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Suspicious transaction alert and blocking system for cryptocurrency exchanges in metaverse’s social media universes: RG-guard

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Neural Computing and Applications 2024年第30期36卷 18825-18840页

作者： Gürfidan, Remzi Computer Programming Department Yalvaç Technical Sciences Vocational School Isparta University of Applied Science Isparta Turkey

In this study, we propose an effective system called RG-Guard that detects potential risks and threats in the use of cryptocurrencies in the metaverse ecosystem. In order for the RG-Guard engine to detect suspicious transactions, Ethereum network transaction information and phishing wallet addresses were collected, and a unique dataset was created after the data preprocessing process. During the data preprocessing process, we manually distinguished the features within the original dataset that contained potential risk indicators. The learning process of the RG-Guard engine in risk classification was achieved by developing a deep learning model based on LSTM + Softmax. In the training process of the model, RG-Guard was optimised for maximum accuracy, and optimum hyperparameters were obtained. The reliability and dataset performance of the preferred LSTM + Softmax model were verified by comparing it with algorithms used in risk classification and detection applications in the literature (Decision tree, XG boost, Random forest and light gradient boosting machine). Accordingly, among the trained models, LSTM + Softmax has the highest accuracy with an F1-score of 0.9950. When a cryptocurrency transaction occurs, RG-Guard extracts the feature vectors of the transaction and assigns a risk level between 1 and 5 to the parameter named βrisk. Since transactions with βrisk > = 3 are labelled as suspicious transactions, RG-Guard blocks this transaction. Thus, thanks to the use of the RG-Guard engine in metaverse applications, it is aimed to easily distinguish potential suspicious transactions from instant transactions. As a result, it is aimed to detect and prevent instant potential suspicious transactions with the RG-Guard engine in money transfers, which have the greatest risk in cryptocurrency transactions and are the target of fraud. The original dataset prepared in the proposed study and the hybrid LSTM + Softmax model developed specifically for the model are expected to c

关键词： Decision trees

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Effects of Different Optimizations at Detection of Vehicles and Pedestrians with YOLOv8 8

Effects of Different Optimizations at Detection of Vehicles ...

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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Iren, Ecem İzmir Kavram Vocational School Computer Programming Program İzmir Turkey

ISBN: (纸本)9798331531492

Improvements in deep learning has facilitated traffic management by providing analyzing and prediction capabilities on the road. In this study, vehicle and pedestrian detection with YOLOv8 was carried out with different optimizations such as Adam, Stochastic Gradient Descent (SGD) and RMSprop, at the end of the experiments, the model with SGD which produced 87.1 % in mAP was seen as the best optimizer when compared with others at detecting objects. © 2024 IEEE.

关键词： Adam optimization deep learning pedestrian detection RMSprop Optimization SGD optimization vehicle detection YOLOv8

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Effects of Different Optimizations at Detection of Vehicles and Pedestrians with YOLOv8

Effects of Different Optimizations at Detection of Vehicles ...

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International Artificial Intelligence and Data Processing Symposium (IDAP)

作者： Ecem İren Computer Programming Program İzmir Kavram Vocational School İzmir Türkiye

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

Improvements in deep learning has facilitated traffic management by providing analyzing and prediction capabilities on the road. In this study, vehicle and pedestrian detection with YOLOv8 was carried out with different optimizations such as Adam, Stochastic Gradient Descent (SGD) and RMSprop, at the end of the experiments, the model with SGD which produced $87.1 \%$ in mAP was seen as the best optimizer when compared with others at detecting objects.

关键词： Deep learning YOLO Pedestrians Roads Stochastic processes Optimization methods Traffic control Real-time systems Pattern analysis Monitoring

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Sheffer stroke operation on L-algebras via an algorithmic approach

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Soft Computing 2024年第19期28卷 10801-10813页

作者： Gürsoy, Necla Kırcalı Öner, Tahsin Gürsoy, Arif Ülker, Alper Department of Computer Programming Ege Vocational School Ege University İzmir Bornova35100 Turkey Department of Mathematics Ege University İzmir Bornova35100 Turkey Department of Mathematics and Computer Science İstanbul Kültür University Bakırköy Istanbul34156 Turkey Izmir Biomedicine and Genome Center İzmir Balçova35340 Turkey

In this study, we introduce the Sheffer stroke L-algebra and prove some fundamental theorems, propositions and lemmas of Sheffer Stroke L-algebras. The notions of filter and ultrafilter for Sheffer stroke L-algebra are studied. We give subalgebra and normal subset definitions of a Sheffer stroke L-algebras. Moreover, a homomorphism between Sheffer stroke L-algebras is introduced and isomorphism theorems are presented. Finally, we give three new algorithms for Sheffer stroke L-algebras. Thus, it is contributed to researchers on different application areas by presenting an algorithmic approach on this subject, for the first time in the literature. © The Author(s) 2024.

关键词： Algebra

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Application-based spam detection with machine learning algorithms 27

Application-based spam detection with machine learning algor...

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27th International Conference on Information Society and University Studies, IVUS 2022

作者： Erbey, Ali Barışçı, Necaattin Uşak University Distance Education Vocational School Department of Computer Programming Uşak Turkey Gazi University Faculty of Technology Department of Computer Engineering Ankara Turkey

We investigated the process of unsupervised generative learning and the structure of informative generative representations of images of handwritten digits (MNIST dataset). Learning models with the architecture of sparse convolutional autoencoder with constraints to produce low-dimensional representations achieved successful generative learning demonstrated by high accuracy of generation of images. A well-defined, continuous and connected structure of generative representations was observed and described. Structured informative representations of unsupervised generative models can be an effective platform for investigation of origins of intelligent behaviors in artificial and biological learning systems. © 2022 Copyright for this paper by its authors.

关键词： Neural networks

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Using LSTM as Intelligent Machine Learning Method to Forecast the Annual Average Relative Humidity: A Case Study for Konya, Turkiye

Using LSTM as Intelligent Machine Learning Method to Forecas...

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Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference

作者： Mayanja, Abubakar Eryürük, Şule Eryürük, Kağan Graduate School in Science Department of Computer Engineering Selcuk University Konya Turkey Vocational School of Commerce and Industry Department of Computer Programming KTO Karatay University Konya Turkey Department of Industrial Engineering KTO Karatay University Konya Turkey Department of Civil Engineering Necmettin Erbakan University Konya Turkey

ISBN: (纸本)9783031397769

Nowadays, one of the most demanding areas of Artificial Intelligence application is to forecast the weather condition as rapid and accurate as possible. The ratio of air vapor pressure to saturation vapor pressure is defined as relative humidity which is represented by a percentage of how much moisture the atmosphere can hold at the same temperature and pressure. The relative humidity has a significant impact on climate change and global warming which have had a negative impact on agriculture, economy, food security, and public health. In addition, accurate weather information and humidity forecasts are frequently required to warn of natural disasters result from unexpected changes in climatic conditions such as relative humidity. Therefore, recent decades have seen a rise in interest in this field of study. The aim of this study is to use Long Short-Term Memory (LSTM) models to forecast the annual relative humidity in Konya, Turkey between 1931 and 2010 by utilizing the annual average amounts of rain, sunlight, wind speed, and temperature. This paper represents a novel approach to predict annual relative humidity in Konya, which has not been previously explored in this location and between the given dates. As a result, the findings were able to forecast the monthly average humidity for next five years in advance with a mean square error of 0.1705% within 150 episodes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Long short-term memory

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Analysis of Leakage Magnetic Field of Rectangular Shaped Defects in Magnetic Materials and Investigation of Magnetic Fluids in the Field

Analysis of Leakage Magnetic Field of Rectangular Shaped Def...

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International Conference on Nanomaterials: Application & Properties (NAP)

作者： Esma Nur Savranguler Samed Gumus Yavuz Ozturk Coskun Harmansah Mechanical Engineering Izmir Democracy University Izmir Turkey Electrical Electronics Engineering Ege University Izmir Turkey Computer Programming Vocational School Izmir Turkey

ISBN: (数字)9798350380125

ISBN: (纸本)9798350380132

Magnetic Particle Inspection (MPI) is a non-destructive testing (NDT) technique widely used in industry to detect surface and sub-surface flaws in ferromagnetic materials. MPI is a simple, fast, and cost-effective technique, particularly useful for evaluating the integrity of welds and critical parts. Detecting surface and sub-surface defects in ferromagnetic materials is essential for ensuring product quality, safety, performance, and regulatory compliance across various industries. Therefore, in this study, the magnetic field and its gradient for rectangular prism-shaped defects in ferromagnetic materials were analyzed. The magnetic fields were calculated using analytical equations. The magnetic field of a 2x7x19 mm sized permanent magnet was measured using a gaussmeter to verify theoretical results. Subsequently, these analyses were used to interpret the forces acting on non-interacting micro to nano-sized particles and magnetic fluid on the crack region. The EFH-1 light hydrocarbon-based magnetic fluid, with an average particle size of about 11.6 nm, was used to experimentally study a 1x1.5x62.5 mm crack on a solid magnetic material. The distribution of the magnetic fluid on the crack was studied for different heights and magnetic field values. For a 2 ml layer of EFH-1 at a height of 2-3.5 mm from the magnetized material, a nearly Gaussian-shaped fluid distribution with a width of around 3.7-5.0 mm was observed. The experimental results are presented and compared with the theoretical analysis.

关键词： Industries Ferrofluid Welding Magnetic liquids Ferromagnetic materials Solids Size measurement Magnetic analysis Magnetic fields Testing

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Estimating Breathing Levels of Asthma Patients with Artificial Intelligence Methods

Estimating Breathing Levels of Asthma Patients with Artifici...

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International Conference on Intelligent and Fuzzy Systems, INFUS 2020

作者： Yüksel, Asım Sinan Tan, Fatma Gülşah Computer Engineering Department Süleyman Demirel University Isparta Turkey Vocational School Computer Programming Department Manisa Celal Bayar University Manisa Turkey

ISBN: (纸本)9783030511555

Pollen contains highly allergic proteins. One of the major causes of allergic diseases is the pollen in the air we breathe. Asthma patients are known to show allergic reaction to pollens. Therefore, they need to be more careful and avoid the factors that trigger asthma. In this study, the first step was taken to develop an artificial-intelligence-based decision support system to improve the quality of life of asthma patients. Finding the ratio of pollen in nature is a long process, and it is measured by using different instruments and hours of calculations in laboratories. In this study, Adaptive Network-Based Fuzzy Extraction System (ANFIS) and normalization methods were applied to meteorological data to estimate the breathing levels of asthma patients according to the amount of pollen in the air. The data were tested via the Artificial Neural Networks method, and it was found that the model produced better results that are very close to real values when compared to the results in ANFIS. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision support systems

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Performance Evaluation of Metaheuristic Optimization Techniques in Insulation Problem 8

Performance Evaluation of Metaheuristic Optimization Techniq...

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8th International Conference on Information Technology Trends, ITT 2022

作者： Gunal, Ozen Akpinar, Mustafa Akpinar, Kevser Ovaz Turgutlu Vocational School Manisa Celal Bayar University Department of Computer Programming Manisa Turkey Sakarya University Department of Software Engineering Sakarya Turkey School of Engineering Architecture and Interior Design Amity University Dubai Dubai United Arab Emirates Rochester Institute of Technology Department of Computing Security Dubai United Arab Emirates

ISBN: (数字)9781665487429

ISBN: (纸本)9781665487412

Selection of insulation material and determination of its thickness are the two most important parameters that prevent heat loss. Too much thickness complicates the price and use. The low coefficient of thermal conductivity of the insulation material reduces heat loss and increases the cost. One of the most important factors when determining the material thickness and conductivity coefficient is the price. In this study, two cases were examined under certain conditions, independent of the price. In the first case, the insulation material is determined, whereas the material thickness is determined in the second case. Six different current metaheuristic algorithms were used to calculate the two cases. It has been observed that harris hawks optimization (HHO), salp swarm optimization (SSO), and whale optimization (WO) algorithms determine the global optimum point without any error in determining the targets of the problem while the processing times were short. Overall, it has been seen that all six algorithms can create solutions to heat transfer problems with very low errors. © 2022 IEEE.

关键词： Optimization

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Word-Based Turkish Lip Reading System with Two Stream 3dcnn and Bilstm

SSRN

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SSRN 2023年

作者： Erbey, Ali Barışçı, Necaattin Department of Computer Programming Distance Education Vocational School Usak University Usak64200 Turkey Department of Information Systems Informatics Institute Gazi University Ankara Turkey Department of Computer Engineering Faculty of Technology Gazi University Ankara06500 Turkey

Lip reading is a technique that aims to understand spoken words by analyzing people's lip movements. Deep learning algorithms are used as a powerful tool for detecting and recognizing lip movements more accurately. In this study, we present a lip-reading architecture for the Turkish language. The architecture is trained to recognize Turkish words using 3DCNN (3D Convolutional Neural Network) architecture and two-stream deep learning methods. The model was trained on video data representing lip movements and learned to classify words accurately. The Turkish lip reading system has been trained and tested on a large Turkish lip reading dataset. In the experiments, an accuracy of 87.12% was achieved with the proposed model, indicating successful results in the Turkish language. The proposed model demonstrates high accuracy rates in recognizing Turkish words, showcasing its effectiveness. This paper introduces a novel architecture, a lip-reading system that employs deep learning techniques, providing a robust tool for successful Turkish lipreading applications. © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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