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2023 IEEE International Conference on Big Data, BigData 2023

作者： Deng, Yuyang Zhao, Ni Huang, Xin Johns Hopkins University Dept. of Applied Mathematics and Statistics Baltimore United States Johns Hopkins University Dept. of Biostatistics Baltimore United States Towson University Dept. of Computer and Information Sciences Baltimore United States

ISBN: (纸本)9798350324457

Since its launch, ChatGPT has achieved remarkable success as a versatile conversational AI platform, drawing millions of users worldwide and garnering widespread recognition across academic, industrial, and general communities. This paper aims to point a portrait of early GPT users and understand how they evolved. Specific questions include their topics of interest and their potential careers;and how this changes over time. We conduct a detailed analysis of real-world ChatGPT datasets with multi-turn conversations between users and ChatGPT. Through a multi-pronged approach, we quantify conversation dynamics by examining the number of turns, then gauge sentiment to understand user sentiment variations, and finally employ Latent Dirichlet Allocation (LDA) to discern overarching topics within the conversation. By understanding shifts in user demographics and interests, we aim to shed light on the changing nature of human-AI interaction and anticipate future trends in user engagement with language models. © 2023 IEEE.

关键词： ChatGPT Latent Dirichlet allocation Natural Language Processing Sentiment Analysis Topic Modeling

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Prediction of Typhoid Using Machine Learning and ANN Prior to Clinical Test

Prediction of Typhoid Using Machine Learning and ANN Prior t...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Bhuiyan, Md. Atik Rad, Sharaf Shahariare Johora, Fatema Tuj Islam, Abdullah Hossain, Md Ismail Khan, Aliza Ahmed Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh Rhine-Waal University of Applied Sciences Dept. of Communication and Environment Kamp-Lintfort Germany

ISBN: (纸本)9798350348217

One of the most prevalent illnesses, typhoid causes a large number of fatalities each year, primarily in Africa. A quick and accurate diagnosis is essential in the medical sector. Self-medication, delayed diagnosis, a lack of medical expertise, and inadequate healthcare facilities all contribute to the high incidence of typhoid fever mortality. Machine learning as well as deep learning has worked wonders for extrapolative analysis in the health industry, and as a result, more health industries are utilizing machine learning techniques. This is the earliest evaluation where a typhoid fever prediction model is being developed which predicts prior to a clinical trial. In this paper, deep learning and machine learning have been employed to develop the model. Ten algorithms have been utilized here, and the XGBoost classifier is the best performer with 97.87% accuracy. © 2023 IEEE.

关键词： Neural networks

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Post Quantum Blockchain Assisted Privacy Preserving Protocol for Internet of Medical Things

Post Quantum Blockchain Assisted Privacy Preserving Protocol...

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2023 IEEE World Conference on applied Intelligence and Computing, AIC 2023

作者： Yadav, Dushyant Kumar Yadav, Dinesh Pal, Yogesh Chaudhary, Dharminder Sahu, Hemlal Manasa, A.S.L. Govt. J. Y. Chhattisgarh College Dept. of Mathematics Raipur India Shivalik College of Engineering Dept. of Computer Science and Engineering Raipur India Greater Noida India Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350310061

Internet of Medical Things (IoMT) is an advanced ecosystem made with the help of Internet of Things technology, smart sensors, medical equipment, and connected end users. Internet of Medical Things is very helpful to enhance efficiency, accuracy, and qualities traditional healthcare system. A smart IoMT contains end users, heterogeneous network, and private data is transferred through public channel. Therefore, it becomes hard to achieve both security and privacy in the IoMT system. In this article, we have focused on designing privacy preserving authenticated key mechanism between two parties. We have proposed a blockchain assisted authenticated key exchange mechanism for IoMT systems supporting fog computing paradigm. The proposed privacy preserving authenticated key exchange protocol is based post quantum assumption 'ring learning with errors' and physical unclonable function (PUF), respectively. A detailed security analysis ensures that proposed protocol is secure against most of the existing attacks. This protocol is also secure against quantum attacks based on the assumption. © 2023 IEEE.

关键词： Authentication

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Detection of Brain Tumor Using CNN and CNN-SVM 3

Detection of Brain Tumor Using CNN and CNN-SVM

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3rd International Conference for Emerging Technology, INCET 2022

作者： Duvvuri, Kavya Kanisettypalli, Harshitha Jayan, Sarada Amrita School of Engineering Dept. of Computer Science Engineering Bengaluru India Amrita School of Engineering Dept. of Mathematics Bengaluru India

ISBN: (纸本)9781665494991

Brain tumor is a type of cancerous growth that may occur in the brain. Early diagnosis of the disease is crucial for proper treatment. Diagnosis of brain tumors is usually done using images obtained through magnetic resonance imaging (MRI). MRI images can be classified using a Convolutional Neural Network (CNN), which is a technique in deep learning. It is suitable for classifying large image datasets. Support Vector Machine (SVM) is a technique in machine learning that is predominantly used for classification and in various regression problems. In this paper, we classified brain MRI images using pre-trained models like AlexNet, VGG16, InceptionV3, and ResNet50. Finally, a CNN model and an SVM model are trained with the same dataset. Using the results thus obtained a hybrid CNN-SVM model has been built to get better accuracy and prediction results. © 2022 IEEE.

关键词： Support vector machines

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Blockchain-based federated learning with checksums to increase security in Internet of Things solutions

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Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 4685-4694页

作者： Prokop, Katarzyna Polap, Dawid Srivastava, Gautam Lin, Jerry Chun-Wei Faculty of Applied Mathematics Silesian University of Technology Kaszubska 23 Gliwice44-100 Poland Dept. of Math and Computer Science Brandon University BrandonMBR7A 6A9 Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Western Norway University of Applied Sciences Bergen Norway Dept. of Computer Science and Math Lebanese American University Beirut1102 Lebanon

Federated learning is becoming a practical solution for machine learning (ML) in industry. This is due to the possibility of implementing artificial intelligence (AI) systems and training its models on private data sets. However, this is not an ideal solution as it is possible to manipulate or even intercept the model during its transmission between the server and workers. In this paper, we propose a solution to ensure the security of the model transmitted between units in FL. The given model is encrypted with AES, DES, RSA algorithms, then a checksum is determined. This checksum with a private key is stored as a transaction on a blockchain. In the case of sending the model and its modification, the recipient can easily verify whether it is correct. The proposed solution has been described, tested, and compared to indicate its advantages and disadvantages. Conducted experiments were based on analyzing the communication time between participants, the accuracy of machine learning models, and attack detection. In terms of attack detection on the blockchain, we reached 81% thanks to the checksum mechanism. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Blockchain

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Fraud Detection in Online Transactions Using Machine Learning 1

Fraud Detection in Online Transactions Using Machine Learnin...

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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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Vector Model Based Information Retrieval System with Word Embedding Transformation 10

Vector Model Based Information Retrieval System with Word Em...

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10th International Conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET-SIP-22 2022

作者： Brundha, J. Meera, K.N. Amrita School of Engineering Dept. of Computer Science and Engineering Bengaluru India Amrita School of Engineering Dept. of Mathematics Bengaluru India

ISBN: (纸本)9781665467414

Vector based information retrieval system has been one of the trending methods in Natural Language Processing. The embeddings vector generated from a document helps in identifying most relevant document related to the query. There is various approach were embedding vectors can be generated and some of them which have implemented are Word2vec, Glove2vec and Sentence BERT. For information retrieval system also used word embedding transformation like PCA and Factor Analysis to improvise the model's performance. Most of information retrieval system involves getting query from the user, preprocessing of the query and generating most relevant information to the query. Results obtained by post processing methods such as PCA and Factor Analysis shows a comparatively better results with an increase of 2-3% of Mean average precision. © 2022 IEEE.

关键词： Analytical models Machine learning algorithms Bit error rate Machine learning Information processing Euclidean distance Information retrieval

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Developing a Python-Based QR Code Generation System 1

Developing a Python-Based QR Code Generation System

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

作者： Kaushik, Priyanka Shamna Parveen, S. Shalu Panda, Sucheta Agarwal, Swati Sridevi, P. R. Sukanya Chandigarh University Dept. of CSE Chandigarh India Dhanlaxmi Srinivasan College Of Engineering Dept. of CSE Coimbatore India Manav Rachna University Dept. of Computer S. and Technology Faridabad India Usha Martin University Dept. of Computing and IT Jharkhand India Usha Martin University Dept. of Mathematics Jharkhand India Meenakshi A.H.E.R Dept. of Computer Science Chennai India

ISBN: (纸本)9798350356816

This project entails importing necessary modules, creating the main window, and obtaining user inputs such as text or URL, the location to store the QR code, the name of the QR code, and its size. The paper delves into the underlying principles of QR code technology, covering aspects such as its structure, error correction capabilities, and applications across various industries. It highlights how Python serves as a powerful, flexible, and user-friendly tool for generating QR codes. This project is designed to be utilized by software developers, researchers, and enthusiasts, enabling them to gain a comprehensive understanding of QR code generation and to create their own customized QR code generation applications. By providing a detailed technical guide for QR code generation using Python, this research paper makes a significant contribution to the field of QR code technology. © 2024 IEEE.

关键词： Python

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Faster R-CNN in Healthcare and Disease Detection: A Comprehensive Review

Faster R-CNN in Healthcare and Disease Detection: A Comprehe...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Tian, Jiawei Lee, Seungho Kang, Kyungtae Major in Bio Artificial Intelligence Hanyang University Dept. of Computer Science and Engineering Ansan-si Korea Republic of Hanyang University Dept. of Applied Artificial Intelligence Ansan-si Korea Republic of Hanyang University Dept. of Artificial Intelligence Ansan-si Korea Republic of

ISBN: (纸本)9798331510756

This review examines the applications, challenges, and prospects of Faster Region-based Convolutional Neural Networks (Faster R-CNN) in healthcare and disease detection. Through a meta-analysis of Web of Science literature from 2017 to 2024, we provide insights into the evolving landscape of Faster R-CNN in medical contexts. The algorithm can be applied to medical image analysis across various modalities, including radiography, computed tomography, magnetic resonance imaging, ultrasound, microscopy, and endoscopy. Its applications extend to optical RGB images for dermatological and surgical uses, as well as broader healthcare areas such as posture detection, medication recognition, and assistive technologies. Despite its success, Faster R-CNN faces challenges in handling subtle abnormalities, addressing class imbalance in medical datasets, ensuring result interpretability, and managing patient privacy. Our analysis reveals dominant application areas, technological advancements, and integration trends with other artificial intelligence technologies. The review highlights Faster R-CNN's significant impact on improving diagnostic accuracy and healthcare delivery while acknowledging the need for continued research to address limitations. Emerging directions include real-time disease detection and advancements in personalized medicine, underscoring Faster R-CNN's potential to further transform healthcare practices and patient outcomes. © 2025 IEEE.

关键词： computerized tomography

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Analysis of Capacitive Coupling and Crosstalk in the PCB of Switch-Mode Power Supplies 20

Analysis of Capacitive Coupling and Crosstalk in the PCB of ...

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20th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2024

作者： Di Capua, Giulia Maffucci, Antonio Femia, Nicola Chiariello, Andrea Gaetano Univ. of Cassino and Southern Lazio Dept. Electrical and Information Engineering 'Maurizio Scarano' FR Cassino Italy University of Salerno Electrical Eng. and Applied Mathematics Dept. of Information Eng SA Fisciano Italy University of Campania 'L. Vanvitelli' Dept. of Engineering CE Aversa Italy

ISBN: (纸本)9798350351927

The increasing use of wide bandgap devices like Gallium Nitride (GaN) allows for shrinking passive components and power electronics boards thanks to the higher operable switching frequency. As sizes decrease and frequency increases, unwanted electromagnetic (EM) coupling between traces becomes more relevant, leading to crosstalk noise that can severely affect signal integrity. This paper discusses models and methods for evaluating the parasitic coupling capacitance between the traces of a switch-mode power supply. The fundamental theoretical concepts underlying its evaluation are illustrated, and guidelines are provided for its practical and reliable calculation using popular simulation tools. © 2024 IEEE.

关键词： Gallium nitride

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