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21st IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2025

作者： Adediran, Anthonia Oluwatosin Ajibade, Samuel-Soma M. Jasser, Muhammed Basheer Bashir, Faizah Mohammed Aminu Dodo, Yakubu Huda Mohd Yassin, Yasni Nurul Issa, Bayan Faculty of Built Environment Universiti Malaya Department of Real Estate Kuala Lumpur Malaysia School of Engineering and Technology Sunway University Department of Data Science and Artificial Intelligence Selangor Darul Ehsan 47500 Malaysia Sunway University Selangor Darul Ehsan 47500 Malaysia College of Engineering University of Hail Department of Decoration and Interior Design Engineering Hail55427 Saudi Arabia College of Engineering Najran University Architectural Engineering Department Najran66426 Saudi Arabia Universiti Teknologi Malaysia Faculty of Artificial Intelligence Kuala Lumpur Malaysia University of Aleppo Faculty of Informatics Engineering Syria

ISBN: (纸本)9798331522193

This study examines the academic field of Artificial Intelligence in Housing by examining the publications and indexed documents in Scopus and web of science (WOS) from 2000 to 2024. Consequently, we examined the publishing trends, exemplary articles, and the most engaged participants and funding institutions in the domain of AI and Housing research. A bibliometric analysis was performed to examine the network of co-authorships, keywords, and citations in the domain of AI and Housing research. The Scopus and WOS search resulted in the discovery of 246 related publications on AI and Housing. With an increase from 1 publication to 68 publications within the observed years. Additionally, trends analysis indicated a significant increase of 5,016 in publications between 2000 and 2024. Articles are the most often utilized document type, including 172 of the overall amounts. The most prolific researchers in the field of AI and Housing are Ahmad, Chatterjee, Cho, Lee, Mehmood, Shaheen, Yang, and Zhao. All having published 3 documents each. Additionally, ten institutions have produced the highest number of published documents. The output of the affiliations active in the topic is primarily attributed to the funding, specifically the National science Foundation, which is the leading funding institution based in the United States and has contributed to 8 publications. An analysis of keyword co-occurrence revealed 4 main study areas that cover the essential tools, theories, methodologies, and socioeconomic and financial aspects of artificial intelligence and Housing. Future research in the field of artificial intelligence and Housing will focus on employing sophisticated machine learning, and neural network algorithms to precisely predict Housing transactions patterns while further enhancing housing market transactions across countries. © 2025 IEEE.

关键词： Bibliographic retrieval systems

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design decisions for integrating Prescriptive Analytics Use Cases into Smart Factories

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Procedia CIRP 2024年 128卷 424-429页

作者： Julian Weller Nico Migenda Sebastian von Enzberg Martin Kohlhase Wolfram Schenck Roman Dumitrescu Fraunhofer Institute for Mechatronic Systems Design Zukunftsmeile 1 33102 Paderborn Germany Center for Applied Data Science Gütersloh Faculty of Engineering and Mathematics Bielefeld University of Applied Sciences and Arts Schulstraße 10 33330 Gütersloh Germany Chair for Advanced Systems Engineering Heinz Nixdorf Institute Fürstenallee 11 33102 Paderborn Germany

The emerging trend of Prescriptive Analytics tries to prescribe actionable decisions. The goal is to help production experts in automating their decisions in the factory. Decision making processes in factories mostly rely on experts inside the company. Current trends such as demographic shifts and shortages of skilled personnel are challenging organizations to find ways to become less dependent on the ad hoc availability of expert knowledge through digitalization and data analytics. Existing data analytics approaches for smart factories usually focus on description, diagnosis, and prediction. An approach to differentiate between different application areas of Prescriptive Analytics in a smart factory is presented. Focus areas for a useful application and integration of Prescriptive Analytics are derived. Based on these applications, design principles are constructed. They help decision makers to select future lighthouse use cases for development and help smart factory advocates to strategically decide which prescriptive analytics approaches to highlight. The resulting design principles are based on a literature review and validated in a workshop with experts from research. Approaches are differentiated and key characteristics for future Use Case design decisions are derived.

关键词： Prescriptive Analytics Smart Factory design methodology tools technologies

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Models of Exchanged datasets and Interactions of Buyers in the data Market: Toward Multi-Agent Simulators for System design

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Procedia Computer science 2022年 207卷 1695-1704页

作者： Teruaki Hayashi Hiroyasu Matsushima Hiroki Sakaji Yoshiaki Fukami Takumi Shimizu Department of Systems Innovation School of Engineering The University of Tokyo Tokyo Japan Center for Data Science Education and Research Shiga University Shiga Japan Department of International Digital and Design Management Tokyo University of Science Tokyo Japan Faculty of Policy Management Keio University Kanagawa Japan

Value creation by reusing data as exchangeable resources has been widely studied as a new source of innovation, resulting in the establishment of a data market and its ecosystem. However, owing to the nascent nature of the market, observable information on exchanged datasets and interactions among stakeholders, such as buyers and providers, in the market are limited. Therefore, a lack of observable information that contributes to designing the market system and formulating regulations to promote the sound growth of data markets exists. This study modeled exchanged datasets and data buyers as agents, the smallest units of the data market components, and prepared seven scenarios for four market sizes. We simulated the effects of datasets and agent models with different market sizes on the agents’ data purchases and emergence of popular datasets and discussed the factors that affect the purchase frequency distribution of the datasets. We present the experimental results and new implications for developing a data market simulator. The development of this simulator is expected to significantly advance research on the market understanding and system design in data markets.

关键词： data market data exchange data ecosystem system design simulation

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Advanced Analytics in Smart Factories: Towards an actionable Taxonomy for Prescriptive Analytics Use Cases

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Procedia CIRP 2025年 134卷 229-234页

作者： Julian Weller Sumit Nalavade Oliver Gmelch Nico Migenda Tim Heuwinkel Roman Dumitrescu Fraunhofer Institute for Mechatronic Systems Design Zukunftsmeile 1 33102 Paderborn Germany Technische Hochschule Ingoldstadt“ Esplanade 10 85049 Ingolstadt Center for Applied Data Science Gütersloh Faculty of Engineering and Mathematics Bielefeld University of Applied Sciences and Arts Schulstraße 10 33330 Gütersloh Germany Chair for Advanced Systems Engineering Heinz Nixdorf Institute Fürstenallee 11 33102 Paderborn Germany

Prescriptive analytics use cases support in the decision-making process and focus on providing actionable guidance (e.g. in the smart factory) based on a set of problems and possible solutions. Smart factories represent the core of Industry 4.0 and can greatly benefit from the implementation of advanced analytics use cases. Prescriptive analytics enables operational excellence in smart factories by providing actionable insights and decisions to (autonomously) steer and govern areas of a factory. Still, a widespread adoption of principles of prescriptive analytics is not reached, due to the complexity and interconnectivity of different use cases. Additionally, a lack of methodological support for developing prescriptive analytics use cases is observed. We support the adoption and ideation of prescriptive analytics use cases by providing a taxonomy for prescriptive analytics use cases in smart factories for researchers. To make the findings from the taxonomy actionable, we develop a concept on how to transform existing analytics use cases or use case ideas into prescriptive analytics use cases to support practitioners. We focus on the transformation of existing analytics use cases into prescriptive analytics use cases to lower the barrier to entry for the development of prescriptive use cases. The findings are based on the taxonomy development method by Nickerson. Our evaluation is supported by expert interviews as well as focus groups.

关键词： "Smart Factory Advanced Analytics Prescriptive Analytics Taxonomy"

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Computational modelling of bone growth and mineralization surrounding biodegradable Mg-based and permanent Ti implants

arXiv

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arXiv 2024年

作者： Pohl, Nik Priebe, Domenik AlBaraghtheh, Tamadur Schimek, Sven Wieland, D.C. Florian Krüger, Diana Trostorff, Sascha Willumeit-Römer, Regine Köhl, Ralf Zeller-Plumhoff, Berit Institute of Metallic Biomaterials Helmholtz-Zentrum Hereon Germany Institute of Surface Science Helmholtz-Zentrum Hereon Germany Department of Mathematics Faculty of Mathematics and Natural Sciences Kiel University Germany Kiel Nano Surface and Interface Science - KiNSIS Kiel University Germany Data-driven Analysis and Design of Materials Faculty of Mechanical Engineering and Marine Technologies University of Rostock Germany Department Life Light & Matter University of Rostock Rostock18051 Germany

In silico testing of implant materials is a research area of high interest, as cost- and labour-intensive experiments may be omitted. However, assessing the tissue-material interaction mathematically and computationally can be very complex, in particular when functional, such as biodegradable, implant materials are investigated. In this work, we expand and refine suitable existing mathematical models of bone growth and magnesium-based implant degradation based on ordinary differential equations. We show that we can simulate the implant degradation, as well as the osseointegration in terms of relative bone volume fraction and changes in bone ultrastructure when applying the model to experimental data from titanium and magnesium-gadolinium implants for healing times up to 32 weeks. An additional sensitivity analysis highlights important parameters and their interactions. Moreover, we show that the model is predictive in terms of relative bone volume fraction with mean absolute errors below 6%. © 2024, CC BY.

关键词： Sensitivity analysis

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Customer Segmentation Using Clustering Analysis

Customer Segmentation Using Clustering Analysis

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Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Moses Makuei Jiet Aahash Kamble Chetan Puri Prajyot Yesankar Prateek Verma Rajendra Rewatkar Department of Computer Science & Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Biomedical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

This research focuses on the crucial role of the clustering technique in data mining, specifically in market forecasting and planning. The study presents a comprehensive report on utilizing the k-means clustering technique and data visualization tools to develop a real- time system for a supermarket, predicting sales across various annual cycles. An open-source dataset of a mall with n = 200 customers divided into k = 3 groups, demonstrated high accuracy in predicting day-to-day sales statistics when compared with actual results. The research highlights the effectiveness of the clustering technique in real-time sales prediction for supermarkets.

关键词： Sociology Refining Decision making data visualization Prediction algorithms Real-time systems Planning

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Can LLMs Assist Computer Education? an Empirical Case Study of DeepSeek

arXiv

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arXiv 2025年

作者： Xiao, Dongfu Gao, Chen Luo, Zhengquan Liu, Chi Shen, Sheng Faculty of Data Science City University of Macau China Design and Creative Technology Vertical Torrens University Australia NSW Australia

This study presents an empirical case study to assess the efficacy and reliability of DeepSeek-V3, an emerging large language model, within the context of computer education. The evaluation employs both CCNA simulation questions and real-world inquiries concerning computer network security posed by Chinese network engineers. To ensure a thorough evaluation, diverse dimensions are considered, encompassing role dependency, cross-linguistic proficiency, and answer reproducibility, accompanied by statistical analysis. The findings demonstrate that the model performs consistently, regardless of whether prompts include a role definition or not. In addition, its adaptability across languages is confirmed by maintaining stable accuracy in both original and translated datasets. A distinct contrast emerges between its performance on lower-order factual recall tasks and higher-order reasoning exercises, which underscores its strengths in retrieving information and its limitations in complex analytical tasks. Although DeepSeek-V3 offers considerable practical value for network security education, challenges remain in its capability to process multimodal data and address highly intricate topics. These results provide valuable insights for future refinement of large language models in specialized professional environments. © 2025, CC BY.

关键词： Computer network performance evaluation

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Xgboost Machine Learning Algorithm Utilised for Predictive Modelling of Bearing Capacity of Soils Stabilised by Cementitious Additives’-Enriched Eco-Friendly Pozzolans

SSRN

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SSRN 2022年

作者： Eyo, Eyo U. Abbey, Samuel J. Onyekpe, Uche Faculty of Environment and Technology Department of Engineering Design and Mathematics Civil Engineering Cluster University of the West of England Bristol United Kingdom Department of Data Science School of Science Technology and Health York St. John University United Kingdom

An estimation of the California Bearing Ratio (CBR) of a soil stabilised by an environmentally-friendly binder composite is one of the most important steps in the development of a correct mix design for subsequent application in the field. In this study, the Extreme Gradient Boosting (XGBoost) machine learning (ML) algorithm is applied to intelligently model the soaked CBR of potential soil subgrade materials stabilised by cementitious additives’-enriched agro-based pozzolanic materials. ML prediction suggests the range of proportion of the binder composite that could be adopted to stabilise a soil subgrade to achieve an optimum CBR value is between 10 – 15% by dry weight of the natural soil but with the quantity of agro-based pozzolans being 4 times more in the mix. Satisfactory performance of the XGBoost algorithm used is confirmed by the high coefficient of determination (R2) of 0.924 and mean absolute percentage error (MAPE) of 2.032 achieved from ML regression. For ML multiclass classification of the stabilised subgrade quality, very high performance (an average AUC of 0.910) indicated the model’s capacity to distinguish between class labels. ML diagnostic training and testing suggested a very strong dependence of CBR on the binders, compaction parameters at optimum conditions and the soils’ specific gravity. © 2022, The Authors. All rights reserved.

关键词： Soils

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Software package for regression algorithms based on Gaussian Conditional Random Fields

Software package for regression algorithms based on Gaussian...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Tijana Markovic Vladan Devedzic Fang Zhou Zoran Obradovic School of Innovation Design and Engineering Malardalen University Vasteras Sweden Faculty of Organizational Sciences University of Belgrade Belgrade Serbia School of Data Science and Engineering East China Normal University Shanghai China Center for Data Analytics and Biomedical Informatics Temple University Philadelphia USA

The Gaussian Conditional Random Fields (GCRF) algorithm and its extensions are used for machine learning regression problems in which the attributes of objects and the correlation between objects should be considered when making predictions. These algorithms can be applied in different domains where problems can be seen as graphs, but their implementation requires complex calculations and good programming skills. This paper presents an open source software package that includes a tool with graphical user interface (GCRFs tool) and Java library (GCRFs library). GCRFs tool is software that integrates various GCRF-based algorithms and supports training and testing of those algorithms on real-world datasets. The main goal of GCRFs tool is to provide a straightforward and user-friendly graphical user interface that will simplify the use of GCRF-based algorithms. GCRFs Java library contains basic classes for GCRF concepts and can be used by researchers who have experience in Java programming. Also, this paper presents the results of a pilot usability evaluation of the GCRFs tool, where the software was evaluated with expert and non-expert users. This evaluation gave us detailed insight into the experiences and opinions of the users and helped us outline priorities for future development.

关键词： Training Java Machine learning algorithms Software packages Software algorithms Programming Prediction algorithms

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Long-term prediction of El Niño-Southern Oscillation using reservoir computing with data-driven realtime filter

arXiv

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arXiv 2025年

作者： Jinno, Takuya Mitsui, Takahito Nakai, Kengo Saiki, Yoshitaka Yoneda, Tsuyoshi School of Sustainable Design University of Toyama Toyama930-8555 Japan Faculty of Health Data Science Juntendo Univerity Chiba Urayasu279-0013 Japan The Graduate School of Environment Life Natural Science and Technology Okayama University Okayama700-8530 Japan Graduate School of Business Administration Hitotsubashi University Tokyo186-8601 Japan Graduate School of Economics Hitotsubashi University Tokyo186-8601 Japan

In recent years, the application of machine learning approaches to time-series forecasting of climate dynamical phenomena has become increasingly active. It is known that applying a band-pass filter to a time-series data is a key to obtaining a high-quality data-driven model. Here, to obtain longer-term predictability of machine learning models, we introduce a new type of band-pass filter. It can be applied to realtime operational prediction workflows since it relies solely on past time series. We combine the filter with reservoir computing, which is a machine-learning technique that employs a data-driven dynamical system. As an application, we predict the multi-year dynamics of the El Niño-Southern Oscillation with the prediction horizon of 24 months using only past time series. © 2025, CC BY-NC-SA.

关键词： Adversarial machine learning

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