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35th Annual European Simulation and Modelling Conference (ESM)

作者： Studzinski, Jan Ziolkowski, Andrzej Polish Acad Sci Syst Res Inst Warsaw Poland

ISBN: (纸本)9789492859181

The issue of leak detection is a problem faced by most water utilities, especially those operating old and failing or poorly managed water supply networks. In such cases, water losses due to failures may reach a dozen or even more percent of produced water, exposing the company to large financial losses. Thus, it is a significant economic problem, but also an environmental one, as water losses deplete water resources. Of course, the remedy can be a properly planned revitalization of the water supply network, changing the pump pressure control in pumping stations and zone pumping stations, but also early detection and removal of failures, which is the subject of the paper. The development of algorithms to solve the problem of water leaks detection is essential for the proper operation of water supply networks, however, it should be borne in mind that their implementation is also associated with high costs and organisational difficulties for waterworks company. The paper discusses methods related to the detection and location of emergency states on water supply networks and presents two algorithms of varying degrees of complexity, developed at the Institute of System Research of the Polish Academy of Sciences.

关键词： water networks location of water leaks ai algorithms neural classifiers

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Machine learning for software engineering: Case studies in software reuse

Machine learning for software engineering: Case studies in s...

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14th IEEE International Conference on Tools with Artificial Intelligence

作者： Di Stefano, JS Menzies, T W Virginia Univ Lane Dept Comp Sci Morgantown WV 26506 USA

ISBN: (纸本)0769518494

There are many machine learning algorithms currently available. In the 21st century, the problem no longer lies in writing the learner, but in choosing which learners to run on a given data set. In this paper, we argue that the final choice of learners should not be exclusive;in fact, there are distinct advantages in running data sets through multiple learners. To illustrate our point, we perform a case study on a reuse data set using three different styles of learners: association rule, decision tree induction, and treatment Software reuse is a topic of avid debate in the professional and academic arena;it has proven that it can be both a blessing and a curse. Although there is much debate over where and when reuse should be instituted into a project, our learners found some procedures which should significantly improve the odds of a reuse program succeeding.

关键词： ai algorithms ai in software engineering ai in data mining machine learning reuse empirical studies treatment learning association rule learning decision tree learning C4.5 J4.8 J4.8 PART APRIORI TAR2

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Application of ai Algorithm in Video Indexing and Retrieval

Application of AI Algorithm in Video Indexing and Retrieval

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3rd International Symposium on Intelligent Information Technology Application

作者： Li Xuemei Li Yan Li Jincheng Zhongkai Univ Agr & Engn Sch Mech & Elect Engn Guangzhou 510225 Guangdong Peoples R China

ISBN: (纸本)9780769538594

Due to the rapid technological developments in image/video capturing, huge data storage, video compression and networking, huge amount of video data are produced each day all over the world. Finding effective ways to store, index and retrieve these video remains a hot researching area. It is especially important for the producers/editors of television programs, since to keep track of the 1000's of short video clips that are used to make a film without help from the computer is unimaginable. Typically 25 hours worth of material is required for a 50-minute show. For this reason, the producers/editors must be able to store these video clips efficiently, and be able to quickly locate and retrieve the necessary clips when editing. The ways to store and index the video clips with the extracted features, and the ways to retrieve them quickly and correctly on the user's query, are introduced in this paper.

关键词： ai algorithms vedio indexing application extracted features

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Advancements in Home Energy Management Systems: A Review of Energy Optimization Strategies and Algorithmic Approaches 12

Advancements in Home Energy Management Systems: A Review of ...

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12th IFAC Symposium on Control of Power and Energy Systems (CPES)

作者： Elargoubi, Fatima Zohra Chakir, Houssam Eddine El Khaili, Mohamed Choukri, Karim Et-Taoussi, Mehdi Mricha, Rabia Hassan II Univ EEIS Lab ENSET Mohammedia Casablanca Morocco Hassan II Univ ENSEM Casablanca LESE Lab Casablanca Morocco

Most of the energy generated globally is used by residential and commercial structures. Due to the expansion of the world economy and population, there is a growing need for energy. Due to ineffective energy management, this has resulted in increased unit electricity prices, frequent strain on the primary electrical system, and carbon *** Home Energy Management System optimizes electricity usage, reduces costs, and limits CO2 emissions by using advanced algorithms to improve the peak-to-average ratio (PAR) and dynamically adjust energy consumption. By using the Services Category (Load Shifting, Valley Filling..., households significantly improve their energy efficiency, resulting in substantial savings. Integrating smoothly with smart grids, HEMS enables real-time monitoring, empowering owners to do informed energy consumption choices. The incorporation of renewable energy sources further enhances their impact, reducing dependence on traditional grids and contributing to a collective reduction in CO2 emissions. HEMS are crucial tools for sustainable and efficient residential energy management, especially in a futur of smart grids and environmentally friendly lifestyles. In this article, we are carried out a comparative study of the different algorithms used in the field of Home Energy Management (HEMS) to promote sustainable residential energy consumption. We highlight the goals, results, and limitations of each algorithm.

关键词： Home Energy Management System (HEMS) Peak-to-average Ration Renewable energy sources Smart Grid Demand-side Management Smart home Metaheuristic algorithms ai algorithms

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Xpression: A Unifying Metric to Optimize Compression and Explainability Robustness of ai Models 2nd

Xpression: A Unifying Metric to Optimize Compression and Exp...

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2nd World Conference on Explainable Artificial Intelligence (xai)

作者： Arazo, Eric Stoev, Hristo Bosch, Cristian Suarez-Cetrulo, Andres L. Simon-Carbajo, Ricardo Univ Coll Dublin CeADAR Ireland Ctr Appl AI Dublin Ireland

ISBN: (纸本)9783031637865;9783031637872

Trustworthiness and efficiency have recently become crucial aspects of applied ai. The intersection of interpretability and model compression, however, still poses significant challenges. As models undergo compression for improved efficiency, maintaining explainability needs to remain a priority. In this paper, we propose a novel metric to evaluate both aspects simultaneously and help practitioners navigate this trade-off. In particular, we delve into the effect that knowledge distillation, quantization, and pruning have on the Infidelity explainability metric. Our goal is for Xpression metric to guide the optimization of compression whilst the model keeps its infidelity robustness. Experimental results across several neural network architectures show the effectiveness of the proposed metric in combining efficiency and relative interpretability with respect to the original model. This work contributes to advancing the understanding of compression techniques and provides a valuable framework for evaluating their implications on model interpretability.

关键词： Xai Model interpretability ai algorithms Compression Efficiency Quantization Knowledge Distillation Pruning Infidelity

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Image segmentation using a generic, fast and non-parametric approach

Image segmentation using a generic, fast and non-parametric ...

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10th International Conference on Tools with Artificial Intelligence

作者： Fiorio, C Nock, R LIRMM F-34790 Montpellier 5 France

ISBN: (纸本)0780352149

In this paper, we investigate image segmentation by region merging. Given any similarity measure between regions, satisfying some weak constraints, we give a general predicate for answering if two regions are to be merged or not during the segmentation process. Our predicate is generic and has six properties. The first one is its independance with respect to the similarity measure, that leads to a user-independant and adaptative predicate. Second, it is non-parametric, and does not rely on any assumption concerning the image. Third, due to its weak constraints, knowledge may be included in the predicate to fit better to the user's behaviour. Fourth, provided the similarity is well-chosen by the user, we are able to upperbound one type of error made during the image segmentation. Fifth, it does not rely on a particular segmentation algorithm and can be used with almost all region-merging algorithms in various application domains. Sixth, it is calculated quickly, and can lead with appropriated algorithms to very efficient segmentation.

关键词： vision and image processing ai algorithms machine learning

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Why do users trust algorithms? A review and conceptualization of initial trust and trust over time

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European Management Journal 2022年第5期40卷 685-706页

作者： Cabiddu, Francesca Moi, Ludovica Patriotta, Gerardo Allen, David G. Department of Economics and Business Sciences University of Cagliari Italy School of Management University of Bath United Kingdom Luther Henderson University Chair in Management and Leadership at the TCU Neeley School of Business (USA) Distinguished Research Environment Professor at Warwick Business School United Kingdom

algorithms are increasingly playing a pivotal role in organizations' day-to-day operations;however, a general distrust of artificial intelligence-based algorithms and automated processes persists. This aversion to algorithms raises questions about the drivers that lead managers to trust or reject their use. This conceptual paper aims to provide an integrated review of how users experience the encounter with ai-based algorithms over time. This is important for two reasons: first, their functional activities change over the course of time through machine learning;and second, users' trust develops with their level of knowledge of a particular algorithm. Based on our review, we propose an integrative framework to explain how users’ perceptions of trust change over time. This framework extends current understandings of trust in ai-based algorithms in two areas: First, it distinguishes between the formation of initial trust and trust over time in ai-based algorithms, and specifies the determinants of trust in each phase. Second, it links the transition between initial trust in ai-based algorithms and trust over time to representations of the technology as either human-like or system-like. Finally, it considers the additional determinants that intervene during this transition phase. © 2022 Elsevier Ltd

关键词： ai algorithms Initial trust Integrative review Trust Trust over time

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Transforming Rapid Diagnostic Tests into Trusted Diagnostic Tools in LMIC using ai

Transforming Rapid Diagnostic Tests into Trusted Diagnostic ...

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IEEE Conference on Artificial Intelligence (IEEE Cai)

作者： Gupta, Krishnan Ruan, Yongshao Ibrahim, Ahmed Mendonca, Rouella Cooper, Shawna Morris, Sarah Hattery, David Audere Seattle WA 98101 USA

ISBN: (纸本)9798350339840

In low and middle-income countries (LMICs), Rapid Diagnostic Tests (RDTs) are often the only way to diagnose diseases such as malaria, HIV, and COVID efficiently and cost effectively, especially in rural settings. However, basic RDTs are often misinterpreted, reducing their reliability for medical treatment or official case counts. ai-based mobile solutions are difficult to implement in LMICs due to limited resources available on commonly used phones and unstable Internet connectivity. HealthPulse ai algorithms aim to address these issues by providing a lightweight, yet highly accurate library of Computer Vision (CV) models for the detection and interpretation of common RDTs for conditions such as malaria, HIV, and COVID. The complete system can function end-to-end offline on phones with as little as 1 GB of total device memory. In addition to detecting the RDT type and interpreting the results, the system can flag image quality issues such as bad lighting or blurriness. If required, it can ask the user for a photo retake in real-time, reducing the need for re-testing. The system provides accurate and consistent result interpretation for surveillance or decision support use cases, helping health systems better understand current disease prevalence which may help mitigate the next pandemic. The ai algorithm pipeline uses deep learning to analyze RDT images, with multiple computer vision models working together to confirm the presence of the expected RDT, flag adverse image conditions, and provide accurate and consistent results. HealthPulse ai prioritizes privacy, accountability, and accessibility while aiming to revolutionize care delivery in LMICs by transforming low-cost RDTs into trusted diagnostic tools using computer vision and ai.

关键词： RDT rapid diagnostic test rapid test ai artificial intelligence ai algorithms ML machine learning CV computer vision

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Dialectical proofs accounting for strength of attacks in Argumentation Systems

Dialectical proofs accounting for strength of attacks in Arg...

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22nd International Conference on Tools with Artificial Intelligence

作者： Cayrol, C. Devred, C. Lagasquie-Schiex, M-C Univ Toulouse 3 IRIT 118 Route Narbonne F-31062 Toulouse 9 France Univ Angers LERIA F-49045 Angers 01 France

ISBN: (纸本)9780769542638

We consider argumentation systems taking into account several attack relations of different strength. We focus on the impact of various strength attacks on the semantics of such systems, and particularly on the decision problem of credulous acceptance: namely, focussing on one particular argument, a classical issue is to compute a proof, under the form of an admissible set containing this argument. Taking into account attacks of various strength leads to search for the "best" proofs.

关键词： Knowledge representation and reasoning ai algorithms Argumentation Dialectical proofs

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ai tools in Emergency Radiology reading room: a new era of Radiology

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EMERGENCY RADIOLOGY 2023年第5期30卷 647-657页

作者： Dundamadappa, Sathish Kumar UMass Chan Med Sch UMass Mem Med Ctr 55 Lake Ave North Worcester MA 01655 USA

Artificial intelligence tools in radiology practices have surged, with modules developed to target specific findings becoming increasingly prevalent and proving valuable in the daily emergency room radiology practice. The number of US Food and Drug Administration-cleared radiology-related algorithms has soared from just 10 in early 2017 to over 200 presently. This review will concentrate on the present utilization of ai tools in clinical ER radiology setting, including a brief discussion of the limitations of the technique. As radiologists, it is essential that we embrace this technology, comprehend its constraints, and use it to improve patient care.

关键词： Deep learning ai algorithms ER radiology Opportunistic screening

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