TR-Dizin İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/396

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3D Sampling of K-Space With Non-Cartesian Trajectories in MR Imaging
(Gazi Univ, Fac Engineering Architecture, 2025) Dundar, Mehmet Sait; Gumus, Kazim Z.; Yilmaz, Bulent
This study presents an innovative approach to 3D k-space sampling in MR imaging using non-Cartesian concentric shell trajectories. The method involves 32 concentric shells of varying radii, allowing for rapid data acquisition through undersampling techniques. Simulations using IDEA software demonstrate that this approach can fill the k-space in less than one second, a significant time reduction compared to traditional FLASH sequences that can take 3-4 minutes. The concentric shell model enhances imaging efficiency by minimizing artifacts and ensuring uniform k-space filling, leading to higher resolution and faster scans. This technique shows promise for clinical applications, particularly in dynamic imaging scenarios such as acute stroke and pediatric radiology, where speed and precision are critical. As illustrated in Figure A, the concentric shell trajectories enable uniform k-space filling, significantly reducing scan times and improving image quality. These results are based on the simulations conducted with IDEA software.
Citation - WoS: 4
Citation - Scopus: 4
All-Polymer Ultrasonic Transducer Design for an Intravascular Ultrasonography Application
(Tubitak Scientific & Technological Research Council Turkey, 2019) Hah, Dooyoung
Intravascular ultrasonography (IVUS), a medical imaging modality, is used to obtain cross-sectional views of blood vessels from inside. In IVUS, transducers are brought to the proximity of the imaging targets so that high-resolution images can be obtained at high frequency without much concern of signal attenuation. To eliminate mechanical rotation rendered in conventional IVUS, it is proposed to manufacture a transducer array on a flexible substrate and wrap it around a cylindrical frame. The transducer of consideration is a capacitive micromachined ultrasonic transducer (CMUT). The whole device needs to be made out of polymers to be able to endure a high degree of bending (radius: 1 mm) Bending of the devices leads to considerable changes in the device characteristics, including resonant frequency and pull-in voltage due to geometrical dimension changes and stress induced. The main purpose of this work is to understand the effect of bending on the device characteristics by means of finite element analysis. Another objective of the work is to understand the relationships between such an effect and the device geometries. It is learned that the bending-induced stress depends strongly on anchor width, membrane thickness, and substrate thickness. It is also learned that resonant frequency and pull-in voltage become lower in most cases because of using a flexible substrate in comparison to those of the device on a rigid substrate. Bending-induced stress increases the spring constant and hence increases resonant frequency and pull-in voltage, although this effect is relatively weaker. For most of the device geometries, pull-in voltage is too high for the polymer material to endure. This is the main drawback of the all-polymer CMUT. In order to meet the design goal of 20 MHz resonant frequency, the membrane radius has to be smaller than 7.7 mu m for a thickness of 3 mu m.
Citation - WoS: 2
Citation - Scopus: 2
Analysis of Optical Gyroscopes With Vertically Stacked Ring Resonators
(Tubitak Scientific & Technological Research Council Turkey, 2021) Hah, Dooyoung
Without any moving part, optical gyroscopes exhibit superior reliability and accuracy in comparison to mechanical sensors. Microring-resonator-based optical gyroscopes emerged as alternatives for bulky conventional Sagnac interferometer sensors, especially attractive for applications with limited footprints. Previously, it has been reported that planar incorporation of multiple resonators does not bring about improvement in sensitivity for a given area because the increase in Sagnac phase accumulation does not outrun the increase of area. Therefore, it was naturally suggested to consider vertical stacking of ring resonators because then, the resonators can share the same footprint. In this work, sensitivity performances of such configurations with vertically stacked microring resonators are analyzed and compared to that of a basic (single-resonator) configuration. Through comprehensive study, it is learned that the sensitivity performance of the devices with vertically-stacked resonators (either with a single bus waveguide or with two bus waveguides) does not exceed that of the basic sensor device (single resonator with one bus waveguide), i.e. the basic structure is yet to be remained as the most efficient configuration.
Citation - WoS: 1
Citation - Scopus: 2
Complementary Medicines Used in Ulcerative Colitis and Unintended Interactions With Cytochrome P450-Dependent Drug-Metabolizing Enzymes
(Tubitak Scientific & Technological Research Council Turkey, 2022) Sen, Alaattin
Ulcerative colitis (UC) is an idiopathic, chronic inflammatory disease with multiple genetic and a variety of environmental risk factors. Although current drugs significantly aid in controlling the disease, many people have led to the application of complementary therapies due to the common belief that they are natural and safe, as well as due to the consideration of the side effect of current drugs. Curcumin, cannabinoids, wheatgrass, Boswellia, wormwood and Aloe vera are among the most commonly used complementary medicines in UC. However, these treatments may have adverse and toxic effects due to unintended interactions with drugs or drug-metabolizing enzymes such as cytochrome P450s; thus, being ignorant of these interactions might cause deleterious effects with severe consequences. In addition, the lack of complete and controlled long-term studies with the use of these complementary medicines regarding drug metabolism pose additional risk and unsafety. Thus, this review aims to give an overview of the potential interactions of drug-metabolizing enzymes with the complementary botanical medicines used in UC, drawing attention to possible adverse effects.
Efficiency of L-DOPA+TiO2 Modified RO Membrane on Salinity Gradient Energy Generation by Pressure Retarded Osmosis
(Pamukkale Univ, 2024) Ates, Nuray; Saki, Seda; Gokcek, Murat; Uzal, Nigmet
Harvesting energy from the salinity gradient of seawater and river water using pressure retarded osmosis (PRO) has been a major research topic of recent years. However, there is a need for efficient PRO membranes that can generate high power density and are pressure resistant, as the performance of current membranes on the market is poor. In this study, specific energy potential of PRO process using LDOPA+TiO2 modified BW30-LE membrane was evaluated on synthetic and real water samples. Polyamide BW30-LE RO membrane was modified by L-DOPA, L-DOPA+0.5 wt% TiO2 and L-DOPA+1 wt% TiO2. The effect of hydraulic pressure and temperature on generation of power density were evaluated for 5, 10, and 15 bar pressures, as well as 10 degrees C, 20 degrees C, and 30 degrees C degrees. The incorporation of TiO2 nanoparticles with L-DOPA increased the water flux by increasing the surface hydrophilicity and roughness of the membrane surface. The maximum specific power was observed as 1.6 W/m(2) for L-DOPA+1 wt% TiO2 modified BW30-LE membrane at 15 bar pressure. Besides, Mediterranean and Aegean, Black Sea water samples were used as draw solution and Seyhan, Ceyhan, Buyuk Menderes, Gediz, Yesilirmak, and Kizilirmak Rivers were used as feed solution. The highest osmotic power density was obtained by using L-DOPA+1 wt% TiO2 modified BW30-LE membrane with Ceyhan River as feed and Mediterranean Sea water as draw solution, which have the highest differences in salinity. In the mixture of Mediterranean and Ceyhan River, the highest power density was obtained at 10 bar pressure at 30 +/- 5 degrees C with 0.70 W/m(2).
Elastic Modulus Prediction for Fiber-Reinforced Concretes
(Pamukkale Univ, 2020) Yagmur, Eren
In this study, the effects of different discrete fiber types on the elastic modulus of concrete are investigated. For this purpose, 260 cylindrical pressure test specimens are compiled. The fiber types considered are steel, PVA, polypropylene, polyolefin, basalt and olefin. The results of the study are showed that if the ratio of coarse aggregate to fine aggregate exceeds 1.5 for all fiber types, the compressive strength of concrete decreases. It has been observed that the elastic modulus increases in cases where the fiber aspect ratio of the steel fibers is less than and equal to 60, while the elastic modulus decreases for values greater than 60. An elastic modulus equation, which applies to all fiber types considered, is proposed. The proposed equation is compared with the experimental results and the other formulas in the literature and the validity of the equations for different cases are questioned.
Enlightening the Molecular Mechanisms of Type 2 Diabetes With a Novel Pathway Clustering and Pathway Subnetwork Approach
(Tubitak Scientific & Technological Research Council Turkey, 2022) Bakir-Gungor, Burcu; Yazici, Miray Unlu; Goy, Gokhan; Temiz, Mustafa
Type 2 diabetes mellitus (T2D) constitutes 90% of the diabetes cases, and it is a complex multifactorial disease. In the last decade, genome-wide association studies (GWASs) for T2D successfully pinpointed the genetic variants (typically single nucleotide polymorphisms, SNPs) that associate with disease risk. In order to diminish the burden of multiple testing in GWAS, researchers attempted to evaluate the collective effects of interesting variants. In this regard, pathway-based analyses of GWAS became popular to discover novel multigenic functional associations. Still, to reveal the unaccounted 85 to 90% of T2D variation, which lies hidden in GWAS datasets, new post-GWAS strategies need to be developed. In this respect, here we reanalyze three metaanalysis data of GWAS in T2D, using the methodology that we have developed to identify disease-associated pathways by combining nominally significant evidence of genetic association with the known biochemical pathways, protein-protein interaction (PPI) networks, and the functional information of selected SNPs. In this research effort, to enlighten the molecular mechanisms underlying T2D development and progress, we integrated different in silico approaches that proceed in top-down manner and bottom-up manner, and presented a comprehensive analysis at protein subnetwork, pathway, and pathway subnetwork levels. Using the mutual information based on the shared genes, the identified protein subnetworks and the affected pathways of each dataset were compared. While most of the identified pathways recapitulate the pathophysiology of T2D, our results show that incorporating SNP functional properties, PPI networks into GWAS can dissect leading molecular pathways, and it could offer improvement over traditional enrichment strategies.
Evaluation of Sub-Network Search Programs in Epilepsy-Related GWAS Dataset
(Pamukkale Univ, 2022) Adanur Dedeturk, Beyhan; Bakir Gungor, Burcu
The active sub-network detection aims to find a group of interconnected genes of disease-related genes in a protein-protein interaction network. In recent years, several algorithms have been developed for this problem. In this study, the analysis of disease-specific sub-network identification programs is evaluated using epilepsy data set. Under the same conditions and with the same data set, 9 different programs are run and results of their Greedy algorithm, Genetic algorithm, Simulated Annealing Algorithm, MCC (Maximal Clique Centrality) algorithm, MCODE (Molecular Complex Detection) algorithm, and PEWCC (Protein Complex Detection using Weighted Clustering Coefficient) algorithm are shown. The top-scoring 5 modules of each program, are compared using fold enrichment analysis and normalized mutual information. Also, the identified subnetworks are functionally enriched using a hypergeometric test, and hence, disease-associated biological pathways are identified. In addition, running times and features of the programs are comparatively evaluated.
Forecasting the Consumer Price Index in Türkiye Using Machine Learning Models: A Comparative Analysis
(Gazi Univ, 2025) Söylemez, İsmet; Ünlü, Ramazan; Nalici, Mehmet Eren
This study utilizes machine learning models to forecast Türkiye's Consumer Price Index (CPI), thereby addressing a critical gap in inflation prediction methodologies. The central research problem involves the forecasting of CPI in a volatile economic environment, which is essential for informed policymaking. The primary objective of this study is to evaluate the performance of three machine learning models, such as Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM), in forecasting CPI over periods ranging from one to six months, utilizing data from 2012 to 2024. The study's unique contribution lies in the application of the \"SelectKBest\" method, which identifies the most relevant indices, thereby enhancing the efficiency of the models. An ensemble method, Averaging Voting, is also employed to combine the strengths of these models, producing more accurate and robust predictions. The findings indicate that while the RF model consistently generates the most accurate forecasts across all shifts, the SVM model demonstrates a particular strength in the domain of short-term predictions. The ensemble model demonstrates a substantial performance improvement, with a R2 value of 0.962 for one-month ahead of estimates and 0.956 for five-month forecasts. This combined approach has been shown to outperform individual models, offering a more reliable framework for CPI forecasting. The findings offer valuable insights for economic policymakers, enabling more precise and stable inflation predictions in Türkiye.
Geological-Geochemical Signatures of Opal Occurrences in Keciborlu (Isparta-Turkey)
(Pamukkale Univ, 2022) Baspinar Tuncay, Ebru; Koken, Ekin; Kuscu, Mustafa; Cengiz, Oya; Aydemir, Fatih; Raimov, Rahmen
Silica-rich solutions, considered as the final products of acidic volcanism, which started from the Late Miocene to throughout the Plio-Quaternary around Isparta, are effective along the main fault observed around the Keciborlu (Isparta) sulfur deposit. Therefore, opal occurrences are intensively observed along this fault zone. Opal occurrences are in various colors such as gray, beige, yellowish, reddish, blackish. Opals with a massive structure, observed as bands, are sharp -edged, conchoidal diffraction, translucent, matte, oily glossy surface opals are iron oxidized. Some opals contain brecciated rock fragments. The locations of the opal occurrences in the field were determined in this study. Using representative samples, structural and textural properties of opals were determined by thin section, scanning electron microscopy analyses, and mineral paragenesis was analyzed via x-ray diffraction and Fourier transform infrared spectroscopy analyses. Geochemical findings revealed chemical compositions. Based on the thin-section studies, it was observed that the opalized samples lost their primary properties due to the effect of hydrothermal solutions and they became iron oxidized, laminated, and argillized. In addition, they contain opaque minerals such as magnetite and hematite. Different micro textures such as amorphous, granular, desert rose, and lepisphere quartz associations were observed in SEM images. In the XRD and FTIR analyzes, it was determined that most of the opals were Opal CT and some of them were defined as Opal C type. Based on the geochemical analyses considering Ba <120 ppm and Ca >200 ppm, the remarkable changes in loss on ignition values, and the relative relationship between C/T ratio and Ga, such hydrothermal alterations in opals the Keciborlu opals were found to have the magmatic origin.
Citation - WoS: 2
Citation - Scopus: 3
Investigation of Hydrogen Diffusion Profile of Different Metallic Materials for a Better Understanding of Hydrogen Embrittlement
(Gazi Univ, 2023) Kapci, Mehmet Fazil; Bal, Burak
In this study, hydrogen diffusion profiles of different metallic materials were investigated. To model hydrogen diffusion, 1D and 2D mass diffusion models were prepared in MATLAB. Iron, nickel and titanium were selected as a material of choice to represent body-centered cubic, facecentered cubic, and hexagonal closed paced crystal structures, respectively. In addition, hydrogen back diffusion profiles were also modeled after certain baking times. Current results reveal that hydrogen diffusion depth depends on the microstructure, energy barrier model, temperature, and charging time. In addition, baking can help for back diffusion of hydrogen and can be utilized as hydrogen embrittlement prevention method. Since hydrogen diffusion is very crucial step to understand and evaluate hydrogen embrittlement, current set of results constitutes an important guideline for hydrogen diffusion calculations and ideal baking time for hydrogen back diffusion for different materials. Furthermore, these results can be used to evaluate hydrogen content inside the material over expensive and hard to find experimental facilities such as, thermal desorption spectroscopy.
Citation - WoS: 1
Citation - Scopus: 2
Investigation of the Interaction of Adipose-Derived Mesenchymal Stem Cells With Ε-Polycaprolactone and EGG White Scaffolds
(Gazi Univ, 2023) Oztel, Olga N.; Yilmaz, Hilal; Isoglu, I. Alper; Allahverdiyev, Adil
The development of three-dimensional (3D) cell culture models is becoming increasingly important due to their numerous advantages over conventional monolayer culture. This study aimed to examine the interaction of adipose tissue-derived mesenchymal stem cells (AD-MSCs) with scaffolds composed of e-polycaprolactone (e-PCL) and egg white. In our study, e-PCL and egg white scaffolds were produced from their monomers by tin octoate catalyzed and heat polymerization, respectively. Characterization of e-PCL was carried out by Gel Permeation Chromatography (GPC), Fourier Transform Infrared Spectrophotometry (FTIR), Proton Nuclear Magnetic Resonance (H-NMR), Differential Scanning Calorimetry (DSC) and Scanning Electron Microscopy (SEM). AD-MSCs labeled with red fluorescent CellTracker CM-DiI were cultured on egg white and e-PCL scaffolds for 12 days. Cell viability was determined using 3-(4.5Dimethylthiazol-2yl)-2.5-diphenyltetrazolium bromide (MTT) and nitric oxide (NO) level was evaluated for toxicity. The results showed that the number of AD-MSCs in the egg white scaffold increased periodically for 12 days compared to the other groups. Although the number of ADMSCs in the e-PCL scaffold increased until day 6 of the culture, the number of cells started to decrease after day 6. These results were associated with the toxic effect of lactic acid release on cells resulting from the decomposition of e-PCL scaffolds through catabolic reactions. Therefore, these results indicated that the egg white scaffold enhanced and maintained cell adhesion and cell viability more than the e-Polycaprolactone scaffold and could be used as a scaffold in tissue engineering studies involving stem cells.
Citation - WoS: 8
Citation - Scopus: 10
Lung Cancer Subtype Differentiation From Positron Emission Tomography Images
(Tubitak Scientific & Technological Research Council Turkey, 2020) Ayyildiz, Oguzhan; Aydin, Zafer; Yilmaz, Bulent; Karacavus, Seyhan; Senkaya, Kubra; Icer, Semra; Kaya, Eser
Lung cancer is one of the deadly cancer types, and almost 85% of lung cancers are nonsmall cell lung cancer (NSCLC). In the present study we investigated classification and feature selection methods for the differentiation of two subtypes of NSCLC, namely adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). The major advances in understanding the effects of therapy agents suggest that future targeted therapies will be increasingly subtype specific. We obtained positron emission tomography (PET) images of 93 patients with NSCLC, 39 of which had ADC while the rest had SqCC. Random walk segmentation was applied to delineate three-dimensional tumor volume, and 39 texture features were extracted to grade the tumor subtypes. We examined 11 classifiers with two different feature selection methods and the effect of normalization on accuracy. The classifiers we used were the k-nearest-neighbor, logistic regression, support vector machine, Bayesian network, decision tree, radial basis function network, random forest, AdaBoostM1, and three stacking methods. To evaluate the prediction accuracy we performed a leave-one-out cross-validation experiment on the dataset. We also considered optimizing certain hyperparameters of these models by performing 10-fold cross-validation separately on each training set. We found that the stacking ensemble classifier, which combines a decision tree, AdaBoostM1, and logistic regression methods by a metalearner, was the most accurate method for detecting subtypes of NSCLC, and normalization of feature sets improved the accuracy of the classification method.
Citation - WoS: 2
Citation - Scopus: 3
Magnetic Separation of Micro Beads and Cells on a Paper-Based Lateral Flow System
(Gazi Univ, 2023) Farooqi, Muhammed Fuad; Icoz, Kutay
Paper based lateral flow systems are widely used biosensor platforms to detect biomolecules in a liquid sample. Proteins, bacteria, oligonucleotides, and nanoparticles were investigated in the literature. In this work we designed a magnetic platform including dual magnets and tested the flow of micron size immunomagnetic particles alone and when loaded with cells on two different types of papers. The prewetting conditions of the paper and the applied external magnetic field are the two dominant factors affecting the particle and cell transport in paper. The images recorded with a cell phone, or with a bright field optical microscope were analyzed to measure the flow of particles and cells. The effect of prewetting conditions and magnetic force were measured, and it was shown that in the worst case, minimum 90% of the introduced cells reached to the edge of the paper. The paper based magnetophoretic lateral flow systems can be used for cell assays.
Citation - WoS: 1
Citation - Scopus: 2
Metacognitive Monitoring and Mathematical Abilities: Cognitive Diagnostic Model and Signal Detection Theory Approach
(Turkish Education Assoc, 2021) Basokcu, Oguz Tahsin; Guzel, Mehmet Akif
Besides various in-class assessments, there exist some standardized assessment tools that are administered in several countries, such as PISA (Programme for International Student Assessment) and TIMMS (Trends in International Mathematics and Science Study). The questions' contents, type of responding, grading, and the analyses in these large-scale tests have been diversified in years. In this study, it was aimed to identify the abilities that are measured at PISA mathematics test in a single testing procedure and by utilizing the methods of analyses of Cognitive Diagnostic Model (CDM) as well as Signal Detection Theory (SDT), which have not been used so far in the assessment of these abilities. Therefore, a randomly selected sample of 6th-grade students (N=230) in Izmir was tested with a PISA-equivalent 12-item mathematics test, where the items are graded dichotomously (correct vs. incorrect). CDM estimates were calculated by using the Deterministic Input Noisy Output and Gate (DINA) Model. The participants were asked to report whether they thought they could solve the question correctly, guess even if they thought they could not solve the question, and then, rate their confidence levels on the correctness of their answers in turn so as to allow us to measure their "metacognitive monitoring performance" with the SDT method, which refers to the ability to differentiate correct and incorrect responses. In short, a better metacognitive monitoring performance was obtained by measuring how well once could differentiate their correct and incorrect responses with the observation of they prefer reporting and then giving high confidence levels to the actually correct responses and prefer passing to give an answer yet rate lower confidence levels to the actually incorrect responses given as pure guesses. The results showed that CDM fits well to the assessment of PISA test and those who were better at the ability of "reasoning and developing strategies" in particular among four possible abilities detected with CDM ("representing and communicating", "mathematization", "reasoning and developing strategies", "using symbolic and technical language") had also better metacognitive monitoring performance. The present study, therefore, contributes to the research that investigates what features the ability of better differentiating correct and incorrect responses are actually linked. Based on the results, it is suggested that a better metacognitive monitoring ability is linked to having a better ability of "reasoning and developing strategies" in particular. Additionally, it is suggested that measuring metacognitive monitoring performance at PISA -or even any other possible tests- with the SDT calculation method, that has a relatively straightforward testing procedure, may yield various estimates for the students' abilities measured at the test as well as their related higher-order abilities.
Citation - WoS: 3
Citation - Scopus: 3
MicroRNA Prediction Based on 3D Graphical Representation of RNA Secondary Structures
(Tubitak Scientific & Technological Research Council Turkey, 2019) Sacar Demirci, Muserref Duygu
MicroRNAs (miRNAs) are posttranscriptional regulators of gene expression. While a miRNA can target hundreds of messenger RNA (mRNAs), an mRNA can be targeted by different miRNAs, not to mention that a single miRNA might have various binding sites in an mRNA sequence. Therefore, it is quite involved to investigate miRNAs experimentally. Thus, machine learning (ML) is frequently used to overcome such challenges. The key parts of a ML analysis largely depend on the quality of input data and the capacity of the features describing the data. Previously, more than 1000 features were suggested for miRNAs. Here, it is shown that using 36 features representing the RNA secondary structure and its dynamic 3D graphical representation provides up to 98% accuracy values. In this study, a new approach for ML-based miRNA prediction is proposed. Thousands of models are generated through classification of known human miRNAs and pseudohairpins with 3 classifiers: decision tree, naive Bayes, and random forest. Although the method is based on human data, the best model was able to correctly assign 96% of nonhuman hairpins from MirGeneDB, suggesting that this approach might be useful for the analysis of miRNAs from other species.
Modified Self-Adaptive Local Search Algorithm for a Biobjective Permutation Flow Shop Scheduling Problem
(Tubitak Scientific & Technological Research Council Turkey, 2019) Alabas Uslu, Cigdem; Dengiz, Berna; Aglan, Canan; Sabuncuoglu, Ihsan
Interest in multiobjective permutation flow shop scheduling (PFSS) has increased in the last decade to ensure effective resource utilization. This study presents a modified self-adaptive local search (MSALS) algorithm for the biobjective permutation flow shop scheduling problem where both makespan and total flow time objectives are minimized. Compared to existing sophisticated heuristic algorithms, MSALS is quite simple to apply to different biobjective PFSS instances without requiring effort or time for parameter tuning. Computational experiments showed that MSALS is either superior to current heuristics for Pareto sets or is incomparable due to other performance indicators of multiobjective problems.
Citation - Scopus: 6
Network Intrusion Detection Based on Machine Learning Strategies: Performance Comparisons on Imbalanced Wired, Wireless, and Software-Defined Networking (SDN) Network Traffics
(Turkiye Klinikleri, 2024) Hacilar, Hilal; Aydin, Zafer; Güngör, Vehbi Çağrı
The rapid growth of computer networks emphasizes the urgency of addressing security issues. Organizations rely on network intrusion detection systems (NIDSs) to protect sensitive data from unauthorized access and theft. These systems analyze network traffic to detect suspicious activities, such as attempted breaches or cyberattacks. However, existing studies lack a thorough assessment of class imbalances and classification performance for different types of network intrusions: wired, wireless, and software-defined networking (SDN). This research aims to fill this gap by examining these networks’ imbalances, feature selection, and binary classification to enhance intrusion detection system efficiency. Various techniques such as SMOTE, ROS, ADASYN, and SMOTETomek are used to handle imbalanced datasets. Additionally, eXtreme Gradient Boosting (XGBoost) identifies key features, and an autoencoder (AE) assists in feature extraction for the classification task. The study evaluates datasets such as AWID, UNSW, and InSDN, yielding the best results with different numbers of selected features. Bayesian optimization fine-tunes parameters, and diverse machine learning algorithms (SVM, kNN, XGBoost, random forest, ensemble classifiers, and autoencoders) are employed. The optimal results, considering F1-measure, overall accuracy, detection rate, and false alarm rate, have been achieved for the UNSW-NB15, preprocessed AWID, and InSDN datasets, with values of [0.9356, 0.9289, 0.9328, 0.07597], [0.997, 0.9995, 0.9999, 0.0171], and [0.9998, 0.9996, 0.9998, 0.0012], respectively. These findings demonstrate that combining Bayesian optimization with oversampling techniques significantly enhances classification performance across wired, wireless, and SDN networks when compared to previous research conducted on these datasets. © 2024 Elsevier B.V., All rights reserved.
New Modeling of Reconfigurable Microstrip Antenna Using Hybrid Structure of Simulation Driven and Knowledge Based Artificial Neural Networks
(Pamukkale Univ, 2020) Aoad, Ashrf; Aydin, Zafer
Knowledge-based modeling has a critical role to embed existing knowledge to improve modeling performance. Since reconfigurable antenna can provide more operational frequencies than the classical antennas, a knowledge-based hybrid structure is used in this work to obtain efficient model and producing optimum new models for a reconfigurable microstrip antenna. The hybrid structure consists of two phases. The first phase generates initial knowledge which is used in knowledge-based modeling structure to obtain design parameters. Artificial neural network based multilayer perceptron can generate necessary knowledge for a knowledge-based model after the training process. Knowledge-based modeling improves the accuracy of the initial model to determine design parameters corresponding to the design target. Source difference, prior knowledge Input and prior knowledge input with difference can be applied to realize an efficient knowledge-based strategy. 3D-EM simulation generates the new model in terms of the design parameters of the proposed application. It has three switching states for operating, which are organized by two resistor circuits representing ON/OFF states. Switch positions and geometrical parameters can be used for satisfying design targets between 1 GHz and 6 GHz for the efficient antenna design.
New Proofs of Fejer's and Discrete Hermite-Hadamard Inequalities With Applications
(Ankara Univ, Fac Sci, 2023) Sekin, Cagla; Tamar, Mehmet Emin; Aliyev, Ilham A.
New proofs of the classical Fejer inequality and discrete Hermite-Hadamard inequality (HH) are presented and several applications are given, including (HH)-type inequalities for the functions, whose derivatives have inflection points. Morever, some estimates from below and above for the first moments of functions f : [a, b] -> R about the midpoint c = (a+b)/2 are obtained and the reverse Hardy inequality for convex functions f : (0, infinity) -> (0, infinity) is established.

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