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2019 Practice of Enterprise Modelling Conference Forum, PoEM 2019 Forum

作者： Rajashekar, Nikhitha Hacks, Simon Silva, Nuno Research Group Software Construction RWTH Aachen University Germany Division of Network and Systems Engineering KTH Royal Institute of Technology Sweden Department of Computer Science and Engineering Technical University of Lisbon Lisbon Portugal

Enterprise Architecture (EA) models help enterprise architects to make business decisions and to support organisations to understand and analyse its structure. Creating and maintaining such an EA model is expensive and time-consuming in large organisations. In this paper, the authors provide a review of how graph analytic methods can be used to support EA model maintenance. To achieve the best results for the duplicate identification, it is necessary to evaluate and optimise different graph analytic and Machine Learning (ML) algorithms to support the maintenance process. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Machine learning

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Effective Intrusion Detection and Classification using Fuzzy Rule based Classifier in Cloud Environment

Effective Intrusion Detection and Classification using Fuzzy...

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Automation, Computing and Renewable systems (ICACRS), International Conference on

作者： C. Veena S. Ramalakshmi V. Bhoopathy Minakshi Dattatraya Bhosale C. G. Magadum Abirami. S. K. Department of CSE Kommuri Pratap Reddy Institute of Technology Ghanpur (V) Hyderabad Telangana India Department of Computer Science and Applications Don Bosco College (Arts and Science) Puducherry India Department of Computer Science and Engineering Malla Reddy College of Engineering Telangana India Faculty of MCA Yashoda Technical Campus Satara Maharashtra India Smt. Kasturbai Walchand College Sangli Maharashtra India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamilnadu India

ISBN: (纸本)9781665460859

Cloud computing (CC) is the demonstration of the technology that makes use of the substructure for computing in a proficient fashion. This sort of computing offers great quantity of consequences in augmenting the productivity that verifies the risk handling management and decreases the cost. Intrusion detection system (IDS) is widely applied for detecting malicious actions in its host and the communication network. IDS is a procedure of discovering activities that take place in a network and attempts to fulfill the confidentiality, network, or security accessibility to smear the trust procedure. This article introduces an Effective Intrusion Detection and Classification using Fuzzy Rule based Classifier (EIDC-FRC) model in Cloud Environment. The goal of the presented EIDC-FRC model is to determine the occurrence of intrusions and normal data traffic in the cloud environment. In order to achieve this, the presented EIDC-FRC model applies FRC for data classification process. The parameter tuning of the FRC model is performed using enhanced bird swarm algorithm (EBSA). The FRC is an effective model in pattern recognition that offers effective outcomes by the use of linguistic labels in the antecedents of the rules. The simulation analysis of the EIDC-FRC model ensured the enhancements of the presented approach compared to recent state of art approaches.

关键词： Cloud computing Analytical models Art Computational modeling Intrusion detection Linguistics Data models

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A Brief Review of Explainable Artificial Intelligence in Healthcare

arXiv

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arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, CC BY-NC-ND.

关键词： Decision making

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Efficient Random Subspace Decision Forests with A Simple Probability Dimensionality Setting Scheme

SSRN

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SSRN 2022年

作者： Wang, Quan Wang, Fei Li, Zhongheng Jiang, Peilin Ren, Fuji Nie, Feiping Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing100094 China School of Software Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China Northwestern Polytechnical University Shaanxi Xi’an710072 China

The random subspace concept is widely used in decision forests. However, there is not a reasonable approach to specify the appropriate number of randomly selected features. Previous random subspace decision forests simply specify the random subspace dimensionality d s to a preset function value such as [log 2 d ] or [√ d ] where d is the number of the total feature dimensions. Actually, this preset value may be a relatively poor choice for high dimensional data sets. In this paper, a novel framework named Efficient Random Subspace decision forest (ERS) is proposed which pays attention to how to set the random subspace dimensionality for each decision tree of decision forests. Specifically, a simple discrete uniform distribution is employed to set with probability the number of randomly selected features for each tree in random subspace decision forests. The proposed Half Range Discrete Uniform distribution-based Varied Dimensionality setting (HRDUVD) method removes the hesitation of how to preset an appropriate d s for different data sets but also results in good enough classification performance along with relatively short running time. Plenty of experiments on public benchmark data sets have demonstrated the effectiveness and the efficiency of the proposed ERS. © 2022, The Authors. All rights reserved.

关键词： Decision trees

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A Fuzzy Logic-Based Computational Framework for Precision Triage in Androgenetic Alopecia: A Simulated Biomarker-Driven Approach

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International Journal of Computational Intelligence systems 2025年第1期18卷 1-30页

作者： Al-Samarraay, Mohammed S. Magableh, Aws A. Mahmood, Rana I. Joudar, Shahad Sabbar Zahid, Idrees A. Al-Obaidi, Jameel R. Al-Saffar, Ali Z. Albahri, A. S. Albahri, O. S. Alamoodi, A. H. Abdullah, Mohd Faizal Nizam Lee Electrical and Electronic Engineering Department College of Engineering Gulf University Sanad Bahrain Faculty of Information Technology and Computer Sciences Department of Information Systems Yarmouk University Irbid Jordan Department of Software Engineering Prince Sultan University Riyadh Saudi Arabia Department of Biomedical Engineering College of Engineering Al-Nahrain University Jadriya Baghdad Iraq University of Technology Baghdad Iraq Department of Biology Faculty of Science and Mathematics Universiti Pendidikan Sultan Idris Tanjong Malim Malaysia Applied Science Research Center Applied Science Private University Amman Jordan Department of Molecular and Medical Biotechnology College of Biotechnology Al-Nahrain University Baghdad Iraq Technical Engineering College Imam Ja’afar Al-Sadiq Baghdad Iraq University of Information Technology and Communications (UOITC) Baghdad Iraq Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Victorian Institute of Technology Melbourne Australia GUST Engineering and Applied Innovation Research Center (GEAR) Gulf University for Science and Technology Mishref Kuwait Department of Mathematics Faculty of Science and Mathematics Universiti Pendidikan Sultan Idris Tanjong Malim Malaysia

Androgenetic alopecia (AGA) is a common cause of hair loss affecting both men and women. Although its precise etiology remains uncertain, genetic and hormonal factors are recognized as major contributors. This study introduces a computational intelligence framework employing fuzzy logic and multi-criteria decision-making (MCDM) to simulate a robust triage system for AGA management. Using a simulated dataset of 100 AGA patients, we applied the fuzzy-weighted zero-inconsistency (FWZIC) method to assign weights to 11 bioactive criteria associated with AGA. These weights informed a novel triage procedure for alopecia patients (TPAP), which stratified patients into seven severity levels (level 1: minor; level 7: severe). This study presents a computationally intelligent triage model tailored for AGA, emphasizing the applicability of fuzzy MCDM techniques in medical decision support. The TPAP framework can assist in resource allocation and treatment planning, paving the way for personalized and timely interventions in hair loss management.

关键词： Biomarkers

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A Brief Review of Explainable Artificial Intelligence in Healthcare

SSRN

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SSRN 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalo, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Swinburne University of Technology HawthornVIC3122 Australia Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

关键词： Decision making

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Skolem Meets Schanuel

arXiv

引用

arXiv 2022年

作者： Bilu, Yuri Luca, Florian Nieuwveld, Joris Ouaknine, Joël Purser, David Worrell, James Institut de Mathématiques de Bordeaux Université de Bordeaux CNRS Talence France School of Mathematics University of the Witwatersrand Johannesburg South Africa Research Group in Algebraic Structures & Applications King Abdulaziz University Saudi Arabia Max Planck Institute for Software Systems Saarland Informatics Campus Germany University of Warsaw Poland Department of Computer Science University of Oxford United Kingdom

The celebrated Skolem-Mahler-Lech Theorem states that the set of zeros of a linear recurrence sequence is the union of a finite set and finitely many arithmetic progressions. The corresponding computational question, the Skolem Problem, asks to determine whether a given linear recurrence sequence has a zero term. Although the Skolem-Mahler-Lech Theorem is almost 90 years old, decidability of the Skolem Problem remains open. The main contribution of this paper is an algorithm to solve the Skolem Problem for simple linear recurrence sequences (those with simple characteristic roots). Whenever the algorithm terminates, it produces a stand-alone certificate that its output is correct - a set of zeros together with a collection of witnesses that no further zeros exist. We give a proof that the algorithm always terminates assuming two classical number-theoretic conjectures: the Skolem Conjecture (also known as the Exponential Local-Global Principle) and the p-adic Schanuel Conjecture. Preliminary experiments with an implementation of this algorithm within the tool Skolem point to the practical applicability of this method. © 2022, CC BY.

关键词： Computation theory

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A framework for managing enterprise architecture debts - Outline and research directions 10

A framework for managing enterprise architecture debts - Out...

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10th International Workshop on Enterprise Modeling and Information systems Architectures, EMISA 2020

作者： Alexander, Peter Hacks, Simon Jung, Jürgen Lichter, Horst Steffens, Ulrike Uludağ, Ömer RWTH Aachen University Research Group Software Construction Aachen Germany KTH Royal Institute of Technology Network and Systems Engineering Stockholm Sweden Frankfurt University of Applied Sciences Faculty of Computer Science and Engineering Frankfurt Germany Hamburg University of Applied Sciences Department of Computer Science Hamburg Germany Technical University of Munich Department of Informatics Munich Germany

Even though enterprise architecture management (EAM) offers a wide range of methods and tools for aligning business with IT, an architect’s work is challenged by reality. The evolution of enterprise architecture (EA) and given constraints (e.g. legacy systems and processes) lead to debts which may complicate and hinder opportunities;however, the management of such debts has not been considered in EAM research. This paper presents a framework for strategically managing EA-debt-related issues and propose open questions as well as future research directions in this field. Copyright © 2020 for this paper by its authors.

关键词： Legacy systems

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science of design for societal-scale cyber-physical systems: challenges and opportunities

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Cyber-Physical systems 2019年第3期5卷 145-172页

作者： Sztipanovits, Janos Koutsoukos, Xenofon Karsai, Gabor Sastry, Shankar Tomlin, Claire Damm, Werner Fränzle, Martin Rieger, Jochem Pretschner, Alexander Köster, Frank Institute for Software Integrated Systems Vanderbilt University NashvilleTN United States Electrical Engineering and Computer Sciences University of California BerkeleyCA United States Department of Computer Science University of Oldenburg Oldenburg Germany Computer Science Technical University of Munich München Germany Braunschweig Germany

Emerging industrial platforms such as the Internet of Things (IoT), Industrial Internet (II) in the US and Industrie 4.0 in Europe have tremendously accelerated the development of new generations of Cyber-Physical systems (CPS) that integrate humans and human organizations (H-CPS) with physical and computation processes and extend to societal-scale systems such as traffic networks, electric grids, or networks of autonomous systems where control is dynamically shifted between humans and machines. Although such societal-scale CPS can potentially affect many aspect of our lives, significant societal strains have emerged about the new technology trends and their impact on how we live. Emerging tensions extend to regulations, certification, insurance, and other societal constructs that are necessary for the widespread adoption of new technologies. If these systems evolve independently in different parts of the world, they will ‘hard-wire’ the social context in which they are created, making interoperation hard or impossible, decreasing reusability, and narrowing markets for products and services. While impacts of new technology trends on social policies have received attention, the other side of the coin–to make systems adaptable to social policies–is nearly absent from engineering and computer science design practice. This paper focuses on technologies that can be adapted to varying public policies and presents (1) hard problems and technical challenges and (2) some recent research approaches and opportunities. The central goal of this paper is to discuss the challenges and opportunities for constructing H-CPS that can be parameterized by social context. The focus in on three major application domains: connected vehicles, transactive energy systems, and unmanned aerial vehicles. Abbreviations: CPS: Cyber-physical systems;H-CPS: Human-cyber-physical systems;CV: Connected vehicle;II: Industrial Internet;IoT: Internet of Things. © 2019, © 2019 Informa UK Limited, trading a

关键词： Cyber Physical System

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The orbit problem for parametric linear dynamical systems

arXiv

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arXiv 2021年

作者： Baier, Christel Jantsch, Simon Lefaucheux, Engel Ouaknine, Joël Whiteland, Markus A. Funke, Florian Karimov, Toghrul Luca, Florian Purser, David Worrell, James Technische Universität Dresden Germany Max Planck Institute for Software Systems Saarland Informatics Campus Germany School of Mathematics Wits University Johannesburg South Africa Research Group in Algebraic Structures & Applications King Abdulaziz University Saudi Arabia Department of Computer Science University of Oxford United Kingdom

We study a parametric version of the Kannan-Lipton Orbit Problem for linear dynamical systems. We show decidability in the case of one parameter and Skolem-hardness with two or more parameters. More precisely, consider a d-dimensional square matrix M whose entries are algebraic functions in one or more real variables. Given initial and target vectors u, v ∈ Qd, the parametric point-to-point orbit problem asks whether there exist values of the parameters giving rise to a concrete matrix N ∈ Rd×d, and a positive integer n ∈ N, such that Nnu = v. We show decidability for the case in which M depends only upon a single parameter, and we exhibit a reduction from the well-known Skolem Problem for linear recurrence sequences, suggesting intractability in the case of two or more parameters. Copyright © 2021, The Authors. All rights reserved.

关键词： Linear control systems

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