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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Marek Kvet Jaroslav Janácek Michal Kvet Faculty of Management Science and Informatics University of Zilina Zilina Slovakia

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

Classical version of the neighborhood search based on exchange operation tests objective function of solution obtained by the exchange operation and depending on the objective function value decides on further progress. When a series of Pareto-optimal solutions of the p-location problem is determined, huge number of such operations must be tested and evaluated. As many of exchanges are repeated with the same consequence for the further progress, it is questionable whether the previously obtained information about their success can be taken into account in the decision process. In this contribution, we focus on answering this question. We perform the associated research of influence of information about previous success or failure on effectiveness of the neighborhood search application to determination of a set of Pareto-optimal solutions.

关键词： Costs Decision making Transportation Search problems linear programming Approximation algorithms Informatics System analysis and design Machine intelligence

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Optimization Study of Flexibility Scheduling Strategies for Independent and Interactive Operation of Regional Integrated Energy Systems

Optimization Study of Flexibility Scheduling Strategies for ...

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Smart Grid and Green Energy (ICSGGE), International Conference on

作者： Xusheng Wang Qifen Li Bangpeng Xie Yiming Yuan Shanghai University of Electric Power Energy and Environment Engineering Institute Shanghai China State Grid Shanghai Pudong Electric Power Supply Company Shanghai China

ISBN: (数字)9798331542139

ISBN: (纸本)9798331542146

In today’s increasingly severe energy crisis, integrated energy systems have become an important way to solve the above problems because they can realize multi-energy complementarity and improve energy efficiency. The mathematical model of the integrated energy subsystem and the regional integrated energy system is constructed, and its objective function and constraints are analyzed, and then, based on the example, the data analysis of the operating costs of the multiple systems under the interactive operation mode and the independent operation mode is carried out. The results show that the system can save $6.61 \%$ of the total cost compared with the independent operation, which has better economic efficiency and energy utilization. By considering the changes in the costs of independent and interactive operation of the integrated district energy system at different confidence levels, it is concluded that the interactive operation mode is superior to the individual operation mode at any confidence level.

关键词： Costs Data analysis Power measurement Optimization models Size measurement linear programming Smart grids Power systems Thermal energy Optimization

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Towards Robust Subject Identification From EEG Segments With Data Augmentation Techniques

Towards Robust Subject Identification From EEG Segments With...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Meka Nani Supriyo Banerjea Raghavan R Speech Information Processing Lab Indian Institute of Technology Hyderabad India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Electroencephalography (EEG) has a wide gamut of information, from subject identity to their emotional states encoded in it. Given a segment of the EEG signal, our study aims to identify the subject in a robust way using the EEGChannel-Net architecture. To achieve this goal, we add gaussian noise to the given 32-channel EEG signal, and minimize the ℓ 2 distance between embeddings of the original signal and that of the noisy signal in addition to the categorical cross entropy loss for classification of the 26 subjects in the dataset. Addition of noise to each channel at a fixed signal-to-noise ratio (SNR) of 15 dB gave the best balanced accuracy of 98.27% in the validation set. Our study concludes by justifying the optimality of the 15 dB SNR and highlights the superiority of our proposed objective function.

关键词： Accuracy Gaussian noise linear programming Brain modeling Electroencephalography Entropy Robustness Noise measurement Speech processing Signal to noise ratio

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Distributed Secretary Problem: A Case of Two Uneven Queues 25

Distributed Secretary Problem: A Case of Two Uneven Queues

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Companion Proceedings of the ACM on Web Conference 2025

作者： Ke Ding Bo Li Fangxiao Wang Department of Computing The Hong Kong Polytechnic University Hong Kong Hong Kong

ISBN: (纸本)9798400713316

Secretary problem is one of the most widely studied online stochastic models, in which an employer wants to hire the best candidate from n candidates who arrive in a random order. It is well-known that the optimal success probability is 1/e. However, in reality, things are more complex because employers often have interviewers in different cities, interviewing candidates in a distributed manner. This motivates us to study the secretary problem with multiple queues. Feldman and Tennenholtz [EC 2012] studied this assuming the candidates are distributed evenly. In particular, when there are two even queues, the optimal success probability is 1/4. In this work, we move to the general problem when the queues are arbitrary and design the optimal online algorithm for the case of two queues. Our results characterize the exact success probability curve, connecting the cases of a single queue and two equal queues. Our technique is grounded on the linear programming framework introduced by Buchbinder et al. [Math. Oper. Res. 2014] and a novel analysis.

关键词： linear programming

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Generating Cardiovascular Data by Integration of Physiological Information

Generating Cardiovascular Data by Integration of Physiologic...

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Computational Intelligence in Health and Medicine (CIHM), IEEE Symposium on

作者： Johannes Kummert Alexander Schulz Moriz Habigt Maike Stemmler Christina Kohler Dirk Abel Rolf Rossaint Barbara Hammer Machine Learning Group Bielefeld University Bielefeld Germany Anaesthesiology Clinic RWTH Aachen University Aachen Germany Institute of Automatic Control RWTH Aachen University Aachen Germany

ISBN: (数字)9798331508333

ISBN: (纸本)9798331508340

Medical data is a valuable resource that is under privacy constraints and often requires a substantial effort and cost to obtain. This is the case for physiological cardiovascular data that is among others utilized to develop assistive heart devices. In the present work, we aim to improve a GAN based generative approach from the literature by integrating physiological knowledge in form of pressure-value curves together with a dynamic time warping cost function into the model. We demonstrate that our adapted model generates more realistic cardiovascular signals as measured by targeted physiological markers and showcase that such generated signals can be used to improve a downstream task on real data.

关键词： Heart Adaptation models Visualization Data privacy Time series analysis Focusing Inspection linear programming Physiology Data models

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Optimal Marker Genes for c-Separated Cell Types 29th

Optimal Marker Genes for c-Separated Cell Types

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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Borozan, Bartol Borozan, Luka Ševerdija, Domagoj Matijević, Domagoj Canzar, Stefan School of Applied Mathematics and Informatics University of Osijek Osijek Croatia Faculty of Informatics and Data Science University of Regensburg Regensburg Germany

ISBN: (纸本)9783031902512

The identification of cell types in single-cell RNA-seq studies relies on the distinct expression signature of marker genes. A small set of target genes is also needed to design probes for targeted spatial transcriptomics experiments and to target proteins in single-cell spatial proteomics or for cell sorting. While traditional approaches have relied on testing one gene at a time for differential expression between a given cell type and the rest, more recent methods have highlighted the benefits of a joint selection of markers that together distinguish all pairs of cell types simultaneously. However, existing methods either impose constraints on all pairs of individual cells which becomes intractable even for medium-sized datasets, or ignore intra-cell type expression variation entirely by collapsing all cells of a given type to a single representative. Here we address these limitations and propose to find a small set of genes such that cell types are c-separated in the selected dimensions, a notion introduced previously in learning a mixture of Gaussians. To this end, we formulate a linear program that naturally takes into account expression variation within cell types without including each pair of individual cells in the model, leading to a highly stable set of marker genes that allow to accurately discriminate between cell types and that can be computed to optimality efficiently. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： linear programming

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Prescriptive Analytics for Logistics: Bridging Predictive Models and Actionable Decisions Through Advanced Optimization Techniques

Prescriptive Analytics for Logistics: Bridging Predictive Mo...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),Conference on

作者： Oussama El Azzouzy Tarik Chanyour Said Jai Andaloussi Department of Mathematics and Computer Science Laboratory of Data Engineering and Intelligent Systems(LIDSI) Hassan II University Casablanca Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

Modern logistics is facing increasing challenges due to the growing complexity of supply chains, high consumer expectations, and economic and environmental pressures. While predictive analytics, powered by artificial intelligence (AI) and massive data, has transformed the ability to forecast trends and risks, its application remains limited by the lack of effective mechanisms to convert these predictions into actionable decisions in dynamic and constrained environments. This paper proposes an innovative hybrid approach that combines advanced predictive models, notably the Temporal Fusion Transformer (TFT), with prescriptive algorithms, such as linear programming and deep reinforcement learning (DRL). The TFT demonstrated superior performance in terms of predictive accuracy (significant reduction in MAE, RMSE, and MAPE), while the prescriptive algorithms optimized complex logistics decisions in real time, even in uncertain environments. This integrated methodology was validated through use cases simulating critical logistics scenarios, such as delivery route optimization. Experimental results show a substantial improvement in operational efficiency, with reductions in costs, lead times and resource utilization. The prescriptive approach made it possible to automate complex decisions, while taking into account multiple constraints, such as costs, lead times and sustainability objectives. By integrating real-time data collected via IoT devices and logistics management systems, this architecture offers a robust, adaptable and scalable solution. It thus meets the requirements of modern logistics by enabling proactive, optimized and resilient decision-making. This research makes a significant contribution by bridging the gap between predictive and prescriptive analysis, while laying the foundations for new applications in the field of intelligent logistics.

关键词： Costs Supply chains Predictive models Prediction algorithms linear programming Transformers Real-time systems Thin film transistors Resource management Optimization

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Animal welfare and economic optimisation of farrowing systems

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Animal Welfare 2023年第1期20卷 57-67页

In many countries, including the UK, the majority of domestic sows are housed in farrowing crates during the farrowing and lactation periods. Such systems raise welfare problems due to the close confinement of the sow. Despite the fact that many alternative housing systems have been developed, no commercially viable/feasible option has emerged for large scale units. Current scientific and practical knowledge of farrowing systems were reviewed in this study to identify alternative systems, their welfare and production potential. The aim was to establish acceptable trade-offs between profit and welfare within alternative farrowing systems. linear programming (LP) was used to examine possible trade-offs and to support the design of welfare-friendly yet commercially viable alternatives. The objective of the LP was to optimise the economic performance of conventional crates, simple pens and designed pens subject to both managerial and animal welfare constraints. Quantitative values for constraints were derived from the literature. The potential effects of each welfare component on productivity were assessed by a group of animal welfare scientists and used in the model. The modelled welfare components (inputs) were extra space, substrate and temperature. Results showed that, when using piglet survival rate in the LP based on data drawn from the literature and incorporating costs of extra inputs in the model, the crates obtained the highest annual net margin and the designed pens and the pens were in second and third place, respectively. The designed pens and the pens were able to improve their annual net margin once alternative reference points, following expert-derived production functions, were used to adjust piglet survival rates in response to extra space, extra substrate and modified pen heating. The non-crate systems then provided higher welfare and higher net margin for sows and piglets than crates, implying the possibility of a win-win situation.

关键词： alternative housing systems animal welfare economic optimisation farrowing systems linear programming pig

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Optimizing Machine Learning for Malicious Blockchain Node Detection Using Modified Metaheuristic

Optimizing Machine Learning for Malicious Blockchain Node De...

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AIML-Applications for Engineering & Technology (ICAET), International Conference on

作者： Nebojsa Bacanin Marko Vićentijevic Luka Jovanovic Angelina Njegus Branislav Radomirovic Miodrag Zivkovic Faculty of Informatics and Computing Singidunum University Belgrade Serbia Faculty of Technical Sciences Singidunum University Belgrade Serbia

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

The decentralized architecture of blockchain tech-nology introduces various challenges, particularly in Proof of Stake (PoS) networks, where malicious nodes can undermine the consensus process and threaten the integrity of the network. Nodes with significant stakes may disproportionately influence validation, weakening the network's decentralization. Despite ongoing advancements in blockchain security, there remains a notable gap in research on the integration of artificial intelli-gence (AI) and optimization techniques for enhancing blockchain security. This paper proposes an approach, utilizing a CatBoost classifier optimized with a modified Particle Swarm Optimization (PSO) algorithm, designed specifically for this study. A compar-ative analysis, performed on a real-world dataset, demonstrates the effectiveness of the proposed method, with the most accurate models achieving an accuracy of 87.13%. The findings highlight the potential of AI -driven optimization in improving blockchain security. Future research will focus on the application of AI and optimization techniques to further refine blockchain protocols, enhance detection and prevention of security threats, and improve overall network performance.

关键词： Proof of stake Analytical models Accuracy Prevention and mitigation Metaheuristics Machine learning linear programming Security Particle swarm optimization Convergence

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Optimal Smoothed Analysis of the Simplex Method

arXiv

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arXiv 2025年

作者： Bach, Eleon Huiberts, Sophie EPFL Switzerland CNRS Université Clermont Auvergne Clermont Auvergne INP Mines Saint-Etienne LIMOS France

Smoothed analysis is a method for analyzing the performance of algorithms, used especially for those algorithms whose running time in practice is significantly better than what can be proven through worst-case analysis. Spielman and Teng (STOC’01) introduced the smoothed analysis framework of algorithm analysis and applied it to the simplex method. Given an arbitrary linear program with d variables and n inequality constraints, Spielman and Teng proved that the simplex method runs in time O(σ−30d55n86), where σ > 0 is the standard deviation of Gaussian distributed noise added to the original LP data. Spielman and Teng’s result was simplified and strengthened over a series of works, with the current strongest upper bound being O(σ−3/2d13/4 log(n)7/4) pivot steps due to Huiberts, Lee and Zhang (STOC’23). We prove that there exists a simplex method whose smoothed complexity is upper bounded by O(σ−1/2d11/4 log(n)7/4) pivot steps. Furthermore, we prove a matching high-probability lower bound of Ω(σ−1/2d1/2 ln(4/σ)−1/4) on the combinatorial diameter of the feasible polyhedron after smoothing, on instances using n = ⌊(4/σ)d⌋ inequality constraints. This lower bound indicates that our algorithm has optimal noise dependence among all simplex methods, up to polylogarithmic factors. © 2025, CC BY.

关键词： linear programming

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