Informatica最新文献_第2页

Pricing Powered by Artificial Intelligence: An Assessment Model for the Sustainable Implementation of AI Supported Price Functions 人工智能定价：可持续实施人工智能支持的价格功能的评估模型

IF 2.9 4区计算机科学

Informatica Pub Date : 2024-05-22 DOI: 10.15388/24-infor559

Anett Erdmann, Morteza Yazdani, Jose Manuel Mas Iglesias, Cristina Marin Palacios

{"title":"Pricing Powered by Artificial Intelligence: An Assessment Model for the Sustainable Implementation of AI Supported Price Functions","authors":"Anett Erdmann, Morteza Yazdani, Jose Manuel Mas Iglesias, Cristina Marin Palacios","doi":"10.15388/24-infor559","DOIUrl":"https://doi.org/10.15388/24-infor559","url":null,"abstract":"Artificial Intelligence (AI) in the price management process is being applied in business practice and research to a variety of pricing use cases that can be augmented or automated, providing opportunities as a forecasting tool or for price optimization. However, the complexity of evaluating the technology to prioritize implementation is challenging, especially for small and medium enterprises (SMEs), and guidance is sparse. Which are the relevant stakeholder criteria for a sustainable implementation of AI for pricing purpose? Which type of AI supported price functions meet these criteria best? Theoretically motivated by the hedonic price theory and advances in AI research, we identify nine criteria and eight AI supported price functions (AISPF). A multiple attribute decision model (MADM) using the fuzzy Best Worst Method (BWM) and fuzzy combined compromise solution (CoCoSo) is set up and evaluated by pricing experts from Germany and Spain. To validate our results and model stability, we carried out several random sensitivity analyses based on the weight of criteria exchange. The results suggest accuracy and reliability as the most prominent attribute to evaluate AISPF, while ethical and sustainable criteria are sorted as least important. The AISPF which best meet the criteria are financial prices followed by procurement prices.\u0000PDF  XML","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"52 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141151491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ontology and Fuzzy Theory Application in Information Systems: A Bibliometric Analysis 本体论和模糊理论在信息系统中的应用：文献计量分析

IF 2.9 4区计算机科学

Informatica Pub Date : 2024-05-16 DOI: 10.15388/24-infor557

Diana Kalibatienė, Jolanta Miliauskaitė, Asta Slotkienė

引用次数: 0

ALMERIA: Boosting Pairwise Molecular Contrasts with Scalable Methods ALMERIA：利用可扩展方法提高成对分子对比度

IF 2.9 4区计算机科学

Informatica Pub Date : 2024-05-14 DOI: 10.15388/24-infor558

Rafael Mena-Yedra, Juana López Redondo, Horacio Pérez-Sánchez, Pilar Martinez Ortigosa

引用次数: 0

A Deep Learning Model for Multi-Domain MRI Synthesis Using Generative Adversarial Networks 使用生成式对抗网络的多域核磁共振成像合成深度学习模型

IF 2.9 4区计算机科学

Informatica Pub Date : 2024-04-29 DOI: 10.15388/24-infor556

Le Hoang Ngoc Han, Ngo Le Huy Hien, Luu Van Huy, Nguyen Van Hieu

{"title":"A Deep Learning Model for Multi-Domain MRI Synthesis Using Generative Adversarial Networks","authors":"Le Hoang Ngoc Han, Ngo Le Huy Hien, Luu Van Huy, Nguyen Van Hieu","doi":"10.15388/24-infor556","DOIUrl":"https://doi.org/10.15388/24-infor556","url":null,"abstract":"In recent years, Magnetic Resonance Imaging (MRI) has emerged as a prevalent medical imaging technique, offering comprehensive anatomical and functional information. However, the MRI data acquisition process presents several challenges, including time-consuming procedures, prone motion artifacts, and hardware constraints. To address these limitations, this study proposes a novel method that leverages the power of generative adversarial networks (GANs) to generate multi-domain MRI images from a single input MRI image. Within this framework, two primary generator architectures, namely ResUnet and StarGANs generators, were incorporated. Furthermore, the networks were trained on multiple datasets, thereby augmenting the available data, and enabling the generation of images with diverse contrasts obtained from different datasets, given an input image from another dataset. Experimental evaluations conducted on the IXI and BraTS2020 datasets substantiate the efficacy of the proposed method compared to an existing method, as assessed through metrics such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR) and Normalized Mean Absolute Error (NMAE). The synthesized images resulting from this method hold substantial potential as invaluable resources for medical professionals engaged in research, education, and clinical applications. Future research gears towards expanding experiments to larger datasets and encompassing the proposed approach to 3D images, enhancing medical diagnostics within practical applications.\u0000PDF  XML","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"36 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140810285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Temporal Variable-Scale Clustering Method on Feature Identification for Policy Public-Opinion Management 用于政策民意管理的特征识别时变尺度聚类方法

IF 2.9 4区计算机科学

Informatica Pub Date : 2024-04-26 DOI: 10.15388/24-infor554

Ai Wang, Xuedong Gao, Mincong Tang

{"title":"A Temporal Variable-Scale Clustering Method on Feature Identification for Policy Public-Opinion Management","authors":"Ai Wang, Xuedong Gao, Mincong Tang","doi":"10.15388/24-infor554","DOIUrl":"https://doi.org/10.15388/24-infor554","url":null,"abstract":"The development of various digital social network platforms has caused public opinion to play an increasingly important role in the policy making process. However, due to the fact that public opinion hotspots usually change rapidly (such as the phenomenon of public opinion inversion), both the behaviour feature and demand feature of netizens included in the public opinion often vary over time. Therefore, this paper focuses on the feature identification problem of public opinion simultaneously considering the multiple observation time intervals and key time points, in order to support the management of policy-focused online public opinion. According to the variable-scale data analysis theory, the temporal scale space model is established to describe candidate temporal observation scales, which are organized following the time points of relevant policy promulgation (policy time points). After proposing the multi-scale temporal data model, a temporal variable-scale clustering method (T-VSC) is put forward. Compared to the traditional numerical variable-scale clustering method, the proposed T-VSC enables to combine the subjective attention of decision-makers and objective timeliness of public opinion data together during the scale transformation process. The case study collects 48552 raw public opinion data on the double-reduction education policy from Sina Weibo platform during Jan 2023 to Nov 2023. Experimental results indicate that the proposed T-VSC method could divide netizens that participate in the dissemination of policy-focused public opinion into clusters with low behavioural granularity deviation on the satisfied observation time scales, and identify the differentiated demand feature of each netizen cluster at policy time points, which could be applied to build the timely and efficient digital public dialogue mechanism.\u0000PDF  XML","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"1 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140799774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Three New Design Patterns for Scalable Agent-Based Computing and Simulation 基于代理的可扩展计算和仿真的三种新设计模式

IF 2.9 4区计算机科学

Informatica Pub Date : 2024-04-19 DOI: 10.15388/24-infor555

Mateusz Najdek, Mateusz Paciorek, Wojciech Turek, Aleksander Byrski

{"title":"Three New Design Patterns for Scalable Agent-Based Computing and Simulation","authors":"Mateusz Najdek, Mateusz Paciorek, Wojciech Turek, Aleksander Byrski","doi":"10.15388/24-infor555","DOIUrl":"https://doi.org/10.15388/24-infor555","url":null,"abstract":"Multi-agent approach is very popular for modelling and simulation of complex phenomena, design and programming of decentralised computing systems. Asynchronous beings, which do not share state but communicate using messages are a convenient abstraction for representing various phenomena observed in the real world. When the number of the considered agents grows, the designers and developers of such systems must address the problem of performance. Introducing distribution is often a weapon of choice, which, however, does not guarantee obtaining proper scalability and efficiency. The intensity of communication in a large-scale agent-based system can easily exceed the abilities of a distributed hardware architecture, leading to poor performance. After analysing various distributed agent-based systems, we identified several reasons for limited performance and several architectural solutions, which can help overcoming this problem. The main aim of the presented work is identification and systematization of these architectural solutions in the form of design patterns. As a result, we propose three new design patterns for building scalable distributed agent-based systems. A systematic description of their aims, structure, variants and features is provided, together with examples of applications.\u0000PDF  XML","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"9 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140630145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A General Framework for Providing Interval Representations of Pareto Optimal Outcomes for Large-Scale Bi- and Tri-Criteria MIP Problems 为大规模双标准和三标准 MIP 问题提供帕累托最优结果区间表示的一般框架

IF 2.9 4区计算机科学

Informatica Pub Date : 2024-04-12 DOI: 10.15388/24-infor549

Grzegorz Filcek, Janusz Miroforidis

{"title":"A General Framework for Providing Interval Representations of Pareto Optimal Outcomes for Large-Scale Bi- and Tri-Criteria MIP Problems","authors":"Grzegorz Filcek, Janusz Miroforidis","doi":"10.15388/24-infor549","DOIUrl":"https://doi.org/10.15388/24-infor549","url":null,"abstract":"The Multi-Objective Mixed-Integer Programming (MOMIP) problem is one of the most challenging. To derive its Pareto optimal solutions one can use the well-known Chebyshev scalarization and Mixed-Integer Programming (MIP) solvers. However, for a large-scale instance of the MOMIP problem, its scalarization may not be solved to optimality, even by state-of-the-art optimization packages, within the time limit imposed on optimization. If a MIP solver cannot derive the optimal solution within the assumed time limit, it provides the optimality gap, which gauges the quality of the approximate solution. However, for the MOMIP case, no information is provided on the lower and upper bounds of the components of the Pareto optimal outcome. For the MOMIP problem with two and three objective functions, an algorithm is proposed to provide the so-called interval representation of the Pareto optimal outcome designated by the weighting vector when there is a time limit on solving the Chebyshev scalarization. Such interval representations can be used to navigate on the Pareto front. The results of several numerical experiments on selected large-scale instances of the multi-objective multidimensional 0–1 knapsack problem illustrate the proposed approach. The limitations and possible enhancements of the proposed method are also discussed.\u0000PDF  XML","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"49 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140567342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing Research Reproducibility in Machine Learning through Blockchain Technology 通过区块链技术提高机器学习研究的可重复性

IF 2.9 4区计算机科学

Informatica Pub Date : 2024-04-11 DOI: 10.15388/24-infor553

Ernestas Filatovas, Linas Stripinis, Francisco Orts, Remigijus Paulavičius

{"title":"Advancing Research Reproducibility in Machine Learning through Blockchain Technology","authors":"Ernestas Filatovas, Linas Stripinis, Francisco Orts, Remigijus Paulavičius","doi":"10.15388/24-infor553","DOIUrl":"https://doi.org/10.15388/24-infor553","url":null,"abstract":"Like other disciplines, machine learning is currently facing a reproducibility crisis that hinders the advancement of scientific research. Researchers face difficulties reproducing key results due to the lack of critical details, including the disconnection between publications and associated models, data, parameter settings, and experimental results. To promote transparency and trust in research, solutions that improve the accessibility of models and data, facilitate experiment tracking, and allow audit of experimental results are needed. Blockchain technology, characterized by its decentralization, data immutability, cryptographic hash functions, consensus algorithms, robust security measures, access control mechanisms, and innovative smart contracts, offers a compelling pathway for the development of such solutions. To address the reproducibility challenges in machine learning, we present a novel concept of a blockchain-based platform that operates on a peer-to-peer network. This network comprises organizations and researchers actively engaged in machine learning research, seamlessly integrating various machine learning research and development frameworks. To validate the viability of our proposed concept, we implemented a blockchain network using the Hyperledger Fabric infrastructure and conducted experimental simulations in several scenarios to thoroughly evaluate its effectiveness. By fostering transparency and facilitating collaboration, our proposed platform has the potential to significantly improve reproducible research in machine learning and can be adapted to other domains within artificial intelligence.\u0000PDF  XML","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"52 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140567462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Minimal Surfaces and the Plateau Problem: Numerical Methods and Applications 最小曲面与高原问题：数值方法与应用

IF 2.9 4区计算机科学

Informatica Pub Date : 2024-04-09 DOI: 10.15388/24-infor552

Mifodijus Sapagovas, Vytautas Būda, Saulius Maskeliūnas, Olga Štikonienė, Artūras Štikonas

引用次数: 0

Optimized Multi-Modular Services: Empowering Earth Observation Data Processing 优化的多模块服务：增强地球观测数据处理能力

IF 2.9 4区计算机科学

Informatica Pub Date : 2024-04-04 DOI: 10.15388/24-infor551

Arthur Lalayan, Hrachya Astsatryan, Suren Poghosyan, Gregory Giuliani

引用次数: 0