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

共 1578 条记录，以下是411-420 订阅

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ML-driven Emotion Identification for Feedback Analysis in E-learning Platforms

ML-driven Emotion Identification for Feedback Analysis in E-...

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2024 OPJU international Technology conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Chitteti, Chengamma Kopparam, Runvika Ganesh, B.V.S.S. Sutraya, Sukerthi Kamakshi, V. Jangam, Shiridinath School of Computing Dept. of Data Science Tirupati India Dept. of Csse Tirupati India G. Narayanamma Institute of Technology and Science for Women Dept. of Data Science Hyderabad India

ISBN: (纸本)9798350373783

With an emphasis on the cognitive and emotional effects of e-learning platforms, this study fills a research gap concerning user experience studies. The study suggests a nonintrusive technique to evaluate the psychological impacts of e-learning platforms by considering feedback data of users in the form of videos and analyzes emotions from video and decides the user feedback level. This process automates the understanding of feedback in a short amount of time for anyone, without requiring specific knowledge of the system. Feedback analysis for E-Learning platforms using deep learning methods for facial expression analysis is proposed in this work. Convolutional Neural Networks (CNNs), a category of deep learning, has become increasingly popular for its ability to precisely analyze and comprehend emotions from facial photos. These models may efficiently learn and extract essential features by being trained on large datasets of tagged facial expression photos. This application takes video feedback as input for various E-learning platforms and analyzes emotions and displays the predicted emotions on video. A publicly accessible dataset is used to test the proposed technique, and the results show that it achieves 90.3% accuracy. This indicates that the approach is highly effective in accurately recognizing facial emotions. © 2024 ieee.

关键词： Contrastive Learning

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Single-Sided Auction based Multi-Channel Allocation with Spectrum Reuse in CR Networks 15

Single-Sided Auction based Multi-Channel Allocation with Spe...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Devi, Monisha Sarma, Nityananda Deka, Sanjib K. Girijananda Chowdhury University Computer Science & Engineering Assam Guwahati India Tezpur University Computer Science & Engineering Assam Tezpur India

ISBN: (纸本)9798350370249

cognitive Radio (CR) is an advanced wireless communication technology that intelligently senses its operating environment to identify available licensed spectrum gaps and adaptively operate on these vacant bands, thereby enhancing spectrum utilization efficiently. Allocation of these free bands to unlicensed users is becoming an inherent necessity in order to overcome the issue of adequate spectrum while satisfying their requisite spectrum demands. Hence, with an intent of enhancing radio spectrum usage, this paper proposes a channel allocation method which carries out single-sided auction in CR networks. Channels are sold out at appropriate prices using the single-sided auction to achieve finer spectrum use, along with reuse of the unused spectrum amongst non-interfering users. Further, with the CR limitations, multi-channel allocation can relevantly improve the channel utilization by performing multiple auction rounds with regard to channel availability time. Rate of data transfer over the heterogeneous nature of channels specifies the bidding language. Reuse of idle bands is provided through a group formation approach following which winning bidders decide their payments. Finally, theoretical analysis and simulation results demonstrate the improvements in performance of the proposed model. © 2024 ieee.

关键词： cognitive radio networks heterogeneous channels single-sided auction spectrum utilization

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Implementing Fuzzy Logic in cognitive Sensor Networks for Environmental Monitoring 3

Implementing Fuzzy Logic in Cognitive Sensor Networks for En...

引用

3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Aarif, Mohd Anjum, Afsana Sharma, Tripti Arikrishnan, A. Rao, Vuda Sreenivasa Balakumar, A. Aligarh Muslim University Department of Commerce Uttar Pradesh Aligarh India Jazan University Dept. of Information Technology & Security Jazan Saudi Arabia Rungta College of Engineering & Technology Department of Cse Chhattisgarh India V.R.S College of Engineering and Technology Department of Mathematics Villupuram Arasur India Koneru Lakshmaiah Education Foundation Department of Cse Andhra Pradesh Vaddeswaram India K. Ramakrishnan College of Engineering Department of Ece Trichy Samayapuram India

ISBN: (纸本)9798350369083

cognitive Sensor Networks are sophisticated systems that use adaptive mechanisms, fuzzy logic, fuzzy clustering algorithms, and other cutting-edge technologies to improve environmental monitoring through flexibility, accurate decision-making, and efficient handling of sensor data uncertainties. The goal of this research is to better monitor the environment by integrating fuzzy logic into cognitive sensor networks. Fuzzy Inference Systems (FIS), rule-based decision making, fuzzy logic controllers (FLC), adaptive fuzzy systems, and fuzzy clustering techniques are some of the components of the complete framework suggested to address the issues given by uncertainties and imprecisions inherent in environmental data. By creating language variables and membership functions, the FIS represents the complex interactions between input sensor data and ambient conditions. Fuzzy rules and input sensor data provide the foundation for the well-informed judgements made by FLCs. Fuzzy output is transformed into useful control signals through the use of defuzzification techniques. The system has adaptive mechanisms that allow fuzzy logic parameters to be dynamically adjusted in response to changing environmental conditions. With 89.8% accuracy, 91.2% precision, 88.6% recall, and an F1 score of 89.9%, the suggested system outperforms the current approaches, PN-WSNA and CogLEACH, after much testing and analysis. © 2024 ieee.

关键词： Environmental monitoring

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TEMPORAL INCONSISTENCY-BASED ACTIVE LEARNING 49

TEMPORAL INCONSISTENCY-BASED ACTIVE LEARNING

引用

49th ieee international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wan, Tianjiao Dou, Yutao Xu, Kele Gao, Zijian Ding, Bo Feng, Dawei Wang, Huaimin Natl Univ Def Technol Changsha Peoples R China Key Lab Software Engn Complex Syst Changsha Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Deep supervised learning has demonstrated strong capabilities;however, such progress relies on massive and expensive data annotation. Active Learning (AL) has been introduced to selectively annotate samples, thus reducing the human labeling effort. Previous AL research has focused on employing recently trained models to design sampling strategies, based on uncertainty or representativeness. Drawing inspiration from the issue of model forgetting, we propose a novel AL framework called Temporal Inconsistency-Based Active Learning (TIR-AL). In this framework, multiple snapshots of the models across consecutive cycles are jointly utilized to select samples with higher temporal inconsistency, by computing the proposed self-weighted nuclear norm metric. Furthermore, we introduce a consistency regularization term to mitigate the issue of forgetting. Together, these components make full use of the potential of data and facilitate effective interaction within the AL loop. To demonstrate the efficacy of TIR-AL, we conducted a set of experiments illustrating how our approach outperforms state-of-the-art methods without incurring any additional training costs.

关键词： Active learning Temporal inconsistency Consistency regularization Self-weighted nuclear norm

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English MOOC data Analysis Model Based on Modified Particle Swarm Optimization (PSO) Algorithm 3

English MOOC Data Analysis Model Based on Modified Particle ...

引用

3rd international conference on data Analytics, computing and Artificial Intelligence, ICDACAI 2024

作者： Shi, Xiaojing Lan, Jie College of Foreign Languages Xinjiang University Urumqi Xinjiang830046 China

ISBN: (纸本)9798331519032

With the development of big data and artificial intelligence technology, online education has become one of the hot topics in the current education reform. This study aimed to conduct in-depth analysis of English MOOC systems by changing Particle Swarm Optimization (PSO) algorithms. PSO algorithm is more suitable for processing large-scale and complex English MOOC system data. The improved PSO algorithm was applied to the data analysis of the English MOOC system, including learner behavior, learning outcomes, course design, and other aspects. Through comparative analysis, it was found that the improved PSO algorithm has higher computational efficiency and accuracy when processing English MOOC system data. Through the experiment, it was found that the number of students satisfied with the system was 497, 300 teachers were 279, and 200 English major teachers were 182. These findings have important reference value for optimizing the design and operation of English MOOC systems and improving their teaching effectiveness. © 2024 ieee.

关键词： Particle swarm optimization (PSO)

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Unraveling the complex Interplay between Socioeconomic Status, Air Pollution, and Heart Disease Hospitalizations in an Urban Population 21

Unraveling the Complex Interplay between Socioeconomic Statu...

引用

21st international conference on Electrical Engineering, computing Science and Automatic Control, CCE 2024

作者： Minutti-Martinez, Carlos Center for Research and Innovation in Information and Communication Technologies INFOTEC Aguascalientes Mexico

ISBN: (纸本)9798350377545

Cardiovascular diseases (CVDs) remain the leading cause of global mortality, with significant contributions from ischemic heart disease and stroke. This study explores the multifaceted risk factors for heart disease hospitalizations in Mexico City, focusing on socioeconomic status (SES) and air pollution. Using a dataset of 11,031 hospitalization records from 2015 to 2020, we employ negative binomial regression and Gradient Boosting Machine (GBM) models to analyze the effects of economic and social SES components, as well as air pollution indices, on the frequency and severity of hospitalizations. Our results reveal that both economic and social aspects of SES, alongside exposure to particulate matter and carbon monoxide, significantly impact hospitalization rates for various heart disease categories. The study highlights that socioeconomic disparities and environmental pollution are crucial determinants of cardiovascular health, emphasizing the need for targeted public health policies and interventions in urban settings. By integrating detailed SES indicators and pollution data, our findings provide a nuanced understanding of the complex relationships influencing heart disease hospitalizations. © 2024 ieee.

关键词： Diseases

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Intellectual Property data Trading Through NFTization 2

Intellectual Property Data Trading Through NFTization

引用

2nd international conference on Big data and Privacy computing, BDPC 2024

作者： Ji, Ziyang Zhang, Jie Guan, Steven Man, Ka Lok School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou215123 China

ISBN: (纸本)9798350307597

Intellectual Property (IP) is a special type of data that has broad and high trading demands. Existing blockchain-based IP data trading schemes can promote the IP data trading market by removing the dependence on centralized platforms. However, the problem of trading fairness among sellers and buyers is more challenging compared to centralized approaches. This paper addresses the trading fairness problem by representing the data as Non-Fungible Tokens (NFTs) and separating usage rights and ownership. An NFTized IP data trading system is designed and a two-stage fair trading scheme is proposed. They ensure that buyers need not pay additional money if the IP content is not useful for them, and the sellers will not lose the IP ownership until they receive additional money in the second stage of trading. A prototype for the system is realized, and based on it, a set of experiments are carried out to evaluate the performance. The experimental results show the cost is acceptable. © 2024 ieee.

关键词： Marketplaces

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Research on Damage Effect Classification in Wargame Replay data Based on Deep Neural Network 9

Research on Damage Effect Classification in Wargame Replay D...

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9th international conference on Cloud computing and Big data Analytics, ICCCBDA 2024

作者： Shi, Yang Yang, Yong Chen, Longliang Xu, Lei Officers College of PAP Chengdu China

ISBN: (纸本)9798350373554

In recent years, wargame competitions have become more and more important. However, there is no targeted tools to analyze the massive wargame data, which can be analyzed to increase the probability of winning. In this paper we focus on the analysis of direct aiming attack data in the wargame replay data, which is very important in the wargame confrontation, but there are many factors affecting the damage effect, so the calculation rules are complicated. In this paper we propose a model of deep neural network can obtain a good training effect, which can accurately predict the damage effect in various complex battlefield situations. © 2024 ieee.

关键词： Deep neural networks

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Systematic cognitive computing Framework Application Using Medical Information Processing 10

Systematic Cognitive Computing Framework Application Using M...

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10th ieee Uttar Pradesh Section international conference on Electrical, Electronics and Computer Engineering, UPCON 2023

作者： Kanna, R. Kishore Ambikapathy, A. Al-Hameed, Mazin Riyadh Sumalatha, Ippa Singh, Navdeep Jeruslaem College of Engineering Department of Biomedical Engineering Chennai India Galgotias College of Engineering and Technology Department of of Electrical and Electronics Engineering India Collage of Engineering National University of Science and Technology Dhi Qar Iraq Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350382471

In recent years, the convergence of cognitive computing and natural language processing (NLP) has emerged as a critical field of study, promising significant breakthroughs in medical imaging. This research digs into the integration of cognitive computing approaches with NLP to increase the interpretation and comprehension of complicated medical narratives. We offer a unique framework that harnesses the cognitive capacities of computers to process, analyze, and interpret huge quantities of unstructured medical material. Our technique combines deep learning architectures and semantic analysis to extract therapeutically important information from radiology reports, patient histories, and other textual data sources. Preliminary findings suggest a considerable increase in the accuracy and efficiency of medical picture annotations, leading to more accurate diagnostic insights. Furthermore, the system exhibits an adeptness in understanding sophisticated medical jargons, acronyms, and context-dependent interpretations. This discovery not only emphasizes the promise of cognitive computing in changing medical imaging but also establishes a precedent for its use in other sectors needing complex language interpretation. © 2023 ieee.

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

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When IoT Meet LLMs: Applications and Challenges

When IoT Meet LLMs: Applications and Challenges

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2024 ieee international conference on Big data, Bigdata 2024

作者： Kök, Ibrahim Demirci, Orhan Özdemir, Suat Ankara University Department of Ai and Data Engineering Ankara Turkey Hacettepe University Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350362480

Recent advances in Large Language Models (LLMs) have positively and efficiently transformed workflows in many domains. One such domain with significant potential for LLM integration is the Internet of Things (IoT), where this integration brings new opportunities for improved decision making and system interaction. In this paper, we explore the various roles of LLMs in IoT, with a focus on their reasoning capabilities. We show how LLM-IoT integration can facilitate advanced decision making and contextual understanding in a variety of IoT scenarios. Furthermore, we explore the integration of LLMs with edge, fog, and cloud computing paradigms, and show how this synergy can optimize resource utilization, enhance real-time processing, and provide scalable solutions for complex IoT applications. To the best of our knowledge, this is the first comprehensive study covering IoT-LLM integration between edge, fog, and cloud systems. Additionally, we propose a novel system model for industrial IoT applications that leverages LLM-based collective intelligence to enable predictive maintenance and condition monitoring. Finally, we highlight key challenges and open issues that provide insights for future research in the field of LLM-IoT integration. © 2024 ieee.

关键词： Predictive maintenance

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