International Journal of Intelligent Networks最新文献_第3页

Enhancing road safety in internet of vehicles using deep learning approach for real-time accident prediction and prevention 利用深度学习方法实时预测和预防事故，加强车联网中的道路安全

International Journal of Intelligent Networks Pub Date : 2024-01-01 DOI: 10.1016/j.ijin.2024.05.002

Xu Wei

{"title":"Enhancing road safety in internet of vehicles using deep learning approach for real-time accident prediction and prevention","authors":"Xu Wei","doi":"10.1016/j.ijin.2024.05.002","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.05.002","url":null,"abstract":"<div><p>The paper proposes an Internet of Vehicles (IoV)-based Accident Prediction and Prevention System that leverages the Internet of Things (IoT) to tackle the road safety challenges arising from the increased rate and volume of traffic due to population growth. In order to enhance road safety and efficiency, the IoV devices enable real-time data transmission and analysis. The proposed multi-tier framework tracks vehicle and roadside unit (RSU) data, encompassing road traffic conditions and vehicle data. The framework integrates vehicles, road traffic, weather conditions, and external factors. On a cloud-based control server, the proposed Spatio-Temporal Conv-Long Short-Term Memory Autoencoder (STCLA) framework deals with and analyzes the resulting data. This research addresses road safety on the Internet of Vehicles via DL. It proposes a novel framework for real-time accident prevention and prediction, demonstrating its effectiveness and potential impact. In a year-long research in Hubei Province, China, data from two road segments demonstrated a substantial boost in predictive accuracy, achieving an Area Under the Receiver Operating Characteristic Curve (AUROC) score of 0.94.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 212-223"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000216/pdfft?md5=391fca7ae3352f4576588898ec427410&pid=1-s2.0-S2666603024000216-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140951010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on intelligent vehicle Traffic Flow control algorithm based on data mining 基于数据挖掘的智能车辆交通流量控制算法研究

International Journal of Intelligent Networks Pub Date : 2024-01-01 DOI: 10.1016/j.ijin.2024.02.004

Lihua Cheng , Ke Sun

{"title":"Research on intelligent vehicle Traffic Flow control algorithm based on data mining","authors":"Lihua Cheng , Ke Sun","doi":"10.1016/j.ijin.2024.02.004","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.02.004","url":null,"abstract":"<div><p>Traffic Congestion (TC) is increasing due to urban growth and vehicle numbers, rendering the development of cities and people's well-being difficult. Traffic Prediction (TP) and control systems have been required to improve Traffic Flow (TF) and reduce TC because standard methods are unsuitable. The paper proposes an innovative method for traffic control using the Dynamic Zone Segmentation Algorithm (DZSA) to solve this significant issue. The algorithm uses real-time data and road conditions to partition city traffic into manageable units, enhancing the adaptability and accuracy of Traffic Prediction (TP) performance. Applying DZSA, the recommended Long Short-Term Memory + Bayesian Structural Time Series (LSTM + BSTS) learning model optimizes TP by integrating the best features of conventional and Machine Learning (ML) methods. The model optimized quality performance when experimentally tested against other benchmark models using metrics like Mean Absolute Error, Mean Absolute Scaled Error, Accuracy Percent, Root Mean Squared Error, and Mean Absolute Percent Error. The recommended model, LSTM + BSTS, shows a minimal error rate of 6.68%, indicating its success.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 92-100"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000101/pdfft?md5=c87971d1e870a72ddbce209838375d01&pid=1-s2.0-S2666603024000101-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139748845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data structure and privacy protection analysis in big data environment based on blockchain technology 基于区块链技术的大数据环境下的数据结构与隐私保护分析

International Journal of Intelligent Networks Pub Date : 2024-01-01 DOI: 10.1016/j.ijin.2024.02.005

Yu Wang

{"title":"Data structure and privacy protection analysis in big data environment based on blockchain technology","authors":"Yu Wang","doi":"10.1016/j.ijin.2024.02.005","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.02.005","url":null,"abstract":"<div><p>In today's digital world, the rapid advancement of Information Technology (IT) has made it crucial to prioritize the protection and management of data storage and retrieval. It is vital to challenge the difficulties related to dispersed and decentralized data to build strong mechanisms for access control and provide effective authorization and authentication in data processing. In the contemporary context of IT, the imperative to secure data storage and retrieval has become distinctly observed. The challenges posed by distributed and decentralized data demand the development of robust mechanisms for access control, demanding a focus on proper authorization and authentication in transaction processing. This research addresses the existing gap by comprehensively adapting data structures effectively to the evolving needs of secure access and storage control. It uses the Enhanced Merkle Tree (EMT) as a novel data structure. This article initially modifies the conventional Merkle Tree (MT) structure used in Blockchain technology to suit e-healthcare Systems (e-HS) requirements. The EMT enhances data security in access and storage and significantly improves data integrity management. Its constant three-degree MT with multiple leaves, branches, and a single root node enables updated data authentication, verification, and validation procedures. The proposed method is applied to the e-HS scenario, and the proposed EMT outperforms existing <em>state-of-the-art</em> techniques, achieving a minimal verification time of 14.26 m for 100 transactions. This research, therefore, contributes to the discourse on data security by presenting an innovative and efficient solution tailored to the unique challenges of e-health systems.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 120-132"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000113/pdfft?md5=8cfe3cff84ce9d5df986f7cbe903e86e&pid=1-s2.0-S2666603024000113-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139945011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modelling and analysis of fiber Bragg grating temperature sensor for Internet of things applications (FBG-4-IoT) 物联网应用光纤布拉格光栅温度传感器（FBG-4-IoT）的建模与分析

International Journal of Intelligent Networks Pub Date : 2024-01-01 DOI: 10.1016/j.ijin.2024.05.006

Paul Stone Macheso , Mohssin Zekriti

{"title":"Modelling and analysis of fiber Bragg grating temperature sensor for Internet of things applications (FBG-4-IoT)","authors":"Paul Stone Macheso , Mohssin Zekriti","doi":"10.1016/j.ijin.2024.05.006","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.05.006","url":null,"abstract":"<div><p>The integration of Fiber Bragg Grating (FBG) sensors into the Internet of Things (IoT) has garnered significant attention in recent years because of their immunity to electromagnetic and radio frequency interference, small size and weight, and corrosion resistance. This paper aims to enhance the performance characteristics of FBG sensors for temperature measurement by proposing a specific design of their parameters, thus facilitating their implementation in IoT applications. The FBG temperature sensor is designed to operate in the 1500–1600 nm wavelength range. The outcomes display a high sensitivity of 0.61 nm/°C with a Full Width Half Maxima (FWHM) of 7.893 nm and a Figure of Merit (FOM) of 7.72 x 10<sup>−2</sup>/°C. The calculated Quality Factor (Q) of the sensor was 195.67. When compared with previous studies in the literature, our obtained results confirm the enhanced performance of the proposed design of the FBG sensor, which render it suitable for utilization in most industrial use cases, especially in harsh environments.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 224-230"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000253/pdfft?md5=a7598bca698b0ec7a293ae86f8b93768&pid=1-s2.0-S2666603024000253-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141068734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep CNN based brain tumor detection in intelligent systems 智能系统中基于深度 CNN 的脑肿瘤检测

International Journal of Intelligent Networks Pub Date : 2024-01-01 DOI: 10.1016/j.ijin.2023.12.001

Brij B. Gupta , Akshat Gaurav , Varsha Arya

{"title":"Deep CNN based brain tumor detection in intelligent systems","authors":"Brij B. Gupta , Akshat Gaurav , Varsha Arya","doi":"10.1016/j.ijin.2023.12.001","DOIUrl":"10.1016/j.ijin.2023.12.001","url":null,"abstract":"<div><p>The early detection of brain tumor is crucial for effective treatment and improved patient prognosis in Industrial Information Systems. This research introduces a novel computational model employing a three-layer Convolutional Neural Network (CNN) for the identification of brain tumors in Industrial Information Systems. Leveraging advanced computational techniques, this proposed model can autonomously detect intricate patterns and features from medical imaging data, resulting in more accurate and expedited diagnoses. With an impressive 90 % precision rate, our model demonstrates the potential to serve as a valuable tool for medical professionals working in the field of neuroimaging. By presenting a dependable and precise computational model, this study contributes to the advancement of brain tumor identification within the domain of medical imaging. We anticipate that our methodology will aid healthcare providers in making more accurate diagnoses, thereby leading to enhanced patient outcomes. Potential avenues for future research encompass refining the model's fundamental architecture and exploring real-time therapeutic applications.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 30-37"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603023000465/pdfft?md5=dfed15092cf58564d51a55c1d9f1edbe&pid=1-s2.0-S2666603023000465-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139637278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multimodal spatio-temporal framework for real-world affect recognition 真实世界情感识别的多模态时空框架

International Journal of Intelligent Networks Pub Date : 2024-01-01 DOI: 10.1016/j.ijin.2024.10.001

Karishma Raut , Sujata Kulkarni , Ashwini Sawant

{"title":"Multimodal spatio-temporal framework for real-world affect recognition","authors":"Karishma Raut , Sujata Kulkarni , Ashwini Sawant","doi":"10.1016/j.ijin.2024.10.001","DOIUrl":"10.1016/j.ijin.2024.10.001","url":null,"abstract":"<div><div>Deep learning models show great potential in applications involving video-based affect recognition, including human-computer interaction, robotic interfaces, stress and depression assessment, and Alzheimer's disease detection. The low complex Multimodal Diverse Spatio-Temporal Network (MDSTN) has been analysed to effectively capture spatio-temporal information from audio-visual modalities for affect recognition using the Acted Facial Expressions in the Wild (AFEW) dataset. The scarcity of data is handled by data augmented parallel feature extraction for visual network. Visual features extracted by carefully reviewing and customizing Convolutional 3D architecture over different ranges are combined to train a neural network for classification. Multi-resolution Cochleagram (MRCG) features from speech, along with spectral and prosodic audio features, are processed by a supervised classifier. The late fusion technique is explored to integrate audio and video modalities, considering their processing over different temporal spans. The MDSTN approach significantly boosts the accuracy of basic emotion recognition to 71.54 % on the AFEW dataset. It demonstrates exceptional proficiency in identifying emotions such as disgust and surprise, thus exceeding current benchmarks in real-world affect recognition.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 340-350"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A new intelligent controller based on integral sliding mode control and extended state observer for nonlinear MIMO drone quadrotor 基于积分滑模控制和扩展状态观测器的非线性 MIMO 无人机四旋翼新型智能控制器

International Journal of Intelligent Networks Pub Date : 2024-01-01 DOI: 10.1016/j.ijin.2024.01.005

Moussa Abdillah , El Mehdi Mellouli , Touria Haidi

{"title":"A new intelligent controller based on integral sliding mode control and extended state observer for nonlinear MIMO drone quadrotor","authors":"Moussa Abdillah , El Mehdi Mellouli , Touria Haidi","doi":"10.1016/j.ijin.2024.01.005","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.01.005","url":null,"abstract":"<div><p>Unmanned aerial vehicles (UAVs) control faces major challenges such as dynamic complexity, unknown external disturbances, parametric uncertainties, time-varying states and delays. The literature proposes different techniques to address these challenges, but little attention has been paid to the design of a hybrid controller combining the advantages of these techniques to improve system performance. This research therefore aims to investigate the design of such a hybrid controller. In this paper, we present a novel intelligent controller based on Integral Sliding Mode Control (ISMC) and Extended State Observer (ESO) for a nonlinear Multiple Input Multiple Output (MIMO) drone quadrotor. First, the kinematic and dynamic models of our quadrotor drone are presented. Second, the ESO is used to estimate external disturbances and model uncertainties. Third, to overcome the problem of the reaching phase and the steady-state error, a new nonlinear ISMC is designed. The additive term of the ISMC structure has also overcome the problem of external disturbances and modelling errors, as well as observational errors. Fourth, an Adaptive Neural Network (ANN) switching control law is developed to surmount the chattering phenomenon. In addition, the stability of the control system is verified using Lyapunov stability theory. Finally, the effectiveness and superiority of the proposed control method are proved by simulation results. The results show that the proposed approach can handle external disturbances and eliminate chatter, leading to smooth control laws and lower power consumption, which is excellent from an energy efficiency perspective.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 49-62"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000058/pdfft?md5=bf10b8e94c36179eb2344a4711679e92&pid=1-s2.0-S2666603024000058-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139714214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generative artificial intelligence for distributed learning to enhance smart grid communication 用于分布式学习的生成式人工智能增强智能电网通信

International Journal of Intelligent Networks Pub Date : 2024-01-01 DOI: 10.1016/j.ijin.2024.05.007

Seyed Mahmoud Sajjadi Mohammadabadi , Mahmoudreza Entezami , Aidin Karimi Moghaddam , Mansour Orangian , Shayan Nejadshamsi

{"title":"Generative artificial intelligence for distributed learning to enhance smart grid communication","authors":"Seyed Mahmoud Sajjadi Mohammadabadi , Mahmoudreza Entezami , Aidin Karimi Moghaddam , Mansour Orangian , Shayan Nejadshamsi","doi":"10.1016/j.ijin.2024.05.007","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.05.007","url":null,"abstract":"<div><p>Machine learning models are the backbone of smart grid optimization, but their effectiveness hinges on access to vast amounts of training data. However, smart grids face critical communication bottlenecks due to the ever-increasing volume of data from distributed sensors. This paper introduces a novel approach leveraging Generative Artificial Intelligence (GenAI), specifically a type of pre-trained Foundation Model (FM) architecture suitable for time series data due to its efficiency and privacy-preserving properties. These GenAI models are distributed to agents, or data holders, empowering them to fine-tune the foundation model with their local datasets. By fine-tuning the foundation model, the updated model can produce synthetic data that mirrors real-world grid conditions. The server aggregates fine-tuned model from all agents and then generates synthetic data which considers all data collected in the grid. This synthetic data can be used to train global machine learning models for specific tasks like anomaly detection and energy optimization. Then, the trained task models are distributed to agents in the grid to leverage them. The paper highlights the advantages of GenAI for smart grid communication, including reduced communication burden, enhanced privacy through anonymized data transmission, and improved efficiency and scalability. By enabling a distributed and intelligent communication architecture, GenAI introduces a novel way for a more secure, efficient, and sustainable energy future.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 267-274"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000265/pdfft?md5=b36de28bb4f3c1a5f7cec09e98576268&pid=1-s2.0-S2666603024000265-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141291759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing aspect category detection in imbalanced online reviews: An integrated approach using Select-SMOTE and LightGBM 增强不平衡在线评论中的方面类别检测：使用 Select-SMOTE 和 LightGBM 的综合方法

International Journal of Intelligent Networks Pub Date : 2024-01-01 DOI: 10.1016/j.ijin.2024.10.002

Chuanjun Zhao , Zhihe Yan , Xuzhuang Sun , Meiling Wu

{"title":"Enhancing aspect category detection in imbalanced online reviews: An integrated approach using Select-SMOTE and LightGBM","authors":"Chuanjun Zhao , Zhihe Yan , Xuzhuang Sun , Meiling Wu","doi":"10.1016/j.ijin.2024.10.002","DOIUrl":"10.1016/j.ijin.2024.10.002","url":null,"abstract":"<div><div>Aspect category detection (ACD) is a pivotal subtask within the field of sentiment analysis in natural language processing, aiming to identify implicit aspect category information in online review texts. In real-world scenarios of online review category detection tasks, data imbalance often arises, leading to skewed distributions among distinct review categories. This phenomenon poses substantial challenges for accurately recognizing minority categories through modeling. To address this, we propose a method for detecting imbalanced aspect categories by combining the selective synthetic over-sampling (Select-SMOTE) algorithm with the light gradient boosting machine (LightGBM). Our approach commences with text data representation through features, followed by a strategy involving joint sample partitioning and boundary optimization within the feature space to generate minority class samples. This partitioning strategy aligns generated data more closely with the original distribution, while the boundary optimization module enhances classification performance by eliminating samples near boundaries. Subsequently, the balanced dataset is input to the LGB model, enabling the extraction of aspect category information through parameter optimization and class weight assignment. Finally, our method is evaluated using the SemEval and SentiHood datasets and compared with prevailing sampling methods and classification models. Empirical results manifestly demonstrate the method’s superiority across diverse metrics, reflecting robustness and effective mitigation of imbalanced data challenges in ACD.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 364-372"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Face recognition technology for video surveillance integrated with particle swarm optimization algorithm 集成粒子群优化算法的视频监控人脸识别技术

International Journal of Intelligent Networks Pub Date : 2024-01-01 DOI: 10.1016/j.ijin.2024.02.008

You Qian

{"title":"Face recognition technology for video surveillance integrated with particle swarm optimization algorithm","authors":"You Qian","doi":"10.1016/j.ijin.2024.02.008","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.02.008","url":null,"abstract":"<div><p>With the rapid development of video surveillance technology, face recognition has become an important security and surveillance tool. To improve the accuracy and applicability of face recognition in video surveillance, this study improved the Inertia Weight (IW) and Learning Factor (LF) based on the Particle Swarm Optimization (PSO) algorithm. Support Vector Machine (SVM) algorithm and Local Binary Mode (LBP) were used to optimize the processing. The results showed that the optimal solution could be obtained after 10 iterations, and the recognition accuracy reached 92.3%. When the number of iterations reached 40, the recognition accuracy inertia weight reached 99.7%. The average operating time of the original PSO algorithm and the optimized PSO algorithm was 26.3 s and 24.7 s, respectively. This shows that the optimization algorithm not only improves the recognition accuracy, but also shortens the operation time, and enhances the convergence performance and robustness to varying degrees. The improved model can improve the recognition rate of video surveillance system, indicating that the optimization algorithm has great application potential in the video surveillance face recognition.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 145-153"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000149/pdfft?md5=3d3263b33fe3d1c605dd0e3b65dc3425&pid=1-s2.0-S2666603024000149-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140031357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0