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

共 13501 条记录，以下是4971-4980 订阅

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A Differential Constrained optimization Algorithm Based on Component Model

A Differential Constrained Optimization Algorithm Based on C...

引用

Computer, Big Data and Artificial Intelligence (ICCBD+AI), international conference on

作者： Yuan Chen Xin Nie School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798350373233

ISBN: (纸本)9798350373240

the resolution and implementation of constrained optimization problems consistently remain a prominent subject in the realm of contemporary complex engineering applications. In this paper, the amalgamation of extraction and partitioning technology with the fundamental differential evolution algorithm is achieved, facilitating the transplantation of feasible component information through the algorithm’s inherent mutation operation and the segmentation technology’s feasible component breeding pool. Simultaneously, the adroit integration of adaptive constraint processing technology occurs within the original selection operation. In this paper, a component-based differential constraint algorithm is proposed and substantiated through numerical experiments conducted on common test functions. the experimental findings showcase the algorithm’s comparable or superior performance in terms of effectiveness, robustness, accuracy, and ease of implementation when compared to certain existing algorithms.

关键词： Adaptation models Computational modeling Robustness Stability analysis Data models Partitioning algorithms Numerical models

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threshold-free Anomaly Detection for Streaming Time Series through Deep learning 20

Threshold-free Anomaly Detection for Streaming Time Series t...

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20th IEEE international conference on Machine learning and applications (ICMLA)

作者： Zhang, Jing Wang, Chao Li, Zezhou Zhang, Xianbo JDTech Beijing Peoples R China

ISBN: (纸本)9781665443371

Anomaly detection for streaming time series is a key issue in real applications, especially in the IT industry like e-commerce. Instead of employing the traditional threshold-based approach to achieve anomaly detection, we propose a threshold-free approach through deep learning in this paper. Two parallel pipelines: the intelligent baseline (a neural network assisted with several optimization steps) and the unsupervised detection (a combination of neural network and multiple machine learning algorithms) cooperatively and comprehensively analyze the streaming time series. the intelligent baseline performs well in cases where time series show clear periodic morphology, while the unsupervised detection excels at cases where efficiency is highly required and the periodicity is less clear. With this complementary design of the two parallel modules, the threshold-free anomaly detection can he achieved without the dependence on careful threshold design. Experiments prove that the proposed threshold-free approach obtains accurate predictions and reliable detections.

关键词： anomaly detection time series machine learning deep neural network

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Multi-Agent Deep Reinforcement learning Offloading Algorithm Based on Resource Reservation and Task Adjustment

Multi-Agent Deep Reinforcement Learning Offloading Algorithm...

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international conference on Computer Engineering and applications (ICCEA)

作者： Hua Qu Qian Gao Jihong Zhao Yucheng Xue School of Software Xi'an Jiaotong University Xi'an Shaanxi China School of Information and Communication Engineering Xi 'an Jiaotong University Xi'an Shaanxi China

With the popularization of the Internet of things and mobile Internet, the data shows explosive growth. Adopting cloud computing mode will lead to network congestion and high energy consumption, and can't meet the low latency requirements of applications. Computing offloading technology offloads tasks to the edge of the network for processing, which can effectively make up for the shortcomings of the cloud computing model. However, in the multi-user and multi-server edge system, it is challenging to formulate the task offloading strategy. In this paper, we consider divisible and latency sensitive tasks and edge server load dynamics. then we model the edge computing scenario, and formulates a task offloading problem to minimize the overall task processing latency while meeting the application latency requirements. We propose a resource reservation strategy to expand the candidate destination nodes for task offloading and propose task adjustment strategy to adjust the resource allocation of elastic tasks which can improve the task carrying capacity of MEC system and then improve the average quality of experience. We use a multi-agent deep reinforcement learning method to solve optimization problems. the simulation results show that our method can significantly reduce the application latency rate and improve the average quality of experience and resource utilization of MEC system.

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Federated learning for Resource Allocation in Maritime IoT Networks with Compressed Sensing

Federated Learning for Resource Allocation in Maritime IoT N...

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Communication Technology and Information Technology (ICCTIT), international conference on

作者： Jiwei Liu Baiyi Li Shutong Liu Tingting Yang Zhongyang Mao Navigation College Dalian Maritime University Dalian China Department of Electronic and Information Engineering Naval Aviation University Yantai China

ISBN: (数字)9798331528973

ISBN: (纸本)9798331528980

Maritime transportation and shipping are vital components of global economic *** order to enhance shipping efficiency and ensure safety, a distributed machine learning architecture can be utilized to achieve it. Due to concerns about data privacy, many shipping data are not shared. Federated learning $(\mathbf{F L})$, as a form of distributed machine learning, offers the advantage of preserving privacy. the problem of communication efficiency and resource joint optimization in FL over communication networks has been studied, and proposed a communication resource allocation algorithm with compressed sensing. through experiments, we found that compared to the basic FL method with uniform resource allocation, the proposed RACS algorithm significantly improves the communication efficiency of wireless IoT network FL, as well as the learning performance in terms of training loss and testing accuracy.

关键词： Wireless communication Training Wireless sensor networks Federated learning Transportation Safety Resource management Compressed sensing optimization Testing

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Synergies of Federated learning and Blockchain for Patient-Centric Sustainable Next Generation Healthcare through Distributed Medical Internet of things 4

Synergies of Federated Learning and Blockchain for Patient-C...

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4th international conference on Distributed Sensing and Intelligent Systems, ICDSIS 2023

作者： Manna, Sarbajit Sarkar, Arindam Khan, Mohammad Zubair Noor, Ayman Department of Computer Science and Electronics Ramakrishna Mission Vidyamandira Belur Math West Bengal Howrah711202 India Department of Computer Science and Information Taibah University Madinah42353 Saudi Arabia Department of Computer Science College of Computer Science and Engineering Taibah University Madinah42353 Saudi Arabia

ISBN: (纸本)9781839539954

this study investigates how blockchain and Federated learning (FL) may work together to create a patient-centered, long-lasting healthcare ecosystem. the paper presents an innovative method for improving data privacy, security, and interoperability within healthcare systems by utilizing the distributed Medical Internet of things (MIoT). the next generation of healthcare services will be revolutionized by the synergistic combination of FL and blockchain, enabling a decentralized framework for enhanced patient care and data-driven decision-making. Centralized Machine learning (ML)-based methods lack security of patient’s sensitive medical information as all information of patient are trained in central server. To tackle with this problem, this paper offers amalgamation of FL and blockchain technology which enables training of ML models collectively by making use of distributed MIoT data, enhances the accuracy and also guarantees privacy of patient’s personal medical record. To preserve privacy over patient’s personal medical record, blockchain technology is used. the proposed approach offers a lot of benefits: (1) To predict disease more perfectly and effectively, this paper utilizes AdaBoost, Decision Tree (DT), Extra Tree Classifier (ETC), Linear Discriminant Analysis (LDA) and FL algorithm. (2) Each algorithm is tested against different data splitting, different cross validation and cross validation with feature selection techniques, hyperparameter tuning for model optimization and finding the best model. (3) the proposed methodology ensures collective learning among several healthcare organizations without sending patient’s private medical records and thus safeguarding privacy. (4) Accuracy, Markedness (MK), False Omission Rate (FOR), Negative Likelihood Ratio (NLR), Prevalence threshold (PT) are calculated to choose the best algorithm and also to show how well the FL algorithm works. (5) the paper is unique as the best algorithm in terms of different model evalua

关键词： Federated learning

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Optimizing Digital Marketing Strategies through Search Engine optimization

Optimizing Digital Marketing Strategies Through Search Engin...

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Contemporary Computing and Communications (InC4), IEEE international conference on

作者： Reshmi Tatikonda Jainath Ponnala Ramya thatikonda Dileep Kumar Yendluri M. Kempanna Bhuvanesh Ananthan Department of Computer Information University of the Central Missouri Warrensburg USA Department of Electronics and Communication Engineering Jawaharlal Technological University Hyderbad India Department of Artificial Intelligence and Machine Learning Bangalore Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (数字)9798350383652

ISBN: (纸本)9798350383669

this article examines SEO and its significance in digital marketing. the goal is to comprehend the main elements that impact achieving high rankings in search engines and the consequences of the presence of websites. Additionally, it strives to study its influence on website visibility, organic traffic, and rank in search results. this document comprehensively examines search engine optimization techniques, including optimizing website content and external factors, optimizing textual materials, and enhancing technical factors. Moreover, it examines the connection between SEO and UX (user experience). It emphasizes the significance of maximizing user happiness and participation. the document further explores the possible future of search engine optimization and online advertising. It contains new trends, expected challenges, and advice for future investigations and real-world applications. In general, the main objective of this paper is to present valuable viewpoints regarding the realm of SEO and its importance in the digital promotion arena.

关键词： Visualization Privacy Machine learning algorithms Machine learning Search engines Market research Prediction algorithms

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An Active learning based Latency Prediction Approach for Neural Network Architecture

An Active Learning based Latency Prediction Approach for Neu...

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Neural Networks, Information and Communication Engineering (NNICE), international conference on

作者： Yong Ji Daikun Wei Leiyang Shi Peng Liu Cheng Li Jun Yu Xu Cai Qingdao Power Supply Company State Grid Shandong Electric Power Company Qingdao China PR Ruixin Technology (Hebei Xiong'an) Co. Ltd Baoding China PR School of Computer Science Wuhan University Wuhan China PR

the multi-objective Neural Architecture Search (NAS) automates the process of neural network architecture design. It also evaluates and balances the accuracy and performance during the design process. the latency is used as a performance optimization object by many multi-objective NAS methods. A latency evaluation approach based on a latency prediction model can solve the efficiency problem of latency evaluation, but building a latency dataset of a certain scale for training the latency prediction model requires high computational costs. this paper uses multilayer perceptron (MLP) as the latency prediction model, takes an iterative selection of architectures to collect latency, trains the latency prediction model and proposes an active learning latency prediction model construction approach based on the performance of the latency prediction model to automatically stop collecting a large amount of latency data. It can avoid the high time cost problem. In the experiments, compared with the control group, using the active learning process to construct a latency prediction model for FPGA devices can save about 94% of the latency collection cost and achieve almost the same evaluation performance.

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4th international conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2024

4th International Conference on Advanced Network Technologie...

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4th international conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2024

ISBN: (纸本)9783031837821

the proceedings contain 109 papers. the special focus in this conference is on Advanced Network Technologies and Intelligent Computing. the topics include: Exploratory Study on Different Transformer Models;visionLingua: Empowering Multimodal Understanding with Cutting-Edge Language Models;human Action Recognition Using Mediapipe Holistic Keypoints: A Deep learning Approach;machine learning for Waste-to-Energy: optimization and Predictive Analytics;enhanced Traffic Forecasting for Urban Planning Using DeepWalk Embeddings and Spatio-Temporal Graph Ordinary Differential Equation Model;fault Detection in Semiconductor Manufacturing Using Advanced Machine learning Techniques with Interpretability;fine-Tuning RetinaNet for Few-Shot Fruit Detection;Smart Street: AI-Powered Traffic Flow Enhancement with Adaptive Signal Control;a Hybrid Machine learning Approach for Accurate Milk Quality Classification;Designing VoiceGPT Chatterbox Concierge: A Personal AI Assistant for Daily Services Using Supervised Machine learning Based Speech Recognition Techniques;improved Classification Techniques Using Convolutional Neural Networks for Rice Plant Disease Detection;Enhancing Customer Segmentation and Behavior Prediction through Machine learning and Medium Gaussian SVM;Grapevine SDLC Model for Real-Time Fake News Classification;Detecting AI-Generated Text Using Fine-Tuned Transformers: A Study on Academic Integrity;MoReco: AI Model Recommendation and optimization for B5G Networks;file Prefetching Accuracy Enhancement Using Deep learning Approaches;Mamba Based Conditional GAN for Optical Cloud Removal of Satellite Images;a Hybrid Quantum Machine learning Model for Multi-class Classifier;adaptive Resource Management in Massive Machine-Type Communications Using Reinforcement learning;Advanced Machine learning and Features Based Data Analytics of Electricity Consumption in USA and India;cross-Domain Adversarial Attacks and Robust Defense Mechanisms for Multimodal Neural Networks;deep Featu

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Machine learning in Sports Analytics: Volleyball Match Outcome Prediction

Machine Learning in Sports Analytics: Volleyball Match Outco...

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Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), international conference on

作者： Hsin-Yu Lin Jenn-Jier James Lien Ya-Ning Chang Miin Wu School of Computing National Cheng Kung University Tainan Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

ISBN: (数字)9798350391183

ISBN: (纸本)9798350391190

In the competitive realm of sports, accurate predictions of match outcomes can provide significant strategic advantages. this study aims to develop an accurate prediction system to assist coaches in making informed decisions. While there has been extensive research on AI applications in various sports, volleyball remains relatively underexplored. To bridge this gap, we first developed two comprehensive datasets from the Taiwan Volleyball League, one for male teams and one for female teams, each consisting of a range of performance metrics. We then leveraged various machine learning (ML) techniques, including Ridge Regression, Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM) models, to predict volleyball match results. To enhance model prediction, we also applied LASSO regression for feature selection and hyperparameter optimization for parameter selection. the results demonstrated that our ML models could effectively predict match outcomes, providing valuable insights for coaches to develop strategies and improve team performance.

关键词： Measurement Accuracy Mechatronics Computational modeling Predictive models Hyperparameter optimization Feature extraction Long short term memory Random forests Sports

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Research on Small Object Detection in Remote Sensing Images Based on Deep learning

Research on Small Object Detection in Remote Sensing Images ...

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IEEE international conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Haijian Wang Ziliang Hu Xuemei Zhao Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology School of Mechanical and Electrical Engineering Guilin University of Electronic Technology Guilin Guangxi China School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin Guangxi China

ISBN: (数字)9798350389418

ISBN: (纸本)9798350389425

Small object detection in remote sensing images has significant applications in fields such as military operations, environmental monitoring, and disaster warning. However, traditional detection methods face challenges such as small target size, complex background, and low signal-to-noise ratio, leading to suboptimal detection performance. In recent years, deep learning technology, with its powerful feature extraction capabilities, has gradually become a key tool for improving the performance of small object detection. this paper designs an efficient model for small object detection in remote sensing images based on deep learning algorithms, focusing on dataset construction, network architecture design, and training optimization strategies. through experimental validation, the proposed algorithm outperforms traditional methods in both accuracy and efficiency. Furthermore, this paper analyzes the potential engineering applications of the algorithm and discusses the challenges and future research directions.

关键词： Deep learning Training Accuracy Image resolution Object detection Feature extraction Environmental monitoring Remote sensing optimization Signal to noise ratio

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