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Compact NSGA-II for Multi-objective Feature Selection

Compact NSGA-II for Multi-objective Feature Selection

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2023 IEEE international conference on systems, Man, and Cybernetics, SMC 2023

作者： Miyandoab, Sevil Zanjani Rahnamayan, Shahryar Bidgoli, Azam Asilian Ontario Tech University Department of Electrical Computer and Software Engineering OshawaON Canada Brock University Department of Engineering St. CatharinesON Canada Wilfrid Laurier University Faculty of Science WaterlooON Canada

ISBN: (纸本)9798350337020

Feature selection is an expensive challenging task in machine learning and data mining aimed at removing irrelevant and redundant features. This contributes to an improvement in classification accuracy, as well as the budget and memory requirements for classification, or any other post-processing task conducted after feature selection. In this regard, we define feature selection as a multi-objective binary optimization task with the objectives of maximizing classification accuracy and minimizing the number of selected features. In order to select optimal features, we have proposed a binary Compact NSGA-II (CNSGA-II) algorithm. Compactness represents the population as a probability distribution to enhance evolutionary algorithms not only to be more memory-efficient but also to reduce the number of fitness evaluations. Instead of holding two populations during the optimization process, our proposed method uses several Probability Vectors (PVs) to generate new individuals. Each PV efficiently explores a region of the search space to find non-dominated solutions instead of generating candidate solutions from a small population as is the common approach in most evolutionary algorithms. To the best of our knowledge, this is the first compact multi-objective algorithm proposed for feature selection. The reported results for expensive optimization cases with a limited budget on five datasets show that the CNSGA-II performs more efficiently than the well-known NSGA-II method in terms of the hypervolume (HV) performance metric requiring less memory. The proposed method and experimental results are explained and analyzed in detail. © 2023 IEEE.

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Computational Proposal for the SAPEVO-M Method: Technological Framework for Complex Decision-Making 11

Computational Proposal for the SAPEVO-M Method: Technologica...

引用

11th international conference on Information Technology and Quantitative Management, ITQM 2024

作者： Lellis Moreira, Miguel Ângelo Costa, Igor Pinheiro de Araújo Costa, Arthur Pinheiro de Araújo Teixeira, Luiz Frederico Horácio S.B. Castro Junior, Marcos Alexandre Pinto de Pereira, Maria Teresa Gomes, Carlos Francisco Simões dos Santos, Marcos Fluminense Federal University RJ Niterói24210-240 Brazil Porto School of Engineering Polytechnic University of Porto Porto4200-072 Portugal Naval Systems Analysis Center RJ Rio de Janeiro20091-000 Brazil Military Institute of Engineering Urca RJ 22290-270 Brazil

This paper introduces a computational framework based on the SAPEVO-M method to enhance decision-making processes in complex scenarios. By integrating technological advancements with the SAPEVO-M methodology, the framework facilitates a more efficient, accurate analysis based on multi-criteria decision-making problems. The paper presents a literature background of the methodology and its axiomatic structure, highlighting its capability for decision-making for a group of stakeholders, thus offering favorable improvements as a technological aid for decision-making analysis. The results demonstrate the potential of the framework, providing decision-makers with robust and reliable insights, and making it an invaluable tool in fields requiring complex decision analysis. This study not only contributes a novel and free computational approach to the decision-making literature but also paves the way for future research and development, applying the axiomatic methodology for problem-solving in diverse areas of science, searching for increase and transparency concerning decision-making processes worldwide. © 2024 The Authors.

关键词： Decision support systems

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Assessing Completeness of Production data in Context of Predictive Quality Applications 2nd

Assessing Completeness of Production Data in Context of Pred...

引用

2nd international conference on Cyber-Physical systems and Control, CPS and C 2021

作者： Schlegel, Peter Schmitt, Robert H. Laboratory for Machine Tools and Production Engineering WZL RWTH Aachen University Aachen52074 Germany Fraunhofer Institute for Production Technology IPT Aachen52074 Germany

ISBN: (纸本)9783031208744

Creating a database of high quality is a considerable challenge when building Predictive Quality models for multi-stage production processes. These models aim to make predictions regarding the occurrence of defects based on acquired production data, therefore enabling the user to reduce scrap at an early stage and optimize process parameters. The performance of these models strongly depends on the quality and thus the completeness of the data acquired, which is often not known to the user. Since the completeness depends very much on the expert knowledge of the respective application, a context-specific methodology is needed for its evaluation and visualization. This work proposes such a methodology, which is consisting of four major steps. The first step includes process modeling and collection of data sources, taking into account information classes relevant for data-driven defect prediction. Then, based on the knowledge of process experts, potential quality-influencing variables are identified and weighted. In the third step, a metric is defined based on which the completeness of existing data for individual process steps and information classes is quantified. The fourth step includes the user-friendly visualization of the assessment. The transparency thus created with regard to the completeness of data in the context of Predictive Quality applications allows the identification of data gaps and the derivation of needs for action. This work, therefore, starts even before the actual modeling process and provides a tool for improving the underlying database. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data completeness data quality data suitability Defect prediction Predictive quality Quality control

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An approach towards Zero Defect manufacturing by combining IIoT data with Industrial Social Networking 4

An approach towards Zero Defect Manufacturing by combining I...

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4th international conference on Industry 4.0 and Smart manufacturing, ISM 2022

作者： Alexopoulos, Kosmas Tsoukaladelis, Thodoris Dimitrakopoulou, Chrysa Nikolakis, Nikolaos Eytan, Amit University of Patras Patras26504 Greece Strada Torino 43 Orbassano10043 Italy

Zero Defect manufacturing (ZDM) is intrinsically a multi-disciplinary approach. Recently a lot of attention has been given towards implementing data analytics using machine learning approaches upon timeseries data collected by Industrial Internet of Things (IIoT) systems during production. However, other types of data, such as text, that capture the human knowledge and experience from people working in manufacturing can be useful for ZDM. In this context, Industrial Social Network (ISN) solutions can be very efficient for serving communication needs in a manufacturing setting and can be used for capturing such informal knowledge, also facilitating sharing of know-how among employees. This work presents a method implemented in software prototype combining IIoT with textual data available in ISN into a knowledge base towards enabling ZDM. A dashboard for informal discussions of employees on the shopfloor status as depicted through IIoT data has been conceived and implemented within ISN. An ontology has been designed and implemented in semantic repository for linking IIoT information with textual information shared through the dashboard. The proposed system has been validated with a set of simulated IIoT data and textual information stored in ISN. © 2022 The Authors. Published by ELSEVIER B.V.

关键词： Ontology

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HeteFedRec: Federated Recommender systems with Model Heterogeneity

HeteFedRec: Federated Recommender Systems with Model Heterog...

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international conference on data engineering

作者： Wei Yuan Liang Qu Lizhen Cui Yongxin Tong Xiaofang Zhou Hongzhi Yin The University of Queensland Shandong University Beihang University The Hong Kong University of Science and Technology

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Owing to the nature of privacy protection, feder-ated recommender systems (FedRecs) have garnered increasing interest in the realm of on-device recommender systems. However, most existing FedRecs only allow participating clients to collaboratively train a recommendation model of the same public parameter size. Training a model of the same size for all clients can lead to suboptimal performance since clients possess varying resources. For example, clients with limited training data may prefer to train a smaller recommendation model to avoid excessive data consumption, while clients with sufficient data would benefit from a larger model to achieve higher recommendation accuracy. To address the above challenge, this paper introduces HeteFedRec, a novel FedRec framework that enables the assignment of personalized model sizes to partici-pants. Specifically, we present a heterogeneous recommendation model aggregation strategy, including a unified dual-task learning mechanism and a dimensional decorrelation regularization, to allow knowledge aggregation among recommender models of different sizes. Additionally, a relation-based ensemble knowledge distillation method is proposed to effectively distil knowledge from heterogeneous item embeddings. Extensive experiments conducted on three real-world recommendation datasets demonstrate the effectiveness and efficiency of HeteFedRec in training federated recommender systems under heterogeneous settings.

关键词： Training Learning systems Privacy Additives Training data data engineering data models

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Eye-Tracking Insights into Traffic Controllers’ Situation Awareness Levels and Workload Conditions 31

Eye-Tracking Insights into Traffic Controllers’ Situation A...

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31st ISTE international conference on Transdisciplinary engineering, TE 2024

作者： Wong, Martin Yu, Xiaoqing Chen, Chun-Hsien Xia, Ziqing School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore639798 Singapore

ISBN: (纸本)9781643685502

Traffic control systems, encompassing both air traffic control (ATC) and vessel traffic service (VTS), play a pivotal role in ensuring the safety and efficiency of transportation across aviation and maritime domains. High situation awareness (SA), which includes the ability to perceive information (Level 1 SA), comprehend the meaning (Level 2 SA), and predict future states (Level 3 SA), is crucial for traffic controllers to manage their respective traffic environments effectively under various workload conditions. Eye-tracking (ET) is a prominent physiological method to quantitatively assess operators’ cognitive workload and situation awareness as eye movements are closely related to one’s attention and information-processing mechanisms. This study aims to investigate the effects of SA levels and workload conditions on traffic controllers’ eye movement patterns using ET technology. Using ATC as a case study, experiments were conducted to collect eye movement data of 26 participants while they monitored aircraft on a simulated radar screen and answered freeze-probe queries of different SA levels (Level 1 and Level 2). Workload conditions were varied by the number of aircraft participants needed to monitor. Two-way repeated measures ANOVA showed significant main effects and interaction effects of SA and workload levels on certain ET metrics (e.g., total fixation duration, average fixation duration). This study contributes insights that could enhance the existing knowledge of SA levels in traffic control tasks and reinforce the evidence base linking eye movements and cognitive processes. Applying these insights to maintain optimal SA could reduce human error and elevate safety standards across different transportation sectors. © 2024 The Authors.

关键词： Air traffic control

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Voltage Output Characteristics of PZT-5H Disk Under Temperature, Humidity and Traffic Load

Voltage Output Characteristics of PZT-5H Disk Under Temperat...

引用

international conference on Intelligent manufacturing and Energy Sustainability, ICIMES 2022

作者： Kumar, Dubey Ankush Biplab, Das Prateek, Gupta Department of Mechanical Engineering Guru Ghasidas Vishwavidyalaya Chhattisgarh Koni Bilaspur495009 India

ISBN: (纸本)9789811984969

As the population is increasing, the demand of energy is also increasing. This results in the threat of energy crisis. In order to combat energy crisis, researchers explore alternative energy technologies that can be harvest from the ambient environment. Piezoelectric transduction is the prominent mechanical energy harvesting mechanism owing to its high electromechanical coupling factor and piezoelectric coefficient compared to electrostatic, electromagnetic and triboelectric transductions. This paper presents experimental study of the effects of temperature, humidity and strain impacts on the voltage generation of PZT-5H piezoelectric panel in real-time environmental condition. The PZT panel was placed on highway and data’s were recorded with the help of indigenously fabricated real-time data logger. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Temperature

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international Scientific and Technical conference on Integrated Computer Technologies in Mechanical engineering, ICTM 2022

International Scientific and Technical Conference on Integra...

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international Scientific and Technical conference on Integrated Computer Technologies in Mechanical engineering, ICTM 2022

ISBN: (纸本)9783031362002

The proceedings contain 62 papers. The special focus in this conference is on Integrated Computer Technologies in Mechanical engineering. The topics include: Shape of Part Edges with the Various Surface Finish of the Adjacent Surfaces;study of a Geometry Accuracy of the Bracket-Type Parts Using Reverse engineering and Additive manufacturing Technologies;design of High Fatigue Life Joints of Fuselage Structures Considering Fracture Mechanics;analysis of Gluing Assembly Defects of Ultra-Thin Composite Face Sheets with Honeycomb Core;information Technology for Determining the Flight Performance of a Paraglider Wing;research of Mathematical Models Based on Optimization Paraboloid and Alternative Method of Regression;a Model of a Transport Multi-section Conveyor Based on a Neural Network;Simulation of the Impact of Migration Caused by the Russian War in Ukraine on the Dynamics of COVID-19 in Poland;experimental Setup with Stand-Alone Automatic Modular Logger of Parameters of Thermal Pulse Processing;limiting Properties of Boundary Value Problems Solutions for Some Higher-Order Equations;method for Correcting the Mathematical Model in Case of Empirical data Asymmetry;model for Determining the Ballistic-Temporal Characteristics of the Movement of a Controlled Parachute System;design Improvements of an Industrial Centrifugal Fan Based on the Computer Mathematical Simulation;bagging Model for Heart Stroke Detection;modification of the Goland-Reissner Overlapped Adhesive Joint Model to Describe Stress Values in Circular Joints;stress State of the Adhesive Joint with Longitudinal Defect. Analytical and Finite Element Models;modeling the Structure of an Aggregation of Toxic Cyanobacteria When Planning Their Elimination from the Air.

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Sentiment Analysis of Social Media Reviews using Machine Learning and Word Embedding Techniques 4

Sentiment Analysis of Social Media Reviews using Machine Lea...

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4th IEEE Annual Flagship India Council international Subsections conference, INDISCON 2023

作者： Lavanya, B.N. Shenoy, P Deepa Venugopal, K.R. Visvesvaraya College of Engineering Bangalore University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350333558

The enormous amount of data generated and exchanged daily on social media raises the need for knowledge-extraction tasks to be mechanized. Sentiment analysis is an active area of information extraction research with various challenges to overcome. Any business ought to be highly interested in client feedback. Customers mainly rely on reviews to determine where to dine. Sentiment analysis is vital in classifying restaurant reviews into positive, negative, and neutral polarity to determine whether the cuisine is good, securely produced, and worth picking over other restaurants. The Yelp dataset for restaurant reviews is used in this study to test different word embedding approaches, including Bag of Words (BoW), Term Frequency-Inverse Document Frequency(TF-IDF), GloVe, Word2Vec, and Doc2Vec. Supervised Machine Learning (ML) algorithms like Logistic Regression (LR) and Support Vector Machine (SVM) are evaluated based on the performance metrics such as the F1-Score, Accuracy, Recall, and Precision. Comparative findings demonstrate that combining a Support Vector Machine and TF-IDF word embedding technique produced results with an accuracy of 98%. © 2023 IEEE.

关键词： Sentiment analysis

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Improving Traffic Safety at the Sorting of Railway Transportation of the Republic of Uzbekistan

Improving Traffic Safety at the Sorting of Railway Transport...

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international conference on Industrial engineering, Applications and manufacturing (ICIEAM)

作者： Janibek F. Kurbanov Zokhid B. Toshboyev Department of “Radio electronic devices and systems” Tashkent State Transport University Tashkent Uzbekistan Department of “Automation and Remote Control” Tashkent State Transport University Tashkent Uzbekistan

ISBN: (数字)9798331512194

ISBN: (纸本)9798331512200

Today, at “Uzbekistan Temir Yollari” JSC, train traffic management is continuously improved and updated based on international regulatory and legal standards. This article addresses the pressing issue of digitalizing the control and monitoring systems for the signaling, centralization, and blocking of the sorting hill at the sorting station managed by “Uzbekistan Temir Yollari” JSC using modern wireless communication technologies. The method for the automated microprocessorbased control system of signaling, centralization, and blocking devices for the sorting hill at the railway station is based on the digitalization of remote control methods for braking and stopping devices for wagons descending the hill. These braking and stopping devices automatically adjust to the weight and speed of the descending wagons. A graph showing the dependence of the wagons braking time on its weight and speed is created, ensuring traffic safety at sorting hills. The digitalization of signaling, centralization, and blocking systems, the elimination of the human factor, and the integration of these systems with electrical centralization based on digital data are aimed at solving this problem.

关键词： Wireless communication Transportation Control systems Rail transportation Safety manufacturing Monitoring Standards Remote control Sorting

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