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arXiv 2024年

作者： Goel, Anubha Hansen, Henri Kanniainen, Juho Computing Science/Financial Computing and Data Analytics Group Tampere University Tampere33720 Finland

Cascade models serve as effective tools for understanding the propagation of information and diseases within social networks. Nevertheless, their applicability becomes constrained when the states of the agents (nodes) are hidden and can only be inferred through indirect observations or symptoms. This study proposes a Mapper-based strategy to infer the status of agents within a hidden information cascade model using expert knowledge. To verify and demonstrate the method we identify agents who are likely to take advantage of information obtained from an inside information network. We do this using data on insider networks and stock market transactions. Recognizing the sensitive nature of allegations of insider trading, we design a conservative approach to minimize false positives, ensuring that innocent agents are not wrongfully implicated. The Mapper-based results systematically outperform other methods, such as clustering and unsupervised anomaly detection, on synthetic data. We also apply the method to empirical data and verify the results using a statistical validation method based on persistence homology. Our findings highlight that the proposed Mapper-based technique successfully identifies a subpopulation of opportunistic agents within the information cascades. The adaptability of this method to diverse data types and sizes is demonstrated, with potential for tailoring for specific applications. © 2024, CC BY-SA.

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Q-Rung Orthopair Fuzzy Sets-Enhanced FMEA for COVID-19 Risk Assessment

Informatica (Slovenia)

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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

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Time-Series Foundation Model for Value-at-Risk Forecasting

arXiv

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arXiv 2024年

作者： Goel, Anubha Pasricha, Puneet Kanniainen, Juho Tampere University Department of Computing Sciences Financial Computing and Data Analytics Group Kalevantie 4 Tampere33100 Finland IIT Ropar Department of Mathematics Punjab Ropar140001 India

This study is the first to analyze the performance of a time-series foundation model for Value-at-Risk (VaR), which essentially forecasts the left-tail quantiles of returns. Foundation models, pre-trained on diverse datasets, can be applied in a zero-shot setting with minimal data or further improved through finetuning. We compare Google’s TimesFM model to conventional parametric and non-parametric models, including GARCH and Generalized Autoregressive Score (GAS), using 19 years of daily returns from the S&P 100 index and its constituents. Backtesting with over 8.5 years of out-of-sample data shows that the fine-tuned foundation model consistently outperforms traditional methods in actual-over-expected ratios. For the quantile score loss function, it performs comparably to the best econometric model, GAS. Overall, the foundation model ranks as the best or among the top performers across the 0.01, 0.025, 0.05, and 0.1 quantile forecasting. Fine-tuning significantly improves accuracy, showing that zero-shot use is not optimal for VaR. © 2024, CC BY.

关键词： Prediction models

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Sparse Index Tracking via Topological Learning

arXiv

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arXiv 2023年

作者： Goel, Anubha Pasricha, Puneet Kanniainen, Juho Computing Science/Financial Computing and Data Analytics Group Tampere University Finland Ecole Polytechnique Fédérale de Lausanne LausanneCH 1015 Switzerland Department of Mathematics Indian Institute of Technology Punjab Ropar140001 India

In this research, we introduce a novel methodology for the index tracking problem with sparse portfolios by leveraging topological data analysis (TDA). Utilizing persistence homology to measure the riskiness of assets, we introduce a topological method for data-driven learning of the parameters for regularization terms. Specifically, the Vietoris-Rips filtration method is utilized to capture the intricate topological features of asset movements, providing a robust framework for portfolio tracking. Our approach has the advantage of accommodating both 1 and 2 penalty terms without the requirement for expensive estimation procedures. We empirically validate the performance of our methodology against state-of-the-art sparse index tracking techniques, such as Elastic-Net and SLOPE, using a dataset that covers 23 years of S&P 500 index and its constituent data. Our out-of-sample results show that this computationally efficient technique surpasses conventional methods across risk metrics, risk-adjusted performance, and trading expenses in varied market conditions. Furthermore, in turbulent markets, it not only maintains but also enhances tracking performance. © 2023, CC BY.

关键词： Commerce

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Classification of Cybercrime Indicators in Open Social data 7th

Classification of Cybercrime Indicators in Open Social Data

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7th Annual International Conference on Information Management and Big data, SIMBig 2020

作者： Ullah, Ihsan Lane, Caoilfhionn Buda, Teodora Sandra Drury, Brett Mellotte, Marc Assem, Haytham Madden, Michael G. Computer Science National University of Ireland Galway Galway Ireland Insight Center for Data Analytics National University of Ireland Galway Galway Ireland Cognitive Computing Group Innovation Exchange IBM Dublin Ireland

ISBN: (纸本)9783030762278

Posting information on social media platforms is a popular activity through which personal and confidential information can leak into the public domain. Consequently, social media can contain information that provides an indication that an organization has been compromised or suffered a data breach. This paper describes a technique for inferring if an organization has been compromised from information posted on social media. The proposed strategy forms the basis of an alarm system which generates an alert for possible unreported cybercrime incidents. The proposed strategy used two social media cybercrime related datasets that were collected from the Irish and New York regions from financial organizations’ Twitter accounts. The Tweets are labelled as either containing cybercrime indicators or not, and then the cybercrime Tweets were labelled further into crime categories. A deep dense pyramidal Neural Network model is used to classify the Tweets. This approach achieves an AUC of 0.85±0.03 which outperforms the baseline of deep convolutional neural networks. © 2021, Springer Nature Switzerland AG.

关键词： Classification (of information)

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 22

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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Proceedings of the 6th International Conference on Advances in Artificial Intelligence

作者： Mohamad Faiz Dzulkalnine Mohd Saberi Mohamad Yee Wen Choon Muhammad Akmal Remli Hany Alashwal Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Malaysia and Department of Data Science Universiti Malaysia Kelantan Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms’ cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy.

关键词： Gene Knockouts Particle Swarm Optimization Artificial Bee Colony Ethanol Flux Balance Analysis

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Women's Health Diary: Tracker System for Menstruation, Intermenstrual, Ovulation, and Postnatal Bleeding Using Rules-Based Technique

Women's Health Diary: Tracker System for Menstruation, Inter...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Nursyuhada binti Abdullah Azwa bin Abdul Aziz Wan Azani Mustafa Muntather Almusawi Abbas Hameed Abdul Hussein Faculty of Informatics & Computing Universiti Sultan Zainal Abidin (UniSZA) Besut Campus Terengganu Malaysia Data Science & Analytics (DASA) Special Interest Group (SIG) Universiti Sultan Zainal Abidin (UniSZA) Terengganu Malaysia Centre of Excellence Advanced Computing (CoE AdvComp) Universiti Malaysia Perlis Arau Perlis Malaysia Faculty of Engineering The Islamic University Najaf Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq

Menstruation, a natural physiological process in women, entails regular vaginal bleeding as part of the monthly menstrual cycle. While existing systems facilitate menstrual cycle tracking, they often lack the capability to differentiate between various types of vaginal bleeding, such as menstruation (haid), intermenstrual bleeding (istihadah), or postnatal bleeding (nifas). To address this limitation, the Women's Health Diary: Tracker System for Menstruation, Intermenstrual, Ovulation, and Postnatal Bleeding) was developed using a rule-based technique. This system employs an iterative method comprising eight phases: initial planning, planning, requirements, analysis and design, implementation, testing, evaluation, and deployment. By ensuring the optimal development and performance of this system, women can effectively manage their menstrual blood, a vital aspect of their overall health. Moreover, this system holds particular significance for every women, as it facilitates tracking and identification of regular and irregular cycles, enabling them to uphold the legitimacy of their worship.

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Implementation of Artificial Intelligence In Engineering to Achieve Sustainable Development Goals (SDGs)

Implementation of Artificial Intelligence In Engineering to ...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Muhammad D. Zakaria Azwa Abdul Aziz Azrul Amri Jamal Wan Azani Mustafa Laith H. Alzubaidi A.H. A. Hussein Faculty of Informatics & Computing Universiti Sultan Zainal Abidin (UniSZA) Tembila Campus Terengganu Malaysia Data Science & Analytics (DASA) Special Interest Group (SIG) Universiti Sultan Zainal Abidin (UniSZA) Gong Badak Terengganu Malaysia Centre of Excellence Advanced Computing (CoE AdvComp) Universiti Malaysia Perlis Arau Perlis Malaysia College of Pharmacy Ahl Al Bayt University Karbala Iraq Faculty of Engineering The Islamic University Najaf Iraq

Artificial Intelligence (AI) is implicit to revolutionize the engineering industry by automating tasks, minimizing costs, improving quality, and creating new opportunities for innovation. The article explores the implementation of AI in engineering as a means to achieve the Sustainable Development Goals (SDGs) set forth by the United Nations. AI technologies hold significant potential in optimizing resource utilization, enabling data-driven decision-making, and fostering innovation in various engineering domains. This study provides an overview of AI applications highlighting their alignment with specific SDGs. It examines the advantages, challenges, and ethical considerations connected to AI integration in engineering practices. Moreover, the article provides recommendations for policymakers, researchers, and practitioners to ensure responsible and inclusive AI development. By harnessing the transformative power of AI in engineering, we can increase the process towards SDGs, creating a more sustainable and prosperous future for all.

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 6

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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6th International Conference on Advances in Artificial Intelligence, ICAAI 2022

作者： Dzulkalnine, Mohamad Faiz Mohamad, Mohd Saberi Choon, Yee Wen Remli, Muhammad Akmal Alashwal, Hany Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Johor81310 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University P.O. Box 17666 Al Ain Abu Dhabi United Arab Emirates Big Data Analytics Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Data Science Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al-Ain United Arab Emirates

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms' cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy. © 2022 Association for computing Machinery.

关键词： Ethanol

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Pricing methods for α-quantile and perpetual early exercise options based on Spitzer identities

arXiv

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arXiv 2021年

作者： Phelan, Carolyn E. Marazzina, Daniele Germano, Guido Financial Computing and Analytics Group Department of Computer Science University College London United Kingdom Department of Mathematics Politecnico di Milano Systemic Risk Centre London School of Economics United Kingdom

We present new numerical schemes for pricing perpetual Bermudan and American options as well as α-quantile options. This includes a new direct calculation of the optimal exercise barrier for early-exercise options. Our approach is based on the Spitzer identities for general Lévy processes and on the Wiener-Hopf method. Our direct calculation of the price of α-quantile options combines for the first time the Dassios-Port-Wendel identity and the Spitzer identities for the extrema of processes. Our results show that the new pricing methods provide excellent error convergence with respect to computational time when implemented with a range of Lévy *** Codes 60E10, 60G51, 91-08, 91-10, 91G20, 91G50, 91G60 © 2021, CC BY.

关键词： Costs

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