Network-Computation in Neural Systems最新文献_第2页

Optimization-assisted deep two-layer framework for ddos attack detection and proposed mitigation in software defined network. 软件定义网络中基于优化辅助的深度两层ddos攻击检测框架及缓解方案。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2026-05-01 Epub Date: 2025-02-13 DOI: 10.1080/0954898X.2024.2443611

Karthika Perumal, Karmel Arockiasamy

{"title":"Optimization-assisted deep two-layer framework for ddos attack detection and proposed mitigation in software defined network.","authors":"Karthika Perumal, Karmel Arockiasamy","doi":"10.1080/0954898X.2024.2443611","DOIUrl":"10.1080/0954898X.2024.2443611","url":null,"abstract":"Security has become crucial as Internet of Things (IoT) applications proliferate. IoT vulnerabilities are widespread, as demonstrated by a recent distributed denial-of-service (DDoS) assault, which many IoT devices unintentionally assisted with. IoT device management may be done safely with the help of the new software-defined anything (SDx) paradigm. In this study, a five-phase SDN design will be equipped with a detection and mitigation system of DDoS attack. Data cleaning is a method of pre-processing raw data that is crucial to the flow of information. The suitable features are chosen from the retrieved features using the augmented chi-square method. A deep two-layer architecture with four classifiers is utilized to characterize the attack's detection stage. Using the recently created hybrid optimization method known as the MUAE approach, the weight of the QNN is adjusted. Until the optimized QNN detects an attacker, regular data routing occurs. In that scenario, control is passed along to the mitigation of attacks step. For training rates of 60, 70, 80, and 90, the predicted accuracy of the model is 94.273%, 94.860%, 94.93%, and 96.02%. Finally, the decided system is verified against traditional ways to demonstrate its superiority in both mitigation and attack detection.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"312-347"},"PeriodicalIF":1.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143411548","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

Investigation on the reliability calculation method of gravity dam based on CNN-LSTM and Monte Carlo method. 基于 CNN-LSTM 和 Monte Carlo 方法的重力坝可靠性计算方法研究。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2026-05-01 Epub Date: 2024-12-29 DOI: 10.1080/0954898X.2024.2447281

Ming-Wei Li, Jun-Qi Ren, Jing Geng, Hsin-Pou Huang, Wei-Chiang Hong

引用次数: 0

New delay-dependent uniform stability criteria for fractional-order BAM neural networks with discrete and distributed delays. 具有离散和分布延迟的分数阶BAM神经网络的新的与延迟相关的一致稳定性准则。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2026-05-01 Epub Date: 2025-03-26 DOI: 10.1080/0954898X.2024.2448534

Shafiya Muthu

引用次数: 0

A novel efficient data storage and data auditing in cloud environment using enhanced child drawing development optimization strategy. 采用增强的儿童绘图开发优化策略，在云环境中实现了一种新的高效数据存储和数据审计。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2026-05-01 Epub Date: 2025-01-17 DOI: 10.1080/0954898X.2024.2443622

Aruna Kari Balakrishnan, Arunachalaperumal Chellaperumal, Sudha Lakshmanan, Sureka Vijayakumar

{"title":"A novel efficient data storage and data auditing in cloud environment using enhanced child drawing development optimization strategy.","authors":"Aruna Kari Balakrishnan, Arunachalaperumal Chellaperumal, Sudha Lakshmanan, Sureka Vijayakumar","doi":"10.1080/0954898X.2024.2443622","DOIUrl":"10.1080/0954898X.2024.2443622","url":null,"abstract":"The optimization on the cloud-based data structures is carried out using Adaptive Level and Skill Rate-based Child Drawing Development Optimization algorithm (ALSR-CDDO). Also, the overall cost required in computing and communicating is reduced by optimally selecting these data structures by the ALSR-CDDO algorithm. The storage of the data in the cloud platform is performed using the Divide and Conquer Table (D&CT). The location table and the information table are generated using the D&CT method. The details, such as the file information, file ID, version number, and user ID, are all present in the information table. Every time data is deleted or updated, and its version number is modified. Whenever an update takes place using D&CT, the location table also gets upgraded. The information regarding the location of a file in the Cloud Service Provider (CSP) is given in the location table. Once the data is stored in the CSP, the auditing of the data is then performed on the stored data. Both dynamic and batch auditing are carried out on the stored data, even if it gets updated dynamically in the CSP. The security offered by the executed scheme is verified by contrasting it with other existing auditing schemes.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"348-388"},"PeriodicalIF":1.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143016854","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

Performance analysis of image retrieval system using deep learning techniques. 基于深度学习技术的图像检索系统性能分析。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2026-05-01 Epub Date: 2025-01-20 DOI: 10.1080/0954898X.2025.2451388

Selvalakshmi B, Hemalatha K, Kumarganesh S, Vijayalakshmi P

{"title":"Performance analysis of image retrieval system using deep learning techniques.","authors":"Selvalakshmi B, Hemalatha K, Kumarganesh S, Vijayalakshmi P","doi":"10.1080/0954898X.2025.2451388","DOIUrl":"10.1080/0954898X.2025.2451388","url":null,"abstract":"The image retrieval is the process of retrieving the relevant images to the query image with minimal searching time in internet. The problem of the conventional Content-Based Image Retrieval (CBIR) system is that they produce retrieval results for either colour images or grey scale images alone. Moreover, the CBIR system is more complex which consumes more time period for producing the significant retrieval results. These problems are overcome through the proposed methodologies stated in this work. In this paper, the General Image (GI) and Medical Image (MI) are retrieved using deep learning architecture. The proposed system is designed with feature computation module, Retrieval Convolutional Neural Network (RETCNN) module, and Distance computation algorithm. The distance computation algorithm is used to compute the distances between the query image and the images in the datasets and produces the retrieval results. The average precision and recall for the proposed RETCNN-based CBIRS is 98.98% and 99.15% respectively for GI category, and the average precision and recall for the proposed RETCNN-based CBIRS are 99.04% and 98.89% respectively for MI category. The significance of these experimental results is used to produce the higher image retrieval rate of the proposed system.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"484-504"},"PeriodicalIF":1.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143016857","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

Automatic screening of retinal lesions for detecting diabetic retinopathy using adaptive multiscale MobileNet with abnormality segmentation from public dataset. 利用自适应多尺度 MobileNet 对公共数据集进行异常分割，自动筛查视网膜病变以检测糖尿病视网膜病变。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2026-02-01 Epub Date: 2024-11-09 DOI: 10.1080/0954898X.2024.2424242

Nandhini Selvaganapathy, Saravanan Siddhan, Parthasarathy Sundararajan, Sathiyaprasad Balasundaram

{"title":"Automatic screening of retinal lesions for detecting diabetic retinopathy using adaptive multiscale MobileNet with abnormality segmentation from public dataset.","authors":"Nandhini Selvaganapathy, Saravanan Siddhan, Parthasarathy Sundararajan, Sathiyaprasad Balasundaram","doi":"10.1080/0954898X.2024.2424242","DOIUrl":"10.1080/0954898X.2024.2424242","url":null,"abstract":"Owing to the epidemic growth of diabetes, ophthalmologists need to examine the huge fundus images for diagnosing the disease of Diabetic Retinopathy (DR). Without proper knowledge, people are too lethargic to detect the DR. Therefore, the early diagnosis system is requisite for treating ailments in the medical industry. Therefore, a novel deep model-based DR detection structure is recommended to fix the aforementioned difficulties. The developed deep model-based diabetic retinopathy detection process is performed adaptively. The DR detection process is imitated by garnering the images from benchmark sources. The gathered images are further preceded by the abnormality segmentation phase. Here, the Residual TransUNet with Enhanced loss function is used to employ the abnormality segmentation, and the loss function in this structure may be helpful to lessen the error in the segmentation procedure. Further, the segmented images are passed to the final phase of retinopathy detection. At this phase, the detection is carried out through the Adaptive Multiscale MobileNet. The variables in the AMMNet are optimized by the Adaptive Puzzle Optimization to obtain better detection performance. Finally, the effectiveness of the offered approach is confirmed by the experimentation procedure over various performance indices.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-33"},"PeriodicalIF":1.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142632789","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

Kruskal Szekeres generative adversarial network augmented deep autoencoder for colorectal cancer detection. 用于结直肠癌检测的 Kruskal Szekeres 生成对抗网络增强型深度自动编码器。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2026-02-01 Epub Date: 2024-11-16 DOI: 10.1080/0954898X.2024.2426580

Suresh Kumar Krishnamoorthy, Vanitha Cn

{"title":"Kruskal Szekeres generative adversarial network augmented deep autoencoder for colorectal cancer detection.","authors":"Suresh Kumar Krishnamoorthy, Vanitha Cn","doi":"10.1080/0954898X.2024.2426580","DOIUrl":"10.1080/0954898X.2024.2426580","url":null,"abstract":"Cancer involves abnormal cell growth, with types like intestinal and oesophageal cancer often diagnosed in advanced stages, making them hard to cure. Symptoms are like burning sensations in the stomach and swallowing difficulties are specified as colorectal cancer. Deep learning significantly impacts the medical image processing and diagnosis, offering potential improvements in accuracy and efficiency. The Kruskal Szekeres Generative Adversarial Network Augmented Deep Autoencoder (KSGANA-DA) is introduced for early colorectal cancer detection and it comprises two stages; Initial stage, data augmentation uses Affine Transform via Random Horizontal Rotation and Geometric Transform via Kruskal-Szekeres that coordinates to improve the training dataset diversity, boosting detection performance. The second stage, a Deep Autoencoder Anatomical Landmark-based Image Segmentation preserves edge pixel spatial locations, improving precision and recall for early boundary detection. Experiments validate KSGANA-DA performance and different existing methods are implemented into Python. The results of KSGANA-DA are to provide higher precision by 41%, recall by 7%, and lesser training time by 46% than compared to conventional methods.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"34-60"},"PeriodicalIF":1.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645136","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

Optimizing tomato detection and counting in smart greenhouses: A lightweight YOLOv8 model incorporating high- and low-frequency feature transformer structures. 优化智能温室中的番茄检测和计数：结合高频和低频特征变换器结构的轻量级 YOLOv8 模型。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2026-02-01 Epub Date: 2024-11-21 DOI: 10.1080/0954898X.2024.2428713

Zhimin Tian, Huijuan Hao, Guowei Dai, Yajuan Li

{"title":"Optimizing tomato detection and counting in smart greenhouses: A lightweight YOLOv8 model incorporating high- and low-frequency feature transformer structures.","authors":"Zhimin Tian, Huijuan Hao, Guowei Dai, Yajuan Li","doi":"10.1080/0954898X.2024.2428713","DOIUrl":"10.1080/0954898X.2024.2428713","url":null,"abstract":"Tomato harvesting in intelligent greenhouses is crucial for reducing costs and optimizing management. Agricultural robots, as an automated solution, require advanced visual perception. This study proposes a tomato detection and counting algorithm based on YOLOv8 (TCAttn-YOLOv8). To handle small, occluded tomato targets in images, a new detection layer (NDL) is added to the Neck and Head decoupled structure, improving small object recognition. The ColBlock, a dual-branch structure leveraging Transformer advantages, enhances feature extraction and fusion, focusing on densely targeted regions and minimizing small object feature loss in complex backgrounds. C2fGhost and GhostConv are integrated into the Neck network to reduce model parameters and floating-point operations, improving feature expression. The WIoU (Wise-IoU) loss function is adopted to accelerate convergence and increase regression accuracy. Experimental results show that TCAttn-YOLOv8 achieves an mAP@0.5 of 96.31%, with an FPS of 95 and a parameter size of 2.7 M, outperforming seven lightweight YOLO algorithms. For automated tomato counting, the R2 between predicted and actual counts is 0.9282, indicating the algorithm's suitability for replacing manual counting. This method effectively supports tomato detection and counting in intelligent greenhouses, offering valuable insights for robotic harvesting and yield estimation research.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"61-97"},"PeriodicalIF":1.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683524","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

Design of a neural transformer for Spanish to Mexican Sign Language automatic translation/interpretation. 西班牙语到墨西哥语手语自动翻译/口译的神经转换器设计。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2026-02-01 Epub Date: 2024-12-11 DOI: 10.1080/0954898X.2024.2435495

Diana Vania Lara-Ortiz, Rita Q Fuentes Aguilar, Isaac Chairez

引用次数: 0

Modified ensemble machine learning-based plant leaf disease detection model with optimized K-Means clustering. 基于优化K-Means聚类的改进集成机器学习植物叶片病害检测模型。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2026-02-01 Epub Date: 2024-12-09 DOI: 10.1080/0954898X.2024.2435492

Vijayaganth Viswanathan, Krishnamoorthi Murugasamy

{"title":"Modified ensemble machine learning-based plant leaf disease detection model with optimized K-Means clustering.","authors":"Vijayaganth Viswanathan, Krishnamoorthi Murugasamy","doi":"10.1080/0954898X.2024.2435492","DOIUrl":"10.1080/0954898X.2024.2435492","url":null,"abstract":"In the farming sector, the automatic detection of plant leaf disease is considered a vital landmark. Farmers move long distances to consult pathologists to observe the disease, which is expensive and time-consuming. Moreover, detection of disease in a premature period is a difficult process in the existing model. Thus, all these challenges motivate us to develop an inventive plant leaf disease detection model. In the developed model, the data is gathered initially and given as input to the pre-processing step using Contrast Limited Adaptive Histogram Equalization (CLAHE). Next, the leaves are segmented from the pre-processed images, and then abnormality segmentation is done by the K-means clustering system. Here, parameters are optimized using the Opposition-based Bird Swarm Algorithm (O-BSA). Further, features were extracted from abnormality-segmented images in feature extraction. The extracted features are given in the classification step, where leaf disease detection is carried out using Optimized Ensemble Machine Learning (OEML), where, parameter optimization is done by O-BSA. Finally, the developed plant leaf detection approach is evaluated with various performance metrics, and given an accuracy of up to 92.26. These findings show that the developed model is promising over conventional methods and its effectiveness in detecting plant leaf disease.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"161-205"},"PeriodicalIF":1.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803550","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