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A Pipeline for Immersive data Visualization in CAVE Systems

A Pipeline for Immersive Data Visualization in CAVE Systems

引用

IEEE Southeastcon

作者： Koundinya Challa Chandra Jaiswal Abhinav Pendem Balakrishna Gokaraju Dept. Computational Data Science and Engineering North Carolina A&T State University Greensboro USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

Virtual Reality (VR) has transformed data visualization by enabling immersive and interactive experiences. CAVE (Cave Automatic Virtual Environment) systems provide a unique platform for exploring complex datasets in an intuitive, spatially immersive manner. However, a standardized pipeline for developing VR scripts tailored to CAVE-based data visualization remains underexplored. This paper presents a structured pipeline for creating and deploying immersive data visualization in CAVE systems, specifically using the WorldViz Prism CAVE environment. Our approach facilitates seamless integration of multi-dimensional datasets, enabling real-time interaction with 27 visual graphs within a single VR scene. The system allows dynamic graph manipulation, interactive exploration, and enhanced user engagement. By streamlining VR script development, this pipeline improves accessibility for researchers and practitioners, fostering a more intuitive understanding of complex datasets. Experimental results demonstrate the effectiveness of this approach in enhancing data interpretability and decision-making.

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Personalized Federated Learning for Smart Healthcare Using SDN-Driven Edge Computing

Personalized Federated Learning for Smart Healthcare Using S...

引用

2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Basu, Deborsi Chaudhary, Ritu Ghosh, Uttam Datta, Raja G. S. Sanyal School of Telecommunications Indian Institute of Technology Kharagpur India Indian Institute of Technology Dept. of Electronics and Electrical Communication Engineering Kharagpur India School of Applied Computational Sciences Dept. of Computer Science and Data Science Mmc TN United States

ISBN: (纸本)9798331504847

The advancement in distributed Federated Learning (FL) techniques is transforming next-generation edge computing, especially in e-Helathcare systems where data integrity and security are critical concerns. Collecting, processing and analyzing such substantial medical data is highly challenging due to their non-identical and versatile nature. Conventional FL approaches often show unsatisfactory and inaccurate results while operating on those data. Traditional FL methods also lack optimal dynamic management mechanisms for edge devices in ehealthcare, which is crucial to ensure that only authorized devices participate in handling any system failures and new registrations. To tackle such important aspects, in this work, we propose a personalized FL technique to handle abrupt label shifts among clients' data. The proposed approach further targets to achieve personalized models for local edge clients. A statistical batch normalization is proposed on customized edge servers, keeping the data specificity intact. To make the proposed system more secure and robust, a Software Defined Network-based (SDN-based) centralized server is used that allows only authorized and efficient edge clients to participate. The proposed comprehensive experiments on two healthcare datasets demonstrate better accuracy than traditional FL methods with enhanced security and privacy. © 2025 IEEE.

关键词： mHealth

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Simplified Micropayment Mechanism to Eliminate the Risk of Double Payment in E-Commerce

Simplified Micropayment Mechanism to Eliminate the Risk of D...

引用

2023 International Conference on Advances in Intelligent Computing and Applications, AICAPS 2023

作者： Chahar, Narendra Kumar Singh, Krishan Pal Hussain, Muzzammil Central University of Rajasthan Dept. of Data Science and Analytics Ajmer India Malaviya National Institute of Technology Dept. of Computer Science and Engineering Jaipur India Central University of Rajasthan Dept. of Computer Science and Engineering Ajmer India

ISBN: (纸本)9798350333817

In recent years, the emergence of the Internet and E-commerce has steered significant growth in digital transactions. Businesses today need mobile wallets, credit and debit cards, and e-cash to digitize payments. Digital payment systems are in transition and promise amazing advancements, but they also pose many risks and as the number of online transactions is increasing tremendously, we need a security system that follows all security norms. In this paper, we study digital transaction systems and evaluate various components of E-commerce plat-forms to address the security of these services. We evaluate the attributes that affect the security of digital payment processes and identify several barriers that hinder their performance to propose a simplified payment mechanism for micro-payments that eliminates the double payment problem. © 2023 IEEE.

关键词： Online systems

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Designing of Creator's Information Hub using Machine Learning 2

Designing of Creator's Information Hub using Machine Learnin...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Gabhane, Diksha Gawande, Ujwalla Gaikwad, Sanjana Gajarlewar, Swati Bhawalkar, Rita Yeshwantrao Chavan College of Engineering Dept. of Computer Science & Design Nagpur India Yeshwantrao Chavan College of Engineering Dept. of Information Technology Nagpur India NCPharmacy Wanadongri Nagpur India G H Raisoni College of Engineering and Management Dept. of Cyber Security and Data Science Pune India

ISBN: (纸本)9798331528713

The proliferation of educational content on platforms like YouTube has transformed digital learning, yet it presents challenges in effective creation, dissemination, and impact assessment. This paper proposes a strategic initiative to collaborate with 6,000 active educational YouTubers to enhance global educational outreach. Central to this initiative is the compilation and analysis of a comprehensive dataset containing vital information about content creators and their audiences. Leveraging advanced machine learning algorithms, the analysis aims to identify collaboration opportunities, optimize engagement strategies, enhance content relevance and impact, facilitate knowledge sharing, and measure outcomes. The problem statement highlights fragmentation in educational content, lack of data-driven insights, and suboptimal collaboration opportunities. Addressing these challenges requires a structured approach to connect creators, leverage data analytics, and establish a collaborative ecosystem for enriching educational experiences worldwide. © 2024 IEEE.

关键词： Adversarial machine learning

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RSM-NLP at BLP-2023 Task 2: Bangla Sentiment Analysis using Weighted and Majority Voted Fine-Tuned Transformers 1

RSM-NLP at BLP-2023 Task 2: Bangla Sentiment Analysis using ...

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1st Workshop on Bangla Language Processing, BLP 2023

作者： Seth, Pratinav Goel, Rashi Mathur, Komal Vemulapalli, Swetha Dept. of Data Science & Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Dept. of Computer Science & Engineering Dept. of Information and Communication Technology

ISBN: (纸本)9798891760585

This paper describes our approach to submissions made at Shared Task 2 at BLP Workshop - Sentiment Analysis of Bangla Social Media Posts(Hasan et al., 2023a;Islam et al., 2021;Hasan et al., 2023b). Sentiment Analysis is an action research area in the digital age. With the rapid and constant growth of online social media sites and services and the increasing amount of textual data, the application of automatic Sentiment Analysis is on the rise. However, most of the research in this domain is based on the English language. Despite being the world’s sixth most widely spoken language, little work has been done in Bangla. This task aims to promote work on Bangla Sentiment Analysis while identifying the polarity of social media content by determining whether the sentiment expressed in the text is Positive, Negative, or Neutral. Our approach consists of experimenting and finetuning various multilingual and pre-trained BERT-based models on our downstream tasks and using a Majority Voting and Weighted ensemble model that outperforms individual baseline model scores. Our system scored 0.711 for the multiclass classification task and scored 10th place among the participants on the leaderboard for the shared task. Our code is available at https://***/ptnv-s/RSM-NLP-BLP-Task2. © 2023 Association for computational Linguistics.

关键词： Sentiment analysis

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An Empirical Study on: Time Series Forecasting of Amazon Stock Prices using Neural Networks LSTM and GAN models 5

An Empirical Study on: Time Series Forecasting of Amazon Sto...

引用

5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Bhardwaj, Anjul Singh, Uday Pratap Dept. Artificial Intelligence & Data Science Poornima Institute of Engineering & Technology Rajasthan Jaipur India

ISBN: (纸本)9798400709418

The OHLCV (Open, High, Low, Close, Volume) data used in this study is used to forecast time series and anticipate stock price movement. We investigate a wide variety of models, including traditional statistical approaches and cutting-edge deep learning strategies combined with sentiment analysis, feature extraction, and hyperparameter tweaking. Instead of focusing on absolute stock prices, our main goal is to predict swings in stock prices, as this has been shown to produce more accurate outcomes. We start our research by obtaining historical Amazon stock data via the Yahoo API, and then we go on a thorough analytical journey. We generate features on the OHLCV data first, and then we design and test Fourier and Autoregressive Integrated Moving Average (ARIMA) models. We then switch to more sophisticated deep learning methods, using the pre-processed data to apply Long Short-Term Memory (LSTM) models. Interestingly, we add sentiment analysis to the LSTM study, which expands its scope and lets us consider market sentiment as a possible influencing factor. To guarantee the stability of our models, we use a careful train-test split technique and organize the data in a time series manner. The field of financial forecasting and trading methods will ultimately benefit from the insightful information our study's findings provide on the efficacy of different modeling techniques and their capacity to forecast stock price movements. © 2023 ACM.

关键词： Long short-term memory

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Android Malware Detection

Android Malware Detection

引用

2023 IEEE World Conference on Communication and Computing, WCONF 2023

作者： Gagan, N. Sai Kumar, S. Keerthana, M. Vaidya, Samarth S Rastogi, Vanshika Dept. Artificial Intelligence and Data Science East Point College of Engineering and Technology Bangalore India

ISBN: (纸本)9798350311204

Since android smartphones are so popular, malware writers have found them to be lucrative targets, which poses a serious risk to user security and privacy. To address this problem, several malware detection strategies have been put out recently. In this review paper, we examine an efficient method for detecting malware on Android that makes use of machine learning techniques. Our method divides Android applications into benign and harmful categories using a combination of feature extraction methods and machine learning algorithms. Overall, our suggested method offers a dependable and efficient alternative for detecting Android malware, giving consumers a more dependable and safe environment for mobile computing. © 2023 IEEE.

关键词： Smartphones

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POSIX: A Prompt Sensitivity Index For Large Language Models

POSIX: A Prompt Sensitivity Index For Large Language Models

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Chatterjee, Anwoy Kowndinya Renduchintala, H.S.V.N.S. Bhatia, Sumit Chakraborty, Tanmoy Dept. of Electrical Engineering Indian Institute of Technology Delhi India Media and Data Science Research Adobe Inc. India

ISBN: (纸本)9798891761681

Despite their remarkable capabilities, Large Language Models (LLMs) are found to be surprisingly sensitive to minor variations in prompts, often generating significantly divergent outputs in response to minor variations in the prompts, such as spelling errors, alteration of wording or the prompt template. However, while assessing the quality of an LLM, the focus often tends to be solely on its performance on downstream tasks, while very little to no attention is paid to prompt sensitivity. To fill this gap, we propose POSIX - a novel PrOmpt Sensitivity IndeX as a reliable measure of prompt sensitivity, thereby offering a more comprehensive evaluation of LLM performance. The key idea behind POSIX is to capture the relative change in log-likelihood of a given response upon replacing the corresponding prompt with a different intent-preserving prompt. We provide thorough empirical evidence demonstrating the efficacy of POSIX in capturing prompt sensitivity and subsequently use it to measure and thereby compare prompt sensitivity of various open-source LLMs. We find that merely increasing the parameter count or instruction tuning does not necessarily reduce prompt sensitivity whereas adding some few-shot exemplars, even just one, almost always leads to significant decrease in prompt sensitivity. We also find that alterations to prompt template lead to the highest sensitivity in the case of MCQ-type tasks, whereas paraphrasing results in the highest sensitivity in open-ended generation tasks. The code for reproducing our results is open-sourced at https://***/kowndinyarenduchintala/POSIX. © 2024 Association for computational Linguistics.

关键词： computational linguistics

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Hyperledger Blockchain and Lightweight Bcrypt Symmetric Key Encryption to Boost Cloud Computing Security Effectiveness

Hyperledger Blockchain and Lightweight Bcrypt Symmetric Key ...

引用

2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Banushri, A. Karthika, R.A. Vels Institute of Science Technology & Advanced Studies Dept. of Computer Science & Engineering Pallavaram Chennai India SRM Institute of Science & Technology Dept. of Computational Intelligence Kattangulathur Chennai India

ISBN: (纸本)9798350333244

One of the key business models of contemporary information technology is cloud computing. It offers users a range of services (hardware, software) with little user engagement and at a reasonable price. Utilising a cloud environment raises several challenges, chief among them being security and privacy. This study explores the issues and proposes a Lightweight Bcrypt Symmetric Key (LBSK) data encryption and decryption technique with key rotation. A variety of algorithms and Blockchain technologies are used to protect the information. Encryption is one of the most fascinating features of data security technology. An Aggregated Authority Certificate Provider (AACP) is recommended with blockchain authentication for secured cloud data audit, which lessens the burden on the data owner. The suggested AACP continuously runs on a cloud server and tracks user requests. For the non-trusted provider, the Hyperledger blockchain proxy re-encrypts data to increase protection and privacy. The Blockchain algorithm was used to encrypt the data from beginning to end, and it was then saved in the cloud. Without storing the data locally, a security degree, public verification, and performance factors must be taken into consideration. Compared to current methods, the proposed strategy is intended to deliver a better result. The Blockchain technique was used to encrypt the data from beginning to end, and it was then saved on the cloud. Public verification and performance considerations raise the security level for remote data verification without storing the data locally. In comparison to current methods, the proposed method offers a better result. © 2023 IEEE.

关键词： Blockchain

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Microfluidic Lab-on-a-Chip Design and Analysis for GFAP Biomarker Detection Using Machine Learning Algorithms 3

Microfluidic Lab-on-a-Chip Design and Analysis for GFAP Biom...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Balaji, P. Devasena, D. Rithick, P. Karthi, T. Rishmitha, K. Roopa, R. Sri Ramakrishna Engineering College Dept. Of. Electronics and Instrumentation Engineering Tamilnadu Coimbatore India Sri Ramakrishna Engineering College Dept. of Robotics and Automation Engineering Tamilnadu Coimbatore India Kongu Engineering College Dept. of Automobile Engineering Erode India Science Arjun College of Technology Dept. of Artificial Intelligence and Data Tamilnadu Coimbatore India

ISBN: (纸本)9798331528201

The detection of Glial Fibrillary Acidic Protein (GFAP) is a crucial biomarker for diagnosing neurological conditions such as traumatic brain injuries (TBI) and astrocytoma. This paper presents the design and analysis of a novel microfluidic Lab-on-a-Chip (LoC) platform for GFAP biomarker detection. The system integrates microfluidic channels with embedded gold nanoparticle-functionalized electrochemical sensors for high-sensitivity biomarker capture. The microfluidic chip is designed using soft lithography and PDMS (Polydimethylsiloxane), allowing for precise fluid handling and minimal sample consumption. The electrochemical sensors generate voltage signals corresponding to GFAP concentration levels, which are processed using digital filtering techniques to mitigate noise and improve signal quality. For accurate biomarker quantification, machine learning algorithms, including Random Forest ,KNN and Support Vector Machine (SVM), are employed to classify sensor output data into clinically significant concentration ranges. The model is trained on both simulated and experimentally collected sensor data, achieving an accuracy of over 93 percent in distinguishing low (below 50 ng/mL) and high (above 50 ng/mL) GFAP concentrations. The combination of microfluidic precision, sensitive electrochemical detection, and data analysis enables rapid and reliable detection of GFAP , providing a scalable and portable solution for early diagnosis of brain injuries and neurodegenerative diseases. This system highlights the potential of integrating microfluidics and machine learning in next-generation point-of-care diagnostic devices. © 2024 IEEE.

关键词： Polydimethylsiloxane

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