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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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Deep Learning Approaches for Precision Agriculture: Weed and Cotton Crop Classification Using YOLO and Faster RCNN

Deep Learning Approaches for Precision Agriculture: Weed and...

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Majeed, Asad Faridoon, Fatima Irfan, Muhammad Rashid, Ahmar Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and Technology Topi Pakistan Department of Electrical Engineering Sukkur IBA University Sukkur Pakistan

ISBN: (纸本)9798331504465

Weed infestation in cotton fields significantly challenges agricultural productivity by competing for essential nutrients and water resources. This study presents a comprehensive comparative analysis of two deep learning-based object detection models - YOLO and Faster R-CNN - for automated weed detection in cotton fields. We developed and annotated a custom dataset comprising field images captured under various environmental conditions, labelled in both COCO and YOLO formats using Label Studio. Performance evaluation revealed the YOLO model’s superior capabilities, achieving a mAP50 of 0.775, precision scores of 0.859 for cotton and 0.656 for weed detection, and recall rates of 0.849 and 0.543 for cotton and weed respectively on the test dataset. In comparison, Faster R-CNN showed lower performance with AP scores of 0.708 for cotton and 0.269 for weed detection, particularly struggling with objects of varying sizes. The YOLO model maintained consistent performance across both validation and test datasets, with validation metrics showing mAP50 of 0.982 and mAP50-95 of 0.829. These results establish YOLO’s effectiveness as a reliable tool for automated weed detection in cotton fields, offering a practical solution for precision agriculture applications. The developed model demonstrates the potential for integration into automated crop management systems, contributing to more efficient and targeted weed control strategies. © 2024 IEEE.

关键词： Cotton

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Integration of Fuzzy Model with the IOT Model to Achieve Better Health(medical) Care Design System 4

Integration of Fuzzy Model with the IOT Model to Achieve Bet...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Verma, Apurv Rastogi, Shailesh Meenakshi, R. India Srv Media Pvt. Limited India Sri Balaji University Faculty of Management It & Business Analytics Pune India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India India Computer Science and Engineering Chennai Institute of Technology India

ISBN: (纸本)9798350360165

The Internet of Things is a pervasive network that utilizes sensor-equipped devices by the billions to observe the real-world environment. The Internet of Things technology presents considerable potential for the development of various industries, including medical treatment, where it can decrease the impact that the combination of old age and chronic ailments puts on health care systems. This paper tends to put forward a secure hierarchical routing approach, so-called Fuzzy Logic-based secure Hierarchical Routing using the Firefly Algorithm, to mitigate the issued challenges in IoT technologies for healthcare. A Fuzzy Trust Framework, an Inter-cluster Routing Framework, and a Firefly Algorithm-based Clustered Framework are the three main components of the proposed FSRF. The Fuzzy Security Framework identifies the trustworthiness of IoT devices in the network to resist routing attacks. The Firefly Algorithm-based Colony Framework selects IoT devices as cluster head nodes using the fitness function that measures the trust level, residual power hop count, connectivity radius, and centrality. FSRF goes further to apply an on-demand routing technique to ensure efficient data transmission. Simulation analysis compared with the current protocol was presented that showed that FSRF significantly improves the network lifetime, and energy efficiency, but security is the issue that needs further concern to improve the system. © 2024 IEEE.

关键词： Sensor nodes

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Economical Camera-based Measuring Scheme for Roundabout Traffic

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Periodica Polytechnica Transportation engineering 2025年第2期53卷 235-241页

作者： Kovács, Tamás Kovács, Lóránt Department of Information Technology GAMF Faculty of Engineering and Computer Science John von Neumann University Izsáki út 10. KecskemétH-6000 Hungary

Counting the turning movements in a four-leg roundabout is a challenging task and often executed by vehicle recognition and tracking on traffic videos. In order to obtain accurately all the 12 flow values of the origin-destination (O-D) matrix we need to measure arbitrary chosen 12 linearly independent flow values. However, the larger the area to be observed the more cameras are needed because the good pixel resolution of the vehicles and the proper camera inclination angles are essential. Here we propose a novel measuring scheme, which observes only two roundabout legs instead of observing all the four, as a result obtaining all the 12 flow values requires a reduced number of cameras. The directly measured flow values and the mathematical method of determining the O-D matrix are given in details. © 2025 Budapest University of technology and Economics. All rights reserved.

关键词： Object recognition

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Advancing Brain Tumor Analysis through Dynamic Hierarchical Attention for Improved Segmentation and Survival Prognosis

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computers, Materials & Continua 2023年第12期77卷 3835-3851页

作者： S.Kannan S.Anusuya Department of Computer Science and Engineering Malla Reddy College of EngineeringSecunderabadTelunganaIndia Department of Information Technology Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSIMATSChennaiTamil Nadu602117India

Gliomas,the most prevalent primary brain tumors,require accurate segmentation for diagnosis and risk *** this paper,we develop a novel deep learning-based method,the Dynamic Hierarchical Attention for Improved Segmentation and Survival Prognosis(DHA-ISSP)*** DHA-ISSP model combines a three-band 3D convolutional neural network(CNN)U-Net architecture with dynamic hierarchical attention mechanisms,enabling precise tumor segmentation and survival *** DHA-ISSP model captures fine-grained details and contextual information by leveraging attention mechanisms at multiple levels,enhancing segmentation *** achieving remarkable results,our approach surpasses 369 competing teams in the 2020 Multimodal Brain Tumor Segmentation *** a Dice similarity coefficient of 0.89 and a Hausdorff distance of 4.8 mm,the DHA-ISSP model demonstrates its effectiveness in accurately segmenting brain *** also extract radio mic characteristics from the segmented tumor areas using the DHA-ISSP *** applying cross-validation of decision trees to the selected features,we identify crucial predictors for glioma survival,enabling personalized treatment *** the DHA-ISSP model and the desired features,we assess patients’overall survival and categorize survivors into short,mid,in addition to long *** proposed work achieved impressive performance metrics,including the highest accuracy of 0.91,precision of 0.84,recall of 0.92,F1 score of 0.88,specificity of 0.94,sensitivity of 0.92,area under the curve(AUC)value of 0.96,and the lowest mean absolute error value of 0.09 and mean squared error value of *** results clearly demonstrate the superiority of the proposed system in accurately segmenting brain tumors and predicting survival outcomes,highlighting its significant merit and potential for clinical applications.

关键词： Survival prediction 3D multimodal MRI brain tumors segmentation CNN U-Net deep learning

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The Intensive Model Design using FL for Smart HC System Design 4

The Intensive Model Design using FL for Smart HC System Desi...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Raman, Ramakrishnan Verma, Apurv Kumar, Vikram Rastogi, Shailesh Pillai, Biju G. Meenakshi, R. India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India Srv Media Pvt. Limited India India Sri Balaji University Faculty of Management It & Business Analytics Pune India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350360165

Using cutting-edge technology like wearables, internet access of Things, and smartphones, smart healthcare has become a crucial component of health policy services. This revolutionary method enables seamless connection and ongoing information access between patients, medical equipment, the facilities, allowing for intelligent responses to a variety of demands within the medical ecosystem. No matter when or where they are, patients may get urgent assistance, diagnostic services, and monitoring via the digital platform of the Smart Health care Management Systems (SHMS). This research emphasizes innovation with direct healthcare services and suggests the use of the Smart the management of healthcare Examination using Fuzzy Decision-Taking (SHME-FDM) technique to measure the effectiveness of technology integration. Furthermore, the Fuzzy Asymmetric Hierarchy Process-Technique for Orders of Preference by The sameness to Ideal Solution (Fuzzy AHP-TOPSIS) is used to evaluate the privacy protection and healthcare data inside the SHMS. In order to make predictions about healthcare, the research uses a fuzzy-based neural network. Through experimental analysis, reliability, accuracy, and error rates are evaluated. Notably, a security risk analysis highlights the proposed fuzzy model's potential for protecting healthcare data by demonstrating its improved risk assessment performance when compared to current methods. © 2024 IEEE.

关键词： Risk assessment

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An empirical intelligent water irrigation system using soft computing and IoT

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International Journal of Information technology (Singapore) 2024年第6期16卷 3983-3989页

作者： Patel, Warish Sangeetha, Y. Gopagoni, Deepa Rani Mallik, D. M. Arvind Agrawal, Rakshal Mishra, Saket Sunil, M.P. Kolandaisamy, Raenu Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat India Department of Information Technology VR Siddhartha Engineering College Andhra Pradesh Vijayawada India Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India PES Institute of Technology and Management Karnataka Shivamogga India Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Maharashtra Nagpur India Centre of Interdisciplinary Research in Business and Technology Chitkara Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Electronics and Communication Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Karnataka Bengaluru India Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia

Water loss from fruit and vegetable production is mostly caused by leakage in the transportation system and handling of land, as well as by using traditional processing techniques. In order to increase the wellness of plants, save power, and optimize consumption of water, information should be widely applied in designing intelligent irrigation systems, which is possible with the help of the Internet of Things (IoT). In the given paper, an IoT-based soft computing recommendation framework is proposed for performing irrigation in a smarter way. The system works in such a way that IoT devices are positioned in the field to gather information about crops and groundwater levels. The acquired data is then fed to a cloud repository, which makes use of soft computing techniques such as neural networks for analysing and providing effective recommendations to farmers on their smartphones. In this way, the framework is tuned and the farmers are also updated from time to time regarding judicious irrigation processes. Lastly, the performance of the proposed system is validated against traditional techniques by taking crop datasets into consideration. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Efficient water management Intelligent devices IoT Smart irrigation Soft computing

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Automated Classification and Segmentation of Brain Tumor Images using Backboned ResUNet Framework 6

Automated Classification and Segmentation of Brain Tumor Ima...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Prasanalakshmi, B. Anitha, Cuddapah Balamanigandan, R. Mahaveerakannan, R. Gade, N.V. Ratnakishor Adjunct Faculty Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Computer Science and Engineering School of Computing Mohan Babu University Andhra Pradesh Tirupati India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9798331522667

One of the most challenging issues in computer imaging is the automated segmentation of brain tumors using Magnetic Resonance Images (MRI). Several approaches are explored using Deep Neural Networks in image segmentation since they perform well in automated tumor segmentation using tumor scans. Due to the complexity, training deeper neural networks usually takes a long time and many processing resources. An automated approach for classifying and segmenting images of Brain tumor MRI is proposed. As a hybrid transfer learning approach, modified ResNet50 identifies the class of MRI images as Benign or malignant. After identifying the Benign images, the tumor’s location is segmented using a hybrid network involving ResUNet with the Xception network as Backbone. This combination approach is chosen after its comparative analysis with other backbones. Various combination approaches are carried out, and performance metrics like Jaccard, Dice, and Tversky coefficient are done to prove the ResUNet backboned with Xception plays a better role concerning accuracy and speed. The proposed approach was tested on the dataset Cancer Imaging Archive to validate its efficiency. The proposed model has an average dice similarity coefficient score of 0.94, outperforming the conventional transfer learning training method with all the layers. © 2025 IEEE.

关键词： Magnetic resonance imaging

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Graph Topology Identification Based on Covariance Matching

Graph Topology Identification Based on Covariance Matching

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Han, Yongsheng Natali, Alberto Leus, Geert Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands

ISBN: (纸本)9798350368741

This paper addresses graph topology identification for applications where the underlying structure of systems like brain and social networks is not directly observable. Traditional approaches based on signal matching and spectral templates have limitations, particularly in handling scale issues and sparsity assumptions. We introduce a novel covariance matching methodology that efficiently reconstructs the graph topology using observable data. For the structural equation model (SEM) using an undirected graph, we demonstrate that our method can converge to the correct result under relatively soft conditions. Furthermore, we extend our methodology to polynomial models and any known distribution of latent variables, broadening its applicability and utility in diverse graph-based systems. © 2025 IEEE.

关键词： covariance matching graph topology identification polynomial model structural equation model

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An efficient computational procedure to solve the biological nonlinear Leptospirosis model using the genetic algorithms

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Soft Computing 2024年第1期28卷 607-625页

作者： Sabir, Zulqurnain Ali, Mohamed R. Raja, Muhammad Asif Zahoor Sadat, R. Department of Mathematics and Statistics Hazara University Mansehra Pakistan Faculty of Engineering and Technology Future University in Egypt New Cairo11835 Egypt Basic Engineering Science Department Benha Faculty of Engineering Benha University Banha Egypt Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Mathematics Zagazig Faculty of Engineering Zagazig University Zagazig Egypt Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon

The purpose of this work is to present the numerical solutions of the nonlinear mathematical Leptospirosis disease (LD) model using the computational performances of the artificial neural networks (ANNs) along with the optimization procedures based on the global search genetic algorithm (GA) and local search active-set algorithm (ASA) scheme, i.e., ANNs-GA-ASA. LD is a zoonotic disease that is occurring in the whole world, obtained by rodents that become the reason of death in the people. The LD model based on the susceptible-infected-recovered, i.e., SIR, is used to solve the mechanisms of disease spread. The optimization of an error-based fitness function, which is constructed through the differential mathematical system using the hybrid computing proficiencies of the ANNs-GA-ASA for solving the LD model. The stochastic ANNs-GA-ASA procedures are applied to the LD model to authenticate the precision, exactness, reliability and competence of the ANNs-GA-ASA. The obtained results using the ANNs-GA-ASA of the LD model will be compared using the Runge–Kutta scheme, which authenticate the importance of the ANNs-GA-ASA. Furthermore, statistical analysis through different actions for the LD model approve the convergence and precision of the ANNs-GA-ASA. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Neural networks

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