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Comparative Analysis of ML Algorithms for Fraud Detection 4

Comparative Analysis of ML Algorithms for Fraud Detection

引用

4th IEEE International Conference on Smart Information Systems and Technologies, SIST 2024

作者： Abitova, Gulnara Abalkanov, Miras Dep. of Eletrical Engineering and Computer Science Dep. of Computer Engineering State University of New York Astana IT University BinghamtonNY United States Astana Kazakhstan Department of Computer Engineeering Astana IT University Astana Kazakhstan

ISBN: (纸本)9798350374865

The challenge of credit card fraud presents a significant threat to both consumers and financial institutions alike, leading to substantial economic detriment. In response, this research applies various machine learning strategies to sift through transactional data for the identification of fraudulent activities. Specifically, we employ logistic regression, decision trees, and random forest algorithms to analyze a compiled dataset of genuine and deceptive transactions. This dataset undergoes segmentation into training and testing phases, allowing for a comprehensive assessment of each algorithm's effectiveness. The determination of accuracy scores across these phases serves as a metric for model evaluation. The findings lay down an essential groundwork for ongoing exploration into leveraging machine learning for the mitigation of credit card fraud. The integration of these analytical models promises to bolster the fraud detection capabilities of both financial entities and individual cardholders, significantly reducing the incidence of financial loss. © 2024 IEEE.

关键词： Logistic regression

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RF-Based Lightweight Machine Learning for Comprehensive Drone Activity Classification 2

RF-Based Lightweight Machine Learning for Comprehensive Dron...

引用

2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Alqodah, Mohammed A. Tahsin, Mahdia Omari, Mohammad H. Matalgah, Mustafa M. Harrison, David University of Mississippi Dep. of Electrical & Computer Engineering UniversityMS United States University of Mississippi Dept. of Computer and Information Science UniversityMS United States

ISBN: (纸本)9798331529529

The rapid rise of commercially available drones, driven by miniaturization and affordability, has introduced potential security risks alongside numerous societal benefits. While drones serve various industries, their unregulated use necessitates robust detection and classification methods. This paper proposes a novel, efficient model for classifying unwanted drone activity. We leverage statistical and time-domain features extracted from radio frequency (RF) signals and machine learning (ML) classifiers. This approach aims to not only detect drones but also distinguish their models and operational modes. Utilizing the DroneRF dataset, our feature selection strategy prioritizes informative signals in the low-band spectrum, achieving {100%} accuracy in both drone detection and drone model identification tasks. Furthermore, our hybrid model, combining time-domain and frequency-domain features, achieves an accuracy of {97.66%} in classifying different flight modes. This demonstrates the effectiveness of this comprehensive feature extraction approach for drone activity classification. © 2024 IEEE.

关键词： Drones

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Study on the Robotic Arm Trajectory Compensation System with Deviation Sensing 3

Study on the Robotic Arm Trajectory Compensation System with...

引用

3rd International Conference on Mechatronics and Automation Technology, ICMAT 2024

作者： Liu, Chao-Chung Liu, Jun-Chi Liu, Chao-Shu Dep. of Computer Science Zhaoqing University Zhaoqing526061 China Dep. of Industrial Design National United University Miaoli360301 Taiwan Dep. of Mechanical Engineering National Kaohsiung University of Science and Technology Kaohsiung80778 Taiwan

ISBN: (纸本)9781643685649

Robotic arms play an important role in the automation industry. Robotic arms plan processing paths based on the shape of the workpiece. However, the processing path often deviates due to inaccurate placement of the workpiece. To solve the issue of deviations, this paper develops a trajectory compensation system for a robotic arm with offset perception. This system uses the concept of workpiece boundary projection and designs an offset calculation algorithm by comparing the dimensions of the workpiece drawings, correcting errors caused by missing corners of the workpiece. In terms of operation, the system uses photoelectric sensors to quickly scan the workpiece boundary and immediately calculate the displacement and rotational offset of the workpiece. Subsequently, when deviations occur due to different placement positions of the workpiece, the robotic arm's motion path and action must be corrected and compensated to maintain precision. Tests on selected workpieces show that the corrected rotational angle error is less than 0.4 degrees, and the translational error is less than 0.7 millimeters. Since these correction error values are lower than the 1.0 mm error threshold for the robotic arm's trajectory tracking control. The offset-sensing robotic arm trajectory compensation system developed in this study can satisfy the requirements of smart automation production lines and arrive at the manufacturer's application standards. © 2024 The Authors.

关键词： Robotic arms

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Energy Access Assessment Report Focusing on Energy Poverty and Energy Demand for Rwanda 3

Energy Access Assessment Report Focusing on Energy Poverty a...

引用

3rd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Asiegbu, Adimchinobi Almaktoof, Ali Khalat, Abdurazag Cape Peninsula University of Technology Dep. of Electrical Electronics and Computer Engineering Cape Town South Africa Sabratha University Dep. of Electrical and Electronics Engineering Sabratha Libya

ISBN: (纸本)9798350319897

This technical report is a researched document that examines energy access in Rwanda, focusing on energy poverty assessment and energy demand assessment. The key findings and observations can help mitigate the energy access problems and improve access to modern energy in Rwanda. Studies have shown that sub-Saharan African countries have poor access or no access to modern energy, Rwanda being part of sub-Saharan Africa also has Poor access to modern and clean energy. To carefully study these energy access problems and proffer solutions to increase energy access in Rwanda based on realities in today's Africa, (where most households fall below poverty lines, with social issues, low Purchasing Power Parity (PPP) and Gross Domestic Power (GDP)), an assessment of these energy problems is done based on Energy Poverty Assessment (EPA) and Energy Demand Assessment (EDA) Composite Multidimensional Indicators (CMDI), respectively. Based on the findings of this research, non-poor energy households increased their energy consumption by 29.26% to 0.68 billion kWh. The clean cooking gas usage in Rwanda is very low at 2.1%, The 2016 estimated electricity consumption is 0.5273 billion kWh, growing by 57.41% to 0.83 billion kWh for energy-poor households in 2021, compared to 52.6% predicted by International Energy Agency (IEA). Finally, there is a direct relationship between energy access and economic development including quality of life, standard of living, per capita income, urbanization, modern infrastructure, etc. Thus, the key findings and observations in this research report will improve economic and infrastructural development in Rwanda, if implemented. © 2023 IEEE.

关键词： Smart power grids

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Biological age estimation from ECG using Artificial Intelligence approaches 2

Biological age estimation from ECG using Artificial Intellig...

引用

2nd Jordanian International Biomedical engineering Conference, JIBEC 2024

作者： Sleiman, Jana Daher, Mohamad Khalil, Mohamad Diab, Ahmad Kassem, Abdallah Lebanese University Computer and Communications Engineering Tripoli Lebanon Faculty of Science Faculty of Applied Sciences & Technology Dep. of Computer Science Dep. of Computer Science & Information Technology Tripoli Lebanon Lebanese University Electrical Engineering Tripoli Lebanon Lebanese International University Biomedical Engineering Beirut Lebanon Notre Dame University Faculty Of Engineering ECCE Dept. Zouk Mosbeh Lebanon

ISBN: (纸本)9798331539542

This paper explores the application of Artificial Intelligence (AI) techniques in healthcare, specifically focusing on electrocardiogram (ECG) data analysis and biological age estimation. The study begins with an overview of ECG as a diagnostic tool and discusses the integration of Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), in healthcare for various applications. Previous studies reviewed in this context consistently identify CNNs as the superior model for ECG analysis and prediction tasks. The primary research investigates the use of Machine Learning (ML) algorithms to estimate biological age from ECG data, employing metrics such as Mean Absolute Error (MAE) and variance to evaluate model performance. Subsequently, DL models are implemented and compared, demonstrating superior accuracy in estimating the biological age of new patients. While both ML and DL approaches were initially explored, this paper primarily focuses on deep learning methodologies due to their demonstrated superiority in healthcare applications. The findings highlight the efficacy of deep learning over traditional ML methods, underscoring its potential for enhancing diagnostic accuracy and personalized patient care. For instance, the most accurate deep learning model, the Convolutional Neural Network (CNN), achieved a Mean Absolute Error (MAE) of 1.49 and an R2 score of 0.53, whereas the best-performing traditional ML model, Random Forest, achieved an MAE of 1.54 and an R2 score of 0.55. These results clearly indicate the advantage of deep learning models in capturing complex patterns in ECG data, leading to more precise and reliable estimations of biological age. © 2024 IEEE.

关键词： Convolutional neural networks

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Detecting 5G Signal Jammers with Autoencoders Based on Loose Observations

Detecting 5G Signal Jammers with Autoencoders Based on Loose...

引用

2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Varotto, Matteo Valentin, Stefan Tomasin, Stefano University of Padova Dep. of Information Engineering Padova Italy Darmstadt University of Applied Sciences Dep. of Computer Science Darmstadt Germany University of Padova Dep. of Information Engineering Dep. of Mathematics Padova Italy

ISBN: (纸本)9798350370218

Fifth-generation (5G) networks are prone to jamming attacks, which are particularly dangerous in mission-critical applications such as factory automation. In this paper, we present a simple method to detect jamming attacks with machine learning techniques operating on in-phase and quadrature (IQ) modulated signals. In particular, a convolutional autoencoder (CAE) learns the structure of the clean signal to distinguish it from jammed signals in real time. This approach requires only a loose synchronization to the OFDM symbol, while equalization and decoding are not necessary. Despite its simplicity, our technique has shown high detection rates in experiments on a 5G testbed. © 2023 IEEE.

关键词： 5G Convolutional Autoencoder IQ Signals Jamming Detection New Radio Wireless Intrusion Prevention

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Investigation of a 7-Axis Suspended Robotic Arm for Automated Spraying System 3

Investigation of a 7-Axis Suspended Robotic Arm for Automate...

引用

3rd International Conference on Mechatronics and Automation Technology, ICMAT 2024

作者： Liu, Chao-Chung Liu, Jun-Chi Liu, Chao-Shu Dep. of Computer Science ZhaoQing University ZhaoQing526061 China Dep. of Industrial Design National United University Miaoli360301 Taiwan Dep. of Mechanical Engineering National Kaohsiung University of Science and Technology Kaohsiung80778 Taiwan

ISBN: (纸本)9781643685649

This study successfully develops a 7-axis suspended robotic arm for an automated spraying system, considering factors such as flexible operation modes, spray area dimensions, workpiece geometry, and the degrees of freedom required for effective operation. By using SolidWorks software for verification, the endpoints of the robotic arm can reach all positions within the workspace. The design and fabrication of the arm are completed according to specifications for structural strength, load capacity, rated torque output, and rotational speed. Kinematic analyses, both forward and inverse, are applied to determine the posture and position of the 7-axis robotic arm, facilitating the planning and design of the spraying trajectory. The control system architecture utilizes a multi-axis servo control board, integrated with a manifold deformation control method for trajectory tracking. Simulations and experiments are conducted to use a car shell workpiece provided by the manufacturer. The results from the simulations of MATLAB analyses show that the maximum tracking error is less than 0.4 mm. In experiments with the robotic arm, the maximum error is less than 0.7 mm. The results of the simulation and experiment are quite close, indicating that the deformation control method effectively operates the posture and movement of the 7-axis suspended robotic arm. Therefore, the 7-axis suspended robotic arm developed in this study will be effectively utilized in the production line of the automated spraying system. © 2024 The Authors.

关键词： MATLAB

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MPI Impact in C+L+S Multiband Transmission Reach

MPI Impact in C+L+S Multiband Transmission Reach

引用

2024 Photonic Networks and Devices, NETWORKS 2024

作者： Cancela, Luís G. Pires, João O. Instituto de Telecomunicações Dep. Information Science and Technology Iscte Instituto Universitário de Lisboa Portugal Instituto de Telecomunicações Dep. Electrical and Computer Engineering Instituto Superior Técnico Portugal

Multipath interference (MPI) impact is assessed in a C+L+S multiband transmission scenario. For a typical -34 dB/span MPI, transmission reach in the L-band suffers a 20% reach penalty considering the QPSK modulation f... 详细信息

关键词： C++ (programming language)

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PV Grid Forecasting Analysis using Artificial Neural Network 25

PV Grid Forecasting Analysis using Artificial Neural Network

引用

25th International Middle East Power System Conference, MEPCON 2024

作者： Aboelmagd, Mohamed R. Selim, Ali Abdel-Akher, Mamdouh Aswan University Faculty of Engineering Department of Electrical Engineering Aswan Egypt Almaaqal University Department of Control and Computer Engineering Dep. Basra Iraq

ISBN: (纸本)9798350379648

In addition to its use in building and agriculture, global solar irradiance is one of the most critical aspects in designing and considering any solar station's volume. Because the Iraqi metrological organization and seismology lack exact information regarding irradiance, this project aims to use historical global data, develop numerical analysis using artificial neural networks, and forecast hourly irradiance. The test is run over Basra to determine references to their nearest places. The learning technique in this study is a forward neural network (FNN) and time series neural network. Their comparison dep.nds on seven input variables: temperature, wind speed, wind direction, sunshine duration, and date. One of the most essential integrated and impactful events for managing the system of new and renewable energy sources in the power grid is accurate solar energy forecasting. The study offers a valuable method for forecasting solar energy generation using neural networks. It is difficult to apply neural networks to anticipate PV power generation because of the unpredictability of variations in the weather;unlike traditional mathematical models, which struggle to handle complicated nonlinear interactions and provide poor predictions, neural network approaches achieve superior prediction performance. The suggested forecasting model based on neural networks provides a practical means of raising the accuracy of PV generation estimation. © 2024 IEEE.

关键词： Solar irradiance

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Power-Based Method for Computation of Induction Motor Per-Phase Equivalent Circuit Parameter Values Using Nameplate and Technical Catalogue Data

Power-Based Method for Computation of Induction Motor Per-Ph...

引用

2022 International Conference on electrical Machines, ICEM 2022

作者： Ferreira, Fernando J. T. E. Dep. of Electrical and Computer Engineering Coimbra Portugal

ISBN: (数字)9781665414326

ISBN: (纸本)9781665414326

This paper proposes and validates a power-based numerical search method for computation of the per-phase equivalent circuit parameter values of commercial IEC-compliant three-phase squirrel-cage induction motors using nameplate and technical catalogue data. For five different motors, the simulated input active power, total losses, input reactive power, stator current, efficiency, and power factor, at 100%, 75% and 50% load, using the equivalent circuit with the computed parameter values, are compared to the reference values obtained directly from the motor nameplate and technical catalogue data. This method leads to a relatively low percentage absolute difference between the simulated and reference values for a motor operating range between no-load and full load, regardless of the motor rated power. It is a fast, sufficiently accurate, and simple-to-apply method to obtain the equivalent circuit parameter values for steady-state simulation of line-operated three-phase induction motors, having relatively low sensitivity to numerical rounding, tolerances, and uncertainties associated with the values stated on the nameplate and in the technical catalogues of IEC-compliant motors. © 2022 IEEE.

关键词： Energy efficiency

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