检索结果-内蒙古大学图书馆

Clustering in Dynamic Environments: A Framework for Benchmark Dataset Generation with Heterogeneous Changes

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Yazdani, Danial Branke, Juergen Khorshidi, Mohammad Sadegh Omidvar, Mohammad Nabi Li, Xiaodong Gandomi, Amir H. Yao, Xin Faculty of Engineering & Information Technology University of Technology Sydney Australia Warwick Business school University of Warwick United Kingdom School of Computing Leeds University Business School University of Leeds United Kingdom RMIT University Australia Faculty of Engineering & Information Technology University of Technology Sydney Australia University Research and Innovation Center [EKIK Obuda University Hungary Department of Computing and Decision Sciences Lingnan University China and CERCIA School of Computer Science University of Birmingham United Kingdom

Clustering in dynamic environments is of increasing importance, with broad applications ranging from real-time data analysis and online unsupervised learning to dynamic facility location problems. While meta-heuristics have shown promising effectiveness in static clustering tasks, their application for tracking optimal clustering solutions or robust clustering over time in dynamic environments remains largely under-explored. This is partly due to a lack of dynamic datasets with diverse, controllable, and realistic dynamic characteristics, hindering systematic performance evaluations of clustering algorithms in various dynamic scenarios. This deficiency leads to a gap in our understanding and capability to effectively design algorithms for clustering in dynamic environments. To bridge this gap, this paper introduces the Dynamic Dataset Generator (DDG). DDG features multiple dynamic Gaussian components integrated with a range of heterogeneous, local, and global changes. These changes vary in spatial and temporal severity, patterns, and domain of influence, providing a comprehensive tool for simulating a wide range of dynamic scenarios. © 2024, CC BY-NC-SA.

关键词： Clustering algorithms

EVMAS: An energy-aware virtual machine allocation scheme in fog centers 2

学校读者我要写书评

暂无评论

EVMAS: An energy-aware virtual machine allocation scheme in ...

2nd International Conference for Emerging technology, INCET 2021

作者： Naik, Madhusudan Barik, Lalbihari Kandpal, Meenakshi Patra, Sudhansu Shekhar Jena, Sudarson Barik, Rabindra K. Department of Computer Science Hakimpada Angul India King Abdul Aziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Saudi Arabia KIIT Deemed to Be University School of Computer Engineering Bhubaneswar India Sambalpur University SUIIT Department of Computer Science and Engineering Sambalpur India

ISBN: (纸本)9781728170299

Due to the rapid growth of the Industrial IoT (IIoT), social media and digitization, and wireless communication technology in various sectors, data volume is increasing rapidly. Cloud computing is an emerging solution with fog computing assistance for handling and processing a huge volume of data. It is a soar of means to improve the quality of services provided to users through cloud computing, which has been more overwhelmed by the massive data flow. Transmitting all the data to the cloud and getting it back from the cloud causes high latency and requires high network bandwidth. In the IIoT applications, there is enough energy needed in the fog layer, a promising area to be handled by the cloud service providers. An important factor that contributes to the energy consumption in fog servers is VM allocation. This paper proposes EVMAS, i.e., an energy-aware VM allocation scheme based on Lagrange's undetermined multiplier with the Hessian matrix, which improves the Quality of Service (QoS) in parallel with service level agreement (SLA). The suggested technique outperforms in comparison to the other state of the algorithm. © 2021 IEEE.

关键词： Fog computing

Brownian-Distribution Algorithm Optimized Efficientnet Features for Normal/Glaucoma Retinal Fundus Image Classification

学校读者我要写书评

暂无评论

Brownian-Distribution Algorithm Optimized Efficientnet Featu...

Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： Venkatesan Rajinikanth Robertas Damaševičius David Taniar Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai TN India Centre of Real Time Computer Systems Kaunas University of Technology Kaunas Lithuania Faculty of Information Technolog Monash University Melbourne VIC Australia

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

The eye is a critical sensory organ in human physiology, and any abnormality in the eye will result in vision problems ranging from mild to severe. Image-guided methods are frequently employed to conduct clinical-level eye health assessments, and retinal fundus imaging (RFI) is among the most frequently employed modalities for eye analysis. This research aims to implement a Deep-Learning (DL)-based technique for classifying the Normal/Glaucoma RFI database. This scheme consist following phases, image collection and labelling, feature extraction with EfficientNet, feature optimization using a novel Brownian-Distribution Algorithm (BDA), and binary classification with 3-fold cross validation. Initially, the proposed approach performs the classification with EfficientNet-model features and documents the results. Subsequently, the BDA is employed to optimize the features in order to identify the most significant ones. Serial features-fusion is then implemented to generate a new features vector. The results of this study confirm that this approach is capable of achieving a 100 % accuracy rate when the BDA optimized feature vector is utilized for the classification task. In the future, this application may be utilized to analyze the RFI collected from hospital.

关键词： Glaucoma Accuracy Hospitals Databases Retina Feature extraction Vectors Physiology Classification algorithms Optimization

An Electromagnetically Consistent and Mutual Coupling-Aware Near-Field Communication Model for Holographic MIMO

学校读者我要写书评

暂无评论

IEEE Transactions on Wireless Communications 2025年

作者： Ye, Lv Song, Yunchao Li, Bo Hu, Shengbo Zhu, Lei Liu, Chen Nanjing University of Posts and Telecommunications College of Electronic and Optical Engineering Nanjing210023 China Guizhou Normal University Institute of Intelligent Information Processing Guiyang550001 China University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology 550001 China

As a potential technology to support the extreme requirements of next-generation communications, holographic multiple-input multiple-output (H-MIMO) refers to arrays with a large number of individually controlled and densely deployed antennas. An important challenge to realize this innovative concept is the need to integrate information theory, electromagnetic theory and circuit theory in modeling, which is also required for electromagnetic information theory (EIT). This paper aims to connect information theory and electromagnetic theory using circuit theory to provide electromagnetically consistent and mutual coupling-aware near-field communication models for H-MIMO systems. Specifically, first, the considered H-MIMO system model is given and represented as a linear multiport network in circuit theory. Unlike the mainstream models that only consider the wireless propagation environment, the circuit-based model is physically consistent and considers the radio frequency components, mutual coupling between different antenna elements, noise, etc. Second, the mutual coupling and propagation effects are represented in the circuit model by the impedance, which requires relating the quantities of the circuit to the fields generated by the antenna. The relationship between the quantities of the circuit and the fields generated by the antenna is given according to the reciprocity and reaction theorems. Furthermore, the method of moments (a computational electromagnetic method) is introduced to calculate the impedance in the line-of-sight case. Finally, the integration of the physical model with the digital model provides a framework for the design of H-MIMO. In the numerical simulations, the H-MIMO performance is provided using the proposed model. © 2002-2012 IEEE.

关键词： Linear network analysis

Educational Data Mining: Inverted Classroom as a Teaching Strategy for Empowering Student Autonomy

学校读者我要写书评

暂无评论

Educational Data Mining: Inverted Classroom as a Teaching St...

IEEE Colombian Communications Conference (COLCOM)

作者： Wilson Chango Chango Patricio Sailema Estefania jativa Hugo Sanchez Computer Science and Electronics Information Technology ESPOCH Riobamba Ecuador Department of Exact Sciences Universidad de las Fuerzas Armadas ESPE Sanlgolquı Ecuador Environmental engineering Faculty of Sciences ESPOCH Riobamba Ecuador

ISBN: (数字)9798331504724

ISBN: (纸本)9798331504731

This study sought to analyze the effectiveness of using the inverted classroom methodology as a teaching strategy to foster autonomy in high school students. The research was carried out with a non-probabilistic sample of 108 high school students, with the objective of obtaining an initial and exploratory knowledge about the topic addressed. The bibliographic review was the most relevant part of the project. The scientific methods applied were analysis, synthesis and experimentation. A questionnaire based on the Likert scale was used to collect data. To determine the internal consistency, Cronbach’s Alpha coefficient was used, obtaining a value of 0.89, which indicates high reliability and validity. To find the degree of statistical relationship between strategies, methodology and autonomy, the selection of the best characteristics was applied, which is based on the concept of information gain, observing a positive correlation between the strategy used and student autonomy. The results allowed concluding that the strategies are strongly correlated with autonomy, being particularly relevant in leadership, teamwork, self-management and learning orientation.

关键词： Education Online services Data mining Reliability Sampling methods Surveys Data collection Statistical analysis Educational programs Autonomous systems Land transportation Vehicles

Femoropopliteal Artery Stent Detection in Fluoroscopy Using Otsu's Preprocessing and CNN Segmentation

学校读者我要写书评

暂无评论

Femoropopliteal Artery Stent Detection in Fluoroscopy Using ...

Technological Advancements in Computational Sciences (ICTACS), International Conference on

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

computer algorithm supported medical image analysis is a prevalent method in clinics, which effectively alleviates the diagnostic burden associated with traditional image assessment methods. This study aims to create a method that utilizes a Convolutional-Neural-Network (CNN) to accurately segment the Femoropopliteal Artery Stent (FAS) in Digital Fluoroscopy Images (DFI). In order to improve the accuracy of segmentation, this study used the combination of Otsu's tri-level thresholding and the Lévy-Flight Distribution Algorithm (LFDA). The suggested technique consists of many stages: collecting and resizing images and masks, boosting the DFI using Otsu and LFDA, using CNN-segmentation to extract the stent region, comparing segmented stent with mask, and calculating the required performance metrics. The efficacy of the suggested system is validated by analyzing the raw and thresholded DFI. The investigational results of this work affirm that this method achieves a higher level of accuracy (>96%) in segmenting images when the Otsu's preprocessed image is utilized. The experimental study employs the UNet-variants, and in the future, alternative CNN-segmentation methodologies can be utilized to validate the effectiveness of the presented scheme.

关键词： Measurement Image segmentation Accuracy Image analysis Boosting Arteries Medical diagnostic imaging

Data Dams: A Novel Framework for Regulating and Managing Data Flow in Large-Scale Systems

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Bouke, Mohamed Aly Abdullah, Azizol Cengiz, Korhan Ivković, Nikola Mihaljević, Ivan Mohamud, Mudathir Ahmed Kowrina, Ahmed Department of Communication Technology and Network Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang43400 Malaysia Department of Electrical-Electronics Engineering Biruni University Istanbul Turkey Faculty of Organization and Informatics University of Zagreb Pavlinska 2 Varaždin42000 Croatia Faculty of Computing SIMAD University Mogadishu252 Somalia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Serdang43400 Malaysia Département d’Informatique et Mathématiques Faculté des Sciences et Techniques Université de Nouakchott Al Aasriya Nouakchott880 Mauritania

In the era of big data, managing dynamic data flows efficiently is crucial as traditional storage models struggle with real-time regulation and risk overflow. This paper introduces Data Dams, a novel framework designed to optimize data inflow, storage, and outflow by dynamically adjusting flow rates to prevent congestion while maximizing resource utilization. Inspired by physical dam mechanisms, the framework employs intelligent sluice controls and predictive analytics to regulate data flow based on system conditions such as bandwidth availability, processing capacity, and security constraints. Simulation results demonstrate that the Data Dam significantly reduces average storage levels (371.68 vs. 426.27 units) and increases total outflow (7999.99 vs. 7748.76 units) compared to static baseline models. By ensuring stable and adaptive outflow rates under fluctuating data loads, this approach enhances system efficiency, mitigates overflow risks, and outperforms existing static flow control strategies. The proposed framework presents a scalable solution for dynamic data management in large-scale distributed systems, paving the way for more resilient and efficient real-time processing architectures. © 2025, CC BY.

关键词： Queueing theory

Cooperative Coevolution for Non-Separable Large-Scale Black-Box Optimization: Convergence Analyses and Distributed Accelerations

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Duan, Qiqi Shao, Chang Zhou, Guochen Yang, Haobin Zhao, Qi Shi, Yuhui Harbin Institute of Technology Heilongjiang Harbin150001 China Faculty of Engineering and Information Technology University of Technology Sydney 15 Broadway Ultimo SydneyNSW2007 Australia Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China

Given the ubiquity of non-separable optimization problems in real worlds, in this paper we analyze and extend the large-scale version of the well-known cooperative coevolution (CC), a divide-and-conquer black-box optimization framework, on non-separable functions. First, we reveal empirical reasons of when decomposition-based methods are preferred or not in practice on some non-separable large-scale problems, which have not been clearly pointed out in many previous CC papers. Then, we formalize CC to a continuous-game model via simplification, but without losing its essential property. Different from previous evolutionary game theory for CC, our new model provides a much simpler but useful viewpoint to analyze its convergence, since only the pure Nash equilibrium concept is needed and more general fitness landscapes can be explicitly considered. Based on convergence analyses, we propose a hierarchical decomposition strategy for better generalization, as for any decomposition, there is a risk of getting trapped into a suboptimal Nash equilibrium. Finally, we use powerful distributed computing to accelerate it under the recent multi-level learning framework, which combines the fine-tuning ability from decomposition with the invariance property of CMA-ES. Experiments on a set of high-dimensional test functions validate both its search performance and scalability (w.r.t. CPU cores) on a clustering computing platform with 400 CPU cores. Copyright © 2023, The Authors. All rights reserved.

关键词： Nash equilibrium

Domain-adaptive Person Re-identification without Cross-camera Paired Samples

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Li, Huafeng Mao, Yanmei Zhang, Yafei Qi, Guanqiu Yu, Zhengtao Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Computer Information Systems Department State University of New York at Buffalo State BuffaloNY14222 United States

Existing person re-identification (re-ID) research mainly focuses on pedestrian identity matching across cameras in adjacent areas. However, in reality, it is inevitable to face the problem of pedestrian identity matching across long-distance scenes. The cross-camera pedestrian samples collected from long-distance scenes often have no positive samples. It is extremely challenging to use cross-camera negative samples to achieve cross-region pedestrian identity matching. Therefore, a novel domain-adaptive person re-ID method that focuses on cross-camera consistent discriminative feature learning under the supervision of unpaired samples is proposed. This method mainly includes category synergy co-promotion module (CSCM) and cross-camera consistent feature learning module (CCFLM). In CSCM, a task-specific feature recombination (FRT) mechanism is proposed. This mechanism first groups features according to their contributions to specific tasks. Then an interactive promotion learning (IPL) scheme between feature groups is developed and embedded in this mechanism to enhance feature discriminability. Since the control parameters of the specific task model are reduced after division by task, the generalization ability of the model is improved. In CCFLM, instance-level feature distribution alignment and cross-camera identity consistent learning methods are constructed. Therefore, the supervised model training is achieved under the style supervision of the target domain by exchanging styles between source-domain samples and target-domain samples, and the challenges caused by the lack of cross-camera paired samples are solved by utilizing cross-camera similar samples. In experiments, three challenging datasets are used as target domains, and the effectiveness of the proposed method is demonstrated through four experimental settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Cameras