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检索条件"丛书名=Communications in Computer and Information Science"

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Using Artificial Intelligence and X-ray Images to Train and Predict COVID-19 and Pneumonia: Tool for Diagnosis and Treatment 12th

Using Artificial Intelligence and X-ray Images to Train and ...

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12th Ecuadorian Conference on information and Communication Technologies

作者： Juarez-Gonzalez, Bryan Villalba-Meneses, Fernando Cruz-Varela, Jonathan Tirado-Espin, Andres Vizcaino-Imacana, Paulina Cadena-Morejon, Carolina Guevara, Cesar Almeida-Galarraga, Diego Univ Yachay Tech Sch Biol Sci & Engn Urcuqui 100119 Ecuador Univ Yachay Tech Sch Math & Computat Sci San Miguel de Urcuqui 100119 Ecuador Univ Internac Ecuador Quito Ecuador Univ Tecnol Indoamer Ctr Invest Mecatron & Sistemas Interactivos MIST Machala & Sabanilla Quito 170103 Ecuador

ISBN: (纸本)9783031754302;9783031754319

In January 2022, Ecuador experienced a peak in COVID-19 cases, with 890,541 confirmed cases and 35,658 deaths. During 2019-2020, influenza and pneumonia also ranked among the top causes of death. Traditional chest X-rays and chest CT scans are commonly used for diagnosing COVID-19, but studies by Wong et al. indicate that chest Xrays are less sensitive compared to CT scans unless artificial intelligence is utilized. Tahir et al. highlighted that AI models such as U-net and neural networks achieve high diagnostic accuracy, with U-Net++ and ResNet18 models showing sensitivities above 99% and perfect specificity using a large dataset of 33,920 chest X-ray images. The rapid detection of symptoms could have helped in prioritizing critical care, potentially reducing deaths. In the current study, it is demonstrated that combining AI with chest X-rays can achieve a binary accuracy above 98% for COVID-19 detection using transfer learning with Xception, VGG16, and VGG19 neural networks. Using 27,052 chest X-ray images, the VGG19 model achieved an excellent F1-score of 98.53% for COVID-19 and normal class classification. The VGG19 model also performed well in multiclass classification with an F1-score above 89%. The study concludes that AI-enhanced diagnostic tools, such as VGG19, are valuable for hospitals in diagnosing COVID-19 and future improvements might include larger datasets and enhanced segmentation techniques.

关键词： Artificial intelligence COVID-19 Pneumonia chest X-ray transfer learning Xception VGG16 VGG19

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EfiMon: A Process Analyser for Granular Power Consumption Prediction 11th

EfiMon: A Process Analyser for Granular Power Consumption Pr...

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11th Latin American Conference on High Performance Computing

作者： Leon-Vega, Luis G. Tosato, Niccolo Cozzini, Stefano Univ Trieste Trieste Italy AREA Sci Pk Trieste Italy

ISBN: (纸本)9783031800832;9783031800849

High-performance computing (HPC) and supercomputing are critical in Artificial Intelligence (AI) research, development, and deployment. The extensive use of supercomputers for training complex AI models, which can take from days to months, raises significant concerns about energy consumption and carbon emissions. Traditional methods for estimating the energy consumption of HPC workloads rely on metering reports from computing nodes' power supply units, assuming exclusive use of the entire node. This assumption is increasingly untenable with the advent of next-generation supercomputers that share resources to accelerate workloads, as seen in initiatives like Acceleration as a Service (XaaS) and cloud computing. This paper introduces EfiMon, an agnostic and non-invasive tool designed to extract detailed information about process execution, including instructions executed within specific time windows and CPU and RAM usage. Additionally, it captures comprehensive system metrics, such as power consumption reported by CPU sockets and PSUs. This data enables the development of prediction models to estimate the energy consumption of individual processes without requiring isolation. Using a regression-based mathematical model, our tool is able to estimate single processes' power consumption in isolated and shared resource environments. In shared scenarios, the model demonstrates robust performance, deviating by a maximum of 2.2% on Intel-based machines and 4.4% on AMD systems compared to non-shared cases. This significant accuracy showcases EfiMon's potential for enhancing energy accounting in supercomputing, contributing to more efficient and energy-aware optimisation strategies in HPC.

关键词： Energy measurement Supercomputers Microprocessors Multicore processing Load modeling High performance computing Cluster computing

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Manufacturing-as-a-Service: A Systematic Review of the Literature 5th

Manufacturing-as-a-Service: A Systematic Review of the Liter...

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5th 5th Olympus - International Conference on Supply Chain

作者： Karamanli, Anastasia Xanthopoulos, Alexandros Gasteratos, Antonios Koulouriotis, Dimitrios Democritus Univ Thrace Sch Engn Dept Prod & Management Engn V Sofias 12 Xanthi 67100 Greece Natl Tech Univ Athens Sch Mech Engn Heroon Polytech 9 Athens 15772 Greece

ISBN: (纸本)9783031693434;9783031693441

The Manufacturing-as-a-Service (MaaS) paradigm has received considerable attention in the research and business community over the past fewyears. MaaS provides manufacturing resources and capabilities as service, available for utilization by organizations via a digital thread, allowing them to concentrate in their significant competencies. This literature review enriched with bibliometrics, aims to examine the current state of MaaS by analyzing selected and contemporary references from Scopus database. The analysis was conducted with the help of specialized bibliometrics software, and the quantitative results include annual trends, leading publications, journals and organizations, citation distributions, co-occurrence of keywords, and journal co-citations. In addition, the publications have been classified according to the methodologies implemented for the data collection and analysis, their objectives, the results obtained, as well as their contribution to the field of manufacturing. This study sheds light on the status and trends in the field ofMaaS and has the potential to aid researchers in forming their research agenda and advancing their work on this topic.

关键词： Manufacturing-as-a-Service Smart Manufacturing Industry 4.0 Cloud Manufacturing Literature Review Bibliometric Analysis

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Incident Alert Priority Levels Classification in Command and Control Centre Using Word Embedding Techniques 12th

Incident Alert Priority Levels Classification in Command and...

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12th Ecuadorian Conference on information and Communication Technologies

作者： Orellana, Marcos Cubero Lupercio, Jonnathan Emmanuel Lima, Juan Fernando Garcia-Montero, Patricio Santiago Zambrano-Martinez, Jorge Luis Univ Azuay Comp Sci Res & Dev Lab LIDI Cuenca Ecuador

ISBN: (纸本)9783031754302;9783031754319

This research analyzes the textual content of emergency calls that arrive at command and control centers to classify them according to their priority. The aim is to respond promptly and make appropriate decisions in situations that require immediate attention. Text mining techniques construct a computational model with preprocessing techniques such as tokenization, case folding, and stop words removal. The calls are then represented using Word Embeddings with the Skip-Gram architecture to obtain word vectorization, and the Clustering algorithm is applied for classification. The results show an improvement in classification accuracy, achieving 95% precision in classification tests using high and low-priority categories and 81% in classification tests using four alert categories. These techniques enhance the syntactic and semantic understanding of emergency calls and reduce the risk of loss of human life.

关键词： Classification Clustering Emergency calls Machine learning Text mining Skip-Gram Word2Vec

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Simulation Modelling of Dynamic Production Scheduling on Parallel Machines with Sequence-Independent Setups 5th

Simulation Modelling of Dynamic Production Scheduling on Par...

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5th 5th Olympus - International Conference on Supply Chain

作者： Karamanli, Anastasia Xanthopoulos, Alexandros Kansizoglou, Ioannis Gasteratos, Antonios Koulouriotis, Dimitrios Democritus Univ Thrace Sch Engn Dept Prod & Management Engn V Sofias 12 Xanthi 67100 Greece Natl Tech Univ Athens Sch Mech Engn Heroon Polytech 9 Athens 15772 Greece

ISBN: (纸本)9783031693434;9783031693441

More and more emphasis is allocated to production job scheduling by the modern organizations in order to preserve their performance indicators stable. This research addresses the dynamic scheduling of jobs in a production system with a single input queue and parallelmachines. The processing times and the times between job arrivals are assumed probabilistic. Jobs belong to different classes and a due date is assigned to each job at the time of its arrival. A machine needs to be setup every time it switches production from some job class to another. In this article, is considered a set of alternative priority rules for dynamic job scheduling using discrete-event simulation. The priority heuristics are compared in respect to several performance metrics in a series of simulation experiments. The behaviour of the scheduling heuristics is assessed under the influence of various parameters. Moreover, are offered managerial insights for scheduling decisions in industry based on the numerical results.

关键词： Dynamic Scheduling Stochastic System Parallel Machines Sequence-Independent Setups Discrete-Event Simulation Priority Rules

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A Service-Based Pipeline for Complex Linguistic Tasks Adopting LLMs and Knowledge Graphs 18th

A Service-Based Pipeline for Complex Linguistic Tasks Adopti...

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18th Symposium and Summer School on Service Oriented Computing

作者： Bianchini, Filippo Calamo, Marco De Luzi, Francesca Macri, Mattia Mecella, Massimo Sapienza Univ Roma Dipartimento Ingn Informat Automat & Gest Antonio Rome Italy

ISBN: (纸本)9783031725777;9783031725784

This paper introduces a microservices-based architecture designed for executing complex linguistic tasks using Large Language Models (LLMs) and Knowledge Graphs (KGs). It has been conceived by focusing on the legal domain, and it integrates Domain-specific KGs and Constraint KGs to address tasks such as law extraction and reasoning. We outline how the pipeline works through a running example involving the extraction of legislative references from legal documents. Furthermore, we discuss a methodology for building KGs from unstructured documents and employing zero-shot prompt engineering techniques to facilitate information extraction. Finally, we present a validation process leveraging the Constraint KG to ensure the coherence and correctness of generated outputs.

关键词： Large Language Models Knowledge Graphs microservices complex linguistic tasks

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Exploring the Improvement of Detection Networks Through Multilevel Filter Modules 5th

Exploring the Improvement of Detection Networks Through Mult...

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5th China Intelligent Robotics Academic Conference

作者： Ju, Moran Mao, Tengkai Ma, Jitong Niu, Buniu Guo, Zuofu Dalian Maritime Univ Sch Informat Sci & Technol Dalian Peoples R China Lanzhou Univ Sch Informat Sci & Engn Lanzhou Peoples R China

ISBN: (纸本)9789819616138;9789819616145

Synthetic aperture radar (SAR) image detection is an advanced remote sensing technology that acquires high-resolution images of the ground surface or target objects by transmitting and receiving radar waves. To date, many experiments and studies related to SAR images have been conducted, however, the detection of targets in SAR images is an important topic. Nevertheless, owing to the imaging principle of SAR images, it is difficult to avoid the introduction of noise in the imaging process, which creates a considerable obstacle to the detection of SAR images. Although many models for SAR image detection exist, most of them are focused on detection network enhancement, few studies can combine filtering and detection to solve the SAR image detection problem. In this paper, we offer a new filtering module, which consists of two filtering mechanisms and a fusion mechanism, which can filter out the low-level interference noise and high-level interference noise respectively, similarly, it can be added to different detectors to enhance the detection performance of the model. The experimental results showthat our proposed MLF filtering module can significantly improve the detection performance of the target detection model. For the common dataset and detection model, compared with the detection without the filtering module, the detection model with the MLF improves the mAP value by 5.34% in the inshore test, 0.36% in the offshore test, and 1.58% in the comprehensive test.

关键词： SAR Filter module Detection model Multitasking

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A Multi-mechanism Collaborative Seagull Optimization Algorithm for Optimizing BP Neural Network Classification Model 5th

A Multi-mechanism Collaborative Seagull Optimization Algorit...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Liu, Xinyu Shao, Peng Jiangxi Agr Univ Sch Comp & Informat Engn Nanchang 330045 Jiangxi Peoples R China

ISBN: (纸本)9789819770007;9789819770014

The seagull optimization algorithm (SOA) is a novel metaheuristic algorithm based on group behavior. However, since SOA suffers from slow convergence, insufficient population diversity, and easily falls into local optimality, a multi-mechanism seagull optimization algorithm (MSOA) that incorporates nonlinear convergence factors, nonlinear weights, evolutionary boundary constraint processing, and Gaussian mutation is proposed as a result. Meanwhile, to verify the signature of the algorithm's optimization-seeking effect, six excellent metaheuristic algorithms are selected for comparison based on 14 different features of the test functions. The experimental results show that MSOA not only has a higher optimization-seeking accuracy but also has a faster convergence speed and stronger robustness. In addition, a BP neural network classification model based on MSOA was designed using the iris dataset as the data source, and according to the experimental statistics, the BP neural network classification model improved by MSOA has practicality and effectiveness.

关键词： BP Neural Network SOA Nonlinear Weights Gaussian Mutation Evolutionary Boundary Constraint

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Underwater Light Mask Transformer for Underwater Image Enhancement 5th

Underwater Light Mask Transformer for Underwater Image Enhan...

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5th China Intelligent Robotics Academic Conference

作者： Guo, Sen Zhang, Weishi Zhao, Xinlong Dalian Maritime Univ Dalian 116026 Peoples R China China Unicom Software Inst Beijing 100176 Peoples R China

ISBN: (纸本)9789819616138;9789819616145

Underwater optical images exhibit color distortion and hazy blurring effects due to complex changes in the underwater environment and the absorption and scattering characteristics of water. The traditional Transformer model pays equal attention to all regions of the processed image, which increases the number of models computations and easily leads to overfitting. To address these challenges, we designed a Transformer method based on an underwater light mask (LMT-UIE). The overall LMT-UIE we designed consists of two parts: the underwater light mask generation module (LGM) and the underwater light maskbased Transformer module (MTF). We designed the LGM to generate an underwater light mask to mitigate the effects of uneven underwater illumination. We design the MTF to utilize the underwater light mask for feature aggregation, which makes the Transformer focus more on the key information and enhances the model's comprehension of the underwater image. Furthermore, we introduce parallel attention in the MTF to improve the Transformer's local modeling ability as well as its generalization ability. The LMT-UIE method not only effectively mitigates the color distortion and blurring problems of underwater images but also has a stronger ability to extract and model key information, which significantly improves the performance improvement of underwater image enhancement tasks.

关键词： Underwater Image Image Enhancement Artificial Intelligence

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Decoding Student Feedback in Engineering Education: A Latent Dirichlet Allocation and PyLDAvis Approach 4th

Decoding Student Feedback in Engineering Education: A Latent...

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4th International Conference on Artificial Intelligence and Knowledge Processing

作者： Alhassan, Ilyas Rege, Manjeet Univ St Thomas Dept Software Engn & Data Sci St Paul MN 55105 USA

ISBN: (纸本)9783031734762;9783031734779

This study investigates the efficacy of Latent Dirichlet Allocation (LDA) and PyLDAvis in analyzing feedback from final-year engineering students. By dissecting responses to strategically selected questions, we identify key themes that include collaboration, innovation, and technical skills development. This research provides actionable insights for educators to refine teaching strategies and curriculum design to align with industry trends and student experiences. Our findings underscore the unique capabilities of LDA and PyLDAvis in capturing and visualizing students' nuanced perspectives, thereby offering a valuable tool for enhancing responsive engineering education.

关键词： Engineering Education Student Feedback LDA Analysis PyLDAvis Visualization Curriculum Development

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