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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431-440 订阅

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Performance Analysis of Face and Emotion Recognition Systems in Video conference Based on Web Application 11

Performance Analysis of Face and Emotion Recognition Systems...

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11th international conference on ICT for Smart Society, ICISS 2024

作者： Hakim, Umar Siedharta, Vincentius Verel Ghaida, Dhiya Risalah Septiana, Asyifa Imanda Mutijarsa, Kusprasapta Rosmansyah, Yusep School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Indonesia University of Education Cibiru Regional Campus Bandung Indonesia

ISBN: (纸本)9798350389654

this research tackles the difficulty of evaluating emotions in online education, where restricted interaction impedes comprehension. Our proposal is a web application constructed on the Google Cloud Platform, Vercel, ***, and FastAPI, which employs a Convolutional Neural Networks (CNNs) model for real-time emotion recognition during online courses. By capturing conference video and analyzing participant faces in screenshots, the application presents emotions on a user-friendly dashboard. Although the front-end performance shows potential, back-end optimization is necessary. this application possesses the potential to improve emotion assessment and ultimately enhance online learning results. © 2024 IEEE.

关键词： Convolutional neural networks

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

ICICN 2023 - 2023 IEEE 11th International Conference on Info...

引用

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

ISBN: (纸本)9798350314014

the proceedings contain 156 papers. the topics discussed include: the wide diameter of strong product graphs of complete graphs and any graphs;the AI-driven permissioned blockchain system for the services of future Internet of things;DID-IDS: a novel diffusion-based imbalanced data intrusion detection system;design and development of automatic detection system for locomotive wheel rims RCF cracks based on ACFM;unsupervised learning exploration for duplicate bug report detection;RIS-aided rate optimization research for PS-SWIPT system;research on acoustic emission signal acquisition system based on frequency shifting;network security situation prediction based on sequential extreme learning machine;vehicle trajectory prediction based on spatial-aware transformer;and an intelligent multiple access protocol: federated transfer reinforcement learning for satellite-ground network.

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AddShare+: Efficient Selective Additive Secret Sharing Approach for Private Federated learning 11

AddShare+: Efficient Selective Additive Secret Sharing Appro...

引用

11th IEEE international conference on data Science and Advanced Analytics, DSAA 2024

作者： Asare, Bernard Atiemo Branco, Paula Kiringa, Iluju Yeap, Tet EECS University of Ottawa Faculty of Engineering OttawaON Canada

ISBN: (纸本)9798350364941

Federated learning (FL) enables collaborative training of Machine learning (ML) models while maintaining user data privacy. However, leaked model updates can reveal private training data. Existing solutions using additive secret sharing introduce intermediary servers, increasing complexity and communication overhead, and often lack privacy guarantees. We propose AddShare+, which enhances efficiency and scalability by creating additive shares for a subset of model weight parameters and using the Elliptic Curve Integrated Encryption Scheme (ECIES) for faster, lighter model encryption. By sampling and splitting a percentage of local weight parameters, AddShare+ reduces computation and communication costs while maintaining model accuracy. We implemented and evaluated AddShare+ on multiple datasets, comparing it with baseline approaches including FedAvg, SCOTCH, FedShare, and AddShare. Results demonstrate that AddShare+ maintains accuracy while significantly reducing running time per round. Notably, sharing as low as 25% of model weights decreases bandwidth demands by over 5x while preserving accuracy within 0.05 % of the full model. Our empirical results demonstrate significant reductions in running time per round with strong privacy guarantees, highlighting the potential of lightweight partial sharing solutions for privacy-preserving FL in resource-constrained environments, paving the way for more efficient and secure collaborative learning systems. © 2024 IEEE.

关键词： additive secret sharing federated learning privacy preservation secure aggregation

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Fuzzy-Based Modeling with semi-automatic Rule learning for Eye-Tracking data Analysis: A Case Study on Raven's Progressive Matrices 11

Fuzzy-Based Modeling with semi-automatic Rule Learning for E...

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IEEE 11th international conference on Serious Games and Applications for Health (SeGAH)

作者： da Silva, Abner Cardoso de Mesquita, Cintia Machado Silva-Calpa, Greis Sierra-Franco, Cesar A. Raposo, Alberto Barbosa Pontif Catholic Univ Rio de Janeiro Dept Informat Rio De Janeiro Brazil Univ Fed Rio de Janeiro Inst Psychiat Rio De Janeiro Brazil Pontif Catholic Univ Rio de Janeiro Tecgraf Inst Rio De Janeiro Brazil

ISBN: (纸本)9798350346077

Eye-tracking allows for the identification of events inherent to human vision, capable of revealing implicit aspects of one's behavior. Identifying and correlating eye-tracking data with domain-specific knowledge can be a complex task. In this context, intelligent systems can be employed to combine specialist's knowledge and experience with eye-tracking data, to better understand the implicit information contained in human visual behavior. this works proposes a framework for developing eye-tracking-based systems that enhance assessment accuracy for specific tasks. this framework result in a model that captures the specialist's knowledge of subjective aspects. the focus of this study is to evaluate the correlation between visual behavior and efficacy in solving tests inspired by Raven's Progressive Matrices. We chose an approach based on fuzzy rules, as it allows us to represent knowledge more legibly to end-users. the model's rules were developed and validated alongside a psychology expert. the system was tested with user data and showed promising results.

关键词： Expert systems Eye-tracking Fuzzy Rules

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IoT Integrated intelligent Entry Monitoring System for Covid-19 Detection 18

IoT Integrated Intelligent Entry Monitoring System for Covid...

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Prasanna, B. Lakshmi Reddy, M. Pala Prasad Kumar, S. Satheesh Karthik, A. Devaraju, T. Reddy, N. Sreeramula Institute of Aeronautical Engineering Department of Ece Hyderabad India Institute of Aeronautical Engineering Department of Eee Hyderabad India Institute of Aeronautical Engineering Department of It Hyderabad India Department of Eee Tirupati India Annamacharya Institute of Technology and Sciences Department of Eee Rajampet India

ISBN: (纸本)9789380544519

the occurrence of the COVID-19 disease has led to a decline in the number of visitors to public spaces such as Schools, Colleges, parks, libraries, and museums. Despite the Indian Government easing restrictions for public visits to these places in early 2021, people are hesitant to venture out due to the lingering fear associated with the impact of the disease even for today. To overcome this issue a Bilateral Screening Process is proposed for safe visiting procedure. It involves measuring the body temperature of a person using IR temperature sensor and the availability of a face mask will be monitored by Open Computer Vision. If any deviation is detected the system promptly triggers mail alerts to notify relevant people and also prevent access through gate control. With seamless connectivity and centralized management facilitated by IoT integration, the system enables real-time monitoring, analysis, and data processing. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： COVID-19 Face mask detection IoT MLX90614 Pi Camera Raspberry Pi3 Temperature detection

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META-learning ADAPTIVE DEEP KERNEL GAUSSIAN PROCESSES FOR MOLECULAR PROPERTY PREDICTION 11

META-LEARNING ADAPTIVE DEEP KERNEL GAUSSIAN PROCESSES FOR MO...

引用

11th international conference on learning Representations, ICLR 2023

作者： Chen, Wenlin Tripp, Austin Hernández-Lobato, José Miguel University of Cambridge MPI for Intelligent Systems United Kingdom University of Cambridge United Kingdom

We propose Adaptive Deep Kernel Fitting with Implicit Function theorem (ADKF-IFT), a novel framework for learning deep kernel Gaussian processes (GPs) by interpolating between meta-learning and conventional deep kernel learning. Our approach employs a bilevel optimization objective where we meta-learn generally useful feature representations across tasks, in the sense that task-specific GP models estimated on top of such features achieve the lowest possible predictive loss on average. We solve the resulting nested optimization problem using the implicit function theorem (IFT). We show that our ADKF-IFT framework contains previously proposed Deep Kernel learning (DKL) and Deep Kernel Transfer (DKT) as special cases. Although ADKF-IFT is a completely general method, we argue that it is especially well-suited for drug discovery problems and demonstrate that it significantly outperforms previous state-of-the-art methods on a variety of real-world few-shot molecular property prediction tasks and out-of-domain molecular property prediction and optimization tasks. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Gaussian distribution

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RISK-AWARE REINFORCEMENT learning WIth COHERENT RISK MEASURES AND NON-LINEAR FUNCTION APPROXIMATION 11

RISK-AWARE REINFORCEMENT LEARNING WITH COHERENT RISK MEASURE...

引用

11th international conference on learning Representations, ICLR 2023

作者： Lam, thanh Verma, Arun Low, Bryan Kian Hsiang Jaillet, Patrick Department of Computer Science National University of Singapore Singapore Department of Electrical Engineering and Computer Science MIT United States

We study the risk-aware reinforcement learning (RL) problem in the episodic finite-horizon Markov decision process with unknown transition and reward functions. In contrast to the risk-neutral RL problem, we consider minimizing the risk of having low rewards, which arise due to the intrinsic randomness of the MDPs and imperfect knowledge of the model. Our work provides a unified framework to analyze the regret of risk-aware RL policy with coherent risk measures in conjunction with non-linear function approximation, which gives the first sub-linear regret bounds in the setting. Finally, we validate our theoretical results via empirical experiments on synthetic and real-world data. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Reinforcement learning

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Boosting Performance Classification of Multiple Intelligence learning Styles in learning Management System Using Feature Extraction Technique 11

Boosting Performance Classification of Multiple Intelligence...

引用

11th international conference on ICT for Smart Society, ICISS 2024

作者： Maulany, Gerzon Jokomen Santosa, Paulus Insap Hidayah, Indriana Universitas Gadjah Mada Department of Electrical and Information Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350389654

the demand for personalized and adaptive learning according to learner needs and preferences is based on the learner model;learning style is one of the main components that make up the learner model. One learning style based on the cognitive approach is the multiple-intelligence learning style, which has performed well in face-to-face learning. However, the application of multiple intelligence learning styles in e-Iearning still needs to be improved. the main obstacle is the low performance of this learning style detection classification in online learning. this research aims to improve the classification performance of multiple intelligence learning styles detection in online learning by applying feature extraction techniques to the dataset. this research uses data analysis methods, including data collection from learning interactions with the module-based learning Management System (LMS) owned by the Directorate General of Teachers and Education Personnel, Ministry of Education, Culture, Research and Technology, Republic of Indonesia to form a dataset, using the Multiple Intelligences Inventory to obtain the dominant intelligence class value, data preprocessing, application of SMOTE-Tomek resampling technique to overcome the problem of the imbalanced dataset, applying feature extraction techniques Independent Component Analysis (ICA) and Singular Value Decomposition (SVD), selection of machine learning classifier models namely K-Nearest Neighbor (K-NN), Random Forest (RF), and Support Vector Machine (SVM), model validation using k-fold cross-validation technique, and model evaluation using accuracy, precision, recall, and fl-score metrics. the experimental results show that the SVM classification performance accuracy reaches 83.33 % on the dataset using SVD, 80.56% on the dataset using ICA, and RF reaches 80.11 % on the dataset using SVD, a promising result for future development. © 2024 IEEE.

关键词： Contrastive learning

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A CNN-Transformer Deep learning Model for Real-time Sleep Stage Classification in an Energy-Constrained Wireless Device 11

A CNN-Transformer Deep Learning Model for Real-time Sleep St...

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11th international IEEE EMBS conference on Neural engineering (IEEE/EMBS NER)

作者： Yao, Zongyan Liu, Xilin Univ Toronto Engn Sci Toronto ON Canada Univ Toronto Dept Elect & Comp Engn Toronto ON Canada Univ Hlth Network UHN Toronto ON Canada

ISBN: (纸本)9781665462921

this paper presents a lightweight deep learning (DL) model for classifying sleep stages based on single-channel EEG. the DL model was designed to run on energy- and memory-constrained devices for real-time operation with all processing on the edge. Four convolutional filter layers are used to extract features and reduce the data dimension, and transformers were utilized to learn the time-variant features of the data. EEG recordings from a publicly available dataset (Sleep-EDF) are used to train and test the model. Subject-specific training was implemented to improve model performance. the testing F1 score was 0.91, 0.37, 0.84, 0.877, and 0.73 for the stages of awake, N1, N2, N3, and rapid eye movement (REM), respectively. the performance of the model was comparable to the state-of-the-art works with significantly greater computational costs. A reduced-size version of the model has been successfully tested on a low-cost Arduino Nano 33 BLE board. this design holds great promise for future integration into a low-power wireless EEG sensor with edge DL for sleep research in pre-clinical and clinical experiments, such as real-time sleep modulation.

关键词： Sleep research

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Survival Analysis of Heart Failure Patients with Advanced Machine learning Models 11th

Survival Analysis of Heart Failure Patients with Advanced Ma...

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11th international conference on intelligent Computing and Applications, ICRTC 2023

作者： Venkata Suryanarayana, S. Makam, Pravalika CVR College of Engineering Hyderabad India Department of IT CVR College of Engineering Hyderabad India

ISBN: (纸本)9789819717231

According to world health organization reports, it is estimated that the deaths due to cardiovascular diseases (CVD) are rising every year across the world and have reached 1.79 crore ever. In many cases, cardiovascular diseases are caused due to ill healthy practices in daily routine such as smoking, physical inactivity, and consumption of alcohol in more frequent attempts, etc. Detection of CVD at the early stage is of significant requirement so as to reduce the risk of life and benefit the patient to undergo preventive medication and counseling. the work proposed in this paper is to adopt advanced machine learning models like Random Forest, AdaBoost, and XGBoost on a given data of heart failure patients who continued the follow-up at regular intervals without being affected by external constraints. In addition to the above-mentioned machine-learning methods, statistical survival analysis techniques like Kaplan–Meier and Cox regression are also used to identify the potential parameters that significantly affect the death rate. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adaptive boosting

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