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A Region convergence Analysis for Multi-mode Stochastic Optimization Based on Double-Well Function 19th

A Region Convergence Analysis for Multi-mode Stochastic Opti...

引用

19th international conference on Advanced Intelligent computing technology and Applications (ICIC)

作者： Yang, Guosong Wang, Peng Yin, Xinyu Chinese Acad Sci Chengdu Inst Comp Applicat Chengdu Peoples R China Southwest Minzu Univ Sch Comp Sci & Engn Chengdu Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9789819947546;9789819947553

In multi-mode heuristic optimization, the output fitness of an algorithm cannot converge to the global optimal value if its search points have not converged to the region with optimal solution. Generally, more samplings and more converged points in this optimal region may result in a higher probability of fitness convergence toward the optimal value. However, studies focus mainly on fitness convergence rather than region convergence (RC) of search points. This is partly because, for most objective functions, it is usually hard to track the region of search points in dynamic optimization. To remedy this, a novel analysis method is proposed using the double-well function (DWF), since it has a unique fitness landscape that makes it convenient to trace these points. First, a mathematical analysis of the DWF is given to explore its landscape. Then, RC is defined and discussed using DWF. On these bases, experiments are conducted and analyzed using Particle Swarm Optimization (PSO), and much useful information about its RC is revealed. Besides, this method can be used to analyze the RC of similar optimization algorithms as well.

关键词： Region convergence Multi-mode Optimization Double-well Function Heuristic Optimization Procedure-oriented Testing

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Dynamic Offloading of Complex Tasks Based on Generative Adversarial Q-Networks in Edge computing 5

Dynamic Offloading of Complex Tasks Based on Generative Adve...

引用

5th international conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2024

作者： Zhang, Lijuan Chen, Zhuo Li, Yan Chongqing University of Technology Chongqing China

ISBN: (纸本)9798331506612

In edge computing environment, dynamic offloading complex tasks is a key issue that affects the overall system performance and user experience. In cloud-edge-end collaborative systems, efficiently offloading tasks with dependencies and data sensitivity, while maximizing resource allocation and quality of service, is a challenging problem for edge service *** address this, we propose an optimization method based on the Generative Adversarial Q Network (GAQN).By incorporating Generative Adversarial Networks (GAN), we enhance Q-value estimation, improving the accuracy and stability of the policy. Specifically, we model complex task offloading in edge computing as a Markov Decision Process (MDP). Then,We design and implement GAQN, combining a generative adversarial network with a dueling deep Q network (Dueling DQN) to optimize task scheduling. Experimental results demonstrate that GAQN exhibits good stability and convergence with proper training, yielding optimal Q values. In terms of service quality, GAQN effectively reduces total system delay and energy consumption. Additionally, GAQN adapts to different network environments and efficiently handles task dependencies and data sensitivity, ensuring data privacy and smooth task execution. © 2024 IEEE.

关键词： Generative adversarial networks

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Joint Optimization of Offloading Scheduling and Path Planning for Space-air-ground Integrated Edge computing Systems 13

Joint Optimization of Offloading Scheduling and Path Plannin...

引用

13th international conference on Information and Communication technology convergence, ICTC 2022

作者： Kim, Doyoung Jeong, Seongah School of Electronic and Electrical Engineering Kyungpook National University Daegu41566 Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

In this paper, we propose a joint optimization of offloading scheduling and path planning for space-air-ground integrated edge computing systems so to minimize the total system energy cost. To this end, we formulate the problem, which is solved by an alternating algorithm based on the successive convex approximation (SCA) approach. Via numerical results, the performance of the proposed three-layer edge computing system design is verified. © 2022 IEEE.

关键词： Edge computing

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Multi-Layer Resource computing Offloading Based on Power Price Difference 23

Multi-Layer Resource Computing Offloading Based on Power Pri...

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23rd IEEE international conference on Communication technology, ICCT 2023

作者： Wang, Bo Guo, Chengrui Huang, Dongyan Guilin University of Electronic Technology Key Laboratory of Wideband Communication and Signal Processing Guilin China

ISBN: (纸本)9798350325959

Sixth-generation (6G) mobile communication technologies will drive convergence in areas such as cloud computing and multi-access edge computing (MEC) through integrated satellite-terrestrial networks, aiming to provide quality computing services to user equipment (UE). However, it is important to note that the power price may vary among different regions. This paper aims to study a multi-layer computing resource system that takes into account power price differences. For the multi-layer resource computing offloading problem, the optimization objective is defined as minimizing the weighted sum of the time cost and offloading cost. To address this problem, a Q-Learning-based multi-layer resource offloading (Q-MRO) scheme is proposed. Simulation results show that the Q-MRO scheme can obtain a lower total cost compared with other benchmark schemes. © 2023 IEEE.

关键词： computing offloading Multi-access edge computing (MEC) Multi-layer resource Power price Q-Learning

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Mobility-Aware Vehicle Selection Strategy for Federated Learning in the Internet of Vehicles

Mobility-Aware Vehicle Selection Strategy for Federated Lear...

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2024 IEEE international conference on Communications, computing, Cybersecurity and Informatics, CCCI 2024

作者： Chang, Xing Obaidat, Mohammad S. Xue, Xiaoping Ma, Jingxiao Duan, Xiaowen College of Electronic and Information Engineering Tongji University Shanghai201804 China King Abdullah II School of Information Technology The University of Jordan Amman11942 Jordan School of Engineering The Amity University UP Noida201301 India School of Computing SRM University SRM Nagar Kattankulathur603203 India School of Information Science and Engineering Lanzhou University Lanzhou730000 China

ISBN: (纸本)9798350349832

Federated Learning (FL) in the Internet of Vehicles (IoV) is a distributed machine learning technology that allows Intelligent Connected Vehicles (ICVs) to collaboratively train models without sharing raw data. In vehicular FL, the selection of participating nodes directly impacts the efficiency and accuracy of the model. Specifically, selecting appropriate ICVs ensures that participating nodes provide high-quality data while possessing sufficient computational resources and communication capabilities. While some existing studies have focused on optimizing resource allocation, improving data quality, and proposing ICV selection strategies, research on the comprehensive impact of ICV mobility on these factors remains limited. This paper proposes a dynamic optimization-based ICV selection algorithm aimed at effectively addressing the impact of mobility on FL performance in IoV. Our strategy combines factors such as ICVs' geographic location, speed, and data quality, selecting the optimal subset of ICVs within learning time constraints to ensure that participating ICVs can provide high-quality data and complete training tasks on time. Simulation results show that this method significantly improves the convergence speed and accuracy of FL models, validating the effectiveness of the proposed approach. © 2024 IEEE.

关键词： Federated learning

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Emotional Contribution to Parkinson's Disease Diagnosis using Machine Learning for EEG Dataset Generation 6

Emotional Contribution to Parkinson's Disease Diagnosis usin...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Arockiya Selvi, S. Kamalakannan, T. Department of Computer science VEL’S Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

ISBN: (纸本)9798331522667

Emotion is the root cause of nearly all harmful illnesses in humans. Emotion affects blood pressure, heart rate, kidney function, and a few disorders of the nervous system. Parkinson's disease, which causes impairment of movement, is one of the brain disorders. This issue is caused by a reduction in dopamine release in the cortex of the brain. Dopamine production deviations have been related to essential hypertension. Dopamine has a crucial role in anxiety modulation in multiple regions of the brain. Patients with Parkinson's disease frequently indicate that acute stress worsens their motor symptoms, especially gait freezing, dyskinesia, and tremors and the emotions play a substantial role in Parkinson's disease. This study focuses on the Classification Learner algorithms and the Electro Encephalogram (EEG) dataset, both of which covered the diagnostic range of PD development. The classifiers were evaluated using key performance indicators such as error rate, accuracy, sensitivity, specificity, and precision. The findings illustrate the convergence of emotional analysis, machine learning (ML), and neuroscience in improving diagnostic capabilities by demonstrating how a combination of EEG datasets with KNN algorithms offers a reliable and accurate approach for early Parkinson's disease identification. © 2025 IEEE.

关键词： Blood pressure

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Big Data based Potential Fixed-Mobile convergence User Mining 21

Big Data based Potential Fixed-Mobile Convergence User Minin...

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21st IEEE international conference on Trust, Security and Privacy in computing and Communications (IEEE TrustCom)

作者： Zhang, Qingqing Zhang, Tao Jiang, Shikun Zhang, Qiang Han, Yuhui Cheng, Xinzhou Wang, Yunyun He, Xin Xiao, Tian China United Network Commun Corp Res Inst Beijing Peoples R China China United Network Commun Grp Corp Govt & Enterprise Customer Business Grp Beijing Peoples R China China United Network Commun Corp Qinghai Prov Branch Xining Peoples R China

ISBN: (纸本)9781665494250

With the disappearance of the demographic dividend and the saturation of the public telecom market, telecom operators need new development strategies urgently. New services formed by the convergence of mobile network services and broadband network services (referred to as fixed-mobile convergence services) have become an important strategy. Through business innovation, telecom operators can bundle mobile services with broadband services, which can enhance user stickiness and increase business revenue. Based on the joint analysis of mobile network data and broadband network data, this paper proposes a rule-based and model-based integrated method for mining potential fixed-mobile convergence target users. After applying this method to the real market for single mobile contract user transferring to convergent contract, results show that the proposed method for exploiting potential target users can increase the conversion rate of convergence users.

关键词： Big Data Analysis Precision Marketing fixed- mobile convergence

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Rating Prediction from User Feedback Reviews Using BERT-RS 9

Rating Prediction from User Feedback Reviews Using BERT-RS

引用

9th IEEE international conference for convergence in technology, I2CT 2024

作者： Dinata, Asfa Amalia Baizal, Z.K.A. Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9798350394474

Skincare is part of skin treatment. It is important for users to choose skincare products appropriately to avoid more skin problems and money waste. To avoid such problems, skincare buyers tend to read reviews from other users before buying skincare products. However, reading too many reviews causes information overload, making users even more confused. The skincare recommender system that has been built has not been able to resolve the sparsity of explicit ratings and information overloading of reviews. Therefore, we introduced a skincare recommender system using Bidirectional Encoder Representations from Transformers for Recommender System (BERT-RS) that combines Neural Collaborative Filtering (NCF) and BERT. This approach can improve the NCF model by adding the ability to analyze potential and emotional factors in textual comments. In addition, we also use additional attributes such as skin type, skin problems, brand name, and product price. Experiments show that the use of additional attributes significantly improves model performance, with an Root Mean Squared Error (RMSE) of 0.3039. Besides that, the experiments conducted also highlight the importance of data normalization in influencing model performance. Normalized data results in better RMSE compared to models that use unnormalized data. © 2024 IEEE.

关键词： Recommender systems

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Detection and Classification of Speech Disorder using FOA-SCNet 3

Detection and Classification of Speech Disorder using FOA-SC...

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3rd international conference on computing and Information technology, ICCIT 2023

作者： Sujigarasharma, K. Shri, M. Lawanya Gangadevi, E. Dhanaraj, Rajesh Kumar Narmatha, C. Balusamy, Balamurugan Vellore Institute of Technology Site Vellore India Loyola College Chennai India Maharashtra Pune India University of Tabuk FCIT Tabuk Saudi Arabia Shiv Nadar University Uttar Pradesh Noida India

ISBN: (纸本)9798350321487

Speech disorders have turned out a great brunt on the ability of speech. The persons have speech disorders were struggling with communication and emotional expressions. The proposed work detects and classifies the speech disorders using combined CNN and SVM techniques. To improve the prediction accuracy of the proposed model, we use the Fruitfly optimization algorithm for improving the convergence rate and accuracy results. The study mainly focuses on young people in which the speech disorders are prevalent. The machine algorithm is utilized to predict a person has healthy or pathological voice accurately. © 2023 IEEE.

关键词： Support vector machines

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Analyzing Urban Transportation Services using RideShare Data Insights 9

Analyzing Urban Transportation Services using RideShare Data...

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9th IEEE international conference for convergence in technology, I2CT 2024

作者： Jashwanth, Kandukuri Sai Praneeth Reddy, Koti Leela Sai Snehitha, Munaga Sampath, Nalini Nair, Priyanka C. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

In this fast-growing world, the need for choosing or booking a ride is increasing. Since the fare displayed on booking apps such as Uber, Rapido, etc., and the fare after the ride is completed differ, it is imperative to predict the fare. There is a large difference in those prices. To address these issues, an optimized model for ride fare prediction should be there. These rideshare services brought a large change in the transportation system but predicting the fare system has to change. The study presents various machine-learning models of regression such as the Selection Operator regressor, Elastic-Net regressor, Ridge regressor, Random Forest regressor, Least Absolute Shrinkage, and XGBoost regressor. The XGBoost regressor outperformed its counterparts with an R2 value of 0.9734. After the regression models are applied the label values are classified into three categories: high, medium, and low. The obtained ordinal label values are given into classification algorithms like Naive Bayes, Extra Trees classifier, Support Vector Machine, and Multi-layer Perceptron are applied. A comparative analysis is done and SVM performed the best in classification algorithms with an F1 score of 99.53. © 2024 IEEE.

关键词： Forecasting

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