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检索条件"任意字段=4th International Conference on Optimization Learning Algorithms and Applications"

共 13501 条记录，以下是131-140 订阅

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Automatic hyperparameter tuning with hyperband for side-channel analysis in deep learning 4

Automatic hyperparameter tuning with hyperband for side-chan...

引用

4th international conference on Advanced algorithms and Neural Networks, AANN 2024

作者： Zhu, Xiongjie Tian, Xi Li, Yuan Huang, Wenbo Sun, Pengyue School of Electronic Science National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781510686106

Deep learning methods provide strong support for side channel analysis, and a large number of research results prove the advantages of this method in the field of side channel applications. Using deep learning for side-channel analysis offers advantages that traditional methods lack, such as its ability to counter certain protective measures like masking, and it doesn't require complex feature extraction processes. these advantages have made deep learning methods the primary tool for side-channel ***, this does not mean that the use of the method is without drawbacks. One of the biggest difficulties is to find the appropriate hyperparameters for the neural network to bring out the best performance of the side-channel analysis. In this paper, we propose an automatic hyperparameter tuning method for deep learning based on Hyperband in the field of side-channel analysis. this method can speed up the hyperparameter search by adaptive resource allocation. Experiments show that regardless of the type of leakage model and neural network, the hyperparameter optimization scheme in this paper performs well. Compared with Bayesian optimization and random search, the proposed scheme has better tuning performance and saves more than half of the tuning time. © 2024 SPIE.

关键词： Resource allocation

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Research on UFS Firmware optimization Method 4

Research on UFS Firmware Optimization Method

引用

4th IEEE international conference on Power, Electronics and Computer applications, ICPECA 2024

作者： Cao, Jia Chang, Xinfang Yang, Wenhao Gao, Xuezhu FAW-VW Corporation Limited Changchun130013 China

ISBN: (纸本)9798350359589

In order to solve the problem of slow data transmission in large capacity storage media UFS in vehicle domain controllers, this paper proposes a firmware optimization method that improves code execution efficiency and saves code execution time by regrouping firmware level codes;At the same time, by storing the optimal voltage in the chip, the time to find the optimal voltage is reduced. the experimental results indicate that the data transmission speed of UFS is significantly improved during low-temperature shock starting. © 2024 IEEE.

关键词： Firmware update low-temperature shock NAND UFS

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Novel Abnormal Data Mining and Detection Algorithm for Intelligent Systems 4

Novel Abnormal Data Mining and Detection Algorithm for Intel...

引用

4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Xu, Shaomei Fan, Yanling Lin, Bing Continuing Education Service Center Shandong Institute of Commerce and Technology Shandong Jinan250103 China

ISBN: (纸本)9798331523923

In intelligent systems, as the amount of data increases, how to analyse data and extract abnormal information is an important task. Based on this, in order to improve the detection efficiency and accuracy of abnormal data in intelligent systems, this study proposes a new and efficient abnormal data mining and detection algorithm. In the initial modelling process, this study introduced the minimum modification cost principle based on cross entropy for modelling. this algorithm optimizes the time series data cleaning process, thereby removing noise data while maintaining the integrity of the data sequence. then, the extreme gradient boosting algorithm is combined with the Gaussian kernel function to achieve data dimension reduction. this operation can reduce data dimensions and feature redundancy. Finally, the overall anomaly detection operation by integrating the improved Ada boost algorithm with the sampling balance technology can improve the accuracy of the classifier while solving the problem of unbalanced data. the experimental results show that the proposed method is superior to the traditional algorithms in comprehensive indicators. © 2025 IEEE.

关键词： Abnormal Data Detection Data Mining algorithms Efficiency optimization Intelligent Systems

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Deep learning-based ECG signal processing algorithms and their applications in cardiac health monitoring 4

Deep learning-based ECG signal processing algorithms and the...

引用

4th international conference on Advanced algorithms and Signal Image Processing, AASIP 2024

作者： Zhang, Zhiguo Zhonghuan Information College Tianjin University of Technology Tianjin China

ISBN: (数字)9781510682818

ISBN: (纸本)9781510682801

this paper explores deep learning-based algorithms for electrocardiogram (ECG) signal processing and their applications in cardiac health monitoring. Initially, we provide an overview of the fundamental principles of ECG signals and the suitability of deep learning for ECG signal processing. Subsequently, we discuss data preprocessing techniques and the selection and construction of deep learning models aimed at enhancing the accuracy of ECG signal processing. Finally, the focus is on the application of these algorithms in the detection of arrhythmias and the diagnosis of myocardial ischemia, validated through experimental results. the findings of this study offer new technological approaches for cardiac health monitoring and foster the application of deep learning in the field of ECG signal processing. © 2024 SPIE.

关键词： Electronic health record

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WEB Content Recommendation algorithms in Distance Education 24

WEB Content Recommendation Algorithms in Distance Education

引用

4th international conference on Internet of things and Machine learning

作者： Ma, Lin Ma, Lulu Wu, Yonglan Luo, Feng Cao, Xingtian Fuyang Presch Educ Coll Dept Primary Educ Fuyang City Anhui Peoples R China Ningbo Univ Business Coll Ningbo Zhejiang Peoples R China Jiangxi Univ Chinese Med Modern Ind Coll Educ Practice Dept Nanchang Jiangxi Peoples R China Huaihua Univ Phys Educ & Hlth Coll Huaihua Hunan Peoples R China Tongcheng Teachers Coll Dept Presch Educ Anqing Anhui Peoples R China

ISBN: (纸本)9798400710353

In the realm of modern distance education, the application of web content recommendation algorithms has emerged as a pivotal means to enhance learning efficiency and personalize the educational experience. this paper aims to explore the various technologies of web-based personalized recommendation within the context of remote education and their impact on learning outcomes. through the analysis of recommendation methods based on content, rules, collaborative filtering, demographic information, and association rules, this study systematically discusses the design and implementation of personalized recommendation algorithms in distance education. Notably, a detailed analysis of collaborative filtering algorithms, including user-based and item-based collaborative filtering as well as model-based approaches, provides both theoretical and technical support for remote education. Experimental results indicate that personalized recommendation algorithms not only significantly improve the matching of learning resources but also effectively enhance students' satisfaction and learning outcomes. this research offers valuable insights for the design and optimization of future distance education systems.

关键词： WEB personalized recommendations content recommendation algorithms distance learning

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Enhancing Abstractive Text Summarization with Proximal Policy optimization 4

Enhancing Abstractive Text Summarization with Proximal Polic...

引用

4th international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Reddy, K. Lokeshwar Phani Shanmukh, M. Charan Kumar, M. Kumar M, tharun Arjun Kumar, C. Prasanna Kumar, R. Venkatraman, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

In the realm of Natural Language Processing (NLP), Abstract Text Summarization (ATS) holds a crucial position, involving the transformation of lengthy textual content into concise summaries while retaining essential information. this research paper delves into the utilization of advanced algorithms such as Seq-Seq Transfer learning, Pegasus, and Proximal Policy optimization for the abstraction text summarization process. Specifically, this study incorporates cutting-edge techniques like deep neurotemporal models and Reinforcement learning (RL), with a focus on Preorder Language Models (PTLM). the integration of knowledge into the development process further enhances the efficacy of ATS applications. the paper meticulously examines the recent advancements in abstract concept extraction, addressing both historical and existing challenges while proposing innovative solutions. Emphasizing the significance of data and metrics in abstract ATS, this research incorporates the widely used ROUGE metric for evaluation. By comparing influential models, the study provides a comprehensive overview of the field's evolution. In addition, it explores the integration of transformative graph-based Transformer architecture and PTLM, which have revolutionized NLP. the research paper concludes by offering valuable insights into the future of abstract text summarization, paving the way for further exploration and innovation in the domain. © 2024 IEEE.

关键词： Abstract Text Summarization DQN Algorithm Natural Language Processing Pegasus Algorithm Seq-Seq Algorithm Transfer learning

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Enhanced Medicare Fraud Detection Using Graph Convolutional Networks 4

Enhanced Medicare Fraud Detection Using Graph Convolutional ...

引用

4th international conference on Artificial Intelligence and Signal Processing

作者： Rakesh, Molkam Shetty, Pushparaj D. Natl Inst Technol Karnataka Dept Math & Computat Sci Surathkal India

ISBN: (纸本)9798350350661;9798350350654

this paper explores the applications of Graph neural networks (GNN) for enhancing Medicare fraud detection. Graph convolutional network (GCN) is a type of graph neural network. Governments and insurance companies are continuously adapting new technologies to detect and prevent fraud activities and trying to minimize financial losses and improve services because every year they lose billions of dollars due to Medicare fraud. Machine learning algorithms fail to analyze the graph data structure but Graph neural networks are good at analyzing the complex relational data and they directly integrate with the learning process. Machine learning algorithms are facing scalability and generalization across diverse graphs. GNN works on graph data structure, using unique IDs as nodes in a graph, with edges illustrating their relationships. Graph Neural Networks is used to improve the accuracy and efficiency of fraud detection by learning the complex relational information obtained from providers, beneficiaries, and physicians. We created a graph database based on the healthcare provider dataset. In this graph database, two types of heterogeneous nodes are there that are beneficiary and medicare provider nodes. the connection between the beneficiary and medicare providers is a power edge and the connection between providers is a shared-physician edge. We developed a fraud detection model using both machine learning and graph neural networks. Our Graph convolutional Network (GCN) model performed well compared to the basic machine learning (Logistic regression) model. the complex relationships between provider and beneficiary, provider and physician helped to detect medicare fraud using our model.

关键词： Graph convolutional networks Graph neural networks Deep learning Machine learning Logistic regression Medicare fraud detection

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Exploring Deep learning Algorithm for Credit Scoring 4

Exploring Deep Learning Algorithm for Credit Scoring

引用

4th international conference on Intelligent Cybernetics Technology and applications, ICICyTA 2024

作者： Putri, Velani Febriana Fahrudin, Tora School of Applied Science Telkom University West Java Bandung Indonesia

ISBN: (纸本)9798331506490

this study investigates the use of deep learning algorithms in credit scoring, a crucial instrument for determining a borrower's creditworthiness. As technology develops, there is a rising movement to incorporate technology including credit scoring into the financial sector. Numerous research endeavours have attempted to ascertain whether deep learning can enhance the credit scoring system. the capacity of deep learning algorithms to study and understand vast volumes of data makes them extremely effective methods with noticeable increases in classification performance in a variety of machine learning applications. this study assesses the effectiveness of models on deep learning using an extensive dataset of borrower data. In addition to using three methods Deep learning, Convolutional Neura Network (CNN), Feedfordward Neural Network (FNN), and Autoendoders, classification metrics were used as evalution tools. Deep learning algorithms are therefore thought to hold a lot of promise for raising the credit scoring system's precision and effectiveness. © 2024 IEEE.

关键词： Adversarial machine learning

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Recent Advances in SAR Image Analysis Using Deep learning Approaches: Examples of Speckle Denoising and Change Detection 4

Recent Advances in SAR Image Analysis Using Deep Learning Ap...

引用

4th international conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2024

作者： Imad, Hamdi Sara, Zada Hajji, Morad Yassine, Tounsi Abdelkrim, Nassim Superior School Of Technology Moulay Ismail University Image Laboratory Meknes Morocco Chouaib Doukkali University Instrumentation Measurement And Control Team Faculty Of Science El Jadida Morocco

ISBN: (纸本)9798350309508

Deep learning is a machine learning technique that has significantly improved results in many areas such as computer vision, speech recognition, machine translation, and biomedical imaging analysis and understanding. Recently in the field of synthetic aperture radar (SAR) images analysis, deep learning approaches have become a powerful tool making information extraction from SAR images very accurate and giving good interpretations related to environment and earth surface observation. this work examines the recent development of scientific productions on the applications of deep learning approaches in the SAR imaging field. these applications concern the speckle noise reduction from SAR images and the algorithms-based method for change detection and classification of these remote sensing images. Moreover, an analysis of scientific production in this field is discussed by exploiting the Scopus database. © 2024 IEEE.

关键词： Change detection

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Solar Energy Forecasting using Ensemble learning Method 13

Solar Energy Forecasting using Ensemble Learning Method

引用

13th international conference on Renewable Energy Research and applications

作者： Ersoz, Betul Tasdelen, Muhammed Cafer Eren, Sueda Sagiroglu, Seref Oter, Ali Gazi Univ Comp Educ & Instruct Technol Ankara Turkiye Gazi Univ Comp Engn Ankara Turkiye Kahramanmaras Sutcu Imam Univ Elect & Automat Kahramanmaras Turkiye

ISBN: (纸本)9798350375596;9798350375589

Integrating renewable energy resources with new technologies such as artificial intelligence (AI) is critical for balancing energy supply and demand. the predictability of variable energy sources, such as solar Energy, plays a vital role in maintaining the stability and efficiency of electrical grids. In this context, this study examines the use of various algorithms in AI applications within renewable energy systems. A comprehensive literature analysis reveals the advantages and challenges of popular AI techniques such as Artificial Neural Networks, Support Vector Machines, and Decision Trees in energy prediction and optimization. this study mainly focuses on the effectiveness of boosting algorithms such as XGBoost, CatBoost, AdaBoost, LightGBM algorithms and Ensemble learning in estimating alternating current (AC). the results have shown that the Ensemble model achieved the highest R-2 value (0.942) and the lowest error metrics MAE (0.040) to demonstrate its superior ability to explain variance in data and make precise predictions. this paper critically evaluates existing methods and proposes an innovative approach to AC prediction in solar power systems using advanced machine-learning techniques.

关键词： Artificial Intelligence Ensemble learning Renewable Energy Solar Energy Alternating Current

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