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检索条件"任意字段=5th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2004"

共 182 条记录，以下是1-10 订阅

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5th international conference on intelligent data engineering and automated learning, ideal 2004

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5th international conference on intelligent data engineering and automated learning, ideal 2004

ISBN: (纸本)3540228810

the proceedings contain 125 papers. the special focus in this conference is on Bioinformatics, data Mining and Knowledge engineering. the topics include: Modelling and clustering of gene expressions using RBFs and a shape similarity metric;a novel hybrid GA/SVM system for protein sequences classification;building genetic networks for gene expression patterns;SVM-based classification of distant proteins using hierarchical motifs;knowledge discovery in lymphoma cancer from gene-expression;a method of filtering protein surface motifs based on similarity among local surfaces;qualified predictions for proteomics pattern diagnostics with confidence machines;an assessment of feature relevance in predicting protein function from sequence;a new artificial immune system algorithm for clustering;the categorisation of similar non-rigid biological objects by clustering local appearance patches;unsupervised dense regions discovery in DNA microarray data;visualisation of distributions and clusters using ViSOMs on gene expression data;prediction of implicit protein-protein interaction by optimal associative feature mining;exploring dependencies between yeast stress genes and their regulators;prediction of natively disordered regions in proteins using a bio-basis function neural network;the effect of image compression on classification and storage requirements in a high-throughput crystallization system;a hybrid approach to human core-promoter prediction;synergy of logistic regression and support vector machine in multiple-class classification;deterministic propagation of blood pressure waveform from human wrists to fingertips;pre-pruning decision trees by local association rules and a new approach for selecting attributes based on rough set theory.

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intelligent data engineering and automated learning--ideal 2004 : 5th international conference, Exeter, UK, August 25-27, 2004 : Proceedings 1

引用

丛书名： Lecture Notes in Computer Science 3177

2004年

作者： Yang Zheng Rong. Everson Richard Yin Hujun

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An Ontology-Lexicon-Driven Approach for Refining Sentiment Analysis Processes 25th

An Ontology-Lexicon-Driven Approach for Refining Sentiment A...

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25th international conference on intelligent data engineering and automated learning

作者： Toldy, Francisco Belo, Orlando Univ Minho ALGORITMI Res Ctr LASI P-4710059 Braga Portugal

ISBN: (纸本)9783031777370;9783031777387

the high number of sentiment analysis systems and applications developed over the last few years provided companies with very sophisticated analysis tools, allowing them to establish preferences, trends and patterns of customer behavior. this is quite important for companies intending to change their way of being, promoting work actions aimed at specific customer segments, to obtain business advantages and improve their image and performance in the market in which they work. In this paper, we present and describe a sentiment analysis system that combine techniques based on ontologies and domain lexicons, to provide relevant indicators to support the evaluation of the degree of user satisfaction and know the influence of each ontological element incorporated in opinion texts in sentiment classification.

关键词： Sentiment Analysis Opinion Analysis Ontologies Lexical Classification data Preparation Tagging Texts Marketing Applications

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Drowsiness Detection Using Vital Sign Sensors and Deep learning on Smartwatches 25th

Drowsiness Detection Using Vital Sign Sensors and Deep Learn...

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25th international conference on intelligent data engineering and automated learning

作者： da Rosa Pereira, Vitor Augusto Berri, Rafael Alceste Osorio, Fernando Santos Univ Sao Paulo BR-13566590 Sao Carlos Brazil Fed Univ Rio Grande BR-96203900 Rio Grande RS Brazil

ISBN: (纸本)9783031777301;9783031777318

Drowsy drivers contribute to high rates of road accidents, leading to numerous fatalities and injuries. the dangers of drowsiness extend beyond the roads, affecting workplaces that require continuous attention and focus. this work aims to develop a non-intrusive, real-time system capable of assessing the user's drowsiness level by processing physiological signals collected by smartwatch sensors. the study utilized photoplethysmography (PPG) and heart rate data from a smartwatch, labeled with an adapted Karolinska Sleepiness Scale (KSS). Deep learning techniques were used to fine-tune hyperparameters and train a 1-dimensional Convolutional Neural Network to reach better prediction performance on drowsiness data. Besides, a 5-fold cross-validation technique was used to evaluate performance and create an ensemble, enhancing prediction robustness through election method. the results show significant prediction accuracy using the ensemble on test data (91.41%). the final pre-trained model was integrated into the smartwatch, creating a real-time drowsiness detection system. this system alerts the user through sound and vibration feedback, preventing them from falling asleep and reducing the risk of accidents.

关键词： Drowsiness Smartwatch Photoplethysmography (PPG) Artificial neural networks Deep learning Ensemble model

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Exploring data Symbion EI Deep learning and Model Sharing Modules 25th

Exploring Data Symbion EI Deep Learning and Model Sharing Mo...

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25th international conference on intelligent data engineering and automated learning

作者： Huszcza, Rafael Mendes, Amanda Lopes, Jeferson Borges, Eduardo N. Lucca, Giancarlo B. Guilherme, Pablo D. B. Pereira, Leandro A. Univ Fed Rio Grande FURG Ctr Ciencias Computac C3 Rio Grande Brazil Univ Catolica Pelotas Ctr Ciencias Sociais & Tecnol Mestrado Engenharia Eletron & Computacao Pelotas RS Brazil Univ Estadual Parana UNESPAR Ctr Educ Humanidades Ciencias Biolog Paranagua Brazil Inst Fed Parana IFPR Eixo Tecnolog Meio Ambiente Paranagua Brazil

ISBN: (纸本)9783031777370;9783031777387

Considering the growing volume of available information, storing and managing data is an increasing challenge. Leveraging these raw datasets to extract meaningful knowledge and make informed decisions requires data mining methods, especially when combined with Machine learning algorithms to uncover hidden patterns in data. Considering this context, this work describes a project to contribute to datadriven initiatives by seamlessly integrating a data repository system with Machine learning (ML) tools. this integration allows users to implement pipelines within a single system-from storing and managing datasets to training and deploying ML models, making easier, more efficient, and intuitive data science workflow accessible to all, not just computer scientists. Furthermore, the system aims to enhance knowledge sharing among researchers and practitioners by enabling users to share datasets and trained machine learning models within the platform. this work highlights the system's key features, particularly the Machine learning module, which includes classic ML algorithms for tabular data and the newly introduced Deep learning algorithms for image classification. the new Model Sharing module also emphasizes promoting collaboration by allowing users to share and manage trained models effectively.

关键词： data Repository Machine learning Deep learning Model sharing data Symbion EI

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Prediction of Covid-19 Recovery Chances Using Supervised Machine learning Classification 5th

Prediction of Covid-19 Recovery Chances Using Supervised Mac...

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5th international conference on Innovative Product Design and intelligent Manufacturing Systems, ICIPDIMS 2023

作者： Dhanalakshmi, R. Nivashini, A. Vijayaraghavan, N. Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering MNM Jain Engineering College Chennai India Department of Mathematics KCG College of Technology Chennai India

ISBN: (纸本)9789819767311

Predictive analytics if combined with machine learning approaches have the potential to play a significant role in forecasting the spread of respiratory infections. Machine learning approaches aid in the mining of data to better assess and forecast the presence or absence of covid-19 infection. In this paper, XGBoost Ensemble Classification using a fine-tuned approach is being used. Various machine learning algorithms are being analyzed and XGBoost Ensemble Classification algorithm is used to forecast and predict the covid-19 infected patient recovery chances that is the possible outcome of a covid-19 infected patient along with the prediction probability of recovery. the suggested categorization model is applied to a variety of situations. When compared to other ensemble models like random forest, hybrid ensemble and other individual classifiers such as decision tree, logistic regression, K-nearest neighbor, support vector machine and Naive Bayes, the XGBoost Ensemble Classification model gave the highest accuracy. Predicting the correct class is important. thus, the proposed system discusses the possibility of automated prediction for covid-19 infected patient recovery chances along with the probability of recovery and the effectiveness of such predictions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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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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IoT-Driven intelligent Waste Management System with Disinfectant Spray

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

引用

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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Fast Dynamic Calibration of Millimeter-Wave Radar External Parameters: Research and Applications

Fast Dynamic Calibration of Millimeter-Wave Radar External P...

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Sensors and Information Technology (ICSI), international conference on

作者： Jie Yang Xiaohui Li Xianjin He Qin Xia Juncheng Huang China Automotive Engineering Research Institute Co. Ltd Chongqing China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

With the rapid development of intelligent connected technologies, the application of millimeter-wave radar in roadside perception systems has become increasingly widespread [1], [2]. Known for its excellent anti-interference capabilities and resistance to adverse weather conditions, millimeter-wave radar has become a key sensor in these systems [3]. However, the accuracy of radar data is still limited by the calibration of the coordinate system, especially in dynamic environments [4]. Traditional calibration methods rely on manual adjustments, which are inefficient [5]. this paper proposes an automated dynamic millimeter-wave radar calibration method based on deep reinforcement learning, combined with multi-sensor data fusion, to address the high-precision calibration issue between the radar coordinate system and the world coordinate system in dynamic environments. Experiments on open roads show that both the new method and the method incorporating deep reinforcement learning (DQN) significantly improve calibration performance. the calibration accuracy increased by 11.11% and 48.15%, respectively, compared to traditional methods, while the calibration speed was also greatly improved. the proposed method effectively reduces human intervention, lowers system deployment costs, and demonstrates strong scalability, making it highly valuable for practical applications in intelligent connected vehicles and roadside perception systems.

关键词： Accuracy Roads Manuals Millimeter wave radar Sensor fusion Deep reinforcement learning Calibration Trajectory Vehicle dynamics Optimization

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