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Pre-processing of the Text of ECU 911 Emergency Calls 12th

Pre-processing of the Text of ECU 911 Emergency Calls

引用

12th Ecuadorian Conference on information and Communication Technologies

作者： Orellana, Marcos Molina Pinos, Pablo Andres Garcia-Montero, Patricio Santiago Zambrano-Martinez, Jorge Luis Univ Azuay Comp Sci Res & Dev Lab LIDI Cuenca Ecuador

ISBN: (纸本)9783031754302;9783031754319

The function of the Integrated Security Service (ECU 911) is to receive emergency calls and coordinate the response through various emergency services. These interactions are recorded and stored as a backup of each call. The present research proposes the pre-processing of the text contained in said calls through the application of selected text formalization techniques, such as the use of dictionaries of idioms, consultation in the Dictionary of the Royal Spanish Academy (RAE), the use of spell checkers and Named Entity Recognition (NER). A bitmap is proposed as an auxiliary tool to facilitate decision-making and correct formalization during the formalization process. Finally, the score obtained by a semantic comparison model is evaluated, in which the informal transcribed text of the calls is compared with the formalized text to determine whether the formalization of text improves the performance of the Artificial Intelligence models The results demonstrated that performance was improved or maintained in 74% of the test cases thanks to the combination of these techniques.

关键词： Text mining Named Entity Recognition Text Formalization Natural Language Processing Emergency calls

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Adaptive Allocation Method of Terminal Container Parameters Based on Evolutionary Game 5th

Adaptive Allocation Method of Terminal Container Parameters ...

引用

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

作者： Wu, Yu Wu, Linfeng Yang, Xiaowei Chen, Shunli Xie, Han State Grid Chongqing Elect Power Co Mkt Serv Ctr Chongqing 400023 Peoples R China Chongqing Guanghui Power Supply Serv Co Ltd Customer Serv Branch Chongqing 400023 Peoples R China

ISBN: (纸本)9789819770038;9789819770045

In order to solve the problems of poor operational stability and high failure rate caused by the increasing number of terminal equipment devices and unreasonable resource allocation, an evolutionary game algorithm is used to construct an adaptive allocation method of terminal container parameters. First, deploy the smart metering terminal architecture and monitor the operating status of the terminal equipment. Subsequently, based on the idea of dominant resource balancing, the container placement order is determined by calculating the dominant resource request ratio of each terminal container, and a coarse-grained resource deployment strategy is obtained. Finally, an evolutionary game algorithm is used for fine-grained resource allocation and deployment to obtain the optimal strategy selection for terminal containers. Experimental results show that the proposed method can effectively improve resource utilization, improve the stable operation control capabilities and refined management level of remote terminal equipment, and ensure the reliable development of various upper-level business applications in marketing.

关键词： terminal container coarse-grained deployment evolutionary game adaptive parameter adjustment resource allocation and deployment

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A Model for Recognizing Mexican Sign Language Using Convolutional Neural Networks

A Model for Recognizing Mexican Sign Language Using Convolut...

引用

4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Marquez, Bogart Yail Castro-Villa, Trinidad Alanis, Arnulfo Bermudez-Jimenez, Eugenia Tecnol Nacl Mexico Campus Tijuana Tijuana Mexico

ISBN: (纸本)9783031834349;9783031834356

Implementing the Mexican Sign Language (LSM) system presents several challenges. One of the primary issues is ensuring the accuracy of the motion capture data, as even slight deviations can lead to misinterpretations. Additionally, the system must handle the vast variability in individual signing styles and regional sign language variations, which requires extensive and diverse training data. In the first phase, the motion capture system must collect data on the gestures executed by LSM users, using cameras to monitor hand and finger movements and sensors that record the position and movements of the forearms and hands. The second phase involves the real-time comparison of an individual's signs in front of a camera with the meanings of the signs stored in the repository. A pattern recognition algorithm, supported by machine learning techniques, identifies specific motion capture system data patterns. In the third phase, the results are evaluated to generate a text translated from sign language to natural language. A successful model promises a significant impact on the hearing-impaired community, facilitating communication and improving access to services. This computational approach has the potential to positively transform the lives of people with hearing disabilities by providing them with a more effective avenue for communication and improving their access to essential services.

关键词： Mexican Sign Language Convolutional Neural Networks Artificial Intelligence Inclusion

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Towards a Human-Centric Industry 5.0: Exploring Team Roles to Improve Human-Machine Collaboration 5th

Towards a Human-Centric Industry 5.0: Exploring Team Roles t...

引用

5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Monopoli, Domenico Caggiano, Mariateresa Semeraro, Concetta Dassisti, Michele Polytech Univ Bari Dept Mech Math & Management DMMM Bari Italy Univ Sharjah Dept Ind & Management Engn Sharjah 27272 U Arab Emirates

ISBN: (纸本)9783031807749;9783031807756

Industry 5.0 aims to enhance the strengths of the current Industry 4.0 paradigm and broaden its scope by emphasizing the human operators within the working environment through harmonious interactions with machines and other human collaborators. One of the three pillars of Industry 5.0 is the redefined role that human operators would assume within rapidly evolving production systems due to significant technological improvements descending from the I4.0 paradigm. This new role is becoming a major organizational and technological challenge to ensure maximum productivity while preserving the health and well-being of the operator. In this context, the primary objective of this study was to investigate the impact of various human-machine team configurations on process performance and worker well-being. To achieve this, a collaborative workstation was designed and implemented to simulate different configurations of collaborative scenarios, each involving distinct roles for humans and machines. Preliminary findings indicate that key factors such as experience and attitude toward technology necessitate different collaborative setups. Specifically, experienced operators prefer supervisory roles within the team, with the machine primarily used for final inspections. In contrast, less experienced operators tend to rely more on the machine's decisions, adopting a more apprentice-like role.

关键词： Industry 5.0 Human-Centricity Human-Machine interaction

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The Evolutional Portfolio Optimization System (EPOS) 8th

The Evolutional Portfolio Optimization System (EPOS)

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2024 World Congress in computer science, computer Engineering and Applied Computing-CSCE

作者： Loukeris, N. Boutalis, Y. Eleftheriadis, I. Arampatzis, A. Livanis, S. Gikas, G. Univ West Attica Athens 12244 Greece Democritus Univ Thrace Xanthi 67100 Greece Univ Macedonia Thessaloniki 54006 Greece

ISBN: (纸本)9783031856273;9783031856280

We introduce a new methodology that incorporates advanced higher moments evaluation in a new approach of the Portfolio Selection problem, supported by effective Computational Intelligence models. The Evolutional Portfolio Optimization System (EPOS) extracts hidden patterns out of the numerous accounting data and financial statements filtering misguiding effects such as noise or fraud, offering an optimal portfolio selection method.

关键词： Isoelastic Utility function Higher moments Recurrent Neural Networks Time-Lag Recurrent Neural Networks Jordan-Elman Neural Networks MLP Hybrids Genetic Algorithms Portfolio Optimization

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Unsupervised Attack Detection on MIL-STD-1553 Bus for Avionic Platforms 2nd

Unsupervised Attack Detection on MIL-STD-1553 Bus for Avioni...

引用

2nd International Conference on information Technologies and Their Applications

作者： Evcil, Mustafa Tok, Zaliha Yuce Sandikkaya, Mehmet Tahir ASELSAN Inc Ankara Turkiye Istanbul Tech Univ Dept Comp Engn Istanbul Turkiye

ISBN: (纸本)9783031734168;9783031734175

Avionic platforms are increasingly reliant on sophisticated onboard software and next-generation communication systems to carry out their operations. However, the expanding connectivity capabilities also introduce security vulnerabilities and the potential for attacks, posing significant challenges to the safety of these systems. In particular, the MIL-STD-1553 military standard, widely used as a serial communication protocol for mission-critical systems, lacks specific design considerations for security. The absence of authentication mechanisms in it allows for the potential impersonation of the Bus Controller (BC) and the injection of fake command words. Such attacks pose significant risks to the safety and integrity of mission-critical systems, highlighting the need for robust security measures. In this paper, we propose an unsupervised machine learning technique for anomaly detection on the MIL-STD-1553 communication bus using real platform data. We introduce a novel voting ensemble technique that combines Nearest Neighbors with thresholding algorithms such as Elliptical Boundary, Median Absolute Deviation, and Z-Score. The evaluation results demonstrate that our solution outperforms existing unsupervised machine learning techniques. The proposed method is very likely to be dependable and useful, as it provides a zero false positive rate.

关键词： MIL-STD-1553 anomaly detection machine learning aviation cybersecurity ensemble learning

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Bridge Model for Individualized Digital NasoAlveolar Molding Using Uniform Cross-Section Elliptic Segment 2nd

Bridge Model for Individualized Digital NasoAlveolar Molding...

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2nd International Conference on information Technologies and Their Applications

作者： Parakarn, Hathaichanok Wangsrimongkol, Buddhathida Eua-Anant, Nawapak Katanyukul, Tatpong Khon Kaen Univ Div Orthodont Fac Dent Khon Kaen Thailand Khon Kaen Univ Dept Comp Engn Fac Engn Khon Kaen Thailand

ISBN: (纸本)9783031734168;9783031734175

Digital orthodontics has emerged as a new paradigm of NasoAlveolar Molding (NAM). NAM is a well adopted adjunctive therapy before undergoing a lip surgery in patients with cleft lip and palate. Its mechanism is to apply differential force through a NAM plate. Conventional NAM uses a single plate, but with multiple adjustments. This causes high burden of care on patients and their families. Employing intra-oral scan and CAD/CAM, digital NAM simplifies NAM procedure and lessens clinical chair side. This also reduces a risk associated to mouth casting and allows plate reproducibility (in case of accidentally broken plates). However, early digital NAM requires design and fabrication of multiple plates throughout the process. A recent study proposes individualized Digital NAM (iDNAM) using biomechanics insight to minimize a number of plates. iDNAM has been shown to be clinically effective. Nonetheless iDNAM in its current state requires a personnel with craniofacial expertise and proficiency in 3D computer modeling. This article presents a pilot study toward an automatic iDNAM-plate design, using ellipse and non-parametric cross-section models. The approach takes advantages of specific data characteristics, allowing low-computation techniques to achieve three-dimension surface generation. The resulting design was assessed to provide a viable bridge model, one of the most challenging parts in plate design. This also supports our hypothesis of the elliptic-segment pattern of an alveolar ridge and shows prospect of our approach in a coveted fully automatic iDNAM-plate design.

关键词： NasoAlveolar Molding iDNAM Alveolar Ridge Approximation Elliptic Model Non-Parametric Model Cross-Section Model Three-Dimension Geometry CAD/CAM in Dentistry Computational science in Health Care

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Assessing the Impact of Prompt Strategies on Text Summarization with Large Language Models 37th

Assessing the Impact of Prompt Strategies on Text Summarizat...

引用

37th International Conference on computer Applications in Industry and Engineering

作者： Onan, Aytug Alhumyani, Hesham Izmir Katip Celebi Univ Fac Engn & Architecture Dept Comp Engn TR-35620 Izmir Turkiye Taif Univ Coll Comp & Informat Technol Dept Comp Engn POB 11099 Taif 21944 Saudi Arabia

ISBN: (纸本)9783031762727;9783031762734

The advent of large language models (LLMs) has significantly advanced the field of text summarization, enabling the generation of coherent and contextually accurate summaries. This paper introduces a comprehensive framework for evaluating the performance of state-of-the-art LLMs in text summarization, with a particular focus on the impact of various prompt strategies, including zero-shot, one-shot, and few-shot learning. Our framework systematically examines how these prompting techniques influence summarization quality across diverse datasets, namely CNN/Daily Mail, XSum, TAC08, and TAC09. To provide a robust evaluation, we employ a range of intrinsic metrics such as ROUGE, BLEU, and BERTScore. These metrics allow us to quantify the quality of the generated summaries in terms of precision, recall, and semantic similarity. We evaluated three prominent LLMs: GPT-3, GPT-4, and LLaMA, each configured to optimize summarization performance under different prompting strategies. Our results reveal significant variations in performance depending on the chosen prompting strategy, highlighting the strengths and limitations of each approach. Furthermore, this study provides insights into the optimal conditions for employing different prompt strategies, offering practical guidelines for researchers and practitioners aiming to leverage LLMs for text summarization tasks. By delivering a thorough comparative analysis, we contribute to the understanding of how to maximize the potential of LLMs in generating high-quality summaries, ultimately advancing the field of natural language processing.

关键词： Large Language Models Text Summarization Prompt Strategies Zero-shot Learning One-shot Learning Few-shot Learning ROUGE BLEU BERTScore

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Stockyard Planning and Optimization Using Intelligent Search 5th

Stockyard Planning and Optimization Using Intelligent Search

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Pissarek, Adele Hofstedt, Petra Loeffler, Sven Brandenburg Tech Univ Cottbus Chair Programming Languages & Compilers Cottbus Germany

ISBN: (纸本)9783031807749;9783031807756

Stockyard planning and optimization in ports is an important logistic task. It describes the calculation of efficient task sequences for unloading of bulk cargo from import vessels, the transportation, the storage and blending of material and for loading export ships. At this, the optimization goal is to minimize the laytimes of the import and export ships by optimized material operation sequences. The paper presents a new approach on stockyard planning by intelligent search methods (greedy, beam, and Monte-Carlo Tree Search) and using domain-specific expert knowledge. The approach is evaluated through a series of tests on the model of a real-world port configuration.

关键词： Stockyard planning and optimization Logistics AI Heuristic search Monte-Carlo tree search

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Deep Learning Algorithms for Colon Cancer Detection: A Comparative Study with Traditional Machine Learning Methods 4th

Deep Learning Algorithms for Colon Cancer Detection: A Compa...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Nabti, Ilhem Kouari, Zakaria Ferradji, Mohamed Abderraouf Merabet, Asma Univ Ferhat Abbes Setif 1 Setif Algeria Univ Larbi Ben Mhidi Oum El Bouaghi LIAOA Lab Oum El Bouaghi Algeria

ISBN: (纸本)9783031821523;9783031821530

This work aims to highlight the distinctions between deep learning models and traditional machine learning methods in colon cancer detection. Based on the LC25000 dataset, we analyze the performance of Support Vector Machine (SVM) and Random Forest (RF) compared to advanced deep learning models such as InceptionV3, VGG19, and EfficientNetV2. Our methodology involves a detailed analysis of each algorithm's Recall, F1 score, Precision, and accuracy. The results of this research show that deep learning algorithms, and EfficientNetV2 in particular, have outperformed traditional machine learning methods. These results highlight the need to enhance colon cancer detection accuracy by adopting deep learning technology in medical imaging diagnosis.

关键词： colon cancer detection deep learning machine learning SVM Random Forest VGG19 InceptionV3 EfficientNetV2

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