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Öğe Stochastic resonance in a single autapse-coupled neuron(Pergamon-Elsevier Science Ltd, 2023) Baysal, Veli; Çalım, AliThe signal detection ability of nervous system is highly associated with nonlinear and collective behaviors in neuronal medium. Neuronal noise, which occurs as natural endogenous fluctuations in brain activity, is the most salient factor influencing this ability. Experimental and theoretical research suggests that noise is beneficial, not detrimental, for regular functioning of nervous system. In this regard, there is a general agreement that noise at an adequate intensity can engage rhythmic activity in brain and noise-induced oscillations enhances performance of the weak signal processing, especially when frequency of the signal is around that of the noise-induced rhythmic oscillation. This behavior in biological neural systems is explained by the notion of stochastic resonance. Another factor that plays a key role in regulating neuronal behaviors, including motor and cognitive tasks by maintaining signaling between cells, is characteristics of synapses different in structure and functioning. Here, we study stochastic resonance in Hodgkin-Huxley neuron that has a peculiar synaptic connection called autapse, known as a biophysical feedback mechanism, under presynaptic noise originating from superposition of inhibitory and excitatory Poisson bombardment. Our results show that, under certain conditions, autapse dynamics are able to improve the weak signal detection performance of Hodgkin-Huxley neuron via stochastic resonance. This study provides novel insights into functional role of autapse in neural information processing by revealing a biophysical aspect of stochastic resonance with numerical computations.Öğe Chaotic resonance in an astrocyte-coupled excitable neuron(Pergamon-Elseiver Science Ltd, 2023) Çalım, Ali; Baysal, VeliWe study the chaotic resonance phenomenon whereby the response of a neuron to a weak signal is amplified with the help of chaotic current stemming from background activity in the brain. This resonance behavior exhibits a bell-shaped curve in terms of detection quality due to increasing chaotic current intensity. Recent experimental studies have shown that astrocytes, which are the most abundant types of glial cells, may be responsible for the regulation of electrophysiological events in neuronal medium. Hence, we consider here a realistic neuronal system which is constituted by a bipartite network consisting of an excitable neuron and an astrocyte. Our analysis reveals that signal detection quality can be greatly enhanced with the astrocyte contribution obtained by appropriate neuronal and astrocytic cell dynamics. We find that depolarization -induced astrocytic glutamate release is able to improve chaotic resonance performance considerably in the presence of an adequately strong interaction between the astrocyte and the excitable neuron receiving a weak signal with a relatively higher frequency. We also show that a moderate production rate of gliotransmitters is required for the astrocyte to affect resonance performance of the neuron. Except for those conditions where the facilitating effect of astrocyte is observed, it can also reduce signal detection performance in the neuron. Furthermore, we demonstrated that intrinsic neuronal excitability is regulated by the astrocyte, via a comparison of resonance behaviors under effects of bias and astrocytic current separately. Taken together, our findings provide a novel insight into the functioning of astrocyte-neuron circuits, in particular the encoding weak signals via chaotic resonance, and suggest that astrocytes play a key role in intrinsic regulation and selectivity in neuronal information processing.Öğe Strain energy prediction of single wall carbon nanotubes using general regression neural network and adaptive neuro - Fuzzy Inference System(Taylor & Francis Inc, 2023) Eyecioğlu, Önder; Kayışlı, Korhan; Beken, MuratSingle Wall Carbon Nanotubes (SWCNTs) play crucial roles in the field of nanotechnology research and applications. Due to their quantum mechanical nature and the intricate structure of SWCNTs, performing direct or indirect experimental processes can be exceedingly challenging. Simulation methods like Tight-Binding Molecular Dynamics (TBMD) can serve as viable alternatives to experimentation. However, it's worth noting that these methods often demand extensive computational runtime. To address this computational time challenge, artificial intelligence algorithms such as the General Regression Neural Network (GRNN) and the Adaptive Neuro Fuzzy Interface System (ANFIS) have been proposed in this study. These models aim to calculate the energetic properties of SWCNTs more efficiently, offering practical and quicker predictive methods with reduced computational workloads. The study's findings demonstrate a strong correlation between the predicted energy values of SWCNTs using GRNN and ANFIS models and the results obtained through TBMD simulations. Consequently, it is believed that these models can be suitable and effective approaches for computing the energetic properties of SWCNTs.Öğe A study on interactions of 14.7-MeV protons and 3.6-MeV alphas in 93Nb target(Taylor & Francis Ltd, 2024) Yiğit, Mervenur; Kara, Ayhan; Yılmaz, AliNiobium is an important alloying material in nuclear reactors because of its enormous strength, low density, low neutron absorption, and high melting point. This study is structured on nuclear data calculations that are based on a Monte Carlo simulation approach. The GEANT4, SRIM, and TALYS codes were used to create a comprehensive simulation of 3.6-MeV alphas and 14.7-MeV protons on a Nb-93 target. We present calculation results on nuclear parameters as ion energy losses, displacements, vacancies, projected ranges, and cross sections. A comparison between the GEANT4 and SRIM codes was made for the projected ranges and ion energy losses. Besides, the calculations of cross sections in the TALYS code were carried out using level densities on the Skyrme energy density functional and the Fermi gas model.Öğe Analyzing the web sites of metropolitan municipalities in Turkey in terms of digital access indicators(Institute for Local Self-Government Maribor, 2023) Tutar, Hasan; Parlak, İsmail Hakkı; Tuzcuoğlu, Ferruh; Başpınar, Nuran Öztürk; Nam, SelçukE-government and e-municipality practices, which have gained critical importance in terms of the spread of citizen-oriented service and participatory management approach in local governments, have become a state policy in many countries. In Turkey, within the framework of restructuring the public administration and the e -transformation strategy, e-municipality practices are being expanded in local governments. One of the critical developments in the field of public administration in Turkey recently is e-government applications and e-municipality, which is its reflection on local governments. E -municipality applications are used as a local government strategy to quickly meet the citizens' municipal needs and reduce service costs. In this study, e-municipality services of metropolitan municipalities in Turkey; It is intended to be analyzed in terms of accessibility, performance, Searches Engine Optimization, and Best Practice indicators. The research findings show that the e-municipal services of metropolitan municipalities in Turkey should be improved significantly, given the mentioned indicators.Öğe SenDemonNet: Sentiment analysis for demonetization tweets using heuristic deep neural network(Springer, 2022) Kayıkçı, ŞafakSentiment analysis is one of the efficient models for extracting opinion mining with identification and classification from unstructured text data such as product reviews or microblogs. It is used to gain feedback from political campaigns, brand reviews, marketing analysis, and customers. The sentiment analysis on Twitter data is a recent research field in the natural processing. The dataset is gathered from the Twitter package in R along with Twitter API. The main intent of this paper is to understand the public opinion on the recently implemented demonetization policy using the proposed SenDemonNet. Initially, the tweet preprocessing was done, which is intended for cleaning the text data. Then, the feature extraction is performed by Bag of n-grams, TF-IDF, and the word2vec algorithm. The main objective of this work is a weighted feature selection that is developed by the hybrid Forest-Whale Optimization Algorithm (F-WOA) to get the best classification outcome. With these features, the Heuristic Deep Neural Network (HDNN) is adopted for classification, where the proposed FOA and WOA tune the parameter of DNN for reaching the maximum accuracy rate. From the statistical analysis, the performance of the designed F-WOA-DNN is 1.8%, 1.9%, 1.86%, and 2% enhanced than PSO-DNN, GWO-DNN, WOA-DNN, FOA-DNN, SVM, CNN, LSTM, and DNN respectively. Extensive experimental results show that SenDemonNet outperforms its competitors, producing an impressive increase in the classification accuracy on the benchmark dataset.Öğe Computational insights of bioconvective third grade nanofluid flow past a riga plate with triple stratification and swimming microorganisms(Hindawi Ltd, 2022) Kayıkçı, ŞafakThe goal of this study is to examine the heat-mass effects of a third grade nanofluid flow through a triply stratified medium containing nanoparticles and gyrostatic microorganisms swimming in the flow. The heat and mass fluxes are considered as a non-Fourier model. The governing models are constructed as a partial differential system. Using correct transformations, these systems are converted to an ordinary differential model. Ordinary systems are solved using convergent series solutions. The effects of physical parameters for fluid velocity, fluid temperature, nanoparticle volume percentage, motile microbe density, skin friction coefficients, local Nusselt number, and local Sherwood number are all illustrated in detail. When the values of the bioconvection Lewis number increase, the entropy rate also rises. The porosity parameter and modified Hartmann number show the opposite behaviour in the velocity profile.Öğe Diagnosis and classification of Parkinson's disease using ensemble learning and 1D-PDCovNN(Pergamon-Elsevier Science Ltd, 2023) Nour, Majid; Şentürk, Ümit; Polat, KemalIn this paper, we proposed a novel approach to diagnose and classify Parkinson's Disease (PD) using ensemble learning and 1D-PDCovNN, a novel deep learning technique. PD is a neurodegenerative disorder; early detection and correct classification are essential for better disease management. The primary aim of this study is to develop a robust approach to diagnosing and classifying PD using EEG signals. As the dataset, we have used the San Diego Resting State EEG dataset to evaluate our proposed method. The proposed method mainly consists of three stages. In the first stage, the Independent Component Analysis (ICA) method has been used as the pre-processing method to filter out the blink noises from the EEG signals. Also, the effect of the band showing motor cortex activity in the 7-30 Hz frequency band of EEG signals in diagnosing and classifying Parkinson's disease from EEG signals has been investigated. In the second stage, the Common Spatial Pattern (CSP) method has been used as the feature extraction to extract useful information from EEG signals. Finally, an ensemble learning approach, Dynamic Classifier Selection (DCS) in Modified Local Accuracy (MLA), has been employed in the third stage, consisting of seven different classifiers. As the classifier method, DCS in MLA, XGBoost, and 1D-PDCovNN classifier has been used to classify the EEG signals as the PD and healthy control (HC). We first used dynamic classifier selection to diagnose and classify Parkinson's disease (PD) from EEG signals, and promising results have been obtained. The performance of the proposed approach has been evaluated using the classification accuracy, F-1 score, kappa score, Jaccard score, ROC curve, recall, and precision values in the classification of PD with the proposed models. In the classification of PD, the combination of DCS in MLA achieved an accuracy of 99,31%. The results of this study demonstrate that the proposed approach can be used as a reliable tool for early diagnosis and classification of PD.Öğe A novel cuffless blood pressure prediction: Uncovering new features and new hybrid ml models(MDPI, 2023) Nour, Majid; Polat, Kemal; Şentürk, Ümit; Arıcan, MuratThis paper investigates new feature extraction and regression methods for predicting cuffless blood pressure from PPG signals. Cuffless blood pressure is a technology that measures blood pressure without needing a cuff. This technology can be used in various medical applications, including home health monitoring, clinical uses, and portable devices. The new feature extraction method involves extracting meaningful features (time and chaotic features) from the PPG signals in the prediction of systolic blood pressure (SBP) and diastolic blood pressure (DBP) values. These extracted features are then used as inputs to regression models, which are used to predict cuffless blood pressure. The regression model performances were evaluated using root mean squared error (RMSE), R-2, mean square error (MSE), and the mean absolute error (MAE). The obtained RMSE was 4.277 for systolic blood pressure (SBP) values using the Matern 5/2 Gaussian process regression model. The obtained RMSE was 2.303 for diastolic blood pressure (DBP) values using the rational quadratic Gaussian process regression model. The results of this study have shown that the proposed feature extraction and regression models can predict cuffless blood pressure with reasonable accuracy. This study provides a novel approach for predicting cuffless blood pressure and can be used to develop more accurate models in the future.Öğe Machine learning models for estimating the AC conductivity mechanism of Edirne Kufeki stone reinforced Polypyrrole composites(WILEY, 2022) Eyecioğlu, ÖnderIn this study, the detailed temperature and frequency-dependent (ac) conductivity analyzes of Polypyrrole/Edirne Kufeki Stone (PPy/EKS) composites have been realized by considering both experimental and Machine Learning (ML) algorithms predicted data. In this respect, the experimental ac conductivity data of pure PPy, PPy/5% EKS, PPy/10% EKS, and PPy/20% EKS composites between 1 Hz and 40 MHz at 296, 313, and 333 K temperatures have been used for the data set of ML. First, a benchmark study has been done for applied ML algorithms to obtain an eligible model. It is found that the Gaussian process regression (GPR) algorithm provided the best prediction performance. Since it has been observed a good conformity between the experimental and prediction data of GPR model, the ac conductivity (sigma(ac)) versus angular frequency (w) curves of the composites produced experimentally have been estimated for new temperature values, which were not treated experimentally. Then, the f(w) curves of at temperature values have been estimated by GPR which is for the EKS composites at various contributions that have not been experimentally produced. Ultimately, the GPR algorithm developed in the present work enables us to determine the optimum EKS additive percentage, working temperature, and frequency band for the PPy polymer matrix.Öğe Thermal analysis of radiative water- and glycerin-based carbon nanotubes past a Riga plate with stratification and non-Fourier heat flux theory(Springer, 2023) Kayıkçı, Şafak; Eswaramoorthi, S.; Postalcıoğlu, Seda; Loganathan, K.The impact of thermal stratification and thermally radiative flow of carbon nanotubes on a Riga plate with injection/suction and heat generation/consumption is investigated. The two varieties of base fluids, like, water and glycerin with single-wall nanotubes and multi-wall carbon nanotubes, are incorporated in this investigation. Cattaneo-Christov heat flux theory is utilized to frame the energy equation. The controlling PDEs are remodeled into ODEs using the appropriate variables. The obtained ODEs are analytically solved by applying the HAM procedure and numerically solved by using the BVP4c scheme. The consequences of the physical parameters on fluid velocity, fluid temperature, skin friction coefficients and local Nusselt number are explained through tables, graphs and charts. It is detected that the fluid velocity in both directions diminishes when raising the suction/injection and velocity slip parameters. The fluid temperature downturns when enhancing the suction/injection and stratification parameters. The surface shear stress suppresses when increasing the Forchheimer number. The radiation parameter leads to strengthening the heat transfer gradient.Öğe Wireless communications beyond 5 g: teraherzwaves, nano-communications and the internet of bio-nano-things(Springer, 2022) Akkaş, Mustafa Alper; Sokullu, RadosvetaTwo promising technologies cosidered for the Beyond 5G networks are the terahertz and nano-technologies. Besides other possible application areas they hold the commitment to numerous new nano-scale solutions in the biomedical field. Nano-technology, as the name implies, examines the construction and design of nano-sized materials. These two interconnected emerging technologies have the potential to find application in quite many areas, one of the most importan being healthcare. This overview paper discusses the specifics of these technologies, their most important characteristics and introduces some of the trends for their application in the healthcare sector. In the first section terahertz frequency radio waves and their specific properties depending on the surrounding environment are discussed, followed by an introduction to nano-scale communications. Terahertz waves mandate the use of nano-scale antennas, which in turn brings us to the concept of nano-scale nodes. Nano-scale nodes are units that can perform the most basic functions of nano-machines and inter-nano-machine communications, which allow distributed nano-machines to perform more complex functions. Beyond 5G the development of these nano-communications is expected to lead to the emergence of new complex network systems. In the second part of this paper the paradigms of the Internet of Nano Things, molecular commnications and the Internet of Bio-Nano Things are discussed followed by details on their integration in healthcare related applications. The main goal of the article is to provide an introduction to these intriguing issues discussing advanced nano-technology enablers for Beyond 5G networks such as terahertz and molecular communications, nano-communications between nano-machines and the Internet of Bio-Nano-Things in light of health related applications.Öğe Deep learning with game theory assisted vertical handover optimization in a heterogeneous network(World Scientific Publishing Co Pte Ltd, 2023) Kayıkçı, Şafak; Unnisa, Nazeer; Das, Anupam; Kanna, S. K. Rajesh; Murthy, Mantripragada Yaswanth Bhanu; Preetha, N. S. NinuProblem: In next-generation networks, users can optimize or tune their preferences with a seamless transfer of diverse access methodologies for maximizing the Quality of Service (QoS) and cost savings. In these heterogeneous wireless environments, users are prepared with several multimode wireless devices for maximizing media services through several access networks. Such networks may vary regarding energy usage, available bandwidth, technology, coverage range, monetary cost, etc. In recent days, vertical handover has attained higher performance owing to the improvements in mobility models through adopting the Fourth Generation (4G) technologies. On the other hand, these improvements are restricted to some cases, so, it does not offer support for generic mobility. Consequently, diverse strategies were implemented by considering these mobility models. However, it suffers from improper network selection, late and too-early handovers, repeated handovers, high packet loss, etc.Aim: This paper tackles the problem of vertical handover problem in the heterogeneous network using deep learning with game theory.Methods: The proposed model develops a non-cooperative game approach, in which all base stations compete selfishly to transmit at higher power. The overall performance in terms of throughput, handover, energy consumption, and load balancing is attained by optimizing the transmission power by the game theory. For performing this model, the required data like path loss, SINR, data rate, load, etc are generated by the deep learning called Recurrent Neural Network (RNN).Results: From the simulation findings, the handoff probability of the recommended RNN+Game Theory is correspondingly secured at 6.9%, 22.6%, and 8.2% superior to TOPSIS, ABC-PSO, and game theory when taking the time like 5 secs for user velocity as 30 km/h.Conclusion: Results show that the proposed game theoretical approach with deep learning provides a throughput enhancement while reducing the power consumption, in addition, to minimizing the unnecessary handover and balancing the load between base stations.Öğe Variable-bandwidth model and capacity analysis for aerial communications in the terahertz band(IEEE-Inst Electrical Electronics Engineers Inc, 2021) Saeed, Akhtar; Gürbüz, Özgür; Bicen, Ahmet Ozan; Akkaş, Mustafa AlperIn this work, 0.75-10 THz band is explored for non-terrestrial communications, by leveraging the improved atmospheric conditions at various altitudes. A channel model for aerial communications at THz band is proposed to calculate the common flat bands for frequency-selective path gain and the colored noise spectrums, both of which are highly affected by the atmospheric conditions. With capacity computation based on common flat bands, we consider aerial vehicles at different altitudes and distances, under realistic weather and channel fading conditions, due to beam misalignment and also multi path fading. An extensive capacity analysis is presented, considering equal and water-filling power allocation. It is shown that, when there is no fading, capacity for aerial links is several orders of magnitude larger than the sea-level capacity. When ergodic capacity is computed for the fading scenarios, it is shown that the impact of fading vanishes with altitude. Sea-level ergodic capacity is increased by an order of magnitude for drone-to-drone communications, providing several Tbps at 10 m range, while 10s of Tbps is achievable among jet planes and UAVs, and several 100s of Tbps is possible for satellites/cubesats at 1 km under fading, suggesting that THz band is a promising alternative for aerial communications.Öğe Detecting snow layer on solar panels using deep learning(IEEE, 2021) Eyecioğlu, Önder; Öztürk, Oktay; Hangün, BatuhanRenewable energy now plays a significant role in meeting rising energy demand while protecting the environment. Solar energy, generated by enormous solar panel farms, is a rapidly developing environmentally friendly technology. However, its efficiency degrades due to some factors. The climate is one of the most impactful factors that affect the electricity generation of a photovoltaic cell-especially countries with snowy climates face those downside effects. Hence, detection and removal of the snow layer on the solar panels are crucial. Firstly, most of the snow detection approaches are based on time series or momentary sensor data. Secondly, the removal of snow is based on surface coatings, heating, and mechanical clearing. Nowadays, vision-based solutions for detecting and removal of snow are trending. Since eliminating the human factor is a priority in physical labor, drones are suitable for vision-based operations. This paper presents a new deep learning-based approach that can be deployed on drones for detecting snowy conditions on solar panels using deep learning-based algorithms. As they are state-of-the-art neural networks in computer vision applications, ResNet-50, VGG-19, and InceptionV3 have been selected. In order to increase generalization in the training phase, we augmented the dataset using different image manipulation techniques. Our results show that we obtain 100%, 99%, and 91% Fl-Score from InceptionV3, VGG-19, and ResNet-50 respectively.Öğe Analytical investigation of magnetohydrodynamic non-newtonian type casson nanofluid flow past a porous channel with periodic body acceleration(Wiley-Hindawi, 2021) Thamaraikannan, N.; Karthikeyan, S.; Chaudhary, Dinesh Kumar; Kayıkçı, ŞafakThe consequence of periodic body acceleration and thermal radiation in the pulsating flow of MHD Casson nanofluid through a porous channel is addressed. A flow of the nanofluid injected through the lower plate is considered while sucked out through the upper plate with a similar velocity. The thermal radiation term is incorporated in the heat transfer equation. The governing equations corresponding to velocity and temperature are converted from partial differential equations to a system of ordinary differential equations by employing similarity variables. The perturbation technique is applied to solve the governing flow equations. The impact of diverse parameters on flow features is graphically analyzed. The result reveals that adding the nanoparticle has enhanced the velocity profile of the base fluid. Moreover, an increase in the periodic body acceleration results in enlarging velocity and temperature.Öğe Heat transfer analysis of 3-d viscoelastic nanofluid flow over a convectively heated porous riga plate with cattaneo-christov double flux(Frontiers Media Sa, 2021) Loganathan, Karuppusamy; Alessa, Nazek; Kayıkçı, ŞafakThe impact of heat-absorbing viscoelastic nanofluidic flow along with a convectively heated porous Riga plate with Cattaneo-Christov double flux was analytically investigated. The Buongiorno model nanofluid was implemented with the diversity of Brownian motion and thermophoresis. Making use of the transformations; the PDE systems are altered into an ODE system. We use the homotopy analysis method to solve these systems analytically. The reaction of the apposite parameters on fluid velocity, fluid temperature, nanoparticle volume fraction skin friction coefficients (SFC), local Nusselt number and local Sherwood number are shown with vividly explicit details. It is found that the fluid velocities reflect a declining nature for the development of viscoelastic and porosity parameters. The liquid heat becomes rich when escalating the radiation parameter. In addition, the nanoparticle volume fraction displays a declining nature towards the higher amount of thermophoresis parameter, whereas the inverse trend was obtained for the Brownian motion parameter. We also found that the fluid temperature is increased in viscoelastic nanofluid compared to the viscous nanofluid. When we change the fluid nature from heat absorption to heat generation, the liquid temperature also rises. In addition, the fluid heat is suppressed when we change the flow medium from a stationary plate to a Riga plate for heat absorption/generation cases.Öğe Zero and nonzero mass flux effects of bioconvective viscoelastic nanofluid over a 3D Riga surface with the swimming of gyrotactic microorganisms(Hindawi Ltd, 2021) Karthik, Thirumalai Sampath; Loganathan, K.; Shankar, A. N.; Carmichael, M. Jemimah; Mohan, Anand; Kayıkçı, ŞafakThis work addresses 3D bioconvective viscoelastic nanofluid flow across a heated Riga surface with nonlinear radiation, swimming microorganisms, and nanoparticles. The nanoparticles are tested with zero (passive) and nonzero (active) mass flux states along with the effect of thermophoresis and Brownian motion. The physical system is visualized via high linearity PDE systems and nondimensionalized to high linearity ordinary differential systems. The converted ordinary differential systems are solved with the aid of the homotopy analytic method (HAM). Several valuable and appropriate characteristics of related profiles are presented graphically and discussed in detail. Results of interest such as the modified Hartmann number, mixed convection parameter, bioconvection Rayleigh number, and Brownian motion parameter are discussed in terms of various profiles. The numerical coding is validated with earlier reports, and excellent agreement is observed. The microorganisms are utilized to improve the thermal conductivity of nanofluid, and this mechanism has more utilization in the oil refinery process.Öğe Mixed convection and thermally radiative flow of MHD Williamson nanofluid with Arrhenius activation energy and Cattaneo-Christov heat-mass flux(HINDAWI LTD, 2021) Eswaramoorthi, S.; Alessa, Nazek; Sangeethavaanee, M.; Kayıkçı, Şafak; Namgyel, NgawangIn this paper, we explored the impact of thermally radiative MHD flow of Williamson nanofluid over a stretchy plate. The flow in a stretchy plate is saturated via Darcy-Forchheimer relation. Cattaneo-Christov heat-mass flux theory is adopted to frame the energy and nanoparticle concentration equations. Additionally, the mass transfer analysis is made by activation energy and binary chemical reaction. Activation energy is invoked through the modified Arrhenius function. The intention of the current investigation is to enhance the heat transfer rate in industrial processes. The non-Newtonian nanofluids have more prominent thermal characteristics compared to ordinary working fluids. The governing models are altered into ODE models, and these models are numerically solved by applying the MATLAB bvp4c algorithm. The graphical and tabular interpretations have scrutinized the impact of sundry distinct parameters. The fluid speed escalates for enhancing the Richardson number, and it falls off for higher values of the Weissenberg number. It is noticed that the fluid temperature declines for higher values of the Brownian motion parameter and it grows for larger values of the thermophoresis parameter. The activation energy enriches the heat transfer gradient and suppresses the local Sherwood number. Additionally, the more significant heat transfer gradient occurs in heat-absorbing nonradiative viscous nanofluid and a smaller heat transfer gradient occurs in heat-generating radiative Williamson nanofluid. Also, we noticed that a higher heat transfer gradient appears in the Fourier model than in the Catteneo-Christov model. In addition, the comparative results are confirmed and reached an outstanding accord.Öğe Estimation of gas emission values on highways in Turkey with machine learning(IEEE, 2021) Kurt, Nursaç; Öztürk, Oktay; Beken, MuratDue to its geographical location, Turkey has been home to many civilizations for centuries. It has always acted as a bridge between west and east and will continue to do so. The development of road networks in Turkey and the difference in transportation methods are increasing the number of national and international traveling vehicles day by day. In this study, gas emission (CO2, CH4, N2O) value changes have been predicted according to vehicle types of vehicle mobility on highways using machine learning (Linear Regression, Bayesian Ridge, Random Forest Regressor, MLP Regressor, SVR) algorithms. Based on these results, the gas emission value and environmental impact that may occur in the future are estimated-each method evaluated with MAE, MSE, RMSE, and R2 statistical metrics. As a result, we obtain R square scores of 0.963231 for CO2, 0.9856 for CH4, and 0.982404 for N2O from the random forest regressor, random forest regressor, and MLP regressor, respectively.
