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检索条件"机构=Computer Engineering and Software Engineering Dept."

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Generative Adversarial Networks for Anomaly Detection: A Systematic Literature Review 4

Generative Adversarial Networks for Anomaly Detection: A Sys...

引用

4th International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Noor, Shah Bazai, Sibghat Ullah Ghafoor, Muhammad Imran Marjan, Shah Akram, Saira Ali, Fatima Dept. Computer Science Quetta Pakistan Pakistan Television Corporation Dept. of Electrical Engineering Superior University Lahore Pakistan Dept. of Software Engineering Quetta Pakistan Dept. of Computer Engineering Quetta Pakistan

ISBN: (纸本)9798350335316

In numerous research areas, anomaly identification is a major problem. Identifying and properly classifying data as anomalous is a challenging task that is resolved in various manners over the years. Different approaches like traditional, supervised, unsupervised, and semi-supervised are used for anomaly detection. In the literature, various machine learning-based anomaly detection algorithms exist. It is challenging to choose one anomaly detection algorithm from the several available algorithms because each algorithm puts forward its good detection performance. In recent years, generative adversarial networks have shown remarkable results for anomaly classification. This paper aims to represent a systematic literature review of generative adversarial network-based approaches for anomaly detection and highlights their pros. © 2023 IEEE.

关键词： Anomaly detection

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GolfPoseNet: Golf-Specific 3D Human Pose Estimation Network

GolfPoseNet: Golf-Specific 3D Human Pose Estimation Network

引用

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

作者： Park, BoJeong Kim, JongWoo Mun, SeoYeong Choi, Younglim Kim, Hyunseok Dong-A University Dept. of Electric Engineering Busan Korea Republic of Dong-A University Dept. of Software Innovation Center Busan Korea Republic of Dong-A University Dept. of Computer Engineering Busan Korea Republic of

ISBN: (纸本)9798331510756

This paper aims to develop a robotic golf trainer using a wheeled robot and create a model that accurately recognizes the user's golf motion. Since existing 3D pose models show limitations in golf motion recognition [1], [2], we propose an optimized recognition model by combining various methods and preprocessing golf datasets to overcome these problems. We used HRNet to estimate 2D pose from video frames and fed these data into a transformer-based model to generate three hypotheses through residual linking and embedding layers. These were integrated to finally design a structure to predict 3D pose, and batch normalization, dropout, and LeakyReLU were used to improve the stability and nonlinearity handling of the model. Experimental results show that the proposed model effectively learns temporal information from multi-view 2D pose data without 3D annotation and performs well even when 3D data is scarce. The proposed method can be used in various applications such as human-robot interaction and sports motion analysis including golf, and further research on multiple datasets and real-world environments is expected to improve further and generalize the model's performance. © 2025 IEEE.

关键词： Human robot interaction

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An In-dept. Exploration of Advanced Driver Drowsiness Detection Systems for Enhanced Road Safety 2

An In-Depth Exploration of Advanced Driver Drowsiness Detect...

引用

2nd International Conference on computer, Communication and Control, IC4 2024

作者： Abirami, A. Bhuvaneswari, S. Vishnuvardhan, M. Karthik, Yadam Shankar, Saisuraj Easwari Engineering College Dept. of Computer Science and Engineering Chennai India Student of Easwari Engineering College Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350387933

Drowsiness, among drivers, plays a role in causing road accidents leading to loss of life, injuries, and financial setbacks. To address the issue of driver fatigue related accidents it is crucial to develop methods for detecting driver fatigue. This survey paper presents an overview of approaches used to identify driver fatigue. These approaches can be grouped into three categories: Behavioural, Vehicular and Physiological parameters. Behavioural parameters, such as eye closure percentage, blink rate patterns, yawning, head movements and facial expressions offer intrusive techniques that examine the physical state of the driver's body and provide information about their drowsiness level. Vehicular parameter-based techniques evaluate drowsiness by observing vehicle behaviour such as lane deviations, pedal usage, and unpredictable steering movements. Physiological indicators are also employed to detect tiredness. The paper also discusses the methodology employed in the research study including image processing techniques classifiers used and measurement metrics utilized, proposed methodology of fatigue detection with adding some extra features and Lastly the paper presents conclusions based on a study that can guide future researchers in identifying potential areas for further exploration in this field. © 2024 IEEE.

关键词： Physiological models

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AROhI: An Interactive Toolkit for Estimating ROI of Data Analytics 15

AROhI: An Interactive Toolkit for Estimating ROI of Data Ana...

引用

15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Zambare, Noopur Idoko, Jacob Acharya, Jagrit Ginde, Gouri Indian Institute of Technology Dept. of Mechanical Engineering Jodhpur India University of Calgary Dept. of Electrical and Software Engineering Canada

ISBN: (纸本)9798331507695

The cost of adopting new technology is rarely analyzed and discussed;however, it is vital for any technological investment because of the cost and effort associated with it. Thus, it is crucial to consider Return On Investment (ROI) when performing data analytics. Decisions on 'How much analytics is needed?' are hard to answer. ROI could guide decision support on the What?, How?, and How Much? Analytics for a given problem. This work details a comprehensive tool that provides conventional and advanced ML approaches for demonstration using software requirements dependency extraction and their ROI analysis as a use case. Utilizing advanced ML techniques such as Active Learning, Transfer Learning and primitive Large language model: BERT (Bidirectional Encoder Representations from Transformers) as its various components for automating dependency extraction, the tool outcomes demonstrate a mechanism to compute the ROI of ML algorithms to present trade-offs between the cost and benefits of a technology investment. © 2025 IEEE.

关键词： Adversarial machine learning

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RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Multi-DNN Inference 30

RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Mul...

引用

30th IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS 2024

作者： Kang, Woosung Lee, Jinkyu Lee, Youngmoon Oh, Sangeun Lee, Kilho Chwa, Hoon Sung DGIST Dept. of Electrical Engineering and Computer Science Korea Republic of Dept. of Computer Science and Engineering Korea Republic of Hanyang University Dept. of Robotics Korea Republic of Ajou University Department of Software and Computer Engineering Korea Republic of School of AI Convergence Soongsil University Korea Republic of

ISBN: (纸本)9798350358414

The increasing complexity and memory demands of Deep Neural Networks (DNNs) for real-Time systems pose new significant challenges, one of which is the GPU memory capacity bottleneck, where the limited physical memory inside GPUs impedes the deployment of sophisticated DNN models. This paper presents, to the best of our knowledge, the first study of addressing the GPU memory bottleneck issues, while simultaneously ensuring the timely inference of multiple DNN tasks. We propose RT-Swap, a real-Time memory management framework, that enables transparent and efficient swap scheduling of memory objects, employing the relatively larger CPU memory to extend the available GPU memory capacity, without compromising timing guarantees. We have implemented RT-Swap on top of representative machine-learning frameworks, demonstrating its effectiveness in making significantly more DNN task sets schedulable at least 72% over existing approaches even when the task sets demand up to 96.2% more memory than the GPU's physical capacity. © 2024 IEEE.

关键词： Deep neural networks

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Vote Bank Forecast Based on Social Media Sentiment Analysis 17

Vote Bank Forecast Based on Social Media Sentiment Analysis

引用

17th International Conference on Open Source Systems and Technologies, ICOSST 2023

作者： Zaland, Zubair Bazai, Sibghat Ullah Ghafoor, Muhammad Imran Akram, Saira Waseem, Muhammad Qaseem, Muhammad Dept. of Software Engineering Quetta Pakistan Dept.of Computer Engineering Quetta Pakistan Pakistan Television Corporation Lahore Pakistan Dept. of Engineering Pakistan

ISBN: (纸本)9798350381320

Sentiment Analysis of human behavior from social media revolutionized many fields, such as health, business, and election prediction. Sentiment analysis is the process of analyzing an individual's opinions, attitudes, views, sentiments, and emotions. In order to predict sentiments, predicting the polarity of words and categorizing them as positive or negative are the major goals. A quick and effective way to determine what the public thinks about one's own political party, the opposition party, and political figures is through Twitter. prior sentiment analysis techniques have primarily struggled with classification accuracy because biases in the training data cause a sizable portion of tweets to be incorrectly classified. As a remedy, we set out to collect real-Time data in order to forecast Pakistan's next election results. Users can quickly share real-Time updates on a variety of activities and events on Twitter, promoting the rapid diffusion of information and news. To do this, we accessed the Twitter API through the 'Tweepy'' Python package and retrieved real-Time data from the service. Through keyword searches, we were able to find more than 5,000 tweets. We attributed feelings to particular tweets using Python's 'TextBlob'' package. We are able to generate prediction graphics by determining the frequency of positive attitudes with respect to a particular candidate. © 2023 IEEE.

关键词： Forecasting

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Enhancing Transaction Security through Iris Recognition 4

Enhancing Transaction Security through Iris Recognition

引用

4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Arepalli, Gopi Suresh Bhavana, Paluvai Krishna, Yakkanti Venkata Sai Surendrababu, Chittibomma Lakireddy Bali Reddy College of Engineering Dept. of computer Science and Engineering Mylavaram India Dept. of Computer Science and Engineering Mylavaram India

ISBN: (纸本)9798350385793

UPI frauds have led to increased use of biometrics for personal identification to ensure security and accuracy. Biometric UPI based on iris recognition technology improves customer service by providing a safe and paperless environment. This system replaces the existing UPI pin and affords protection to the UPI payment from cyber-attacks. During enrollment, genuine user's iris samples and UPI ID are retained in the database, and transactions are completed by capturing and matching iris patterns by using G-6 iris recognizer and biometric Framework. This innovative technology has revolutionized the way to make payments and has significantly reduced the risk of fraudulent activities. With biometric UPI, customers can enjoy a seamless experience when making transactions. The use of biometrics also ensures that there is no chance of identity theft or unauthorized access to sensitive information. © 2024 IEEE.

关键词： Authentication Protocol

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An Overview on IoT in Electric Vehicles: Efficiency and Sustainability 2

An Overview on IoT in Electric Vehicles: Efficiency and Sust...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Gupta, Shashank Sounkhla, Indu Dubey, Ranjeet Kumar Yadav, Mithilesh Kumar Singh Yadav, Sandeep NIT Dept. of Computer Science and Engineering Jalandhar India KIPM Dept. of Computer Science and Engineering Gorakhpur India Buddha Institute of Technology Dept. of Computer Science and Engineering Gorakhpur India

ISBN: (纸本)9798350368109

Internal combustion engine (ICE) vehicles are very polluting and release high nitrogen oxides, carcinogens, and soot into the environment. ICE vehicles are the best choice in this era, but replacing ICE vehicles with electric vehicles (EVs) and automated vehicle control is a potential transition process. The most important turning point in the history of the automobile occurred with the entry of EVs into the vehicle market. The rise of the Internet of Things (IoT) paradigm in electric vehicles (EVs) has changed the way we look at large-scale transportation by making it intelligent and more sustainable. Recently, with the advent of connectivity through IoT platforms, IoT in EVs has improved performance and reduced environmental impact: from any on-off switch and adoption of IoT through our smartphones placed on the charging base. We can implement smart charging solutions for better energy consumption management, reduction of grid load, and battery life enhancement. IoT allows electric vehicles to communicate directly with charging stations and even the power grid, providing a better way to create balanced transportation networks. But implementing IoT in electric vehicles also raises some issues, including data security and user privacy. With all of these issues, there is huge potential in IoT usage with electric vehicles to help produce cleaner transportation and create a more sustainable future. © 2024 IEEE.

关键词： Electric Vehicles Internal Combustion engine Internet of Things

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Enhancing Mutation Testing through Grammar Fuzzing and Parse Tree-Driven Mutation Generation 15

Enhancing Mutation Testing through Grammar Fuzzing and Parse...

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15th International Conference on Information and Knowledge Technology, IKT 2024

作者： Khorsandi, Mohammad Saei, Alireza Dastmalchi Sharbaf, Mohammadreza Dept. of Software Engineering University of Isfahan Isfahan Iran

ISBN: (纸本)9798331522254

Mutation testing is a technique used to assess the effectiveness of software test suites. It works by deliberately introducing small, controlled changes, called mutations, into the code of the software under test (SUT). A robust and thorough test suite should be able to identify and detect these intentionally seeded errors. The key point is to ensure that the resulting mutant program can still be successfully loaded and executed, without causing compilation or runtime errors. The effectiveness of mutation testing directly depends on the nature and scope of the introduced mutations, as more advanced mutations and even targeted mutations can pose additional challenges to the test suite. This paper presents a novel approach leveraging parse trees and grammar fuzzing to create syntactically valid mutations. By generating a parse tree from the SUT’s source code, our method allows precise selection of target nodes and controls mutation granularity through Lexar and parser rules. A custom grammar fuzzer generates new code fragments, which are then semantically validated by a language-specific analyzer to ensure correctness. To address potential compilation issues, we propose selecting deeper parse tree nodes for mutations. Our approach enhances mutation testing precision, flexibility, and automation, ensuring valid and contextually appropriate code mutations. © 2024 IEEE.

关键词： Semantics

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Guided Machine Learning for Mobile User Interface Design Through HCI Principles

Guided Machine Learning for Mobile User Interface Design Thr...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Wickramarathne, Jagath Kodagoda, Nuwan Ganegoda, Devanshi Sliit Dept. of Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350350821

Mobile Application User Interfaces (UIs) are crucial for user experience, with well-designed UIs enhancing usability and poorly designed ones causing frustration. The increasing needs of humans, demands UIs that support diverse tasks and contexts. Designing user interfaces requires prior experience and knowledge in designing interfaces that meet user needs. However, the shortage of specialized designers frequently results in designs that are not as user-centered. This study proposes a design guideline framework to annotate Android mobile UI images collected from RICO dataset, by combining Material Design Guidelines with most common HCI principles, to identify and report mobile UI design issues through machine learning. © 2024 IEEE.

关键词： Adversarial machine learning

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