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Mitigating Credit Card Fraud: A Machine Learning Perspective 2

Mitigating Credit Card Fraud: A Machine Learning Perspective

2nd International Conference on Recent Trends in Microelectronics, Automation, computing, and Communications Systems, ICMACC 2024

作者： Bhushan, Shivam Gupta, Indrajeet Angayarkanni, S.A. Balakiruthiga, B. Pandimurugan, V. Rajaram, V. School of Computing SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur India Acharya Institute of Technology Computer Science and engineering Bangalore India

ISBN: (纸本)9798350366570

In this day and age of online and digital transactions, credit cards have come up as a popular option of payment. This has led to the increase in the fraudulent credit card transactions and as a result the banks and the customers have to bear the loss. It is very vital for the credit card companies to identify and stop the fraud transactions. The main aim of this project is to create a model that can distinguish fraudulent credit card transactions from real ones. The proposed model uses the transaction patterns of previous credit card transaction data which contains both genuine and fraud data to train itself using suitable ML algorithms and identify the fraudulent transactions and nip it in the bud. We will be using using SMOTE to process the data considering the imbalance between legitimate and fraudulent transactions, hyperparameter tuning the model for increased effectiveness and using random forest classifier and decision tree classifier to compare results. By using the above mentioned techniques we saw an increase in accuracy and recall. These models offer a scaleable solution to the problem of real time fraud detection in an ever growing and vast financial landscape © 2024 IEEE.

关键词： Random forests

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EdgeMove: Pipelining Device-Edge Model Training for Mobile Intelligence 23

EdgeMove: Pipelining Device-Edge Model Training for Mobile I...

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32nd ACM World Wide Web Conference, WWW 2023

作者： Dong, Zeqian He, Qiang Chen, Feifei Jin, Hai Gu, Tao Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia School of Computing Macquarie University Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Training machine learning (ML) models on mobile and Web-of-Things (WoT) has been widely acknowledged and employed as a promising solution to privacy-preserving ML. However, these end-devices often suffer from constrained resources and fail to accommodate increasingly large ML models that crave great computation power. Offloading ML models partially to the cloud for training strikes a trade-off between privacy preservation and resource requirements. However, device-cloud training creates communication overheads that delay model training tremendously. This paper presents EdgeMove, the first device-edge training scheme that enables fast pipelined model training across edge devices and edge servers. It employs probing-based mechanisms to tackle the new challenges raised by device-edge training. Before training begins, it probes nearby edge servers' training performance and bootstraps model training by constructing a training pipeline with an approximate model partitioning. During the training process, EdgeMove accommodates user mobility and system dynamics by probing nearby edge servers' training performance adaptively and adapting the training pipeline proactively. Extensive experiments are conducted with two popular DNN models trained on four datasets for three ML tasks. The results demonstrate that EdgeMove achieves a 1.3 × -2.1 × speedup over the state-of-the-art scheme. © 2023 ACM.

关键词： Internet of things

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Machine Learning to Enhance the V2V Communication-based IoD Using Companding Dataset 22

Machine Learning to Enhance the V2V Communication-based IoD ...

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22nd IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2025

作者： Daoud, O. Hamarsheh, Q. Damati, A. Al Jaafrah, S. Qasem, O. Al-Khawaldah, M. School of Engineering and Computing American International University Electrical and Computing Engineering Al-Jahra Kuwait Philadelphia University Faculty of Engineering and Technology Electrical Engineering Department Amman Jordan Researcher

ISBN: (纸本)9798331542726

This paper investigates the improvement of Vehicle-to-Vehicle (V2V) communications in the Internet of Drones (IoD) scenario for the reduction of the Peak-to-Average Power Ratio (PAPR) problem in Orthogonal Frequency Division Multiplexing (OFDM) systems. Motivated by the recent research on machine learning and deep neural network, this study will attempt to optimize Quality of Service (QoS) by means of a multiprocessing approach based on Daubechies wavelets. The proposed scheme integrates high-performance PAPR reduction schemes, such as μ-law companding, hybrid precoding-companding, and neural network-based adaptive algorithms. Deep reinforcement learning-driven approach in clipping and filtering is further proposed, which can offer real-time adaptation for the high-demanding communications. Results show a considerable gain in PAPR, over 60% PAPR reduction under high SNR conditions. These findings show that IoD can sustain stable V2V connections in urban use cases, thereby providing a scalable solution for 5G and beyond networks. © 2025 IEEE.

关键词： 5G and 6G ANN IoD Machine Learning OFDM

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Survey on Security Based Approach to Prevent Attacks in SDN Using IoT Framework 2

Survey on Security Based Approach to Prevent Attacks in SDN ...

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2nd International Conference on Edge computing and Applications, ICECAA 2023

作者： Prabhu, V. Balamurugan, P. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computer Science and Engineering Kattankulathur Tamilnadu Chengalpattu603 203 India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur Tamilnadu Chengalpattu603 203 India

ISBN: (纸本)9798350347579

Network evolution trendincludes Internet of Things (IoT) and Software-Defined Networking (SDN). By separating the data plane from the control plane, the software defined networking architectural framework is used and it will makes the network admins works more easily. SDN mainly focuses on network management and controls at the higher levels to monitoring and sharing the connection between the devices with the help of network connectivity in real time using the Internet of Things concepts. Almost in all the fields, IoT devices used so have to improve the security of IoT devices. The SDN has the centralized control logic, if the number of network traffic increases then centralized controller may not be handle all the network traffic requests at the same time, because the SDN controller is a single point of failure, its failure can bring the entire network down. The Software Defined Network (SDN) is easily exposed to various attacks mostly to DDoS attacks. Majority of the existing research is concentrated only on sensing of HR-DDoS attacks occurring over the control layer and not focusing on LR-DDoS attack occurring over the data layer because the accuracy of attacks on the LR-DDoS attacks are very low. This study mainly focusing on how to design the multi controller SDN architecture to improve the scalability, how to increase the detection accuracy of low-rate DDoS attacks on the SDN data layer and how to detect the attacks using machine learning algorithms. © 2023 IEEE.

关键词： Internet of things

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Optimizing Fabric Welding Using Image Processed Laser Inspection 5th

Optimizing Fabric Welding Using Image Processed Laser Inspec...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Ansari, Tabish Sekhar, Malepati Chandra Alam, Intekhab Bargavi, Manju Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India

ISBN: (纸本)9789819780426

Material welding is used in expanding industrial and industrial applications and depends on accuracy and precision to ensure satisfaction and protection. Laser inspection technology has been developed to help enhance the procedure's accuracy and performance by presenting rapid, reliable imaging that may be processed to decide where changes are wished. Through photograph processing, particular modifications to cloth welding can be recognized quickly and effectively. Additionally, photograph-processed laser inspection can hit upon any irregularities or defects in the welding method that can cause premature failure or decreased functionality. With this generation, designers and manufacturers can also develop higher control structures for his or her tasks, making more accurate and price-effective use of their assets. Sooner or later, the laser inspection era can offer actual-time remarks for automatic tactics, allowing changes to be made quickly with minimal disruption. Using this image processing technology, fabric welding groups can improve exceptional accuracy and value across merchandise. Optimizing material welding through image-processed laser inspection is a valuable method for controlling the pleasantness of welds. This technique uses a laser scanner, mild source, and a digital camera to seize detailed photos of the welds before and after welding. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Inspection

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FIREfly Project: Forest Fire Monitoring and Prediction Using Machine Learning

FIREfly Project: Forest Fire Monitoring and Prediction Using...

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2024 International Conference on Artificial Intelligence and Machine Learning Research, CAIMLR 2024

作者： Ong, Si Hui Ang, Hui Lun Lian, Elroy Tan, Timothy Tan, Kai Yang Tay, Emanuel Yau, Peter C.Y. Hesse, Henrik Woradit, Kampol Bhatia, Dinesh School of Computing Science University of Glasgow United Kingdom James Watt School of Engineering University of Glasgow United Kingdom Department of Computer Engineering Chiang Mai University Thailand School of Science Engineering & Technology RMIT University Viet Nam

ISBN: (纸本)9781510691797

Forest fires and extreme wildfire events pose a major threat to ecosystems worldwide. This paper implements various machine learning algorithms for the prediction of forest fires in Northern Thailand, a region which is severely impacted by fire events and the resulting pollution. Using publicly available satellite data of fires and weather information, two prediction models, namely the Random Forest and Support Vector Machines, were developed and tested for their accuracy in forecasting forest fire occurrences. Initial results indicate that both models have an accuracy of approximately 60% in predicting forest fires. The real-time prediction data based on current weather conditions is further displayed in a dashboard. The online dashboard has been integrated with Project FIREfly which is a collaboration with Chiang Mai University and the University of Glasgow to visualize real-time data of forest fires. Through the integration of the predictive models, the online dashboard is able to show the probability of forest fires which improves situational awareness for emergency response services and enables them to take proactive measures in managing forest fires. © 2025 SPIE.

关键词： Support vector machines

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A lightweight identity based generalized signcryption scheme for secure communication in standard model

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International Journal of Information technology (Singapore) 2024年第2期16卷 1221-1229页

作者： Singh, Tej Kumar, Mukesh Gelmecha, Demissie Jabir Thakur, Ganesh Kumar Tyagi, Vikas Sori, Worku Jifara Department of Applied Sciences ABES Engineering College UP Ghaziabad India Institute of Applied Sciences & Humanities GLA University UP Mathura India Department of Electronics and Communication Engineering School of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia Department of CSE IMS Engineering College UP Ghaziabad India Department of Computer Science School of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia

The implementation of secure communication in the Industrial Internet of Things (IIoT) has made and it is a significant security concern. Identity-based generalized signcryption scheme is one among the cryptographic tools available that offers a secure way out to meet the security needs of IIoT. Our proposal is a lightweight scheme, an efficient identity-based generalized signcryption that works as an encryption signature, or signcryption scheme using only one algorithm in a standard model. This scheme is appropriate for storage-limited environments like wireless sensor networks and smart cards due to its lightweight nature. This means that it can be implemented in a range of IIoT applications where resources are limited, without compromising on security or efficiency. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Bilinear Pairing Generalized signcryption Identity-based cryptography Signcryption

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In-Memory computing Accelerator for Iterative Linear Algebra Solvers

IEEE Computer Architecture Letters

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IEEE Computer Architecture Letters 2025年第1期24卷 161-164页

作者： Liu, Rui Li, Zerun Zhang, Xiaoyu Chen, Xiaoming Han, Yinhe Tang, Minghua Xiangtan University School of Materials Science and Engineering Xiangtan411105 China Chinese Academy of Sciences Institute of Computing Technology Beijing100190 China University of Chinese Academy of Sciences Beijing100190 China

Iterative linear solvers are a crucial kernel in many numerical analysis problems. The performance and energy efficiency of iterative solvers based on traditional architectures are severely constrained by the memory wall bottleneck. computing-in-memory (CIM) has the potential to enhance solving efficiency. Existing CIM architectures are mostly customized for specific algorithms and primarily focus on handling fixed-point operations, which makes them difficult to meet the demands of diverse and high-precision applications. In this work, we propose a CIM architecture that natively supports various iterative linear solvers based on floating-point operations. We develop a new instruction set for the accelerator, which can be flexibly combined to implement various iterative solvers. The evaluation results show that, compared with the GPU implementation, our accelerator achieves more than 10.1× speedup and 6.8× energy savings when executing different iterative solvers. © 2002-2011 IEEE.

关键词： Fixed point arithmetic

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Design and Analysis of Cumulative Regenerative System for a 42 kWh Battery Pack 1st

Design and Analysis of Cumulative Regenerative System for a ...

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1st International Conference on Energy and Materials Technologies , ICEMT 2021

作者： Sabareesh, M.C. Raja, R. Karthik Doss, Arockia Selvakumar Arockia Short, Michael School of Mechanical Engineering Vellore Institute of Technology Chennai India School of Electrical Engineering Vellore Institute of Technology Chennai India School of Computing Engineering and Digital Technologies Teesside University Middlesbrough United Kingdom

ISBN: (纸本)9789811934667

Electric vehicles (EV) have been the spotlight of automobile industry. Over the past few decades, major research and development are being carried out to design an optimal battery pack in order to establish an economically suitable EV model with enhanced the endurance range and by minimizing frequent charging cycles. This paper focuses on the endurance efficiency study of a battery pack using cumulative regenerative technology analysis of battery pack discharge analysis, control system analysis of regenerative braking model considering voltage, torque and time as parameters during the deceleration of vehicle during braking, control system analysis for regenerative suspension, control system analysis of solar charging module based on assumed solar panel configuration, solar irradiance analysis of various states of different countries, SOC comparative analysis of battery pack with and without regeneration are also studied. In addition, this research also addresses the configuration and numerical study of battery pack model. It also describes the MATLAB-Simulink modeling for the design and analysis of control systems for the battery pack charging with and without the individual regenerative systems. The designed and configured 4s5p battery pack was found to be satisfactory. The study of cumulative regenerative model yielded satisfactory results with an overall increase in endurance efficiency by 39.95%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Automotive industry

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Parallel Software-Based Self-Testing with Bounded Model Checking for Kilo-Core Networks-on-Chip

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Journal of Computer Science & technology 2023年第2期38卷 405-421页

作者：张颖季鹏飞朱潘玮 Zebo Peng 李华伟江建慧 School of Software Engineering Tongji UniversityShanghai 200092China Department of Computer and Information Science Linkoping UniversityLinkoping 58183Sweden Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Online testing is critical to ensuring reliable operations of the next generation of supercomputers based on a kilo-core network-on-chip(NoC)interconnection *** present a parallel software-based self-testing(SBST)solution that makes use of the bounded model checking(BMC)technique to generate test sequences and parallel *** this method,the parallel SBST with BMC derives the leading sequence for each router’s internal function and detects all functionally-testable faults related to the function.A Monte-Carlo simulation algorithm is then used to search for the approximately optimum configuration of the parallel packets,which guarantees the test quality and minimizes the test ***,a multi-threading technology is used to ensure that the Monte-Carlo simulation can reach the approximately optimum configuration in a large random space and reduce the generating time of the parallel *** results show that the proposed method achieves a high fault coverage with a reduced test ***,by performing online testing in the functional mode with SBST,it effectively avoids the over-testing problem caused by functionally untestable turns in kilo-core NoCs.

关键词： software-based self-testing(SBST) parallel test kilo-core networks-on-chip(NoCs) online testing

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