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Fraud Detection in Financial Transactions using Machine Learning - A Comparative Study 3

Fraud Detection in Financial Transactions using Machine Lear...

引用

3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Jayanthkumar, Avati Sathwik, Mayakuntla Sai Reddy, Shagam Srinivasa Kumar, Donadula Mounish Samir, Chougule Saish Patni, Jagdish Chandra Bahadure, Nilesh Kumar, Rohit Alliance School of Advanced Computing Alliance University Dept. of Computer Science Bengaluru India School of Technology Gsfc University Department of Computer Science and Engineering Vadodara India

ISBN: (纸本)9798331518592

Data analysis techniques using machine learning approaches are the most prominent when we are considering the online fraud, it may be a credit card fraud or any other online fraud in the banking system. Using Machine Learning techniques, we can easily manage the information of fake transactions by extensive data analysis of time, location and amount in the real time. This paper gives the comprehensive study of the changing scene of the fraud discovery in monetary exchanges, investigating the most recent patterns, advances, and techniques utilized by financial institutions and other administrative bodies. An extensive review of various machine learning techniques has been done to find out the effective solution to protect the online fraud. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimizing Telecom Operations with Segmentation and Churn Prediction 3

Optimizing Telecom Operations with Segmentation and Churn Pr...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Neha, Dandu Ameesha, Mohammed Dheeraj, Mukka Sangeeta, K. Dept.Computer Science and Engineering Institute of Aeronautical Engineering Telangana Hyderabad India Institute of Aeronautical Engineering Telangana Hyderabad India

ISBN: (纸本)9798331527549

Client turnover is a significant issue for large organizations, particularly within the telecom sector, where it has a direct impact on profitability. To address this, telecom providers are increasingly focusing on developing predictive models to forecast customer churn and reduce its adverse effects. Identifying the key factors influencing churn is critical for implementing effective retention strategies. This research presents the development of a churn prediction model designed to help telecom companies identify customers at a high risk of leaving. The study involves exploring various data analysis techniques and presenting the results through graphical visualizations. Using machine learning methods, the model leverages large-scale datasets and introduces an innovative approach to feature design and selection. Historical customer data from previous months was utilized for training, evaluation, and validation of the model. Four machine learning algorithms were tested: Logistic Regression, XGBoost, Gradient-Boosted Machines (GBM), Random Forest, and Decision Trees. To further improve model accuracy, parameter tuning was applied during the training phase. The primary objective of this work is to enhance telecom operations by optimizing customer segmentation and churn prediction, ultimately improving both operational efficiency and customer retention. © 2025 IEEE.

关键词： Sales

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SCALABLE HPC JOB SCHEDULING AND RESOURCE MANAGEMENT IN SST

SCALABLE HPC JOB SCHEDULING AND RESOURCE MANAGEMENT IN SST

引用

2024 Winter Simulation Conference, WSC 2024

作者： Abdurahman, Abubeker Hossain, Abrar Brown, Kevin A. Yoshii, Kazutomo Ahmed, Kishwar Dept of Electrical Engineering and Computer Science The University of Toledo ToledoOH United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States

ISBN: (纸本)9798331534202

Efficient job scheduling and resource management contributes towards system throughput and efficiency maximization in high-performance computing (HPC) systems. In this paper, we introduce a scalable job scheduling and resource management component within the structural simulation toolkit (SST), a cycle-accurate and parallel discrete-event simulator. Our proposed simulator includes state-of-the-art job scheduling algorithms and resource management techniques. Additionally, it introduces a workflow management components that supports the simulation of task dependencies and resource allocations, crucial for workflows typical in scientific computing and data-intensive applications. We present validation and scalability results of our job scheduling simulator. Simulation shows that our simulator achieves good accuracy in various metrics (e.g., job wait times, number of nodes usage) and also achieves good parallel performance. © 2024 IEEE.

关键词： Job shop scheduling

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LG-FAL: Locality-customized GSA Federated Active Learning 15

LG-FAL: Locality-customized GSA Federated Active Learning

引用

15th IEEE International Conference on Knowledge Graph, ICKG 2024

作者： Wang, Shuwen Zhu, Xingquan Portland State University Department of Computer Science Portland United States Florida Atlantic University Dept. of Electrical Engineering & Computer Science Boca Raton United States

ISBN: (纸本)9798331508821

Federated Learning (FL) is a learning paradigm which constructs machine learning models using decentralized datasets, with the goal of training a globally optimized model while preserving data privacy. In practice, annotating training data for FL is expensive and time-consuming. To solve this problem, researchers proposed to integrate active Learning (AL) strategy as Federated Active Learning (FAL) framework. However, existing research on FAL has two main limitations: (1) In the active learning part, more attention is paid to the global model, while ignoring the local information;(2) In the federated learning part, the popular Federated averaging (FedAvg) method relies on the assumption that the corresponding nodes in distributed neural networks share the same importance when averaging. In this paper, in order to tackle the two limitations, we propose a locality-customized GSA federated active learning (LG-FAL) method. Specifically, (i) both local and global information are taken into consideration to evaluate and select informative data samples to annotate for active learning;(ii) we integrate Gravitational Search Algorithm (GSA) to dynamically average local network parameters into the global parameter, by mimicking the principles of gravity and motion in the universe, for effective federated learning. As a result, LG-FAL can select a small subset of informative samples considering both local and global information, and at the same time, provide an improved trade-off between communication cost and learning accuracy. Experimental results show that LG-FAL significantly outperforms the current state-of-the-art baselines in terms of performance and effectiveness. © 2024 IEEE.

关键词： Federated learning

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Electronic Market for Farmers 14

Electronic Market for Farmers

引用

14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Vidhya, S.G. Dhruva, M.S. Gowda, Nisha J. Shilpa Shree, H. Gowda, Shravana P. Dept. of Information Science and Engineering BGSIT India Dept. of Computer Science and Engineering BGSIT India

The Covid-19 pandemic is a global health crisis that has affected everyone. One negative impact has left local farmers struggling to find other means of income besides marketing their produce to customers. It is difficult for customers to find good quality fresh goods in super markets. The objective of this paper is two-part: first, to evaluate the existing and manual system of how consumers buy their fresh produce during the Covid19 pandemic, to design the and implement the delivery system for local farmers to sell fresh product through mobile app. © Grenze Scientific Society, 2023.

关键词： Sales

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Personal AI Trainer Using Two-Way Mirror and Raspberry Pi

Personal AI Trainer Using Two-Way Mirror and Raspberry Pi

引用

2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Singh, Ratan Tejnarayan Wasnik, Aarush Mistry, Krishna Modi, Jayen Koshti, Dipali Fr. Conceicao Rodrigues College of Engineering Dept. of Electronics and Computer Science Mumbai India

ISBN: (纸本)9798331512965

A novel smart mirror system is proposed to enhance users' daily routines by integrating advanced technologies. Utilizing a Raspberry Pi, an electronic display, a two-way mirror, a camera, and control mechanisms powered by OpenCV and MediaPipe, the system provides personalized interactions through facial recognition. This feature allows the smart mirror to identify users and tailor its responses accordingly. Beyond its aesthetic appeal, the system functions as an interactive fitness coach, guiding users through workouts, correcting their form, and delivering real-time feedback. This paper explores the potential of smart mirrors to revolutionize personal fitness and care, offering an innovative approach to interactive user engagement. © 2025 IEEE.

关键词： Face recognition

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Comparative Advances in Financial Sentiment Analysis:A Review of BERT,FinBert, and Large Language Models 3

Comparative Advances in Financial Sentiment Analysis:A Revie...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mahendran, Manish Barath Gokul, Aswin Kumar Lakshmi, Poornima Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

Capturing market sentiments and supporting well-informed financial decision-making depend on the developing field of Financial Sentiment Analysis (FSA). Natural language processing (NLP) has made significant strides in comprehending and categorizing sentiment in intricate financial texts, especially with the use of Large Language Models (LLMs). The application of various LLMs, such as Bidirectional Encoder Representations form Transformers(BERT) and Financial BERT (FinBERT), as well as distilled models, such as DistilBERT and DistilRoBERTa, on a variety of financial datasets, including Financial Phrase Bank and LexisNexis news articles, was the main focus of this review article. highlighting different approaches such as model fine-tuning, zero-shot and few-shot learning, and prompt engineering. The study focuses on practical model predictions through a case study of sentimental analysis of cryptocurrencies. While FinBERT, a financial variant of BERT, exhibits high accuracy and robustness, other LLMs exhibit varying degrees of success based on the dataset and domain requirements. The analysis concentrates on the difficulties, compromises, and potential paths for improving LLMs for financial sentiment analysis. © 2025 IEEE.

关键词： Zero-shot learning

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Efficient Host Intrusion Detection using Hyperdimensional Computing

Efficient Host Intrusion Detection using Hyperdimensional Co...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Nam, Yujin King, Rachel Burke, Quinn Zhou, Minxuan McDaniel, Patrick Rosing, Tajana Uc San Diego Dept. of Computer Science and Engineering La Jolla United States University of Wisconsin-Madison Dept. of Computer Sciences Madison United States Illinois Institute of Technology Computer Science Department Chicago United States

ISBN: (纸本)9798350362480

Modern host-based intrusion detection systems (HIDS) rely on querying provenance graphs - graph representations of activity history on a system - to detect and respond to security threats present on a system. However, as the complexity and number of applications running on a system increase, the size of provenance graphs also increase, and thus the latency to query them. State-of-the-art designs deliver query latencies that are impractical for modern threat detection. In this paper, we introduce a hyper-dimensional computing (HDC) approach to querying provenance graphs for HIDS. By encoding provenance graphs and attack patterns/signatures into hyper-dimensional vectors, we can implement a query engine using simple vector operations. Our approach is hardware accelerator compatible, providing further speedups under resource-constrained environments. Our evaluation on a real-world dataset shows that our approach achieves > 90% detection accuracy and up to 4, 242× speedups over the state-of-the-art. This shows that HDC-based approaches can effectively deal with scaling issues in modern HIDS. © 2024 IEEE.

关键词： Network intrusion

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Detecting and Mitigating Malicious TPAs in Cloud-Based Data Storage Through Public Auditing 7

Detecting and Mitigating Malicious TPAs in Cloud-Based Data ...

引用

7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Balaram, Pawar Ankush Jyoti, Baviskar Kashmira, Dalvi Computer Science and Engineering Vishwaniketan's iMEET Affiliated to Mumbai University Mumbai India SIES Nerul Affiliated to Mumbai University Computer Engineering Dept. Mumbai India

ISBN: (纸本)9798331534271

The concept of data auditing has been developed due to the increasing concern about the safety of data stored in the cloud which can be attacked externally or modified without authorization. The main objective of this work is to involves assessing data integrity through a Third-Party Auditor (TPA). The audit request triggers an Anomaly Detection module which analyzes patterns to identify any unusual or malicious behavior. This study focuses on identifying and mitigating malicious TPAs in cloud-based data storage using privacy-preserving public auditing. Simulation results carried out using Python tool. The improvement enables TPA to handle multiple authorization audits concurrently thus enhancing the overall efficiency of the auditing system. The suggested system is both secure and efficient, according to a thorough evaluation of its performance and security. © 2024 IEEE.

关键词： Cloud storage

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Factors Affecting Mental Health of High School Students due to COVID-19: An Exploratory Study

Factors Affecting Mental Health of High School Students due ...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Reza, Md. Rokonuzzaman Islam, Muhammad Nazrul Islam, Mohian Mumu, Afnan Alauddin Faisal, Faiz Al The International University of Scholars Dept. of Computer Science and Engineering Dhaka Bangladesh Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh LTIMindtree Ltd. Washington United States Green University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350316001

Mental health is one of the most significant factors in the human life span. It also influences how we interact with people, manage stress, and make good decisions. From childhood and youth through maturity, mental health is crucial at every stage of life. In the era of the COVID-19 pandemic, it becomes more vulnerable and affects teenagers as others. In this study, high school students' (10-19 years old) mental health states and the most contributing factors that affect their mental health were assessed through a statistical and machine learning-based approach. The study data were collected through an online survey and received responses from 158 respondents. The data were analyzed statistically with a chi-square test and path analysis considering all the possible factors of anxiety and depression to determine the most contributing factors. Besides, a few Machine Learning (ML) algorithms have been developed such as Logistic Regression(LR), Support Vector Machine(SVM), Naive Bayes(NB), AdaBoost(AB), and Random Forest(RF) for the prediction of anxiety and depression and found that AdaBoost achieved the highest score (86%). As outcomes, the overall analysis revealed that academic concerns such as poor understanding of online classes, daily activities like sleep pattern disruption, and extended uses of electronic devices are the major causes of the deterioration of high school students' mental health. © 2023 IEEE.

关键词： COVID-19

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