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International Conference on Intelligent and Fuzzy Systems (INFUS)

作者： Musaev, A. A. Grigoriev, D. A. ITMO Univ St Petersburg Russia St Petersburg State Univ SPBU Ctr Econometr & Business Analyt CEBA St Petersburg Russia

ISBN: (纸本)9783031671913;9783031671920

This article presents a research focused on improving the control of stochastic search procedures within an e-neighborhood surrounding current monitoring outcomes of control parameters in technological processes. The research aimed to enhance the effectiveness of control measures through the utilization of state-of-the-art Random Search (RS) technology. A mathematical structure was established to define an e-neighborhood for manipulating parameters and setting boundaries for the search space. Various methods were explored for selecting manipulation parameters using operator-driven processes via Human-Machine Interface (HMI) tools. The RS-Control Module's functional structure accounted for the dynamics of controlled parameter evolution using a sliding observation window. The RS-Optimization Module's software implementation allowed for forecasting output parameters and assessing forecast accuracy using quality indicators. The implementation of the main program provided a user-friendly interface for adjusting process parameters, optimizing criteria, and monitoring control based on RS forecasts. The research demonstrated the effectiveness of the proposed approach in improving output and technological parameters in industrial processes.

关键词： Stationarity violation Random Search algorithms Hybrid algorithms predictive algorithms

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Knowledge, algorithmic predictions, and action

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Asian Journal of Philosophy 2024年第2期3卷 1-17页

作者： Cresto, Eleonora CONICET - Instituto de Investigaciones Filosóficas SADAF Bulnes 642 Buenos Aires 1176 Argentina

I discuss the epistemic status of algorithmic predictions in the legal realm. My main claim is that algorithmic predictions do not give us knowledge, not even probabilistic knowledge. The situation, however, is relevantly different from the one in which we find ourselves at the time of assessing statistical evidence in general, and it is rather related to the fact that algorithmic fairness in legal contexts is essentially undetermined. In the light of this, we have to settle for justified beliefs and justified credences. I end by drawing some morals for the Knowledge Norm of action. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

关键词： Knowledge norm predictive algorithms Probabilistic knowledge Statistical evidence

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An Innovative Approach for Manipulating Tidal-Based Computing Power 10

An Innovative Approach for Manipulating Tidal-Based Computin...

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IEEE 10th International Conference on Edge Computing and Scalable Cloud (IEEE EdgeCom) / 11th IEEE International Conference on Cyber Security and Cloud Computing (IEEE CSCloud)

作者： Li, Fang Wu, Gang Lv, Tianjing Lu, JianHua QianYun Informat Technol Co Ltd Shanghai Peoples R China

ISBN: (纸本)9798350377149;9798350377132

This invention situates itself at the forefront of computational resource management, particularly delineating a novel method of tidal computing power governance within contexts characterized by hybrid deployment scenarios. It ingeniously orchestrates a dynamic adjustment mechanism for computing resources to seamlessly adapt to fluctuating workloads, thereby ensuring an efficacious allocation and management of the expansive computing power resources resident within heterogeneous data centers. By instituting an elaborate scoring model for computing nodes, coupled with a dynamic task scoring framework, and conducting a meticulous evaluation of resources and tasks across diverse deployment junctures, it meticulously identifies the most propitious node for task execution. This methodological approach not only facilitates a dynamic redistribution of resources but also engenders an intelligent scheduling paradigm for workloads. The resultant architecture markedly amplifies both performance and efficiency metrics, whilst concurrently precipitating a substantial diminution in operational expenditures. Moreover, this methodology incorporates sophisticated predictive algorithms to forecast workload trajectories, thereby enabling a preemptive optimization of resources. This anticipatory mechanism aligns computing capabilities with the actual exigencies of demand, engendering a computing environment that is both responsive and cost-efficient, and it represents a paradigmatic shift towards a more adaptable and sustainable computational resource management framework.

关键词： resource management predictive algorithms cost-efficient

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A Systematic Review on Formwork Pressure Exerted by Self-Compacting Concrete: Parameters, Prediction Models, and Advances in Monitoring Technologies

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Iranian Journal of Science and Technology - Transactions of Civil Engineering 2025年 1-26页

作者： Jebitha, D. Annlin Kannan, M. Ramesh Karthiyaini, S. School of Civil Engineering Vellore Institute of Technology Tamil Nadu Chennai India Division of Structural Engineering Department of Civil Engineering College of Engineering Guindy Campus Anna University Tamil Nadu Chennai India

The development of Self-Compacting Concrete (SCC) significantly impacts the construction industry due to its exceptional flowability and compaction qualities. Consequently, understanding how this type of concrete behaves with formwork structures is crucial. This review article evaluates formwork pressure dynamics in SCC, enhancing existing knowledge through careful research and extensive coverage over the years. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses flowchart method, along with innovative analytical visualizations using chord diagrams, this study thoroughly documented and investigated every aspect of the scholarly work, from its initial stages to the current state of research. The study explores the chronological progression and integration of various perspectives on key parameters that influence the behaviour of SCC. These parameters include the rheological properties of SCC, the pouring rate, the qualities of the formwork used, and the environmental conditions affecting lateral pressure. The development of predictive algorithms, particularly machine learning techniques, to forecast formwork pressures is also emphasized in this study. This effort combines data-driven insights with practical construction parameters. In addition, a comprehensive analysis is provided of advancements in real-time monitoring technologies, ranging from basic mechanical models to sophisticated sensor networks. These developments play a crucial role in monitoring and managing formwork pressures. The detailed analysis of the collected data offers valuable insights, highlighting the importance of safety, cost efficiency, and optimization of construction workflow. This discussion underscores the need to address current shortcomings, foster interdisciplinary collaboration, and establish standardized models for prediction, aiming to enhance the practice and implementation of SCC in various formwork systems and pave the way for future research. © The Author

关键词： Chord diagram Construction technology Formwork monitoring Formwork pressure Machine learning predictive algorithms PRISMA Rheological properties Self-compacting concrete (SCC)

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The Post-Choice Society: Algorithmic Prediction and the Decentring of Choice

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THEORY CULTURE & SOCIETY 2025年

作者： Schwarz, Ori Bar Ilan Univ Ramat Gan Israel

Choice has played a key role in late-modern economy, society, culture and politics, but recently predictive algorithms are decentring choice and replacing many of its instantiations, offering an alternative way to match individuals with information, cultural goods, and consumer products - and to govern people. This article has two contributions: first, it contextualizes the decentring of choice within the history of capitalism, showing how it emerged once psychological and economics knowledge transformed the meaning of choice from an economic engine into a problem ('friction' or 'overload'). Second, it explores the wider significance and cultural-cum-political implications of this transformation. Algorithmic 'individuation without choice' challenges the role of choice in neoliberal subjectification. Choice is increasingly marginalized in everyday practice while retaining its role in justification (as predictions are construed as what actors would have chosen). As the decentring of choice expands from social media and consumption towards governance, it may pose a threat to democracy.

关键词： choice consumer culture post-neoliberalism predictive algorithms TikTok

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Photonic Sensors-assisted ML pipeline for Precise Control of Curing Process in Advanced Thermosetting Composite Manufacturing 8

Photonic Sensors-assisted ML pipeline for Precise Control of...

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Conference on Optical Sensing and Detection VIII

作者： Kyriazi, Evrydiki Petsinis, Georgios Poulopoulos, Ioannis Syriopoulos, Georgios Prousalidi, Thenia Neale, Geoffrey Asareh, Mehdi Toumasis, Panagiotis Zervos, Charalampos Skordos, Alexandros A. Avramopoulos, Hercules Natl Tech Univ Athens Sch Elect & Comp Engn Athens Greece Talos Analyt IKE Athens Greece Cranfield Univ Sch Aerosp Transport & Mfg Cranfield MK43 0AL Beds England

ISBN: (纸本)9781510673175;9781510673168

This study introduces an innovative approach to enhance the utilization of carbon fiber thermosetting composites in advanced structural engineering by addressing the challenges of high manufacturing costs and limited production rates. We develop, deploy and test an ML pipeline utilizing PIC-based sensors (SOI technology, 220 nm thick, fabricated at IMEC's MPW). They are based on a Bragg structure, packaged using ball lenses and suitable for operating at 180 degrees Celsius and 5 bar pressure. The focus is on accurately predicting two crucial parameters: Cure time and Temperature Overshoot, vital for determining the process duration and part quality. Using advanced tools and sensors, this study achieves a high prediction accuracy of 98% in millisecond scale while effectively handling the outliers. The ML pipeline allows the real-time process optimization of manufacturing process, minimizing the cost, and providing insights into the quality of the composite part through the in-depth monitoring of the process.

关键词： Machine learning algorithms PIC-based sensors Bragg grating structure optimization of manufacturing process predictive algorithms ML pipeline process monitoring XGBoost regressor

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Profit-Oriented Task Allocation for Mobile Crowdsensing With Worker Dynamics: Cooperative Offline Solution and predictive Online Solution

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2021年第8期20卷 2637-2653页

作者： Tao, Xi Song, Wei Univ Montreal Dept Comp Sci & Operat Res Montreal PQ H3T 1J4 Canada Univ New Brunswick Fac Comp Sci Fredericton NB E3B 5A3 Canada

Mobile crowdsensing (MCS) is a new paradigm of data collection with large-scale sensing. A group of mobile users are recruited as workers to move around in a specific region and carry out sensing tasks. A challenging problem of MCS is task allocation, especially when the MCS platform needs to assign tasks to selected workers among a large user pool and consider mixed spatial and temporal features, including locations and time windows of tasks, and trajectories and arrival time of workers. In this paper, we take into account these features and study the task allocation problem that assigns tasks to workers over time and guarantees the tasks are accomplished before their deadlines. We consider an offline scenario where the MCS platform is informed of all the information of tasks and workers in advance, and an online scenario where the platform does not know the information of workers before they enter the system. For the offline scenario, we provide a cooperative ant colony algorithm with swarm intelligence to approximate the optimal solution in large-scale cases. For the online scenario with incomplete information, we propose several online algorithms, among which the predictive online algorithm exploits historical records of workers and performs the best. Finally, we conduct simulations and evaluate the differences among the online solutions and offline solutions. The results show that the proposed online solutions can approach the offline optimal solution in small-scale cases, and its approximation obtained by the cooperative offline solution in large-scale cases.

关键词： Task analysis Resource management Sensors Approximation algorithms Prediction algorithms Path planning Microsoft Windows Mobile crowdsensing online task allocation incomplete information swarm intelligence predictive algorithms

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The looming threat of digital determinism and discrimination: Commentary on “The feudal glove of talent-selection decisions in sport – Strengthening the link between subjective and objective assessments” by Bar-Eli et al. (2023)

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Asian Journal of Sport and Exercise Psychology 2024年第1期4卷 29-30页

作者： Morgulev, Elia Kaye Academic College of Education Beer-Sheva Israel Levinsky-Wingate Academic College Israel

Recent technological developments, such as automatic broadcasting systems and computer vision, are enabling cost-effective data gathering, bringing big data analytics to youth sports. In a recent insightful article, Bar-Eli et al. (2023) discussed among other issues the subjective and objective talent selection approaches, advocating for the use of big data analytics to harness the benefits of both methods. My aim in this commentary is to complement that big data predictive tools also were found to be vulnerable to biases that render their decisions unfair. Therefore, artificial intelligence in the domain of youth talent identification should be approached with caution and in-depth consideration of ethical issues needs to be undertaken before these predictive tools can be widely adopted. © 2023

关键词： Algorithmic bias Big data analytics Machine learning predictive algorithms Talent selection

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Bitcoin Price Prognosis Using Machine Learning 9

Bitcoin Price Prognosis Using Machine Learning

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Begum, A. Benazir Bhuvanesh, R. Babu, R.R.M. Mathesh Velammal College of Engineering and Technology Department of Cse Madurai India

ISBN: (纸本)9798350377972

This research explores the application of machine learning techniques to predict Bitcoin price dynamics. Bitcoin, as a decentralized digital currency, has garnered significant attention due to its high volatility and potential for rapid value fluctuations. This study aims to develop a machine learning model that analyzes historical Bitcoin price data to identify patterns and predict future price trends. While precise short-term predictions may be challenging, the model can provide valuable insights into overall price trends and inform investment strategies. This research contributes to a better understanding of Bitcoin price dynamics and has the potential to assist investors in making more informed decisions. © 2024 IEEE.

关键词： Bitcoin Price Prediction Cryptocurrency Dynamics Data Analysis Data Patterns Digital Currency Machine Learning predictive algorithms Price Trends

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predictive modelling of student dropout risk: Practical insights from a South Korean distance university

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HELIYON 2024年第11期10卷 e30960页

作者： Seo, Eui-Yeong Yang, Jaemo Lee, Ji-Eun So, Geunju Hanyang Cyber Univ Educ Innovat Ctr 220 Wangsimni ro Seoul 04763 South Korea Hanyang Cyber Univ Dept Law 220 Wangsimni ro Seoul 04763 South Korea Hanyang Cyber Univ Dept Management Informat & AI Business 220 Wangsimni ro Seoul 04763 South Korea

Distance education supports lifelong learning and empowers individuals in rapidly changing societal conditions, yet it encounters high dropout rates due to a range of individual and societal obstacles. This study addresses the challenge of creating a practical prediction model by analyzing extensive real-world time-point data from a well-established online university in Seoul. Covering 144,540 instances from 2018 to 2022, the study integrates diverse datasets to compare the accuracy of models based on longitudinal, semester-wise, and gender-specific datasets. The demographic, academic, and online metrics identified significant dropout indicators, including age (particularly when binned), residential area, specific occupations, GPA, and LMS log metrics, using a stepwise backward elimination process. The study revealed that, despite societal changes, recent data from the last four semesters can be effectively used for stable prediction training. Gender-based analysis showed different factors influencing dropout risk for males and females. The Light Gradient Boosting Machine (LGBM) algorithm excelled in prediction accuracy, with the ROC-AUC metric affirming its superiority. However, logistic regression also showed its competitive performance and offered in-depth interpretation. In South Korea's distinct educational setting, merging advanced algorithms like LGBM with the interpretive strength of logistic regression is key for effective student support strategies.

关键词： Distance education Dropout risk predictive modelling Feature selection Dataset modelling predictive algorithms

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