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Using Unsupervised Learning to detect Fraud in Cryptocurrency 3

Using Unsupervised Learning to detect Fraud in Cryptocurrenc...

引用

3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Chordia, Sankalp Nilesh Shinde, Sandeep Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350354348

Bitcoin, the most popular form of cryptocurrency, operates independently of central banks or governmental entities. The emphasis on anonymity complicates the task of tracing cryptocurrency payments linked to illicit activities or criminal transactions. Consequently, ransomware developers exploit this environment to extort ransom by encrypting valuable user information. Numerous research endeavors have explored the application of machine learning techniques to detect fraudulent activities within cryptocurrency transactions. However, there is a notable absence of studies showcasing the practical implementation of such methods using real-time blockchain data. The present study is structured into two main sections. Firstly, we delineate the development of various features utilized for training purposes. Subsequently, our approach will be validated using real-time Bitcoin data. The other section highlights the use of open source investigation tool (OSINT) to carry forward the cryptocurrency investigation by taking the input from previous findings. OSINT involves the retrieval of information from a range of publicly accessible data sources, while offering a viable approach to address the difficulties associated with extracting and aggregating intelligence from diverse publicly available information channels and social platforms. Thus narrowing down the search investigation by exploiting the pseudonymity of Bitcoin and tracing back the wallet and IP addresses' digital and physical identity. © 2024 IEEE.

关键词： Adversarial machine learning

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HealthVault: A Blockchain-based Patient Health Records Management System 9

HealthVault: A Blockchain-based Patient Health Records Manag...

引用

9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Jaybhaye, Sangita Desai, Rutuja Choudhari, Gauri Dashetwar, Isha Dagale, Pratik Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350394474

This paper describes a blockchain-based patient record management system that combines a React online user interface with a MongoDB database, IPFS, Hardhat, MetaMask, IPFS smart contracts, and the Ethereum blockchain. Existing systems are often subject to issues such as single-point failures, data loss, high costs and limited scalability. Failure of a server or data center can lead to data loss and system downtime. The proposed work intends to address problems with existing patient records management systems, including data security, privacy, and accessibility. The system uses Ethereum decentralized blockchain with smart contracts to store and manage patient records ensuring data privacy, security and immutability. Ether is the crypto-currency used to pay transaction fees. MetaMask is used to facilitate secure transactions between users and the blockchain network whereas IPFS is used to store large files such as medical images, videos and documents. The web interface provides an intuitive and user-friendly platform for healthcare providers and patients to interact with the system and manage their records. The proposed system provides a secure and efficient way to manage patient records enabling healthcare providers to easily access patient records while maintaining the privacy and security of sensitive medical information. © 2024 IEEE.

关键词： Smart contract

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Traffic Density Prediction System using Deep Learning 2

Traffic Density Prediction System using Deep Learning

引用

2nd International Conference on Data Science and Information System, ICDSIS 2024

作者： Verma, Hritik Sarkar, Sayan Sadhukhan, Souranil Mishra, Aniket Mondal, Md Ashifuddin Narula Institute Of Technology Department Of Computer Science And Engineering India

ISBN: (纸本)9798350381665

In major cities throughout the world, traffic congestion is still a major problem that negatively affects the efficiency of transportation system. Traffic congestion creates several issues that require a creative and adaptable strategy to solve. This work tackles the problem by accurately forecasting vehicle density by merging state-of-the-art deep learning models, specifically YOLO - v8 for real-time vehicles identification and Stacked Long Short-Term Memory (LSTM) models for forecasting traffic density. These methods are combined to create a powerful vehicle density prediction system. The integration of YOLOv8 with LSTM provides a practical response to the growing issues brought on by traffic congestion in cities. The simulation results shows that accuracy of YOLOv8 is 95.36, while average RMSE value of Stacked LSTM models is 0.591. The proposed model is compared with RNN model for vehicle density forecasting and the proposed model performs better than RNN in terms of accuracy. © 2024 IEEE.

关键词： Traffic congestion

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Smart Business with Intelligent Business Cards (SBIBC)

Smart Business with Intelligent Business Cards (SBIBC)

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Kirubakaran, N. Preethi, S.T. Parijatham, R. Pramithi, R. Chennai Institute of Technology Department of Computer Science and Business Systems Chennai India S.A. Engineering College Department of Computer Science and Engineering Chennai India Madha Institute Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

The research emphasizes the creation of a powerful and efficient system for the automaticextraction of contact information from physical calling cards through computer vision and information extraction techniques. This is achieved by integrating Tesseract Optical Character Recognition (OCR) with the YOLO (You Only Look Once) object detection algorithm, which acts as a dynamic combination for text and relevant elements identification and digitization from calling cards. The developed system uses client-server architecture to enable communication and data processing seamlessly. The server handles computationally intensive tasks such as object detection, OCR processing, and data validation while the client side has an interactive user interface that enables easy uploading of images and retrieval of structured contact information. The optimizations include this: YOLO is customized for detecting particular card elements (name, phone number, email) and enhancing the capabilities of Tesseract OCR to improve effectiveness for text extraction from varied card formats or fonts in the system. It is designed to be scalable and efficient, processing large datasets without affecting accuracy. This approach not only eases the digitization of calling card details into databases but also goes a long way in contributing to the domains of computer vision, object detection, and mechanized information extraction. It extends the scope to broad applications in automated data entry, customer relationship management (CRM), and digital archival. It takes forward for AI-driven sustaining technologies the integration of these techniques into practical solutions by overcoming the challenges of data extraction from real-world images. © 2024 IEEE.

关键词： Optical character recognition

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Optimizing NOMA Transmissions to Advance Federated Learning in Vehicular Networks

Optimizing NOMA Transmissions to Advance Federated Learning ...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Chen, Ziru Ni, Zhou Guan, Peiyuan Wang, Lu Cai, Lin X. Hashemi, Morteza Li, Zongzhi Illinois Institute of Technology Department of Electrical and Computer Engineering Chicago United States University of Kansas Department of Electrical Engineering and Computer Science Lawrence United States University of Oslo Department of Informatics Oslo Norway Illinois Institute of Technology Department of Civil Engineering Chicago United States

ISBN: (纸本)9798350351255

Diverse critical data, such as location information and driving patterns, can be collected by IoT devices in vehicular networks to improve driving experiences and road safety. However, drivers are often reluctant to share their data due to privacy concerns. The Federated Vehicular Network (FVN) is a promising technology that tackles these concerns by transmitting model parameters instead of raw data, thereby protecting the privacy of drivers. Nevertheless, the performance of Federated Learning (FL) in a vehicular network depends on the joining ratio, which is restricted by the limited available wireless resources. To address these challenges, this paper proposes to apply Non-Orthogonal Multiple Access (NOMA) to improve the joining ratio in a FVN. Specifically, a vehicle selection and transmission power control algorithm is developed to exploit the power domain differences in the received signal to ensure the maximum number of vehicles capable of joining the FVN. Our simulation results demonstrate that the proposed NOMA-based strategy increases the joining ratio and significantly enhances the performance of the FVN. Index Terms - Federated Vehicular Network, NOMA © 2024 IEEE.

关键词： Data privacy

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Harnessing Natural Entity Recognition for Intuitive Spreadsheet Control: A Voice-Driven Google Sheets Extension 5

Harnessing Natural Entity Recognition for Intuitive Spreadsh...

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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Joglekar, Pushkar Jha, Divyanshu Dhorage, Prathamesh Thakkar, Dhruv Dhumal, Om Department of Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798350361155

This paper introduces an innovative Voice-Controlled Google Sheets Extension employing Natural Language Processing (NLP) capabilities. The system seamlessly integrates a Chrome extension and a meticulously trained Named Entity Recognition (NER) model. Trained on custom datasets, the NER model proficiently extracts crucial entities, such as command types, cell references, and values from user speech, enabling accurate interpretation of complex commands. The extension enhances user interaction with spreadsheets, offering a voice-driven interface that simplifies spreadsheet management. The backend, utilizing Flask-based REST APIs, facilitates seamless communication with the Google Sheets API, ensuring precise execution of actions dictated by the interpreted commands. Experimental results underscore the system's accuracy and efficiency, establishing its potential as an intelligent productivity tool. This research contributes to the evolution of smart computing interfaces, providing users with an intuitive and accessible means of interacting with spreadsheet applications. © 2024 IEEE.

关键词： Natural language processing systems

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An Experimental Analysis of Machine Learning Model in the Context of Sales Forecasting: -A Walmart Dataset as A Case Study 15

An Experimental Analysis of Machine Learning Model in the Co...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Wankhede, Disha Athanikar, Abhishek Borude, Yash Chougule, Raviraj Diware, Om Department of Computer Engineering Vishwakarma Institute of Information Technology India

ISBN: (纸本)9798331300579

Software programmers may get gradually precise at expecting consequences without being clearly coded using machine learning techniques. Machine learning is based on the idea that models and algorithms may collect input data then utilize statistical investigation to determine an output, while updating results as information become obtainable, as underlying principle. For example, models may be adapted and trained to meet management expectations so that correct measures are followed to reach a certain goal. Wall Mart, a one-stop shopping mall, has been used in this system to estimate sales of various products and to study impact that various variables have on sales of products. Predictive models may be built using different features of a Wall Mart dataset and methods used to construct them, and these findings can be used to make better business choices. This Predictive Algorithm is used in many fields over the market world for their profitability. Recently, Lot of companies are going to used this Machine Learning for better AI and new technologies in market. This report Includes combination of machine learning algorithm and Features engineering to predict Walmart sales. The research topic of this paper primarily solves the problems of Walmart sales forecasting that is provided by the Kaggle competition. © Grenze Scientific Society, 2024.

关键词： Input output programs

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Improved Energy Efficient Divergent Path Routing for Energy-Harvesting Wireless Sensor Networks 9

Improved Energy Efficient Divergent Path Routing for Energy-...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Sivakumar, S. Sabaresan, V. Prakash, S. Om Dharmaraj, T.B. Nehru Institute of Engineering and Technology Department of Computer Science and Engineering India St.Joseph's Institute of Technology Department of Computer Science and Engineering India Ppg Institute of Technology Department of Mechanical Engineering India Ppg Institute of Technology Department of Information Technology India

ISBN: (纸本)9798350377972

Since packets are dropped when there is an insufficient connection between each of the sensor nodes in the network, a sensor node in a sensor network is unable to efficiently aggregate the data packets. Since each area has a different sensor node location, the sender node rarely shares information with its neighbour. Only a certain number of packets are received for each communication, the rest are dropped, and the communication is lost. In order to determine each node's minimum residual energy level, the node transmits packets on the path once more. It raises the energy consumption and packet loss rate. Energy-efficient packet aggregation along the network environment is the goal of the suggested Improved Energy Efficient Divergent Path Routing (IEEDP) technique. The node's position is clearly tracked before a different route is assigned for routing. Using metrics such as end-to-end delay, overhead for communication, packet delivery ratio, link stability, consumption of energy, packet loss, and network lifetime using a simulation model, the suggested improved energy efficient divergent path routing (IEEDP) approach outperformed existing methods like Wireless Access Range (WAR) and Congestion-Aware Clustering And Routing (CCR). Energy preservation is a process to extend the network's lifetime by examining the residual Energy Effects on EH-WSN. The Combining Packet Aggregation algorithm is made for sensor networks and collects data packets without any loss during the entire transmission period from the origin node to a target node in a network environment. It lowers energy consumption and packet loss rate. © 2024 IEEE.

关键词： Packet loss

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Real-Time Traffic Management Using Deep Learning and Object Detection using YOLOv8 15

Real-Time Traffic Management Using Deep Learning and Object ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Phatangare, Sheetal Sakpal, Rahul A. Kasurde, Soham N. Punde, Shubhan S. Khan, Saif S. Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

As urban traffic management challenges continue to grow, there is an urgent demand for advanced technologies to improve real-time monitoring and control systems. This study introduces an innovative method for realtime traffic management by combining deep learning with YOLOv8-based object detection. By leveraging the speed and accuracy of YOLOv8, our system achieves precise identification of vehicles and pedestrians in dynamic urban settings. The model seamlessly integrates with existing surveillance infrastructure, allowing for intelligent analysis of live video streams to monitor traffic flow and congestion. The system's adaptability is showcased through training on diverse datasets, and its impact extends to optimizing traffic signal control for enhanced transportation efficiency. Results suggest the potential of our approach to transform urban traffic control systems with improved real-time decision-making capabilities. © 2024 IEEE.

关键词： computer Vision Intelligent transport systems Machine Learning Object detection Real-time traffic management Traffic flow analysis Traffic management YOLOv8

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Decentralized Music Platform: A Blockchain-based Solution for NFT Music Distribution and Royalty Management 5

Decentralized Music Platform: A Blockchain-based Solution fo...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Dhore, M.L. Kate, Sumedh Khandelwal, Dipkul Khandetod, Arya Kharade, Aishwarya Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350367171

Blockchain technology, which rests on impermeable, decentralized ledgers, has found use in a variety of fields beyond its original connection to cryptocurrencies. Its ability to create safe, readable, and unchangeable records is its core strength. The proposed research work presents a novel approach to music distribution that makes use of blockchain technology to enable artists to sell their work directly to customers, doing away with middlemen. To provide secure and transparent transactions, the system uses a decentralized ledger to record and authenticate music transactions. Automated royalty distribution made possible by smart contracts ensures that artists receive just pay. Musicians have more control over their creative property and income by eschewing conventional middlemen. Additionally, the platform incorporates an easy-to-use interface that allows artists to upload, manage, and monetize from their works with ease. This creative strategy reduces concerns of money leakage and piracy while promoting direct relationships between musicians and fans, resulting in a more egalitarian music market. © 2024 IEEE.

关键词： Smart contract

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