International Journal of Intelligent Networks最新文献_第5页

A two-stage CNN model for the classification and severity analysis of retinal and choroidal diseases in OCT images 用于 OCT 图像中视网膜和脉络膜疾病分类和严重程度分析的两级 CNN 模型

International Journal of Intelligent Networks Pub Date : 2024-01-01 DOI: 10.1016/j.ijin.2024.01.002

Neetha George , Linu Shine , Ambily N , Bejoy Abraham , Sivakumar Ramachandran

{"title":"A two-stage CNN model for the classification and severity analysis of retinal and choroidal diseases in OCT images","authors":"Neetha George , Linu Shine , Ambily N , Bejoy Abraham , 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}

引用次数: 0

Markov enhanced I-LSTM approach for effective anomaly detection for time series sensor data 马尔可夫增强型 I-LSTM 方法用于有效检测时间序列传感器数据的异常情况

International Journal of Intelligent Networks Pub Date : 2024-01-01 DOI: 10.1016/j.ijin.2024.02.007

V. Shanmuganathan, A. Suresh

{"title":"Markov enhanced I-LSTM approach for effective anomaly detection for time series sensor data","authors":"V. Shanmuganathan, 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}

引用次数: 0

Tracing delay network in air transportation combining causal propagation and complex network 结合因果传播和复杂网络追踪航空运输中的延误网络

International Journal of Intelligent Networks Pub Date : 2024-01-01 DOI: 10.1016/j.ijin.2024.01.006

DaoZhong Feng , Bin Hao , JiaJian Lai

{"title":"Tracing delay network in air transportation combining causal propagation and complex network","authors":"DaoZhong Feng , Bin Hao , 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}

引用次数: 0

Optimizing land mine detection across diverse mining environments: A hyperspectral data approach with regression models 优化不同采矿环境中的地雷探测：利用回归模型的高光谱数据方法

International Journal of Intelligent Networks Pub Date : 2024-01-01 DOI: 10.1016/j.ijin.2024.08.004

R. Anand , Andrew J. , Ihab Makki

{"title":"Optimizing land mine detection across diverse mining environments: A hyperspectral data approach with regression models","authors":"R. Anand , Andrew J. , 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}

引用次数: 0

Face expression image detection and recognition based on big data technology 基于大数据技术的人脸表情图像检测与识别

International Journal of Intelligent Networks Pub Date : 2023-01-01 DOI: 10.1016/j.ijin.2023.08.002

Shuji Deng

{"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}

引用次数: 0

Application of spatial data and 3S robotic technology in digital city planning 空间数据和3S机器人技术在数字城市规划中的应用

International Journal of Intelligent Networks Pub Date : 2023-01-01 DOI: 10.1016/j.ijin.2023.08.003

Yunyan Chang, Jian Xu

{"title":"Application of spatial data and 3S robotic technology in digital city planning","authors":"Yunyan Chang, 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}

引用次数: 0

Efficient breast cancer detection via cascade deep learning network 基于级联深度学习网络的癌症高效检测

International Journal of Intelligent Networks Pub Date : 2023-01-01 DOI: 10.1016/j.ijin.2023.02.001

Bita Asadi, Qurban Memon

{"title":"Efficient breast cancer detection via cascade deep learning network","authors":"Bita Asadi, Qurban Memon","doi":"10.1016/j.ijin.2023.02.001","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.02.001","url":null,"abstract":"<div><p>Breast calcifications or irregular tissue growth are major health concerns that can lead to breast cancer. To enable early management, which significantly lowers death rates, it is crucial to perform screening and determine if a tumor is benign or malignant. Building a cascade network model that bases predictions on the shape, pattern, and spread of the tumor is how this research approaches the challenge. Pre-processing of images, followed by segmentation and classification, are common methods to accomplish this. The strategy in this research employs a cascade network with UNet architecture for segmentation with a ResNet backbone for classification. To enable classification to make predictions, segmentation process involves separating tumor from the image in the form of a mask. The segmentation model's F1-score measurement came out to be 97.30%. The final decision-making layer's neural network is a straightforward 8-layer network, which follows the ResNet50 model. The proposed model's classification accuracy was 98.61%, with F1 score of 98.41%. Comparative evaluations are conducted together with the comprehensive experimental results.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 46-52"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194630","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}

引用次数: 3

Classification of network slicing threats based on slicing enablers: A survey 基于切片使能因子的网络切片威胁分类：一项调查

International Journal of Intelligent Networks Pub Date : 2023-01-01 DOI: 10.1016/j.ijin.2023.04.002

Mohammad J.K. Abood , Ghassan H. Abdul-Majeed

引用次数: 1

A novel deep neural network heartbeats classifier for heart health monitoring 一种用于心脏健康监测的新型深度神经网络心跳分类器

International Journal of Intelligent Networks Pub Date : 2023-01-01 DOI: 10.1016/j.ijin.2022.11.001

Velagapudi Swapna Sindhu, Kavuri Jaya Lakshmi, Ameya Sanjanita Tangellamudi, K. Ghousiya Begum

{"title":"A novel deep neural network heartbeats classifier for heart health monitoring","authors":"Velagapudi Swapna Sindhu, Kavuri Jaya Lakshmi, Ameya Sanjanita Tangellamudi, K. Ghousiya Begum","doi":"10.1016/j.ijin.2022.11.001","DOIUrl":"https://doi.org/10.1016/j.ijin.2022.11.001","url":null,"abstract":"<div><p>The electrocardiogram (ECG) is a very useful diagnostic tool to examine the functioning of the heart and to detect myocardial infarction (MI) and arrhythmias. It contains the records of the electrical signal of the heart and it is an investigation tool to check the heart's rhythm and thereby analyze heartbeats. Automatic detection of arrhythmia is possible by analyzing a patient's abnormal heartbeats and has become a major research area in recent years, as the manual examination of heart activity is time-consuming and prone to errors. Nowadays, the deployment of artificial intelligence (AI) - based algorithms to predict abnormal heartbeats categorized into five classes namely, non-ectopic (N), supra ventricular ectopic (S), ventricular ectopic (V), fusion (F) and unknown beats (Q) has drawn more attention in detecting arrhythmias. The use of intuitive hand-crafted features with shallow feature learning architectures is one of the key drawbacks of machine learning (ML) techniques. So, we present a novel deep neural network heartbeat classifier to extract and classify the heartbeat signals. The novel one-dimensional convolution neural network (1D CNN) model is developed by modifying the LENET architecture for the classification of heat beats (MIT-BIH Arrhythmia Database) and has attained an accuracy of 97.37%. This model's performance is also enhanced by the implementation of smote oversampling technique and gained an accuracy of 98.41%. Finally, the proposed model's performance is compared with other pre-existing models and various oversampling methods are deployed for analysis.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 1-10"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194734","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}

引用次数: 0

Hybrid cloud storage system with enhanced multilayer cryptosystem for secure deduplication in cloud 混合云存储系统，增强多层密码系统，保证云环境下重复数据删除的安全性

International Journal of Intelligent Networks Pub Date : 2023-01-01 DOI: 10.1016/j.ijin.2023.11.001

Nagappan Mageshkumar , J. Swapna , A. Pandiaraj , R. Rajakumar , Moez Krichen , Vinayakumar Ravi

{"title":"Hybrid cloud storage system with enhanced multilayer cryptosystem for secure deduplication in cloud","authors":"Nagappan Mageshkumar , J. Swapna , A. Pandiaraj , R. Rajakumar , Moez Krichen , Vinayakumar Ravi","doi":"10.1016/j.ijin.2023.11.001","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.11.001","url":null,"abstract":"<div><p>Data deduplication is a crucial technique in the field of data compression that aims to eliminate redundant copies of recurring data. This technique has gained significant popularity in the realm of cloud storage due to its ability to effectively reduce storage requirements and optimize bandwidth utilization. To ensure the safeguarding of sensitive data while simultaneously facilitating deduplication, researchers have put forth the concept of convergent encryption as a potential solution. This technique involves encrypting the data prior to its outsourcing, thereby enhancing the confidentiality of the information. In this work, an earnest endeavor is undertaken to formally tackle the issue of authorized data deduplication, with the aim of enhancing data security. Our approach combines the Diffie-Hellman algorithm and symmetrical external decision to protect and popularize information, ensuring end-to-end encryption to encourage user adoption of cloud storage. The proposed model employs block-level deduplication and guarantees the randomness of ciphertexts by generating encryption keys using the Diffie-Hellman algorithm. This method effectively counters both internal and external brute-force attacks, enhancing data security while reducing computational costs. An extensive experimentation is carried out to demonstrate that our approach is particularly beneficial in scenarios with multiple privilege sets. Overall, the proposed model offers an elaborate framework that maintains data privacy and strengthens security measures, contributing to a more efficient and secure cloud-based document search.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 301-309"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603023000295/pdfft?md5=87683c34f716c34cf5f33cc644cd5155&pid=1-s2.0-S2666603023000295-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138454137","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