Network-Computation in Neural Systems最新文献_第7页

Study the hydrotropic behaviour of butyl stearate using ANN tools 利用 ANN 工具研究硬脂酸丁酯的亲水行为

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-09-11 DOI: 10.1080/0954898x.2024.2393751

Chinnakannu Jayakumar, Venkatesan Sampath Kumar, Chathurappan Raja, Dharmendira Kumar Mahendradas

引用次数: 0

Improved deep neural network (EnhanceNet) for real-time detection of some publicly prohibited items. 改进的深度神经网络（EnhanceNet），用于实时检测一些公共违禁物品。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-09-11 DOI: 10.1080/0954898x.2024.2398531

Chukwuebuka Joseph Ejiyi,Zhen Qin,Chiagoziem Chima Ukwuoma,Grace Ugochi Nneji,Happy Nkanta Monday,Makuachukwu Bennedith Ejiyi,Ijeoma Amuche Chikwendu,Ariyo Oluwasanmi

{"title":"Improved deep neural network (EnhanceNet) for real-time detection of some publicly prohibited items.","authors":"Chukwuebuka Joseph Ejiyi,Zhen Qin,Chiagoziem Chima Ukwuoma,Grace Ugochi Nneji,Happy Nkanta Monday,Makuachukwu Bennedith Ejiyi,Ijeoma Amuche Chikwendu,Ariyo Oluwasanmi","doi":"10.1080/0954898x.2024.2398531","DOIUrl":"https://doi.org/10.1080/0954898x.2024.2398531","url":null,"abstract":"Public safety is a critical concern, typically addressed through security checks at entrances of public places, involving trained officers or X-ray scanning machines to detect prohibited items. However, many places like hospitals, schools, and event centres lack such resources, risking security breaches. Even with X-ray scanners or manual checks, gaps can be exploited by individuals with malicious intent, posing significant security risks. Additionally, traditional methods, relying on manual inspections and conventional image processing techniques, are often inefficient and prone to high error rates. To mitigate these risks, we propose a real-time detection model - EnhanceNet using a customized Scale-Enhanced Pooling Network (SEP-Net) integrated into the YOLOv4. The innovative SEP-Net enhances feature representation and localization accuracy, significantly contributing to the model's efficacy in detecting prohibited items. We annotated a custom dataset of nine classes and evaluated our models using different input sizes (608 and 416). The 608 input size achieved a mean Average Precision (mAP) of 74.10% with a detection speed of 22.3 Frames per Second (FPS). The 416 input size showed superior performance, achieving a mAP of 76.75% and a detection speed of 27.1 FPS. These demonstrate that our models are accurate and efficient, making them suitable for real-time applications.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":"61 1","pages":"1-28"},"PeriodicalIF":7.8,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep learning-based energy prediction and tangent search remora optimization-based secure multi-path data communication mechanism in WSN 基于深度学习的 WSN 能量预测和切线搜索 remora 优化安全多路径数据通信机制

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-09-10 DOI: 10.1080/0954898x.2024.2393750

Muthukrishnan Athinarayanasamy, Karthi Selvakumar, Veluchamy Sivasubbu, Michael Mahesh Kanakam

引用次数: 0

Lung disease prediction based on CT images using REInf-net and world cup optimization based BI-LSTM classification 利用 REInf-net 和基于世界杯优化的 BI-LSTM 分类，基于 CT 图像预测肺部疾病

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-09-09 DOI: 10.1080/0954898x.2024.2392782

Padmini Sankaramurthy, Renukadevi Palaniswamy, Suseela Sellamuthu, Fancy Chelladurai, Anand Murugadhas

引用次数: 0

Transformer-based deep learning networks for fault detection, classification, and location prediction in transmission lines. 基于变压器的深度学习网络，用于输电线路故障检测、分类和位置预测。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-09-03 DOI: 10.1080/0954898X.2024.2393746

Bousaadia Baadji, Soufiane Belagoune, Sif Eddine Boudjellal

{"title":"Transformer-based deep learning networks for fault detection, classification, and location prediction in transmission lines.","authors":"Bousaadia Baadji, Soufiane Belagoune, Sif Eddine Boudjellal","doi":"10.1080/0954898X.2024.2393746","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2393746","url":null,"abstract":"Fault detection, classification, and location prediction are crucial for maintaining the stability and reliability of modern power systems, reducing economic losses, and enhancing system protection sensitivity. This paper presents a novel Hierarchical Deep Learning Approach (HDLA) for accurate and efficient fault diagnosis in transmission lines. HDLA leverages two-stage transformer-based classification and regression models to perform Fault Detection (FD), Fault Type Classification (FTC), and Fault Location Prediction (FLP) directly from synchronized raw three-phase current and voltage samples. By bypassing the need for feature extraction, HDLA significantly reduces computational complexity while achieving superior performance compared to existing deep learning methods. The efficacy of HDLA is validated on a comprehensive dataset encompassing various fault scenarios with diverse types, locations, resistances, inception angles, and noise levels. The results demonstrate significant improvements in accuracy, recall, precision, and F1-score metrics for classification, and Mean Absolute Errors (MAEs) and Root Mean Square Errors (RMSEs) for prediction, showcasing the effectiveness of HDLA for real-time fault diagnosis in power systems.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-21"},"PeriodicalIF":1.1,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142121202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Retraction. 撤回。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-08-03 DOI: 10.1080/0954898X.2024.2385540

引用次数: 0

Bolstering IoT security with IoT device type Identification using optimized Variational Autoencoder Wasserstein Generative Adversarial Network. 利用优化的变异自动编码器 Wasserstein 生成对抗网络识别物联网设备类型，增强物联网安全性。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-08-01 Epub Date: 2024-01-31 DOI: 10.1080/0954898X.2024.2304214

Jothi Shri Sankar, Saravanan Dhatchnamurthy, Anitha Mary X, Keerat Kumar Gupta

{"title":"Bolstering IoT security with IoT device type Identification using optimized Variational Autoencoder Wasserstein Generative Adversarial Network.","authors":"Jothi Shri Sankar, Saravanan Dhatchnamurthy, Anitha Mary X, Keerat Kumar Gupta","doi":"10.1080/0954898X.2024.2304214","DOIUrl":"10.1080/0954898X.2024.2304214","url":null,"abstract":"Due to the massive growth in Internet of Things (IoT) devices, it is necessary to properly identify, authorize, and protect against attacks the devices connected to the particular network. In this manuscript, IoT Device Type Identification based on Variational Auto Encoder Wasserstein Generative Adversarial Network optimized with Pelican Optimization Algorithm (IoT-DTI-VAWGAN-POA) is proposed for Prolonging IoT Security. The proposed technique comprises three phases, such as data collection, feature extraction, and IoT device type detection. Initially, real network traffic dataset is gathered by distinct IoT device types, like baby monitor, security camera, etc. For feature extraction phase, the network traffic feature vector comprises packet sizes, Mean, Variance, Kurtosis derived by Adaptive and concise empirical wavelet transforms. Then, the extracting features are supplied to VAWGAN is used to identify the IoT devices as known or unknown. Then Pelican Optimization Algorithm (POA) is considered to optimize the weight factors of VAWGAN for better IoT device type identification. The proposed IoT-DTI-VAWGAN-POA method is implemented in Python and proficiency is examined under the performance metrics, like accuracy, precision, f-measure, sensitivity, Error rate, computational complexity, and RoC. It provides 33.41%, 32.01%, and 31.65% higher accuracy, and 44.78%, 43.24%, and 48.98% lower error rate compared to the existing methods.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"278-299"},"PeriodicalIF":1.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139643428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved deep belief network for estimating mango quality indices and grading: A computer vision-based neutrosophic approach. 用于估算芒果质量指标和分级的改进型深度信念网络：基于计算机视觉的中性方法。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-08-01 Epub Date: 2024-01-15 DOI: 10.1080/0954898X.2023.2299851

Mukesh Kumar Tripathi, Shivendra

引用次数: 0

M2AI-CVD: Multi-modal AI approach cardiovascular risk prediction system using fundus images. M2AI-CVD：使用眼底图像的多模态人工智能心血管风险预测系统。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-08-01 Epub Date: 2024-01-27 DOI: 10.1080/0954898X.2024.2306988

Premalatha Gurumurthy, Manjunathan Alagarsamy, Sangeetha Kuppusamy, Niranjana Chitra Ponnusamy

{"title":"M2AI-CVD: Multi-modal AI approach cardiovascular risk prediction system using fundus images.","authors":"Premalatha Gurumurthy, Manjunathan Alagarsamy, Sangeetha Kuppusamy, Niranjana Chitra Ponnusamy","doi":"10.1080/0954898X.2024.2306988","DOIUrl":"10.1080/0954898X.2024.2306988","url":null,"abstract":"Cardiovascular diseases (CVD) represent a significant global health challenge, often remaining undetected until severe cardiac events, such as heart attacks or strokes, occur. In regions like Qatar, research focused on non-invasive CVD identification methods, such as retinal imaging and dual-energy X-ray absorptiometry (DXA), is limited. This study presents a groundbreaking system known as Multi-Modal Artificial Intelligence for Cardiovascular Disease (M2AI-CVD), designed to provide highly accurate predictions of CVD. The M2AI-CVD framework employs a four-fold methodology: First, it rigorously evaluates image quality and processes lower-quality images for further analysis. Subsequently, it uses the Entropy-based Fuzzy C Means (EnFCM) algorithm for precise image segmentation. The Multi-Modal Boltzmann Machine (MMBM) is then employed to extract relevant features from various data modalities, while the Genetic Algorithm (GA) selects the most informative features. Finally, a ZFNet Convolutional Neural Network (ZFNetCNN) classifies images, effectively distinguishing between CVD and Non-CVD cases. The research's culmination, tested across five distinct datasets, yields outstanding results, with an accuracy of 95.89%, sensitivity of 96.89%, and specificity of 98.7%. This multi-modal AI approach offers a promising solution for the accurate and early detection of cardiovascular diseases, significantly improving the prospects of timely intervention and improved patient outcomes in the realm of cardiovascular health.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"319-346"},"PeriodicalIF":1.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

State identification for a class of uncertain switched systems by differential neural networks. 用微分神经网络识别一类不确定开关系统的状态。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-08-01 Epub Date: 2024-01-11 DOI: 10.1080/0954898X.2023.2296115

Isaac Chairez, Alejandro Garcia-Gonzalez, Alberto Luviano-Juarez

{"title":"State identification for a class of uncertain switched systems by differential neural networks.","authors":"Isaac Chairez, Alejandro Garcia-Gonzalez, Alberto Luviano-Juarez","doi":"10.1080/0954898X.2023.2296115","DOIUrl":"10.1080/0954898X.2023.2296115","url":null,"abstract":"This paper presents a non-parametric identification scheme for a class of uncertain switched nonlinear systems based on continuous-time neural networks. This scheme is based on a continuous neural network identifier. This adaptive identifier guaranteed the convergence of the identification errors to a small vicinity of the origin. The convergence of the identification error was determined by the Lyapunov theory supported by a practical stability variation for switched systems. The same stability analysis generated the learning laws that adjust the identifier structure. The upper bound of the convergence region was characterized in terms of uncertainties and noises affecting the switched system. A second finite-time convergence learning law was also developed to describe an alternative way of forcing the identification error's stability. The study presented in this paper described a formal technique for analysing the application of adaptive identifiers based on continuous neural networks for uncertain switched systems. The identifier was tested for two basic problems: a simple mechanical system and a switched representation of the human gait model. In both cases, accurate results for the identification problem were achieved.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"213-248"},"PeriodicalIF":1.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139418624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0