International Journal of Intelligent Networks最新文献

{"title":"Research on the application of network security defence in database security services based on deep learning integrated with big data analytics","authors":"Feilu Hang,&nbsp;Linjiang Xie,&nbsp;Zhenhong Zhang,&nbsp;Wei Guo,&nbsp;Hanruo Li","doi":"10.1016/j.ijin.2024.02.006","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.02.006","url":null,"abstract":"<div><p>Every day, more people use the internet to send and receive sensitive information. A lot of confidential data is being transmitted electronically between people and businesses. Cyber-attacks, which are the inevitable result of our growing reliance on digital technology, are a reality that we must face today. This paper aims to investigate the impact of Big Data Analytics (BDA) on information security and vice versa. Additionally, an Artificial Neural Network (ANN)-based Deep Learning (DL) method for Anomaly Detection (AD) is presented in this work. To improve AD, the proposed method uses a DL-based detection method, which is used to parse through many collected security events to develop individual event profiles. The paper also investigated how BDA can be used to address Information Security (IS) issues and how existing Big Data technologies can be adapted to improve BDA's security. This study developed a DL-based Security Information System (DL-SIS) using a combination of event identification for data preprocessing and different Artificial Neural Network (ANN) methods. The feasibility and impact of implementing a Big Data Analytics (BDA) system for AD are investigated and addressed in this study. From this study, we learn that BDA systems are highly effective in securing Critical Information Setup from several discrete cyberattacks and that they are currently the best method available. By analyzing the False Positive Rate (FPR), the system facilitates quick action by security analysts in response to cyber threats. DL-SIS had the highest AD accuracy of 99.40% but performed poorly in the high-dimensional dataset.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 101-109"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000125/pdfft?md5=4ddc5ac4e95d6b926a7cf8f85af0a69e&pid=1-s2.0-S2666603024000125-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139748973","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":"Personal internet of things networks: An overview of 3GPP architecture, applications, key technologies, and future trends","authors":"Fariha Eusufzai,&nbsp;Aldrin Nippon Bobby,&nbsp;Farzana Shabnam,&nbsp;Saifur Rahman Sabuj","doi":"10.1016/j.ijin.2024.02.001","DOIUrl":"10.1016/j.ijin.2024.02.001","url":null,"abstract":"<div><p>The use of the personal Internet of Things (PIoT) is rapidly expanding across a range of application areas. It is needed for real-time monitoring, wireless coverage, remote sensing, delivery, security, surveillance, and civil infrastructure inspection. Smart PIoT devices provide new opportunities in wearable, public, and home automation, making them the next significant development in PIoT technology. The study provides a comprehensive overview of PIoT systems, including their architecture, applications, technology, and future developments. This paper begins with a comprehensive overview of prior research about the PIoT network. Then, we present an overview of PIoT classification and a comprehensive explanation of the PIoT architecture. We also investigate the requirements and technology of the PIoT network. Additionally, we provide a thorough examination of several applications for PIoT networks. Moreover, we emphasize some significant challenges and ongoing problems confronting the PIoT network. Finally, we address recent research trends and outline potential future study directions.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 77-91"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000071/pdfft?md5=ba2ba9fd4da955d707d4394c141822cc&pid=1-s2.0-S2666603024000071-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139819072","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":"Interference-assisted energy harvesting short packet communications with hardware impairments","authors":"Dechuan Chen ,&nbsp;Jin Li ,&nbsp;Jianwei Hu ,&nbsp;Xingang Zhang ,&nbsp;Shuai Zhang ,&nbsp;Dong Wang","doi":"10.1016/j.ijin.2024.05.005","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.05.005","url":null,"abstract":"<div><p>Radio frequency energy harvesting offers a promising solution to provide low power Internet of Things (IoT) devices with convenient and perpetual energy supply. In this work, we investigate the reliable performance of an energy-constrained transmitter communicating with a receiver over Nakagami-<em>m</em> channel, where the effects of transceiver hardware impairments and finite blocklength coding are jointly considered. Specifically, the communication link between the transmitter and receiver operates within the coverage of an existing wireless system, with radio frequency signal from the existing system serving as an energy signal for the transmitter while acting as an interference signal for the receiver. By utilizing the finite-blocklength information theory, we first derive average block error rate (BLER) and asymptotic average BLER in closed-form expressions, which enable us to quantify the extent of reliability loss. Then, we analyze effective throughput of the system, and determine the optimal blocklength that maximizes the effective throughput. Computer simulations are employed to validate the accuracy of our analytical findings, demonstrating the presence of an outage threshold solely due to hardware impairments. Furthermore, if transmission rate exceeds the outage threshold defined by the level of hardware impairments, reliable communication within the system under consideration cannot be achieved, regardless of the transmit signal-to-noise ratio (SNR).</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 231-240"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000241/pdfft?md5=e1b2b3a06d3734c687b06973e9a5a4cf&pid=1-s2.0-S2666603024000241-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141068732","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":"A swarm intelligence and deep learning strategy for wind power and energy storage scheduling in smart grid","authors":"Lin Geng ,&nbsp;Lei Zhang ,&nbsp;Fangming Niu ,&nbsp;Yang Li ,&nbsp;Feng Liu","doi":"10.1016/j.ijin.2024.08.001","DOIUrl":"10.1016/j.ijin.2024.08.001","url":null,"abstract":"<div><p>In today's world, rising energy demands are a significant challenge, and the smart grid emerges as a solution for sustainable energy management. An essential view of advancing the Smart Grid (SG) capabilities is the collaborative scheduling of Wind Power Generation (WPG) and energy storage. It plays a significant role in elevating SG efficiency, reliability, and environmental sustainability. This kind of strategic planning is essential to increase coordination between WPG and flexible deployment of Energy Storage Systems (ESS). Efficient SG functions will be maintained, and energy sources can be regulated with demand variations. Putting an emphasis on assumptions and empirical data is vital in conventional techniques. When it comes to the continuously shifting environment of SG and RE resources, traditional approaches aren't highly reliable or adaptable. The present article uses a hybrid model that integrates Deep Reinforcement Learning (DRL) and Particle Swarm Optimization (PSO) to address those drawbacks. The primary purpose of it is to help with the joint scheduling of WP and ESS. This technique is what permits DRL to reach selections rapidly in convoluted, ever-changing environments. The proposed approach, when combined with PSO's effectiveness for variable optimization, will result in improved scheduling findings. The framework additionally exploits the finest use of ESS, but it also effectively addresses the challenging task of integrating dynamic WP with the SG. Reliable and cost-effective supply is ensured by the system's design. The accuracy, stability, and versatility of the suggested approach to the dynamic features of Wind Energy (WE) and storage management are incomparable to traditional approaches. The findings indicate the method's actual validity and its significance for improving SG functions. Applying <em>state-of-the-art</em> statistical techniques for holistic optimization of RE resources and storage systems is emphasized by the framework. Owing to minimizing Energy Consumption (EC) and lowering greenhouse gas emissions, this study provides a significant step towards achieving the goal of effective and eco-friendly SG functions.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 302-314"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000289/pdfft?md5=563c206b94274c5d11f0a8178d5d3291&pid=1-s2.0-S2666603024000289-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142130068","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":"A two-stage CNN model for the classification and severity analysis of retinal and choroidal diseases in OCT images","authors":"Neetha George ,&nbsp;Linu Shine ,&nbsp;Ambily N ,&nbsp;Bejoy Abraham ,&nbsp;Sivakumar Ramachandran","doi":"10.1016/j.ijin.2024.01.002","DOIUrl":"10.1016/j.ijin.2024.01.002","url":null,"abstract":"<div><p>The advancements in medical imaging techniques have brought exponential increase in the quantity and complexity of data which often require human expertise for interpretation and decision making. However, in real-world clinical settings, there is often a shortage of experts available for timely diagnosis and triage. An automated diagnostic technique aids clinicians in the precise diagnosis and effective management of diseases. In this article, a two-stage classification model using convolutional neural network (CNN) is proposed for the classification and severity analysis of retinal and choroidal diseases in optical coherence tomography (OCT) images. The proposed model can identify abnormal conditions such as Pachychoroid disorders, macular edema, and Drusen. The images are initially classified into four categories-healthy, Drusen, Pachychoroid and macular edema classes. The severity of Pachychoroid conditions, including central serous chorioretinopathy, polypoid choroidal vasculopathy, and choroidal neovascularization, is subsequently determined through a second-level classification. A modified version of the VGG16 architecture is used for the initial classification. A fine-tuned CNN with the same architecture is then employed to determine the severity of Pachychoroid diseases. Further, we make use of the UNet architecture for assessing the severity of macular edema. The proposed hybrid approach for classification and analysis achieved promising results, demonstrating consistency on par with human experts in diagnosing and grading ocular diseases.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 10-18"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000022/pdfft?md5=f0ff5ed7106fc87e30db1de0438c61cf&pid=1-s2.0-S2666603024000022-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139637075","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":"Markov enhanced I-LSTM approach for effective anomaly detection for time series sensor data","authors":"V. Shanmuganathan,&nbsp;A. Suresh","doi":"10.1016/j.ijin.2024.02.007","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.02.007","url":null,"abstract":"<div><p>Users could engage and interact with their immediate surroundings without effort in smart settings. The emergence of intelligent technologies along with software-based services has made this possible. It is clear that technical advancements have ushered in a new era for both computer processing and sensor technology, facilitating the concept of smart surroundings. Even though their implementation faces a number of obstacles, numerous expansive projects are working to advance their adoption. The problem of anomalies in the sensor data could result inappropriate decisions and could lead unamicable situations to the users. Many such algorithms are already there, which does not provide satisfactory predictions for the sensor data for the time series data. Time series anomaly detection problems are typically stated as finding outlier data points in comparison to some norm or typical signal. Better anomaly detection in time series data is provided by the proposed Markov and enhanced LSTM technique. The Markov model and the enhanced LSTM offer accurate predictions for extra- and short-term data, which is highly useful in situations involving intelligent environments. When compared to the KNN algorithm, the technique offers reduced MAE, RMSE, MSE and MAPE errors. The algorithm also performs better than other LSTM and RNN methods. The proposed algorithm provides 0.00047 reduced error in humidity data, 0.00416 reduced error in temperature and 0.01771 reduced MAE value in case of light intensity when comparing with the KNN algorithm.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 154-160"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000137/pdfft?md5=2e07fc6d80b04d2d64e9a2a85d741d65&pid=1-s2.0-S2666603024000137-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140062701","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":"Tracing delay network in air transportation combining causal propagation and complex network","authors":"DaoZhong Feng ,&nbsp;Bin Hao ,&nbsp;JiaJian Lai","doi":"10.1016/j.ijin.2024.01.006","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.01.006","url":null,"abstract":"<div><p>In air transportation, monitoring delays and making informed decisions at a system level is crucial for network managers. Causal selection methods have recently witnessed increased adoption for the analysis of multi-observations. Systematic Path Isolation (SPI) stands out as an effective mechanism for selecting causal pathways in time-series data. However, specific improvements are needed to ensure the effectiveness within the aviation system. This paper proposes an SPI-based causal inference method that incorporates the Granger test and the Kernel-based test, accommodating both linear and non-linear relationships, thereby enabling better condition selection. Additionally, the two-step SPI test employs the Kernel-based Conditional Independence test due to its suitability for handling complex data with nonlinear relationships, and it avoids explicit feature extraction. Validation of delay tracing involves the use of complex network metrics and a specially designed load-embedded metric for identifying daily states. The case study results demonstrate the effectiveness of the network generated by the proposed method in accurately tracing dynamic states, particularly through the proposed indicator. In static propagation detection, network motifs can serve as micro-expressions, particularly with convergence and transmission forms during high delays. This research contributes to refine the depiction of delay propagation in the air transport network, enhancing the ability to trace delay trends in dynamic and static perspectives.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 63-76"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266660302400006X/pdfft?md5=9f19ef81d4c4274e179acb334e890fc8&pid=1-s2.0-S266660302400006X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139718597","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":"Optimizing land mine detection across diverse mining environments: A hyperspectral data approach with regression models","authors":"R. Anand ,&nbsp;Andrew J. ,&nbsp;Ihab Makki","doi":"10.1016/j.ijin.2024.08.004","DOIUrl":"10.1016/j.ijin.2024.08.004","url":null,"abstract":"<div><div>The detection of landmines, namely anti-tank mines, explosive devices, and unexploded ordnance, is a formidable obstacle for the global community. The visible consequences of unobserved explosives in communities affected by war are characterized by significant devastation and human suffering. In order to effectively tackle this matter, it is imperative to use proactive strategies that focus on the identification and mitigation of these perilous substances prior to their potential infliction of harm. Nevertheless, the majority of current solutions exhibit significant deficiencies, such as exorbitant expenses, inefficiencies, and apprehensions over accuracy. These drawbacks are further compounded by the inherent trade-offs that exist between these elements, where improvements in one area often come at the expense of another. Contrarily, recent breakthroughs in the areas of deep learning, unmanned aerial vehicles, and sensor technologies are being recognized as potentially transformative elements in the domain of landmine identification and removal. This paper presents a thorough examination of recent scholarly investigations that integrate computerized technology in the field of landmine detection. To the extent of our current understanding, there has been no prior investigation that has thoroughly examined this particular domain. The main aim of this study is to investigate the incorporation of machine learning based regression methods in the field of landmine detection. The study specifically emphasizes the identification and resolution of existing issues that hinder the development of efficient automated solutions, hence enhancing performance optimization. The Sum of Sine Curve Fit Regression Model is proposed and proved a powerful and adaptable tool for extracting relevant information from this hyperspectral images.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 351-363"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534520","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":"Face expression image detection and recognition based on big data technology","authors":"Shuji Deng","doi":"10.1016/j.ijin.2023.08.002","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.08.002","url":null,"abstract":"<div><p>This research addresses the deficiencies in current dynamic sequence facial expression recognition methods, which suffer from limited accuracy and effectiveness. The primary objective is to introduce an innovative approach that leverages big data technology for improved facial expression detection and recognition. The methodology encompasses several vital steps. The integral graph method is employed to capture dynamic sequences of facial expressions, and a weak facial feature classifier is utilized for image preprocessing. To enhance accuracy, a dynamic sequence model is devised for feature extraction. The study combines the personalized learning algorithm with the optical flow technique to pinpoint critical facial expression junctures and facilitate dynamic sequence recognition. The investigation reveals the inadequacy of current dynamic sequence facial expression recognition methods in accurately categorizing expressions. The proposed approach yields promising results, achieving a peak expression division accuracy of 91.78% in simulations. Notably, the personalized learning recognition method demonstrates enhanced robustness in categorizing expressions, effectively capturing intricate facial details and augmenting overall recognition efficacy. This research thus contributes to advancing facial expression recognition technology, addressing critical shortcomings in current methods.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 218-223"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194620","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":"Application of spatial data and 3S robotic technology in digital city planning","authors":"Yunyan Chang,&nbsp;Jian Xu","doi":"10.1016/j.ijin.2023.08.003","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.08.003","url":null,"abstract":"<div><p>This article uses spatial data and 3S (Spatial, Surveying, and Remote Sensing) technology to enhance digital city planning. The methodology integrates WebGIS Big data, statistical feature extraction techniques, and strategic planning to create a comprehensive framework for digital urban planning. Spatial information point calibration ensures accurate spatial positioning during the planning process. At the same time, data fusion and fuzzy C-means clustering analysis are utilized to detect and analyze WebGIS data within the digital city planning context. The proposed model incorporates a piecewise fitting method within the Big data integration scheduling framework for digital city spatial planning. A C/S (Client/Server) architecture and ARM-embedded technology have been developed to establish a robust digital city planning system to support this approach. This system encompasses modules for WebGIS information collection, bus control, database management, human-machine interaction, and data processing terminals. Simulation results demonstrate that the method significantly reduces delays in digital city planning and design. When analyzing a data scale of 100, the method exhibits an 83.4% lower delay than the fuzzy method. Although delays increase with larger data scales, even at a scale of 400, the method still offers a 43.1% reduction compared to the fuzzy method. Across varying data scales, the proposed method consistently maintains approximately 60% lower latency than the rough set method. This method showcases superior intelligence and exhibits strong capabilities in accessing and scheduling WebGIS data effectively.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 211-217"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194621","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}