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Weighting Attributes Based on the Greedy Algorithm Properties

Procedia computer science 2024年 246卷 4883-4892页

作者： Beata Zielosko Urszula Stańczyk Kamil Jabloński University of Silesia in Katowice Institute of Computer Science Bȩdzińska 39 41-200 Sosnowiec Poland Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

Estimation of importance for considered features is an important issue for any knowledge exploration process and it can be executed by a variety of approaches. In the research reported in this study, the primary aim was the development of a methodology for creating attribute rankings. Based on the properties of the greedy algorithm for inducing decision rules, a new application of this algorithm has been proposed. Instead of constructing a single ordering of features, attributes were weighted multiple times. The input datasets were discretised with several algorithms representing supervised and unsupervised discretisation approaches. Each resulting discrete data variant was exploited to construct a ranking of attributes. The effectiveness of the obtained rankings was confirmed through a rule filtering process governed by weighted attributes. The methodology was applied to the stylometric task of authorship attribution. The experimental outcomes demonstrate the value of the proposed research method, as it generally led to improved predictions while taking into account a noticeably decreased sets of attributes and decision rules.

关键词： Greedy algorithm Weighting attributes Decision rules Rule filtering Authorship attribution

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Welcome to Artificial Intelligence science and Engineering

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人工智能科学与工程 2025年第1期1卷封2,前插1页

作者： Tingwen Huang Faculty of Computer Science and Control Engineering at Shenzhen University of Advanced Technology World Academy of Sciences European Academy of Sciences and Arts International Academy of Systems and Cybernetic Sciences IEEE IAPR AIAA

Over the past decade,artificial intelligence(AI)has evolved at an unprecedented pace,transforming technology,industry,and *** diagnosing diseases with remarkable accuracy to powering self-driving cars and revolutionizing personalized learning,AI is reshaping our world in ways once thought *** fields such as machine learning,deep learning,natural language processing,robotics,and ChatGPT,AI continues to push the boundaries of innovation.

关键词： intelligence engineering science welcome artificial

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Numerical approach to design of low frequency magnetic field irradiation system for lab on chip experiments 14

Numerical approach to design of low frequency magnetic field...

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14th International Conference ELEKTRO, ELEKTRO 2022

作者： Radil, Roman Bajtos, Marek Carnecka, Lucia Hargasova, Klaudia Kamencay, Patrik University of Zilina Department of Electromagnetic and Biomedical Engineering Zilina Slovakia Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Systems Biology and Engineering Gliwice Poland

ISBN: (纸本)9781665467261

The article is focused on design of specific electromagnetic coil system using numerical modelling and simulation methods. The proposed solution would be capable of delivering magnetic field of desired strength/ flux density and homogeneity within the specific area to affect the cultivation process of single cells in lab on chip experiments. © 2022 IEEE.

关键词： Numerical models

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Ranking of attributes-comparative study based on data from stylometric domain 26

Ranking of attributes-comparative study based on data from s...

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26th International Conference on Knowledge-Based and Intelligent Information and Engineering systems, KES 2022

作者： Zielosko, Beata Stanczyk, Urszula Zabinski, Krzysztof Institute of Computer Science University of Silesia in Katowice B edzinska 39 Sosnowiec41-200 Poland Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

The area of feature selection methods constantly expands along with the development of artificial intelligence domain, and has great impact on almost every field, whenever data is processed and explored. The paper presents research where a ranking method was proposed, inspired by an approach which comes from an algorithm for induction of decision rules. The ranking procedure was based on calculation of standard deviation for attributes, taking into account assigned class labels. This method was compared with another ranking mechanism, a modified version of popular Relief algorithm, with incorporating characteristics of variables by supervised discretisation. Comparison of obtained results included the aspect of knowledge representation as well as the perspective of the accuracy for constructed rule-based classifiers. The experiments were performed on datasets from stylometry domain, where authorship attribution was considered as a classification task, and stylometric descriptors as characteristic features defining writing styles of authors. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th International Conference on Knowledge-Based and Intelligent Information & Engineering systems (KES 2022)

关键词： Knowledge representation

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An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

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computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

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On-Board Intelligent Decision Support System Development for Aviation Complexes Using Machine-Oriented Methods

On-Board Intelligent Decision Support System Development for...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Alexander Vorobev Ilya Elesin Alexander Shevadronov Faculty Computer Science Artificial Intelligence and Control Systems Bauman Moscow State Technical University Moscow Russian Federation

This paper considers the possibility of using an integrated distributed on-board intelligent decision support system development by a CAD-centric integrated pipeline approach. The problems and solutions of developing and integrating such pipeline into aviation complexes are explained. Developed demonstration for making onboard intelligent decision expert system step-by-step. Key features and use cases are listed for CAD pipeline. The need to split a single monolithic system across multiple CADs to meet various requirements is considered. CAD pipeline consists of: CAD "Expert", CAD "Message", CAD "Astra-DB" and CAD "Scenario". In paper explained improvements given by CAD-centric approach, which increases speed of development and its overall quality summarized.

关键词： Decision support systems Visualization Solid modeling Power engineering Instruments Pipelines Aerospace electronics

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Model-based epidemic data reconstruction using feedback linearization

Model-based epidemic data reconstruction using feedback line...

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2022 IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2022

作者： Csutak, Balazs Polcz, Peter Szederkenyi, Gabor Pázmány Péter Catholic University Faculty of Information Technology and Bionics Budapest Hungary Institute for Computer Science and Control Systems and Control Laboratory Kende u. 13-17 BudapestH-1111 Hungary

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

In this paper, a model-based method is proposed for the reconstruction of non-measured epidemic data of the COVID-19 pandemic in Hungary. Only the data series showing the daily number of hospitalized people are used for the reconstruction together with a nonlinear dynamical model of epidemic spread containing 8 compartments. The unknown input of the model is the infection rate, which is computed through the solution of a feedback linearization-based asymptotic output tracking problem, where the reference is the actually observed number of hospitalized people. Computations show good match with of hospitalized people. Computations show good match with previous reconstruction results, and show a roughly 3.5-4-fold underdetection of infections until the Omicron wave. © 2022 IEEE.

关键词： Feedback linearization

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Industrial Scenario of Renewable Energy-Based Electromobility 3rd

Industrial Scenario of Renewable Energy-Based Electromobilit...

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3rd International Conference on Energy systems, Drives and Automations, ESDA 2020

作者： Szymanski, Jerzy Zurek-Mortka, Marta Faculty of Transport Electrical Engineering and Computer Science Kazimierz Pulaski University of Technology and Humanities Radom Poland Department of Control Systems Lukasiewicz Research Network Institute for Sustainable Technology Radom Poland

ISBN: (纸本)9789811672736

The article presents the concept of a hybrid network topology in the enterprises with the use of a solar power plant and energy storage as well as a drive frequency converter for charging of transportation battery of wheels vehicles and powering of electric caterpillar mining machines. Two important functionalities of the drive frequency converter as a unidirectional EV battery charger and for powering electric motors are presented. The obtained results of the simulation and experimental tests of the presented model confirm the possibility of controlling the DC current value of connected to drive frequency converter of the diode rectifier unit charging the EV battery. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Solar energy

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Analysis and Detection of Melanoma through Collective Intelligence with AI

Analysis and Detection of Melanoma through Collective Intell...

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Annual IEEE Symposium on computer-Based Medical systems

作者： Enrique Fernández-Morales Carlos L. Sanchez-Bocanegra Rafael Pastor-Vargas José-Juan Pereyra-Rodriguez Juan M. Haut José Alberto Benítez-Andrades Communications and Control Systems Dpt. Computer Science Faculty UNED Madrid Spain Communications and Control Systems Dpt. Computer Science Faculty UNED Malaga Spain Virgen Del Rocio Hospital Universitario Seville Spain School of Tecnology University of Extremadura Cáceres Spain Dept. of Electric Systems and Automatics Engineering Universidad de León León Spain

ISBN: (数字)9798350384727

ISBN: (纸本)9798350384734

Cancer is a widespread global health problem, claiming millions of lives each year, and skin cancer represents a significant threat as it is one of the most common types. Early tumor detection via medical imaging is critical for effective treatment. Leveraging artificial intelligence, particularly novel models like Transformers, presents promising avenues for improved diagnosis. This paper explores the efficacy of a Collective Intelligence approach using AI in classifying cancerous and non-cancerous tumors, aiming to reduce classification errors and support clinical decision-making. We created five different configurations using various datasets to compare the results. The results show solid performance for the CI in the evaluated tasks, reaching up to 75.89% accuracy. The lack of images in certain classes significantly contributes to overfitting. It is suggested to explore data expansion strategies and improve consistency in image capture for future work.

关键词： Accuracy Melanoma Collective intelligence Image capture Transformers Solids Artificial intelligence

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Reference Tracking control of a Nonlinear Epidemiological Model with State Estimation

Reference Tracking Control of a Nonlinear Epidemiological Mo...

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Balázs Csutak Gábor Szederkényi Faculty of Information Technology and Bionics Pazmany Peter Catholic University Budapest Hungary Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

In this paper, a reference tracking controller for an 8-compartment epidemic model is proposed. The dynamical model describing the disease spread and progression is given in nonlinear input-affine form. The manipulable input is the transmission rate, while the output to be tracked is the number of infected people. The model parameters correspond to the COVID-19 pandemic. The control design uses a simple SEIR model and it is based on feedback linearization combined with extended Kalman filter for state estimation. Simulation results show good tracking performance even with model mismatch and significant parameter uncertainty.

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