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Data-Filtered Prediction with Decomposition and Amplitude-Aware Permutation Entropy for Workload and Resource Utilization in Cloud Data Centers

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IEEE Internet of Things Journal 2025年第12期12卷 19189-19201页

作者： Yuan, Haitao Hu, Qinglong Wang, Meijia Wang, Shen Bi, Jing Buyya, Rajkumar Shi, Shuyuan Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Hong Kong999077 Hong Kong Beijing University of Technology School of Software Engineering The Faculty of Information Technology Beijing100124 China Lab School of Computing and Information Systems MelbourneVIC3010 Australia Beihang University School of Integrated Circuit Science and Engineering Beijing China CSSC Systems Engineering Research Institute Beijing China Southern Methodist University Department of Computer Science The Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy. © 2014 IEEE.

关键词： Quality of service

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Forensic Eye: Convolutional Neural Network-based Crime Scene Object Detection system 2

Forensic Eye: Convolutional Neural Network-based Crime Scene...

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2nd International Conference Computational and Characterization Techniques in Engineering and sciences, IC3TES 2024

作者： Sudha, I. Vadivel, M. Lakshmiramana, P. Kalpana, N. Kumar, Nellore Manoj Vinoth, R. Saveethe University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science Tamilnadu Chennai India Excel Engineering College Department of Information Technology Komarapalayam Namakkal India Madanapalle Institute of Technology & Science Department of Computer Science & Technology Andhra Pradesh India Psna College of Engineering and Technology Department of Computer Science and Engineering India Saveetha School of Engineering Department of Mathematics Tamilnadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Puduvoyal India

ISBN: (纸本)9798350364699

Forensic investigations rely heavily on the accurate detection and analysis of crime related objects present at crime scenes. Traditional methods of object detection often involve manual labor and are error prone process to human error. In recent years, Convolutional Neural Networks (CNNs) have shown remarkable success in various computer vision tasks, including in object detection. In this article, we introduce ForensicEye, a novel CNN-based crime scene object detection system designed to assist forensic investigators in identifying the objects related to the crime. It utilizes a state-of-the-art CNN architecture trained on a large dataset of crime scene images, enabling robust detection even in challenging lighting and environmental conditions. We evaluated the performance of the ForensicEye on benchmark datasets with various preprocessing techniques, demonstrating its superior accuracy and efficiency compared to traditional methods. Our proposed model achieved the accuracy of 96.2%, precision of 97.9%, Recall of 96.9%, F1-score of 97.4%. © 2024 IEEE.

关键词： Convolutional neural networks

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Detection and Prognosis of Pulmonary Tuberculosis Using ML Classifiers with Soft Computing 5

Detection and Prognosis of Pulmonary Tuberculosis Using ML C...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Bisht, Girish Singh Ganesh, D. Kumar, Santosh Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Internet of Things Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350383522

Several intriguing research works on soft computing techniques have recently been presented and for the creation of a pulmonary TB diagnosis system, researchers continue to build an accurate and trustworthy intelligent decision-making approach. The current diagnostic testing system processes are cumbersome and time-consuming to analyse. Since increased sensitivity and specificity can be attained, the diagnosis of TB still has to be improved upon using new speedy and accurate diagnostic models and procedures, which will help with disease prevention and control. Therefore, this review paper's significance lies in its ability to discriminate between the many soft computing methodologies used to help the diagnosis, identification, prediction, and intelligent categorization of pulmonary TB illness. Researchers and medical professionals anticipate employing soft computing methods in the field. 'Artificial neural networks, genetic algorithms, support vector machines, fuzzy logic', and others are a few of these. The most recent diagnostic techniques involve artificial neural networks. Predictive inference accuracy and easy-to-optimize, adoptable non-linear modelling of large data sets are some additional advantages of artificial neural networks, showing that they could be an important decision support tool in a variety of industries, including medicine. © 2024 IEEE.

关键词： Artificial Neural Network Fuzzy and Fuzzy Logic Genetic Algorithm Pulmonary Tuberculosis Support Vector Machine

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SoulMate: Short-Text Author Linking Through Multi-Aspect Temporal-Textual Embedding (Extended Abstract) 40

SoulMate: Short-Text Author Linking Through Multi-Aspect Tem...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Najafipour, Saeed Hosseini, Saeid Hua, Wen Kangavari, Mohammad Reza Zhou, Xiaofang School of Information Technology and Electrical Engineering Iran University of Science and Technology Tehran Iran Sohar University Faculty of Computing and Information Technology Sohar Oman Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350317152

We devise a neural network-based temporal-textual framework that generates subgraphs with highly correlated authors from short-text contents. Our approach computes the relevance score (edge weight) between authors by considering a portmanteau of contents and concepts. It then employs a stack-wise graph-cutting algorithm to extract communities of related authors. Experimental results show that our multi-aspect vector space model can gain higher performance than other knowledge-centered competitors in linking short-text authors. © 2024 IEEE.

关键词： Semantics

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Effective NSGA-II Algorithm for a Limited AGV Scheduling Problem in Matrix Manufacturing Workshops with Undirected Material Flow

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Complex system Modeling and Simulation 2025年第1期5卷 68-85页

作者： Xuewu Wang Jianing Zhang Yi Hua Rui Yu School of Information Science and Engineering East China University of Science and TechnologyShanghai 200237China Institute for Sustainable Manufacturing and also with the Department of Electrical and Computer EngineeringUniversity of KentuckyLexingtonKY 40506USA

Automatic guided vehicles(AGVs)are extensively employed in manufacturing workshops for their high degree of automation and *** paper investigates a limited AGV scheduling problem(LAGVSP)in matrix manufacturing workshops with undirected material flow,aiming to minimize both total task delay time and total task completion *** address this LAGVSP,a mixed-integer linear programming model is built,and a nondominated sorting genetic algorithm II based on dual population co-evolution(NSGA-IIDPC)is *** NSGA-IIDPC,a single population is divided into a common population and an elite population,and they adopt different evolutionary strategies during the evolution *** dual population co-evolution mechanism is designed to accelerate the convergence of the non-dominated solution set in the population to the Pareto front through information exchange and competition between the two *** addition,to enhance the quality of initial population,a minimum cost function strategy based on load balancing is *** local search operators based on ideal point are proposed to find a better local *** improve the global exploration ability of the algorithm,a dual population restart mechanism is *** tests and comparisons with other algorithms are conducted to demonstrate the effectiveness of NSGA-IIDPC in solving the LAGVSP.

关键词： limited automatic guided vehicle(AGV)scheduling problem nondominated sorting genetic algorithm II(NSGA-II) dual population co-evolution matrix manufacturing workshop

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Short-Text Author Linking Through Multi-Facet Temporal-Textual Embedding (Extended Abstract) 40

Short-Text Author Linking Through Multi-Facet Temporal-Textu...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Najafipour, Saeed Najafipour Hosseini, Saeid Hua, Wen Reza Kangavari, Mohammad Zhou, Xiaofang School of Information Technology and Electrical Engineering Iran University of Science and Technology Tehran Iran Sohar University Faculty of Computing and Information Technology Sohar Oman The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350317152

关键词： Semantics

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Supplier Qualification Using FMEA in a Meat Company

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Periodica Polytechnica Transportation Engineering 2023年第4期51卷 323-328页

作者： Boldizsár, Adrienn Török, Erika Pásztor, Attila Department of Information Technology GAMF Faculty of Engineering and Computer Science John von Neumann University Izsáki u. 10. KecskemétH-6000 Hungary

The analysis of the processes between supplier and customer and the detection and handling of defects is based on objective, quantified criteria so that customer complaints can be handled as efficiently as possible, with the least possible inconvenience. One possible risk-management method is FMEA, or Failure Mode(s) and Effect(s) Analysis, which allows a team to anticipate and prevent failures from occurring anywhere. It is an analytical methodology to identify and check for potential problems when designing a product, service or process. It can be used to reduce the number of defective supplies and thus indirectly increase customer satisfaction (by more efficient purchasing, the price can be kept lower and the quality higher, and fewer customer complaints from end users can be expected). It was used in this study to assess the supplier quality of a Hungarian industrial company in the FMCG sector and to make tangible improvements in a short time to eliminate supplier defects. © 2023 Budapest University of technology and Economics. All rights reserved.

关键词： Defects

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Navigating Mobile Robots in 2D Environments using Model Predictive Control 6

Navigating Mobile Robots in 2D Environments using Model Pred...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Al-Dahhan, Mohammed Rabeea Hashim Alsaadi, Mahmood Abdulrazzaq Al-Dahhan, Ruqayah R. Aliesawi, Salah A. College of Computer Science and Information Technology University of Anbar Ramadi Anbar Iraq Computer Science Department Al- Maarif University Collage Ramadi Anbar Iraq

ISBN: (纸本)9798350394634

This paper describes a model of predictive designing and implementing a control system for a mobile robot in MATLAB. The controller is designed to move the robot around in a 2D environment with various test scenarios. To set up the MPC problem, both the translation and rotation motions of this mobile robot are modelled. Such points include the definition of states and inputs, prediction horizon, formulation of cost function et cetera. In this instance, an algorithm was developed using MATLAB which optimizes control inputs based on system dynamics and given objectives. The performance of the controller will be tested through various tests like trajectory tracking and goal-reaching tasks among others. Different situations will be considered during simulations showing how well the MPC works through using as a navigation tool for different environments with varying challenges. This study contributes towards improving self-driving methods used in mobile robots employed in industries ranging from transport to warehousing or even security services, especially during search operations, where human life may be at stake. © 2024 IEEE.

关键词： Mobile robots

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Insights into the Future: XAI Integration in O-RAN and Space Communication systems

Insights into the Future: XAI Integration in O-RAN and Space...

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2024 Multimedia University Engineering Conference, MECON 2024

作者： Tahir, H. Ahmed Nabi, Fahad Tariq, Muhammad Ziad Khan, Ahmad Fasih Mahmud, Azwan Multimedia University Faculty of Engineering Cyberjaya Malaysia University of Kiel Department of Electrical Engineering & Information Technology Kiel Germany Department of Computer Science SZABIST University Islamabad Pakistan Roots School System Islamabad Pakistan

ISBN: (纸本)9798331530747

This paper outlines the merging of Explainable Artificial Intelligence (XAI) with Open Radio Access Network (O-RAN) and space communication systems. The usage of AI drives decision-making in critical domains of telecommunications and space explorations requires transparency and interpretability. However, traditional AI models often operate as 'black boxes,' making it difficult to understand their decision-making processes. This lack of explainability poses significant risks, including misinterpretation of signals, undetected anomalies, and erroneous decision-making, which can compromise the integrity of communication systems. Specifically, the challenges include accurately distinguishing between legitimate signals and attacks in anti-jamming scenarios, understanding the behaviour of complex models like LSTM in traffic prediction, and ensuring the reliability of telemetry data despite errors and noise. Integration of XAI techniques within O-RAN architecture and space communication protocols can ensure trust, reliability and safety in AI-enabled systems. This paper investigates the opportunities and challenges of incorporating XAI in O-RAN and space communications. Additionally, it presents the implications of XAI, the need for interpretability in autonomous spacecraft operations, anomaly detection and decision support for mission-critical tasks. By bridging the gap in AI transparency and advanced communication technology, this paper aims to present a detailed analysis of implemented AI and Machine Learning (ML) in the telecommunication domain. It also presents the challenges currently faced in the intersection of XAI and communication. Use cases of XAI in the domain of Open Radio Access Network and Space Communications in light of LIME and SHAP techniques were presented to give a hypothetical modelling for future experimentation to provide local and global interpretation and explanations for the currently employed AI/ML models in the respective domains. Overall, this

关键词： Radio access networks

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The Combination of the Leiden Algorithm and Graph Coloring for Influencers Detection in Large Scale Social Networks

The Combination of the Leiden Algorithm and Graph Coloring f...

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computer sciences, Engineering, and technology Innovation (ICoCSETI), International Conference on

作者： Handrizal Poltak Sihombing Erna Budhiarti Nababan Mohammad Andri Budiman Doctoral Program in Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Information Technology Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

The role of influencers, especially on social media platforms, has grown significantly. A commonly used feature among business professionals today is follower grouping. However, this feature is limited to identifying influencers based solely on mutual followership, underscoring the need for a more sophisticated approach to influencer *** study proposes a new method for influencer detection that integrates the Leiden Coloring Algorithm and Matrix Centrality. This approach leverages network analysis to identify patterns and relationships in large-scale datasets. First, the Leiden Coloring Algorithm partitions the network into various communities, which are considered potential influencer groups. Furthermore, Eigenvector and Degree Centrality augment this process by identifying nodes with high connectivity, representing potential *** proposed method is validated using crawled data from the Twitter (X) social media platform with the keyword "GarudaIndonesia." The results of the Leiden Coloring Algorithm recommend 10 accounts as influencers. Based on Eigenvector Centrality and Degree Centrality for a dataset of 1,000 rows, it is observed that the first and second ranks consistently identify the same influencers, namely IndonesiaGaruda and GarudaCares. However, the third to tenth ranks suggest different influencers. For a dataset of 5,000 rows, both methods again identify IndonesiaGaruda as the top-ranked influencer, while the second to tenth ranks differ between the two *** modularity value for the first test scenario is 0.9396, while for the second test scenario, it is 0.9381. The processing time for the first test scenario is 29.5493 seconds, compared to 434.1838 seconds for the second test scenario. Additionally, the number of communities identified by the Leiden Coloring Algorithm increases with dataset size, with 505 communities for the first test scenario and 1,969 communities for the second. This demonstrates that larger datasets res

关键词： Technological innovation Social networking (online) Blogs Network analyzers Feature extraction Partitioning algorithms Business

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