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An Introduction of Explainable Artificial Intelligence to Winter Sports Performance Analysis

An Introduction of Explainable Artificial Intelligence to Wi...

引用

2023 IEEE International Workshop on Sport, technology and Research, STAR 2023

作者： Odong, Lawrence A. Bouquet, Paolo University of Trento Dept. of Information Engineering and Computer Science Trento Italy

ISBN: (纸本)9798350316056

In recent years, the utilization of machine learning techniques has revolutionized performance analysis in the field of sports. However, a crucial drawback of these algorithms is their inherent black box nature, which hinders stakeholders' ability to comprehend the underlying rationale behind their decisions. This lack of transparency presents challenges for coaches, trainers, and athletes who heavily rely on analysis outcomes to shape their strategies and training approaches. To address this limitation, this paper introduces the concept of Explainable Artificial Intelligence (XAI) to sports performance analysis. We employ SHAP (SHapley Additive exPlanations), a model-agnostic explainable method, to provide a clear and interpretable representation of the contribution of different features towards the final model prediction. Ski jumping is chosen as the initial test case to illustrate the effectiveness of this approach. © 2023 IEEE.

关键词： Sports

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Resource Allocation Optimization in 5G Networks Using Variational Quantum Regressor 1

Resource Allocation Optimization in 5G Networks Using Variat...

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1st International Conference on Quantum Communications, Networking, and Computing, QCNC 2024

作者： Pathak, Param Oad, Vidhi Prajapati, Aditya Innan, Nouhaila Sardar Vallabhbhai Patel Institute of Technology Dept. of Computer Engineering India Vishwakarma Government Engineering College Dept. of Electronics and Communication India Dhirubhai Ambani Institute of Information and Communication Technology Dept. of Information and Communication Technology India Quantum Physics and Magnetism Team LPMC Faculty of Sciences Ben M'sick Hassan II University of Casablanca Morocco

ISBN: (纸本)9798350366778

Quantum Machine Learning (QML) is a powerful tool for addressing complex telecommunications challenges. As 5G technology continues to evolve, our research provides a roadmap for integrating QML into the resource allocation framework, potentially improving the efficiency and performance of 5G networks. Through the application of the Variational Quantum Regressor (VQR), our study demonstrates the potential for quantum models to enhance predictive accuracy in resource allocation scenarios. Our VQR accurately aligns predicted quantum states with their intended targets, thereby reducing discrepancies. Such precision in regression analysis, particularly in the context of 5G networks, represents a significant breakthrough. A key highlight of our algorithm is its ability to substantially reduce the mean squared error to an impressively low value of 0.0081, indicating near-perfect prediction accuracy. This innovative approach is set against the backdrop of the evolving landscape of communication networks and computational systems, where the challenge of resource allocation is increasingly pronounced due to the rising demand for bandwidth-intensive and computation-heavy services. © 2024 IEEE.

关键词： Mean square error

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Investigating the Effectiveness of Artificial Intelligence in Watermarking and Steganography for Digital Media Security 21

Investigating the Effectiveness of Artificial Intelligence i...

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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Abdalwahid, Shadan M.J.A Hashim, Wassan Adnan Saeed, Mohammed Ganim Altaie, Sarmad A. Kareem, Shahab Wahhab Engineering College Erbil Polytechnic University Information System Engineering Dept. Erbil Iraq AIQAlam University College Medical Instruments Techniques Dept Kirkuk Iraq Dohuk Polytechnic University Department of Information Technology Dohuk Iraq University of Technology Computer Engineering Department Baghdad Iraq Erbil Technical Engineering College Erbil Polytechnic University Information System Engineering Dept. Erbil Iraq

ISBN: (纸本)9798350374131

Watermarking and Steganography are methods of embedding digital information within images or other media, such as text or audio, for the purpose of Copyright Protection or covert communication. This field of study is not recent and has been ongoing for several years, culminating in its current advanced stage. The utilization of Artificial Intelligence algorithms has played a pivotal role in revolutionizing various aspects, including security concerns and the precision of outcomes, as compared to traditional methods. This paper focuses on doing an in-dept. analysis of cutting-edge research, techniques, and methodologies employed in the domain of Watermarking and Steganography, specifically in conjunction with Artificial Intelligence. By thoroughly examining and evaluating a collection of recent studies in this domain, we have scrutinized the outcomes of each study with respect to its research goal, the acquired results, the employed algorithm, and the research's robustness in terms of susceptibility to various forms of attacks and the technique of data embedding. Our findings indicate that the use of Artificial Intelligence algorithms has a substantial influence on enhancing result precision, system resilience, and establishing data ownership. Deep Neural Networks (DNN) are essential in Watermarking and Steganography due to their robustness, effectiveness, and accuracy. Novel methodologies and systems improve security, incorporation rates, precision of detection, and speed of convergence. Deep learning is being investigated in techniques such as data concealment, information hiding, and steganography to improve security. © 2024 IEEE.

关键词： Embeddings

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Secure Authentication in Smart Home Environment Using SGX and Biometrics: Survey 1

Secure Authentication in Smart Home Environment Using SGX an...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Rajeh, Zahra M. Alhomdy, Sharaf A. Thabit, Fursan Dept. Inforamtion Technology Sana’a University Sana’a Yemen Dept. Computer Engineering Ege University İzmir Turkey

ISBN: (纸本)9798331533557

Recently, our lifestyles have evolved to be smarter, with various devices becoming available for daily activities, such as smartphones, smart watches, smart lighting, and smart cameras. A smart home, also known as a connected home, uses smart internet-connected devices that enable remote monitoring and administration of appliances and systems. This study aims to analyze the environment of smart homes, focusing on the authentication processes that secure these systems. It explores modern technologies supporting authentication, such as Intel software guard extensions (SGX) and biometrics systems. SGX utilizes isolation techniques to protect data in secure enclaves, while biometrics measure people's unique physical and behavioral characteristics. These technologies are used to build many layers of defense to protect homeowners' privacy. Moreover, the Internet of Things (IoT) is crucial in enhancing smart home functionalities. This study also examines the implementation of IoT in smart homes and the ways they are utilized and communicated. Additionally, it discusses the lessons learned and identifies current challenges in this evolving environment. © 2024 IEEE.

关键词： Smart homes

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Immersive networked music performance systems: identifying latency factors

Immersive networked music performance systems: identifying l...

引用

2023 Immersive and 3D Audio: from Architecture to Automotive, I3DA 2023

作者： Turchet, Luca Tomasetti, Matteo University of Trento Dept. of Information Engineering and Computer Science Trento Italy

ISBN: (纸本)9798350311044

Music playing with binaural systems has thus far received remarkably little attention by the research community. Such an activity is particularly relevant for the case of networked music performances (NMPs), where geographically displaced musicians play together over a telecommunication network. Recent findings obtained in simulated settings have shown the preference of musicians for spatialized listening during collaborative playing using headphones, as opposed to listening with conventional stereophonic systems. This result has highlighted the need to enhance current NMP systems with the spatial rendering of the acoustic scene, leading to the development of immersive networked music performance (INMP) systems. A crucial aspect that needs to be addressed is the contribution of the spatial audio system to the overall latency of the audio processing and transmission chain between the network nodes. In this paper, we provide an overview of the INMP topic and identify the latency contributions of the components of an INMP system. We then relate such components to state-of-the-art hardware and software systems. Based on our analysis, we draw concluding remarks and discuss the open challenges for progressing the INMP field. © 2023 IEEE.

关键词： Music

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Leveraging Sentiment Analysis and Negation Phrase Identification for Mitigating information Overload in E-commerce 3

Leveraging Sentiment Analysis and Negation Phrase Identifica...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Ranganatha, K. Rao, Abhishek S. Shilpa, B. Nayak, Vasanth Shet, Nakul N Canara Engineering College Dept. of Information Science & Engineering Benjanapadavu India NMAM Institute of Technology Dept. of Information Science & Engineering Nitte India

ISBN: (纸本)9798350317022

In the era of e-commerce, online reviews have become crucial for both consumers and merchants. However, the overwhelming volume of reviews has resulted in information Overload (10). To address this, we employ Sentiment Analysis (SA) on user comments, utilizing machine learning to classify sentiments. Our study focuses on the challenge of accurately predicting sentiment orientation, particularly noting that adjectives and adverbs with negative prefixes can convey opposite sentiments. We introduce a negation phrase identification algorithm to address this issue. By combining sentiment analysis and negation phrase identification, our research aims to alleviate information overload, providing valuable insights for e-commerce stakeholders. Our model, utilizing the Bayes classifier, achieves an 89% accuracy, showcasing the effectiveness of machine learning in understanding customer sentiment. The research findings indicate that employing machine learning algorithms, particularly the Bayes algorithm, on textual data will allow businesses to gain valuable insights into customer sentiment, surpassing the limitations of manual efforts. The combined approach positions the sentiment analysis methodology as a forefront contender in natural language understanding, promising more reliable and contextually accurate assessments of sentiment from user-generated text data. © 2023 IEEE.

关键词： e-commerce natural language processing opinion mining product reviews sentiment analysis

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AttRes-UNet: A Dual-Model Approach for Brain Tumor Segmentation 4

AttRes-UNet: A Dual-Model Approach for Brain Tumor Segmentat...

引用

4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Kolekar, Samruddhi Maheshkumar Yadav, Agnesh Chandra Yadav, Suchita Dash, Daba Prasad Thaper Institue of Engineering and Technology Dept. Electrical Engineering Patiala India India Instite of Technology Patna Dept. Electrical Engineering Patna India Bennett University Dept. of Computer Science and Engineering Gr. Noida India

ISBN: (纸本)9798350364729

Accurate segmentation of brain tumors in MRI images is crucial for diagnosis, treatment planning, and disease monitoring. Traditional manual segmentation methods are time- consuming and prone to variability, while standard deep learning architectures such as U-Net face challenges in capturing the complex, heterogeneous structures of brain tumors. In this study, we proposed AttRes-UNet, a novel dual-model architecture combining Attention U-Net and Residual U-Net to enhance segmentation accuracy. Attention Gates allow the model to focus on critical tumor regions, while Residual Blocks improve deep feature learning, enabling the model to handle diverse tumor morphologies with greater precision. We evaluated the model using the BraTS 2021 dataset, achieving high performance with an accuracy of 0.996, a dice coefficient of 0.8340, and an Intersection over Union of 0.8210. These results demonstrate the effectiveness of AttRes-UNet in improving segmentation accuracy, making it a valuable tool for automated brain tumor delineation, ultimately reducing manual workload and improving clinical outcomes. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Investigating the Impact of Heuristic Algorithms on Cyberthreat Detection 2

Investigating the Impact of Heuristic Algorithms on Cyberthr...

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2nd International Conference on Advances in Computation, Communication and information technology, ICAICCIT 2024

作者： Kalla, Dinesh Mohammed, Abdul Sajid Boddapati, Venkata Nagesh Jiwani, Nasmin Kiruthiga, T. Dept. of Computer Science Colorado Technical University CharlotteNC United States Dept of School of Computer and Information Sciences University of the Cumberlands Kentucky United States Dept. of Computer Science Microsoft CharlotteNC United States Dept. of Information Technology University of The Cumberlands WilliamsburgKY United States Department of ECE Vetri Vinayaha College of Engineering and Technology Tamil Nadu Trichy India

ISBN: (纸本)9798331541217

This paper investigates the effect of heuristic algorithms on cybersecurity hazard detection and mitigation. To be able to do so, the authors analyze the performance of two heuristics-based algorithms-the simple Heuristic set of rules (SHA) and greedy Heuristic set of rules (GHA)-on 4 distinct datasets. The datasets contain malicious and non-malicious packets. Effects of the experiments show that each algorithms exhibit extraordinarily correct predictions of malicious packets while as compared to the floor-reality labels, and showcase an excessive level of detection accuracy in detecting threats. The SHA set of rules tested the strongest performance, achieving an accuracy of as much as 95 and ninety-seven percentage on datasets. The authors further take a look at that once both algorithms are utilized in aggregate, the overall performance progressed. The research findings may be generalized in further contexts and assist the notion that heuristics algorithms may be effective in threat detection and mitigation. © 2024 IEEE.

关键词： Cybersecurity

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Effect of Hyper-Parameter Tuning on the Performance of Augmented Random Search 1

Effect of Hyper-Parameter Tuning on the Performance of Augme...

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1st International Conference on Informatics, ICI 2022

作者： Rana, Misha Kumar, Nimesh Sharma, Himanshi Pandey, Kavita Jaypee Institute of Information Technology Dept. of Computer Science & Engineering and Information Technology Noida India

ISBN: (纸本)9781665437219

Recently, researchers working in the field of Artificial Intelligence have been keen on finding new algorithms that provide optimal results. One such reinforcement learning algorithm is the Augmented Random Search. This algorithm is imperative as it provides better results over other reinforcement learning algorithms. Finding the optimal values of hyper-parameters helps the algorithm to work up to its full potential. In this work, we have made an effort to find out the best hyper-parameter settings for the learning rate and direction, for this specific task. Our results show how a change in these values can impact the final maximum rewards obtained during the training process. We report novel findings of the impact of hyper-parameters on the performance of ARS algorithm. Finally, an effort has been made to find out by what percentage the ARS performs better than other Reinforcement Learning algorithm, TRPO. © 2022 IEEE.

关键词： Reinforcement learning

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A Comprehensive Survey on Automotive Hacking 1

A Comprehensive Survey on Automotive Hacking

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Maodah, Khawla A. Alhomdy, Sharaf A. Thabit, Fursan Dept. Inforamtion Technology Sana’a University Sana’a Yemen Dept. Computer Engineering Ege University İzmir Turkey

ISBN: (纸本)9798331533557

Using cutting-edge technologies in automobiles has increased concerns about car hacking. Network connections like Bluetooth, Wi-Fi, and 4G LTE have expanded the attack surface. The weaknesses in hardware and software may allow unauthorized access to private systems, endangering drivers and passengers. Hackers exploit the Controller Area Network (CAN) bus to manipulate car functionality, potentially causing accidents. Vehicle manufacturers and cybersecurity experts are implementing stronger security measures to mitigate this threat. Comprehensive security risk assessments are conducted throughout a vehicle's lifespan, involving identifying assets, monitoring security threats, and analyzing concerns. Car hacking highlights the need for modern cybersecurity safeguards. This paper briefly overviews the rise in cyber threats against smart technologies, such as attacks connected to smart vehicles. It discusses major developments in connected and autonomous car technology, the challenges posed by growing cyber threats, and how important automotive cybersecurity and IT are to preserving these breakthroughs. © 2024 IEEE.

关键词： Cyber attacks

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