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93rd Annual Meeting of the International Association of applied Mathematics and Mechanics, GAMM 2023

作者： Lißner, Julian Fritzen, Felix Data Analytics in Engineering Institute of Applied Mechanics University of Stuttgart Stuttgart Germany

In multiscale modeling, the response of the macroscopic material is computed by considering the behavior of the microscale at each material point. To keep the computational overhead low when simulating such high performance materials, an efficient, but also very accurate prediction of the microscopic behavior is of utmost importance. Artificial neural networks are well known for their fast and efficient evaluation. We deploy fully convolutional neural networks, with one advantage being that, compared to neural networks directly predicting the homogenized response, any quantity of interest can be recovered from the solution, for example, peak stresses relevant for material failure. We propose a novel model layout, which outperforms state-of-the-art models with fewer model parameters. This is achieved through a staggered optimization scheme ensuring an accurate low-frequency prediction. The prediction is further improved by superimposing an efficient to evaluate U-net, which captures the remaining high-level features. © 2023 The Authors. Proceedings in applied Mathematics & Mechanics published by Wiley-VCH GmbH.

关键词： Forecasting

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Developing a Customer Churn Prediction Framework Using LFMCP Value Segmentation: Insights from the Thai Insurance Sector 11th

Developing a Customer Churn Prediction Framework Using LFMCP...

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11th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2025

作者： Pongsathornwiwat, Akkaranan Kulmanochwong, Pattaraporn Department of Logistics Management Graduate School of Applied Statistics National Institute of Development Administration Bangkok Thailand Department of Business Analytics and Data Science Graduate School of Applied Statistics National Institute of Development Administration Bangkok Thailand

ISBN: (纸本)9789819646029

This study explores the application of the Length, Frequency, Monetary, Claim, and Profit (LFMCP) customer value model to predict customer churn in a Thai general insurance company with a primary focus on B2B clients. The research provides valuable insights into the insurance industry, particularly for organizations aiming to enhance customer retention strategies. The churn prediction model is developed in two phases. In the first phase, LFMCP variables are used to distinguish between valued and non-valued customers. In the second phase, the classification performance of five predictive techniques—k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Decision Tree, Random Forest (RF), and XGBoost (XGB)—is compared. Results show that LFMCP significantly improves churn classification, with XGBoost emerging as the best-performing method. The study also investigates the importance of LFMCP variables using the Analytical Hierarchy Process (AHP). While the analysis explores varying weight configurations, findings suggest that equal weighting of the LFMCP variables results in superior predictive performance for churn classification in the insurance context. By incorporating additional predictors beyond the traditional RFM (Recency-Frequency-Monetary) model, the study introduces a novel and effective framework for churn prediction in the insurance industry. This expanded model, combined with insights from the AHP evaluation, forms the key contribution of the research, offering practical value for enhancing decision-making and customer retention strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Support vector machines

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New Results on Interval Type-3 Fuzzy Control for Nonlinear Time-Delay Systems Using Convex Relaxation Technique

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3630-3641页

作者： Harikaran, B. Harshavarthini, S. Lee, Sangmoon Sakthivel, R. Sathiyaraj, T. School of Advanced Sciences Department of Mathematics Chennai600127 India Kyungpook National University School of Electronic and Electrical Engineering Daegu41566 Korea Republic of Bharathiar University Department of Applied Mathematics Coimbatore641046 India Institute of Actuarial Science and Data Analytics UCSI University Kuala Lumpur56000 Malaysia

In this article, the interval type-3 fuzzy-based state feedback control is proposed for the stabilization problem of interval type-3 fuzzy systems (IT3FSs) subject to time-varying delay. Specifically, to improve the model accuracy and control capabilities, we proposed a nonparallel distributed compensation-based controller approach, be precise, the system and controller adopts the distinct membership functions. Moreover, the stability analysis of IT3FSs in the state space form is studied newly in this article. Besides the primary membership function that exists for interval type-2 fuzzy systems (IT2FSs), the IT3FSs possess the secondary membership function to handle the overall uncertainty which enacts the superiority of the proposed work. Notably, the adequate stability criteria are derived in the form of linear matrix inequality (LMI) using the Lyapunov stability theorem. Additionally, the convex relaxation technique is used to strengthen the design flexibility and achieve the faster convergence rate, which converts an optimization problem with a vector variable to a convex program with a matrix variable, via a lifting technique. A noteworthy aspect is that the application of the convex relaxation technique in the context of IT3FSs is proposed for the first time in literature. Finally, the comparative results between IT2FSs and IT3FSs are shown via two illustrative examples, including the inverted-pendulum model, to underscore the efficacy and practical implementations of the developed control methodology. © 2013 IEEE.

关键词： Membership functions

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Optimal testing using combined test statistics across independent studies 37

Optimal testing using combined test statistics across indepe...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Vuursteen, Lasse van der Vaart, Aad Szabó, Botond van Zanten, Harry Delft Institute of Applied Mathematics Delft University of Technology Netherlands Department of Decision Sciences Institute for Data Science and Analytics Bocconi University Italy Mathematics Department Vrije Universiteit Amsterdam Netherlands

ISBN: (纸本)9781713899921

Combining test statistics from independent trials or experiments is a popular method of meta-analysis. However, there is very limited theoretical understanding of the power of the combined test, especially in high-dimensional models considering composite hypotheses tests. We derive a mathematical framework to study standard meta-analysis testing approaches in the context of the many normal means model, which serves as the platform to investigate more complex models. We introduce a natural and mild restriction on the meta-level combination functions of the local trials. This allows us to mathematically quantify the cost of compressing m trials into real-valued test statistics and combining these. We then derive minimax lower and matching upper bounds for the separation rates of standard combination methods for e.g. p-values and e-values, quantifying the loss relative to using the full, pooled data. We observe an elbow effect, revealing that in certain cases combining the locally optimal tests in each trial results in a sub-optimal meta-analysis method and develop approaches to achieve the global optima. We also explore the possible gains of allowing limited coordination between the trial designs. Our results connect meta-analysis with bandwidth constraint distributed inference and build on recent information theoretic developments in the latter field. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Statistics

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Challenges and Opportunities for Enabling the Next Generation of Cross-Domain dataspaces 2

Challenges and Opportunities for Enabling the Next Generatio...

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2nd International Workshop on Semantics in dataspaces, SDS 2024

作者： Deshmukh, Rohit A. Collarana, Diego Gelhaar, Joshua Theissen-Lipp, Johannes Lange, Christoph Arnold, Benedikt T. Curry, Edward Decker, Stefan Fraunhofer Institute for Applied Information Technology FIT Sankt Augustin Germany RWTH Aachen University Aachen Germany Fraunhofer Institute for Software and Systems Engineering ISST Dortmund Germany Insight Centre for Data Analytics University of Galway Galway Ireland

dataspaces are regarded as a standardized solution for sharing data in a trusted way. However, providing and sharing high-quality data across dataspaces poses several scientific and technical challenges, opening new research avenues. Developing governance models and technologies for supporting cross-domain integration of data and services from the existing single-domain dataspaces represents a significant challenge. In this vision paper, we discuss the challenges for enabling next-generation dataspaces and propose an innovative approach that aims at developing a vision and identifying requirements and building blocks for next-generation dataspaces, followed by defining a roadmap and practical migration paths for the existing dataspaces towards the next-generation dataspaces. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Interoperability

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Accurate Location Estimation of Smart Dusts Using Machine Learning

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Computers, Materials & Continua 2022年第6期71卷 6165-6181页

作者： Shariq Bashir Owais Ahmed Malik Daphne Teck Ching Lai Institute of Applied Data Analytics(IADA) Universiti Brunei Darussalam(UBD)GadongBE1410Brunei Darussalam School of Digital Sciences(SDS) Universiti Brunei Darussalam(UBD)GadongBE1410Brunei Darussalam

Traditional wireless sensor networks(WSNs)are not suitable for rough terrains that are difficult or impossible to access by *** dust is a technology that works with the combination of many tiny sensors which is highly useful for obtaining remote sensing information from rough *** tiny sensors are sprinkled in large numbers on rough terrains using airborne distribution through drones or aircraftwithout manually setting their *** it is clear that a number of remote sensing applications can benefit from this technology,but the small size of smart dust fundamentally restricts the integration of advanced hardware on tiny *** raises many challenges including how to estimate the location of events sensed by the smart *** solutions on estimating the location of events sensed by the smart dusts are not suitable for monitoring rough terrains as these solutions depend on relay sensors and laser patterns which have their own limitations in terms of power constraint and uneven *** study proposes a novel machine learning based localization algorithm for estimating the location of *** approach utilizes timestamps(time of arrival)of sensed events received at base stations by assembling them into a multidimensional vector and input to a machine learning classifier for estimating the *** to the unavailability of real smart dusts,we built a simulator for analysing the accuracy of the proposed approach formonitoring forest *** experiments on the simulator show reasonable accuracy of the approach.

关键词： Smart dust sensor localization remote sensing machine learning algorithms Internet of Things sensor applications

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Research on Large-scale Structured and Unstructured data Processing based on Large Language Model 24

Research on Large-scale Structured and Unstructured Data Pro...

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2024 International Conference on Machine Learning, Pattern Recognition and Automation Engineering, MLPRAE 2024

作者： Li, Bohang Jiang, Gaozhe Li, Ningxin Song, Chaoda Department of Data Science Shopee Pte. Ltd. Singapore Institute of Operations Research and Analytics National University of Singapore Singapore Fu Foundation School of Engineering and Applied Science Columbia University United States School of Engineering Department of Computer and Data Sciences United States

ISBN: (纸本)9798400709876

Since the beginning of the internet era, there has been an explosion of growth in structured data (such as numbers, symbols, and labels) as well as unstructured data (including images, videos, and text). Efficient and accurate mixed query of these two types of data is a key technology to achieve high-quality information retrieval, and it is also a major challenge that needs to be solved urgently in the industry. In this study, we employ an advanced Transformer model that combines strategies and fine-tuning techniques for multi-task learning. Specifically, the model is first pre-trained on a large-scale, general-purpose dataset to learn different types of data representations and basic language comprehension skills. After that, we fine-tuned the parameters of the model to better suit these specific data processing tasks for specific application scenarios, such as image annotation, video content analysis, and structured data query. At the heart of the model is the self-attention mechanism, which allows the model to automatically emphasize the important parts and ignore irrelevant information when processing the input data. In addition, we have introduced task-specific adaptation layers that are designed to add additional processing power to the original Transformer architecture, such as a semantic analysis layer for unstructured text data and a relational extraction layer for structured data. This combination of general pre-training and task-specific fine-tuning allows the model to flexibly process and integrate information from different data sources, improving processing efficiency and accuracy. Experimental results show that the model performs well in a variety of data processing tasks, significantly improves the accuracy and efficiency of information retrieval, and verifies the strong potential and adaptability of large language models in processing mixed data types. © 2024 ACM.

关键词： data accuracy

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Multi-Modal Artificial Intelligence in Additive Manufacturing: Combining Thermal and Camera Images for 3D-Print Quality Monitoring 25

Multi-Modal Artificial Intelligence in Additive Manufacturin...

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25th International Conference on Enterprise Information Systems, ICEIS 2023

作者： Bauer, Markus Uhrich, Benjamin Schäfer, Martin Theile, Oliver Augenstein, Christoph Rahm, Erhard Institute for Applied Informatics Goerdelerring 9 Leipzig Germany Center for Scalable Data Analytics and Artificial Intelligence Humboldtstraße 25 Leipzig Germany Siemens AG Siemensdamm 50 Berlin Germany

ISBN: (纸本)9789897586484

With emerging technologies such as high-precision Laser Powder Bed Fusion (LPBF), rapid prototyping has gained remarkable importance in metal manufacturing. Furthermore, cloud computing and easy-to-integrate sensors have boosted the development of digital twins. Such digital twins use data from sensors on physical objects, to improve the understanding of manufacturing processes as a whole or of certain production parameters. That way, digital twins can demonstrate the impact of design changes, usage scenarios, environmental conditions or similar variables. One important application of such digital twins lies in early detection of manufacturing faults, such that real prototypes need to be used less. This reduces development times and allows products to be individually, affordable, powerful, robust and environmentally friendly. While typically simple USB-camera setups or melt-pool imaging are used for this task, most solutions are difficult to integrate into existing processes and hard to calibrate and evaluate. We propose a digital-twin-based solution, that leverages information from camera-images in a self-supervised fashion, and creates a heat transfer based AI quality monitoring. For that purpose, artificially generated labels and physics simulation were combined with a multisensor setup and supervised learning. Our model detects printing issues at more than 91% accuracy. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： 3D printing

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Marine Transport Business: A datamesh-Based data Asset Strategy 23

Marine Transport Business: A Datamesh-Based Data Asset Strat...

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Proceedings of the 2023 8th International Conference on Information Systems Engineering

作者： Kitipong Viseschart Worapol Pongpech Department of Management of Analytics and Data Technologies School of Applied Statistics National Institute of Development Administration (NIDA) Thailand

ISBN: (纸本)9798400709173

Modern organisations increasingly view data as a valuable asset, the management of which is essential due to its diminishing value over time. However, implementing data management technologies within various marine transportation systems, including vessels and ports, presents significant challenges. These difficulties primarily stem from issues such as unreliable data transmissions, incompatible programs, and the requirement to handle large volumes of data, particularly geographic information, for advanced processing. As a result, there is a pressing need to revise traditional data management methods to address these challenges effectively. The modern solution of the data Mesh has been attracting substantial interest from various organisations. This innovative approach adopts an in-house data management model that enhances the standard centralised team-based process and reduces the reliance on centralised personnel. Under the data Mesh paradigm, individual data management tasks are delegated to personnel within their specific business domains, yielding several benefits such as improved data quality, enhanced data utilisation, and better alignment with organisational strategic goals. This study applies the data Mesh model to construct a comprehensive framework for marine transport organisations and bulk carrier shipping companies. This framework encompasses decentralised data management, data product development, and the lifecycle of data assets and creates strategies for data assets.

关键词： data analytics data Assets data Management data Products Marine Transport Business

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Does parental migration affect children’s health in rural India? A fresh look at retrospective data using LASSO Regression Method

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SN Social Sciences 2025年第1期5卷 1-24页

作者： Chakraborty, Monalisha Mukherjee, Subrata Department of Data Sciences and Analytics School of Social Sciences MS Ramaiah University of Applied Sciences Gnanagangothri Campus New BEL road MSR Nagar Bangalore 560054 India Institute of Development Studies Kolkata 27/D DD Block Sector I Salt Lake Kolkata 700 064 India

The paper analyses India Human Development Survey-II (2011–12) data to discern the effect of parental out-migration on nutritional health status and morbidity among rural children of 0–14 years age. Adaptive LASSO logistic regression, a machine learning technique, was used to select the predictors, and state cluster fixed effect model was used to find the effect of select variables on children’s nutritional health status and morbidity. Results show that although parental migration lowers underweight among accompanied children of returned migrant parents, left-behind children of current migrant parents are found more likely to be underweight than their peers. Also, accompanied children of returned migrant parents are more likely to be affected by diarrhoea than their peers living in the countryside. ARI is found lower among the children left behind by returned migrant parents as well as by current migrant parents. However, children of remittance-recipient households are found to have performed well in terms of stunting among them. Multi-prolonged strategies are required towards mitigating the negative effects of parental migration on - the children. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Child health Morbidity Nutritional status Parental migration Rural India

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