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33rd Southern African Universities Power Engineering conference, SAUPEC 2025

作者： Donkor, David N. Ogudo, Kingsley A. Rameshar, Vikash University of Johannesburg Department of Electrical Engineering Technology South Africa

ISBN: (纸本)9798331535162

This study presents a comprehensive review of recent advancements in data-driven control techniques applied to industrial heating furnaces. The investigation focuses on three prominent approaches: fuzzy-PID controllers, neural network controllers utilizing model reference control, and Genetic Algorithm (GA) techniques for PID parameter optimization. These data-driven methodologies demonstrated good performance metrics compared to conventional control strategies based on their results. This paper contributes to the field by synthesizing current knowledge, identifying research gaps, and proposing future directions that could lead to more efficient, robust, and widely applicable control solutions for diverse heating furnace systems in the heating industry. © 2025 ieee.

关键词： Genetic algorithms

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Smart Grid Management with AI driven Analytics in Solar Energy 2

Smart Grid Management with AI Driven Analytics in Solar Ener...

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2nd International conference on Machine learning and Autonomous systems, ICMLAS 2025

作者： Timande, Devanshi Agade, Nishant Gudadhe, Amit Faculty of Engineering & Technology Department of Artificial Intelligence and Data Science Wardha India Faculty of Engineering & Technology Wardha India

ISBN: (纸本)9798331505745

This paper presents an AI-driven framework for real-time smart grid optimization tailored specifically for solar energy integration. The proposed system features a multi-layered architecture that integrates data acquisition from solar irradiance sensors, smart meters, and meteorological sources with advanced pre-processing techniques to ensure data quality and fusion. Using advanced deep learning architectures, such as Long Short-Term Memory networks for forecasting solar irradiance and regression methodologies for predicting load, our proposed framework dynamically adjusts energy dispatch and demand response measures to address the inherent variability associated with solar power. Additionally, a combined strategy that incorporates reinforcement learning is implemented to improve real-time grid stability and operational efficiency, and explainable artificial intelligence methods are used to provide clarity in decision-making processes. Simulation studies conducted on an enhanced ieee 24-bus test system have shown to reduce forecast error by up to 30% and prevent the occurrence of grid instability events by about 40% in contrast with traditional methods. These results are promising for AI-based analytics since it promises to enhance smart grid functionality and enable more sustainable use of renewable resources. Further research will develop this model with additional renewable energy sources and increase cybersecurity. © 2025 ieee.

关键词： Reinforcement learning

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AI-driven Clinical Decision Support systems: Revolutionizing Healthcare with Predictive Models

AI-Driven Clinical Decision Support Systems: Revolutionizing...

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2025 ieee International conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Kukreti, Sanjeev Shrivastava, Anurag Chandrashekar, Rakesh Rani, K. Pushpa Badhoutiya, Arti Lakhanpal, Sorabh Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India GLA University Department of Electrical Engineering Mathura India Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798331512965

The integration of artificial intelligence (AI) into Clinical Decision Support systems (CDSS) is changing the face of healthcare by allowing AI powered predictive, data driven insights, enabling medical practitioners to prolong their diagnosis and treatment planning. To make better and faster clinical judgements, CDSS based on AI using machine learning techniques such as ensemble methods and deep learning is discussed in this paper. He said most of the focus is on models that can predict future events, diagnose plagues and deliver tailored treatment programs sifting through mountains of health care data, such as genetics, medical imaging and EHRs. Some of the main problems we work with as we implement AI driven CDSS is its data privacy, model interpretability and integration to current healthcare processes. In addition this study summarizes recent advances in artificial intelligence (AI), enabling real time analysis and making real time decisions to improve patient outcomes and reduce workloads of healthcare personnel. Case examples that employ AI-CDSS to identify diseases, optimize treatments and predict risk are provided to show how they can change the face of clinical practice. Finally, we explore where AI healthcare may be going from here with a focus on the importance of deploying these systems with patient well being in mind and with full compliance to the relevant regulations. © 2025 ieee.

关键词： AI-driven CDSS Clinical Decision Support Healthcare Machine learning Predictive Models

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Managing data Heterogeneity for Ontology-driven Models: Application to Gamified E-learning Contexts 17

Managing Data Heterogeneity for Ontology-Driven Models: Appl...

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17th International conference on Computer Supported Education, CSEDU 2025

作者： Gomaa, Yara Lahoud, Christine Abel, Marie-Hélène Moussa, Sherin Laboratoire HEUDIASYC Université de Technologie de Compiègne Sorbonne Universités 57 Avenue Landshut Compiègne60200 France Université Française d’Egypte 21 Ismailia Desert Road Shorouk City Cairo11837 Egypt CIAD UR 7533 Univ. BourgogneFranche-Comté UTBM BelfortF-90010 France Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9789897587467

data heterogeneity within gamified e-learning systems exposes a challenge for ontology-driven models, specifically ontology-based recommender systems. These systems can help teachers who are unfamiliar with gamification by offering personalized recommendations to gamify their pedagogical resources. Yet, developing such systems requires collecting and integrating diverse data about users, resources, and game elements, originating from multiple sources, like learning management systems and educational repositories, each with varying formats and inconsistent semantics. This paper proposes an approach to manage the complexities of collecting and preparing heterogeneous data for an ontology-driven model within gamified e-learning contexts. A full overview is provided on the data workflow, which consists of two main phases: (1) data collection, which combines automated techniques through APIs and web scraping, and (2) data Integration by means of mapping the collected data into our Teacher in Gamified e-learning Context (TGC) ontology to produce coherent and semantically enriched structure. The resulting data repository facilitates semantic queries, inference, and knowledge enrichment, overcoming challenges like cold-start scenarios and supporting the dynamic generation of personalized recommendations. This proposed approach aims to establish a robust approach that addresses the challenges of data heterogeneity, ensuring consistent and meaningful integration for ontology-based recommender systems in gamified e-learning contexts. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Recommender systems

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Model-driven Bayesian Reinforcement learning for IRS-Assisted Massive MIMO-OFDM Channel Feedback, Beamforming, and IRS control

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ieee TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第3期24卷 2514-2529页

作者： Bi, Yuanyuan Lau, Vincent K. N. Tsang, Danny H. K. Hong Kong Univ Sci & Technol Acad Interdisciplinary Studies Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Hong Kong Univ Sci & Technol Guangzhou Internet Things Thrust Guangzhou 511400 Guangdong Peoples R China

In Intelligent Reflecting Surface (IRS)-assisted massive Multiple-Input Multiple-Output (MIMO) systems, the downlink channel state information (CSI) needs to be fed back to the base station (BS) and utilized to perform the beamforming and IRS control for high spectral efficiency performance. However, the intricate nature of these systems, characterized by a vast number of antennas, subcarriers, and IRS elements, exacerbates the CSI feedback overhead and complicates the optimization of beamforming and IRS parameters, potentially compromising spectral efficiency. Addressing these challenges, this paper introduces a Bayesian Reinforcement learning (BRL)-based approach, named IRS-CSI-BRL, for efficient CSI feedback, beamforming, and IRS control. Firstly, the IRS-CSI-BRL approach utilizes the equivalent CSI for optimization, aligning with current channel estimation protocols without necessitating extensive modifications. Secondly, it employs a practical IRS control model that optimizes the effective capacitance of IRS control circuits rather than IRS reflection coefficients, accurately reflecting the IRS's frequency-responsive behavior to enhance system performance. Additionally, we advocate bypassing the reconstruction of the CSI at the BS to eliminate information irrelevant to beamforming and IRS control, thereby boosting feedback efficiency. Another distinctive feature of the proposed scheme is that its output format is probability distributions, which enables the incorporation of model-assisted knowledge about the latent space and boosts the algorithm's robustness. Simulation results demonstrate that the proposed IRS-CSI-BRL scheme significantly outperforms start-of-the-art solutions in feedback overhead reduction and system data rate enhancement while maintaining exceptional robustness. Furthermore, this approach maintains flexibility, allowing for the incorporation of an additional training loss function for full CSI reconstruction if needed.

关键词： Array signal processing Integrated circuit modeling Optimization Reflection coefficient Channel estimation Capacitance Training Protocols Downlink Circuits IRS massive MIMO CSI feedback beamforming Bayesian reinforcement learning

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Research on the Application of Knowledge Graph-driven Two-Dimensional Convolutional Embedding Methods in Recommender systems 1

Research on the Application of Knowledge Graph-Driven Two-Di...

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1st International conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Dong, Peng School of Business Stevens Institute of Technology HobokenNJ United States

ISBN: (纸本)9798331529246

This article proposes a knowledge graph-driven recommendation method that integrates user preferences and contrastive learning to enhance the performance of the recommendation system. The study introduces knowledge-aware aggregation technology to extract user features from the user-project interaction graph, capturing preference characteristics using user-target factors. Additionally, a two-dimensional convolution method, incorporating both spatial and channel attention mechanisms, is employed to extract project features from the knowledge graph, with recommendation prediction achieved through the inner product. To address challenges posed by noisy knowledge graph data and the reliance on high-quality data, the paper introduces a model combining relationship learning with contrastive learning. This model leverages data augmentation techniques to generate subgraphs, enhancing the knowledge graph's content. By comparing the subgraphs to the original graph, hierarchical features are learned to improve both the accuracy and robustness of the recommendations. The results show that the KG-UIR and KGCRL models significantly enhance recommendation performance and system robustness. They also help mitigate issues related to data sparsity and cold-start problems to some extent. © 2025 ieee.

关键词： Knowledge graph

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Graph-Based learning in Core and Edge Virtualized O-RAN for Handling Real-Time AI Workloads

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ieee TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2025年第1期12卷 302-318页

作者： Tam, Prohim Kim, Seokhoon Amer Univ Phnom Penh Sch Digital Technol Phnom Penh 12106 Cambodia Soonchunhyang Univ Dept Software Convergence Asan 31538 South Korea Soonchunhyang Univ Dept Comp Software Engn Asan 31538 South Korea

AI-empowered applications have been deployed in many aspects of networking, and federated learning (FL) has emerged as a complementary approach due to its ability to enable privacy-preserving model training and inference. However, without self-organizing capability, practical FL systems face several issues to co-exist in real-time networking. Therefore, this paper aims to design autonomous FL management with integrated graph neural networks (GNN) and deep reinforcement learning (DRL), namely AutoFedGDRL, to sustain heterogeneous FL execution in optimized open radio access network (O-RAN) and intelligent core network architectures and offer automated policy-driven orchestration by intelligent agent controller. Edge cloud virtualized O-RAN is integrated to assist model computation and support multiple services with elastic containerized resource scaling. The practicability of FL systems is stimulated by modelling the participants and aggregators as a graph representation and subsequently analyzing to predict the accessibility and trustworthiness of the nodes, bandwidth capacities, and virtual link relationship. Our proposed AutoFedGDRL aims to obtain specifications of hidden FL, service, and networking states in order to control the main policies, such as training management, resource sharing, aggregation scheduling, and service prioritization. In the experiment, AutoFedGDRL surpassed reference models (non-federated training) in global accuracy, achieving 98.23% for MNIST and 97.12% for CIFAR-10, compared to 98.22% and 95.89% for PrimaryGNN-FL. The proposed scheme also improved end-to-end convergence speed, with execution times 10.58 ms to 32.79 ms faster. Model delivery ratios reached 99.98%, ensuring the federated system's reliability and sharing workload efficiency.

关键词： Open RAN Cloud computing Computational modeling Training data models Resource management Virtualization Optimization Real-time systems Intelligent agents Deep reinforcement learning (DRL) edge cloud virtualization federated learning (FL) graph neural networks (GNN) open radio access network (O-RAN)

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Large Pre-Trained Models and Few-Shot FineTuning for Virtual Metrology: A Framework for Uncertainty-driven Adaptive Process control in Semiconductor Manufacturing

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ieee Transactions on Automation Science and Engineering 2025年 22卷 15351-15370页

作者： Lin, Chin-Yi Tseng, Tzu-Liang Emon, Solayman Hossain Tsai, Tsung-Han University of Texas at EL Paso Department of Industrial Manufacturing and Systems Engineering Texas El PasoTX79968 United States Computational Science Program University of Texas at EL Paso Texas El PasoTX79968 United States National Taipei University of Business Institute of Information and Decision Sciences Taipei10051 Taiwan

High-precision wafer metrology poses significant cost and throughput challenges in modern semiconductor manufacturing, where frequent process changes and recipe variations demand highly adaptive and scalable solutions. In this paper, we present a Generative-FewShot-Active Virtual Metrology (GFA-VM) framework that unifies large-scale generative modeling, few-shot fine-tuning, and uncertainty-driven active sampling into a single, data-centric system. A foundational generative model, built on a hybrid architecture of Transformer networks and Variational Autoencoders (VAEs), learns diverse sensor characteristics in an offline stage without relying on extensive labeled data. During online inference, the model produces both wafer quality predictions and predictive uncertainties;samples exceeding a dynamic uncertainty threshold are selected for physical measurement and few-shot model recalibration. This selective sampling both reduces measurement costs and adapts rapidly to new process conditions (e.g., novel recipes or equipment upgrades), requiring only a handful of freshly labeled wafers. The paper further addresses the long-term stability of the system through a self-updating mechanism that adjusts the uncertainty threshold when distributional shifts occur. Empirical evaluations confirm that our GFA-VM approach achieves state-of-the-art accuracy while significantly reducing metrology overhead compared to conventional virtual metrology methods. Additionally, rigorous theoretical analyses—including proofs of convergence and label cost bounds—demonstrate the reliability of using a generative foundation plus meta-learning technique. By fostering on-demand adaptation within a closedloop framework, GFA-VM offers a comprehensive, scalable strategy for next-generation semiconductor process control. © 2004-2012 ieee.

关键词： Semiconductor device measurement Metrology Semiconductor device modeling Semiconductor process modeling Uncertainty Adaptation models Accuracy data models Predictive models Manufacturing

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Scale-driven Tensor Representation-Based Multiview Clustering

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning systems 2025年

作者： Zhang, Chuanbin Chen, Long Ding, Weiping Zhao, Kai Shi, Zhaoyin Wang, Yingxu Chen, C. L. Philip Zhaoqing Univ Sch Comp Sci & Software Zhaoqing 526061 Peoples R China Univ Macau Fac Sci & Technol Dept Comp & Informat Sci Taipa Macau Peoples R China Nantong Univ Sch Artificial Intelligence & Comp Sci Nantong 226019 Peoples R China City Univ Macau Fac Data Sci Taipa Macau Peoples R China Chongqing Univ Sch Automat Chongqing 400044 Peoples R China Shenzhen Univ Coll Mechatron & Control Engn Shenzhen 518060 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Peoples R China

Real-world data tends to exhibit an inherent hierarchical structure, providing a natural multiview perspective where features at different scales can be treated as distinct views. However, most existing multiview clustering algorithms primarily focus on the inter-sample relationships at a single level. These methods overlook the hierarchical structures present in the data and are specifically designed for native multiview data. This article introduces a comprehensive multiview clustering framework that transforms both typical data and images into a unified multiview feature representation. The framework allows for extracting multiscale features from the raw data and clustering different types of data with the same algorithm. A novel scale-driven pre-processing approach unifies the feature structure across various data types and explores local relationships among samples at multiple scales. Features at larger scales delineate the global cluster contours, while features at smaller scales reveal fine-grained local details. Subsequently, the proposed method learns the view-specific partitions from different scales of views and derives consensus features through tensor low-rank representation. By optimizing these consensus features, the approach effectively captures the precise cluster shapes from coarse to fine-grained levels. The final label indicator matrix is directly obtained from these consensus features. To demonstrate the effectiveness and versatility of the proposed method, we conducted experimental comparisons with state-of-the-art (SOTA) algorithms in both multiview clustering and image segmentation across diverse datasets. The source code and datasets are released at https://***/ChuanbinZhang/SDTR

关键词： Tensors Clustering algorithms Feature extraction Image segmentation data mining Transforms Technological innovation Matrix decomposition Computer science Urban areas Low-rank tensor multiview clustering scale-driven unsupervised learning

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Quality control in Extrusion-Based Additive Manufacturing: A Review of Machine learning Approaches

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ieee Transactions on Cybernetics 2025年第6期55卷 2522-2534页

作者： Pereira, Adailton Gomes Barbosa, Gustavo Franco Filho, Moacir Godinho Shiki, Sidney Bruce Silva, Andrea Lago da Universidade Federal de São Carlos Department of Production Engineering São Carlos13565-905 Brazil Universidade Federal de São Carlos Department of Mechanical Engineering São Carlos13565-905 Brazil Metis Lab EM Normandie Business School Le Havre76600 France

Additive manufacturing (AM) revolutionizes product creation with its unique layer-by-layer construction method but faces obstacles in widespread industrial use due to quality assurance and defect challenges. Integrating machine learning (ML) into AM quality control (QC) systems presents a viable solution, utilizing ML’s ability to autonomously detect patterns and extract important data, reducing the reliance on manual intervention. This study conducts an in-depth literature review to scrutinize the role of ML in augmenting QC mechanisms within extrusion-based AM processes. Our primary objective is to pinpoint ML models that excel in monitoring manufacturing activities and facilitating instantaneous defect corrections via parameter adjustments. Our analysis highlights the efficacy of convolutional neural networks (CNNs) models in defect detection, leveraging camera-based systems for an in-depth examination of printed parts. For 1-D data processing, support vector machines (SVMs) and long short-term memory (LSTM) networks have shown significant application and effectiveness. Furthermore, the study classifies various sensors and defects that can effectively benefit from ML-driven QC approaches. Our findings accentuate the essential role of ML, especially CNNs, in detecting and rectifying production flaws and also detail the synergy between different sensor technologies in creating a comprehensive monitoring framework. By integrating ML with a multisensor approach and employing real-time corrective strategies, such as dynamic parameter adjustments and the use of advanced control systems, this research underscores ML’s transformative potential in elevating AM QC. Thus, our contribution lays the groundwork for harnessing ML technologies to ensure superior quality parts production in AM, paving the way for its broader industrial adoption. © 2013 ieee.

关键词： Extrusion

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