Julien Biaudet^1,*, Lisa Laroussi-Libeault², Mauricette Michallet³, Laurie Panse², Raymond Merle⁴

Psycho-Oncologie, Vol.18, No.1, pp. 17-22, 2024, DOI:10.32604/po.2023.043536

Abstract Cet article vise à partager une expérience innovante d’organisation et de financement de la recherche ayant impliqué les premiers concernés : les patients. Le groupe de travail « ECLAIR » du Cancéropôle Lyon Auvergne-Rhône-Alpes (CLARA) a été créé en fin d’année 2020 dans le but de contribuer à l’élaboration d’un appel à projets portant sur l’expérience patient en cancérologie, ouvert en janvier 2021. Constitué au départ de 8 membres dont 7 patients, coordonné par un chef de projets du CLARA, le groupe de travail ECLAIR a activement contribué à l’écriture du cahier des charges de l’appel à projets, à l’élaboration… More >

Yue Xue¹, Dunqiu Fan², Shen Su^1,3,*, Jialu Fu¹, Ning Hu¹, Wenmao Liu², Zhihong Tian^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 69-101, 2024, DOI:10.32604/cmes.2023.031488

Abstract Decentralized finance (DeFi) is a general term for a series of financial products and services. It is based on blockchain technology and has attracted people’s attention because of its open, transparent, and intermediary free. Among them, the DeFi ecosystem based on Ethereum-based blockchains attracts the most attention. However, the current decentralized financial system built on the Ethereum architecture has been exposed to many smart contract vulnerabilities during the last few years. Herein, we believe it is time to improve the understanding of the prevailing Ethereum-based DeFi ecosystem security issues. To that end, we investigate the Ethereum-based DeFi security issues: 1)… More >

Weisi Chen^1,*, Walayat Hussain^2,*, Francesco Cauteruccio³, Xu Zhang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 187-224, 2024, DOI:10.32604/cmes.2023.031388

Abstract Financial time series prediction, whether for classification or regression, has been a heated research topic over the last decade. While traditional machine learning algorithms have experienced mediocre results, deep learning has largely contributed to the elevation of the prediction performance. Currently, the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking, making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better, what techniques and components are involved, and how the model can be designed and implemented. This review article provides an overview of techniques, components and… More > Graphic Abstract

Anne-Fleur Guillemin, Iris Cervenka, Jérôme Foucaud^*

Psycho-Oncologie, Vol.17, No.3, pp. 113-121, 2023, DOI:10.32604/po.2023.045035

Abstract La recherche interventionnelle en santé des populations (RISP) a été initiée en prévention primaire, en proposant un paradigme de recherche centré sur l’intervention et les solutions aux questions de santé. L’intervention se co-construit avec les parties prenantes dans une approche globale jusqu’à son déploiement sur les territoires. Face au développement de la RISP, se pose la question de son application à la prévention tertiaire. L’objectif de cette étude est de proposer—à partir d’une analyse descriptive des projets financés en RISP par l’Institut national du cancer (INCa) sur les dispositifs de soutien auprès des personnes touchées par un cancer et leur… More >

Fang Yu¹, Wenbin Bi², Ning Cao^3,4,*, Hongjun Li¹, Russell Higgs⁵

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1-17, 2023, DOI:10.32604/csse.2023.018349

Abstract In view of the fact that the prediction effect of influential financial customer churn in the Internet of Things environment is difficult to achieve the expectation, at the smart contract level of the blockchain, a customer churn prediction framework based on situational awareness and integrating customer attributes, the impact of project hotspots on customer interests, and customer satisfaction with the project has been built. This framework introduces the background factors in the financial customer environment, and further discusses the relationship between customers, the background of customers and the characteristics of pre-lost customers. The improved Singular Value Decomposition (SVD) algorithm and… More >

Munawar Hassan¹, Shafqat Iqbal², Harish Garg^3,*, Shahbaz Gul Hassan⁴, Yunxian Yan^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 559-584, 2023, DOI:10.32604/cmes.2022.021385

Abstract The main objective of this paper is to present an integrated approach to evaluate the level of satisfaction of borrowers with the products and services of microfinance institutions (MFI) at different criterion levels. For this, the study adopts the concept of FCEM (Fuzzy Comprehensive Evaluation Method) in concurrence with the AHP (Analytical Hierarchy Process). In our day-to-day situation, the researchers have made many efforts to assess the impact of Microfinance on poverty reduction, but borrowers’ satisfaction is always overlooked. Since the multiple factors impact the borrower’s satisfaction, each factor is made of different items. Thus, as the factors items increase,… More >

Jian-Guo Hu^1,*, Shao-Yi Wu^1,*, Yi Yang¹, Qin-Sheng Zhu¹, Xiao-Yu Li¹, Shan Yang²

Journal of Quantum Computing, Vol.4, No.1, pp. 53-61, 2022, DOI:10.32604/jqc.2022.027683

Abstract As one of the major methods for the simulation of option pricing, Monte Carlo method assumes random fluctuations in the distribution of asset prices. Under certain uncertainties process, different evolution paths could be simulated so as to finally yield the expectation value of the asset price, which requires a lot of simulations to ensure the accuracy based on huge and expensive calculations. In order to solve the above computational problem, quantum Monte Carlo (QMC) has been established and applied in the relevant systems such as European call options. In this work, both MC and QM methods are adopted to simulate… More >

Hanbo Liu^*, Guang Sun

Journal on Big Data, Vol.3, No.4, pp. 175-182, 2021, DOI:10.32604/jbd.2021.024074

Abstract With the advent of the era of big data, traditional financial management has been unable to meet the needs of modern enterprise business. Enterprises hope that financial management has the function of improving the accuracy of corporate financial data, assisting corporate management to make decisions that are more in line with the actual development of the company, and optimizing corporate management systems, thereby comprehensively improving the overall level of the company and ensuring that the company can be in business with the assistance of financial integration, can better improve and develop themselves. Based on the investigation of enterprises and universities,… More >

Zheng Liu¹, Juanjuan Song¹, Hui Wu^2,*, Xiaomin Gu², Yuanjun Zhao³, Xiaoguang Yue⁴, Lihua Shi¹

Computer Systems Science and Engineering, Vol.39, No.3, pp. 391-401, 2021, DOI:10.32604/csse.2021.014527

Abstract Finance is the core of modern economy, and a strong country cannot do without the support of financial system. With the rapid development of economy and society, the traditional financial services can not support the increasingly large and complex economic system. As a brand-new format, financial technology can help the financial industry to restructure and upgrade. At the same time, as an international consensus, green development is the only way for China to achieve sustainable development. Therefore, it is of great practical significance to study the impact of finance on the regional development of green finance. Based on the essence… More >

Liangliang Shi^1,2, Peili Lu³, Junchi Yan^4,5,*

Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 873-885, 2020, DOI:10.32604/iasc.2020.010121

Abstract Causality learning has been an important tool for decision making, especially for financial analytics. Given the time series data, most existing works construct the causality network with the traditional regression models and estimate the causality by pairs. To fulfil a holistic one-shot inference procedure over the whole network, we propose a new causal inference method for the multidimensional time series data, specifically related to some case studies for the industrial finance analytics. Specifically, the time series are first converted to the event sequences with timestamps by fluctuation the detection, and then a multidimensional point process is used for learning the… More >