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检索条件"任意字段=25th International Conference on Intelligent Data Engineering and Automated Learning"

共 3588 条记录，以下是141-150 订阅

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Predicting Emotional States from Multi-modal Physiological data Using Multi-expert Ensemble Systems and Graph Neural Networks 6th

Predicting Emotional States from Multi-modal Physiological ...

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6th international conference on Biomedical and Health Informatics, ICBHI 2024

作者： Giaccone, Paolo Sasso, Daniele Matarrese, Margherita A. G. Merone, Mario Pecchia, Leandro Research Unit of Intelligent Health Technologies Department of Engineering Università Campus Bio-Medico di Roma Via Alvaro del Portillo 21 Rome00128 Italy Research Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Via Alvaro del Portillo 21 Rome00128 Italy Fondazione Policlinico Universitario Campus Bio-Medico di Roma Via Alvaro del Portillo 200 Rome00183 Italy

ISBN: (纸本)9783031863226

this study focused on identifying emotional states by analyzing brain and physiological data, aiming to improve diagnostic and intervention strategies. Sixteen patients watched videos that elicited positive, negative, or neutral emotions, and they rated these on a 9-level valence scale. the study collected data from three sources: BOLD fMRI signals, PPG (photoplethysmography), and respiratory data across 30 trials of 25 s each. A multi-expert ensemble system was used to predict valence ratings from these physiological signals, employing advanced AI models tailored to each data type. For fMRI data, graph representations were created to capture neural activity, which were then processed using a Graph Attention Network (GAT). PPG and respiratory signals were analyzed using a sliding window approach, focusing on changes from a baseline to identify significant features. these features were processed using Fully Connected Networks (FCN). the models were trained using nested Leave-One-Subject-Out cross-validation, and their predictions were combined using a meta-learning approach, which involved training an additional AI model (either Random Forest or FCN) on the outputs of the initial models. the study demonstrated the feasibility of a multi-expert meta-learning approach for emotion detection from multi-modal physiological data, showing promise for enhancing accuracy in emotional state analysis. Further optimization of single-modality models is expected to improve performance. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Photoplethysmography

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Next-Gen Energy Management: IoT Gateway for Smart Monitoring and Appliance Control

Next-Gen Energy Management: IoT Gateway for Smart Monitoring...

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Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

作者： Sohum Kabra Prithvi Nadagouda Yash Nikam Manoj S. Kavedia Department of Electronics and Telecommunications Engineering (EXTC) Thadomal Shahani Engineering College Mumbai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

this research study proposes an IoT-based solution for intelligent energy management, designed to monitor and optimize power consumption in both residential and industrial environments. the system will integrate an AI meter for real-time tracking of voltage, current, and power usage, alongside environmental sensors that measure parameters like temperature, humidity, light intensity, and motion. these data inputs are processed by an IoT gateway based on an ESP32 microcontroller and Raspberry pi, which will handle the data locally or transmits it to a cloud-based or edge machine learning model. the machine learning model analyzes real-time sensor data combined with historical consumption patterns to predict and implement optimal appliance control strategies. the system aims to reduce power consumption by up to 20-25%, contributing to energy efficiency without compromising operational performance and delays. Appliances are controlled remotely using IR Blasters and relays, allowing for dynamic, real-time adjustments based on environmental factors. the solution is scalable, making it applicable for both household energy management and large-scale warehouse automation. By utilizing predictive analytics and intelligent control, the system optimizes energy usage, offering a sustainable, automated solution for modern smart homes and industries. this integration of IoT and machine learning provides a significant step toward more efficient and sustainable energy management practices.

关键词： Temperature sensors Temperature measurement Home appliances Analytical models Voltage measurement Machine learning Logic gates Predictive models Real-time systems intelligent sensors

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IoT-Enabled intelligent System for Helmet Detection and automated Penalty Enforcement using AI & ML

IoT-Enabled Intelligent System for Helmet Detection and Auto...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： P. Vishnu Kumar Nandasree Shathanakota Saimanaswini Rachamadugu Uppara Layavardhini Department of Internet of Things G. Pullaiah College of Engineering and Technology Kurnool Andhra Pradesh India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

the increasing number of road accidents due to non-compliance with helmet regulations highlights the need for intelligent systems to enhance rider safety. this paper presents an IoT-enabled system that utilizes Artificial Intelligence (AI) and Machine learning (ML) to detect riders without helmets and enforce penalties automatically. the system integrates a Raspberry Pi with a camera module to capture real-time images of motorcyclists at traffic signals. AI-powered object detection models, such as YOLOv5, determine helmet usage, while Optical Character Recognition (OCR) techniques extract license plate details for violators. the system transmits violation data to a centralized server via IoT communication protocols, where an automated penalty system processes the information and notifies the rider through SMS or email. By automating helmet detection and penalty enforcement, this system enhances road safety, reduces manual intervention, and ensures adherence to traffic regulations.

关键词： Head Road accidents Manuals Machine learning Road safety Regulation Real-time systems Safety intelligent systems Monitoring

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intelligent IoT System with AI and ML Integration for Smart Home Automation

Intelligent IoT System with AI and ML Integration for Smart ...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： P. Vishnu Kumar Allam Prathyusha Nowly HimaBindu Palukuru Manaswini Department of Internet of Things G. Pullaiah College of Engineering and Technology Kurnool Andhra Pradesh India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

the integration of Artificial Intelligence (AI), Machine learning (ML), and the Internet of things (IoT) is transforming smart home automation by enhancing security, energy efficiency, and user convenience. this paper presents an AI-powered IoT system that employs real-time monitoring and adaptive automation to optimize household operations. the system utilizes ultrasonic sensors for water level monitoring, motion sensors for automated lighting and fan control, and DHT11 sensors for climate regulation. AI-driven facial recognition ensures secure access control, while predictive energy management reduces electricity consumption by dynamically adjusting appliance usage. the system effectively detects hazards such as gas leaks and fires in real time, triggering immediate alerts and safety measures. With seamless device coordination and automated responses, this framework enhances home security, energy efficiency, and overall automation reliability. Despite challenges in data security and system integration, the findings highlight that AI-ML-driven IoT automation is a viable solution for creating self-sustaining and intelligent smart homes.

关键词： Automation Face recognition Smart homes Machine learning Real-time systems Energy efficiency Sensor systems Hazards Internet of things intelligent sensors

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Equivariant Diffusion-Based Sequential Hypergraph Neural Networks with Co-attention Fusion for Information Diffusion Prediction 25th

Equivariant Diffusion-Based Sequential Hypergraph Neural Net...

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Lu, Ye Zhang, Ji Yu, Ting Yang, Gaoming Nanjing University of Aeronautics and Astronautics Nanjing China The University of Southern Queensland Darling Heights Australia Zhejiang Lab Hangzhou China Anhui University of Science and Technology Chuzhou China

ISBN: (纸本)9789819605729

Information spread within social networks is a complex process with broad implications. Predicting information diffusion is crucial for understanding information spread within social networks. However, previous research has primarily focused on the homogeneity characteristics of internal cascades, such as temporal and social relationships, neglecting the impact of external information propagation. Additionally, conventional methods of feature integration simply merge cascade and user embeddings, which may introduce excessive redundant information and result in the loss of valuable contextual information critical for accurate predictions. To address these limitations, we present a novel model, the Equivariant Diffusion-based Sequential Hypergraph Neural Network with Co-Attention Fusion (EDSHNN-CAF). Within its cascade feature learning module, the model proposes hypergraphs with equivariant diffusion operators to incorporate external cascade influences alongside internal features. this approach effectively captures complex high-order interconnections and accurately reflects the dynamics of information diffusion. In the feature fusion and prediction module, a co-attention mechanism is designed to seamlessly integrate cascade and user embeddings, revealing their complex interdependencies and significantly enhancing predictive capabilities. Experimental results on four real datasets showcase the promising performance of EDSHNN-CAF in predicting information diffusion, outperforming existing state-of-the-art information diffusion prediction models. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Spatio-temporal data

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REXIO: Indexing for Low Write Amplification by Reducing Extra I/Os in Key-Value Store Under Mixed Read/Write Workloads 25th

REXIO: Indexing for Low Write Amplification by Reducing Ex...

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Wang, Zizhao Qu, Qiang Han, Nan Deng, Zhelang Ma, Yizhuo Huang, Xiaowen Meng, Jintao Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China Shenzhen University Shenzhen China Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9789819605781

In mixed read/write (r/w) workloads, Key-Value stores that utilize Log-Structured Merge trees face significant write amplification (WA) due to frequent writing, resulting in extensive data compaction. While current LSM-based KV stores effectively address WA in write-heavy workloads, they struggle with increased block cache invalidation and increased r/w I/O conflicts in mixed r/w workloads. this paper introduces REXIO, a novel indexing approach to reduce WA. It decouples RAM from Solid State Drives (SSDs) and stores addresses of KV pairs in the In-RAM hashing table, which reduces buffer invalidations and eliminates unnecessary data reorganization to lower extra I/Os, thereby reducing I/O conflicts. Additionally, REXIO stores keys and values in different blocks and employs In-blocking logging to optimize updating and deleting. the experimental results show that REXIO achieves a 68.3% reduction of WA and 3.4x throughput compared to state-of-the-art LSM-Tree approaches. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Amplification

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Did You Tell a Deadly Lie? Evaluating Large Language Models for Health Misinformation Identification 25th

Did You Tell a Deadly Lie? Evaluating Large Language Models...

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25th international conference on Web Information Systems engineering, WISE 2024

作者： thapa, Surendrabikram Rauniyar, Kritesh Veeramani, Hariram Shah, Aditya Razzak, Imran Naseem, Usman Virginia Tech BlacksburgVA24060 United States Delhi Technological University New Delhi India University of California Los AngelesCA United States University of New South Wales SydneyNSW Australia Macquarie University SydneyNSW Australia

ISBN: (纸本)9789819605750

the rapid spread of health misinformation online poses significant challenges to public health, potentially leading to confusion, undermining trust in health authorities, and hindering effective health interventions. Large Language Models (LLMs) have shown promise in various natural language processing tasks, including misinformation detection. However, their effectiveness in identifying health-specific misinformation has not been extensively benchmarked. this study evaluates the performance of seven state-of-the-art LLMs - GPT-3.5, GPT-4, Gemini, Flan-T5 XL, Gemma, LLaMA-2, and Mistral - on the task of health misinformation detection across four datasets (Monkeypox-V1, Monkeypox-V2, COVID-19, and CoAID). the models were tested under five different settings: zero-shot classification, 5-shot random examples, 10-shot random examples, 5-shot sampled examples, and 10-shot sampled examples. Performance was evaluated using macro F1-score, and inter-model agreement was assessed using Cohen’s Kappa and Fleiss’ Kappa scores. By comprehensively benchmarking these LLMs, this study aims to determine which models excel in particular scenarios and provide insights into their potential for combating health misinformation in online environments. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Zero-shot learning

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IoT-Driven intelligent Waste Management System with Disinfectant Spray

IoT-Driven Intelligent Waste Management System with Disinfec...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： N. S. Swapna Basutkar Kalyan Rao Kummari Netra Paradesi Abhinav Department of Internet of Things G. Pullaiah College of Engineering and Technology Kurnool India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

the rapid increase in urban waste generation demands innovative solutions to improve public health and operational efficiency. this paper presents an IoT-driven intelligent waste management system that integrates real-time monitoring, dynamic route optimization, and automated disinfectant mechanisms. Smart bins equipped with ultrasonic, gas, rain, and infrared sensors transmit data via ESP8266 Wi-Fi/GSM modules to a cloud platform (thingSpeak), enabling anomaly detection and route optimization using a modified Travelling Salesman Problem (TSP) algorithm. A solenoid-based disinfectant system reduces bacterial load by 98% (verified through lab testing [5]), while the TSP algorithm reduces collection time by 40% and fuel consumption by 30% compared to traditional methods [4]. A three-level dashboard (admin, collector, user) provides actionable insights through a mobile app. Deployed in a 1 km 2 testbed with 10 bins, the system demonstrates scalability and cost-effectiveness. Future work includes integrating machine learning for predictive analytics and automated waste segregation. this solution aligns with UN Sustainable Development Goals (SDGs) 11 and 13 [1], promoting cleaner cities and climate action.

关键词： Waste management Rain Machine learning algorithms Urban areas Real-time systems Mobile applications Predictive analytics Optimization Gas detectors intelligent sensors

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28th international conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2024, and 23rd international conference on Information and Knowledge engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

28th International Conference on Image Processing, Computer ...

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28th international conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2024, and 23rd international conference on Information and Knowledge engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

ISBN: (纸本)9783031859328

the proceedings contain 28 papers. the special focus in this conference is on Image Processing, Computer Vision, and Pattern Recognition. the topics include: Image-Based Seal Recognition: Approaches and Challenges in Current automated Systems;Deep learning Techniques for Lunar Impact Crater Identification Based on CCD and DEM data;silicon Wafer Map Defect Classification Using Artificial Intelligence Models;low Light Image Enhancement Using Autoencoder-Based Deep Neural Networks;towards Elephants intelligent Monitoring in Zakouma National Park, Chad;a Review of Multi-modal and Multi-view Applications in Hand-Drawn Sketch Images;residential Real Estate Image Classification for Property Valuation;Novel Method to Investigate Decay in Rotting Bananas Using RGB Color Images;lalitha: A Hand Gesture-Based Computer Control System;dishari: A Novel Gesture-Based Educational Application for Specially Challenged People;mobility Anomaly Detection with intelligent Video Surveillance;Weakly-Supervised Video Anomaly Detection Using Modified Anomaly Score Module and Modified BERT;Contour Detection of Seeds Based on Traditional and Convolutional Neural Network (CNN) Based Algorithms;Early Detection of Lameness in Dairy Cattle Using Activity data, Image Analysis, AI and ML - An Approach for Improved Animal Welfare and Economic Impact;implications for Designing Hawks Detection with data Augmentation and Network Optimizations;a Review of Detecting and Quantification of Cracks Using Convolutional Neural Networks and Image Processing Techniques;using Linkage Context for automated Correction in Unsupervised Entity Resolution;the Rising threat Against Modern Technology in Cybersecurity;an Online Bookstore Design and Implementation;cybersecurity: Sight and Foresight;the Application of Blockchain Technology in the Transmission of Semiconductor Process Recipes;the Use of Social Media and the Internet for the Facilitation of Human Trafficking.

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Health Prognostic for Lithium-Ion Battery via Time-Series Transfer learning 8th

Health Prognostic for Lithium-Ion Battery via Time-Series ...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Zhu, Yehui Cheng, Hanjing Wang, Xiaoliang Li, Zhongpeng Cao, Ke-Cai Wu, Hongjie Gu, Juping School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou China Jiangsu Industrial Intelligent and Low-carbon Technology Engineering Center Nanjing China

ISBN: (纸本)9789819602315

Lithium-ion batteries have wide applications in the energy sector, and effective health prediction is crucial for ensuring their safety and performance. Most machine learning-based State of Health (SOH) estimation methods operate under the assumption that battery data follows the same distribution. However, the statistical characteristics of time-series data may change over time, affecting the accuracy of predictions. To address this issue, an adaptive time-series transfer learning method based on knee point is proposed in this paper. For different battery datasets, a knee method is employed to determine segmentation points of the time series, dividing the battery degradation curve into two stages. then, healthy features are extracted from battery charge-discharge data for input into the transfer learning model, and the effectiveness of these features is validated. Finally, time-series transfer learning is used to train the health features, exploiting the shared information learned from different domains for SOH prediction. Experimental results demonstrate that the proposed method could achieve good performance across multiple battery data. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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