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

共 9504 条记录，以下是761-770 订阅

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How to Build a Controlled Vocabulary of Concepts for Deep Semantic Search

How to Build a Controlled Vocabulary of Concepts for Deep Se...

引用

Intelligent systems Conference

作者： Bense, Hermann Bense Com GmbH Schwarze Bruder Str 1 D-44137 Dortmund Germany

ISBN: (纸本)9783031664274;9783031664281

Semantic Search (SS) is well understood and widely applied in search engines, WordNets (PWN) and ontologies. It is based on Semantic Relations (SR) between different concepts. SS in general does not take account of Intra-Concept Relations (ICR). This article describes how concepts can be formally composed as Semantic Compounds (SC) of sub concepts and Semantic Primes (SP) using ICRs. SMs are stored in a Controlled Vocabulary of Concepts (CVC) which is the fundamental for the implementation of a novel Deep Semantic Search (DSS) algorithm also called Search by Meaning (SbM). The evaluation of SbM has been conducted with a large energy ontology (EnArgus) and the mobile app rObby for the automated notification on scientific publications. SbM finds more relevant concepts and publications compared to traditional SS. The results of SbM are also evaluated against those of Google Search (GS) and Princeton WordNet (PWN).

关键词： Concept Composition (CC) Concept Binary Tree (CBT) Search by Meaning (SbM) Vocabulary of Concepts (CVC) Word Sense (WS) SuPerSense (SPS) Semantic Compound (SC) Semantic Prime (SP) Semantic Relation (SR) PrincetonWordNet (PWN) Word Sense Definition (WSD)

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Qualitative Data Analysis in the Health Sector 11th

Qualitative Data Analysis in the Health Sector

引用

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

作者： Veloso, Maria Ferreira, Marta Campos Tavares, Joao Manuel R. S. Univ Porto Fac Engn R Dr Roberto Frias P-4200465 Porto Portugal INESC TEC R Dr Roberto Frias P-4200465 Porto Portugal

ISBN: (纸本)9783031456503;9783031456510

In the health sector, the implementation of qualitative data research is very important to improve overall services. However, the use of these methods remains relatively unexplored when compared to quantitative analyses. This article describes the qualitative data analysis process that is based on the description, analysis and interpretation of data. It also describes a practical case study and the use of NVivo software to assist in the development of a theory-based qualitative analysis process. This article intends to be a step forward in the use of qualitatively based methodologies in future research in the health sector.

关键词： Grounded Theory Qualitative Research Case Study Healthcare NVivo

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Assessing the Effects of E-Waste on the Environment and How to Mitigate It Using Information Technologies: A Case of Agbogbloshie Scrapyard 9th

Assessing the Effects of E-Waste on the Environment and How ...

引用

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

作者： Owusu, Acheampong Univ Ghana Business Sch Legon Accra Ghana

ISBN: (纸本)9789819733040;9789819733057

This research delves into the role of information technologies in the management of electronic waste (e-waste) in Ghana, with a particular focus on the Agbogbloshie scrapyard. The study employs a qualitative research approach and interviews 10 participants selected via purposive sampling technique with thematic analysis as the analytical lens. The findings revealed that despite the economic benefits derived from e-waste processing, the improper management of e-waste in Ghana continues to pose threats to human health and the environment. Furthermore, it was discovered that collaborative efforts have been initiated by e-waste recyclers, non-governmental organizations (NGOs), and policymakers to implement solutions to effectively manage e-waste disposal. However, these efforts have not placed enough emphasis on emerging information technologies as a solution to the e-waste management problem in Ghana. Therefore, it is recommended that researchers, technology enthusiasts, and policymakers direct their attention toward exploring specific approaches to employ information technologies in the management of e-waste in Ghana.

关键词： Agbogbloshie E-waste E-waste management Information technology Ghana

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Is t-SNE Becoming the New Self-organizing Map? Similarities and Differences 15th

Is t-SNE Becoming the New Self-organizing Map? Similarities ...

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15th International Workshop on Self-Organizing Maps, Learning Vector Quantization and Beyond (WSOM)

作者： Lee, John A. Catholic Univ Louvain Mol Imaging Radiotherapy & Oncol Ottignies Belgium

ISBN: (纸本)9783031671586;9783031671593

Self-organizing maps (SOMs) have many advantages as a tool for exploratory data analysis. Combining vector quantization and topological relationships that are defined in a low-dimensional space, they can run on big data sets and are mostly immune to the curse of dimensionality in the data space. SOMs are used mainly for dimensionality reduction and marginally for clustering;however, SOMs also suffer from some shortcomings. Vector quantization makes them unable to embed all data points, only prototypes or centroid are mapped. Being defined as a regular grid in the low-dimensional space, dimensionality reduction and clustering with SOMs are indirect, as compared to methods of direct embedding like multi-dimensional scaling. Since 2008, t-SNE (t-distributed stochastic neighbor embedding) has raised growing interest, first in the machine learning community and now outside of it, with many applications in cell biology, for instance. Primarily used as a 2D embedding and visualization technique, t-SNE is more and more used as a clustering technique, which is capable of identifying meaningful clusters that classical clustering tools struggle to see. Quite counter-intuitively, t-SNE often better separates clusters in low-dimensional embeddings than clustering tools would do in the high-dimensional data space. To understand this paradox, several mechanisms of t-SNE can be framed as a distance transformation with the possibility (i) to denoise distances in high-dimensional spaces and (ii) to impose a strong inductive bias on them, which magnifies inter-cluster gaps. Despite these strengths, t-SNE is not free of drawbacks, which we quickly review to sketch perspectives of future developments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Vector quantization

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Proceedings of the 27th International Conference on systems Engineering, ICSEng 2020 1st ed. 2021

Proceedings of the 27th International Conference on Systems ...

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丛书名： lecture notes in networks and systems

2021年

作者： Henry Selvaraj Grzegorz Chmaj Dawid Zydek

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Artificial Intelligence Techniques for Medical Image Segmentation: A Technical Overview and Introduction to Advanced Applications 4th

Artificial Intelligence Techniques for Medical Image Segment...

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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Sabbar, Hanan Silkan, Hassan Abbad, Khalid Chouaib Doukkali Univ Comp Sci Dept LAROSERI Lab El Jadida Morocco Sidi Mohamed Ben Abdellah Univ Sci Dept Intelligent Syst & Applicat Lab Fes Morocco

ISBN: (纸本)9783031686597;9783031686603

Medical image segmentation plays a crucial role in enhancing diagnostic, treatment, and research applications within the medical field, increasingly relying on sophisticated artificial intelligence (AI) technologies. This survey explores various AI architectures employed in medical image segmentation, including Deep Neural networks, Encoder-Decoder networks, Attention-Based networks, and hybrid models that integrate features from multiple architectures. It methodically assesses these models' performances, focusing on their effectiveness across different medical imaging types. The analysis delves into each technique's specific advantages and limitations, offering valuable insights into their practical applications in medical diagnostics and treatment planning. The comparative study highlights the differences in accuracy, processing time, and the ability to manage complex image structures among the methods reviewed. It also explores the synergistic benefits of hybrid models, which are shown to achieve superior segmentation results. By providing a comprehensive comparison of AI-driven segmentation techniques, the paper significantly enhances our understanding of how these technologies can be optimized and implemented effectively in medical image analysis. The ultimate goal is to guide future research and the practical application of AI in medical image segmentation, aiming to develop more efficient and precise diagnostic tools.

关键词： Segmentation Medical image Artificial intelligent Deep learning Computer vision

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Modeling of Data Link Redundancy and Network Failure Recovery in the AFDX Network 7th

Modeling of Data Link Redundancy and Network Failure Recover...

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7th International Conference on Green Technology and Sustainable Development

作者： Mau, Dung Ong Ind Univ Ho Chi Minh City Fac Elect Technol Ho Chi Minh City Vietnam

ISBN: (纸本)9783031762314;9783031762321

Avionics Full-Duplex Switched Ethernet (AFDX) is standard part seven of the Aeronautical Radio, Incorporated (ARINC) 664 standards. AFDX describes a new data transfer protocol that supports a data rate of 100 Mb/s, is faster, and has numerous improvements over the previous version, ARINC 429. AFDX has been used on a variety of aircraft models, including the Airbus, Boeing, and COMAC. However, current domestic and international research only addresses the level of understanding of the AFDX protocol, while other critical features such as redundant data link characteristics, data link failure recovery, and switching failure recovery features have yet to be announced. Furthermore, building and developing the AFDX network model on the simulation software platform is critical because research is hampered by equipment costs and stringent security requirements for aircraft hardware devices. As a result, in this article, we propose the AFDX network model with full technical specifications in the ARINC 664 standard set for computer network simulation using the specialized software platform OPNET Modeler. The aggregated results from various simulation scenarios are used to assess the technical characteristics and performance of the AFDX network, including processing delays at network elements, end-to-end delays, data link redundancy, and link and switching failure recovery. Finally, our research contributes to the academic community by simulating complete AFDX systems that meet the standards in ARINC-664.

关键词： System and Modeling Avionics Full-Duplex Switched Ethernet Quality-of-Service OPNET Modeler Finite State Machine

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Social Engineering as the Art of Deception in Cyber-Attacks: A Mapping Review 1

引用

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

作者： Guana-Moya, Javier Avila-Pesantez, Diego Pontificia Univ Catolica Ecuador Quito Ecuador Escuela Super Politecn Chimborazo Riobamba Ecuador

ISBN: (数字)9783031456428

ISBN: (纸本)9783031456411;9783031456428

Social engineering is the art of getting users to compromise information systems, presenting itself as a novel deception strategy. Social engineers target individuals with access to information tomanipulate them into disclosing sensitive data or even carrying out malicious attacks through influence and persuasion. These types of attacks have become the most significant cybersecurity threats. This bibliographic review aims to analyze social engineering due to its impact on the productivity and efficiency of organizations, using deception and knowledge of human vulnerabilities to establish gaps in information security and gain access to secure data. We conclude that social engineering acts based on human exposure through attacks in four clearly defined phases. These attacks can be classified, first, considering the target as humans and computers, and finally, according to how the attack is made, as social, technical, and physical.

关键词： Social engineering cybersecurity attacks victims

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Interpretable Machine Learning in Endocrinology: A Diagnostic Tool in Primary Aldosteronism 15th

Interpretable Machine Learning in Endocrinology: A Diagnosti...

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15th International Workshop on Self-Organizing Maps, Learning Vector Quantization and Beyond (WSOM)

作者： Biehl, Michael Pavlov, David Sitch, Alice J. Prete, Alessandro Arlt, Wiebke Univ Groningen Bernoulli Inst Math Comp Sci & Artificial Intelli Groningen Netherlands Univ Birmingham Ctr Syst Modelling & Quantitat Biomed Birmingham W Midlands England Univ Birmingham NIHR Birmingham Biomed Res Ctr Birmingham W Midlands England Univ Birmingham Univ Hosp Birmingham NHS Fdn Trust Birmingham W Midlands England Univ Birmingham Inst Appl Hlth Res Birmingham W Midlands England Univ Birmingham Coll Med & Dent Sci Inst Metab & Syst Res Birmingham W Midlands England Birmingham Hlth Partners Ctr Endocrinol Diabet & Metab Birmingham W Midlands England MRC Lab Med Sci London England Imperial Coll London Fac Med Inst Clin Sci London England

ISBN: (纸本)9783031671586;9783031671593

The use of urinary steroid metabolomics (USM) in combination with machine learning in endocrinology is briefly introduced. We demonstrate the usefulness of the approach for the detection and differential diagnosis of Primary Aldosteronism (PA), which has been addressed in a recent retrospective study. Here, we mainly present results for the application of the prototype based Generalized Matrix Relevance Learning Vector Quantization for the classification of steroid metabolomics profiles. The method allows for the successful diagnosis of PA and provides insights into the importance of the available markers. Moreover, it facilitates the non-invasive identification of a subtype of PA which is associated with adrenal adenoma harboring a particular mutation in the tumor tissue.

关键词： Interpretable Machine Learning Steroid Metabolomics Primary Aldosteronism

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Enhancing Predictive Maintenance in Industrial systems Through Acoustic Monitoring of Servo Motors Using Machine Learning (AI)

Enhancing Predictive Maintenance in Industrial Systems Throu...

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International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Aradi, Attila Varga, Attila Karoly Univ Miskolc ION Tech Kft Miskolc Hungary Univ Miskolc Miskolc Hungary

ISBN: (纸本)9783031671944;9783031671951

The reliability and efficiency of industrial machinery are pivotal in maintaining optimal production levels and minimizing operational costs. Predictive maintenance, as opposed to traditional reactive or time-based approaches, offers a proactive strategy for machinery upkeep, reducing downtime and extending equipment lifespan. This study introduces an innovative method for predictive maintenance focused on acoustic monitoring of servo motors, integrating advanced machine learning (ML) techniques to detect and diagnose potential failures before they escalate into costly repairs or operational halts. By employing an array of microphones, acoustic signatures of servo motors under various operating conditions were collected and analyzed. The data encompasses a wide range of normal operational sounds as well as those indicative of common faults. Utilizing a combination of feature extraction methods and ML algorithms, including convolutional neural networks (CNNs) and support vector machines (SVMs), the study achieves significant accuracy in classifying the condition of servo motors. This approach not only underscores the practical application of acoustic analysis and ML in industrial maintenance but also opens new avenues for the implementation of intelligent monitoring systems in various manufacturing environments. The findings suggest that acoustic monitoring, supported by sophisticated ML models, can serve as a powerful tool in the predictive maintenance toolkit, offering a scalable, non-invasive means of ensuring machinery health and operational efficiency.

关键词： Predictive Maintenance Acoustic Monitoring Servo Motors Machine Learning Fault Detection Convolutional Neural networks (CNNs) Support Vector Machines (SVMs) Industrial Automation Vibration Analysis Operational Efficiency

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