes produce tons of material contaminated with radionuclides such as U and different heavy metals such as Cd, Ni, and Pb. U(VI) adsorbs strongly on bacteria, exhibiting pH-dependent adsorption behavior that is caused by a range of uranyl surface complexes on bacteria cell walls. The Amycolatopsis sp. K47 was isolated from Manisa Koprubasi Kasar open-cast uranium mine and identified for the first time. Using the batch adsorption method, the biosorption potential of this microbe was investigated by studying the effects of changes in pH (1–10), biomass dose (0.1–5 g/l), initial uranium metal concentration (5–200 mg/l), contact time (5–180 min), and temperature (20–60 °C). Interpretation of FTIR data obtained for both the uranium loaded and unloaded Amycolatopsis sp. K47 biomass showed the presence of carboxylic acid, hydroxyl, and amide functional groups that could interact with uranium ions. Scanning electron microscopy images demonstrated that uranium was intensely adsorbed on the microbial biomass surface. The sorption isotherms were investigated by analysis of the Langmuir, Freundlich, and Dubinin–Radushkevich (D–R) models. The Langmuir isotherm model was found to show the best fit for the experimental data obtained. Furthermore, thermodynamic parameters, such as ΔH°, ΔS°, and ΔG°, were calculated using adsorption equilibrium constant obtained from the Langmuir isotherm. The optimal experimental conditions were determined to be pH = 4, C0 = 40 ppm, t = 150 min, temp. = 40 °C, and abs. dose = 1 g/l, and the corresponding U(VI) removal efficiency was about 97 ± 2%.
AtıfScopus16WoS14OpenAlex15İncele - MakaleYalnız künye2017Scopus SJRQ2Effect of temperature and nitrogen concentration on the growth and lipid content of Spirulina platensis and biodiesel production
Seyhaneyildiz Can, Safak; Koru, Edis; Cirik, Semra
In this study, Spirulina platensis (Cyanophyceae) was cultured at five mediums with different amounts of sodium nitrate and three different temperatures. The study was conducted at 1600 lx light intensity and a pH value of 9. The influences of nitrate amounts and temperature on biomass growth and lipid amount were studied during 20 days. The best biomass growth (1.039 ± 0.668 g L−1 dry weight and 0.780 ± 0.501 optical density) for S. platensis was obtained from the group with the temperature value of 35 °C and 2.5 g L−1 of sodium nitrate. The highest lipid production (4.70 ± 0.067% dry weight) was in the nitrate-free medium at 15 °C. The best results for biomass growth and lipid production were obtained from the group at 35 °C and 1.25 g L−1 of sodium nitrate (P < 0.05). At the end of the study, it was determined that algal biomass increased with increasing the nitrate amount but the produced lipid by the algae decreased. Intensive production was performed in accordance with the culture conditions of the most productive groups and biodiesel was obtained from the algae. The density, viscosity, and flash point of the biodiesel were calculated as 0.78, 4.67, and 107 °C, respectively.
AtıfScopus34WoS27OpenAlex35İncele - DergiComplex and Intelligent Systems
- DergiIran Journal of Computer Science
- DergiJournal of King Saud University - Computer and Information Sciences