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European Conference on Numerical mathematics and Advanced Applications, ENUMATH 2019

作者： Nassar, Hiba Podgórski, Krzysztof Cognitive Systems Department of Applied Mathematics and Computer Science Technical University of Denmark Lyngby Denmark Department of Statistics Lund University Lund Sweden

ISBN: (纸本)9783030558734

In implementations of the functional data methods, the effect of the initial choice of an orthonormal basis has not been properly studied. Typically, several standard bases such as Fourier, wavelets, splines, etc. are considered to transform observed functional data and a choice is made without any formal criteria indicating which of the bases is preferable for the initial transformation of the data. In an attempt to address this issue, we propose a strictly data-driven method of orthonormal basis selection. The method uses B-splines and utilizes recently introduced efficient orthornormal bases called the splinets. The algorithm learns from the data in the machine learning style to efficiently place knots. The optimality criterion is based on the average (per functional data point) mean square error and is utilized both in the learning algorithms and in comparison studies. The latter indicate efficiency that could be used to analyze responses to a complex physical system. © 2021, Springer Nature Switzerland AG.

关键词： Mean square error

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Automatic neural robot controller design using Evolutionary Acquisition of Neural Topologies

Automatic neural robot controller design using Evolutionary ...

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19. Fachgesprach Autonome Mobile Systeme, AMS 2005 - 19th Expert Discussion on Autonomous Mobile systems, AMS 2005

作者： Kassahun, Yohannes Sommer, Gerald Department of Cognitive Systems Institute of Computer Science and Applied Mathematics Christian Albrechts University Olshausenstr. 40 D-24098 Kiel Germany

ISBN: (纸本)3540302913

In this paper we present an automatic design of neural controllers for robots using a method called Evolutionary Acquisition of Neural Topologies (EANT). The method evolves both the structure and weights of neural networks. It starts with networks of minimal structures determined by the domain expert and increases their complexity along the evolution path. It introduces an efficient and compact genetic encoding of neural networks onto a linear genome that enables one to evaluate the network without decoding it. The method uses a meta-level evolutionary process where new structures are explored at larger time-scale and existing structures are exploited at smaller time-scale. We demonstrate the method by designing a neural controller for a real robot whichshould be able to move continously in a given environment cluttered with obstacles. We first give an introduction to the evolutionary method and then describe the experiments and results obtained.

关键词： Topology

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DRICORN-K: A Dynamic RIsk CORrelation-driven Non-parametric Algorithm for Online Portfolio Selection 1st

DRICORN-K: A Dynamic RIsk CORrelation-driven Non-parametric ...

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1st Southern African Conference for Artificial Intelligence Research, SACAIR 2020

作者： Sooklal, Shivaar van Zyl, Terence L. Paskaramoorthy, Andrew School of Computer Science and Applied Mathematics University of the Witwatersrand Johannesburg South Africa Institute for Intelligent Systems University of Johannesburg Johannesburg South Africa

ISBN: (纸本)9783030661502

Online Portfolio Selection is regarded as a fundamental problem in Computational Finance. Pattern-Matching methods, and the CORN-K algorithm in particular, have provided promising results. Despite making notable progress, there exists a gap in the current state of the art – systematic risk is not considered. The lack of attention to systematic risk could lead to poor investment returns, especially in volatile markets. In response to this, we extend the CORN-K algorithm to present DRICORN-K – a Dynamic RIsk CORrelation-driven Non-parametric algorithm. DRICORN-K continuously adjusts a portfolio’s market sensitivity based on the current market conditions. We measure market sensitivity using the β measure. DRICORN-K aims to take advantage of upward market trends and protect portfolios against downward market trends. To this end, we implement a number of market classification methods. We find that an exponentially weighted moving linear regression method provides the best classification of current market conditions. We further conducted an empirical analysis on five real world stock indices: the JSE Top 40, Bovespa, DAX, DJIA and Nikkei 225 against twelve state of the art algorithms. The results show that DRICORN-K can deliver improved performance over the current state of the art, as measured by cumulative return, Sharpe ratio and maximum drawdown. The experimental results lead us to conclude that the addition of dynamic systematic risk adjustments to CORN-K can result in improved portfolio performance. © 2020, Springer Nature Switzerland AG.

关键词： Pattern matching

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Selection of objective function for imbalanced classification: An industrial case study 22

Selection of objective function for imbalanced classificatio...

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22nd IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2017

作者： Khan, Abdul Rauf Schiøler, Henrik Kulahci, Murat Department of Electronic Systems Aalborg University Denmark Dept of Applied Mathematics and Computer Science Technical University of Denmark Luleå University of Technology Sweden

In this article we discuss the issue of selecting suitable objective function for Genetic Algorithm to solve an imbalanced classification problem. More precisely, first we discuss the need of specialized objective function to solve a real classification problem from our industrial partner and then we compare the results of our proposed objective function with commonly used candidates to serve this purpose. Our comparison is based on the analysis of real data collected during the quality control stages of the manufacturing process. © 2017 European Union

关键词： Quality control

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An Enhanced Particle Swarm Optimization for ITC2021 Sports Timetabling

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computers, Materials & Continua 2022年第7期72卷 1995-2014页

作者： Mutasem K.Alsmadi Ghaith M.Jaradat Malek Alzaqebah Ibrahim A.Lmarashdeh Fahad A.Alghamdi Rami Mustafa A.Mohammad Nahier Aldhafferi Abdullah Alqahtani Department of MIS College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia Department of Computer Science Faculty of Computer Science and InformaticsAmman Arab UniversityAmmanJordan Department of Mathematics College of ScienceImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia Basic and Applied Scientific Research CenterImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia Computer Information Systems Department College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia

Timetabling problem is among the most difficult operational tasks and is an important step in raising industrial productivity,capability,and *** tasks are usually tackled using metaheuristics techniques that provide an intelligent way of suggesting solutions or *** intelligence techniques including Particle Swarm Optimization(PSO)have proved to be effective *** recent experiments showed that the PSO algorithm is reliable for timetabling in many applications such as educational and personnel timetabling,machine scheduling,***,having an optimal solution is extremely challenging but having a sub-optimal solution using heuristics or metaheuristics is *** research paper seeks the enhancement of the PSO algorithm for an efficient timetabling *** algorithm aims at generating a feasible timetable within a reasonable *** enhanced version is a hybrid dynamic adaptive PSO algorithm that is tested on a round-robin tournament known as ITC2021 which is dedicated to sports *** competition includes several soft and hard constraints to be satisfied in order to build a feasible or sub-optimal *** consists of three categories of complexities,namely early,test,and middle *** showed that the proposed dynamic adaptive PSO has obtained feasible timetables for almost all of the *** feasibility is measured by minimizing the violation of hard constraints to *** performance of the dynamic adaptive PSO is evaluated by the consumed computational time to produce a solution of feasible timetable,consistency,and *** dynamic adaptive PSO showed a robust and consistent performance in producing a diversity of timetables in a reasonable computational time.

关键词： Sports timetabling particle swarm optimization ITC2021 roundrobin tournament dynamic adaptive

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Unsupervised delineation of the vessel tree in retinal fundus images 5th

Unsupervised delineation of the vessel tree in retinal fundu...

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5th Eccomas Thematic Conference on Computational Vision and Medical Image Processing, VipIMAGE 2015

作者： Strisciuglio, N. Vento, M. Azzopardi, G. Petkov, N. Department of Computer Engineering and Electrical Engineering and Applied Mathematics University of Salerno Salerno Italy Johann Bernoulli Institute for Mathematics and Computer Science University of Groningen Groningen Netherlands Department of Intelligent Computer Systems University of Malta Msida Malta

ISBN: (纸本)9781138029262

Retinal imaging has gained particular popularity as it provides an opportunity to diagnose various medical pathologies in a non-invasive way. One of the basic and very important steps in the analysis of such images is the delineation of the vessel tree from the background. Such segmentation facilitates the investigation of the morphological characteristics of the vessel tree and the analysis of any lesions in the background, which are both indicators for various pathologies. We propose a novel method called B-COSFIRE for the delineation of the vessel tree. It is based on the classic COSFIRE approach, which is a trainable nonlinear filtering method. RE filter to be configured by the automatic analysis of any given vessel-like pattern. The responses of a B-COSFIRE filter is achieved by combining the responses of difference-of-Gaussians filters whose areas of support are determined in an automatic configuration step. We configure two types of B-COSFIRE filters, one that responds selectively along vessels and another that is selective to vessel endings. The segmentation of the vessel tree is achieved by summing up the response maps of both types of filters followed by thresholding. We demonstrate high effectiveness of the proposed approach by performing experiments on four public data sets, namely DRIVE, STARE, CHASE_DB1 and HRF. The delineation approach that we propose also has lower time complexity than existing methods. © 2016 Taylor & Francis Group, London.

关键词： Pathology

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Enhancing photovoltaic energy forecasting:a progressive approach using wavelet packet decomposition

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Clean Energy 2024年第3期8卷 95-108页

作者： Khaled Ferkous Mawloud Guermoui Abderahmane Bellaour Tayeb boulmaiz Nadjem Bailek LMTESE Laboratory of Materials Technology of Energy Systems and EnvironmentUniversitéde Ghardaia47000Algeria Renewable Energy Applied Research Unit(URAER) Ghardaia47000Algeria Laboratory of Mathematics Modeling and Applications Department of Mathematics and Computer ScienceFaculty of Sciences and TechnologyAhmed Draia University of AdrarAdrar 01000Algeria

Accurate photovoltaic(PV)energy forecasting plays a crucial role in the efficient operation of PV power *** study presents a novel hybrid machine-learning(ML)model that combines Gaussian process regression with wavelet packet decomposition to forecast PV power half an hour *** proposed technique was applied to the PV energy database of a station located in Algeria and its performance was compared to that of traditional forecasting *** evaluations demonstrate the superiority of the proposed approach over conventional ML methods,including Gaussian process regression,extreme learning machines,artificial neural networks and support vector machines,across all *** proposed model exhibits lower normalized root mean square error(nRMSE)(2.116%)and root mean square error(RMSE)(208.233 kW)values,along with a higher coefficient of determination(R^(2))of 99.881%.Furthermore,the exceptional performance of the model is maintained even when tested with various prediction ***,as the forecast horizon extends from 1.5 to 5.5 hours,the prediction accuracy decreases,evident by the increase in the RMSE(710.839 kW)and nRMSE(7.276%),and a decrease in R2(98.462%).Comparative analysis with recent studies reveals that our approach consistently delivers competitive or superior *** study provides empirical evidence supporting the effectiveness of the proposed hybrid ML model,suggesting its potential as a reliable tool for enhancing PV power forecasting accuracy,thereby contributing to more efficient grid management.

关键词： short photovoltaic power forecasting wavelet packet decomposition sub-series reconstruction machine learning in energy forecasting sustainable power stations renewable energy

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Integration of Fog Computing for Health Record Management Using Blockchain Technology

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computers, Materials & Continua 2022年第5期71卷 4135-4149页

作者： Mesfer AI Duhayyim Fahd N.Al-Wesabi Radwa Marzouk Abdalla Ibrahim Abdalla Musa Noha Negm Anwer Mustafa Hilal Manar Ahmed Hamza Mohammed Rizwanullah Department of Natural and Applied Sciences College of Community AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia&Faculty of Computer and ITSana’a UniversityYemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia&Department of MathematicsFaculty of ScienceCairo UniversityGiza12613Egypt Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Science King Khaled UniversityKSA&Faculty of ScienceDepartment of Mathematics and Computer ScienceMenoufia UniversityEgypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Internet of Medical Things (IoMT) is a breakthrough technologyin the transfer of medical data via a communication system. Wearable sensordevices collect patient data and transfer them through mobile internet, thatis, the IoMT. Recently, the shift in paradigm from manual data storage toelectronic health recording on fog, edge, and cloud computing has been *** advanced computing technologies have facilitated medical services withminimum cost and available conditions. However, the IoMT raises a highconcern on network security and patient data privacy in the health caresystem. The main issue is the transmission of health data with high security inthe fog computing model. In today’s market, the best solution is blockchaintechnology. This technology provides high-end security and authenticationin storing and transferring data. In this research, a blockchain-based fogcomputing model is proposed for the IoMT. The proposed technique embedsa block chain with the yet another consensus (YAC) protocol building securityinfrastructure into fog computing for storing and transferring IoMT data inthe network. YAC is a consensus protocol that authenticates the input datain the block chain. In this scenario, the patients and their family membersare allowed to access the data. The empirical outcome of the proposedtechnique indicates high reliability and security against dangerous *** major advantages of using the blockchain model are high transparency,good traceability, and high processing speed. The technique also exhibitshigh reliability and efficiency in accessing data with secure transmission. Theproposed technique achieves 95% reliability in transferring a large number offiles up to 10,000.

关键词： Fog computing IoMT block chain YAC security IoT consensus protocol

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Acceptance factors of wearable computing: An empirical investigation 11

Acceptance factors of wearable computing: An empirical inves...

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11th International Network Conference, INC 2016

作者： Gribel, Lena Regier, Stefanie Stengel, Ingo School of Computing Electronics and Mathematics University of Plymouth United Kingdom Faculty of Computer Science and Business Information Systems University of Applied Sciences Karlsruhe Germany

ISBN: (纸本)9781841024103

Despite the vast economic potential of wearable technologies, up to now there is only little scientific research on the acceptance determinants of the wearable computing phenomenon. Therefore, the overall aim of this study is to explain psychographic factors that lead to either acceptance or resistance of wearable computing. This paper synthesises a cause and effect model of wearable computing adoption in the European market. The basis of the proposed conceptual framework builds an explorative study consisting of expert interviews and a subsequent qualitative content analysis to identify salient acceptance factors. The results indicate that the strongest factor that supports the acceptance of wearable technologies is perceived usefulness, whilst the main reason for resistance towards these technologies are perceived IT security risks.

关键词： Security of data

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04491 - The Kiel Esterel Processor The Kiel Esterel Processor - A Semi-Custom, Configurable Reactive Processor - Dagstuhl Seminar -

04491 - The Kiel Esterel Processor The Kiel Esterel Processo...

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Synchronous Programming - SYNCHRON 2004

作者： Li, Xin von Hanxleden, Reinhard Real-Time and Embedded Systems Group Institute of Computer Science and Applied Mathematics Faculty of Engineering Christian-Albrechts-Universität zu Kiel Germany

The synchronous language Esterel is an established language for developing reactive systems. It gives an abstract, well-defined and executable description of the application, and can be synthesized into hardware and software. Typically, an Esterel program is first translated into other, lower-level languages (such as VHDL or C), and then compiled further. However, there is also the alternative of executing Esterel-like instructions directly. For example, in the REFLIX and RePIC projects, Roop et al. have augmented traditional processors with custom hardware to execute Esterel instructions. This patch strategy is a convenient approach, but has some shortages. We present the Kiel Esterel Processor (KEP), a semi-custom, configurable reactive processor for the direct execution of Esterel programs. It consists of a reactive core and scalable peripheral elements. KEP supports standard Esterel statements directly, except (so far) for the concurrency operator. Valued signals and counter functions in Esterel statements are supported by KEP. Due to its control path and its cooperation with elements, KEP obeys exact Esterel (preemption and priority) rules, including for example abort/weak abort (nests). © 2005 Dagstuhl Seminar Proceedings. All rights reserved.

关键词： Application programs

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