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N-ary relation extraction using web-scraped data 1

N-ary relation extraction using web-scraped data

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1st ieee international conference on Advances in Electronics, Communication, computing and intelligent Information systems, ICAECIS 2023

作者： Rao, Sachin K Rns Institute of Technology Department of Information Science and Engineering Bangalore India

ISBN: (纸本)9798350348057

Information Extraction refers to the process of parsing through unstructured data and extracting meaningful information into editable and structured data formats. There are two common sub-tasks of information extraction, which are entity extraction and relation extraction. The purpose of relation extraction is to find semantic links between entities which can either be binary or n-ary, where n is greater than two. The objective of this paper is to identify n-ary relations among arguments by first scraping news articles from Wikipedia and using a Bi-LSTM and Multi-Task Learning (MTL) framework which greatly enhances the accuracy of extraction. © 2023 ieee.

关键词： Long short-term memory

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Enhancing Early Lung Cancer Detection using Deep Learning Techniques 4

Enhancing Early Lung Cancer Detection using Deep Learning Te...

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4th international conference on Emerging systems and intelligent computing, ESIC 2024

作者： Badaya, Harsh Mishra, Shruti Satapathy, Sandeep Kumar Cho, Sung-Bae Mallick, Pradeep Kumar Vellore Institute of Technology Computer Science and Engg Tamil Nadu Chennai India Vellore Institute of Technology Centre for Advanced Data Science Tamil Nadu Chennai India Yonsei University Department of Computer Science 50 Yonsei-ro Sudaemoon-gu Seoul03722 Korea Republic of Kiit University School of Computer Engineeing Odisha India

ISBN: (纸本)9798350349856

Lung cancer is one of the most deadly and persistent diseases in the world today, making early identification of this disease crucial. This research focuses on the integration of cutting-edge deep learning technologies, namely Convolutional Neural Networks (CNNs), with modern medical insights to improve lung tumour detection using plain chest X-rays (CXR). Using a novel technique to transfer learning, we optimise our model's effectiveness for better diagnostic results in the early identification of lung cancer. Our suggested model, which combines ResNet50, EfficientNetB0, and DenseNet121, achieves outstanding accuracy (97.41%) while minimising loss (0.0803), exceeding individual models. This unique model combines the characteristics of many architectures to provide improved feature extraction and sophisticated pattern identification in medical imaging. Beyond a literature review, our study adds a real answer that has the potential to revolutionise early detection, giving a powerful weapon for combatting this lethal illness. © 2024 ieee.

关键词： Convolutional neural networks

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Enabling intelligent Observability Volume Management 17

Enabling Intelligent Observability Volume Management

引用

17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Naik, Priyanka Goel, Seep Raichstein, Eran Meth, Kalman Govindarajan, Kavya Chakraborty, Aishwariya Govindarajan, Chander IBM Research Bangalore India IBM Research Haifa Israel

ISBN: (纸本)9798331531195

In the era of centralized clouds, multi-cloud, and edge computing, adaptable observability is essential to efficiently manage the growing volume of metrics data. Observability Volume Manager (OVM) addresses the complexities of dynamic observability data management by offering tailored processing capabilities that ensures efficient and effective observability across diverse infrastructures. This demo highlights OVM's ability to perform dynamic transformations for fine-grained control over observability data collection, while significantly reducing the volume of data collected, processed, and stored. These optimizations drive substantial cost savings and position OVM as an essential solution for cost-efficient observability. © 2025 ieee.

关键词： Distributed cloud

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Motion Intent Recognition Algorithm for Lower Limb Rehabilitation Robots Based on Dual-Physics Sensor Feature Fusion

Motion Intent Recognition Algorithm for Lower Limb Rehabilit...

引用

2024 ieee international conference on Cognitive computing and Complex data

作者： Lu, Haiyu Zhao, Peng Ma, Fengling Ma, Junhao Wu, Xiangning Gao, Xueshan Beibu Gulf Univ Sch Mech & Shipbldg Ocean Engn Qinzhou Peoples R China Beijing Inst Technol Sch Mechatron Engn Beijing Peoples R China Minist Civil Affairs Key Lab Intelligent Control & Rehabil Technol Beijing Peoples R China Guangxi Res Inst Mech Ind CO LTD Nanning Peoples R China

ISBN: (纸本)9798350352900;9798350352894

Existing motion intent recognition systems in lower limb rehabilitation robots primarily rely on the fusion of multiple sensor features. Such systems capture the motion characteristics of healthy volunteers during specific m ovements a nd t hen process the data using machine learning algorithms to accurately recognize human motion events, such as forward and backward movements. We address the complexity and inaccuracies of current intent recognition systems by synthesizing feedback from rehabilitation physicians and patients and adopting modular design principles to develop an integrated human motion intent recognition system for lower limb rehabilitation robots. The system utilizes dual physical sensors to collect data on the movement characteristics of the patient's waist, abdomen, and shoulders, which are then classified using the Transformer-LSTM algorithm. The dataset employed for training and testing the algorithm was gathered from a tertiary care hospital, focusing on the movement characteristics of patients with functional hemiplegia of the lower extremities. Clinical trial results demonstrated that the Transformer-LSTM algorithm achieved an average classification accuracy of 97.54% in recognizing human lower limb movement events, compared to 87.48% with the LSTM algorithm. This lower limb rehabilitation robot also significantly enhances patient motivation and comfort during training.

关键词： Human lower limb motion intention recognition Dual Physical Quantity Sensor Transformer-LSTM Algorithm

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Multi-Agent Decision Making System based on Membrane computing 11

Multi-Agent Decision Making System based on Membrane Computi...

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11th ieee international conference on intelligent data acquisition and advanced computing systems - Technology and Applications (IDAACS)

作者： Munteanu, Silvia Sudacevschi, Viorica Ababii, Victor Borozan, Olesea Ababii, Constantin Lasco, Victor Tech Univ Moldova Kishinev Moldova ICG Engn LTD Kishinev Moldova

ISBN: (纸本)9781665426053

This paper presents certain results obtained in the domain of applying Membrane computing models in the modeling and designing of Multi-Agent systems for decision making. The presented results highlight the method of presenting the topology of Multi-Agent systems and the mode of its formal description in order to allow their automatic implementation in Software products or Hardware architectures. The JSON format, which allows the structuring of code according to the topology of Membrane computing model, is used for the formal description of Membrane computing models. The functioning model of living cells is at the basis of Membrane computing. A cell is associated with a computing system that contains input/output ports, the knowledge base consisting of the set of data and Methods of processing them, and a processor.

关键词： agent multi-agent systems decision making membrane computing artificial intelligence collaborative systems knowledge

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Efficient Fault Detection in Renewable Energy systems through IoT and Isolation Forest Algorithm

Efficient Fault Detection in Renewable Energy Systems throug...

引用

2023 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Singh Thakur, Virendra Raman, Ramakrishnan Symbiosis Law School Maharashtra Pune India Maharashtra Pune India

ISBN: (纸本)9798350319200

As renewable energy sources are becoming more integrated into the power grid, ensuring system reliability is crucial. A unique defect detection method for renewable energy systems leveraging the Internet of Things (IoT) and the Isolation Forest algorithm is presented in this research. The integration of IoT devices provides real-time monitoring and data collection from solar panels, wind turbines, and energy storage units. The machine learning Isolation Forest method effectively finds data abnormalities and defects. The Isolation Forest algorithm can correctly detect problems and abnormalities in renewable energy systems, enabling early maintenance and repair. This proactive defect detection technology improves renewable energy's reliability and efficiency, minimizing downtime and power grid stability. IoT technology and the Isolation Forest algorithm improve problem detection and system performance and creating a more sustainable and cost-effective energy-producing infrastructure. It shows that advanced data analytics and machine learning may make renewable energy systems more reliable and appealing as clean energy sources. © 2023 ieee.

关键词： Anomaly Detection Energy Reliability Fault Detection Proactive Maintenance Renewable energy systems

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Harnessing Quantum systems for Simulating Complex Physical Phenomena 2

Harnessing Quantum Systems for Simulating Complex Physical P...

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2nd ieee international conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2024

作者： Senthilkumar, C. Kongkham, Dinokumar Beenarani, B.B. Nirmala, P. Kumaresan, S. Joshua Saveetha Institute of Medical and Technical Sciences Electronics and Communication Engineering Saveetha School of Engineering Tamil Nadu Chennai India Saveetha School of Engineering Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350306415

This article describes how quantum systems can be used to replicate complex physical occurrences, with emphasis on the use of new quantum algorithms and special data sets. First, let's talk about how quantum computers can handle big and complex data sets that are too hard for classical computers. The focus here is on the use of quantum computing methods. The main focus of our school assignment is on developing and executing quantum algorithms specifically designed to analyze and evaluate these datasets. These quantum algorithm allows us to model a complex system. Explain the basic of tool that can be used to quantum material properties. Instructions: Change the word 'formulas' into another Word and rephrase the sentence accordingly. The study proposes using quantum algorithms to handle large data collections. We explain how to transform classical data to quantum representation for clearer comprehension. Also illustrated is how this encoding may yield simulated outcomes. This approach ensures precise and reliable simulations. Unlike ordinary data storage and correction, quantum computing presents various problem-solving obstacles. To remove these challenges, the simulation is made painstakingly precise and dependable. Case studies may show how quantum systems can simulate complicated situations like astronomical object motions and subatomic particle activity under harsh conditions. These case studies show that quantum simulation can address issues traditional approaches can't. Our findings might affect future study, and quantum computing could transform our knowledge of complex physical events. We also discuss quantum computing and coding's pros and cons, encouraging creative thinking in this fascinating topic. © 2024 ieee.

关键词： Complex Physical Phenomena High-Performance Quantum Quantum Error Correction Quantum Simulation

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Modeling Operational Profile for AI systems: A Case Study on UAV systems 4

Modeling Operational Profile for AI Systems: A Case Study on...

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4th international conference on intelligent computing and Human-Computer Interaction, ICHCI 2023

作者： Pan, Cong Gao, Yan Gu, Ai China Electronics Reliability and Environmental Testing Institute Guangzhou511370 China Key Laboratory of General Quality Technology and Application for Intelligent Manufacturing Equipment China

ISBN: (纸本)9798350343113

Operational profile (OP) and reliability testing is vital to ensure the reliability of artificial intelligence (AI) systems. This paper presents the AIOP (AI-oriented Operational Profile) method, which enhances reliability testing for AI systems, by incorporating environmental and data profiles to comprehensively represent their internal functioning and interaction with the external environment. Through the implementation of the AIOP framework and the introduction of feature-oriented profile modeling for environmental and data profiles, this study offers valuable insights for improving system reliability and reducing potential risks in the development and testing of AI systems across various domains. A case study on the UAV system, DroneResponse, demonstrates the effectiveness of the AIOP method. © 2023 ieee.

关键词： Unmanned aerial vehicles (UAV)

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Proceedings of the international conference on Cloud computing Technology and Science, CloudCom

Proceedings of the International Conference on Cloud Computi...

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15th ieee international conference on Cloud computing Technology and Science, CloudCom 2024

ISBN: (纸本)9798331507589

The proceedings contain 22 papers. The topics discussed include: sampling in cloud benchmarking: a critical review and methodological guidelines;enhancing machine learning performance in dynamic cloud environments with auto-adaptive models;optimal distribution of ML models over edge for applications with high input frequency;Ataru: a lightweight VMM+Runtime for low latency serverless functions;blockchain-based framework for stock market operations: IPOs, trading, and dividend distribution;evaluating fine-tuned BERT-based language models for web API recommendation;enhancing security in EV charging systems: a hybrid detection and mitigation approach;and SW forecaster: an intelligent data-driven approach for water usage demand forecasting.

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Multi-source Analysis of AI Vulnerabilities: Methodology and Algorithms of data Collection

Multi-source Analysis of AI Vulnerabilities: Methodology and...

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ieee international Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications

作者： Oleksii Neretin Vyacheslav Kharchenko Herman Fesenko National aerospace university “KhAI” Kharkiv Ukraine

The initial stage of assessing the cybersecurity of artificial intelligence (AI) systems and tools for cyber-physical systems is the collecting, processing and integrating of vulnerability data from relevant and reliable information sources using Big data tools. The article provides a classification of sources of information about vulnerabilities of AI systems. The possibilities of automatic scanning of these sources using software scanners are analyzed. The analysis methodology and data structures are proposed, and the necessary set of data for further storing and analyzing is selected. Algorithms for direct collection of vulnerability data from various sources are developed. The directions of further research on the development of algorithms and software tools for aggregation, filtering, processing and integration of multisource data on vulnerabilities of AI systems are substantiated.

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