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Identification of Fraudulent Transactions for Enhanced Credit Card Fraud Detection 4

Identification of Fraudulent Transactions for Enhanced Credi...

引用

4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Gupta, Monica Gayathri, V. Narula, Rachna Kumar, Vijay Kaur, Surinder Singh, Neetu Bharati Vidyapeeth's College of Engineering Dept. of Electronics and Communication Engineering New Delhi India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering New Delhi India Bharati Vidyapeeth's College of Engineering Department of Information and Technology New Delhi India

ISBN: (纸本)9798350387490

With the flourishment of the digital era and the evolution of new techniques and technology, this paper highlights the ever-growing risk of credit card fraud. The advancing technology has provided fraudsters with new and untraceable techniques for scamming genuine users. With the increasing impact of fraud, financial institutions must use machine learning tools for scam detection to provide safety and security to their users' accounts. This paper consists of an interdisciplinary comparative analysis of ML algorithms: Random Forest Classifier, XGBoost, ADABoost, Logistic Regression, Decision Trees, and Artificial Neural Networks. The diverse selection of algorithms aims to determine the most effective algorithm with the highest accuracy score for predicting legitimate and fraudulent transactions. The methodology involves fetching data from an Excel file, training the models, testing them on 20% of the dataset, and finally obtaining results in a Word document, having a confusion matrix and accuracy of each algorithm. By comparing the results of the confusion matrix and accuracy score, the Random Forest Classifier and XGBoost demonstrate the highest accuracy scores, while Logistic Regression yields a lower accuracy score. © 2024 IEEE.

关键词： Logistic regression

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Framework for Cloud-Based Data Management in Autonomous Vehicles

Framework for Cloud-Based Data Management in Autonomous Vehi...

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IEEE Students' Conference on Electrical, Electronics and computer science (SCEECS)

作者： Harshith Pallapothu Gopi Krishna G Digvijay Sunil Teja Yadav S Nagasundari S Dept. of Computer Science & Engineering PES University Bengaluru India Dept. of Computer Science & Engineering Research Center for Information Security Forensics and Cyber Resilience PES University Bengaluru India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

The increasing adoption of autonomous vehicles has driven the need for robust data management solutions that support real-time operations and ensure vehicle safety and efficiency. This work introduces a cloud-based framework for management of sensor data from autonomous vehicles, focusing on optimizing payload transmission rates. The framework leverages AWS IoT Core and AWS IoT Analytics to ensure efficient data flow from vehicle sensors to cloud storage. This framework demonstrates its potential for scalable deployment in real-world autonomous vehicle networks, contributing to the evolution of connected vehicle technologies and intelligent transport systems. This framework ensures reliable data transmission up to 250 payloads per second, beyond which data loss occurs.

关键词： Cloud computing Connected vehicles Web services Vehicle safety Propagation losses Real-time systems Reliability Autonomous vehicles Payloads Predictive maintenance

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Active Element Arrangement and Prediction Model for Channel Estimation in IRS-Assisted Systems

Active Element Arrangement and Prediction Model for Channel ...

引用

IEEE Region 10 International Conference TENCON

作者： Yoshihiko Tsuchiya Norisato Suga Kazunori Uruma Masaya Fujisawa Dept. of Information and Computer Technology Tokyo University of Science Tokyo Japan Dept. of Information and Communication Engineering Shibaura Institute of Technology Tokyo Japan Dept. of Computer Science Kogakuin University Tokyo Japan

ISBN: (数字)9798350350821

ISBN: (纸本)9798350350838

Intelligent reflecting surfaces (IRS) that can dynamically control the phase of radio waves and reflect them are attracting attention to realize non line-of-sight communication in the high-frequency band. Channel state information (CSI) is estimated to properly control the phase, however the overhead of the estimation reduces communication efficiency. To reduce the overhead, IRS systems with active elements that perform channel estimation are considered. One approach treats the partial CSI obtained from the active elements as an image and utilizes a super-resolution technique to predict the entire CSI. However, because the active elements are arranged in grid, prediction is difficult when the active elements spacing and the CSI fluctuation period match. In this study, we propose a new arrangement that gathers the active elements in the center of the IRS. Furthermore, we regard prediction as an image-inpainting problem that recovers missing pixels from known pixel values, and we propose applying the U-Net to solve this problem. Numerical experiments show that the proposed method outperforms conventional methods in prediction accuracy and spectral efficiency.

关键词： Fluctuations Accuracy Surface waves Spectral efficiency Superresolution Channel estimation Line-of-sight propagation Predictive models Numerical models IEEE Regions

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A 4x4 MIMO Antenna Array for the Wi-Fi7Access Point on a Metal Wall Application

A 4x4 MIMO Antenna Array for the Wi-Fi7Access Point on a Met...

引用

International Symposium on Antennas and Propagation (ISAP)

作者： Hsin-Lung Su You-Sheng Zhan Dept. of Computer and Communication National Pingtung University Taiwan Dept. of Computer Science and Information Engineering National Pingtung University Taiwan

ISBN: (数字)9798350364774

ISBN: (纸本)9798350364781

4×4 CPW MIMO Antenna array with an artificial magnetic conductor (AMC) reflector is presented for Wi-Fi7 access point on a metal wall application in the study. The 10-dB impedance of the integrated MIMO antenna array covers the Wi-Fi 7 operating bands, and the transmission coefficients are under -15 dB in the whole Wi-Fi7 bands. This proposed integrated MIMO antenna is low profile and has good total radiation efficiency (over 60%). The height of the integrated MIMO antenna array is 6.325 mm.

关键词： Transmitting antennas Metals Directive antennas Reflector antennas Conductors Coplanar waveguides Impedance Wireless fidelity Antenna arrays Antenna radiation patterns

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RIS-Aided Federated Edge Learning Exploiting Statistical CSI

RIS-Aided Federated Edge Learning Exploiting Statistical CSI

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Li, Heju Wang, Rui Wu, Jun Zhang, Wei Soto, Ismael Tongji University Dept. Electronics and Information Shanghai200092 China Fudan University Dept. Computer Science Shanghai200433 China The University of New South Wales Dept. Electrical Engineering and Telecommunications SydneyNSW2052 Australia University of Santiago Dept. Electrical Engineering AE Santiago de Chile3519 Chile

ISBN: (纸本)9798350345384

Federated edge learning (FEEL) as an emerging distributed learning paradigm can effectively resolve the resource constraints and privacy issues in the Internet of Things (IoT) by featuring collaborative training at the edge devices under the premise of data localization. Nevertheless, owing to the scarce resources and the inscrutable communication fading, issues such as model damage and signal deviation will critically diminish the convergence performance and learning accuracy of FEEL. With this in mind, reconfigurable intelligent surface (RIS) has recently been integrated to enhance the communication quality of wireless systems by adaptively reconfiguring the signal propagation environment. To utterly release the applied potency of RIS, the configuration of phase shifts is of paramount importance, where accurate channel state information (CSI) is required. Unfortunately, the accurate instantaneous CSI is extremely challenging to be estimated. In this paper, we focus on the realistic scenario assuming only statistical CSI is known, which can be comparatively easily and accurately explored. On the other hand, considering the wireless outage caused by the random non-line-of-sight channel, we rigorously derive an explicit convergence upper bound of the RIS enabled FEEL framework with respect to the outage probability. Based on the convergence theory, a unified resource allocation problem is further established by jointly optimizing bandwidth allocation, and the RIS configuration matrix. Extensive simulations are conducted to demonstrate that the proposed design dramatically promotes the learning performance compared against the baseline solutions. © 2023 IEEE.

关键词： Channel state information

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Rule-Augmented Neural Networks for Improved Answer Validation in Question Answering Systems

Rule-Augmented Neural Networks for Improved Answer Validatio...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Eman Alduweib Waseem Alromema Nisreen Otoum dept. of Computer Science University of Petra Amman Jordan dept. of Computer Science and Information Taibah University Medinah Saudi Arabia dept. of Software Engineering University of Petra Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This work presents a hybrid method for answer validation in question answering systems, combining neural network methods with explicit logical rules. The Rule-Augmented Neural Network (RANN) framework integrates the expressiveness of neural networks and precision of rule-based systems for improving answer validation accuracy. Using a sample of the Natural Questions dataset, we demonstrate how we can achieve more reliable and interpretable answer validation by complementing traditional neural architectures with domain-specific rules. The experimental results validate the RANN framework. The experimental results emphasize that the hybrid approach outperforms both neural-based models and rule-based reasoning by achieving the best accuracy, precision, recall, and F1 score.

关键词： Accuracy Computational modeling Neural networks computer architecture Question answering (information retrieval) Cognition Reliability Artificial intelligence Computational intelligence

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An Iterated Local Search Algorithm for Commuting Bus Routing Problem with Latest Arrival Time Constraint

An Iterated Local Search Algorithm for Commuting Bus Routing...

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2022 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2022

作者： Hashimoto, H. Hu, Y. Sugiura, T. Takada, Y. Yagiura, M. Tokyo University of Marine Science and Technology Dept. Logistics and Information Engineering Tokyo Japan Tokyo University of Science Dept. Applied Mathematics Tokyo Japan Nagoya University Graduate School of Information Science Dept. Computer Science and Mathematical Informatics Nagoya Japan

ISBN: (数字)9781665486873

ISBN: (纸本)9781665486873

Given a set of bus stops and a set of employees, the commuting bus routing problem with latest arrival time constraint (CBRP-LATC) aims to determine routes of buses to carry every employee from the company to one of the bus stops at which he/she wants to get off. Each employee walks to his/her destination after getting off the bus and has a desired time when he/she wants to arrive there, which is called the latest arrival time. The objective is to find a set of routes with the minimum total traveling cost that covers all employees' destinations under bus capacity and latest arrival time constraints. We propose an iterated local search algorithm in which we use two kinds of neighborhood operations called set-del/1-ins and VRP-OPT∗. The set-del/1ins operation consists of three neighborhood operations, setdel, 1-ins and set-del-ins to remove or assign employees from or to a bus route. The VRP-OPT∗ is a local search algorithm to improve the distances of routes based on four neighborhood operations, 2-opt, 2-opt∗, relocate and cross-exchange. Computational results show the effectiveness of the two neighborhood operations. © 2022 IEEE.

关键词： Personnel

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Cocopal - A Deep Learning Based Intelligent System to Certify and Standardize the Quality of Coconut Based Products 4

Cocopal - A Deep Learning Based Intelligent System to Certif...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Gunawardana, K.H.R. Hemachandra, M.G.S.P. Hettiarachchi, N.M. Deshan, M.P.N. Ganegoda, Devanshi Weerasinghe, Lokesha Dept. of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Dept. of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

The procedure of certifying and standardizing the quality of the coconut-based products is done manually in Sri Lanka at precent. It is a time consuming and labor- intensive task and is conducted by experts. In most cases, the quality is decided solely by visual inspections by buyers and suppliers, with no scientific basis. The paper reports the capacity of bringing modern technology solutions such as Artificial Intelligence (AI), Machine Learning (ML), Image Processing (IP), and decentralized storage to aid in the certification and standardization of the quality of raw *** showed that the accuracy of the proposed system is in the 86% to 90% range and showed that this technique could beimproved and used as an alternative to manual techniques. © 2022 IEEE.

关键词： Deep learning

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An Environmental Sensing and APP Display System Based on IoT

An Environmental Sensing and APP Display System Based on IoT

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2022 IET International Conference on engineering Technologies and Applications, IET-ICETA 2022

作者： Fan, Cheng-De Chang, Chih-Yung Fan, Chun-Chieh Tamkang University Dept. of Electrical and Computer Engineering New Taipei City Taiwan St. John's University Dept. of Computer Science and Information Engineering New Taipei City Taiwan

ISBN: (数字)9781665491389

ISBN: (纸本)9781665491389

This research develops a cheap and multi-functional environment sensing and APP display system. The sensing end of this system is composed of CO2 sensor, PM2.5 sensor, GPS module, memory module, microprocessor, Bluetooth,,,,,etc.. The sensed information can be stored in the memory module, and then use computer and mobile phone to observe the curve of data changes, and proceed further analyze, discuss, and decision-making. This research mainly writes a mobile APP program, uses Bluetooth to control the sensor memory module data, and then displays them on the mobile phone. The values sensed by various sensors can be observed from the mobile phone screen, and the changing curve can be present. In addition, it can also display the sensing time and location of a sensing value in map mode, so that the complete sensing data information can be clearly understood from the mobile phone. © 2022 IEEE.

关键词： Climate change

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Train a Robot to Climb Staircase Using Vision-Base System 10

Train a Robot to Climb Staircase Using Vision-Base System

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10th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2022

作者： Jayawardana, J.T.H. Dilshan, H.V.V. Wijethilaka, R.G.K.H Balasooriya, T.D. Rajapaksha, U.U.S. Harshanath, S.M.B. Jayawardena, Chandimal Sri Lanka Institute of Information Technology Dept. Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. Information Technology Malabe Sri Lanka

ISBN: (数字)9781665401562

ISBN: (纸本)9781665401562

Currently, robots are used for different types of work, such as the manufacturing industry, healthcare, and the hotel industry. According to the current epidemic situation, the usage of robots was increased because of the need to reduce human interaction. As a result, they have to walk around the workplace, because of that, they may have to climb staircases. The world has many types of robots. Here the selected robot is a humanoid. This proposal is concerned with how to detect the staircase, count steps, get dimensions of it, and move the robot on it by keeping body balance. First, want to know what the objects are, then walk. The identified images from the image sensors will get as input. The technology stack that is used for image analysis is a method related to computer vision in deep learning. Other than that, while climbing the stairs robot needs to identify whether the staircase is over or not. Here we introduce a new concept: get the number of steps required to climb before climbing the stairs. It is related to how humans identify things by seeing and making decisions. The need to take the dimensions of the stairs is that when considering the stairs, they have their height, width, and range. Therefore, it is imperative to calculate the dimensions of each staircase separately. Creating a balance system similar to human balance is a great advantage in robotics. To implement such a system, there is an upright pose controller to allow the robot to walk stably by preventing tilting of the robot during walking on uneven floor. In general, for us to do that we need to calculate the global inclination of the floor is a key factor. It can be measured with a 2-axis accelerator meter, and it is installed in the inertial sensor. © 2022 IEEE.

关键词： Stairs

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