检索结果-内蒙古大学图书馆

Informatica (Slovenia) 2025年第1期49卷 81-92页

作者： Abdullah, Lazim Awang, Noor Azzah Qiyas, Muhammad Special Interest Group of Modelling and Data Analytics Faculty of Computer Science and Mathematics Universiti Malaysia Terengganu Kuala Nerus21030 Malaysia College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Department of Mathematics Riphah International University Failsabad Campus Pakistan

Failure Modes and Effects Analysis (FMEA) is a widely used tool for risk analysis, primarily to identify risk factors affecting system quality. Due to the limitations of the traditional FMEA model, several recent models incorporating advanced fuzzy set extensions have been developed to enhance the reliability of risk assessment outcomes. However, most of these models limit expert flexibility in expressing preferences and often overlook the impact of unequal expert weights and the stability of risk ranking results. This study introduces a new FMEA model based on Q-Rung Orthopair Fuzzy Sets (Q-ROFSs), termed Q-ROFSs-FMEA. Q-ROFSs, an extension of intuitionistic fuzzy sets, introduce a new linguistic term. The Q-ROFSs-FMEA model considers the unequal weights of experts, enabling a dynamic representation of expert preferences. These weights and the linguistic evaluation of risk factors are integrated through an aggregation operator, facilitating consensus among experts. The model is applied to a case study on COVID-19 risk factors, revealing that ‘older age’ (risk priority number 0.000012) is the highest risk factor, while ‘gender’ (risk priority number -0.0037) is the lowest. It is found that the ranking of risk factors determined by the Q-ROFSs-FMEA model is obtained as FM1 FM3 FM6 FM4 FM5 FM7 FM8 FM2 . Furthermore, a comparative analysis indicates consistent ranking results across different models, demonstrating the reliability of the proposed model. The case study and comparative analysis validate the effectiveness and applicability of the Q-ROFSsbased risk assessment model. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Failure modes

来源：评论

学校读者我要写书评

暂无评论

A General Framework for Clustering and Distribution Matching with Bandit Feedback

引用

IEEE Transactions on Information Theory 2025年第3期71卷 2116-2139页

作者： Yavas, Recep Can Huang, Yuqi Tan, Vincent Y. F. Scarlett, Jonathan CNRS@CREATE DesCartes Program CREATE Tower 138602 Singapore National University of Singapore Department of Computer Science Queenstown119077 Singapore National University of Singapore School of Computing Department of Information Systems and Analytics Queenstown119077 Singapore National University of Singapore Department of Mathematics Department of Electrical and Computer Engineering Queenstown119077 Singapore Institute of Data Science National University of Singapore Department of Computer Science The Department of Mathematics Queenstown119077 Singapore

We develop a general framework for clustering and distribution matching problems with bandit feedback. We consider a K-armed bandit model where some subset of K arms is partitioned into M groups. Within each group, the random variable associated to each arm follows the same distribution on a finite alphabet. At each time step, the decision maker pulls an arm and observes its outcome from the random variable associated to that arm. Subsequent arm pulls depend on the history of arm pulls and their outcomes. The decision maker has no knowledge of the distributions of the arms or the underlying partitions. The task is to devise an online algorithm to learn the underlying partition of arms with the least number of arm pulls on average and with an error probability not exceeding a pre-determined value δ. Several existing problems fall under our general framework, including finding M pairs of arms, odd arm identification, and N-ary clustering of K arms belong to our general framework. We derive a non-asymptotic lower bound on the average number of arm pulls for any online algorithm with an error probability not exceeding δ. Furthermore, we develop a computationally-efficient online algorithm based on the Track-and-Stop method and Frank-Wolfe algorithm, and show that the average number of arm pulls of our algorithm asymptotically matches that of the lower bound. Our refined analysis also uncovers a novel bound on the speed at which the average number of arm pulls of our algorithm converges to the fundamental limit as δ vanishes. © 1963-2012 IEEE.

关键词： Random variables

来源：评论

学校读者我要写书评

暂无评论

Improved Machine Learning Algorithms for Fraud Detection in Fintech Companies 5th

Improved Machine Learning Algorithms for Fraud Detection in ...

引用

5th International Conference on Financial Technology, ICFT 2024

作者： Nwachukwu, Chukwuemeka Akwiwu-Uzoma, Chukwuebuka Ovuehor, Samuel Durodola-Tunde, Kehinde Department of Applied AI and Data Analytics University of Bradford Bradford United Kingdom Department of Computing University of Dundee Dundee United Kingdom

ISBN: (纸本)9789819638109

The rapid adoption of mobile money is due to the penetration of smart devices and the reduced cost of computing power. Today, many rural dwellers use mobile smart devices (Ofcom, 2022). A financial solution that creates a transaction system through mobile smart devices is instrumental in closing the financial inclusion gap. Mobile money offers digital financial service providers access to an untapped market that is profitable. There has been a shift from traditional banking to digital banking services (mobile money). This is due to a rapid increase in mobile phone penetration and reliance on domestic remittances in many households (Mauree and Kohli, 2013). With the broad internet usage, modern technologies, and the current growth of digital financial service providers, there is a need for financial institutions to mitigate against financial crimes and fraudulent activities (Wewege et al., 2020). The sporadic expansion of digital transformation has made digital financial services providers vulnerable to cyber risks. The continuous partnerships with third parties, technology providers and fintech companies have heightened their vulnerability to cyber-attacks (Tsys, 2017). More people are prioritizing their delicate personal data against hacking, identity theft, cyberattacks and money laundering. Digital banks with lesser security infrastructure and small fintech companies are more prone to financial fraud and cyber risks. Hence, digital financial service providers and fintech companies should continuously update their security infrastructure (Wewege et al., 2020). This study focuses on how fintech companies can maximize improved machine learning algorithms for fraud detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fintech

来源：评论

学校读者我要写书评

暂无评论

Enhancing Organizational Performance: Synergy of Cyber-Physical Systems, Cloud Services, and Crowdsensing

引用

International Journal of Crowd Science 2025年第1期9卷 44-55页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Nabot, Ahmad Jebreen, Issam Alauthman, Mohammad Alangari, Someah Aburub, Faisal Aldweesh, Amjad Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan Department of Information Security University of Petra Amman11196 Jordan Department of Computer Science College of Science and Humanities Dawadmi Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia

In the contemporary business landscape, software has evolved into a strategic asset crucial for organizations seeking sustainable competitive advantage. The imperative of ensuring software quality becomes evident as low-quality software systems pose formidable challenges to organizational performance. This study delves into the profound impact of three key dimensions of information system quality on organizational performance—information quality (IQ), quality of service (QoS), and software quality (SQ). Anchored in the DeLone and McLean information system (IS) success model, a quantitative questionnaire was administered to 360 industry experts and academics. Rigorous data analysis, employing exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM), revealed significant positive effects of all three quality dimensions on organizational performance. Among these dimensions, software quality emerged as the most influential, showcasing substantial total effects, closely followed by information and service qualities. The study underscores the tangible value derived from strategic investments in enhancing software, information, and service quality. Elevating these facets manifests as a catalyst for improved organizational performance, empowering decision-makers with accurate and timely information while enhancing user satisfaction with the system. This research contributes significantly to the IS success literature by empirically validating the synergistic relationship between information quality, service quality, software quality, and organizational outcomes. The systematic analysis offered in this study goes beyond theoretical validation, providing actionable insights for managers. The findings guide the prioritization of quality initiatives and resource allocation, enabling organizations to maximize competitive advantage. As a future research direction, investigating moderator influences and exploring alternate qualit

关键词： data quality

来源：评论

学校读者我要写书评

暂无评论

Bayesian classifier based on cancer prognostic markers using accelerated failure time model with frailty effect

引用

Quality and Quantity 2025年 1-27页

作者： Vishwakarma, Gajendra K. Kumari, Pragya Bhattacharjee, Atanu Ong, Seng Huat Department of Mathematics & Computing Indian Institute of Technology Dhanbad Jharkhand Dhanbad 826004 India Division of Population Health and Genomics School of Medicine University of Dundee Dundee United Kingdom Institute of Actuarial Science and Data Analytics UCSI University Kuala Lumpur 56000 Malaysia Institute of Mathematical Sciences University of Malaya Kuala Lumpur 50603 Malaysia

This work presents a Bayesian classifier technique to categorize patients based on predictive biomarkers of time-to-event data utilizing the Accelerated Failure Time (AFT) model incorporating the frailty effect. Before classification, efficient and significant markers from a high-dimensional gene expression dataset need to be identified. Currently, it is an emerging area in oncology. A conventional three-step feature selection approach is introduced to select the most relevant markers from high-dimensional data. A threshold value for each selected marker is obtained using the proposed Bayesian classification procedure incorporating the AFT model with the frailty effect. The frailty effect is incorporated to account for unobserved heterogeneity in the expression values of subjects, allowing for a more accurate investigation of the risk of cancer-related outcomes. A simulation study is performed to validate the proposed classification approach, and the classification’s performance is evaluated using the Brier score, and the result indicates that it is relatively high. Application of the proposed classification technique is provided for two high-dimensional TCGA datasets of lung cancer patients. Our results provide evidence about the effect of classified gene expression values on the survival risk of lung cancer patients. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Accelerated failure time model Bayesian Cancer’s biomarker Classification Survival analysis

来源：评论

学校读者我要写书评

暂无评论

Feature Selection in Socio-Economic Analysis: A Multi-Method Approach for Accurate Predictive Outcomes

引用

International Journal of Crowd Science 2025年第1期9卷 64-78页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Jebreen, Issam Nabot, Ahmad Rajab, Mohammed Alauthman, Mohammad Aldweesh, Amjad Aburub, Faisal Alangari, Someah Alzgol, Musab Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan University Headquarter University of Anbar Ramadi31001 Iraq Department of Information Security University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan Computer Information Systems Department Faculty of Information Technology Isra University Amman11622 Jordan

Feature selection is a cornerstone in advancing the accuracy and efficiency of predictive models, particularly in nuanced domains like socio-economic analysis. This study explores nine distinct feature selection methods, utilizing a heart disease dataset as a representative model for complex socio-economic systems. Our findings identified four universally recognized features as critical across all selection methods. However, the divergence in significance attributed to other features by different methods underscores the inherent variability in selection techniques. When the top four features were incorporated into twelve classification models, a noticeable surge in predictive accuracy was observed, emphasizing their foundational role in enhancing model outcomes. The variations among methods stress the need for a methodical and discerning approach to feature selection, especially in data-rich socio-economic landscapes. As we venture further into an era defined by data-driven decision-making, rigour and precision in feature selection become indispensable. Future research should extend this approach to broader datasets, ensuring the robustness and adaptability of our findings. © The author(s) 2025.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Real-Time Energy Efficient Communication for Large-Scale Industrial Internet of Things 2nd

Real-Time Energy Efficient Communication for Large-Scale Ind...

引用

2nd International Conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Khan, Musfira Khan, Muazzam A. Farman, Haleem Malik, Asad W. Ramzan, Muhammad Sher Islamabad Pakistan Department of Computer Science Quaid-I-Azam University Islamabad Pakistan ICESCO Chair for Big Data Analytics and Edge Computing Quaid-I-Azam University Islamabad Pakistan Smart Systems Engineering Lab College of Engineering Prince Sultan University Riyadh Saudi Arabia King Abdulaziz University Jeddah Saudi Arabia

ISBN: (纸本)9789819783441

Industrial Internet of Things (IIoTs) comprises large-scale sensors deployed to monitor and control the system. Deploying IoT devices presents challenges due to frequent disconnections and sudden power drainage in intermediate devices responsible for routing data. Consequently, the deployed network becomes sporadic, hindering connectivity with the industrial controller system, especially critical during emergencies. Furthermore, IoT node failures lead to a short network lifetime, which is intolerable in real-time industrial applications. This paper introduces a multi-hop clustering framework designed for reliable communication in industrial environments. The framework dynamically forms clusters, employing cluster heads and backup nodes to stabilize communication in the absence of a cluster head. Each cluster selects a backup node during the initial cluster head selection and later through data piggybacking. The proposed framework provides efficient communication across the IIoT network with the help of a backup and multi-hop clustering mechanism. The proposed framework is compared with two existing protocols i.e. cluster-based coordination routing (CRC) and energy-efficient quality of service routing (EEQSRP) protocols. The results show a considerable decrease in sensor energy consumption and packet delay. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

Quantitative Error Bounds for Scaling Limits of Stochastic Iterative Algorithms

arXiv

引用

arXiv 2025年

作者： Wang, Xiaoyu Kasprzak, Mikolaj J. Negrea, Jeffrey Bourguin, Solesne Huggins, Jonathan H. Department of Mathematics & Statistics Boston University United States Department of Information Systems Data Analytics & Operations ESSEC Business School France Department of Statistics & Actuarial Science University of Waterloo Canada Faculty of Computing & Data Sciences Boston University United States

Stochastic iterative algorithms, including stochastic gradient descent (SGD) and stochastic gradient Langevin dynamics (SGLD), are widely utilized for optimization and sampling in large-scale and high-dimensional problems in machine learning, statistics, and engineering. Numerous works have bounded the parameter error in, and characterized the uncertainty of, these approximations. One common approach has been to use scaling limit analyses to relate the distribution of algorithm sample paths to a continuous-time stochastic process approximation, particularly in asymptotic setups. Focusing on the univariate setting, in this paper, we build on previous work to derive non-asymptotic functional approximation error bounds between the algorithm sample paths and the Ornstein-Uhlenbeck approximation using an infinite-dimensional version of Stein's method of exchangeable pairs. We show that this bound implies weak convergence under modest additional assumptions and leads to a bound on the error of the variance of the iterate averages of the algorithm. Furthermore, we use our main result to construct error bounds in terms of two common metrics: the Lévy-Prokhorov and bounded Wasserstein distances. Our results provide a foundation for developing similar error bounds for the multivariate setting and for more sophisticated stochastic approximation *** Codes 60F17 (Primary) 60J60, 62-08, 68T05, 62E17 (Secondary) Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

来源：评论

学校读者我要写书评

暂无评论

CS2023: Computer Science Curriculum Guidelines: A New Liberal Arts Perspective

引用

ACM Inroads 2025年第1期16卷 40-52页

作者： Holland-Minkley, Amanda Barnard, Jakob E. Barr, Valerie Braught, Grant Davis, Janet Reed, David Schmitt, Karl Tartaro, Andrea Teresco, James D. Department of Computing and Information Studies Washington & Jefferson College 60 S. Lincoln St. WashingtonPA15301 United States Department of Computer Science and Technology University of Jamestown 6401 College Ln. JamestownND58405 United States Department of Computer Science Bard College 30 Campus Road Annandale-on-HudsonNY12504 United States Department of Computer Science Dickinson College P.O. Box 1773 CarlislePA17013 United States Department of Computer Science Whitman College 345 Boyer Ave. Walla WallaWA99362 United States Department of Computer Science Design & Journalism Creighton University Hitchcock 209 OmahaNE68178 United States Department of Computing and Data Analytics Trinity Christian College 6601 West College Drive Palos HeightsIL60463 United States Department of Computer Science Furman University 3300 Poinsett Hwy GreenvilleSC29690 United States Department of Computer Science Siena College 515 Loudon Rd. LoudonvilleNY12211 United States

来源：评论

学校读者我要写书评

暂无评论

Park4U: A Blockchain-Based Parking Reservation and Management for Smart Cities

Park4U: A Blockchain-Based Parking Reservation and Managemen...

引用

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

作者： Amjad Aldweesh Mohammad Alauthman Ahmad Al-Qerem Mohammad Al Khaldy Saad Alateef Someah Alangari College of computing and IT Shaqra University Shaqra Saudi Arabia Faculty of Information Technology University of Petra Amman Jordan Faculty of Information Technology Zarqa University Zarqa Jordan Department of Business Intelligence and Data Analytics University of Petra Amman Jordan School of Computing Science Newcastle University Newcastle UK College of Computing and IT Shaqra University Aldawadmi Saudi Arabia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Urbanization and increasing vehicles in smart cities have created challenges in managing parking efficiently. Traditional systems face issues like lack of transparency, security vulnerabilities, and poor stakeholder coordination. This paper introduces Park4U, a blockchain-based parking reservation and management system for smart cities. It takes advantage of blockchain's decentralized, secure, and transparent nature to improve efficiency, accessibility, and reliability of parking services. Park4U integrates IoT sensors, smart contracts, and a mobile application facilitating seamless reservation, payment, and management. The system aims to enhance resource utilization, reduce congestion, and provide a secure and transparent platform for stakeholders: drivers, operators, and authorities. An evaluation of performance and comparison with traditional systems are presented, showcasing blockchain-based approach's benefits. It also explores potential challenges and future research directions.

关键词： Smart cities Smart contracts Blockchains Mobile applications Stakeholders Security Reliability Intelligent sensors Faces Vehicles

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：