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检索条件"丛书名=Lecture Notes in Networks and Systems"

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Mountain Without Barriers - Method of Evaluating Tourist Trails for Possibilities of Traveling by People with Disabilities Using Specialized Off-Road Vehicle 1

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19th Scientific and Technical Conference of the Transport systems Theory and Practice

作者： Klos, Marcin Jacek Prusicki, Pawel Gozdz, Kamil Silesian Tech Univ Fac Transport & Aviat Engn Katowice Poland

ISBN: (数字)9783031531811

ISBN: (纸本)9783031531804;9783031531811

The article focuses on route planning in selected mountain areas, which is part of the second component of the project. An analysis of the factors influencing route possibilities to complete the vehicle under development was carried out. Elements relating to transit and passenger comfort are included. In order to reduce the number of tests on the vehicle under development in the environment, a method was developed. A method has been developed to select routes for further tests in real life. Both linear infrastructure (e.g., hiking trails) and point infrastructure (e.g., stopping places and tourist service areas) must meet the adopted assumptions to make travel possible. The research presents a method of inventorying tourist infrastructure to determine the feasibility of travel with the specialized off-road vehicle under development.

关键词： GIS spatial analysis transport accessibility disabled people non-urbanized areas tourist routes

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Automatic Optimization-Based Methods in Machine Learning: A Systematic Review

Automatic Optimization-Based Methods in Machine Learning: A ...

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Intelligent systems Conference (IntelliSys)

作者： Shahrabadi, Somayeh Adao, Telmo Alves, Victor Magalhaes, Luis G. Ctr Comp Graph CCG Campus AzuremEdificio 14 P-4800058 Guimaraes Portugal Univ Minho ALGORITMI Res Ctr Guimaraes Portugal

ISBN: (纸本)9783031477232;9783031477249

Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that has been applied to various fields ranging from industrial to medical sectors, to perform miscellaneous Computer Vision tasks such as image classification, image segmentation, object detection, and language modeling. Notwithstanding, having a suitable model with practical applicability requires performing appropriate structural operations upon datasets, building adequate CNN architectures from the scratch or resorting to the ones available in the state-of-the-art, and, either way, parameterizing them to improve machine learning skills, usually, in a trial-and-error fashion. Aligned with this context, despite the several semi-/fully automatic approaches that can be found in the literature, (e.g., grid search for hyperparameter fine-tuning, auto-Machine Learning for self-configurable model development, and automatic methods for data arrangement and augmentation), which are often integrated with combination to establish automatic pipelines for the effective implementation of solutions powered by AI, surveys documenting such topic seem to be scarce. Therefore, the main goal of this work is to present an updated yet extensive literature review focusing on this class of approaches, considering the importance of their role in the perspective of ML Optimization.

关键词： Deep Learning Machine Learning Artificial Intelligence Computer Vision Dataset Learning Optimization Auto ML Automatic Data Augmentation CNN Architecture Hyperparameter Optimization Neural Architecture Search

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The Effect of Oxygen Content Variation in the Combustion Atmosphere on Emission of Pollutants During the Combustion of Different Solid Fuels 10th

The Effect of Oxygen Content Variation in the Combustion Atm...

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International Conference on New Technologies, Development and Application - Advanced Production Processes and Intelligent systems

作者： Kadic, Kenan Hodzic, Nihad JP Elektroprivreda BiH DD Sarajevo Power Util Power Plant Kakanj Kakanj Kakanj 72240 Bosnia & Herceg Univ Sarajevo Fac Mech Engn Sarajevo 71000 Bosnia & Herceg

ISBN: (纸本)9783031662676;9783031662683

This paper presents the research results on the influence of oxidant variation on emission of pollutants during the combustion of different solid fuels. Oxidant variation involves adjusting the oxygen content in the combustion atmosphere. The emission of key components of flue gases, NOx, SO2, CO2, and CO, was analyzed during the combustion of specified fuels in air stream (O-2 = 21%) and oxygen-enriched stream (O-2 = 27%) at a process temperature of 1050 degrees C. Results are provided for three different fuels: brown coal, lignite coal, and waste wood biomass. The oxygen-enriched combustion technology, as part of oxy-fuel combustion technologies, is particularly interesting in terms of observing changes in CO2 emissions. The research results a substantial influence of oxygen content variation on pollutant emissions across all fuel types. A dedicated section discusses the implications for further carbon dioxide capture, storage, or utilization, including insights into Carbon Capture and Storage or Utilization technology, specifically focusing on the CO2 component.

关键词： emission of pollutants oxy-fuel combustion solid fuels oxygen

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A Digital Ecosystem with Pedagogical Aspects to Support the Creation of Accessible Educational Resources 1

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11st World Conference on Information systems and Technologies (WorldCIST)

作者： Eliseo, Maria Amelia Martins, Valeria Farinazzo de la Higuera Amato Amato, Cibelle Albuquerque Silveira, Ismar Frango Univ Presbiteriana Mackenzie BR-01239001 Sao Paulo SP Brazil

ISBN: (数字)9783031456510

ISBN: (纸本)9783031456503;9783031456510

The lack of support tools for teachers and pedagogical content creators in the creation and implementation of accessible digital educational resources encouraged the realization of the Smart Ecosystem for Learning and Inclusion Project - SELI Project, which involved researchers from different countries in Europe, Latin America, and the Caribbean. Based on the difficulties regarding the Digital Exclusion of the countries involved in this project, an authoring tool, called SELI Learning Platform, was implemented to assist and guide teachers in planning, structuring, and building accessible courses. In this context, this paper presents some keys aspects of pedagogical planning for the creation of accessible educational digital resources on the SELI Learning Platform. Eight pilot studies of the development of accessible courses using this ecosystem are implemented to check its efficiency, and feasibility and point out some improvements. How instructional design guide, one of the improvements, based on the ADDIE framework, and accessibility aspects were incorporated into the ecosystem are discussed. The results of the usability evaluation with eight teachers of these pilot studies are presented, with their strengths and weaknesses.

关键词： Accessible Digital Educational Resources Smart Digital Ecosystem Pedagogical Platform Authoring Tool

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Average Dwell-Time Stability of Positive Linear Switched Algebraic systems 9th

Average Dwell-Time Stability of Positive Linear Switched Alg...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Dossou-Yovo, Marie-Louise Degla, Guy Inst Math & Sci Phys Porto Novo Benin

ISBN: (纸本)9789819733019;9789819733026

The stability of the positive linear switchted descriptor systems is addressed in this paper using the Drazin inverse conditions. Linear matrix inequalities and copositive linear Lyapunov functions are also used to establish our results which give sufficient conditions for exponential stability under dwell-time and average dwell-time. As domains of applications, we have the technology of computer disk drives systems, switching power converters, Telecommunications, Electrical networks, Constrained Mechanical systems, Robotics, interconnected large-scale systems, Chemistry Biology, and Economics systems.

关键词： Positive switched descriptor systems Exponential stability Lyapunov function Drazin inverse

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Intelligent Recommendation System for Assessing Hand Hygiene of Healthcare Workers

Intelligent Recommendation System for Assessing Hand Hygiene...

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International Scientific and Technical Conference on Integrated Computer Technologies in Mechanical Engineering - Synergetic Engineering (ICTM)

作者： Yevtushenko, Valeriia Railian, Maryna Chumachenko, Tetyana Natl Aerosp Univ Kharkiv Aviat Inst UA-61070 Kharkiv Ukraine Kharkiv Natl Med Univ UA-61001 Kharkiv Ukraine

ISBN: (纸本)9783031614149;9783031614156

Nosocomial infections pose a significant threat to patient safety in healthcare settings globally, with hand hygiene being a crucial preventive measure. This study aims to develop an intelligent recommendation system encapsulated in a mobile application to assess and enhance healthcare workers' hand hygiene practices, thereby reducing nosocomial infections. The system was developed using the MVVM architecture, ensuring a robust, scalable, and maintainable solution. The application was designed in compliance with iOS Human Interface Guidelines to ensure user-friendliness. It includes departmental, workplace, and individual assessment checklists, with a scoring system ranging from 1 to 25 for each item. Technologies such as Firebase for authentication and data storage, Swift Package Manager for dependency management, and various libraries for enhanced functionality were integrated. The application successfully facilitates comprehensive tracking and analysis of hand hygiene practices. The checklist-based approach allows detailed data collection, providing quantifiable insights into compliance levels. Including dark mode and user-friendly interfaces tailored for different user types (new, non-authorized, and authorized users) enhances the user experience. The application's innovative approach combines data analytics with practical utility, making it a valuable tool in nosocomial infection control. The use of modern technologies and adherence to established design principles ensure its relevance and ease of use in healthcare environments. This intelligent recommendation system represents a significant advancement in the application of technology to healthcare challenges, particularly in infection control.

关键词： hand hygiene nosocomial infections recommendation system Swift mobile app

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Deep Learning Brain MRI Segmentation and 3D Reconstruction: Evaluation of Hippocampal Atrophy in Mesial Temporal Lobe Epilepsy 12th

Deep Learning Brain MRI Segmentation and 3D Reconstruction: ...

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12th World Conference on Information systems and Technologies (WorldCIST)

作者： Chaouch, Aymen Messaoud, Nada Hadj Ben Abdallah, Asma Saad, Jamal Payen, Laurent Hmida, Badii Bedoui, M. Hedi Fac Med Monastir Med Technol & Image Proc Lab Monastir Tunisia H Sousse Univ Sousse Higher Inst Comp Sci & Commun Technol Sousse Tunisia Fattouma Bourguiba Hosp Dept Med Imaging Monastir Tunisia St Denis Hosp Dept Med Imaging St Denis France

ISBN: (纸本)9783031602177;9783031602184

In the present paper, we present an automated approach to analyse the lateralization of the epileptogenic focus within the mesial temporal lobe (mTLE) using brain MRI scans. The proposed method encompasses an initial segmentation stage that utilizes a deep convolutional neural network (CNN), followed by a 3D reconstruction and volume computation for both the right and left hippocampus. Our comprehensive approach involves preprocessing the database, employing augmentation techniques, and evaluating outcomes using standard metrics. We validated our method using a dataset containing MRI images from 50 patients, resulting in encouraging findings. Specifically, our approach achieved an 0.84 Dice score, surpassing the values previously reported for this dataset in the literature. Moreover, our method attained an 89% sensitivity and a Hausdorff distance of 2.59.

关键词： Epilepsy Hippocampus Deep Learning Segmentation Preprocessing MRI 3D reconstruction

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Predicting Anemia in Patients Through Clinical and Hematological Data Using Machine Learning Algorithms 13th

Predicting Anemia in Patients Through Clinical and Hematolog...

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13th Computer Science Online Conference

作者： Meza, Alexis Paucar, Kevin Morales, Edson Ticona, Wilfredo Univ ESAN Lima Peru Univ Tecnol Peru Lima Peru

ISBN: (纸本)9783031702846;9783031702853

Anemia is a prevalent condition affecting millions of individuals world-wide. Notably, Peru has witnessed a concerning increase in anemia cases. This study aims to delves into an extensive analysis of the role of machine learning within healthcare, particularly in the early identification and management of anemia using maching learning. The proposed Methodology consists of five phases: Obtained the dataset, Preprocessing, Model Implementations, Evaluation and Validation. In the proposed method, anemia detection is performed by reviewing relevant studies, encompassing Rodriguez's data analysis methodology, Gomez's exploration of AI applications, Hernandez's optimization-based SVM model, Diaz's email classification, and Davila's algorithms for discerning between healthy and anemic blood samples. The approach involves comprehensive data collection, meticulous preprocessing, feature extraction employing techniques such as cell count and crucial factor identification, and data classification using a range of machine learning algorithms. The results highlight the Artificial Neural Network model as the most optimal, achieving a noteworthy accuracy rate of 90.3\% in correctly identifying anemic blood samples. Finally, this study underscores the pivotal role of machine learning techniques in advancing the diagnosis and treatment of anemia, especially in regions experiencing escalated incidences like Peru, offering promising avenues for addressing this pressing global health concern.

关键词： Plant diseases Smart Agriculture Machine Learning Pest control plant pathology Accuracy

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Increasing the Productivity of Fiber Coating Line in Cable Production by Eliminating Waste in Material Flow Using DMAIC Lean Six Sigma Tools and Minitab Application 10th

Increasing the Productivity of Fiber Coating Line in Cable P...

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International Conference on New Technologies, Development and Application - Advanced Production Processes and Intelligent systems

作者： Alagic, Ismar Int Univ Sarajevo Hrasnicka Cesta 15 Sarajevo 71210 Bosnia & Herceg Univ Zenica Fac Mech Engn Fak 1 Zenica 72000 Bosnia & Herceg

ISBN: (纸本)9783031662676;9783031662683

In this paper, the author will point out the application of Lean Six Sigma tools in order to increase the productivity of the sheathing line in cable production. The previous analysis determined that auxiliary times when changing tools on extruders and replacing inputs and outputs are too large. The problem includes all items, i.e. all product changes on the fibercoating line in cable production. This LSS project follows DMAIC stages and application of following techniques: Project charter, SIPOC, CTQ, VSM, Ishikawa diagram, Cause and Effect Matrix, Histograms, Pareto analysis and SPC are applied. In order to eliminate waste in flow material process, the process capability analysis for changing tools on extruder No.2 is done. This article is the result of several years of author's work in the field Lean Six Sigma project implemented in working conditions of manufacturing oriented companies. This article provides proposal a set of approaches that are the basis for the development and application of the Minitab as statistical tool for Lean Six Sigma approach.

关键词： Lean Six Sigma (LSS) DMAIC fiber coating line cable production productivity Minitab

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Neutrosophic Fuzzy Hybrid Method Using Metaheuristic Algorithms for IMRT Treatment

Neutrosophic Fuzzy Hybrid Method Using Metaheuristic Algorit...

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International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Kumar, K. Keshav Narasimham, N. V. S. L. Jawaharlal Nehru Technol Univ Dept Math Hyderabad 500085 Telangana India G Narayanamma Inst Technol & Sci Dept Humanities & Math Hyderabad 500104 Telangana India

ISBN: (纸本)9783031671913;9783031671920

Intensity-Modulated Radiation Therapy (IMRT) has gained prominence in cancer treatment due to its ability to deliver precise radiation doses to tumor regions while sparing adjacent healthy tissues. Optimizing the treatment planning process in IMRT involves addressing complex, conflicting objectives and uncertainties inherent in medical decision-making. This paper proposes a novel Neutrosophic Fuzzy Hybrid Method (NFHM) integrated with metaheuristic algorithms to enhance the efficiency and robustness of IMRT treatment planning. The Neutrosophic Fuzzy Hybrid Method combines neutrosophic logic, fuzzy logic, and metaheuristic optimization techniques to model and handle uncertainties, imprecisions, and conflicting information present in the IMRT treatment planning domain. Neutrosophic logic provides a valuable framework for representing indeterminacy, while fuzzy logic aids in capturing vagueness in medical data. To optimize IMRT treatment plans, the proposed method incorporates metaheuristic algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). These algorithms explore the solution space efficiently, seeking optimal trade-offs among conflicting treatment objectives, including target coverage, organ-at-risk sparing, and dose uniformity. The performance of the Neutrosophic Fuzzy Hybrid Method is evaluated using benchmark datasets and compared against traditional optimization approaches. The results demonstrate the method's superiority in generating high-quality IMRT plans that exhibit improved dose distribution and conformity while considering uncertainties and imprecisions. In conclusion, the integration of neutrosophic and fuzzy reasoning with metaheuristic optimization algorithms in the proposed NFHM presents a promising approach for enhancing the IMRT treatment planning process. The method's ability to handle uncertainties and find optimal solutions makes it a valuable tool for improving the efficacy and precision

关键词： IMRT Neutrosophic Fuzzy Hybrid Method Optimization Algorithm

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