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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 6841 条记录，以下是21-30 订阅

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Proceedings - 2024 IEEE/ACM 3rd international conference on AI engineering - Software engineering for AI, CAIN 2024

Proceedings - 2024 IEEE/ACM 3rd International Conference on ...

引用

3rd international conference on AI engineering, CAIN 2024, co-located with the 46th international conference on Software engineering, ICSE 2024

ISBN: (纸本)9798400705915

The proceedings contain 48 papers. The topics discussed include: a taxonomy of foundation model-based systems through the lens of software architecture;investigating the impact of SOLID design principles on machine learning code understanding;identifying architectural design decisions for achieving green ML serving;modeling resilience of collaborative ai systems;an exploratory study of V-model in building ML-enabled software: a systems engineering perspective;engineering challenges in industrial AI;what about the data? a mapping study on data engineering for AI systems;unmasking data secrets: an empirical investigation into data smells and their impact on data quality;an exploratory study of dataset and model management in open source machine learning applications;developer experiences with a contextualized AI coding assistant: usability, expectations, and outcomes;innovating translation: lessons learned from BWX generative language engine;and towards a responsible AI metrics catalogue: a collection of metrics for AI accountability.

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Employing machine learning Techniques for Depression Prediction 3

Employing Machine Learning Techniques for Depression Predict...

引用

3rd international conference for Advancement in Technology, ICONAT 2024

作者： Mishra, Swati Srivastava, Divya JSS Academy of Technical Education Department of Electrical and Electronics Engineering Noida India

ISBN: (纸本)9798350354171

This paper focuses on employing various machine learning algorithms- K Nearest Neighbors and Extra Trees. By analyzing patterns in behavioral, physiological, and contextual data, the model aims to identify early indicators of depression, providing a potential tool for proactive intervention and support in mental health. The study emphasizes the integration of machine learning techniques to enhance predictive accuracy and contribute to the development of accessible and timely depression detection systems. F1 score was added which helped to identify the best machine learning algorithm among the applied. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

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Research on Fire Alarm System Problems Based on Comprehensive Evaluation Method and machine learning 3

Research on Fire Alarm System Problems Based on Comprehensiv...

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3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Yang, Juncheng Chang'an University Xi'an China

ISBN: (纸本)9798350380989

machine learning applied to fire alarm systems is an increasingly common optimization problem. In this paper, a method based on comprehensive evaluation and machine learning is proposed. Firstly, relying on the literature and data sets, 10 indicators such as the number of fires, the failure rate, the accuracy rate, etc. are extracted to comprehensively evaluate the performance of each type of alarm component, and the entropy weight method, the information weight method and the TOPSIS method are used to derive the performance level rating of each component, and the fire alarm intelligent research and judgment system is constructed. Then, a BP neural network was used to build a training model and test the alarm data set for a certain region, and finally predicted the real number of alarms of each test device with an accuracy of 95.4%. These results can help the government plan to select more reliable fire detector types and improve the fire alarm accuracy to further optimize the fire alarm system. © 2024 IEEE.

关键词： BP Neural Networks Fire Alarm System machine learning TOPSIS

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A Robust Defense Algorithm for Backdoor Erasure Based on Attention Alignment in Federated learning 3

A Robust Defense Algorithm for Backdoor Erasure Based on Att...

引用

3rd international Joint conference on Information and Communication engineering (JCICE)

作者： Han, Haotong Dai, Xu Li, Dan Wang, Erfu Heilongjiang Univ Elect Engn Coll Harbin Peoples R China

ISBN: (纸本)9798350388732;9798350388725

The widespread adoption of deep learning has led to recurring data security issues. Consequently, there is an even greater need to preserve data privacy, resulting in data becoming increasingly challenging to share and creating what are often referred to as 'data islands', which FL effectively addresses. FL, a new deep learning paradigm, enables collaborative model training across multiple terminals distributively, facilitating data sharing and machine learning modeling while ensuring robust data privacy protection. Individual client could be backdoor attacked by poisoning the data. This paper introduces a robust defense method for backdoor attacks based on reverse trigger engineering, model hardening, and attention distillation mechanisms. In this paper, we conduct a lot of experiments on different classical datasets with different settings, and the results demonstrates that the proposed strategy improve robustness by reducing the attack success rate while preserving main task accuracy.

关键词： federated learning backdoor defense adversarial training attention distillation

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The Role of Linear Algebra in Developing machine learning Solutions 3

The Role of Linear Algebra in Developing Machine Learning So...

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Mhaske, Mayuri Gidhad, Bhagwat Maniyar, Kamalkishor Ghuge, Gajanan Dr. D. Y. Patil Institute of Technology Pimpri Pune India Savitribai Phule Pune University Pune India Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798350354171

The branch of mathematics known as 'linear algebra' deals with vectors, matrices, and linear transformations. It is useful in a variety of disciplines, including computer science, physics, engineering, and machine learning, where it is essential to data analysis, classification, and machine learning algorithms. Additionally, machine learning is used to allow computer programmers to determine, without explicit programming, which of its applications - graphing, regression, and predicating is most closely related to mathematics, primarily linear algebra. Because machine learning is strongly related to statistics, we will briefly explain some statistical ideas before examining what linear algebra and machine learning are in this work. Afterwards, we demonstrate how linear algebra is used in support vector machines and linear regression, two types of machine learning algorithms. © 2024 IEEE.

关键词： Adversarial machine learning

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Prediction of Attention Deficit Hyperactivity Disorder Using machine learning Models 3

Prediction of Attention Deficit Hyperactivity Disorder Using...

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3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Parameswaran, S. Gowsheeba, S.R. Praveen, E. Vishnuram, S.R. KSR Institute for Engineering and Technology Dept of Biomedical Engineering Tiruchengode India

ISBN: (纸本)9798350372120

machine learning models are increasingly utilized to predict ADHD based on various features and behavioural indicators. These models analyze demographic, clinical, and behavioural data to distinguish individuals with ADHD from those without, showing promising results in accurate prediction. Feature selection techniques are applied to identify key factors influencing predictions, and enhancing diagnostic procedures for ADHD. This paper showcases the promising application of machine learning for early ADHD diagnosis and predicting treatment outcomes, thereby enhancing diagnostic tools' efficiency. By employing machine learning to characterize ADHD, valuable insights into its neural correlates and underlying mechanisms are gained, facilitating a deeper understanding of its complexity and variability. Overall, machine learning shows significant promise in improving ADHD diagnosis and intervention strategies. © 2024 IEEE.

关键词： Forecasting

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Design and implementation of career planning system based on machine learning 3

Design and implementation of career planning system based on...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Sun, Chendao Jilin Communications Polytechnic Jilin China

ISBN: (纸本)9798350380989

To better cater to the needs of job seekers and provide enhanced career development directions, this study proposes a novel employment guidance and career planning system integrating machine learning algorithms, along with the creation of a career matchmaking framework. Firstly, leveraging the RippleNet algorithm model, we construct a knowledge graph-based career planning recommendation system. The system-building process involves investigating key technologies in data processing and recommendation algorithms. Subsequently, through the implementation of database security design, we ensure the information security of users within the system, thereby maintaining the efficient operation of the system. The results indicate that the system is well-functioning, exhibits stable performance, and is capable of meeting the fundamental requirements of a career planning system. © 2024 IEEE.

关键词： career planning system machine learning algorithms recommendation system RippleNet algorithm

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Automatic machine learning Participation in Power Load Forecasting Under the Background of Big data 3

Automatic Machine Learning Participation in Power Load Forec...

引用

3rd international conference on Energy, Power and Electrical Technology, ICEPET 2024

作者： Zhang, Xueqiu Han, Jinshan North China Electric Power University School of Economics and Management Beijing China

ISBN: (纸本)9798350352658

Power load forecasting is one of the important tasks in controlling system costs, and accurate and effective load forecasting can reasonably arrange the operating status of power grid generators. machine learning, as an important prediction method, has gradually become an important choice for complex load forecasting. However, due to the numerous machine learning methods, it is difficult to accurately and effectively select suitable algorithms in different regions when selecting prediction methods, and the parameter settings of algorithms often require debugging. This article is based on the automatic machine learning platform PyCaret, analyzing the convenience and effectiveness of automatic selection algorithms, and comparing them with the prediction accuracy of grey prediction and neural network algorithms. The results indicate that using existing platforms can accurately fit data and achieve portable and effective power load forecasting. © 2024 IEEE.

关键词： Load forecasting machine learning PyCaret

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AI-Powered Predictive Cybersecurity in Identifying Emerging Threats through machine learning 3

AI-Powered Predictive Cybersecurity in Identifying Emerging ...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Wang, Ruibin Dongying Vocational Institute Dongying China

ISBN: (纸本)9798350380989

With an emphasis on the incorporation of the Wasserstein Generative Adversarial Network (WGAN) algorithm, this study examines the revolutionary potential of AI-Powered Predictive Cybersecurity in detecting new risks using machine learning. The adoption of an innovative cybersecurity approach within CyberGuard Bank is investigated in this paper through the use of a fictitious case study set in the financial sector. With the use of cutting-edge machine learning techniques and WGAN-generated synthetic data, the model exhibits improved threat detection, false positive reduction, real-time threat mitigation, and scalability capabilities. The implementation places a strong emphasis on ethical issues, including as bias prevention and privacy compliance, which positions the company as a responsible steward of AI-driven cybersecurity. The results highlight the strategic importance of WGAN and AI in strengthening defences against the constantly changing and dynamic array of cyberthreats. © 2024 IEEE.

关键词： AI-Powered Predictive Cybersecurity Ethical Considerations machine learning Real-Time Threat Mitigation Scalability Wasserstein Generative Adversarial Network (WGAN)

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Component-based Approach to Software engineering of machine learning-enabled Systems 24

Component-based Approach to Software Engineering of Machine ...

引用

IEEE/ACM 3rd international conference on AI engineering - Software engineering for AI (CAIN)

作者： Indykov, Vladislav Univ Gothenburg Chalmers Gothenburg Sweden

ISBN: (纸本)9798400705915

machine learning (ML) - enabled systems capture new frontiers of industrial use. The development of such systems is becoming a priority course for many vendors due to the unique capabilities of Artificial Intelligence (AI) techniques. The current trend today is to integrate ML functionality into complex systems as architectural components. There are a lot of relevant challenges associated with this strategy in terms of the overall system architecture and in the context of development workflow (MLOps). The probabilistic nature, crucial dependency on data, and work in an environment of high uncertainty do not allow software engineers to apply traditional software development methodologies. As a result, there is a community request to systematize the most relevant experience in building software architectures with ML components, to create new approaches to organizing the process of developing ML-enabled systems, and to build new models for assessing the system quality. Our research contributes to all mentioned directions and aims to create a methodology for the efficient implementation of ML-enabled software and AI components. The results of the research can be used in the design and development in industrial settings, as well as a basis for further studies in the research field, which is of both practical and scientific value.

关键词： machine learning software architecture software quality

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