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International conference on ent Computing and control Systems (ICICCS)

作者： Pranav Kate Tanya Shruti Rajeswari K Department of Computer Science Pimpri-Chinchwad College of Engineering Pune Pune India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

This research proposes an effective money prediction via a data-driven tool to reduce costs by integrating intelligent data mining techniques. This study plans to improve revenue prediction and find venues for cost stemming by analyzing financial statements for the last 15 years, such as balance sheets and profit and loss reports. data preprocessing, exploratory data analysis (EDA), correlation analysis, regression analysis, clustering, and time series analysis are well integrated in finding feasible solutions. Model performance is evaluated on multiple metrics, including R-square and mean absolute error while considering computational complexity and efficiency. The findings illustrate how business houses can use such analytical methods to achieve better decision-making, financial sustainability, and increased efficiency. The work also discusses practical limitations, such as data availability and the model’s fitting to dynamic market conditions. The study highlights the importance of continuously monitoring and refining the model for the prediction in a real-world context.

关键词： Adaptation models Analytical models Costs Computational modeling Time series analysis Decision making Predictive models data models Forecasting Business

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基于数据驱动的变体机翼跨声速颤振分析

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力学学报 2025年第2期57卷 523-534页

作者：万芸怡黄锐刘豪杰南京航空航天大学航空航天力学及控制全国重点实验室南京210016

激波运动和流动分离等诱发的跨声速气动非线性效应,可能会引发变体飞行器的颤振特性随构型显著变化,导致变体机构的轻量化设计面临严峻挑战.本文针对后缘变弯度机翼,提出了基于数据驱动的跨声速气动弹性建模方法,高效且准确地预测了后... 详细信息

激波运动和流动分离等诱发的跨声速气动非线性效应,可能会引发变体飞行器的颤振特性随构型显著变化,导致变体机构的轻量化设计面临严峻挑战.本文针对后缘变弯度机翼,提出了基于数据驱动的跨声速气动弹性建模方法,高效且准确地预测了后缘变弯度角度参变过程机翼的颤振边界.首先,发展了基于计算流体动力学的变弯度流-固耦合数值模拟方法,对后缘变弯度机翼的跨声速非定常流动进行高精度模拟,获得给定激励信号下机翼表面的压力快照数据及气动力响应快照数据.然后,利用获得的训练数据,结合本征正交分解和带控制的动态模式分解方法,建立了能够可靠描述机翼运动和气动载荷分布之间关系的低阶状态空间模型.最后,利用所构建的低阶模型,进行了后缘变弯度机翼的跨声速气动力响应和颤振特性预测.数值仿真结果表明,所建立的数据驱动模型能够可靠预测跨声速条件下,后缘变弯度角度参变过程中机翼的非定常气动力、表面压力分布及颤振边界,变弯度角度的增加会使跨声速颤振凹坑提前出现.

关键词：气动弹性力学颤振变体机翼模型降阶数据驱动建模

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Conceptual Framework for Integrating Building Information Modelling (BIM) with Pavement Management System (PMS) 20th

Conceptual Framework for Integrating Building Information Mo...

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20th International conference on Computing in Civil and Building Engineering, ICCCBE 2024

作者： Utami, Retno Sandoval, Carlos Osorio Thom, Nick Department of Civil Engineering University of Nottingham NottinghamNG7 2RD United Kingdom Department of Civil Engineering. Politeknik Negeri Bandung Indonesia

ISBN: (纸本)9783031842078

In developing countries, pavement management systems (PMS) face limitations such as insufficient data, budget constraints, and inadequate analysis tools. To address those limitations, simplified PMS are needed to increase efficiency and effectiveness in managing pavement assets. Meanwhile, Building Information Modelling (BIM) is a promising technology that enables collaborative work, process automation, and centralised asset information sharing. However, current BIM implementation for PMS remains scattered across different steps of the pavement management process. A unified mechanism is required to seamlessly integrate all PMS elements into the BIM workflow. This article proposes a conceptual framework to integrate BIM with PMS. It integrates three key modules: (1) an As-Is model for capturing real-world pavement conditions, (2) a prediction model for automated performance analytics and deterioration forecasting, and (3) a decision-making engine to optimise recommended maintenance based on constraints. The unified mechanism enables visualisation, analytics, and data management to increase the efficiency and effectiveness of pavement management. The next step for this research is to implement and validate the framework through a real-world case study. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Budget control

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Methods for Creating Contextual Models of Subject Areas

Methods for Creating Contextual Models of Subject Areas

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International Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Nikita G. Vorobyov Anna Yu. Philippovich Moscow Polytechnic University Moscow Russian Federation Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

The aim of the study is to evaluate the methods of autonomous collection and use of local contexts for selecting an answer option by an intelligent system in the dialog interaction process. The following research methods were used: analysis of technical documentation, scientific papers, conference materials and instructional manuals. The current methods of collecting local contexts used in the most successful projects are analyzed. The methods of coding text words for processing by software are analyzed. The methods of measuring the quality of data collected in this way are analyzed. The result of the work is the presentation of the studied methods of collecting, storing and evaluating local contexts. A method for modeling subcategory contexts by a subject area is proposed in order to improve the quality of the decision-making system. A new method of graph modeling of logical connections of natural language data is proposed. A comparison of the effectiveness of various approaches to modeling local contexts is carried out and the corresponding conclusions are made.

关键词： Training Analytical models Adaptation models Accuracy Computational modeling Machine learning Probabilistic logic Vectors data models Context modeling

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Machine Learning Based Dynamic Spatiotemporal Networks and Bioinspired Optimization for Adaptive data Modelling and data analysis

Machine Learning Based Dynamic Spatiotemporal Networks and B...

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

作者： Chilamakuru Nagesh Manoj Kumar Department of Computer Science & Engineering Shri Venkateshwara University Gajraula Amroha Uttar Pradesh India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

This spatiotemporal complexity presents a very big challenge in effectively modeling and predicting patterns across domains that include climate systems, traffic networks, and disaster progression. Currently, the methods often tend to fail in capturing the time-varying dynamics, optimizing spatial paths, and managing the high dimensionality of such datasets. This leads to suboptimal performance and increased computational overhead. In response to the aforementioned limitations, three new methods of innovative encoding, learning, and optimization are proposed to significantly boost the spatiotemporal analysis of data in this work. First, a dynamic spatiotemporal network called DeepSTNet with adaptive temporal encoding and graph-based spatial attention for capturing region-wise interactions achieved 5-10% higher accuracy and reduced computation by 20-30%. Second, ReinforceSTPath uses a multi-agent reinforcement learning framework to optimize spatial path predictions with temporal feedback, allowing for collaborative learning and achieving a 15-20% reduction in path prediction error while increasing efficiency by 30% compared to deterministic models. Lastly, inspired by the dynamics of an ant colony, BioST-Optimizer introduces a bioinspired swarm intelligence algorithm for feature selection in high-dimensional datasets, reducing dimensions by 30-50% and preserving 98% predictive accuracy while reducing training times by 40%. Collectively, these methodologies advance the state-of-the-art modeling of spatiotemporal data in dynamical alignment of temporal and spatial characteristics, optimization of path prediction, and efficient feature selections. This framework deals with the major shortcomings of previous approaches. Robust, scalable, and efficient solutions with wide applicability and transformative potential for spatiotemporal data analysis are provided in process.

关键词： data analysis Computational modeling Biological system modeling Heuristic algorithms Predictive models Feature extraction data models Encoding Spatiotemporal phenomena Optimization

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The Good, the Bad and the Ugly: Investigating the Effectiveness of Graph Deep Neural Networks for Anomaly Detection in Industrial control Systems 7th

The Good, the Bad and the Ugly: Investigating the Effectiven...

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7th IFIP WG 5.5 International Cross-Domain conference on Internet of Things

作者： Nahalka, Martin Cook, Marco M. Pezaros, Dimitrios Univ Glasgow Glasgow Lanark Scotland

ISBN: (纸本)9783031819025;9783031819001;9783031818998

Industrial control Systems (ICS) are paramount to the efficient operation of Critical National Infrastructure (CNI) ranging from electricity generation and distribution to manufacturing. However, the growing convergence of ICS with Information Technology (IT) systems renders CNI vulnerable to a range of cyber threats. Graph neural networks are being increasingly used for anomaly detection by adding granularity to the detection process. In this paper, we present a comparative study of graph-based deep learning models for ICS anomaly detection. Through the evaluation of four models using three multivariate industrial datasets, we aim to discern the effectiveness of prediction and reconstruction-based graph models in the ICS domain. We investigate data reduction techniques to minimise features needed to represent the window size and examine the representation of sliding window in terms of feature size for time-series analysis. Additionally, we assess the impact of the length of a context window on anomaly detection performance. Our results show that using feature reduction techniques on a longer context window produces better results while having the computational advantages of a shorter window size. Graph autoencoder is the most resilient to feature size reduction by maintaining similar F1 and AUC-PR score regardless of the number of features used to represent a context window. The results also provide insight to the suitability of graph-based models in this domain and offer recommendations for their optimal usage, paving the way for enhanced security and resilience in ICS.

关键词： Anomaly detection Industrial control Systems Deep Learning Graph Neural Networks Machine Learning

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Layered data-Centric AI to Streamline data Quality Practices for Enhanced Automation 19th

Layered Data-Centric AI to Streamline Data Quality Practice...

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19th International conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2024

作者： Akmal, Muhammad Uzair Asif, Saara Koval, Leonid Mathias, Selvine G. Knollmeyer, Simon Grossmann, Daniel AImotion Bavaria Technische Hochschule Ingolstadt Ingolstadt85049 Germany Faculty of Computer Science and Data Processing Technische Hochschule Ingolstadt Ingolstadt85049 Germany

ISBN: (纸本)9783031815416

Most artificial intelligence (AI) applications are designed under the model-centric AI (MCAI) approach, where data scientists aim to optimize the machine learning (ML) models starting with fixed, preprocessed data. However, businesses often struggle with limited datasets, changes in data over time, and limited ML knowledge, making it difficult to maintain data quality. One potential approach is data-centric AI (DCAI), which systematically improves the data quality used to build AI systems. However, rapid growth in data volume leads to challenges in selecting the most suitable operations for enhancing data quality thereby maintaining data accuracy, completeness, consistency, and reliability in real-world applications. To address this gap, we propose a novel framework, namely Layered data-Centric AI (LDCAI) by expanding upon the existing DCAI Pipeline. LDCAI employs a three-layered approach, starting with the DCAI layer which generates the initial training data. This data is then analyzed and refined in the data analysis (DA) layer by a team of data scientists and domain experts. The refined data is later sent to the data Quality control (DQC) layer for additional quality checks and improvements by data scientists. The entire process can be iterated as needed to continuously improve data quality, which can be fed back into the DCAI layer’s model to achieve better predictions and outcomes. The implementation is illustrated through a scalable and customizable architecture designed for optimizing data quality within AI systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data accuracy

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analysis of Influence of Air Pollutant Concentration Based on Multi-Material Balance Model 24

Analysis of Influence of Air Pollutant Concentration Based o...

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Proceedings of the 2024 4th International conference on Computational modeling, Simulation and data analysis

作者： Yuchan Zhang Tingting Yan Chuanxu Wang Qinhang Wu School of Municipal and Environmental Engineering Shenyang Jianzhu University Shenyang Liaoning China School of Science Shenyang Jianzhu University Shenyang Liaoning China School of Mechanical Engineering Shenyang Jianzhu University Shenyang Liaoning China

ISBN: (纸本)9798400711831

The air purifier is of great significance for purifying air. In this paper, aiming at the use of air purifiers, the data preprocessing of ten representative air purifiers is first carried out. After the consistency test of the selected eight indicators, the entropy weight method is used to weight them, and the TOPSIS method is used to process the data set. A comprehensive multi-index evaluation model for evaluating the advantages and disadvantages of air purifiers is established. Based on this model, the selected ten air purifiers are ranked in terms of comprehensive cost performance. Then, the air purifier model and the living room simulation model were established by using SolidWorks software. Based on the dispersion of fluid mechanics, control equations and equations, the control equation model of gas fluid is constructed. The CFD finite element simulation is used to establish a suitable three-dimensional model of the analysis scene. In this paper, the air purifier and the model and the living room simulation model are meshed and the calculation area is adjusted. The control equation model of the air fluid in the scene is solved by the pressure base solver. The equation and the established material balance model are imported into Fluent software for calculation. Finally, the cloud map of the influence of an air purifier placed at different positions in the room on the concentration of air pollutants at different heights in the room is obtained by fluent-post.

关键词： Air fluid control equation

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AI-Powered Defect Detection using Deep Learning: A Pattern-Agnostic Faster R-CNN Approach for SEM Images with GPU Acceleration

AI-Powered Defect Detection using Deep Learning: A Pattern-A...

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IEEE/SEMI conference and Workshop on Advanced Semiconductor Manufacturing

作者： Hosam Hatem Amr Essam Hesham Samir Ahmed ElDessouki Stewart Wu Kiarash Ahi Nathan Greeneltch Srividya Jayaram Germain Fenger Siemens EDA Cairo Egypt Siemens EDA Tainan Taiwan Siemens EDA Fremont USA Siemens EDA Wilsonville USA

ISBN: (数字)9798331531850

ISBN: (纸本)9798331531867

Semiconductor manufacturing requires highly precise defect detection to ensure product quality and yield. This paper presents a deep learning-based defect detection framework using Faster R-CNN to identify and classify defects in Scanning Electron Microscope (SEM) images, specifically within the modeling flow, which is used to simulate and predict semiconductor process behavior based on metrology data. The process begins with manufacturing test patterns through the photolithography process, followed by metrology analysis using a SEM machine. If a defect is present in the SEM image, the associated metrology data may become unreliable, potentially degrading the accuracy of the simulation models built on this data. Using such erroneous data in model creation could lead to inaccurate predictions and weaker *** address this issue, our method filters out defective data, ensuring that only high-quality, defect-free measurements contribute to model development. This leads to more reliable semiconductor process simulations and improved process decisions. Leveraging the proven performance of Faster R-CNN—particularly its effectiveness in detecting small, low-contrast anomalies—the model generalizes across diverse layouts without requiring pattern-specific training. It achieves a mAP of 83% at IoU = 0.5, significantly reducing manual inspection time and improving defect classification accuracy. Our approach enables scalable, automated inspection of SEM data, enhancing the overall efficiency, robustness, and precision of the semiconductor modeling and manufacturing pipeline.

关键词： Training Scanning electron microscopy Accuracy Semiconductor device manufacture Predictive models Metrology Inspection data models Semiconductor process modeling Defect detection

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Spatial modeling with Automated Machine Learning and Gaussian process Regression Techniques for Imputing Wafer Acceptance Test data

Spatial Modeling with Automated Machine Learning and Gaussia...

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Design, Automation and Test in Europe conference and Exhibition

作者： Ming-Chun Wei Chun-Wei Shen Hsun-Ping Hsieh Department of Electrical Engineering National Cheng Kung University Tainan Taiwan Academy of Innovative Semiconductor and Sustainable Manufacturing National Cheng Kung University Tainan Taiwan

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

The Wafer Acceptance Test (WAT) is a significant quality control measurement in the semiconductor industry. However, because the WAT process can be time-consuming and expensive, sampling test is commonly employed during production. This makes root cause tracing impossible when abnormal products have not been tested. Therefore, in our study, we focus on establishing a reliable method to estimate WAT results for non tested shots, including both intra and inter-wafer prediction. Notably, we are the first to combine the use of Chip Probing data with WAT to improve the predictions. Our proposed method first extracts valuable features from Chip Probing test results by using the Automated Machine Learning technique. We then employ Gaussian process Regression to capture the spatiotemporal correlation. Finally, we adopted the linear regression model to ensemble two components and proposed a SMART-WAT model to effectively estimate the wafer acceptance test data. Our method has been tested on a real-world dataset from the semiconductor manufacturing industry. The prediction results of four key WAT parameters indicate that our proposed model outperforms the state-of-the-art methods in both intra and inter-wafer prediction.

关键词： Semiconductor device modeling Industries Correlation Estimation Gaussian processes Semiconductor device manufacture Predictive models Feature extraction data models Semiconductor process modeling

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