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Reconfigurable Intelligent Surface Enabled cognitive Radio Networks

Reconfigurable Intelligent Surface Enabled Cognitive Radio N...

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ieee international conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES)

作者： Vishwas Srivastava Binod Prasad Dept. of Electrical and Electronics Engineering ABV Indian Institute of Information Technology and Management (ABV-IIITM) Gwalior MP India

ISBN: (数字)9798350376135

ISBN: (纸本)9798350376142

In this paper, we investigate the impact of integrating reconfigurable intelligent surface (RIS) in cognitive radio (CR) networks for the enhancement of spectrum sensing and data transmission for unlicensed users (ULU) in a limited energy environment. The idea is to use RIS to help ULU in spectrum sensing as well as energy harvesting operations so that the CR networks perform more efficiently and have better spectral efficiency. To get the full potential of RIS, we employed a nature-inspired soft computing technique named Grey-Wolf optimization (GWO) to tune the reflecting phase of the RIS as needed. With the support of RIS, we investigate the usage of non-orthogonal multiple access (NOMA) technique for data transmission across multiple users. We illustrate the effectiveness of this integrated approach through simulation and provide the receiver operating characteristics (ROC) curve for spectrum sensing performance and outage probability for spectral efficiency.

关键词： NOMA Spectral efficiency Reconfigurable intelligent surfaces Signal processing SPICE Sensors data communication Energy harvesting cognitive radio Surface treatment

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Integrating Decision Trees with Cloud computing for Predicting cognitive Decline in Alzheimer’s Patients

Integrating Decision Trees with Cloud Computing for Predicti...

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Integrated Circuits and Communication Systems (ICICACS), ieee international conference on

作者： K. Meenakshi Bathini Ravinder Mohammed Rehaman Pasha K. Thinakaran Dabbara Jayanayudu S.P. Maniraj Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Vadapalani Tamil Nadu India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Department of Computer Science and Engineering Malla Reddy Institute of Engineering and Technology Hyderabad Telangana India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Electronics and Communication Engineering Siddartha Institute of Science and Technology Puttur Andhra Pradesh India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India

The proposed research work suggests a combined technique based on decision trees and cloud computing for predicting cognitive decline in Alzheimer’s patients. Medical records and data from wearable devices are among the many sources that perform preprocessing and feature engineering. Decision tree models can make the most efficient use of computing resources. Decision tree topologies are tested for performance criteria, including accuracy and F1 score. The system's predictive powers are rather encouraging since it frequently achieves accuracy rates over 90%. However, limitations in data quality and the intricacy of decision tree interpretation provide challenges. The prediction of cognitive deterioration in Alzheimer's patients may be made more scalable and interpretable by integrating decision trees with cloud computing. This method has promise for early identification and intervention despite its limitations. To further improve prediction accuracy and clinical value, future research should increase data quality and make it easier to understand.

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TGC-YOLO: Detection Model for Small Objects in UAV Image Scene

TGC-YOLO: Detection Model for Small Objects in UAV Image Sce...

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

作者： Taoyang Jin Wenhao Wang Chenjin Sun Zhenyang Yu Yanlun Wu Xin Chen Faculty of Computer Engineering Huaiyin Institue of Technology Huaian China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

In the field of drone target detection, existing object detection models often have problems of missed detection and false detection when dealing with challenges such as scale changes, dense targets and irregular arrangement of small targets. Therefore, this paper proposes a new detection model TGC-YOLO. Firstly, the EMA attention mechanism module is introduced to encode global information, and pixel-level features are further aggregated through dimensional interaction, which effectively reduces the attention to irrelevant information and maintains good feature extraction capabilities. Secondly, in terms of network structure, the FasterBlock module is used to replace the traditional BottleNeck module in C2f in YOLOv8n, so as to design a lightweight FasterC2f module, which reduces the number of parameters while ensuring the accuracy of the model. Thirdly, the Slim-neck structure is adopted on the Neck layer, and the model's detection ability for small objects is enhanced through the combination of GSConv and VoV-GSCSP. Then, the TGC three-channel feature fusion structure is designed based on GSConv to effectively aggregate multi-scale feature maps, enhance the feature fusion ability of the model, and improve the performance of small target detection. Finally, the dynamic head (DyHead) based on the attention mechanism is introduced to further improve the accuracy of small target detection by combining the three attention mechanisms of scale, space and task. Compared with YOLOv8n, TGC-YOLO has achieved 7.7%, 6.8% and 7.4% improvement in mAp@50, Precision and Recall respectively. Meanwhile, the model size is reduced by 0.1MB, enabling fast and accurate detection of small targets.

关键词： Accuracy Attention mechanisms Head Aggregates Object detection Feature extraction Autonomous aerial vehicles Neck cognitive systems Drones

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Tag Cloud Visualization integrated with Dimensionality Reduction for Conceptualizing Communication Behavioral data 58

Tag Cloud Visualization integrated with Dimensionality Reduc...

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58th Hawaii international conference on System Sciences, HICSS 2025

作者： Yoo, Sangbong Yoon, Chanyoung Seo, Seongbum Lee, Hyelim Jang, Yun Hong, Hyein Kim, Jeong-Nam Sejong University Korea Republic of Korea University Korea Republic of The University of Oklahoma United States

ISBN: (纸本)9780998133188

Public relations (PR) aims to establish positive relationships of company-customer and government-citizen. PR researchers and scholars achieve this by analyzing communication behavioral data, which encompasses the public's experiences in consumption, policy, and governance. However, labeling this data for analysis in PR is costly and complex, primarily due to its multi-label and the need for human judgment based on the Organization-Public Relationship Assessment (OPRA) concepts. Consequently, these problems limit the development of automated labeling models because more training data is needed for fine-tuning. In this paper, we propose a novel tag cloud visualization integrated with dimensionality reduction for conceptualizing communication behavioral data to address limitations in PR. We use attention-based embedding and dimensionality reduction to visualize the public's experience as a scatter plot according to OPRA concepts. Additionally, we provide PR researchers and scholars with clues for multi-labeling by combining the tag cloud using Bag of Words (BoW) in a scatter plot. We evaluate our proposed interactive visualization through a case study and feedback from two PR researchers. © 2025 ieee Computer Society. All rights reserved.

关键词： Public relations

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Cross Network Layer cognitive Service Orchestration in Edge computing Systems

Cross Network Layer Cognitive Service Orchestration in Edge ...

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ieee international conference on Edge computing (EDGE)

作者： Milad Rafiee Amir Taherkordi Özgü Alay Department of Informatics University of Oslo Oslo Norway Computer Science Department Karlstad University Sweden

ISBN: (数字)9798350368499

ISBN: (纸本)9798350368505

The edge architecture introduces challenges due to the presence of heterogeneous constrained devices and the dynamic nature of the network. Dynamic service orchestration is essential for efficient utilization of network resources in this context. Service orchestration plays a critical role in 5G networks in automating end-to-end service deployment and operations, and 5G network slicing. However, existing approaches on service orchestration primarily focus on resource availability, lacking a comprehensive understanding of network conditions-cross network layer orchestration. As a result, providing efficient service orchestration for delay-sensitive services remains a significant research area. To address this gap, this paper proposes a cognitive service orchestration framework that leverages not only application-level resource demands, but also the real-time status of the network infrastructure. This cognitive framework incorporates Reinforcement Learning, enabling it to dynamically interact with the network environment and continuously update policies for intelligent decision-making in complex 5G networks. The efficacy of the proposed framework is evaluated using an object detection application in smart cities. The evaluation results show that the proposed framework achieves a significant reduction in latency as compared to OpenELB, with a remarkable $58 \%$ decrease, which highlights its efficiency and effectiveness in meeting the requirements of delay-sensitive services.

关键词： 5G mobile communication Smart cities Network slicing Image edge detection Heuristic algorithms Surveillance Real-time systems

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Hanging Around: cognitive Inspired Reasoning for Reactive Robotics 14

Hanging Around: Cognitive Inspired Reasoning for Reactive Ro...

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14th international conference on Formal Ontology in Information Systems, FOIS 2024

作者： Pomarlan, Mihai de Giorgis, Stefano Ringe, Rachel Hedblom, Maria M. Tsiogkas, Nikolaos Applied Linguistics Department University of Bremen Bremen Germany Institute of Cognitive Sciences and Technologies National Research Council Catania Italy Digital Media Lab University of Bremen Bremen Germany Department of Computing Jönköping School of Engineering Jönköping Sweden Department of Computer Science KU Leuven Leuven Belgium

ISBN: (纸本)9781643685618

Situationally-aware artificial agents operating with competence in natural environments face several challenges: spatial awareness, object affordance detection, dynamic changes and unpredictability. A critical challenge is the agent’s ability to identify and monitor environmental elements pertinent to its objectives. Our research introduces a neurosymbolic modular architecture for reactive robotics. Our system combines a neural component performing object recognition over the environment and image processing techniques such as optical flow, with symbolic representation and reasoning. The reasoning system is grounded in the embodied cognition paradigm, via integrating image schematic knowledge in an ontological structure. The ontology is operatively used to create queries for the perception system, decide on actions, and infer entities’ capabilities derived from perceptual data. The combination of reasoning and image processing allows the agent to focus its perception for normal operation as well as discover new concepts for parts of objects involved in particular interactions. The discovered concepts allow the robot to autonomously acquire training data and adjust its subsymbolic perception to recognize the parts, as well as making planning for more complex tasks feasible by focusing search on those relevant object parts. We demonstrate our approach in a simulated world, in which an agent learns to recognize parts of objects involved in support relations. While the agent has no concept of handle initially, by observing examples of supported objects hanging from a hook it learns to recognize the parts involved in establishing support and becomes able to plan the establishment/destruction of the support relation. This underscores the agent’s capability to expand its knowledge through observation in a systematic way, and illustrates the potential of combining deep reasoning with reactive robotics in dynamic settings. © 2024 The Authors.

关键词： Optical flows

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Big data Analysis and Applications in international Trade: Decision Optimization Based on Intelligent Algorithms

Big Data Analysis and Applications in International Trade: D...

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Applied Intelligence and computing (AIC), ieee World conference on

作者： Wenwen Yang Jiangxi College of Foreign Studies Nanchang China

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

In the field of international trade, due to its wide range and complex interest relations, it is necessary to synthesize various information for analysis and decision-making. However, traditional data processing methods have been unable to meet the needs of enterprise development. As a new, efficient, safe and reliable solution, large database technology has been widely used in various fields and achieved good results. The purpose of this paper is to discuss and study the cross-department collaboration problem driven by intelligent algorithm through application examples. This paper first introduces the basic concepts, characteristics and relevant theoretical basis of inter-departmental collaboration. By studying the application of intelligent algorithms in interdepartmental collaboration, effective decision support tools are provided to deal with the complex situations in international trade. After that, this paper conducts a simulation test on the intelligent algorithm-driven decision optimization model, and the test results show that the test range of this model for the risk-return rate in trade is 0.04-0.09.

关键词： international trade Accuracy Machine learning algorithms databases Optimization models Collaboration Machine learning Big data Reliability theory Optimization

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On three fundamental graph enumeration problems and the corresponding graph generation algorithms

On three fundamental graph enumeration problems and the corr...

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ieee/ACM Int'l conference on & Int'l conference on Cyber, Physical and Social computing (CPSCom) Green computing and Communications (GreenCom)

作者： Antoine Bossard Graduate School of Science Kanagawa University Yokohama Japan

ISBN: (数字)9798350351637

ISBN: (纸本)9798350351644

Green supercomputing has been attracting more and more attention due to the acknowledgement of environmental issues. This is especially the case in the field of supercomputing: given the large number of compute nodes involved, the power consumption of these machines is high, in the order of tens of megawatts. Efficient usage of supercomputers is thus key to minimise their power consumption, and one approach is to increase data transfer rates so that the computer can be returned into an energy efficient sleep mode rapidly. Parallel paths and especially disjoint paths routing is one solution to achieve such more efficient data transfers. Yet, the selection of mutually disjoint paths is complex, and in some cases graph enumeration can produce a solution whereas conventional approaches fail. The contribution of this paper is twofold: for the selected three fundamental graph enumeration problems, we give simple, easy to understand proofs that do not involve complex mathematical notions as in previous works; we describe and evaluate the corresponding graph generation algorithms and make comparisons to straightforward or conventional approaches. Not only theoretical evaluation but also empirical evaluation is conducted. Time complexity is shown to be optimal.

关键词： Social computing Power demand Network topology Multiprocessor interconnection Green products Parallel processing Routing data transfer Supercomputers Time complexity

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A Generic Performance Metric for Scientific data Compression

A Generic Performance Metric for Scientific Data Compression

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ieee international conference on e-Science and Grid computing

作者： Zhenlu Qin Jessie Gu Maggie Liu Jinzhen Wang Hongjian Zhu AUM Montgomery AL University of Maryland College Park MD Tenafly High School Tenafly NJ University of North Carolina Charlotte NC X-Byte Research Tenafly NJ

ISBN: (数字)9798350365610

ISBN: (纸本)9798350365627

The performance outcomes of scientific data compression are complex in nature, often with one performance metric being more prioritized/optimized over others in the algorithm. To balance between different performance metrics, this paper formulates a new generic metric and uses it for searching an optimal configuration that leads to a balanced compression performance.

关键词： Measurement data compression

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An Efficient Resource Allocation Model in IIoT Using Federated Reinforcement Learning

An Efficient Resource Allocation Model in IIoT Using Federat...

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ieee international conference on Utility and Cloud computing (UCC)

作者： Mahdi Safaei Yaraziz Richard Hill Department of Computer Science School of Computing and Engineering University of Huddersfield Huddersfield UK

ISBN: (数字)9798350367201

ISBN: (纸本)9798350367218

In the Industrial Internet of Things (IIoT), resource allocation is important for reducing downtime and improving the system's operational performance. This study introduces a novel federated reinforcement learning approach that addresses resource management difficulties by enabling several agents to learn optimum maintenance policies jointly while maintaining data privacy. According to the analysis and evaluation performed, the proposed technique has the potential for implementation in complex industrial contexts, with future work concentrating on integrating advanced predictive models and expanding the algorithm to include multi-objective optimization cases.

关键词： data privacy Machine learning algorithms Computational modeling Reinforcement learning Predictive models Prediction algorithms Maintenance Resource management Optimization Industrial Internet of Things

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