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检索条件"任意字段=2024 IEEE International Conference on Cognitive Computing and Complex Data"

共 1561 条记录，以下是211-220 订阅

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cognitive computing for Natural Language Processing (NLP) and Understanding Medical Imaging Narratives 2

Cognitive Computing for Natural Language Processing (NLP) an...

引用

2nd ieee international conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Makhija, Himanshu Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350369960

In recent years, the intersection of cognitive computing and natural language processing (NLP) has emerged as a pivotal area of research, promising transformative advancements in medical imaging. This paper delves into the integration of cognitive computing techniques with NLP to enhance the interpretation and understanding of complex medical narratives. We introduce a novel framework that leverages the cognitive capabilities of machines to process, analyze, and comprehend vast amounts of unstructured medical text. Our approach employs deep learning architectures and semantic analysis to extract clinically relevant information from radiology reports, patient histories, and other textual data sources. Preliminary results indicate a significant improvement in the accuracy and efficiency of medical image annotations, leading to more precise diagnostic insights. Furthermore, the system demonstrates an adeptness at understanding intricate medical jargons, abbreviations, and context-dependent interpretations. This research not only underscores the potential of cognitive computing in revolutionizing medical imaging but also sets a precedent for its application in other domains requiring nuanced language understanding. © 2023 ieee.

关键词： cognitive computing Medical Imaging Natural Language Processing Radiology Reports Semantic Analysis

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Exploring Advanced Techniques for Enhancing data Privacy and Security in Digital Systems 1

Exploring Advanced Techniques for Enhancing Data Privacy and...

引用

1st ieee international conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Kumari, Rimjhim Sriramulu, Srinivasan Galgotias University Department of Computer Science and Engineering Greater Noida India Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350356816

As digital interactions proliferate, the imperative to fortify data privacy and security becomes paramount. This paper explores advanced techniques - encryption algorithms, biometric authentication, machine learning for anomaly detection, and blockchain technology - as integral components of a comprehensive strategy for safeguarding digital systems. The research reveals that a holistic approach, synthesizing these techniques, offers robust defense against evolving cyber threats. Findings underscore the significance of user-centric security measures, continuous adaptation to emerging threats, and the ethical considerations inherent in technological advancements. The contribution of this research lies in providing practical insights for practitioners and researchers, shaping a nuanced understanding of the dynamic landscape of data privacy and security. Through actionable recommendations, the paper empowers stakeholders to navigate the intricate challenges and opportunities presented by the digital age, ensuring the resilience and trustworthiness of digital systems. The proposed paper talked about how encryption algorithms, biometric authentication, machine learning for anomaly detection and blockchain work. The paper equips stakeholders with tangible recommendations to skillfully navigate the complex landscape of the digital era, promoting the durability and dependability of digital systems. © 2024 ieee.

关键词： Differential privacy

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Security Analysis of Accounting Computerized Information System based on data Mining and Neural Networks 3

Security Analysis of Accounting Computerized Information Sys...

引用

3rd ieee international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Wang, Yan Xi 'An Mingde Institute Of Technology Shaanxi Xi'an China

ISBN: (纸本)9798350318609

The security issue of accounting by electronic data processing information systems is a complex system engineering. How to extract useful data from the massive amount of accounting by electronic data processing information and provide decision-making basis for enterprise decision-makers is a key issue. The article is based on data mining and neural network technology, and studies a security analysis method for accounting by electronic data processing information systems based on data mining and neural networks. This method extracts useful information from data and establishes corresponding models through neural networks to identify key factors that affect the security of accounting by electronic data processing information systems. The results show that the average reliability of the accounting computerization information system based on data mining is 84.38%. The average security of the accounting computerization information system based on neural networks is 94.86%. This article combines the two to create a better accounting by electronic data processing information system. © 2024 ieee.

关键词： data mining

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Subsynchronous Oscillation Suppression Strategy for Grid-Connected Photovoltaic Systems Based on data-Driven Predictive Control

Subsynchronous Oscillation Suppression Strategy for Grid-Con...

引用

2024 international conference on Communications Control and computing Technologies for Smart Grids

作者： Wang, Zihan Cao, Junqi Wang, Yenning Zhang, Xiaonan Li, Gengyin Xn, Yardmi Zhang, Le Xia, Shiwei North China Elect Power Univ Slate Key Lab Alternate Elect Power Syst Renewa Beijing Peoples R China

ISBN: (纸本)9798350318562;9798350318555

With the increasing installed capacity of photovoltaic (PV) in China, there is a risk of subsynchronous oscillations (SSOs) in grid-connected PV systems. As for the grid-connected PV systems, the high complexity of the dynamic behavior and the difficulty of obtaining mathematical models have brought new challenges to the stability control of power systems. To enhance the stability of the modern power systems, this paper proposes a data-driven SSO suppression strategy for grid-connected PV systems. This strategy leverages data-driven predictive control to mitigate SSOs by constructing suitable objective functions and constraints. Furthermore, it employs the rolling optimization and feedback correction in the predictive control to determine the optimal input sequences for grid-connected PV systems, ensuring that the system's state converges to the desired steady state. The data-driven strategy proposed in this paper can be used as a bridge between the measurement data and the dynamic characteristics of the complex power system, which greatly enhances the stability and robustness of the SSO suppression. Finally, the effectiveness and robustness of the suppression strategy are verified in various conditions through MATLAB/Simulink simulation.

关键词： Subsynchronous oscillation suppression data-driven predictive control grid-connected photovoltaic system

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Deep Learning Application in PQRS Segment Detection in ECG data

Deep Learning Application in PQRS Segment Detection in ECG D...

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2024 international conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Pandey, Amit Kumar Gupta, C.L.P. Bansal Institute of Engineering and Technology Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350378092

The electrocardiogram is a vital piece of information about a person's health that provides measurements of changes in the cardiovascular system. When using complex software modules to diagnose diseases, the clinical ECG signal is always tainted by power line interference and electromagnetic fields, leading to inaccurate ***, it's critical to reduce these ECG data acquisition recording inaccuracies in order to perform precise clinical analysis. We are working on an algorithm that will split the data array of three successive ECG signals into three equivalents. The Empirical Mode After decomposing the data matrix and running it through the Independent Component Generation algorithm, we will identify and remove any noisy components using deep learning scheme. To obtain an error-free result of ECG data, we will then apply reverse ICA and EMD features for deep learning. © 2024 ieee.

关键词： Deep learning

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Uncovering the Authentic RF Fingerprint: Exploiting Random Window Slicing and complex-Valued Network

Uncovering the Authentic RF Fingerprint: Exploiting Random W...

引用

international Joint conference on Neural Networks (IJCNN)

作者： Zhang, Zhuo Wang, Yiran Bai, Jing Xiao, Zhu Zhou, Huaji Xidian Univ Sch Artificial Intelligence Xian Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China Natl Key Lab Electromagnet Space Secur Jiaxing Peoples R China

ISBN: (纸本)9798350359329;9798350359312

The Specific Emitter Identification (SEI) technology has broad application prospects in the fields of the Internet of Things and cognitive communication and serves as an effective means for device authentication. Most existing SEI methods based on deep learning primarily operate in the real number domain. However, complex numbers naturally represent radio frequency signals, making complex-valued neural network(CVNN) a superior choice for signal representation. To address the limitations of traditional real-valued neural network methods, such as low recognition accuracy and the requirement for a large number of training samples, we propose an SEI method based on CVNN to learn the true RF fingerprint features. In the data preprocessing stage, we employ a signal slicing strategy with a random window step size to enhance data randomness and improve the model's generalization performance. Given the complex-valued nature of signals, we employ a complex-valued convolutional neural network for feature extraction. Subsequently, we design a feature fusion module based on the complex-valued attention mechanism to eliminate redundant features while preserving radio frequency fingerprint features. Considering the high similarity between emitter signals, we employed a joint loss function based on metric learning that promotes intra-class aggregation and interclass separation. The experimental results demonstrate that the proposed method uncovers the authentic RF fingerprint with high accuracy and robustness under limited training sample conditions.

关键词： Specific emitter identification data preprocessing complex-valued network complex-valued attention mechanism

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Enabling Provenance Tracking in Workflow Management Systems

Enabling Provenance Tracking in Workflow Management Systems

引用

2024 ieee international conference on Big data, Bigdata 2024

作者： Sacco, Ludovica Sopranzetti, Carolina Fiore, Sandro University of Trento Trento Italy

ISBN: (纸本)9798350362480

Provenance in scientific research involves documenting the history of data, focusing on the changes it undergoes and the relationships between various data points throughout its entire lifecycle. By capturing detailed information on how data is processed and transformed, provenance enables researchers to trace, validate and ensure the integrity of their results. This process becomes particularly crucial in complex workflows and high-performance computing environments, where managing intricate tasks and data relationships is essential for maintaining scientific rigor and fostering collaboration. This paper highlights the importance of supporting data reliability and traceability of scientific workflows, providing valuable insights and tools to tracking provenance in Workflow Management Systems. © 2024 ieee.

关键词： data integrity

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cognitive data Underwriting

Cognitive Data Underwriting

引用

2024 ieee international conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Patil, Sonali Chandrashekhar Das, Sudeep Patil, Vaishnavi Patil, Atharva Yadav, Shubham Dr. D.Y.Patil Institute of Technology Department of Information Technology Pimpri Pune India

ISBN: (纸本)9798331539948

The automation of underwriting processes in the insurance industry could significantly enhance operational efficiency by reducing the manual evaluation of risk factors. This paper presented a Natural Language Processing (NLP)based system designed to streamline the underwriting workflow. The system automated key steps such as converting PDF documents into text, pre-processing documents, and selectively extracting relevant data using custom corpora. A classification module was implemented to categorize extracted information into predefined risk classes, optimizing the evaluation process. Additionally, a summarization component condensed lengthy Attending Physician Statements (APS) into concise summaries, enabling quicker and more informed decision-making. The proposed system demonstrated its potential to simplify underwriting tasks, transforming complex documents into actionable insights for risk assessment. © 2024 ieee.

关键词： Risk assessment

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GSDI: Spatio-Temporal Contrastive Learning for Geo-Sensory data Inference

GSDI: Spatio-Temporal Contrastive Learning for Geo-Sensory D...

引用

international Joint conference on Neural Networks (IJCNN)

作者： Ke, Songyu Liang, Yuxuan Yi, Xiuwen Zhang, Junbo Zheng, Yu Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China JD Tech JD Intelligent Cities Res & JD iCity Beijing 100176 Peoples R China Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China

ISBN: (纸本)9798350359329;9798350359312

To keep track of the current state of modern cities, various sensors are deployed throughout the cities to collect geo-sensory data. These data are used for intelligent applications, such as predicting pollution levels and issuing traffic warnings. However, there are a limited number of sensors of a specific type in the city, resulting in coarse-grained geo-sensory data. To obtain more detailed geo-sensory data and improve the quality of service for applications, it is necessary to infer the data at locations without sensors. This task is challenging due to two main factors: 1) only a few labeled locations with sensors, making them rare and sparse;2) the spatio-temporal correlations are complex and dynamic. To overcome the above issues, we propose a novel method based on spatio-temporal contrastive learning for geo-sensory data inference (GSDI). The proposed method consists of two major parts, i.e., spatio-temporal data augmentations and a hybrid spatio-temporal representation network. We propose several spatio-temporal data augmentation methods to generate positive samples for learning robust representations via contrastive learning. We also design a hybrid spatio-temporal representation network to efficiently learn spatio-temporal representations for geo-sensory data inference. Furthermore, experiments on the real-world dataset show the advantages of our proposed methods. Our method achieves at most 5.26% improvement at MAE.

关键词： Urban computing Spatio-Temporal data Contrastive Learning

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Assessing data Quality Maturity Models: Enhancing Organizational Readiness and Improvement

Assessing Data Quality Maturity Models: Enhancing Organizati...

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2024 ieee international conference on computing, ICOCO 2024

作者： Hendrawan, Fadhil Rozi Febriyani, Widia Information System School of Industrial Engineering Telkom University West Java Bandung Indonesia

ISBN: (纸本)9798331530303

data quality is crucial in supporting accurate decision-making in today's digital era. Key attributes such as data completeness, accuracy, consistency, and relevance must be maintained to ensure organizations across various sectors, including finance, healthcare, and education, can optimally utilize data. However, data quality issues such as incompleteness, inaccuracy, and inconsistency often pose significant challenges to accurate decision-making. Invalid or irrelevant data can lead to decision-making errors, ultimately harming business performance and strategy. If data quality is not maintained, decisions based on such data risk being inaccurate and ineffective. In this study, a data quality capability model is proposed and evaluated by six experts, with an average score of 3.7 and a Content Validity Ratio (CVR) of 1, indicating that the model is valid and relevant for implementation. The Fleiss’ Kappa value of 0.481 indicates good agreement among the experts, further supporting the model's relevance for use within organizations. Implementing this model is expected to help organizations manage their data more effectively and efficiently, resulting in high-quality data that supports accurate decision-making. The application of this model is also expected to enhance organizational performance and competitiveness in the face of increasingly complex data management challenges. This model can be a foundation for achieving higher data quality standards, allowing organizations to strategically leverage data in innovation development and operational efficiency. ©2024 ieee.

关键词： data quality

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