Yazar "Isik, Ibrahim" seçeneğine göre listele
Listeleniyor 1 - 9 / 9
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe Analysis and classification of the mobile molecular communication systems with deep learning(Springer Heidelberg, 2022) Isik, Ibrahim; Er, Mehmet Bilal; Isik, EsmeNano networks focused on communication between nano-sized devices (nanomachines) is a new communication concept which is known as molecular communication system (MCs) in literature. The researchers have generally used fixed transmitter and receiver for MCs models to analyze the fraction of received molecules and signal to interference rate etc. In this study, contrary to the literature, a mobile MC model has been used in a diffusion environment by using five bits. It is concluded that when the receiver and transmitter are mobile, distance between them changes and finally this affects the probability of the received molecules at the receiver. After the fraction of received molecules is obtained for different mobility values of Rx and Tx (Drx and Dtx), deep learning's bi-directional long short-term memory (Bi-LSTM) model is applied for the classification of Rx and Tx mobilities to find the best MC model with respect to fraction of received molecules. Finally it is obtained that when the mobilities of Rx and Tx increase, the fraction of received molecules also increases. Bi-LSTM model of Deep learning is used on a data set consisting of five classes. The suggested model's accuracy, precision, and sensitivity values are obtained as 98.05, 96.49, and 98.01 percent, respectively.Öğe Artificial neural network modeling for the effect of fly ash fineness on compressive strength(2021) Demir Şahin, Demet; Isik, Esme; Isik, Ibrahim; Cullu, MustafaIn the literature, there has been a lot of discussion over the compressive strength of concrete made with high-calcium fly ash instead of cement. However, no research has been done to determine the influence of ground fly ash on compressive strength using experimental and artificial neural network (ANN) models when they are used in place of cement at various replacement ratios. The size and replacement ratio of fly ash give maximum durability of the concrete when utilized in the cement composition, according to the ANN model employed in this study, allowing to forecast the result without using a high-cost and energy-intensive operation like grinding. In this study, short and long-term compressive strength of concrete of class C fly ash is analyzed at six different Blaine fineness values (1834, 1852, 1930, 1992, 1995, and 2018 cm2/g) and three different fly ash substitution rates (10, 30, and 50%) for 270 supplemental concrete samples. In addition, based on the experimental results, an ANN model was proposed to simulate and predict the compressive strength of concrete. In this proposed model, substitution rate, Blaine fineness, and curing time (day) were used as input to simulate the value of compressive strength for 3, 7, 28, 56, and 90 curing times with 99% accuracy. Also, the value of compressive strength was predicted for 120 curing days. The predicted target values were compared with the experiment resulted in a better correlation coefficient of 0.99. Thus, the results attained from this ANN model were found to be effective in predicting the relationship between fly ash fineness and compressive strength at any given operating condition.Öğe Bacterial Chemotaxis in Molecular Communication: Experimental and Simulation Analysis of Receiver Placement and Gradient Dynamics(Ieee-Inst Electrical Electronics Engineers Inc, 2026) Duman, Mustafa Ozan; Isik, Ibrahim; Isik, EsmeBacteria-based nanonetworks (BNs) represent a promising strategy for nanoscale information transfer, utilizing bacterial motility and chemotaxis for targeted message delivery. This study analyzes BN performance through both experimental validation and a custom-developed three-dimensional (3D) simulation program built in MATLAB, focusing on receiver (RX) placement, chemoattractant release rate (Q), and bacterial lifespan. The simulation employs experimentally validated parameters and models bacterial behavior under various spatial configurations. Results demonstrate that RX positioning significantly affects communication efficiency, with asymmetric placement causing uneven chemoattractant gradients and reduced success rates. While higher Q values improve reach time and delivery success, bacterial lifespan becomes a limiting factor at extended distances. Experimental findings using agar-based assays confirm a threshold distance beyond which bacterial motility becomes ineffective. These insights provide practical guidance for optimizing BN systems by balancing signal strength with biological constraints. Future work should explore adaptive bacterial strategies and dynamic environmental conditions to further enhance BN reliability and applicability in areas such as targeted drug delivery and biosensing.Öğe Chemotaxis-Driven Molecular Communication in Nanonetworks: Simulating E. coli Behavior and Performance(Institute of Electrical and Electronics Engineers Inc., 2025) Duman, Mustafa Ozan; Isik, Ibrahim; Bilaler, Mehmet; Tagluk, Mehmet Emin; Isik, EsmeBacteria-based nanonetworks (BN) show significant potential for revolutionizing nanoscale communication, particularly in fields like medicine and environmental monitoring. This study models the chemotaxis of Escherichia coli (E. coli) in a 2D environment using a customdeveloped MATLAB simulation to understand communication effectiveness. We investigate the impact of chemoattractant release rate (Q), transmitter-receiver distance (d), and bacterial lifespan. Key findings reveal a trade-off between communication range and energy consumption: while higher Q values extend range, they also increase resource usage. A Q value of 10-14 ~mol / s is identified as providing a balanced approach. Furthermore, simulations highlight that bacterial lifespan inherently limits longer communication range, suggesting the potential for nanomachine relays in future BN designs. Future research will expand these models to incorporate 3D environments and multi-bacterium interactions, enhancing their applicability for real-world scenarios. © 2025 IEEE.Öğe Classification of Diffusion Constants of Transmitter and Receiver and Distance Between Them Using Mobile Molecular Communication via Diffusion Model(Springer Heidelberg, 2025) Er, Mehmet Bilal; Isik, Ibrahim; Kuran, Umut; Isik, EsmeMolecular communication (MC) holds promise for enabling communication in scenarios where traditional wireless methods may be impractical or ineffective, offering unique capabilities for a range of applications in both natural and engineered systems. In this research, a novel approach to MC is explored, diverging from the standard use of stationary transmitter and receiver models typically found in the field. The study introduces a dynamic MC model, where both the transmitter and receiver are mobile within a diffusion environment. This model operates using a 5-bit system. The key finding is that the mobility of these nanodevices alters their distance, which in turn impacts the likelihood of molecule reception at the receiver. The study employs deep learning techniques, specifically a combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, to categorize the mobility patterns of the receiver (Rx) and transmitter (Tx). By analyzing various mobility rates (Drx and Dtx) and distances between the Tx and Rx, the research successfully identifies the most efficient mobile MC model in terms of molecule reception rates. The use of Linear Support Vector Machine alongside the CNN and LSTM hybrid feature vector resulted in an 87.68% accuracy in predicting diffusion coefficients. Moreover, using a Cubic Support Vector with the same hybrid feature vector, the study achieved an 88.09% accuracy in estimating the distance between the transmitter and receiver. The study concludes that an increase in the mobilities of Rx and Tx correlates with a higher rate of molecule reception.Öğe Enhancing the performance of TiO2 nanotube-based hydrogen sensors through crystal structure and metal electrode(Pergamon-Elsevier Science Ltd, 2024) Tasyurek, Lutfi Bilal; Isik, Esme; Isik, Ibrahim; Kilinc, NecmettinIn this research, the effect of metal electrodes and crystalline phase on gas detection of titanium dioxide (TiO2) nanotube-based hydrogen (H2) sensors was investigated. TiO2 nanotubes were produced using glycerol-based electrolyte and annealed at 300 degrees C and 700 degrees C to change the anatase and rutile crystalline phases, respectively. TiO2 nanotubes were coated by platinum (Pt), palladium (Pd), gold (Au) and silver (Ag) electrodes to fabricate metal/TiO2 nanotubes Ti H2 sensor devices and then the current-voltage (I-V) characteristics were investigated at room temperature. The structural properties of TiO2 nanotubes were characterized by SEM, FE-SEM, XRD, and Raman techniques. The H2 detection properties of the sensors were examined at the 1000 ppm - 5% H2 concentration range. The crystal structure and metal electrodes are the main factors that affect the H2 sensing properties of TiO2 nanotube-based sensors. The effect of crystal forms on sensitivity was not the same as for metal electrodes. The underlying sensing mechanisms for different types of metal electrodes and crystal structures are discussed and the relevance of their sensing performance to nanotubes and electronic properties is investigated. In addition, discussion of each metal electrode and crystal structure will make important contributions to the development of H2 sensors. The Pd-coated device annealed at 700 degrees C showed the best detection performance.(c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.Öğe Optimization of distribution function and model parameters for molecular communication via diffusion with OtoO approximation(Elsevier, 2024) Akpamukcu, Mehmet; Ates, Abdullah; Isik, Ibrahim; Isik, EsmeThe analysis is generally conducted in stationary receiver and transmitter models in a diffusion environment for the fundamental Molecular communication (MOC) models. However, a mobile MOC model is employed in this study, deviating from the existing literature. This mobile MOC model considers the mobility of all variables in the diffusion environment, including the transmitter, receiver, and molecules. Firstly, a novel MOC model is proposed, departing from the conventional normal distribution for the mobility of variables. Instead, alternative distribution functions such as the Pareto distribution, extreme value distribution, t-distribution, and generalized extreme value distribution are employed. Furthermore, the system's performance is enhanced by optimizing the distribution function and model parameters, such as the diffusion coefficient, using the optimization of optimization (OtoO) approach. In this approach, the Multi-Verse Optimization (MVO) algorithm serves as the primary algorithm, while the Grey Wolf Optimization (GWO) algorithm functions as the auxiliary algorithm. Essentially, the MVO algorithm optimizes the parameters of the MOC model, while simultaneously, the GWO algorithm optimizes the impact of the optimization processes of MVO on the parameters ``p and ``N as well as the constant parameter of the distribution function. By optimizing both the parameters of the MOC model and the distribution function, the number of received molecules is significantly increased. Therefore, this study not only improves the results of the MOC model structure based on different distribution functions but also optimizes all parameters of the proposed model using the MVO-GWO OtoO approach.Öğe Predictive Modeling of Bacteria-Based Nanonetwork Performance Using Simulation-Driven Machine Learning and Genetic Algorithm Optimization(Wiley-V C H Verlag Gmbh, 2026) Duman, Mustafa Ozan; Isik, Ibrahim; Er, Mehmet Bilal; Tagluk, Mehmet Emin; Isik, EsmeBacteria-based nanonetwork (BN) offers a biologically inspired solution for enabling information exchange between nanomachines (NMs) in environments where traditional communication methods are ineffective. This study presents a 2D simulation model of a BN system that captures the chemotactic behavior of a single Escherichia coli (E. coli) bacterium navigating from a transmitter (TX) toward a receiver (RX) under varying environmental conditions. Key parameters, which are chemoattractant release rate (Q), TX-RX distance (d), and bacterial lifespan (), are systematically varied to evaluate their impact on communication performance, measured in terms of reach time and success rate. To enable accurate performance prediction without the need for computationally expensive repeated simulations, an analytical model is constructed using various machine learning (ML) techniques, including Linear Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP). Hyperparameters of MLP are optimized using a Genetic Algorithm (GA), significantly enhancing predictive accuracy and training stability. The results demonstrate the effectiveness of integrating dynamic simulation with data-driven modeling and hyperparameter optimization to represent complex system behavior. This framework offers valuable design insights for BN system development and supports the creation of efficient, scalable nanonetworks.Öğe THE EFFECT OF CRYSTAL STRUCTURE AND METAL ELECTRODES ON GAS DETECTION IN TiO2 NANOTUBES H2 SENSORS(International Association for Hydrogen Energy, IAHE, 2022) Tasyurek, Lutfi Bilal; Isik, Esme; Isik, Ibrahim; Kilinc, NecmettinIn this study, the effects of various metal electrodes and various crystal structures on hydrogen (H2) gas sensors based on titanium dioxide (TiO2) nanotubes were investigated. For the production of TiO2 nanotubes, anodization method consisting of an electrolyte containing 0.5wt% NH4F in 85% pure glycerol solution was applied. Scanning electron microscope (SEM) images of the obtained TiO2 nanotubes were examined. In order to see the effect of different crystal structures of TiO2, amorphous, anatase and rutile phases were obtained by annealing of samples. Each of the Ti/TiO2 nanotubes/metal (Pd, Pt, Au, and Ag) gas sensors, obtained by coating with palladium (Pd), platinum (Pt), gold (Au) and silver (Ag) electrodes, was tested at room temperature at 1% H2 concentration depending on three different phases. © 2022 Proceedings of WHEC 2022 - 23rd World Hydrogen Energy Conference: Bridging Continents by H2. All rights reserved.
