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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11-20 订阅

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cognitive Observation of Multidimensional Stochastic Fractal Structures of complex Systems as a Tool of XAI 27

Cognitive Observation of Multidimensional Stochastic Fractal...

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27th international conference on Soft computing and Measurements, SCM 2024

作者： Gorokhov, Vladimir L. Brusakova, Irina A. Shirokov, Stanislav I. Saint Petersburg Electrotechnical University 'LETI' St. Petersburg Russia St. Petersburg Branch of The Special Astrophysical Observatory of The Russian Academy of Sciences St. Petersburg Russia

ISBN: (纸本)9798350363708

The methods and means of cognitive dynamic projection of fractal stochastic structures of complex systems are considered. These tools can be used as an explanatory tool for interpreting the results of artificial intelligence (AI) systems working on fractal multidimensional data. In addition to the explanatory effect, the proposed methods can provide quantitative estimates of multidimensional statistical relationships in terms of non-additive statistics (such as: Rainier or Tsalis entropy, conditional concentration of the fractal process, spectrum of fractal dimensions) inherent in both the ideology of multifractal analysis and the ideology of deep learning. © 2024 ieee.

关键词： Fractal dimension

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cognitive computing's Role in Shaping the Future Landscape of Healthcare

Cognitive Computing's Role in Shaping the Future Landscape o...

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2024 ieee international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2024

作者： Jain, Anunay Jain, Neelesh Verma, Ashish Jain, Anupama Shri G. S. Institute of Technology Department of Information Technology Indore India Trinity Institute of Technology and Research Department of Computer Science Engineering Bhopal India Dr. Harisingh Gour Central University School of Mathematical & Physical Sciences Madhya Pradesh Sagar India Sagar Institute of Research & Technology Department of Computer Science Engineering Bhopal India

ISBN: (纸本)9798350348460

As the healthcare industry undergoes a paradigm shift towards data-driven and patient-centric models, cognitive computing emerges as a transformative force. This research paper delves into the profound impact of cognitive computing on the future landscape of healthcare. Through an extensive review of literature, case studies and emerging trends, this paper explores the applications, benefits, challenges, and ethical considerations associated with integrating cognitive computing into healthcare practices. Drawing on interdisciplinary perspectives, we examine how cognitive computing enhances diagnostic precision, optimizes treatment plans, facilitates personalized medicine, and improves overall healthcare delivery. The paper also scrutinizes the ethical implications of leveraging cognitive computing in healthcare, considering issues such as data privacy, bias, and the human-machine interface. By synthesizing existing knowledge and proposing future research directions, this paper contributes to the ongoing discourse on the role of cognitive computing in shaping the trajectory of healthcare. © 2024 ieee.

关键词： cognitive systems Digital healthcare Healthcare system

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Enhancing Knowledge Selection with data Processing Based on Multiple Turns of Dialog in Knowledge-Grounded Open-Domain Conversations

Enhancing Knowledge Selection with Data Processing Based on ...

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international conference on Big data and Smart computing (BigComp)

作者： Joo, Eojin Lee, Do Kyung Youn, Junyoung Sung, Joo-Won Choi, Ho-Jin Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea KT AI2XL Seoul South Korea

ISBN: (纸本)9798350370027;9798350370034

In NLG, recent research in Knowledge-Grounded Text Generation aims to refine sentence specificity and naturalness. When generating texts, considering multiple turns within a conversation is considered important because it allows models to generate sentences that reflect the context of the conversation. Addressing open-domain conversations, determining the optimal conversation history for training knowledge selection models lacks prior exploration. This study aims to improve KGTG models to effectively handle complex utterances by progressively incorporating more turns. This finding offers a foundational direction for boosting knowledge selection models in text generation.

关键词： Knowledge Selection Natural Language Generation Knowledge-Grounded Text Generation Deep Learning

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Transformers and Large Language Models as Used in ETSI ISG Experiential Networked Intelligence 59

Transformers and Large Language Models as Used in ETSI ISG E...

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59th Annual ieee international conference on Communications (ieee ICC)

作者： Forbes, Ray Strassner, John Zeng, Yu Huawei Technol UK ETSI Experiential Networked Intelligence ISG Birmingham W Midlands England Futurewei Technol San Jose CA USA China Telecom ETSI ISG ENI Beijing Peoples R China

ISBN: (纸本)9798350304060;9798350304053

Telecom operators are getting overwhelmed by the multitude of different technologies to use to meet increasingly complex customer goals. This paper discusses the use of Large Language Models and knowledge-based reasoning to create a cognitive network in ETSI's Experiential Networked Intelligence (ENI) group. ENI's cognitive network uses multiple closed control loops to adapt resources and services to user needs, business goals, and environmental conditions. cognitive network can analyze data and information at scale, and use AI algorithms to dynamically optimize behavior based on current and anticipated future conditions. It can also enhance network operations to meet future needs.

关键词： cognitive Networks Large Language Models Semantics

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cognitive Oracles: Provable on-chain assessment through Validity Machine Learning

Cognitive Oracles: Provable on-chain assessment through Vali...

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ieee international conference on Pervasive computing and Communications (PerCom)

作者： Esposito, Marco Bruschi, Francesco Montecalvo, Manuel Sciuto, Donatella Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy Politecn Milan Milan Italy

ISBN: (纸本)9798350304367;9798350304374

Blockchains offer a mechanism for executing code in a credible, transparent and uncensorable way. These characteristics lend themselves to the implementation of applications that guarantee different actors regarding the credible execution of mechanisms, including insurance and impartial certification. A significant challenge in this domain is enabling smart contracts to reliably access real-world state information. Our research centers on scenarios requiring smart contracts to evaluate complex or ambiguous conditions, such as identifying specific elements within an image. The proposed solution introduces an oracle utilizing machine learning classifiers to aid smart contracts in assessing such intricate predicates. We incorporate SNARK arguments to facilitate efficient and secure evaluation of these classifiers, and introduce a jury-based system to address classifier inaccuracies. We evaluate the proposed architecture with respect to an example application.

关键词： blockchain oracle machine learning validity

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IoT-Enabled Smart Agriculture System Using cognitive computing 3

IoT-Enabled Smart Agriculture System Using Cognitive Computi...

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international conference on Advances in computing, Communication and Applied Informatics (ACCAI)

作者： Rajababu, Durgam Boddepalli, Eswararao Survase, Rajesh Bhaskar Pal, Arunabha Dandagala, Sreenivasulu Chakravarthi, M. Kalyan SR Univ Dept EEE Sch Engn Warangal Telangana India Virginia Tech Bradley Dept ECE Blacksburg VA USA Savitribai Phule Pune Univ ES Divekar Coll Varvand Dept Geog Pune Maharashtra India Centurion Univ Technol & Management MS Swaminathan Sch Agr Dept Soil Sci Parlakhemundi 761211 Odisha India Annamacharya Univ Annamacharya Inst Technol & Sci Dept Civil Engn Rajampet 516126 Andhra Pradesh India VIT AP Univ Sch Elect Engn Amaravathi 522237 Andhra Pradesh India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

Together, the Internet of Things (IoT) and cognitive computing have revolutionized the agricultural industry by enabling the implementation of intelligent husbandry systems. Investigating the creation and use of an Internet of Things-enabled smart husbandry system that leverages cognitive computing techniques is the aim of this study. The system includes detectors to gather data on environmental factors like crop health, soil temperature, and humidity in real-time. After that, a centralized platform receives these data. This data is processed by algorithms used in cognitive computing to give growers useful insights like optimal irrigation timing and early detection of crop issues. The application of this method raises agricultural operations' productivity and enhances decision-making processes' effectiveness. This study covers the following topics: the system's armature;the choice and incorporation of Internet of Things bias;the use of cognitive computing models;and the assessment of system performance in actual agricultural scripts. The results show how cognitive computing and the Internet of Things (IoT) may revolutionize traditional husbandry methods into data-driven, intelligent husbandry systems.

关键词： IoT smart agriculture cognitive computing sensor networks data analytics precision farming

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A Review of Unsupervised Anomaly Detection Techniques for Health Insurance Fraud 10

A Review of Unsupervised Anomaly Detection Techniques for He...

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ieee 10th international conference on Big data computing Service and Machine Learning Applications (ieee BigdataService)

作者： Leevy, Joffrey L. Salekshahrezaee, Zahra Khoshgoftaar, Taghi M. Florida Atlantic Univ Boca Raton FL 33431 USA

ISBN: (纸本)9798350366396;9798350366389

This paper surveys the latest unsupervised anomaly detection methodologies applied to health insurance fraud, covering studies from 2017 to 2024. Our review includes a variety of machine-learning approaches, evaluating their effectiveness in handling complex, high-dimensional, and imbalanced healthcare datasets. Techniques such as Isolation Forest, Bayesian hierarchical models, and deep autoencoders demonstrate superior performance compared to traditional methods. Despite significant advancements, gaps remain with regard to transfer learning, interpretability and explainability of models, and the development of real-time, incremental learning algorithms. Future research should focus on these areas to enhance fraud detection accuracy and trust. Our work aims to provide a valuable resource for researchers and practitioners, supporting the development of more robust and adaptive fraud detection systems to protect healthcare integrity and reduce financial losses.

关键词： anomaly detection health insurance fraud unsupervised learning machine learning big data analytics

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UASOS: An Experimental Environment For Assessing Mental Fatigue & cognitive Flexibility During Drone Operations 24

UASOS: An Experimental Environment For Assessing Mental Fati...

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19th Annual ACM/ieee international conference on Human-Robot Interaction (HRI)

作者： Hinss, Marcel F. Vitale, Vincenzo Maria Nhat Tien Phan Roy, Raphaelle N. Brock, Anke M. Univ Toulouse ONERA Federat ENAC ISAE SUPAERO Toulouse France

ISBN: (数字)9798400703225

ISBN: (纸本)9798400703225

Mental fatigue from continuous operations without breaks represents a safety issue for military drone operations, as these systems are complex and operate during long shifts. Military operations are hard to study due to their sensitive nature. The open-access program UASOS serves as a testbed to examine the efects of mental fatigue in an ecologically valid environment. UASOS recreates fundamental aspects of military drone operations in a controllable environment that is easy enough for novices to understand but demanding enough to elicit mental fatigue. Participants alternate between navigating a drone-using either a trackball/mouse or a joystick-and searching for visual targets. The protocol is set up in a way that taxes the cognitive fexibility of participants by constantly requiring them to alternate between tasks. In addition, several parameters such as difculty, duration, questionnaires, training phases, and more can be adapted. The task also allows for synchronization with physiological data using LabStreamingLayer. Implemented in python, the code is set up to be easily installed.

关键词： Physiological computing human-drone interaction adaptive interfaces

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TRRRACED - Towards Reproducible, Replicable and Reusable Affective computing Experiments and data 15

TRRRACED - Towards Reproducible, Replicable and Reusable Aff...

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15th ieee international conference on cognitive Infocommunications, CogInfoCom 2024

作者： Schreiber, Paul Grensing, Florian Maleshkova, Maria Department of Data Engineering Helmut-Schmidt Universität Hamburg Germany

ISBN: (纸本)9798350378245

We present TRRRACED - a framework that aims to support Reproducible, Replicable and Reusable Affective computing Experiments and data. To the best of our knowledge, TRRRACED is the first framework aiming to provide standardised guidelines that facilitate structural unity of experiments and data in the domain of AC research. To this end, state-of-the-art studies considering physiological signals in relation to affective states were examined regarding (i) stimuli used, (ii) experimental setup and (iii) self-reports. Based on this analysis we employ reproducibility, replicability, and reusability as criteria to develop our framework. We propose a protocol for conducting experiments to enhance replicability, guidelines for annotating data, and a sample dataset. © 2024 ieee.

关键词： data assimilation

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Interpreting Temporal Dynamics in Distributed Fuzzy cognitive Maps for Time Series Prediction 11

Interpreting Temporal Dynamics in Distributed Fuzzy Cognitiv...

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11th ieee international conference on Advances in computing and Communications, ICACC 2024

作者： Haritha, K. Judy, M.V. Cochin University of Science and Technology Department of Computer Applications Ernakulam India

ISBN: (纸本)9798350379136

Fuzzy cognitive Maps (FCMs) are powerful tools for the modeling of complex systems, one of which is time series modeling. Although advocated for their interpretability, existing methods measure concept relevance based on static structural features often overlooking the dynamic influences over the time. This paper presents a novel SHapley Additive exPlanations (SHAP) framework based Distributed FCM model. The proposed approach invokes functions to determine the SHAP concept relevance scores for the Distributed Fuzzy cognitive Maps (DFCMs) for time series prediction, marginalizing over each concept and comparing the predicted states to an expected baseline iteratively. This method is interpreted as measuring the time-varying impact of each concept through the dynamic course of the system. The SHAP-based approach improves the interpretability and application of FCMs to the modeling of complex time-course systems. © 2024 ieee.

关键词： Fuzzy cognitive Maps

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