International Journal of Intelligent Networks最新文献

{"title":"Improving distributed systems failure prediction via multi-objective feature selection and deep forest","authors":"Zhidan Yuan ,&nbsp;Yikai Zhang ,&nbsp;Yingqi Yu ,&nbsp;Taizhi Lv ,&nbsp;Tao Huang","doi":"10.1016/j.ijin.2025.07.002","DOIUrl":"10.1016/j.ijin.2025.07.002","url":null,"abstract":"<div><div>Distributed systems failure prediction uses Key Performance Indicator (KPI) metrics to train machine learning models to identify potential system failures. Currently, this approach faces the challenge of KPI metrics redundancy. Although existing studies have used feature selection methods to address this issue, the optimal number of KPI metrics remains difficult to determine. Furthermore, existing failure prediction models have not achieved satisfactory performance. To address the aforementioned challenges, we propose a failure prediction method, MOFSDF, which leverages multi-objective optimization and the deep forest model. The method aims to achieve optimal failure prediction performance with the fewest KPI metrics. MOFSDF frames the selection of KPI metrics as a multi-objective optimization problem, where the objective is to minimize the number of KPI metrics while simultaneously maximizing the model’s performance. This optimization process is achieved through the NSGA-III algorithm. Furthermore, the integration of the deep forest model guarantees both the robustness and generalization capabilities of the failure prediction model. We use the ZTE dataset for the empirical study. The results demonstrate that MOFSDF significantly outperforms other comparative methods across multiple dimensions. Compared to nine classical models, the <span><math><mrow><mi>A</mi><mi>c</mi><mi>c</mi><mi>u</mi><mi>r</mi><mi>a</mi><mi>c</mi><mi>y</mi></mrow></math></span> improvement ranges from 1.1% to 97.9%, while the <span><math><mrow><mi>M</mi><mi>a</mi><mi>c</mi><mi>r</mi><mi>o</mi><mo>−</mo><mi>F</mi><mn>1</mn></mrow></math></span> improvement ranges from 1.1% to 179.4%. The <span><math><mrow><mi>K</mi><mi>a</mi><mi>p</mi><mi>p</mi><mi>a</mi></mrow></math></span> and <span><math><mrow><mi>H</mi><mi>a</mi><mi>m</mi><mi>m</mi><mi>i</mi><mi>n</mi><mi>g</mi><mspace></mspace><mi>L</mi><mi>o</mi><mi>s</mi><mi>s</mi></mrow></math></span> also show significant improvements. Moreover, the findings from the ablation studies further substantiate the rationale and effectiveness of the component configuration within MOFSDF.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"6 ","pages":"Pages 151-165"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GAN-based solar radiation forecast optimization for satellite communication networks","authors":"Chao Chen ,&nbsp;Xin Liu ,&nbsp;Shizhong Zhao ,&nbsp;Muhammad Bilal","doi":"10.1016/j.ijin.2025.07.004","DOIUrl":"10.1016/j.ijin.2025.07.004","url":null,"abstract":"<div><div>Accurate short-term solar radiation forecasting is essential for the stable operation and dispatch of photovoltaic power generation systems. Advanced encoder–decoder architectures, utilizing satellite remote sensing data, are now primary techniques for this forecasting task. However, these methods encounter significant limitations, particularly as forecast horizons extend. In such scenarios, predictions often exhibit spatial texture degradation and distortions in radiation intensity. This significantly reduces precision and reliability, making it difficult to meet the demands of high-precision applications. To address these limitations, this paper proposes GAN-Solar, a novel quality optimization model for short-term solar radiation forecasting based on Generative Adversarial Networks (GANs). GAN-Solar utilizes an ED-AttUNet model, enhanced with conditional inputs, as its generator. A discriminator, incorporating residual structures, progressive downsampling, and conditional information, is employed to distinguish between real and generated forecasts. This adversarial process, guided by a hybrid loss function and a discriminator treated as a learnable objective function, refines the forecast quality. Experimental results on summer solar radiation data demonstrate that GAN-Solar significantly improves forecast quality. It reduces the Root Mean Square Error by approximately 3.2% and increases the Structural Similarity Index from 0.84 to 0.87 when compared to the baseline ED-AttUNet model. The proposed method effectively mitigates issues of texture degradation and intensity distortion, leading to clearer and more accurate solar radiation predictions.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"6 ","pages":"Pages 89-96"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resource optimization algorithm for 5G core network integrating NFV and SDN technologies","authors":"Chunxue Xu","doi":"10.1016/j.ijin.2025.04.001","DOIUrl":"10.1016/j.ijin.2025.04.001","url":null,"abstract":"<div><div>The growth in network demand has driven the development of new network technologies. However, traditional network architecture cannot meet the huge traffic of transportation and different business needs. To address this issue, a specific network service function chain is formed based on the network function virtualization. Dynamic resource awareness algorithms are introduced to construct an adaptive migration model based on network function virtualization. Based on the Multi-Armed Bandit (MAB) algorithm, a dynamic routing model based on MAB is constructed by using a greedy algorithm to search for random actions. When the nodes were 200 and 500, the migration costs of the adaptive migration model based on network function virtualization were 1000 and 3000, respectively. The average migration was 350 and 900 respectively, while destination nodes' average resource occupancy rates were 52 % and 58 %, respectively. When the path failure rates were 4 % and 20 %, the algorithm's safe path rates were 96.25 % and 92.75 %. For fixed and mobile nodes, the link load rate of the dynamic routing model based on the MAB algorithm was low and the load growth was relatively stable. This dynamic routing model's link delay is significantly less than the Dijkstra algorithm. These two models can maximize server resource utilization, reduce cost consumption, and achieve maximum overall benefits.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"6 ","pages":"Pages 36-46"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SMS spam detection using BERT and multi-graph convolutional networks","authors":"Linjie Shen ,&nbsp;Yanbin Wang ,&nbsp;Zhao Li ,&nbsp;Wenrui Ma","doi":"10.1016/j.ijin.2025.06.002","DOIUrl":"10.1016/j.ijin.2025.06.002","url":null,"abstract":"<div><div>The surge in smartphone usage has significantly increased Short Message Service (SMS) traffic and, consequently, SMS spam, posing risks such as phishing, financial losses, and privacy breaches. Traditional rule-based and blacklist methods fail against evolving spamming techniques, prompting the adoption of machine learning and deep learning approaches. However, models like Convolutional Neural Networks and Recurrent Neural Networks struggle to capture global co-occurrence patterns and complex semantics, while transformer-based models like Bidirectional Encoder Representations from Transformers (BERT) lack explicit syntactic and co-occurrence modeling. To address these limitations, we propose the BERT with Triple-Graph Convolutional Networks (BERT-G3CN) model, the first framework to integrate BERT word embeddings with graph embeddings from Co-occurrence, Heterogeneous, and Integrated Syntactic Graphs. This multigraph approach captures diverse features and models both global and local structures using tailored Graph Convolutional Networks. Experiments on two benchmark datasets demonstrate that BERT-G3CN achieves superior accuracy of 99.28 % and 93.78 %, representing an improvement of approximately 2–3 % over competitive baselines.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"6 ","pages":"Pages 79-88"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prostate cancer detection in intelligent big data systems using omnipresent AI with Fox and Golden Jackal Optimizers","authors":"Akshat Gaurav ,&nbsp;Varsha Arya ,&nbsp;Kwok Tai Chui ,&nbsp;Brij B. Gupta","doi":"10.1016/j.ijin.2025.07.005","DOIUrl":"10.1016/j.ijin.2025.07.005","url":null,"abstract":"<div><div>Prostate cancer is one of the most critical health concerns, making early detection essential for improved patient outcomes. In this context, this work used the ability of omnipresent AI and big data to provide accurate and fast prostate cancer detection. Golden Jackal Optimization (GJO) for hyperparameter optimization and the Fox optimizer for feature selection used to optimize the performance of CNN model. After training for five epochs, the model achieved an accuracy of 72%. Comparative analysis with models such as GRU, LSTM, and traditional classifiers demonstrates that the proposed method provides a robust and scalable solution for large-scale, data-driven healthcare applications.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"6 ","pages":"Pages 166-175"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning-enhanced loT and Wireless Sensor Networks for predictive analysis and maintenance in wind turbine systems","authors":"Lei Gong, Yanhui Chen","doi":"10.1016/j.ijin.2024.02.002","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.02.002","url":null,"abstract":"","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"24 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139882379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personal internet of things networks: An overview of 3GPP architecture, applications, key technologies, and future trends","authors":"Fariha Eusufzai, Aldrin Nippon Bobby, Farzana Shabnam, S. Sabuj","doi":"10.1016/j.ijin.2024.02.001","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.02.001","url":null,"abstract":"","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139878827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on secure Official Document Management and intelligent Information Retrieval System based on recommendation algorithm","authors":"Liang Xing","doi":"10.1016/j.ijin.2024.02.003","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.02.003","url":null,"abstract":"","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"41 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139884638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved spectrum prediction model for cognitive radio networks using hybrid deep learning technique","authors":"M.G. Sumithra ,&nbsp;M. Suriya","doi":"10.1016/j.ijin.2024.05.003","DOIUrl":"10.1016/j.ijin.2024.05.003","url":null,"abstract":"<div><p>Cognitive Radio (CR) technology has been highlighted as one of the most likely answers to the issue of spectrum shortage with the rise of fifth generation and beyond communication. Secondary users (SUs) in cognitive radio networks (CRN) must continuously monitor the spectrum to forecast channel occupancy by primary users (PUs) based on fundamental factors, such as location, time, and RF band. A hybrid deep learning model called LSTM-MLP (Long Short-Term Memory-Multilayer Perceptron) is proposed to improve idle channel prediction probability thus reducing the overall sensing time by cognitive users during spectrum sensing. Performance evaluation for the proposed model is done in terms of prediction error and efficiency, the GSM-900 spectrum dataset demonstrates that LSTM-MLP performs better in terms of improved prediction accuracy compared to existing state-of-art prediction techniques.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 286-292"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000228/pdfft?md5=cdc0b0f67bdd877ac91a21ff75bc3bee&pid=1-s2.0-S2666603024000228-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141042987","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}

{"title":"AOF: An adaptive algorithm for enhancing RPL objective function in smart agricultural IoT networks","authors":"Abubakar Wakili,&nbsp;Sara Bakkali","doi":"10.1016/j.ijin.2024.09.001","DOIUrl":"10.1016/j.ijin.2024.09.001","url":null,"abstract":"<div><p>Within the Internet of Things (IoT) ecosystem, the Routing Protocol for Low-Power and Lossy Networks (RPL) serves as a foundational element for network communication. The protocol's effectiveness depends on its Objective Function (OF), which orchestrates route selection based on predefined criteria. However, traditional OFs often struggle to adapt to the dynamic nature of IoT environments. This paper presents the Adaptive Objective Function (AOF), an innovative algorithm designed to dynamically adjust the OF in real-time, responding to fluctuating network conditions and application requirements. AOF comprises: a Network Monitor, an OF Selector, an OF Switcher, and an Event Handler, all working in concert to enhance network performance, reliability, and energy efficiency. Through simulations, AOF has demonstrated superior performance over legacy OFs, achieving a 10 %–20 % reduction in End-to-End Delay (EED), a 1 %–2 % increase in Packet Delivery Ratio (PDR), a 10 %–20 % improvement in Network Lifetime (NLT), and a substantial 50 %–80 % decrease in Control Overhead (COH). The paper also presents a smart agriculture case study that illustrates AOF's practical application in optimizing sensor network data routing—a testament to its versatility and practicality. Future endeavours will concentrate on further refining AOF and broadening its application across various IoT domains.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 325-339"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000320/pdfft?md5=bf0e841f7517d2e4a59787401fa56ed6&pid=1-s2.0-S2666603024000320-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142238819","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}