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检索条件"任意字段=2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023"

共 711 条记录，以下是71-80 订阅

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An Investigation on Spectrum Mobility Mechanisms in cognitive Network Communication 9

An Investigation on Spectrum Mobility Mechanisms in Cognitiv...

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9th international conference on Advanced computing and Communication Systems, ICACCS 2023

作者： Gayathri, N. Anandakumar, H. Sathya, R. Gowri, S. Sri Eshwar College of Engineering Department of Computer Science and Engineering TamilNadu Coimbatore India Kongunadu College of Engineering and Technology Department of Information Technology TamilNadu Trichy India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science TamilNadu Coimbatore India

ISBN: (纸本)9798350397376

In our increasingly digitized environment, the demand for more effective spectrum usage is becoming more and more prominent. By permitting unauthorized individuals to take advantage of the spectrum in an adaptive way, cognitive radio (CR) is a prospective method to increase spectrum efficiency. Spectrum handover, which ensures that Secondary Users (SUs) can leave the available spectrum that is currently in use and identify a suitable target channel to continue the interrupted service, is a critical component of the development of cognitive Radio Networks (CRN). A complete description and investigation of the current spectrum handoff techniques for CRN are discussed in this paper. Numerous research challenges and constraints are also studied in the development of effective spectrum handoff methods. Also, the comparative study on different spectrum handoff mechanisms is presented to discover the least number of reductions in performance during a spectrum handoff. © 2023 IEEE.

关键词： cognitive radio

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The Evolutionary Logic from DIKW to DIKWP, and then to Platform Economy DIKICRWPIP 25

The Evolutionary Logic from DIKW to DIKWP, and then to Platf...

引用

25th IEEE international conferences on High Performance computing and Communications, 9th international conference on data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Dou, Erxiang Cheng, Ailiu Duan, Yucong Mei, Yingtian Peking University Beijing China School of Computer Science and Technology Hainan University Haikou China

ISBN: (纸本)9798350330014

DIKW can be regarded as the overall model for human social cognition and transformation of the world. In terms of improving cognitive efficiency and improving transformation accuracy, the DIKWP model is an evolution. The difference between any animal lies in its original purpose. The clearer the purpose P, the more significant the results of cognition and transformation, and the greater the progress of human society. This evolution is also reflected in the evolution of information carriers, as exemplified by the prosperity of the platform economy. The new platform economy has improved the completeness of the platform on the basis of information carrier technology, especially in terms of the three commonalities of the platform, namely fairness, dialectics, and openness. The new platform is more credible. The modern enterprise system will transition to a 'platform + company' system, which is conducive to the formation of a unified domestic and international market and emerging value. © 2023 IEEE.

关键词： international trade

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Evolutionary Multi-Objective Neural Architecture Search for Generalized cognitive Diagnosis Models 5

Evolutionary Multi-Objective Neural Architecture Search for ...

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5th international conference on data-Driven Optimization of complex Systems, DOCS 2023

作者： Yang, Shangshang Zhen, Cheng Tian, Ye Ma, Haiping Liu, Yuanchao Zhang, Panpan Zhang, Xingyi School of Artificial Intelligence Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China Institutes of Physical Science and Information Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China Automation for Process Industries Northeastern University State Key Laboratory of Synthetical Shenyang China

ISBN: (纸本)9798350393521

cognitive diagnosis models (CDMs) with high generalization are essential for intelligent education systems to reveal students' knowledge states in multiple datasets. However, existing CDMs' architectures are designed dependent on researcher expertise and experience from observing and summarizing partial students' learning behaviors, which makes handcrafted models not cover all learning behaviors well and thus limits their generalization. To develop generalized CDMs, this paper proposes an evolutionary neural architecture search to design CDMs' architectures effective on multiple datasets automatically. Specifically, we first formulate the search task as a multi-objective optimization problem (MOP), which maximizes model performance on multiple datasets containing learning behaviors as many as possible to ensure model generalization. Then, an expressive search space is devised to contain as many potential architectures as possible, where each architecture is denoted by a unified form, taking three given inputs and integrating them in a linear or no-linear manner for prediction. Finally, an evolutionary algorithm with a tailored deduplication strategy is employed to solve the MOP, where each architecture is further represented by a single-root computation tree for easy optimization. Experiments on multiple datasets demonstrate the generalization and effectiveness of the best architecture searched by the proposed approach, and the searched architecture also holds as good interpretability as handcrafted architectures. © 2023 IEEE.

关键词： Multiobjective optimization

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Performance Optimization of Energy-Harvesting Underlay cognitive Radio Networks Using Reinforcement Learning 19

Performance Optimization of Energy-Harvesting Underlay Cogni...

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19th IEEE international Wireless Communications and Mobile computing (IEEE IWCMC)

作者： Tashman, Deemah H. Cherkaoui, Soumaya Hamouda, Walaa Polytechn Montreal Dept Comp & Software Engn Montreal PQ Canada Concordia Univ Dept Elect & Comp Engn Montreal PQ Canada

ISBN: (纸本)9798350333398

In this paper, a reinforcement learning technique is employed to maximize the performance of a cognitive radio network (CRN). In the presence of primary users (PUs), it is presumed that two secondary users (SUs) access the licensed band within underlay mode. In addition, the SU transmitter is assumed to be an energy-constrained device that requires harvesting energy in order to transmit signals to their intended destination. Therefore, we propose that there are two main sources of energy;the interference of PUs' transmissions and ambient radio frequency (RF) sources. The SU will select whether to gather energy from PUs or only from ambient sources based on a predetermined threshold. The process of energy harvesting from the PUs' messages is accomplished via the time switching approach. In addition, based on a deep Q-network (DQN) approach, the SU transmitter determines whether to collect energy or transmit messages during each time slot as well as selects the suitable transmission power in order to maximize its average data rate. Our approach outperforms a baseline strategy and converges, as shown by our findings.

关键词： Energy harvesting reinforcement learning underlay cognitive radio networks

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Empowering the Future: An In-Depth Investigation of Computer Science Graduate Employability cognitive Factors 14

Empowering the Future: An In-Depth Investigation of Computer...

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14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Kansal, Priyank Sadawarti, Harsh Desh Bhagat University Dept. of Cse Mandi Gobindgarh India

ISBN: (纸本)9798350335095

This study investigates the cognitive factors influencing computer science graduates' employability through a two-phase approach, involving data collection from 562 students and exploratory factor analysis (EFA) of 30 variables. Sample adequacy was confirmed, and principal component analysis (PCA) revealed six factors accounting for 78.925% of total variance. Varimax rotation identified the factors as: 1) problem-solving and decision-making skills, 2) knowledge of contemporary issues, 3) competence in specific engineering disciplines, 4) knowledge of science and engineering principles, 5) competency, and 6) engineering system approach. High internal consistency was found for each factor. The results offer valuable insights for educational institutions and employers to enhance skill and knowledge development in computer science graduates. © 2023 IEEE.

关键词： Factor analysis

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Equilibrium Analysis of AI Investment in Financial Markets under Uncertainty

Equilibrium Analysis of AI Investment in Financial Markets u...

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cognitive computing and complex data (iccd), international conference on the

作者： Sahil Varma Penmetsa Computer Information Systems IT University of Central Missouri

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

This paper extends the monotone supermodular games framework to analyze equilibrium outcomes in financial markets involving AI investments. Using Szkup and Trevino’s results, I demonstrate the existence and uniqueness of Bayesian Nash equilibria, where investors allocate resources based on private signals and AI-driven insights. I explore the impact of coordination, open vs. closed AI models, operational risks like cyber disruptions, and the "data reflection" problem. My findings i ndicate that AI investment equilibria minimize private noise, aligning with risk-dominant strategies in global games.

关键词： Uncertainty Computational modeling Noise Games Reflection data models Bayes methods Artificial intelligence cognitive systems Investment

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A Study Of Dynamic Thresholds Power Detection Spectrum Sensing Techniques In CRN 9

A Study Of Dynamic Thresholds Power Detection Spectrum Sensi...

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9th international conference on Advanced computing and Communication Systems, ICACCS 2023

作者： Keerthika, J. AnandaKumar, H. Priya, V. Yuvalatha, S. Gayathri, N. Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Dr NGP Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Bannari Amman Institute of Technology Department of Computer Science and Business System Sathyamangalam India

ISBN: (纸本)9798350397376

A cognitive radio is the mobile communication technologies which help to utilize the efficient need of radio spectrum origin. The performance of CRN is significantly impacted by the spectrum sensing function. The problem of bandwidth has risen as a more specific one with breakthroughs in remote communications. By forgiving unlicensed users to usage the available spectrum in primary networks is the solution to the CR Technology. An innovative method for enhancing the electromagnetic spectrum's use efficiency, cognitive radio is a encouraging technology. Spectrum sensing has high spectral resolution capabilities and aids in the detection of the spectrum holes. The various methodologies are used to enhance the spectrum sensing essence. These techniques are distinguished by a sensing threshold, which is crucial to the performance of the sensing. The most important element in selecting the best threshold to optimize spectrum sensing performance is the dynamic threshold in the proposed system's spectrum detection algorithm. The energy earned from the licensed user over a precise time of observation, then the dynamic threshold is commutated between two levels. The estimation of the noise ambiguity factor forms the basis for threshold evaluations. These criteria are used to hike the likelihood of detection and contraction the possibility of false alarms. © 2023 IEEE.

关键词： cognitive radio

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Cluster Algorithm with Dual-Cluster Heads Based on Multi-Criteria Decision-Making in Heterogeneous cognitive Sensor Network 8

Cluster Algorithm with Dual-Cluster Heads Based on Multi-Cri...

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8th international conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Song, Xiaoying Zhang, Qilong Dalian Neusoft University of Information Dalian116023 China

ISBN: (纸本)9798350302455

cognitive sensor networks can not only alleviate the congestion of unlicensed spectrum, but also improve the utilization of licensed spectrum, which has become a recent research hotspot. However, it faces the dual challenges of high cost and high energy consumption. The clustering routing algorithm has the advantages of high energy utilization and less data redundancy, and has become an effective solution to high energy consumption. At present, the cluster unit constructed by the existing cluster routing algorithm has only one cluster head, which results in a serious imbalance of energy consumption between the cluster head and the member nodes. In addition, the existing cluster routing algorithms do not consider the reliability of transmitted data. Based on the above problems, this paper proposes a cluster routing algorithm with dual-cluster heads based on multi-criteria decision-making for heterogeneous cognitive sensor networks. Firstly, in the process of the cognitive node electing the main cluster head, the multi-criteria decision-making method is used to determine the main cluster head. Secondly, a selection scheme of the slave cluster head and an adjustable primary cluster head competition radius adapted to the random distribution of nodes are proposed. Finally, in the cluster head transmission stage, a routing algorithm based on spectrum detection is proposed to improve the data transmission rate. The simulation results show that the proposed algorithm can not only effectively reduce the energy consumption of the cognitive sensor network, but also improve the channel detection probability of the network, thereby improving the reliability of data transmission. © 2023 IEEE.

关键词： Sensor networks

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The 5thWorkshop on Modeling Socio-Emotional and cognitive Processes from Multimodal data in the Wild (MSECP-Wild) 23

The 5thWorkshop on Modeling Socio-Emotional and Cognitive Pr...

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25th international conference on Multimodal Interaction (ICMI)

作者： Dudzik, Bernd Hrkalovic, Tifany Matej Kuester, Dennis St-Onge, David Putze, Felix Devillers, Laurence Delft Univ Technol Delft Netherlands Univ Bremen Bremen Germany Ecole Technol Super Montreal PQ Canada CNRS LISN Orsay France

ISBN: (纸本)9798400700552

The ability to automatically infer relevant aspects of human users' thoughts and feelings is crucial for technologies to intelligently adapt their behaviors in complex interactions. Research on multimodal analysis has demonstrated the potential of technology to provide such estimates for a broad range of internal states and processes. However, constructing robust approaches for deployment in real-world applications remains an open problem. The MSECP-Wild workshop series is a multidisciplinary forum to present and discuss research addressing this challenge. Submissions to this 5(th) iteration span eforts relevant to multimodal data collection, modeling, and applications. In addition, our workshop program builds on discussions emerging in previous iterations, highlighting ethical considerations when building and deploying technology modeling internal states in the wild. For this purpose, we host a range of relevant keynote speakers and interactive activities.

关键词： Multimodal Modeling Afective computing Human-centered AI

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Proceedings - 2023 IEEE international conference on Big data and Smart computing, BigComp 2023

Proceedings - 2023 IEEE International Conference on Big Data...

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2023 IEEE international conference on Big data and Smart computing, BigComp 2023

ISBN: (纸本)9781665475785

The proceedings contain 95 papers. The topics discussed include: self-supervised learning for climate downscaling;classification of event sequences based on temporal relation features;a machine learning approach to government business process re-engineering;pairs trading strategy optimization using proximal policy optimization algorithms;an auditable and efficient prepaid scheme with privacy preservation in smart grids;federated learning with intermediate representation regularization;lazy node-dropping autoencoder;feed-forward design vs. mimic learning for interpretable deep models: a comparative study;inductive graph-based knowledge tracing;fast integration for multiple graphs with Neumann approximation;evaluating mitigation approaches for adversarial attacks in crowdwork;outlier-aware cross-market product recommendation;and edge-cloud collaboration architecture for efficient web-based cognitive services.

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