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arXiv 2024年

作者： Dürrschnabel, Dominik Priss, Uta Knowledge & Data Engineering Group University of Kassel Kassel Germany Interdisciplinary Research Center for Information System Design Kassel Germany Faculty of Computer Science Ostfalia University of Applied Sciences Wolfenbüttel Germany

Euler diagrams are a tool for the graphical representation of set relations. Due to their simple way of visualizing elements in the sets by geometric containment, they are easily readable by an inexperienced reader. Euler diagrams where the sets are visualized as aligned rectangles are of special interest. In this work, we link the existence of such rectangular Euler diagrams to the order dimension of an associated order relation. For this, we consider Euler diagrams in one and two dimensions. In the one-dimensional case, this correspondence provides us with a polynomial-time algorithm to compute the Euler diagrams, while the two-dimensional case is linked to an NP-complete problem which we approach with an exponential-time *** Codes 06A07, 68R05 Copyright © 2024, The Authors. All rights reserved.

关键词： Formal concept analysis

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Brain Age Estimation Using Structural MRI: A Clustered Federated Learning Approach

Brain Age Estimation Using Structural MRI: A Clustered Feder...

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Omni-layer Intelligent Systems (COINS), International Conference on

作者： Seyyed Saeid Cheshmi Abtin Mahyar Anita Soroush Zahra Rezvani Bahar Farahani Department of Computer and Data Sciences Shahid Beheshti University Tehran Iran School of Computer Science Institute for Research in Fundamental Sciences (IPM) Tehran Iran Department of Computer Science Faculty of Mathematical Sciences Alzahra University Tehran Iran Cyberspace Research Institute Shahid Beheshti University Tehran Iran

Estimating brain age based on structural Magnetic Resonance Imaging (MRI) is one of the most challenging and prominent areas of research in recent medical imaging and neuroscience studies. The significance of brain age prediction in the early diagnosis of neurological disorders has fueled a resurgence of interest in this field. Various studies have addressed this issue using a spectrum of techniques, from traditional machine learning to deep neural networks. The majority of these techniques employ centralized paradigms, which do not adequately preserve privacy. To tackle this problem, a handful of studies have utilized a federated approach. In this study, we propose a novel hierarchical clustered federated learning approach that carefully captures and considers the similarities of the clients’ predictions on a certain benchmark dataset. This method enhances performance in Non-Independent and Identically Distributed (Non-IID) environments while preserving privacy. We use a multi-site dataset that provides a broad variety of MRI scans, characterizing a proper Non-IID environment. Our method achieves a Mean Absolute Error (MAE) of 3.86, while the non-clustered FedAvg federated approach attains a 4.14 MAE on the test set. 1 1 Code is available at https://***/Abtinmy/Clustered-FL-BrainAGE.

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Intelligent Optimization of Laser Marking Parameters Using Neural Networks and Genetic Algorithms 4

Intelligent Optimization of Laser Marking Parameters Using N...

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4th International Conference on Robotics, Automation, and Artificial Intelligence, RAAI 2024

作者： Yan, Teng Li, Wenxian Yu, Zhenxi Sun, Yuxiang Zhuang, Haoxue Zhou, Xiaohong College of Applied Technology Shenzhen University Shenzhen China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China College of Big Data and Internet Shenzhen Technology University Shenzhen China School of Science and Engineering The Chinese University of HongKong Shenzhen China Technical University of Applied Sciences Würzburg-Schweinfurt Faculty of Computer Science and Business Information Systems Schweinfurt Würzburg Germany

ISBN: (纸本)9798331520038

Laser marking technology is essential in precision manufacturing, but optimizing its numerous parameters is com-plex and time-consuming with traditional methods. This study developed a fully automated laser marking system using the Draco series laser from Han's Laser, incorporating automatic loading and unloading mechanisms. By utilizing neural networks and genetic algorithms, we optimized laser marking on ABS materials. The research included COMSOL simulations of laser-material interactions, analysis of parameter variations on col-oration, temperature field distribution under laser irradiation, and prediction of marking effects for practical applications. The aim is to automate processes through algorithmic integration, providing an intelligent laser marking solution. The developed methodologies and algorithms are applicable to other manufac-turing optimizations, offering valuable insights for broader in-dustrial applications. Primary research focuses include COMSOL simulation and parameter analysis, developing an AI-integrated laser production line with color prediction, and exploring au-tomated laser marking on ABS material using neural networks and genetic algorithms. © 2024 IEEE.

关键词： Genetic Algorithms Laser Marking Machine Learning Small data Neural Networks

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Machine learning approaches to medication adherence amongst NCD patients: A systematic literature review

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Informatics in Medicine Unlocked 2023年 38卷

作者： Kanyongo, Wellington Ezugwu, Absalom E. Department of Computer Science Faculty of Science Engineering Bindura University of Science Education Bindura Zimbabwe School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Pietermaritzburg Campus Pietermaritzburg 3201 South Africa Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom 2520 South Africa

Non-adherence to prescribed medication is a major public health concern that escalates the risk of morbidity and death as well as incurring extra expenses associated with hospitalisation. According to the World Health Organization (WHO), only 50% of people suffering from chronic diseases follow the treatment recommendations despite the counsel provided to patients on the importance of medication adherence (MA). Early detection of non-communicable disease (NCD) patients poorly adhering to recommended medications using analytics based on machine learning (ML) may improve the outcomes of NCD patients positively. This paper presents a systematic review of literature involving the application of ML in evaluating MA amongst NCD patients. The articles considered in this study were extracted from Web of science, Google Scholar, PubMed, and IEEE Explore. Twenty-five articles in total met the criteria for inclusion. These were articles that utilised ML techniques to analyse MA in NCDs, with patients suffering from diabetes (n = 8), hypertension (n = 3), cardiovascular disease (CVD) and statin adherence (n = 6), cancer (n = 3), respiratory diseases (n = 2), and other NCD conditions (n = 3). The proportion of days covered (PDC) was typically used to evaluate MA. It emerged from the study that for MA to be considered high, the adherence threshold should be at least 75% of the PDC, a universally accepted threshold. In MA analytics research and practice, a PDC ≥80% threshold is typically regarded as a high level of adherence to prescription medication. Logistic regression (LR) (n = 12), random forest (RF) (n = 11), support vector machine (SVM) (n = 7), neural net (n = 6), ensemble learning (n = 6), MLPs (n = 4), XGBoost (n = 3), Bayesian network (BN) (n = 3), and gradient boosting (n = 3) were the most frequently applied ML techniques in the analytics of MA amongst NCD patients. It should be underscored that leveraging standard ML, deep learning (DL), and ensemble learning has eno

关键词： Cancer Cardiovascular disease (CVD) Chronic disease Deep learning Diabetes Hypertension Machine learning Medication adherence NCD patients Non-communicable disease Respiratory disease

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A Novel Hybrid Optimizer Based on Coati Optimization Algorithm and Differential Evolution for Global Optimization and Constrained Engineering Problems

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International Journal of Computational Intelligence Systems 2025年第1期18卷 1-70页

作者： Biswas, Saptadeep Maiti, Binanda Singh, Gyan Ezugwu, Absalom E. Saleem, Kashif Abualigah, Laith Smerat, Aseel Bera, Uttam Kumar Department of Mathematics National Institute of Technology Agartala Tripura India Unit for Data Science and Computing North-West University Potchefstroom South Africa Department of Computer Science & Engineering College of Applied Studies & Community Service King Saud University Riyadh Saudi Arabia Computer Science Department Al al-Bayt University Mafraq Jordan Faculty of Educational Sciences Al-Ahliyya Amman University Amman Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India Computer Technologies Engineering Mazaya University College Nasiriyah Iraq

This paper presents a novel hybrid metaheuristic, the Hybrid Coati Optimization Algorithm with Differential Evolution (HCOADE), developed to address complex global optimization tasks and constrained engineering design problems. HCOADE integrates the exploration-driven behaviour of the Coati Optimization Algorithm (COA)-inspired by the social foraging and predation strategies of coatis-with the powerful mutation and crossover mechanisms of Differential Evolution (DE), thereby achieving a balanced and adaptive search process. The hybridization enhances global exploration and local exploitation, enabling the algorithm to efficiently navigate diverse and challenging optimization landscapes. To rigorously evaluate its performance, HCOADE is tested on benchmark suites from CEC 2014, 2017, 2020, and 2022, which encompass unimodal, multimodal, hybrid, and composition functions. It is also applied to real-world constrained engineering problems, such as pressure vessel design, cantilever beam optimization, and reinforced concrete beam design. Comparative experiments against state-of-the-art algorithms—including COA, DE, RSA, PSO, SSA, BBO, QIO, DMOA, and others—demonstrate that HCOADE consistently delivers superior solution quality, faster convergence, and higher robustness. Quantitative results show that HCOADE achieved the 1st place average rank across all four benchmark suites. It obtained top performance on 80% of the functions in CEC 2014, 66.7% in CEC 2017, 70% in CEC 2020, and 66.7% in CEC 2022. Furthermore, HCOADE outperformed or matched CEC competition-winning algorithms such as LSHADE-cnEpSin, LSHADE-SPACMA, and CMA-ES on numerous challenging functions of CEC 2017. Statistical analyses, including Wilcoxon Rank Sum Tests and ranking evaluations, confirm the significance and reliability of HCOADE’s performance. Furthermore, convergence behaviour, measurement of exploration and exploitation, and sensitivity analysis highlight the algorithm’s adaptability and stability

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Artificial Intelligence-based Service Chains Scheduling for Medical Emergency in Healthcare 7

Artificial Intelligence-based Service Chains Scheduling for ...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Soni, Mukesh Rusho, Maher Ali Byeon, Haewon Alghamdi, Azzah Hannan, Shaikh Abdul Dave, Parth Rameshchandra Dr. D. Y. Patil School of Science & Technology Tathawade Dr. D. Y. Patil Vidyapeeth Marashtra Pune India University of Colorado Department of Lockheed Martin Engineering Management BoulderCO80309 United States Inje University Department of Ai and Software & Medical Big Data Research Centre Gimhae50834 Korea Republic of Imam Abdalrhman Bin Faisal University College of Computer Science and Information Technology Computer Information Systems Department Khobar Saudi Arabia AlBaha University Faculty of Computing and Information AlBaha Saudi Arabia L. D. College of Engineering Department of Computer Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798350350067

This study analyses the weekly scheduling challenge of health and medical information network service function chains to meet medical emergency categorization and grading needs. Emergency services are divided into four sorts by urgency and service area, with priorities and weights. The health and medical information network architecture, service model, and operation method are built accordingly. To reduce weighted completion time, a service function chain scheduling model is created. The function chain scheduling scheme is solved by matching game and Q -learning reinforcement learning algorithms for different issue scales. Finally, simulation studies demonstrate that this approach can accomplish graded medical emergency service, build health and medical information networks, and progress smart healthcare. © 2024 IEEE.

关键词： Electronic health record

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Embedded Point Iteration Based Recursive Algorithm for Online Identification of Nonlinear Regression Models

arXiv

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arXiv 2022年

作者： Chen, Guang-Yong Gan, Min Chen, Jing Chen, Long The College of Computer and Data Science Fuzhou University Fuzhou350116 China School of Science Jiangnan University Wuxi214122 China The Faculty of Science and Technology University of Macau 99999 China

This paper presents a novel online identification algorithm for nonlinear regression models. The online identification problem is challenging due to the presence of nonlinear structure in the models. Previous works usually ignore the special structure of nonlinear regression models, in which the parameters can be partitioned into a linear part and a nonlinear part. In this paper, we develop an efficient recursive algorithm for nonlinear regression models based on analyzing the equivalent form of variable projection (VP) algorithm. By introducing the embedded point iteration (EPI) step, the proposed recursive algorithm can properly exploit the coupling relationship of linear parameters and nonlinear parameters. In addition, we theoretically prove that the proposed algorithm is mean-square bounded. Numerical experiments on synthetic data and real-world time series verify the high efficiency and robustness of the proposed algorithm. © 2022, CC BY-NC-ND.

关键词： Parameter estimation

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Improving Matrix-vector Multiplication via Lossless Grammar-Compressed Matrices

arXiv

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arXiv 2022年

作者： Ferragina, Paolo Gagie, Travis Köppl, Dominik Manzini, Giovanni Navarro, Gonzalo Striani, Manuel Tosoni, Francesco Department of Computer Science University of Pisa Italy Faculty of Computer Science Dalhousie University Canada M&D Data Science Center Tokyo Medical Dental University Japan Millennium Institute for Foundational Research on Data Department of Computer Science University of Chile Chile Department of Sciences and Technological Innovation University of Piemonte Orientale Italy

As nowadays Machine Learning (ML) techniques are generating huge data collections, the problem of how to efficiently engineer their storage and operations is becoming of paramount importance. In this article we propose a new lossless compression scheme for real-valued matrices which achieves efficient performance in terms of compression ratio and time for linear-algebra operations. Experiments show that, as a compressor, our tool is clearly superior to gzip and it is usually within 20% of xz in terms of compression ratio. In addition, our compressed format supports matrix-vector multiplications in time and space proportional to the size of the compressed representation, unlike gzip and xz that require the full decompression of the compressed matrix. To our knowledge our lossless compressor is the first one achieving time and space complexities which match the theoretical limit expressed by the k-th order statistical entropy of the input. To achieve further time/space reductions, we propose column-reordering algorithms hinging on a novel column-similarity score. Our experiments on various data sets of ML matrices show that, with a modest preprocessing time, our column reordering can yield a further reduction of up to 16% in the peak memory usage during matrix-vector multiplication. Finally, we compare our proposal against the state-of-the-art Compressed Linear Algebra (CLA) approach showing that ours runs always at least twice faster (in a multi-thread setting) and achieves better compressed space occupancy for most of the tested data sets. This experimentally confirms the provably effective theoretical bounds we show for our compressed-matrix approach. © 2022, CC BY-NC-ND.

关键词： Matrix algebra

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Transformer-based Time-to-Event Prediction for Chronic Kidney Disease Deterioration

arXiv

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arXiv 2023年

作者： Zisser, Moshe Aran, Dvir Faculty of Data and Decision Sciences Technion-Israel Institute of Technology Haifa Israel Faculty of Biology Technion-Israel Institute of Technology Haifa Israel The Taub Faculty of Computer Science Technion-Israel Institute of Technology Haifa Israel

Deep-learning techniques, particularly the transformer model, have shown great potential in enhancing the prediction performance of longitudinal health records. While previous methods have mainly focused on fixed-time risk prediction, time-to-event prediction (also known as survival analysis) is often more appropriate for clinical scenarios. Here, we present a novel deep-learning architecture we named STRAFE, a generalizable survival analysis transformer-based architecture for electronic health records. The performance of STRAFE was evaluated using a real-world claim dataset of over 130,000 individuals with stage 3 chronic kidney disease (CKD) and was found to outperform other time-to-event prediction algorithms in predicting the exact time of deterioration to stage 5. Additionally, STRAFE was found to outperform binary outcome algorithms in predicting fixed-time risk, possibly due to its ability to train on censored data. We show that STRAFE predictions can improve the positive predictive value of high-risk patients by 3-fold, demonstrating possible usage to improve targeting for intervention programs. Finally, we suggest a novel visualization approach to predictions on a per-patient basis. In conclusion, STRAFE is a cutting-edge time-to-event prediction algorithm that has the potential to enhance risk predictions in large claims datasets. © 2023, CC BY-NC-ND.

关键词： Deterioration

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Enhancing Financial Stability: Machine Learning Models for Bank Loan Risk Prediction

Enhancing Financial Stability: Machine Learning Models for B...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Shadi Nashwan Issam Jebreen Ahmad Al-Qerem Mohammad A Al Khaldy Mohammed Rajab Faculty of Information Technology Middle East University Jordan Computer Science Department Zarqa University Jordan Business Intelligence Data Analytics University of Petra Amman Jordan University Headquarter University Of Anbar Ramadi Anbar Iraq

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The efficient prediction of loan risk in banking is pivotal for maintaining profitability and financial stability. This paper tackles two main objectives: (1) identifying the most suitable classifier among twenty distinct algorithms (encompassing six learning strategies) to detect loan risk, and (2) evaluating two widely adopted feature selection methods to pinpoint the most influential predictors within the loan dataset. By integrating advanced data mining techniques, including decision trees, ensemble approaches, Bayesian methods, and neural networks, we analyzed a comprehensive set of borrower attributes and loan characteristics. The best-performing classifier (Decision Table) achieved high accuracy, precision, recall, and Fl-score, establishing its reliability for loan risk modeling. Furthermore, we found GainRatioAttributeEval to be the most effective feature selection technique, significantly enhancing predictive performance by highlighting relevant variables. These findings underscore how coupling an optimal classifier with a targeted feature selection method can substantially improve a bank's risk management framework and operational decision-making. In practice, the proposed data-driven approach can bolster financial institutions' resilience, reduce default rates, and ensure the more strategic allocation of capital resources.

关键词： Accuracy Banking Predictive models Feature extraction Stability analysis data models data mining Risk management Resource management Resilience

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