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检索条件"任意字段=22nd International Conference on Intelligent Data Engineering and Automated Learning"

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22nd international conference on intelligent data engineering and automated learning, IDEAL 2021

22nd International Conference on Intelligent Data Engineerin...

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22nd international conference on intelligent data engineering and automated learning, IDEAL 2021

ISBN: (纸本)9783030916077

The proceedings contain 61 papers. The special focus in this conference is on intelligent data engineering and automated learning. The topics include: Radar Echo Image Prediction Algorithm Based on Multi-scale Encoding-Decoding Network;t Line and C Line Detection and Ratio Reading of the Ovulation Test Strip Based on Deep learning;ensemble Synthetic Oversampling with Manhattan Distance for Unbalanced Hyperspectral data;indoor Positioning System for Ubiquitous Computing Environments;developments on Support Vector Machines for Multiple-Expert learning;multimodal Semi-supervised Bipolar Disorder Classification;ACHC: Associative Classifier Based on Hierarchical Clustering;meta-learning Based Feature Selection for Clustering;detecting Communities in Feature-Rich Networks with a K-Means Method;Chimera: A Hybrid Machine learning-Driven Multi-Objective Design Space Exploration Tool for FPGA High-Level Synthesis;spell Checker Application Based on Levenshtein Automaton;tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations;wind Turbine Modelling Based on Neural Networks: A First Approach;evaluating Unidimensional Convolutional Neural Networks to Forecast the Influent pH of Wastewater Treatment Plants;in-Car Violence Detection Based on the Audio Signal;An Implementation of the "Guess Who?" Game Using CLIP;meta-feature Extraction Strategies for Active Anomaly Detection;an intelligent Decision Support System for Production Planning in Garments Industry;evaluating Football Player Actions During Counterattacks;end-to-End Deep learning for Detecting Metastatic Breast Cancer in Axillary Lymph Node from Digital Pathology Images;explainable Artificial Intelligence in Healthcare: Opportunities, Gaps and Challenges and a Novel Way to Look at the Problem Space;countering Misinformation Through Semantic-Aware Multilingual Models;neural Complexity Assessment: A Deep learning Approach to Readability Classification for European Portuguese Corpora;preface.

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2nd international conference on intelligent data engineering and automated learning, IDEAL 2000

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2nd international conference on intelligent data engineering and automated learning, IDEAL 2000

ISBN: (纸本)3540414509

The proceedings contain 82 papers. The special focus in this conference is on data Mining, Financial engineering and intelligent Agents. The topics include: Analyses on the generalised lotto-type competitive learning;extended k-means with an efficient estimation of the number of clusters;an interactive approach to building classification models by clustering and cluster validation;a new nonhierarchical clustering procedure for symbolic objects;information-based classification by aggregating emerging patterns;a new algorithm to select learning examples from learning data;data ranking based on spatial partitioning;visualisation of temporal interval association rules;fuzzy hydrocyclone modelling for particle separation using fuzzy rule interpolation;observational learning with modular networks;a note on learning automata based schemes for adaptation of BP parameters;a general class of neural networks for principal component analysis and factor analysis;integrating KPCA with an improved evolutionary algorithm for knowledge discovery in fault diagnosis;nonlinear and noisy time series prediction using a hybrid nonlinear neural predictor;wavelet methods in PDE valuation of financial derivatives;fast algorithms for computing corporate default probabilities;applying mutual information to adaptive mixture models;feature selection for support vector machines in financial time series forecasting;a computational framework for convergent agents;building an ontology for financial investment;a multi-agent negotiation algorithm for load balancing in CORBA-based environment and combining exploitation-based and exploration-based approach in reinforcement learning.

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Development of an intelligent GUI with ChatGPT for Efficient and Accurate Transformer Fault Diagnosis Using DGA, Duval Triangle Method, and Machine learning Automation 22

Development of an Intelligent GUI with ChatGPT for Efficient...

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22nd international learning and Technology conference, L and T 2025

作者： Babiker, Safia Jadalhaq, Jouri Jadalhaq, Jana Barkat, Enfel Effat University College of Engineering Electrical & Comp. Eng. Dept. Jeddah Saudi Arabia

ISBN: (纸本)9798331532215

This study presents an intelligent graphical user interface (GUI) for transformer fault diagnosis, utilizing Dissolved Gas Analysis (DGA), the Duval Triangle method, and automated machine learning model selection. Key gases - Methane (CH4), Ethylene (C2 H4), and Acetylene (C2 H2) - are analyzed to classify fault types, including partial discharges and thermal/electrical faults, through gas concentration ratios. The system leverages TPOT's model selection capabilities, with the Multi-Layer Perceptron (MLP) emerging as the optimal classifier, achieving over 95% accuracy. The GUI, developed using Gradio, enables CSV uploads for batch processing and real-time fault classification, complemented by ChatGPT-generated explanations and maintenance suggestions. A case study on mineral oil-based transformers demonstrates the tool's high diagnostic accuracy, user-friendly design, and potential for expanded applications in predictive maintenance. © 2025 IEEE.

关键词： Graphical user interfaces

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Exploiting Artificial Intelligence Approaches to Augment Decision-making Processes for an intelligent Performance in Football 22

Exploiting Artificial Intelligence Approaches to Augment Dec...

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22nd international learning and Technology conference, L and T 2025

作者： Boudouda, Souheila Merouani, Aya Marir, Naila University of Abdelhamid Mehri LIRE Laboratory Constantine Algeria Effat University Effat College of Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798331532215

The world of Football has witnessed remarkable advancements, elevating the sport from being merely "The Beautiful Game"to a realm where numbers and data hold immense significance. While some clubs and teams have achieved success through research, there is a pressing need for further investment in research, particularly with the integration of Artificial Intelligence (AI) technologies. AI has the potential to revolutionize team performance analysis by providing real-time insights and facilitating the analysis of instant games. An illustrative instance of the application of reinforcement learning for the purpose of analyzing player styles and conducting comparative assessments among various players can significantly enhance the simulation of these players within video games, such as FIFA games. Furthermore, it can augment the process of recruiting new players in accordance with the distinct training methodologies employed by individual clubs within diverse *** this paper, we propose a novel approach to developing an intelligent system that can analyze and predict Football players' performances in real-time. Our approach utilizes machine learning and deep learning techniques, as well as a recommendation system, to comprehensively analyze players' performance. This proposed system aims to support managers and players in making informed decisions by providing them with valuable insights based on diverse data sources. By leveraging this system, teams can enhance their performance and potentially attain higher international rankings. © 2025 IEEE.

关键词： Recommender systems

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Blind Spots Detection in Autonomous Robots using data Fusion and Deep learning 22

Blind Spots Detection in Autonomous Robots using Data Fusion...

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22nd international learning and Technology conference, L and T 2025

作者： Dwedar, Mohamed Bayram, Fatih Jesser, Alexander Heilbronn University of Applied Science Intelligent Cyber Physical Systems Heilbronn Germany

ISBN: (纸本)9798331532215

The adoption of humanoid robots has surged in recent years, particularly in addressing operational limitations. This study introduces a methodology for detecting blind spots and anomalies in robot motion, using the Nao Robot as a case *** approach integrates multisensor data with deep learning techniques validated on a proprietary dataset covering diverse real-world scenarios, including dynamic non-visible obstacles. High-frequency sensor sampling (T0 = 0.2 s) ensures accurate data, while DBSCAN clustering and ANOVA F-value-based feature selection identify critical features like obstacle distances and motion parameters for collision *** learning with Sigmoid and ReLU activations, the ADAM optimizer, and Sparse Categorical Crossentropy loss achieved a 95% F1 score and 99% AUC-ROC, demonstrating robust performance in dynamic environments also in real life environment. © 2025 IEEE.

关键词： Anthropomorphic robots

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automated Integration of BIM and 3D GIS for Sustainable Infrastructure Management 22

Automated Integration of BIM and 3D GIS for Sustainable Infr...

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22nd international learning and Technology conference, L and T 2025

作者： Zada, Yassmine Sebari, Imane Morel, Victor Pierrot, Amanda School of Geomatics and Surveying Engineering IAV Hassan II Department of Photogrammetry and Cartography Rabat Morocco Setec International Building Information Modeling Hub Paris France Setec International Geographic Information System Hub Paris France

ISBN: (纸本)9798331532215

In this paper, we present an automated integration approach for BIM and 3D GIS data about infrastructure projects to improve human-machine interaction with respect to sustainable project management. Based on the step-by-step process that starts with the preparation of data in BIM about buildings and complex infrastructures, CAD for the buried networks and terrain models, and BIM itself for buildings and complex infrastructures by generating IFC models. This is followed by a cleansing and optimisation step to find geometric conflicts. ETL processes automate the development of a unified spatial database integrating BIM and GIS data, without loss to geometric or attribute data. Automation tools are used to transfer data between BIM and GIS environments. This process enables the design of a 3D GIS application for 3D visualization and advanced spatial queries, such as efficient monitoring and decision making. From a case study in preliminary infrastructure projects, such as underground networks and building structure mapping, significant improvements in workflow efficiency were observed, improving by 30%, and data errors were reduced by 20% compared to manual methods. It is an essential gain for infrastructure integration and management, in particular within the complex environments involving smart cities. This approach contributes to sustainable infrastructure management through the use of global real-time views about projects. © 2025 IEEE.

关键词： Decision making

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Classification of ECG Signals Using Machine learning for Detection of Cardiac Abnormalities 22

Classification of ECG Signals Using Machine Learning for Det...

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22nd international learning and Technology conference, L and T 2025

作者： Alimam, Nawar Alsaggaf, Alhassan Barakat, Enfel Effat University Electrical and Computer Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798331532215

Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, with arrhythmias contributing significantly to these statistics. This study proposes a machine learning framework for automated ECG classification, focusing on Premature Ventricular Contraction (PVC), Atrial Fibrillation (AFib), and Ventricular Tachycardia (VTach). Normal electrocardiogram (ECG) signals from the MIT-BIH Arrhythmia database were integrated with synthetically generated abnormal signals, which were derived using mathematical models and implemented in MATLAB. Signals were segmented into 1-second intervals, with features extracted including P-wave amplitude, QRS width, and T-wave frequency. A Random Forest classifier achieved 0.88 accuracy, with high precision and recall for each class, including normal, atrial fibrillation, and ventricular ectopic beats, demonstrating balanced performance across all categories. The incorporation of synthetic data addressed dataset imbalances, particularly improving detection of minority classes like VTach. The framework demonstrates potential for real-time implementation in wearable devices and telemedicine platforms. Future work will focus on expanding the dataset and exploring deep learning techniques to enhance clinical applicability. © 2025 IEEE.

关键词： Shear waves

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Ensemble learning and Time-Domain Feature Mining based Appliance Power Consumption Pattern Recognition 22

Ensemble Learning and Time-Domain Feature Mining based Appli...

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22nd international learning and Technology conference, L and T 2025

作者： Qaisar, Saeed Mian Effat University Electrical and Computer Engineering Department Jeddah21478 Saudi Arabia

ISBN: (纸本)9798331532215

As technology has advanced, using smart meters instead of conventional ones has changed. These smart meters are essential parts of intelligent grids, offering significant advantages to many stakeholders regarding social, environmental, and economic constraints. A detailed method of fine-grained collection and analysis of the metering data is necessary to benefit the different smart grid stakeholders. This results in collecting, transmitting, and processing a large amount of data with an exponentially raised dimension. It seriously constrains the system processing resources, latency, data management, transmission effectiveness, and analysis response time. In this regard, a new hybridization of ensemble learning and time-domain feature extraction is suggested for the automatic classification of appliances through the processing of their power consumption data. Time-domain feature mining makes up for the previously noted deficiency and can result in a considerable real-time data dimension reduction without sacrificing important information. data in compressed form can therefore be processed, examined, stored, and transmitted with efficiency. It offers a significant reduction in the post cloud-based classification latency along with computational and transmission effectiveness. The classification is carried out using the robust ensemble learning (EL) classifiers. The performance of considered EL classifiers is evaluated for the case of a real multi-class and multi-device smart meter dataset. The devised solution achieves a highest average classification accuracy of 88.33% for a 10-class problem while securing a compression gain of 32.73. © 2025 IEEE.

关键词： Smart power grids

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22nd international conference on Software engineering and Formal Methods, SEFM 2024

22nd International Conference on Software Engineering and Fo...

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22nd international conference on Software engineering and Formal Methods, SEFM 2024

ISBN: (纸本)9783031773815

The proceedings contain 25 papers. The special focus in this conference is on Software engineering and Formal Methods. The topics include: User-Guided Verification of Security Protocols via Sound Animation;a Policy Framework for Regulating External Calls in Smart Contracts;exploiting Assumptions for Effective Monitoring of Real-Time Properties Under Partial Observability;a Formal Modeling Language for Smart Contracts;symbolic Execution for Precise Information Flow Analysis of Timed Concurrent Systems;Validating Traces of Distributed Programs Against TLA+ Specifications;partially-Observable Security Games for Attack-Defence Analysis in Software Systems;secure Smart Contracts with Isabelle/Solidity;Deductive Verification of SYCL in VerCors;minuska: Towards a Formally Verified Programming Language Framework;hierarchical learning of Generative Automaton Models from Sequential data;composing Run-Time Variability Models;model-Checking the Implementation of Consent;grammarForge: learning Program Input Grammars for Fuzz Testing;verified Configuration and Deployment of Layered Attestation Managers;right or Wrong – Understanding How Users Write Software Models in Alloy;an Operational Semantics for Yul;execution-Time Opacity Control for Timed Automata;unlocking the Power of Environment Assumptions for Unit Proofs;towards Quantum Multiparty Session Types;automated Invariant Generation for Efficient Deductive Reasoning About Embedded Systems;formal Analysis of Multi-Factor Authentication Schemes in Digital Identity Cards;leveraging Contracts for Failure Monitoring and Identification in automated Driving Systems.

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Deep learning for sleep disorder diagnosis: Enhancing EEG Analysis with Machine learning Techniques 22

Deep learning for sleep disorder diagnosis: Enhancing EEG An...

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22nd international learning and Technology conference, L and T 2025

作者： Mohamed, Baher Barkat, Enfel Effat University Dept. of Electrical & Computer Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798331532215

The rising prevalence of sleep disorders highlights their disastrous effects on health, well-being, cognitive performance, memory, and learning outcomes. Sleep disorders such as insomnia and sleep apnea normally require extensive instrumentation and expertise for diagnosis, which is not always possible or even available in most cases. This work, therefore, proposes a state-of-the-art use of Machine learning (ML) to develop a model for the classification and detection of sleep patterns from electroencephalogram (EEG) signals. The model is trained on a synthetic dataset, developed using mathematical simulations of real-life EEG data. This is a novelty in this area, which allows this model to be precise yet robust without relying too much on real-world *** application bridges advanced AI techniques with real-world applications and smooths out the experience for healthcare professionals and researchers alike. This research highlights how ML might transform sleep medicine through efficient, extended solutions for EEG data analysis and sleep disorder detection. We provide a realistic EEG simulation combined with an intuitive application, a powerful platform that makes advanced sleep diagnostics more accessible and impactful, which supports the development of educational strategies or technologies. © 2025 IEEE.

关键词： Electroencephalography

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