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检索条件"机构=Dept. of Electrical and Computer Engineering and Computer Science University of Cincinnati"

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Integrating Temporal Analysis with Hybrid Machine Learning and Deep Learning Models for Enhanced Air Quality Prediction 19

Integrating Temporal Analysis with Hybrid Machine Learning a...

引用

19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Omer, Muhammad Ali, Sardar Jaffar Raza, Syed M. Le, Duc-Tai Choo, Hyunseung Dept. of Computer Science and Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of James Watt School of Engineering University of Glasgow Glasgow United Kingdom Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798331507817

Air pollution remains a critical issue, adversely affecting public health and the environment. In this study, we utilize the Air Quality index dataset from Kaggle to analyze temporal and seasonal variations of key pollutants, specifically Carbon Monoxide (CO), Nitrogen Oxides (NOx), and Benzene (C6H6 ). Building upon this analysis, we predict Absolute Humidity (AH), a vital meteorological factor influencing pollutant dispersion, using Machine Learning (ML) and Deep Learning (DL) techniques. Three ML techniques, Linear Regression (LR), Random Forest (RF), and Support Vector Regression (SVR), and three DL techniques, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), are employed for predicting AH. The results indicate that while the RF model achieved the lowest Mean Absolute Error (MAE) among ML methods (0.02), the CNN model, despite having an MAE of 0.04, demonstrated statistical superiority through paired t-tests and Wilcoxon signed-rank tests (p © 2025 IEEE.

关键词： Convolutional neural networks

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DURATION-BASED INVESTIGATION OF USER CONTENT CHOICES IN THE EXIT OF FILTER BUBBLES 6

DURATION-BASED INVESTIGATION OF USER CONTENT CHOICES IN THE ...

引用

6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Bunza, Rabiatu DEPT. OF COMPUTER SCIENCE FACULTY OF SCIENCE AND ENGINEERING SWANSEA UNIVERSITY United Kingdom

ISBN: (纸本)9798350394634

Content personalization on social platforms has been linked to the creation of filter bubbles. The algorithms provide content recommendations based on the user’s browsing history and interests, which limits content diversity. Studies have been conducted on platforms such as Facebook and YouTube to investigate user awareness and strategies to exit filter bubbles. However, no study has employed a multi-platform, user-centered perspective in investigating filter bubble exit through change in search content, as well as the duration of the content transition across multiple platforms. This study examines the duration of user exit of filter bubbles, across three social platforms;TikTok, YouTube Shots, and Instagram Reels, as well as the impact of word similarity on the duration of the transition between contents, across the platforms. The methodology applied involved triggering the filter bubble effect through user interactions with the search content across the different platforms. Findings revealed that the mean time of a transition between search terms on TikTok is significantly higher than the mean time on Instagram reels, and YouTube shorts. Similarly, the study identified differences in the duration of transition across the different platforms, due to the similarity of the search terms. Apart from improving user awareness of a filter bubble exit strategy, this study contributes to Human-computer Interaction research by highlighting some design implications for future social media application design. © 2024 IEEE.

关键词： Social networking (online)

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RF-Based Lightweight Machine Learning for Comprehensive Drone Activity Classification 2

RF-Based Lightweight Machine Learning for Comprehensive Dron...

引用

2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Alqodah, Mohammed A. Tahsin, Mahdia Omari, Mohammad H. Matalgah, Mustafa M. Harrison, David University of Mississippi Dep. of Electrical & Computer Engineering UniversityMS United States University of Mississippi Dept. of Computer and Information Science UniversityMS United States

ISBN: (纸本)9798331529529

The rapid rise of commercially available drones, driven by miniaturization and affordability, has introduced potential security risks alongside numerous societal benefits. While drones serve various industries, their unregulated use necessitates robust detection and classification methods. This paper proposes a novel, efficient model for classifying unwanted drone activity. We leverage statistical and time-domain features extracted from radio frequency (RF) signals and machine learning (ML) classifiers. This approach aims to not only detect drones but also distinguish their models and operational modes. Utilizing the DroneRF dataset, our feature selection strategy prioritizes informative signals in the low-band spectrum, achieving {100%} accuracy in both drone detection and drone model identification tasks. Furthermore, our hybrid model, combining time-domain and frequency-domain features, achieves an accuracy of {97.66%} in classifying different flight modes. This demonstrates the effectiveness of this comprehensive feature extraction approach for drone activity classification. © 2024 IEEE.

关键词： Drones

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Homogenous Ensemble Learning for Air Quality Index Prediction 15

Homogenous Ensemble Learning for Air Quality Index Predictio...

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

作者： Medhi, Shrabani Boruah, Rituraj Baruah, Sagar Pratim Das, Hiranya Kumar Girijananda Chowdhury University Dept. of Computer Science and Engineering Guwahati India Girijananda Chowdhury Institute of Management and Technology Dept. of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331300579

Air pollution is a global issue of concern. Accurate prediction of the air quality index (AQI) plays a crucial role in understanding pollution patterns and implementing effective mitigation strategies. This research paper presents a comprehensive study on the application of ensemble learning techniques for AQI prediction, aiming to enhance prediction accuracy and provide insights into the driving factors behind pollution levels. The primary objective is to conduct a comparative analysis of homogenous ensemble method, namely, bagging in conjunction with individual base models, such as multiple linear regression, support vector regression, decision trees, and artificial neural networks. In total we have created 4 non-ensembled models and 4 ensembled models. Additionally, the research investigates the importance of different features in AQI prediction models, aiming to identify the key factors influencing air pollution. Hyperparameter tuning is done to optimize the models. © Grenze Scientific Society, 2024.

关键词： Prediction models

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K-Band QPSK PMCW Radar with Digital I/Q Constellation Phase Compensation and I/Q Imbalance Correction

K-Band QPSK PMCW Radar with Digital I/Q Constellation Phase ...

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2025 IEEE Topical Conference on Wireless Sensors and Sensor Networks, WiSNeT 2025

作者： Thompson, Derek Li, Changzhi Dept. of Electrical and Computer Engineering Texas Tech University LubbockTX United States

ISBN: (纸本)9798331507336

This paper presents a novel K-Band Quadrature Phase Shift Keying (QPSK) phase-modulated continuous-wave (PMCW) radar front end architecture with digital I/Q constellation phase compensation and I/Q channel imbalance correction. Although QPSK is a very common form of digital phase modulation, it is rarely reported for PMCW radar systems, especially those designed on printed circuit board (PCB) technology. A MATLAB simulation platform is also developed and presented to help verify the benefits of QPSK PMCW radar for range detection. © 2025 IEEE.

关键词： MATLAB

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Post Quantum Code-Based Cryptosystems with Dual Inverse Matrix 13

Post Quantum Code-Based Cryptosystems with Dual Inverse Matr...

引用

13th International Conference on Information Technology in Asia, CITA 2023

作者： Makoui, Farshid Haidary Gulliver, T. Aaron Dakhilalian, Mohammad University of Victoria Dept. of Electrical & Computer Engineering VictoriaBC Canada Isfahan University of Technology Dept. of Electrical & Computer Engineering Isfahan Iran

ISBN: (纸本)9798350326208

The Shor algorithm demonstrates the significant risk that quantum attacks pose to the security of widely used cryptographic primitives. However, code-based cryptography has been shown to be resistant to these attacks. To date, no polynomial-time attack exists that can break code-based cryptosystems such as the McEliece cryptosystem. Despite this, these cryptosystems are not employed in practical applications in domains such as online banking, blockchains, and e-commerce platforms. The primary reason is the large sizes of the public and private keys associated with code-based cryptosystems. In this paper, a new code-based cryptosystem is introduced which employs a dual matrix based on the transpose and inverse of the parity check matrix to reduce the key size compared to the McEliece cryptosystem. © 2023 IEEE.

关键词： Public key cryptography

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Lightweight SNN-based White Space Detector for Cognitive Vehicular Networking

Lightweight SNN-based White Space Detector for Cognitive Veh...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Galib, Md Mehedi Hassan Younis, Mohamed University of Maryland Baltimore County Dept. of Computer Science and Electrical and Engineering BaltimoreMD21250 United States

ISBN: (纸本)9798350351255

The Dedicated Short-Range Communication (DSRC) protocol has become the de facto means for supporting vehicle ad hoc networking (VANET). Yet, the current allocated spectrum for DSRC is insufficient for handling large volumes of data, particularly during accidents or high traffic congestion, where most spectral resources are dedicated to control and emergency awareness, degrading infotainment and multimedia application services. Such a challenge motivates the pursuance of cognitive radio (CR), also known as CR-VANET, where vehicles opportunistically tap to utilize resources within the licensed spectrum as secondary users (SUs). To avoid interference with primary users (PUs), a vehicle needs to accurately sense the medium and detect white space. Given the dynamic nature of the VANET topology and varying volume of message traffic, machine-learning (ML) techniques, particularly those pursuing deep learning models, proved to be a viable option for spectrum sensing. However, the computational complexity of these techniques becomes an obstacle for vehicles. This paper opts to fill the technical gap by proposing a novel lightweight spectrum detector that employs spiking neural networks (SNN). The validation results using a field collected LTE-dataset clearly show that our proposed SNN model enables fast inference time, which ultimately expedites the spectrum sensing process and provides additional spectral resources for vehicular communication. © 2024 IEEE.

关键词： Traffic congestion

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FPGA-based Hardware Acceleration for Model Predictive Control of Power Electronic Converters 50

FPGA-based Hardware Acceleration for Model Predictive Contro...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Guo, Zhengchen Nelms, R. Mark Dept. of Electrical and Computer Engineering Auburn University AuburnAL United States

ISBN: (纸本)9781665464543

Model predictive control (MPC) has gained considerable attention in power electronics but the high computational demands preclude its embedded and real-time implementation. This paper presents a novel FPGA-based hardware acceleration approach for MPC implementation in power electronic converters, enabling real-time, embedded operation with a total processing time potentially less than 1 µs, effectively eliminating the computational bottleneck. Furthermore, a comprehensive design guideline of FPGA-based hardware acceleration is presented using a buck converter as example, including top architecture programming to bottom calculation kernels design. This FPGA-based hardware acceleration framework for MPC is versatile, offering potential applicability across a wide range of power electronic systems. Simulation and experimental results using a buck converter with 5-step prediction horizon validate the efficacy of this approach. © 2024 IEEE.

关键词： Buck converter

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A Medium Voltage Bidirectional Modular LLC Converter With An Extendable Stacked-Switches Leg

A Medium Voltage Bidirectional Modular LLC Converter With An...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Beshara, Maria Cheema, Muhammad Ali Masood Lam, John York University Dept. of Electrical Engineering and Computer Science Toronto Canada Northern Transformer Corporation Toronto Canada

ISBN: (纸本)9798350376067

This paper proposes a new stacked-switches/full-bridge (FB) structured DC/DC bidirectional LLC resonant converter for medium voltage DC grids in energy storage application. With the proposed converter configuration, the extendable stacked-switch leg that interfaces with the MV side results in low voltage stress across the switch. In addition, this leg also facilitates rectification in the reverse power flow direction. The proposed converter utilizes variable frequency control to adjust the battery voltage, with FB synchronous rectification to facilitate efficient power flow for both charging and discharging of batteries. The circuit analysis, design considerations, and the operational framework are provided. Results on a 200kW, 100kHz, 10kV/600V design are provided to demonstrate the merits of this work. © 2024 IEEE.

关键词： energy storage medium voltage Resonant converters soft-switching

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Unravel News: An Efficient Summarization Approach

Unravel News: An Efficient Summarization Approach

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Saha, Ankan Al Shafi, Abdullah Khulna University of Engineering and Technology Dept. Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798331529765

The process of automatically creating a concise and informative summary of a news piece is known as news summarizing. This can be a useful tool for people who want to quickly get the gist of a news story without having to read the entire article. Extractive and abstractive summarization are the two basic methods used to summarize news. The process of extractive summarization entails locating and separating the key phrases from the source text. In this paper, sentence weighting is used for extractive summarization. The words in the headlines are given ten times more emphasis than the other words in the news article. The final summarization is done by rearranging the sentences as per the sequence in the main news article. The paper concludes by discussing the future of news summarization, which is an active area of research. © 2024 IEEE.

关键词： Abstractive Summary Extractive Summary ROUGE Sentence Rank Algorithm Stemming Text Rank Algorithm

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