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Incentive Mechanism for Federated learning Participants Based on Statistical Analysis Features 4

Incentive Mechanism for Federated Learning Participants Base...

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4th international conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2023

作者： Li, Hao Shi, Kai Tianjin University of Technology School of Computer Science and Engineering Tianjin China

ISBN: (纸本)9798350329490

Compared to traditional machine learning approaches, Google's innovative framework for federated learning has emerged as a prominent focus within the realm of collaborative efforts involving multi-source data and multiple users, all aimed at enhancing model performance. Distinguished by its high learning efficiency and robust privacy-preserving capabilities, this framework has garnered significant attention. In the context of cross-device federated learning, potential challenges can stem from participants who display a lack of enthusiasm for engaging in model training or who meticulously manipulate their submitted model parameters. these malicious behaviors carry the potential to undermine the integrity of the global model, thereby exerting a notable influence on the trajectory of the global model training *** tackle these challenges, this paper introduces an incentive mechanism tailored for federated learning participants, grounded in the analysis of statistical features. By harnessing insights from statistical features, this mechanism dynamically adjusts participants' weights, thereby curbing the impact of malicious users. Under the assumption of rational participant behavior, the mechanism not only mitigates potential harm but also encourages proactive involvement. Empirical findings underscore the effectiveness of this adaptive mechanism in constraining the behavior of malicious participants in federated learning, thereby fortifying protective measures for the global model. the practical utility of this mechanism is demonstrated convincingly. © 2023 IEEE.

关键词： learning systems

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Automatic Operator Performance Tumng in a machine learning System on Edge 28

Automatic Operator Performance Tumng in a Machine Learning S...

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28th IEEE international conference on Parallel and Distributed Systems, ICPADS 2022

作者： Xu, Peng Chang, Xinyu Zhao, Jianxin Liu, Chi Harold Beijing Institute of Technology School of Computer Science and Technology Beijing China

ISBN: (纸本)9781665473156

With the current large scale deployment of machine learning technologies, such as those on cloud servers and edge and IoT hardwares, machine learning systems have been widely prevalence. Practical requirement has driven their performance increase in both academia and industry. However, the application requirement varies greatly across different applications, and directly using off-the-shelf systems might not be sufficient in many cases. In this work, we first propose to implement a series of techniques to optimize performance of convolution operation, one of the most important operations, in constructing deep learning networks. Besides, we also propose to apply the automated empirical optimisation of software approach to improve the performance of operators in machine learning system, most notably across various hardware platforms. Evaluation compared to existing libraries on different hardware devices has proved the efficiency of our proposed method. © 2023 IEEE.

关键词： Convolution

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Classification of Brain Strokes in Computed Tomography Images Utilizing Deep learning 4

Classification of Brain Strokes in Computed Tomography Image...

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4th IEEE international conference on Smart Information Systems and Technologies, SIST 2024

作者： Tursynova, Azhar Sakhipov, Aivar Omirzak, Islam Ikram, Zhanseri Smakova, Saida Kutubayeva, Madina Department of Artificial Intelligence and Big Data Al-Farabi Kazakh National University Almaty Kazakhstan Department of Computer Engineering Astana IT University Astana Kazakhstan Department of Information Systems Al-Farabi Kazakh National University Almaty Kazakhstan

ISBN: (纸本)9798350374865

the growing importance of efficient and accurate medical image classification has led to increased research interest in the application of deep learning techniques. this research paper investigates the utilization of Convolutional Neural Networks (CNNs) for the classification of brain strokes from computed tomography (CT) images. Leveraging a diverse dataset, we systematically examine the performance of the CNN model in distinguishing between 'Normal' and 'Stroke' image classes. the study employs a comprehensive set of evaluation metrics, including accuracy, precision, recall, and F-score, to assess the model's diagnostic efficacy. the training process is rigorously monitored, ensuring convergence of learning rates and model stability. Our results demonstrate promising levels of accuracy and an increasing trend in precision, recall, and F-score, underscoring the potential of deep learning in enhancing stroke diagnosis. Furthermore, the study addresses data scarcity challenges through data augmentation techniques, emphasizing their pivotal role in model training. While acknowledging the need for larger and more diverse datasets and potential model enhancements, this research contributes to the broader goal of harnessing artificial intelligence for improved healthcare, particularly in the timely and accurate detection of critical medical conditions like brain strokes through non-invasive imaging modalities. © 2024 IEEE.

关键词： Convolutional neural networks

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Visual Question Generation Using Deep learning 4

Visual Question Generation Using Deep Learning

引用

4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Rathi, Snehal Raje, Atharv Ghule, Gauri Sankpal, Shruti Shitole, Soham More, Priyanka Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vishwakarma Institute of Information Technology Department of Electronics and Telecommunication Pune India Vishwakarma Institute of Information Technology Department of Information Technology Pune India

ISBN: (纸本)9798350306118

the natural task of human intelligence is to ask a question. Asking curious questions is an aspect of human curiosity that leads to inventions and discoveries. the task of converting visual scenes into different languages, questions, answers, and even examples, has been widely interpreted as image-sequencing the program and the reconstruction of the Network in which the image was created. To go beyond the real thing, we have chosen to examine whether questions about any image are often directed at emotional intelligence, that's why in artificial intelligence train systems to generating questions is an important task. We have proposed a model that generates image questions using deep learning algorithms. this is a basic model which can be further extended to do more specific tasks of question generation with the relevant dataset. this question generation technique will help in creating question banks and assist in many teaching activities. © 2023 IEEE.

关键词： Deep learning

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application-Oriented Benchmarking of Quantum Generative learning Using QUARK 4

Application-Oriented Benchmarking of Quantum Generative Lear...

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4th IEEE international conference on Quantum Computing and Engineering, QCE 2023

作者： Kiwit, Florian J. Marso, Marwa Ross, Philipp Riofrío, Carlos A. Klepsch, Johannes Luckow, Andre Bmw Group Munich Germany Ludwig Maximilian University Munich Germany Technical University Munich Germany

ISBN: (纸本)9798350343236

Benchmarking of quantum machine learning (QML) algorithms is challenging due to the complexity and variability of QML systems, e. g., regarding model ansatzes, data sets, training techniques, and hyper-parameter selection. the QUantum computing application benchmaRK (QUARK) framework simplifies and standardizes benchmarking studies for quantum computing applications. Here, we propose several extensions of QUARK to include the ability to evaluate the training and deployment of quantum generative models. We describe the updated software architecture and illustrate its flexibility through several example applications: (1) We trained different quantum generative models using several circuit ansatzes, data sets, and data transformations. (2) We evaluated our models on GPU and real quantum hardware. (3) We assessed the generalization capabilities of our generative models using a broad set of metrics that capture, e. g., the novelty and validity of the generated data. © 2023 IEEE.

关键词： Benchmarking

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Analysing BP Disease Prediction through Transfer learning (machine learning) - A Conceptual Framework and Case Study 4

Analysing BP Disease Prediction through Transfer Learning (m...

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4th IEEE international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Bisht, Rajendra Singh Al-Jawahry, Hassan M. Alsailawi, H.A. Lamba, Anil Kumar Dhabliya, Dharmesh Mohsen, Abdul Hussein Al-Khuzaie, Mohammed Y. Mezaal, Ali Abdulkareem Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Bhimtal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq University of Kerbala College of Medicine Department of Chemistry and Biochemistry Kerbala56001 Iraq Geeta University School of Computer Science and Engineering Delhi NCR Panipat India Vishwakarma Institute of Information Technology Pune India Alkunooze University College Computer Engineer Department Basra Iraq National University of Science and Technology College of Technical Engineering DhiQar64001 Iraq Kut University College 'Business Administration Department wasit Al kut52001 Iraq

ISBN: (纸本)9798350360165

In the evolving landscape of the clever healthcare industry, the detection and prognostication of ailments thru technological improvements present each possibility and demanding situations, mainly within the realm of lethal tumors. this paper delves into the application of switch getting to know in the framework of the Internet of Medical things (IoMT) to enhance the accuracy and reliability of disease predictions. Utilizing the modernA capabilities of the Google Net deep device-learning version, we introduce a relaxed IoMT-based totally technique that substantially outperforms conventional methodologies in predicting lethal most cancers ailments. Our have a study demonstrates a incredible accuracy price of 90 seven.5%, underscoring the ability of switch learning to revolutionize predictive diagnostics within the clever healthcare area. By integrating superior IoMT gadgets and secure information protocols, this method no longer most effective improves illness prediction quotes but moreover addresses important privacy and protection issues inherent in modern scientific information handling. © 2024 IEEE.

关键词： fatal tumors Internet of Medical things (IoMT) privacy security smart healthcare industry

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the thousand Faces of Explainable AI Along the machine learning Life Cycle: Industrial Reality and Current State of Research 4th

The Thousand Faces of Explainable AI Along the Machine Learn...

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4th international conference on Artificial Intelligence in HCI (AI-HCI) Held as Part of the 25th international conference on Human-computer Interaction (HCII)

作者： Decker, thomas Gross, Ralf Koebler, Alexander Lebacher, Michael Schnitzer, Ronald Weber, Stefan H. Siemens AG Munich Germany Ludwig Maximilians Univ Munchen Munich Germany Goethe Univ Frankfurt Frankfurt Germany Tech Univ Munich Munich Germany

ISBN: (纸本)9783031358906;9783031358913

In this paper, we investigate the practical relevance of explainable artificial intelligence (XAI) with a special focus on the producing industries and relate them to the current state of academic XAI research. Our findings are based on an extensive series of interviews regarding the role and applicability of XAI along the machine learning (ML) lifecycle in current industrial practice and its expected relevance in the future. the interviews were conducted among a great variety of roles and key stakeholders from different industry sectors. On top of that, we outline the state of XAI research by providing a concise review of the relevant literature. this enables us to provide an encompassing overview covering the opinions of the surveyed persons as well as the current state of academic research. By comparing our interview results with the current research approaches we reveal several discrepancies. While a multitude of different XAI approaches exists, most of them are centered around the model evaluation phase and data scientists. their versatile capabilities for other stages are currently either not sufficiently explored or not popular among practitioners. In line with existing work, our findings also confirm that more efforts are needed to enable also nonexpert users' interpretation and understanding of opaque AI models with existing methods and frameworks.

关键词： Explainable AI Interpretable machine learning Human-centered Computing machine learning Life Cycle Human-computer-Interaction

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Research on Federated learning Data Management Method Based on Data Lake Technology 4

Research on Federated Learning Data Management Method Based ...

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4th international conference on computers, Information Processing and Advanced Education, CIPAE 2023

作者： Kai, Liu Liang, Zhang Yaojing, Yang Dazhu, Yan Min, Zhao Yunnan Tobacco Company Kunming China Yunnan Huaye Investment Co. Ltd Kunming China Yunnan Tobacco Company Information Center Kunming China

ISBN: (纸本)9798350342710

With the diversified development of data information in the era of big data, the scale of data is also increasing. Due to the multi-source and heterogeneous data characteristics in the information age, the data sources are also more diverse. It is difficult to identify valid data, and data consistency cannot be guaranteed. Based on the above background, we gained a complete understanding of enterprise requirements and challenges after in-depth research on the federated learning data for data lake technology. this paper designs and implem12 -*+ents a data lake federated learning and data management system. Firstly, the application of enterprise big data is analyzed in detail, and the many deficiencies in its sources and query methods are clarified. On this basis, through in-depth customization of the unified query system, based on data lake technology and federated learning technology, the enterprise's multi-heterogeneous data system is re-planned to run stably and reliably and manage enterprise data more efficiently. © 2023 IEEE.

关键词： Lakes

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Revolutionizing Prostate Cancer Diagnosis: Harnessing the Potential of Transfer learning for MRI-Based Classification 4

Revolutionizing Prostate Cancer Diagnosis: Harnessing the Po...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Degadwala, Sheshang Vyas, Dhairya Trivedi, Sanjay Dave, Harsh Nilaykumar, Patel Khush Dalal, Pankaj Sigma University Department of Computer Engineering Gujarat India The Maharaja Sayajirao University of Baroda Gujarat Vadodara India Gjpiasr Surgeon Department of Surgery Gujrat Anand India Smt.B.K.Shah Medical Institute and Research Center Gujarat Vadodara India Charotar University of Science and Technology Changa India Sigma University Computer Science and Applications Gujrat Vadodara India

ISBN: (纸本)9798350300888

this study introduces a novel framework that harnesses the potential of transfer learning for MRI -based classification in prostate cancer diagnosis. the study explores the challenges of accurately classifying prostate cancer from MRI images and proposes a methodology that integrates pre-trained deep neural networks with a large, annotated dataset. the framework demonstrates superior performance compared to conventional methods, achieving higher Accuracy, Precision, Recall, Fl-Score. the approach exhibits robustness across different MRI scanners and patient populations, highlighting its potential for real-world clinical applications. Additionally, this study emphasizes the interpretability of the model through visualization techniques, aiding clinicians in understanding the underlying pathology and making informed treatment decisions. Overall, this research contributes to the revolutionization of prostate cancer diagnosis, offering improved accuracy and efficiency in clinical settings and paving the way for advanced computer-aided diagnostic systems. © 2023 IEEE.

关键词： Magnetic resonance imaging

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5th international conference on 3D Imaging Technologies-Multidimensional Signal Processing and Deep learning, 3DIT-MSP and DL 2023

5th International Conference on 3D Imaging Technologies-Mult...

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5th international conference on 3D Imaging Technologies-Multidimensional Signal Processing and Deep learning, 3DIT-MSP and DL 2023

ISBN: (纸本)9789819751839

the proceedings contain 29 papers. the special focus in this conference is on 3D Imaging Technologies-Multidimensional Signal Processing and Deep learning. the topics include: the Role and Effect of Deep learning in Landscape Design Innovation;application of 3D Image Technology and Deep learning in Landscape Design;the Influence of University Yoga Course Based on VR Technology on University Students’ Physical Fitness and Special Sports Skills;enhancing Mobile Robot Path Planning through Advanced Deep Reinforcement learning;Innovative application of VR Technology in Ceramic Art Design Exhibition;deep learning-Based Species Classification Using Joint ResNet and Data Augmentation;simulation and application of Digital Display Model Based on computer Vision and Virtual Reality;chinese Image Description Generation Model Based on Recurrent Fusion Encoding;rapid 3D Reconstruction of E-commerce Product Scenes Based on Neural Radiance Fields;research on Video Reuse and Structured Spatio-Temporal Big Data Technology Based on 3D Remote Sensing;evaluation of Slicing Scheme Based on Virtual 3D Printing;based on the Deep Study of 3D Printing Defect Detection Technology Research;three-Dimensional Hexagonal Braiding machine Chassis Parametric Modeling;from Titles to Genres: An Exploration of machine learning Techniques in Movie Classification;construction and Optimization Strategy of Financial Shared Service Centers Based on Blockchain Technology in the Digitization Background;three-Dimensional Model Resume Transfer Technology for the Entire Lifecycle of Power Grid Equipment;application of Virtual Reality Technology in Physical Education Teaching Resources and Student Experience;deep learning-Based Research on the Comprehensive Evaluation System for College Faculty Competence.

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