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检索条件"任意字段=4th International Conference on Optimization Learning Algorithms and Applications"

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Towards Adaptive Adjusting and Efficient Scheduling Coflows Based on Deep Reinforcement learning 29

Towards Adaptive Adjusting and Efficient Scheduling Coflows ...

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29th IEEE international conference on Parallel and Distributed Systems, ICPADS 2023

作者： Wang, Zichao Yan, Kedong Chang, Guanxin Huang, Chanying Xiao, Shan Yang, Yuxin Nanjing University of Science and Technology School of Cyber Science and Engineering Jiangyin China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China Fiberhome Communication Technology Co. LTD Fiberhome R & D Center Wuhan China Nanjing Univ. of Science & Technology School of Cyber Science & Engineering Jiangyin China

ISBN: (纸本)9798350330717

the rapid development of current data centers and Industrial Internet of things has brought about the explosive growth of information, which leads to the need for better performance of cluster communication systems. Coflow scheduling has the potential to enhance communication performance among applications in data parallel clusters. However, current coflow scheduling techniques that lack preliminary knowledge often depend on a fixed set of threshold parameters within a multilevel feedback queue (MLFQ), disregarding network variability. though manually tweaking threshold settings may support network flexibility, it negatively impacts real-time performance and increases workload. Furthermore, manual threshold adjustments often fail to react promptly and adaptively to network environmental changes. To address the issues highlighted above, this paper presents D-MLFQ, a novel approach that leverages deep reinforcement learning to dynamically and autonomously regulate the threshold of MLFQ. As a result, D-MLFQ offers enhanced scheduling and communication optimization capabilities. Furthermore, the study performs trace-driven simulations to assess the efficacy of D-MLFQ. Empirical data indicate that D-MLFQ outperforms Aalo, which utilizes fixed threshold, by up to 1.39× in terms of coflow's completion time. Compared to other common scheduling algorithms such as per-flow fairness, D-MLFQ achieves up to 2.13× faster completion time. © 2023 IEEE.

关键词： coflow scheduling deep reinforcement learning dynamic threshold efficiency MLFQ

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Efficient Customer Segmentation Using Silhouette Based K-Means Algorithm

Efficient Customer Segmentation Using Silhouette Based K-Mea...

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Artificial Intelligence and Signal Processing (AISP), INDIA, international conference on

作者： Pavan Karthik Chilla Sunil Kumar Nahak Mudem Veera Venkata Deepika Balivada Sai Rohit Tankala Hemalatha Asish Kumar Dalai Department of Computer Science KL University Vaddeswaram Andhra Pradesh India Department of Department of Computer Science and Engineering NIST University Berhampur Odisha India Department of Electronics and Communication Engineering Vijaya Institute of Technology for Women AP India School of Computer Science and Engineering VIT AP University Amaravati Andhra Pradesh India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

Customer segmentation is now essential for firms seeking to maintain and optimize revenue from consumers. the present research investigates the potential of utilizing behavioral variables such as expenses and income to successfully segment clients, beyond conventional demographic methods. the k-means clustering procedure, which is an unsupervised machine learning methodology, is utilized using the Elbow method and Silhouette score to ascertain the most suitable number of clusters. A Python application, with basic scaling, examines a dataset from a retail mall to visually represent similarities among customers and categorize them into clusters. the project aims to segment customers based on their purchase behavior using the silhouette-based K-means algorithm. the seaborn library will be used for implementation. the objective is to create a resilient consumer segmentation approach that assists organizations in comprehending target markets and enhancing marketing strategies suitably. By employing this algorithm, organizations acquire knowledge about client behavior, facilitating customized marketing campaigns and enhanced income generating.

关键词： Clustering algorithms Signal processing algorithms Data visualization Organizations Machine learning Signal processing Libraries Data mining Elbow optimization

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Prediction of Aviation Spare Parts Consumption Based on Kepler-NeuralProphet Modeling

Prediction of Aviation Spare Parts Consumption Based on Kepl...

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Computer, Control and Robotics (ICCCR), international conference on

作者： Jiaheng Lu Haiyan Zhu Jieru Zheng Ningyun Lu College of Chang Kong Nanjing University of Aeronautics and Astronautics Nanjing China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798350373141

ISBN: (纸本)9798350373158

Aviation spare parts are essential resources for the aviation industry, playing a crucial role in ensuring the safety and reliability of aircraft flights. Nevertheless, because of their high cost and specific requirements, it is necessary to develop precise procurement plans. this paper addresses the challenges associated with poor prediction accuracy and limited explanatory capability of current methods when dealing with small-sample aviation spare parts data. It introduces a small-sample aviation spare parts consumption prediction method based on the Kepler-N euralProphet model. the NeuralProphet model has a strong combinatorial interpretation capability, which allows it to maintain interpretability while achieving accurate predictions. It utilizes trend, seasonal, holiday, and autoregressive terms to extract the features of time series data related to aviation spare parts consumption. In order to enhance the feature extraction capability of NeuralProphet model, the hyperparameters of NeuralProphet model were optimized using improved Kepler algorithm, which is based on the optimization of the Logistic-Tent-Cosine chaotic operator. In the empirical tests, the Kepler-NeuralProphet model yielded MAE, MAPE, and RMSE results of 3.31, 3.39%, and 5.59, respectively, outperforming the NeuralProphet, Prophet, and NARX models.

关键词： Machine learning algorithms Accuracy Time series analysis Machine learning Predictive models Prediction algorithms Feature extraction

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Study on Parameters optimization and Adaptation of Nitrogen Foam Flooding in Reservoirs with High Water Cut and High Permeability 4

Study on Parameters Optimization and Adaptation of Nitrogen ...

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2022 4th international conference on New Energy System and Electrical Engineering, NESEE 2022

作者： Zhang, Yu Li, Binfei Wu, Yizhi Song, Zhichao Lu, Teng Zhang, Mengyuan Zeng, Xianghui Qingdao266580 China Qingdao266580 China Exploration and Development Research Institute Shengli Oilfield Company Sinopec Dongying257000 China Co. LTD. Shenzhen BRANCH Shenzhen518000 China

Most oil reservoirs in China have entered the stage of high water cut. Affected by the heterogeneity of the reservoir, the water flow dominance channel is easily formed between injection-production wells in the plane direction of such reservoirs, and the phenomenon of water channelling is intensified. therefore, a numerical simulation study of nitrogen foam flooding is carried out in this paper. According to the reservoir characteristics of the target block, the adaptability of nitrogen foam flooding to reservoirs in the ultra-high water cut stage is studied, the parameters of injection and production are optimized, and the applicable limits of nitrogen foam and the sensitivity of reservoir parameters are analysed. the optimization results of foam flooding indexes obtained through numerical simulation are as follows: the injection method of nitrogen foam is a mixed injection, the injection volume is 0.3 PV, the injection rate is 200 m3/d, the concentration of foaming agent is 0.5 wt%, and the gas-liquid ratio is 1: 1. the results show that for high-water-cut reservoirs, nitrogen foam flooding technology can effectively improve the water flooding effect of reservoirs and enhance oil recovery. It exhibits excellent adaptability to such reservoirs and has a very board application prospect. the results of numerical simulation optimization can provide theoretical guidance for field applications. © Published under licence by IOP Publishing Ltd.

关键词： Reservoirs (water)

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4th international conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2021

4th International Conference on Futuristic Trends in Network...

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4th international conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2021

ISBN: (纸本)9789811950360

the proceedings contain 80 papers. the special focus in this conference is on Futuristic Trends in Networks and Computing Technologies. the topics include: Design of Mobile Application for Farmers;secured Fog-Based System for Smart Healthcare Application;Meta-Heuristic Optimal Path Planning in Blockchain-Aided UAV Swarm Network;internet of Behavior in Cybersecurity: Opportunities and Challenges;fusion of Federated learning and 6G in Internet-of-Medical-things: Architecture, Case Study and Emerging Directions;rail Track Monitoring System Using Quantum Key Distribution in IoT Scenario;Evaluating Tip Selection algorithms for IOTA Blockchain;LEAPS: Load and Emission Performance Characteristics for Sensor-Driven Green Transport Systems;a Smart Home Automation System Based on Internet of things (IoT) Using Arduino;generative Adversarial Network for Colorization of Mammograms;brain Depletion Recognition through Iot Sensors Empowered with Computational Intelligence;auction-Based Deadline and Priority-Enabled Resource Allocation in Fog–IoT Architecture;research of Basic Characteristics of Wireless Sensor Networks for Energy Monitoring System;An Energy Efficient Routing for Emergency Rescue in IoT-Based WSN;an Energy-Efficient Data Sensing Technique Using Compressive Sensing for IoT-Based Systems;dual-Hop Direct Diffusion Routing Protocol for Energy-Efficient Wireless Sensor Network;design and Development of Smart Waste Bin for Effective Waste Collection and Management;randomly Hiding Secret Data Using I-Blocks and E-Blocks for Image Steganography;on the Efficacy of Deep Image Denoising for Computer Vision applications;diabetic Retinopathy Classification Using Pixel-Level Lesion Segmentation;Coverage, Capacity and Cost Analysis of 4G-LTE and 5G Networks: A Case Study of Ahmedabad and Gandhinagar;password-Protected Lossless Visible Watermarking Technique for Digital Image;survey: Emotion Recognition from Text Using Different Approaches;cyber-Awareness for Dummies.

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A primal-dual-assisted penalty approach to bilevel optimization with coupled constraints 24

A primal-dual-assisted penalty approach to bilevel optimizat...

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Proceedings of the 38th international conference on Neural Information Processing Systems

作者： Liuyuan Jiang Quan Xiao Victor M. Tenorio Fernando Real-Rojas Antonio G. Marques Tianyi Chen Rensselaer Polytechnic Institute Troy NY King Juan Carlos University Madrid Spain

ISBN: (纸本)9798331314385

Interest in bilevel optimization has grown in recent years, partially due to its applications to tackle challenging machine-learning problems. Several exciting recent works have been centered around developing efficient gradient-based algorithms that can solve bilevel optimization problems with provable guarantees. However, the existing literature mainly focuses on bilevel problems either without constraints, or featuring only simple constraints that do not couple variables across the upper and lower-levels, excluding a range of complex applications. Our paper studies this challenging but less explored scenario and develops a (fully) first-order algorithm, which we term BLOCC, to tackle BiLevel optimization problems with Coupled Constraints. We establish rigorous convergence theory for the proposed algorithm and demonstrate its effectiveness on two well-known real-world applications - hyperparameter selection in support vector machine (SVM) and infrastructure planning in transportation networks using the real data from the city of Seville.

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Enhancing Wireless Communication Efficiency: Channel Models and optimization Techniques

Enhancing Wireless Communication Efficiency: Channel Models ...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Rohit Shekdar Vijay Rayar Department of Electronics and Communication Engineering KLE technological University's Dr. MSSCET Belagavi India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Wireless communication technology is an emerging field with a wide range of practical applications, such as environmental monitoring and the development of smart cities. Various factors such as signal transmission, path loss, interference, and environmental conditions have become major factors that affect channel efficiency in communication channels for such applications. this paper provides an exhaustive review of different channel models, including deterministic, empirical, and stochastic ones. the paper discusses their specific applications, the mathematical formulations associated with them, and presents a comprehensive analysis of these models that are largely based on key parameters. Additionally, this paper examines several optimization techniques that could potentially be applied to enhance channel modeling while reducing prediction errors that can enhance the reliability and efficiency of wireless communication systems.

关键词： Wireless communication Smart cities Reviews Stochastic processes Predictive models Propagation losses Mathematical models Channel models Reliability optimization

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Infrared Image Human Detection Based on Improved RT-DETR Algorithm

Infrared Image Human Detection Based on Improved RT-DETR Alg...

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Electronic Information Engineering and Computer Technology (EIECT), international conference on

作者： Long Xie Mingwu Ren School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798331528850

ISBN: (纸本)9798331528867

this study presents a novel approach for human detection in infrared images by enhancing the RT-DETR algorithm. the aim is to overcome challenges related to low detection accuracy and real-time performance issues in infrared target detection. the proposed method involves replacing the original RT-DETR algorithm's backbone network with ConvNeXt to improve feature extraction capabilities for low-resolution infrared images. Additionally, a Feature Pyramid Network (FPN) is incorporated to facilitate multi-scale feature fusion, thereby enhancing small target detection accuracy. Furthermore, optimization of the detection head and the replacement of GIoU loss with CIoU loss are carried out to enhance bounding box localization precision. Experimental results demonstrate that the proposed method achieves higher mean Average Precision (mAP) and precision on the Teledyne FLIR ADAS infrared dataset. this improvement effectively balances inference time and real-time performance, offering an efficient solution for human detection in infrared images suitable for practical applications.

关键词： Location awareness Adaptation models Accuracy Head Object detection Feature extraction Transformers Real-time systems Inference algorithms optimization

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Bayesian optimization considering constrained boundary exploration

Bayesian optimization considering constrained boundary explo...

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Big Data & Artificial Intelligence & Software Engineering (ICBASE), international conference on

作者： Fan Jia Mei Han School of Economics and Management Nanjing University of Aeronautics and Astronautics Nanjing China

Bayesian optimization has become a popular solution for solving black-box or expensive optimization problems. optimization problems accompanied with constraints are more common in practical applications. Existing methods usually focus on finding the optimal solution in the feasible region, and constrained boundaries sometimes lead to inefficiencies and even limit their applicability. therefore, this paper proposes a new strategy that uses the classical Kriging surrogate model and presents a two-stage acquisition function combining the search for a feasible optimal solution with constraint boundary exploration. the aim is to find the optimal solution satisfying the constraints quickly and precisely while minimizing the calls to the objective function and constraints. three numerical examples demonstrate the success of our proposed method.

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Investigating the Impacts of Weather and Personalisation on Asthma Exacerbations using Machine learning 2022

Investigating the Impacts of Weather and Personalisation on ...

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11th international conference on Software and Computer applications, ICSCA 2022

作者： Haque, Radiah Ho, Sin-Ban Chai, Ian Abdullah, Adina Faculty of Computing and Informatics Multimedia University Malaysia Faculty of Medicine University of Malaya Malaysia

ISBN: (纸本)9781450385770

this paper reports the results of investigating the impacts of weather and personalisation on asthma exacerbations. Additionally, several machine learning classification algorithms were applied and evaluated to develop an intelligent mHealth solution that can offer accurate personalised estimations of asthma exacerbations based on weather and demography and provide tailored precautionary guidelines for selfcare to each user. Consequently, the proposed solution will enhance asthma self-management by assisting patients with chronic asthma to plan their daily outdoor activities and limit exposure to weather triggers, thus controlling their asthma exacerbations. © 2022 ACM.

关键词： Machine learning Neural networks Personalisation Asthma self-management Classification algorithm

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