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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

共 7159 条记录，以下是491-500 订阅

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Integrating Digital Twins in Supply Chain Management Education 11

Integrating Digital Twins in Supply Chain Management Educati...

引用

11th IEEE international conference on Software Defined Systems, SDS 2024

作者： Eletter, Shorouq F. Elrefae, Ghaleb A. Qasim, Amer Houhamdi, Zina Al Ain University College of Business United Arab Emirates Al Ain University College of Engineering United Arab Emirates

ISBN: (纸本)9798331518325

Higher education is undergoing a considerable transformation due to the rapid progress of technological advancements. However, higher education in business is currently experiencing a notable deficiency of experimental laboratories that would enable the practical application of the student's theoretical knowledge. Specifically, typical teaching approaches in supply chain management (SCM) are insufficient to provide students with the skills to navigate the complicated dynamics of modern supply networks. Conversely, experiential learning allows students to gain knowledge through active participation, thought, and interpretation of real-world experiences. Digital twins (DT) are a vital new tool in mixed-reality immersive learning that helps with hands-on learning. this study aims to provide a new pedagogical paradigm for teaching SCM using experiential learning. It connects classroom teaching with real-world business and societal issues, emerging trends, and best practices. the study emphasizes the interplay between knowledge and ideation. therefore, it explores using experiential learning theory to teach the SCM course using digital twins. the study advances experiential learning by illustrating its relevance for teaching complex, data-driven decision-making inside a virtual business-oriented framework. It demonstrates how technology may improve learning in higher education. © 2024 IEEE.

关键词： Contrastive learning

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Privacy-Cost Management in Smart Meters: Classical vs Deep Q-learning with Mutual Information 11

Privacy-Cost Management in Smart Meters: Classical vs Deep Q...

引用

11th IEEE international conference on Smart Energy Grid engineering, SEGE 2023

作者： Shateri, Mohammadhadi Department of Systems Engineering Montreal Canada

ISBN: (纸本)9798350340716

In the realm of smart grids, a privacy-cost management framework was recently proposed, utilizing a modified deep double Q-learning algorithm that incorporates mutual information (MI) as the privacy metric. Numerous studies have confirmed the superior performance of deep reinforcement learning (DRL) frameworks compared to classical RL (CRL) approaches in safeguarding electricity consumption data, albeit at the expense of increased complexity. However, the significance of the privacy measure in the transition from classical RL (utilizing a simpler model) to deep RL (employing a more complex model) remains largely unexplored. this research aims to address this gap by investigating the importance of the privacy measure in the context of smart meter data privacy. Specifically, the privacy-cost management model based on the Q-learning algorithm with MI as the privacy metric is studied in two scenarios (CRL versus DRL). Our findings demonstrate that the classical Q-learning approach performs comparably to the deep Q-learning model while employing a simpler model architecture. this finding challenges previous research, which used flatness as a measure of privacy and favored DRL-based frameworks over CRL approaches. Our study underscores the crucial role of the privacy measure in determining the optimal approach. © 2023 IEEE.

关键词： Reinforcement learning

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ON thE IMPORTANCE AND APPLICABILITY OF PRE-TRAINING FOR FEDERATED learning 11

ON THE IMPORTANCE AND APPLICABILITY OF PRE-TRAINING FOR FEDE...

引用

11th international conference on learning Representations, ICLR 2023

作者： Chen, Hong-You Tu, Cheng-Hao Li, Ziwei Shen, Han-Wei Chao, Wei-Lun Department of Computer Science and Engineering The Ohio State University United States

Pre-training is prevalent in nowadays deep learning to improve the learned model's ***, in the literature on federated learning (FL), neural networks are mostly initialized with random *** attract our interest in conducting a systematic study to explore pre-training for *** multiple visual recognition benchmarks, we found that pre-training can not only improve FL, but also close its accuracy gap to the counterpart centralized learning, especially in the challenging cases of non-IID clients' *** make our findings applicable to situations where pre-trained models are not directly available, we explore pre-training with synthetic data or even with clients' data in a decentralized manner, and found that they can already improve FL ***, many of the techniques we explore are complementary to each other to further boost the performance, and we view this as a critical result toward scaling up deep FL for real-world *** conclude our paper with an attempt to understand the effect of pre-training on *** found that pre-training enables the learned global models under different clients' data conditions to converge to the same loss basin, and makes global aggregation in FL more ***, pre-training seems to not alleviate local model drifting, a fundamental problem in FL under non-IID data. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

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Design and Implementation of a Multifunctional Autonomous Underwater Vehicle 11

Design and Implementation of a Multifunctional Autonomous Un...

引用

2023 IEEE 11th international conference on Information, Communication and Networks, ICICN 2023

作者： Wang, Zixuan Wang, Zicheng Ni, Hao Li, Jintian Hu, Qiwei Xu, Dexin Harbin Engineering University College of Intelligent Systems Science and Engineering Harbin China Joint Institute Harbin Engineering University Southampton Ocean Engineering Harbin China Harbin Engineering University College of Underwater Acoustic Engineering Harbin China

ISBN: (纸本)9798350314014

Currently, as an important tool for exploring and developing the ocean, underwater robots have achieved rapid development at China and abroad. However, due to factors such as technology and cost, the development of small autonomous vehicles in the field of underwater surveying and mapping is relatively slow. the writer proposes a multifunctional autonomous underwater vehicle, which is an intelligent high-level system based on AUV and integrating intelligent perception, autonomous navigation, and fusion decision-making. through the optimization design of mechanical structure, the high flexibility and stability of autonomous vehicle navigation have been improved. through effective data fusion of underwater binocular camera, depth sensor, IMU, and single beam sonar sensor, the dynamic positioning control of autonomous vehicle has been achieved, ultimately achieving a low-cost, high-precision, and high-performance multi-functional underwater autonomous vehicle. © 2023 IEEE.

关键词： Autonomous underwater vehicles

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Renewable Energy Integration and Carbon Neutrality Green Technology Innovation based on intelligent Microgrid Control 11th

Renewable Energy Integration and Carbon Neutrality Green Tec...

引用

11th international conference on Applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Duan, Qingqing College of Finance and Economics Guangdong University of Science and Technology Dongguan China

this paper focuses on the topic of renewable energy integration and carbon neutrality green technology innovation based on intelligent microgrid and focuses on the design and optimization of intelligent microgrid control to improve the utilization efficiency of renewable energy and achieve the goal of carbon neutrality. In this paper, the hardware structure of the microgrid is designed, and the solar, wind and biomass power generation systems are integrated, and the battery energy storage and supercapacitor energy storage systems are optimized. Subsequently, a complete software control system was developed, and based on the application of big data processing and machine learning technology, the intelligent management of the microgrid was realized. then, based on the research of intelligent control strategy and optimization algorithm, the overall response speed and operation efficiency of the system were improved, and a simulation platform was built to achieve effective simulation experiments, and then the efficiency and stability of the system were verified. © 2024 the Authors. Published by Elsevier B.V.

关键词： Carbon sequestration

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Classifying Endangered Species in High-Risk Areas Using Deep learning 37th

Classifying Endangered Species in High-Risk Areas Using Deep...

引用

37th international conference on Industrial, engineering and Other Applications of Applied intelligent Systems (IEA-AIE)

作者： Brito, Cristian Engdahl, Andrea Atkinson, John GHS Sustainabil Santiago Chile Mercado Libre Santiago Chile Univ Adolfo Ibanez Santiago Chile

ISBN: (纸本)9789819746767;9789819746774

Endangered animals are protected by national and international regulations as they are part of the environmental, cultural and genetic heritage. Some of these species are difficult to identify and monitor in the wild, hence very little information and data are available about them. Whenever an organization's actions impact this type of species, it can receive huge fines. Despite this situation, there are currently no specific automated methods to accurately identify this type of animal, using small image datasets. this research introduces the use of Deep learning techniques to address a real environmental problem related to the classification of endangered wildlife that lives within the area of influence of large mining projects. Small datasets were used because there are no public databases available for the target species. the overall model achieved high accuracy in classifying images of different quality and those containing high levels of noise, reaching an average accuracy and F1-score greater than 0.97.

关键词： Endangered Animals Machine learning Image Classification data Augmentation Convolutional Neural Networks Transfer learning

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Anomaly Detectionin Network Traffic Scenarios by Resampling and Majority Voting with Concept Drift: A Hybrid Approach 11

Anomaly Detectionin Network Traffic Scenarios by Resampling ...

引用

11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Singh, Richa Srivastava, Nidhi Kumar, Ashwani Amity Institute of Information Technology Amity University Lucknow Campus Lucknow India Srm Institute of Science & Technology Computer Science and Engineering Faculty of Engineering & Technology Ghaziabad India

ISBN: (纸本)9798350350357

Addressing data imbalance is a critical difficulty in the setting of a heavily skewed dataset because the dominant class disproportionately influences classifier accuracy, especially when inadequate data impede the learning of minority class characteristics. For multi-class cases, traditional binary classification approaches are inadequate. the suggested architecture uses a Weighted Majority Voting Classifier (WMVC), noise cleaning, limited under-sampling, and oversampling to overcome this. One important step is to set an average class size limit by dividing the entire sample number by the number of classes. One or more majority classes receive Random Under sampling, all minority classes receive an up sample using the Adaptive Synthetic Sampling Approach, and noise reduction is achieved via Tomek Link. the resulting WMVC is contrasted with Convolutional Neural Network (CNN) classifiers, XGBoost, and six additional ensemble techniques by utilizing tree-based ensemble algorithms. Comparative study shows that balanced data performs better than unbalanced data. WMVC significantly beats CNN, XGBoost, and the other ensemble methods, and it improves performance for difficult minority classes while successfully decreasing classifier bias towards the majority class. © 2024 IEEE.

关键词： Anomaly detection

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Facial Expression Classification Using Extreme learning Machine and Genetic Algorithm 11

Facial Expression Classification Using Extreme Learning Mach...

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11th international conference on Electrical engineering, Computer Science and Informatics, EECSI 2024

作者： Bachtiar, Fitra Abdurrachman Nuha, Mufidatun Universitas Brawijaya Intelligent System Laboratory Computer Science Faculty Malang Indonesia

ISBN: (纸本)9798350355314

Feature extraction is a critical component of facial expression recognition, requiring careful selection to ensure optimal performance. Traditional template-based models often struggle with computational efficiency and are sensitive to factors such as illumination, color variation, and image quality. Although geometry-based models have demonstrated robustness in feature extraction, further optimization remains needed to enhance the recognition accuracy. In this study, the CK+ dataset is utilized, and employ facial landmarks as a geometry-based feature extraction method. the extracted features are then processed using an Extreme learning Machine combined with Genetic Algorithms (ELM-GA) to select a subset of features and classify facial expressions. the proposed model effectively reduces the number of features used in the classification model by more than half, improving computational efficiency. Additionally, the accuracy of the proposed model outperforms the VGGNet and combination of machine learning and Local Binary Patterns (LBP) and machine learning and Gray-Level Co-occurrence Matrix (GLCM) for feature extraction. Although the current model does not represent the best possible solution, the promising accuracy results provide a solid foundation for further refinement and development in facial expression recognition systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Myocardial Infarction Prediction Using RNN Deep learning Algorithm on Phonocardiogram Signals 11

Myocardial Infarction Prediction Using RNN Deep Learning Alg...

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11th international conference on Information and Communication Technology, ICoICT 2023

作者： Amini, Sabilla Suci Mandala, Satria Pramudyo, Miftah Telkom University School of Computing Bandung Indonesia Engineering & School of Computing Bandung Indonesia Padjadjaran University Department of Cardiology and Vascular Medicine Bandung Indonesia

ISBN: (纸本)9798350321982

Myocardial Infarction (MI) is a cardiovascular disease characterized by the death of the heart muscle. Blockage of blood vessels is one of the causes of myocardial infarction. MI causes blood flow to the heart to become blocked. In the worst case, it causes cardiac death. In general, the screening of cardiovascular disease can be performed using an electrocardiogram (ECG), phonocardiogram (PCG), and photoplethysmogram (PPG). Among these three signals, PCG detection is rarely used in MI detection. In fact, PCG signal has advantages such as non-invasive, efficient, and low-cost. Deep learning is also one of the methods used by many previous researchers to classify an object. However, from several previous research, the implementation of deep learning algorithms on PCG signals, especially in cases of myocardial infarction, has yet to be carried out. As a solution to the problem, this research proposes developing a deep-learning model to predict myocardial infarction based on PCG signals. this research uses the Recurrent Neural Network (RNN) model to classify PCG signal data. PCG signal data was obtained from Hasan Sadikin Hospital Bandung, Indonesia. the experiments showed that the RNN model had a good performance, which resulted in a sensitivity of 95.4%, specificity of 95.2%, and accuracy of 95.3%. © 2023 IEEE.

关键词： learning algorithms

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intelligent Pavement Condition Rating System for Cycle Routes and Greenways 11

Intelligent Pavement Condition Rating System for Cycle Route...

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11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Haider Shah, Syed M. Qureshi, Waqar Shahid O’ Dea, Gerard Ullah, Ihsan Insigth SFI Research Center for Data Analytics University of Galway Galway Ireland School of Computer Science University of Galway Galway Ireland Transport Infrastructure Ireland Ireland

ISBN: (纸本)9789897587450

this study introduces an intelligent framework for assessing cycling infrastructure, addressing the limitations of traditional pavement evaluation methods. At the core of the system is the CRSI, a 1-to-5 rating scale specifically designed to evaluate cycle routes based on critical factors like surface quality, vegetation encroachment, and drainage. A dataset of over 40,000 frames, extracted from videos captured using handlebar-mounted GoPro cameras and annotated by experts, forms the foundation of the system. Four deep learning (DL) models LeNet, AlexNet, EfficientNet-B2, and Swin Transformer-Tiny were trained and evaluated for Cycle Route Surface Index (CRSI) classification. Among all models, Swin Transformer-Tiny performed the best, achieving an impressive accuracy of 99.90%. To further test its robustness, we evaluated the system on four new videos, from which four separate frame sets were generated. Among these, Swin Transformer-Tiny again delivered the highest accuracy, reaching 86.67%, confirming its reliability across different datasets. this CRSI-based framework provides a scalable, automated solution for evaluating cycling infrastructure, empowering transportation agencies to improve maintenance and ensure safer, more accessible cycling networks. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Convolutional neural networks

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