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

Energy and time-aware scheduling in diverse virtualized cloud computing environments using optimized self-attention progressive generative adversarial network. 利用优化的自关注渐进生成对抗网络，在多样化虚拟化云计算环境中实现能量和时间感知调度。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-09-25 DOI: 10.1080/0954898X.2024.2391401

G Senthilkumar, S Anandamurugan

{"title":"Energy and time-aware scheduling in diverse virtualized cloud computing environments using optimized self-attention progressive generative adversarial network.","authors":"G Senthilkumar, S Anandamurugan","doi":"10.1080/0954898X.2024.2391401","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2391401","url":null,"abstract":"The rapid growth of cloud computing has led to the widespread adoption of heterogeneous virtualized environments, offering scalable and flexible resources to meet diverse user demands. However, the increasing complexity and variability in workload characteristics pose significant challenges in optimizing energy consumption. Many scheduling algorithms have been suggested to address this. Therefore, a self-attention-based progressive generative adversarial network optimized with Dwarf Mongoose algorithm adopted Energy and Deadline Aware Scheduling in heterogeneous virtualized cloud computing (SAPGAN-DMA-DAS-HVCC) is proposed in this paper. Here, a self-attention based progressive generative adversarial network (SAPGAN) is proposed to schedule activities in a cloud environment with an objective function of makespan and energy consumption. Then Dwarf Mongoose algorithm is proposed to optimize the weight parameters of SAPGAN. Outcome of proposed approach SAPGAN-DMA-DAS-HVCC contains 32.77%, 34.83% and 35.76% higher right skewed makespan, 31.52%, 33.28% and 29.14% lower cost when analysed to the existing models, like task scheduling in heterogeneous cloud environment utilizing mean grey wolf optimization approach, energy and performance-efficient task scheduling in heterogeneous virtualized Energy and Performance Efficient Task Scheduling Algorithm, energy and make span aware scheduling of deadline sensitive tasks on the cloud environment, respectively.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142332541","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

Optimized deep maxout for crowd anomaly detection: A hybrid optimization-based model. 用于人群异常检测的优化深度最大值：基于优化的混合模型

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-09-20 DOI: 10.1080/0954898X.2024.2392772

Rashmi Chaudhary, Manoj Kumar

{"title":"Optimized deep maxout for crowd anomaly detection: A hybrid optimization-based model.","authors":"Rashmi Chaudhary, Manoj Kumar","doi":"10.1080/0954898X.2024.2392772","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2392772","url":null,"abstract":"Monitoring Surveillance video is really time-consuming, and the complexity of typical crowd behaviour in crowded situations makes this even more challenging. This has sparked a curiosity about computer vision-based anomaly detection. This study introduces a new crowd anomaly detection method with two main steps: Visual Attention Detection and Anomaly Detection. The Visual Attention Detection phase uses an Enhanced Bilateral Texture-Based Methodology to pinpoint crucial areas in crowded scenes, improving anomaly detection precision. Next, the Anomaly Detection phase employs Optimized Deep Maxout Network to robustly identify unusual behaviours. This network's deep learning capabilities are essential for detecting complex patterns in diverse crowd scenarios. To enhance accuracy, the model is trained using the innovative Battle Royale Coalesced Atom Search Optimization (BRCASO) algorithm, which fine-tunes optimal weights for superior performance, ensuring heightened detection accuracy and reliability. Lastly, using various performance metrics, the suggested work's effectiveness will be contrasted with that of the other traditional approaches. The proposed crowd anomaly detection is implemented in Python. On observing the result showed that the suggested model attains a detection accuracy of 97.28% at a learning rate of 90%, which is much superior than the detection accuracy of other models, including ASO = 90.56%, BMO = 91.39%, BES = 88.63%, BRO = 86.98%, and FFLY = 89.59%.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142301214","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

EGDP based feature extraction and deep convolutional belief network for brain tumor detection using MRI image. 基于 EGDP 特征提取和深度卷积信念网络的磁共振成像脑肿瘤检测。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-09-16 DOI: 10.1080/0954898X.2024.2389248

Loganayagi T, Pooja Panapana, Ganji Ramanjaiah, Smritilekha Das

{"title":"EGDP based feature extraction and deep convolutional belief network for brain tumor detection using MRI image.","authors":"Loganayagi T, Pooja Panapana, Ganji Ramanjaiah, Smritilekha Das","doi":"10.1080/0954898X.2024.2389248","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2389248","url":null,"abstract":"This research presents a novel deep learning framework for MRI-based brain tumour (BT) detection. The input brain MRI image is first acquired from the dataset. Once the images have been obtained, they are passed to an image preprocessing step where a median filter is used to eliminate noise and artefacts from the input image. The tumour-tumour region segmentation module receives the denoised image and it uses RP-Net to segment the BT region. Following that, in order to prevent overfitting, image augmentation is carried out utilizing methods including rotating, flipping, shifting, and colour augmentation. Later, the augmented image is forwarded to the feature extraction phase, wherein features like GLCM and proposed EGDP formulated by including entropy with GDP are extracted. Finally, based on the extracted features, BT detection is accomplished based on the proposed deep convolutional belief network (DCvB-Net), which is formulated using the deep convolutional neural network and deep belief network.The devised DCvB-Net for BT detection is investigated for its performance concerning true negative rate, accuracy, and true positive rate is established to have acquired values of 93%, 92.3%, and 93.1% correspondingly.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142301213","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

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":null,"pages":null},"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":null,"pages":null},"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

Human hand gesture recognition using fast Fourier transform with coot optimization based on deep neural network. 利用基于深度神经网络的快速傅立叶变换和 coot 优化技术识别人类手势。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-08-21 DOI: 10.1080/0954898X.2024.2389231

Arumugam Arulkumar, Palanisamy Babu

{"title":"Human hand gesture recognition using fast Fourier transform with coot optimization based on deep neural network.","authors":"Arumugam Arulkumar, Palanisamy Babu","doi":"10.1080/0954898X.2024.2389231","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2389231","url":null,"abstract":"Hand motion detection is particularly important for managing the movement of individuals who have limbs amputated. The existing algorithm is complex, time-consuming and difficult to achieve better accuracy. A DNN is suggested to recognize human hand movements in order to get over these problems.Initially, the raw input EMG signal is captured then the signal is pre-processed using high-pass Butterworth filter and low-pass filter which is utilized to eliminate the noise present in the signal. After that pre-processed EMG signal is segmented using sliding window which is used for solving the issue of overlapping. Then the features are extracted from the segmented signal using Fast Fourier Transform. Then selected the appropriate and optimal number of features from the feature subset using coot optimization algorithm. After that selected features are given as input for deep neural network classifier for recognizing the hand movements of human. The simulation analysis shows that the proposed method obtain 95% accuracy, 0.05% error, precision is 94%, and specificity is 92%.The simulation analysis shows that the developed approach attain better performance compared to other existing approaches. This prediction model helps in controlling the movement of amputee patients suffering from disable hand motion and improve their living standard.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142019538","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 and optimization enabled multi-objective for task scheduling in cloud computing. 云计算任务调度的深度学习和优化多目标。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-08-20 DOI: 10.1080/0954898X.2024.2391395

Dinesh Komarasamy, Siva Malar Ramaganthan, Dharani Molapalayam Kandaswamy, Gokuldhev Mony

{"title":"Deep learning and optimization enabled multi-objective for task scheduling in cloud computing.","authors":"Dinesh Komarasamy, Siva Malar Ramaganthan, Dharani Molapalayam Kandaswamy, Gokuldhev Mony","doi":"10.1080/0954898X.2024.2391395","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2391395","url":null,"abstract":"In cloud computing (CC), task scheduling allocates the task to best suitable resource for execution. This article proposes a model for task scheduling utilizing the multi-objective optimization and deep learning (DL) model. Initially, the multi-objective task scheduling is carried out by the incoming user utilizing the proposed hybrid fractional flamingo beetle optimization (FFBO) which is formed by integrating dung beetle optimization (DBO), flamingo search algorithm (FSA) and fractional calculus (FC). Here, the fitness function depends on reliability, cost, predicted energy, and makespan, the predicted energy is forecasted by a deep residual network (DRN). Thereafter, task scheduling is accomplished based on DL using the proposed deep feedforward neural network fused long short-term memory (DFNN-LSTM), which is the combination of DFNN and LSTM. Moreover, when scheduling the workflow, the task parameters and the virtual machine's (VM) live parameters are taken into consideration. Task parameters are earliest finish time (EFT), earliest start time (EST), task length, task priority, and actual task running time, whereas VM parameters include memory utilization, bandwidth utilization, capacity, and central processing unit (CPU). The proposed model DFNN-LSTM+FFBO has achieved superior makespan, energy, and resource utilization of 0.188, 0.950J, and 0.238, respectively.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142009908","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