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Fraud Detection in Online Transactions Using Machine Learning 1

Fraud Detection in Online Transactions Using Machine Learnin...

引用

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Rathore, Rachna Singh, Narayan Prajapat, Dilip Yadav, Arti Vashisht, Manisha Dharshini, P. Priya Jiwaji University Dept. of Mathematics Gwalior India Madhav University Dept. of Electronic Engineering Pindwara India J.S. University Dept. of Computer Science Shikohabad India Lingayas Vidyapeeth Dept. of CSE Faridabad India Meenakshi A.H. Edu. & Research Dept. of English Chennai India

ISBN: (纸本)9798350356816

In the evolving landscape of online transactions, fraud detection remains a critical challenge due to the increasing sophistication of fraudulent activities. This paper explores the application of machine learning techniques to enhance fraud detection in online transactions. Various algorithms, including decision trees, random forests, support vector machines, and neural networks, are investigated to identify and mitigate fraudulent behavior effectively. The study utilizes a large dataset of transaction records, employing feature engineering to extract relevant attributes and enhance model accuracy. Through comparative analysis, it is demonstrated that ensemble methods, particularly random forests and gradient boosting, outperform traditional techniques in detecting fraudulent patterns. Additionally, the implementation of real-time detection systems using these models shows promising results in minimizing false positives and reducing financial losses. The findings highlight the potential of machine learning to offer robust, scalable solutions for fraud detection, contributing to more secure online transaction environments. © 2024 IEEE.

关键词： Adversarial machine learning

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A Blockchain based framework for Lending Digital Assets implemented using NFT

A Blockchain based framework for Lending Digital Assets impl...

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2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Darshan, M. Raswanth, S.R. Kumar, Priyanka Srivastava, Gautam Amrita School of Computing Dept. of Computer Science and Engineering Coimbatore India University of Texas at San Antonio Department of Computer Science San Antonio United States Brandon University Department of Mathematics and Computer Science Brandon Canada

ISBN: (纸本)9798350331790

In recent times, digital assets have been on the rise. Crypto-enthusiasts have been investing in this market segment for quite some time. The returns from these investments for the investors cannot be fathomed. Blockchain Technology is one of the main pillars of the Industry 4.0 revolution. As of now, the entities can buy or sell digital assets but the ability to lend/rent a digital asset is still a question that needs to be addressed. With this motivation, we have proposed a framework as a solution that will allow owners to lend their digital assets to buyers by determining the ownership rights of the asset dynamically. This research work presents the comprehensible approach to the buyer-seller protocol using NFT smart contracts and blockchain technology, to provide proof of ownership and secure transferability in the marketplace. © 2023 IEEE.

关键词： Blockchain

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Modified EfficientNetB3 Deep Learning Model to Classify Colour Fundus Images of Eye Diseases 5

Modified EfficientNetB3 Deep Learning Model to Classify Colo...

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5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2023

作者： Sharma, Riya Gangrade, Jayesh Gangrade, Shweta Mishra, Ashish Kumar, Gautam Kumar Gunjan, Vinit Manipal University Jaipur Dept of Computer Science & Engineering Jaipur India Learning Manipal University Jaipur Dept. of Artificial Intelligence & Machine Jaipur India Manipal University Jaipur Dept of Information Technology Jaipur India Jaypee Institute of Information Technology Dept of Computer Science & Engineering Noida India CMR Institute of Technology Department of Computer Science & Engineering Hyderabad India

ISBN: (纸本)9798350338287

The only way to prevent blindness from eye problems is by early detection and prompt treatment. Although colour fundus photography (CFP) is useful for fundus inspection, there is a need for computer-assisted automated diagnosis tools due to the similarities between the early symptoms of many eye disorders. The suggested approach uses cutting-edge deep learning model to categorize images into several disease categories by learning distinguishing features from the input images. The high-resolution fundus photos from individuals with diabetic retinopathy (DR), glaucoma, cataract, and healthy eyes make up most of the dataset used for this research. The experimental findings show that the suggested system achieve 97% accuracy with modified efficientNetB3 model and surpasses current approaches for categorizing eye diseases. This approach may help doctors diagnose and treat eye conditions earlier, leading to better patient outcomes. © 2023 IEEE.

关键词： Color

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Improved Vehicle Sub-type Classification for Acoustic Traffic Monitoring

Improved Vehicle Sub-type Classification for Acoustic Traffi...

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2023 National Conference on Communications, NCC 2023

作者： Ashhad, Mohd Goenka, Umang Jagetia, Aaryan Akhtari, Parwin Ambat, Sooraj K. Samuel, Mary Jamia Hamdard Dept. of Computer Science & Engineering New Delhi India IIIT Lucknow Dept. of Information Technology Lucknow India Nit Rourkeela Dept. of Mechanical Engineering Roorkela India Defence R&d Organization Naval Physical & Oceanographic Laboratory Kochi India IIIT Lucknow Dept. of Mathematics Lucknow India

ISBN: (纸本)9781665456258

The detection and classification of vehicles on the road is a crucial task for traffic monitoring. Usually, computer Vision (CV) algorithms dominate the task of vehicle classification on the road, but CV methodologies might suffer in poor lighting conditions and require greater amounts of computational power. Additionally, there is a privacy concern with installing cameras in sensitive and secure areas. In contrast, acoustic traffic monitoring is cost-effective, and can provide greater accuracy, particularly in low lighting conditions and in places where cameras cannot be installed. In this paper, we consider the task of acoustic vehicle sub-type classification, where we classify acoustic signals into 4 classes: car, truck, bike, and no vehicle. We experimented with Mel spectrograms, MFCC and GFCC as features and performed data pre-processing to train a simple, well optimized CNN that performs well at the task. When used with MFCC as features and careful data pre-processing, our proposed methodology improves upon the established state-of-the-art baseline on the IDMT Traffic dataset with an accuracy of 98.95%. © 2023 IEEE.

关键词： Machine learning

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Toward Project Success Forecasting in Reward-based Crowdfunding through Wide-and-Deep Computational Learning 15

Toward Project Success Forecasting in Reward-based Crowdfund...

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15th IEEE International Conference on Industry Applications, INDUSCON 2023

作者： Petchhan, Jirayu Phanichraksaphong, Varinya Doungtap, Surasachai Tridet, Natthanicha Dept. of Electrical Engineering National Taiwan University of Science & Technology Taiwan Dept. of Electronic National Taipei University of Technology Taiwan Dept. of Computer Science and Information Engineering National Taipei University of Technology Taiwan Dept. of Business Management National Taipei University of Technology Taiwan

ISBN: (纸本)9798350314182

While reward-based crowdfunding platforms centered like Kickstarter in the United States, allow entrepreneurs or well-off investors like industrial owners to gather assets and seek patron support, they have evolved into an option for generating funds via an internet platform. Conversely, these crowdfunding initiatives pose innovation challenges due to the uncertainty of receiving support and success. Both project developers and supporters may benefit from it for better knowledge and more accurate forecasting of project success. Analyzing a completed project information, this study aims to identify critical success characteristics of crowdfunding. It investigates the effectiveness of wide-and-deep learning in predicting models. These findings can help fundraisers understand the likelihood of successful fundraising initiatives. © 2023 IEEE.

关键词： Kickstarter Project Success Prediction Reward-based Crowdfunding Wide-and-Deep Learning

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Deep Learning-and Transfer Learning-based Models for COVID-19 Detection using Radiography Images

Deep Learning-and Transfer Learning-based Models for COVID-1...

引用

2023 International Conference on Advances in Electronics, Control and Communication Systems, ICAECCS 2023

作者： Mazari, Ahmed Cherif Kheddar, Hamza University of Medea Lsea Lab Dept. of Mathematics and Computer Science Faculty of Science Medea2600 Algeria University of Medea Lsea Lab Dept. of Electrical Engineering Faculty of Technology Medea2600 Algeria

ISBN: (纸本)9781665463102

Image classification and segmentation techniques are still very popular in the medical field (for healthcare), in which the medical image plays an important role in the detection and screening of diseases. Recently, the spread of new viral diseases, namely Covid-19, requires powerful computer models and rich resources (datasets) to fight this phenomenon. In this study, we propose to examine the CNN Deep Learning algorithm and two Transfer Learning models, namely RestNet50 and MobileNetV2 using the pretrained model of the ImageNet database, experimented on the new dataset (COVID-QU-Ex Dataset 2022) offered by the University of Qatar. These models are tested to classify radiography images into two classes (Covid19 and Normal). The results achieved by CNN (Acc =95.97%), ResNet50 (Acc =95.53%) and MobileNetV2 (Acc =97.32%) show that these algorithms are promising in order to combat this Covid-19 disease by detecting it through thoracic images (Chest X-ray type). © 2023 IEEE.

关键词： Image classification

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The Integration of 6G and Blockchain into an Efficient AIoT-Based Smart Education Model 6

The Integration of 6G and Blockchain into an Efficient AIoT-...

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6th World Symposium on Communication engineering, WSCE 2023

作者： Lioupa, Anna Memos, Vasileios A. Stergiou, Christos L. Ishibashi, Yutaka Psannis, Konstantinos E. School of Information Sciences University of Macedonia Dept. of Applied Informatics Thessaloniki Greece Graduate School of Engineering Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan

ISBN: (纸本)9798350339505

The technological advances impact the future classrooms and the whole education system. The evolution of 6G promises faster speeds based on terahertz, wireless communication, the leverage of artificial intelligence (AL), virtually reality (VR) and generally a fully connected world. The use of applications of IoT and AIoT improves the education environment, but the intelligent network services undoubtedly gather, store and analyze big data which are vulnerable to attackers. As a solution we may consider the technology of blockchain. The trust and security of next generation intelligent networks are essential part of their design. In this article we suggest a data security system based on blockchain for AL applications in 6G networks. First, we present two Al-enabled applications, indoor positioning and mobile payment within the framework of 6G in the university area. We present the efficiency of blockchain in data security for Al-enabled systems using two case studies of indoor positioning system and mobile payment. The 6G system which is fully supported by Al and the integration of this in technology of blockchain, is ensured the security in future wireless communication. © 2023 IEEE.

关键词： Blockchain

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A Blockchain-based Technique to Prevent Grade Tampering: A University Perspective 3

A Blockchain-based Technique to Prevent Grade Tampering: A U...

引用

3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Habib, Md. Ahsan Hossen Manik, Md. Motaleb Zaman, Saklain Khulna University of Engineering & Technology Dept. of Computer Science and Engineering Khulna Bangladesh School of Information Studies Syracuse University New York United States

ISBN: (纸本)9798350345360

Education plays a significant role in the advancement of a country. The quality of education that a university provides is crucial to this progress. Almost every university stores the assessed grades on the local server, making them vulnerable to different security attacks e.g. DDoS, SQL injections, cross-site scripting, etc. Any alteration to the assessed grades highly affects the students' personal, academic, and professional lives. This study proposes a blockchain-based secure approach for storing, managing and recovering students' grades by employing both a local server and a blockchain server. The immutability feature of blockchain ensures grade integrity and facilitates the implementation of a grade recovery system. Grades are encrypted before storing on the local and blockchain server to protect privacy. The blockchain server returns the index number of the corresponding block to the user for further use. Finally, the experimental analyses demonstrate the system's effectiveness over state-of-the-art works. © 2023 IEEE.

关键词： Cryptography

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A 200/400 GHz Cascode SiGe Voltage-Controlled Oscillator Exploiting Harmonic Optimization Technique for mmWave Applications

A 200/400 GHz Cascode SiGe Voltage-Controlled Oscillator Exp...

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International Workshop on Integrated Nonlinear Microwave and Millimeterwave Circuits, INMMIC

作者： Mehran Khanehbeygi Giorgio Tatangelo Leonardo Pantoli Giorgio Leuzzi Dept. of Industrial and Information Engineering & Economics University of L’Aquila L’Aquila Italy Dept. of Information Engineering Computer Science and Mathematics University of L’Aquila L’Aquila Italy

ISBN: (数字)9798331519605

ISBN: (纸本)9798331519612

This brief presents a Voltage-Controlled Oscillator (VCO) for operation in the mmWave frequency range. The VCO is based on the Colpitts topology, utilising SiGe:C 130 nm bipolar devices. Unlike traditional configurations, the proposed VCO simultaneously provides both a differential output at the fundamental frequency (200 GHz) and a push-push output at the second harmonic (400 GHz). The overall performance is significantly enhanced through the implementation of an innovative Second Harmonic Extraction Network (SHEN), which efficiently captures and utilizes the second harmonic for improved output power and efficiency.

关键词： Microwave measurement Network topology Voltage-controlled oscillators Power system harmonics Harmonic analysis Topology Millimeter wave communication Power generation Tuning Silicon germanium

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Black Hole Attacks Detection in Mobile Ad Hoc Networks (MANETs) using Bayesian Inference Method 11

Black Hole Attacks Detection in Mobile Ad Hoc Networks (MANE...

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11th International Conference on Electrical engineering, computer science and Informatics, EECSI 2024

作者： Afifah, Nurul Fadli, Muhammad Septian, Tri Wanda Sari, Winda Kurnia Hermansyah, Adi Marieska, Mastura Diana Sriwijaya University Faculty of Comp. Science Computer Engineering Dept. Palembang Indonesia Sriwijaya University COMNETS Research Group Faculty of Comp. Science Palembang Indonesia Sriwijaya University Faculty of Comp. Science Information System Dept. Palembang Indonesia

ISBN: (纸本)9798350355314

MANET has vulnerability in terms of security systems against black hole attacks. The main target of black hole as a malicious node is to absorbs all data. The aim of this research is to identify black hole attacks against MANE Tusing Bayesian inference. The methodology of this research involves several testing phases. In the first phase, all data packets sent by the source node to destination within the range of the black hole node are lost. The second and third testing phases showed that the Bayesian method was successful in detecting black holes by preventing data transmission. The results indicated that traffic in the MANET runs smoothly when the nodes are operating under normal conditions. However, when a black hole node is introduced, a significant number of data packets are lost. Implementing the Bayesian method offers a solution to this issue. © 2024 IEEE.

关键词： Mobile ad hoc networks

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