Network-Computation in Neural Systems最新文献_第3页

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 1.1 3区计算机科学

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

Shafiya Muthu

引用次数: 0

Leveraging the internet of things and optimized deep residual networks for improved foliar disease detection in apple orchards. 利用物联网和优化的深度残留网络来改进苹果园的叶面疾病检测。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-03-24 DOI: 10.1080/0954898X.2025.2472626

Sameera Kuppam, Swarnalatha Purushotham

{"title":"Leveraging the internet of things and optimized deep residual networks for improved foliar disease detection in apple orchards.","authors":"Sameera Kuppam, Swarnalatha Purushotham","doi":"10.1080/0954898X.2025.2472626","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2472626","url":null,"abstract":"Plant diseases significantly threaten food security by reducing the quantity and quality of agricultural products. This paper presents a deep learning approach for classifying foliar diseases in apple plants using the Tunicate Swarm Sine Cosine Algorithm-based Deep Residual Network (TSSCA-based DRN). Cluster heads in simulated Internet of Things (IoT) networks are selected by Fractional Lion Optimization (FLION), and images are pre-processed with a Gaussian filter and segmented using the DeepJoint model. The TSSCA, combining the Tunicate Swarm Algorithm (TSA) and Sine Cosine Algorithm (SCA), enhances the classifier's effectiveness. Moreover, Plant Pathology 2020 - FGVC7 dataset is used in this work. This dataset is designed for the classification of foliar diseases in apple trees. The TSSCA-based DRN outperforms other methods, achieving 97% accuracy, 94.666% specificity, 96.888% sensitivity, and 0.0442J maximal energy, with significant improvements over existing approaches. Additionally, the proposed model demonstrates superior accuracy, outperforming other methods by 8.97%, 6.58%, 2.07%, 1.71%, 1.14%, 1.07%, 0.93%, and 0.64% over Multidimensional Feature Compensation Residual neural network (MDFC - ResNet), Convolutional Neural Network (CNN), Multi-Context Fusion Network (MCFN), Advanced Segmented Dimension Extraction (ASDE), and DRN, fuzzy deep convolutional neural network (FCDCNN), ResNet9-SE, Capsule Neural Network (CapsNet), IoT-based scrutinizing model, and Multi-Model Fusion Network (MMF-Net).","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-37"},"PeriodicalIF":1.1,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694661","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

Parallel convolutional SpinalNet: A hybrid deep learning approach for breast cancer detection using mammogram images. 并行卷积SpinalNet：一种混合深度学习方法，用于使用乳房x光片图像检测乳腺癌。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-03-24 DOI: 10.1080/0954898X.2025.2480299

Vinay Gautam, Anu Saini, Alok Misra, Naresh Kumar Trivedi, Shikha Maheshwari, Raj Gaurang Tiwari

{"title":"Parallel convolutional SpinalNet: A hybrid deep learning approach for breast cancer detection using mammogram images.","authors":"Vinay Gautam, Anu Saini, Alok Misra, Naresh Kumar Trivedi, Shikha Maheshwari, Raj Gaurang Tiwari","doi":"10.1080/0954898X.2025.2480299","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2480299","url":null,"abstract":"Breast cancer is the foremost cause of mortality among females. Early diagnosis of a disease is necessary to avoid breast cancer by reducing the death rate and offering a better life to the individuals. Therefore, this work proposes a Parallel Convolutional SpinalNet (PConv-SpinalNet) for the efficient detection of breast cancer using mammogram images. At first, the input image is pre-processed using the Gabor filter. The tumour segmentation is conducted using LadderNet. Then, the segmented tumour samples are augmented using Image manipulation, Image erasing, and Image mix techniques. After that, the essential features, like CNN features, Texton, Local Gabor binary patterns (LGBP), scale-invariant feature transform (SIFT), and Local Monotonic Pattern (LMP) with discrete cosine transform (DCT) are extracted in the feature extraction phase. Finally, the detection of breast cancer is performed using PConv-SpinalNet. PConv-SpinalNet is developed by an integration of Parallel Convolutional Neural Networks (PCNN) and SpinalNet. The evaluation results show that PConv-SpinalNet accomplished a superior range of accuracy as 88.5%, True Positive Rate (TPR) as 89.7%, True Negative Rate (TNR) as 90.7%, Positive Predictive Value (PPV) as 91.3%, and Negative Predictive Value (NPV) as 92.5%.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-41"},"PeriodicalIF":1.1,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694662","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

HUNHODRL: Energy efficient resource distribution in a cloud environment using hybrid optimized deep reinforcement model with HunterPlus scheduler. HUNHODRL：在云环境中使用混合优化深度强化模型和HunterPlus调度器实现节能资源分配。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-03-24 DOI: 10.1080/0954898X.2025.2480294

Senthilkumar Chellamuthu, Kalaivani Ramanathan, Rajesh Arivanandhan

{"title":"HUNHODRL: Energy efficient resource distribution in a cloud environment using hybrid optimized deep reinforcement model with HunterPlus scheduler.","authors":"Senthilkumar Chellamuthu, Kalaivani Ramanathan, Rajesh Arivanandhan","doi":"10.1080/0954898X.2025.2480294","DOIUrl":"10.1080/0954898X.2025.2480294","url":null,"abstract":"Resource optimization and workload balancing in cloud computing environments necessitate efficient management of resources to minimize energy wastage and SLA (Service Level Agreement) violations. The existing scheduling techniques often face challenges with dynamic resource allocations and lead to inefficient job completion rates and container utilizations. Hence, this framework has been proposed to establish HUNHODRL, a newly-minted DRL-based framework that aims to improve container orchestration and workload allocation. The evaluation of this framework was done against HUNDRL, Bi-GGCN, and CNN methods comparatively under two sets of workloads with datasets on CPU, Memory, and Disk I/O utilization metrics. The model optimizes scheduling choices in HUNHODRL through a combination of destination host capacity vector and active job utilization matrix. The experimental results show that HUNHODRL outperforms existing models in container creation rate, job completion rate, SLA violation reduction, and energy efficiency. It facilitates increased container creation efficiency without increasing the energy costs of VM deployments. This method dynamically adapts itself and modifies the scheduling strategy to optimize performance amid varying workloads, thus establishing its scalability and robustness. A comparative analysis has demonstrated higher job completion rates against CNN, Bi-GGCNN, and HUNDRL, establishing the potential of DRL-based resource allocation. The significant gain in cloud resource utilization and energy-efficient task execution makes HUNHODRL and its suitable solution for next-generation cloud computing infrastructure.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-26"},"PeriodicalIF":1.1,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694660","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

A multi-objective function for deep learning-based automatic energy efficiency power allocation in multicarrier noma system using hybrid heuristic improvement. 基于混合启发式改进的基于深度学习的多载波noma系统能效自动分配多目标函数。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-03-13 DOI: 10.1080/0954898X.2025.2461046

Chiranjeevi Thokala, Pradnya H Ghare

{"title":"A multi-objective function for deep learning-based automatic energy efficiency power allocation in multicarrier noma system using hybrid heuristic improvement.","authors":"Chiranjeevi Thokala, Pradnya H Ghare","doi":"10.1080/0954898X.2025.2461046","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2461046","url":null,"abstract":"Non-Orthogonal Multiple Access (NOMA) is the successive multiple-access methodologies for modern communication devices. Energy Efficiency (EE) is suggested in the NOMA system. In dynamic network conditions, the consideration of NOMA shows high computational complexity that minimizes the EE to degrade the system performance. This research suggested EE for the Multi-Carrier NOMA (MC-NOMA) models by optimization algorithm. The main scope of this research tends to improve the EE by Hybrid of Sewing Training and Lemur Optimization for optimizing the system parameters. The improvement made in this developed HSTLO algorithm can provide significant impact on MC-NOMA system, which it renders better user capacity while effectively optimizing the system parameters. Moreover, the Dilated Dense Recurrent Neural Network (DDRNN) model is developed. Employing the improvement in the deep learning model for the MC-NOMA system could effectively manage and enhance the system performance. Considering the DDRNN model can leverage to provide better generalization outcomes in different network scenarios that ensures to provide fast and reliable solutions compared to existing methods. Addressing the energy consumption problems in this research study will be analysed to show the advancement in MC-NOMA system that help to enhance the system performance.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-32"},"PeriodicalIF":1.1,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143617226","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 bounding box segmentation technique for crowd anomaly detection with optimal trained convolutional neural network. 基于最优训练卷积神经网络的人群异常检测改进边界盒分割技术。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-03-12 DOI: 10.1080/0954898X.2025.2475070

Rohini P S, Sowmy I

{"title":"Improved bounding box segmentation technique for crowd anomaly detection with optimal trained convolutional neural network.","authors":"Rohini P S, Sowmy I","doi":"10.1080/0954898X.2025.2475070","DOIUrl":"10.1080/0954898X.2025.2475070","url":null,"abstract":"A crucial role in many security and surveillance applications is crowd anomaly detection, where seeing unusual activity helps avert possible threats or interruptions. For precise anomaly identification, current models might not successfully incorporate spatial and temporal features. To overcome these drawbacks, a novel Crowd Anomaly Detection based on Opposition Behavior Learning updated Chimp Optimization Algorithm (CAD-OBLChoA) is proposed in this research to enhance the detection of abnormal crowd behaviours in dynamic environments. In this research, bilateral filtering is used for smoothening the image and reducing noise for preprocessing phase. For object detection, a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM)-based bounding box approach is used. Then, features like Colour features, Shape features, and Improved Texture features are extracted. Finally, the anomalies get detected based on the trained extracted feature set in the system. For this, an optimized CNN is used, where training is done by the OBLChoA scheme via tuning the optimal weights. The proposed CAD-OBLChoA scheme achieved a higher specificity of about 0.924 and 0.931 in the 90% training data for datasets 1 and 2. This approach could significantly improve crowd monitoring and security, enabling faster identification of potential threats or emergencies.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-54"},"PeriodicalIF":1.1,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143607155","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

JLeNeT: Jaccard LeNet for Parkinson's disease detection and severity level classification using voice signal in IoT environment. JLeNeT：在物联网环境中使用语音信号进行帕金森病检测和严重程度分类的Jaccard LeNet。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-03-12 DOI: 10.1080/0954898X.2025.2453032

Sundaresan Pragadeeswaran, Subramanian Kannimuthu

{"title":"JLeNeT: Jaccard LeNet for Parkinson's disease detection and severity level classification using voice signal in IoT environment.","authors":"Sundaresan Pragadeeswaran, Subramanian Kannimuthu","doi":"10.1080/0954898X.2025.2453032","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2453032","url":null,"abstract":"The neurodegenerative disorder called Parkinson's disease (PD) is one of the most common diseases now a day. In this research, PD is detected and severity classification is done using the proposed Jaccard LeNet (JLeNet) with the help of voice signal in the IoT environment. Here, the IoT simulation is done. Initially, from which voice signal is collected and the routing process is done by the proposed Chimp Wild Geese Algorithm (ChWGA). This ChWGA is the combination of the Wild Geese Algorithm (WGA) and Chimp Optimization Algorithm (ChOA). Finally, at Base Station (BS), PD is detected and classified. The input voice signal is fed for pre-processing conducted by an adaptive Kalman filter. Following this, feature extraction and feature selection are conducted, where Harmonic mean similarity helps in feature selection. Here, PD is detected using JLeNet, which is the hybridization of LeNet with the Jaccard similarity measure. In this work, routing metrics of energy and delay are superior and recorded with the values of 0.309 J and 0.434 ms for the ChWGA. Moreover, the proposed method attains an Accuracy of 0.910, True Positive Rate (TPR) of 0.903, and True Negative Rate (TNR) of 0.918.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-30"},"PeriodicalIF":1.1,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143607157","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

Hybrid ladybug Hawk optimization-enabled deep learning for multimodal Parkinson's disease classification using voice signals and hand-drawn images. 混合瓢虫鹰优化支持深度学习的多模态帕金森病分类使用语音信号和手绘图像。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-03-04 DOI: 10.1080/0954898X.2025.2457955

Shanthini Shanmugam, Chandrasekar Arumugam

{"title":"Hybrid ladybug Hawk optimization-enabled deep learning for multimodal Parkinson's disease classification using voice signals and hand-drawn images.","authors":"Shanthini Shanmugam, Chandrasekar Arumugam","doi":"10.1080/0954898X.2025.2457955","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2457955","url":null,"abstract":"PD is a progressive neurodegenerative disorder that leads to gradual motor impairments. Early detection is critical for slowing the disease's progression and providing patients access to timely therapies. However, accurately detecting PD in its early stages remains challenging. This study aims to develop an optimized deep learning model for PD classification using voice signals and hand-drawn spiral images, leveraging a ZFNet-LHO-DRN. The proposed model first preprocesses the input voice signal using a Gaussian filter to remove noise. Features are then extracted from the preprocessed signal and passed to ZFNet to generate output-1. For the hand-drawn spiral image, preprocessing is performed with a bilateral filter, followed by image augmentation. Here also, the features are extracted and forwarded to DRN to form output-2. Both classifiers are trained using the LHO algorithm. Finally, from the output-1 and output-2, the best one is selected based on the majority voting. The ZFNet-LHO-DRN model demonstrated excellent performance by achieving a premium accuracy of 89.8%, a NPV of 89.7%, a PPV of 89.7%, a TNR of 89.3%, and a TPR of 90.1%. The model's high accuracy and performance indicate its potential as a valuable tool for assisting in the early diagnosis of PD.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-43"},"PeriodicalIF":1.1,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544402","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

RESNET-50 with ontological visual features based medicinal plants classification. 基于本体视觉特征的药用植物分类RESNET-50。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-03-03 DOI: 10.1080/0954898X.2024.2447878

Sapna Renukaradhya, Sheshappa Shagathur Narayanappa, Pravinth Raja

{"title":"RESNET-50 with ontological visual features based medicinal plants classification.","authors":"Sapna Renukaradhya, Sheshappa Shagathur Narayanappa, Pravinth Raja","doi":"10.1080/0954898X.2024.2447878","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2447878","url":null,"abstract":"The proper study and administration of biodiversity relies heavily on accurate plant species identification. To determine a plant's species by manual identification, experts use a series of keys based on measurements of various plant features. The manual procedure, however, is tiresome and lengthy. Recently, advancements in technology have prompted the need for more effective approaches to satisfy species identification standards, such as the creation of digital-image-processing and template tools. There are significant obstacles to fully automating the recognition of plant species, despite the many current research on the topic. In this work, the leaf classification was performed using the ontological relationship between the leaf features and their classes. This relationship was identified by using the swarm intelligence techniques called particle swarm and cuckoo search algorithm. Finally, these features were trained using the traditional machine learning algorithm regression neural network. To increase the effectiveness of the ontology, the machine learning approach results were combined with the deep learning approach called RESNET50 using association rule. The proposed ontology model produced an identification accuracy of 98.8% for GRNN model, 99% accuracy for RESNET model and 99.9% for the combined model for 15 types of medicinal leaf sets.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-37"},"PeriodicalIF":1.1,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544475","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

A novel skin cancer detection architecture using tangent rat swarm optimization algorithm enabled DenseNet. 一种基于切线鼠群优化算法的新型皮肤癌检测架构实现了DenseNet。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-02-28 DOI: 10.1080/0954898X.2025.2452274

Balashanmuga Vadivu P, Om Prakash Pg, Aravind Karrothu, Sriramakrishnan Gv

{"title":"A novel skin cancer detection architecture using tangent rat swarm optimization algorithm enabled DenseNet.","authors":"Balashanmuga Vadivu P, Om Prakash Pg, Aravind Karrothu, Sriramakrishnan Gv","doi":"10.1080/0954898X.2025.2452274","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2452274","url":null,"abstract":"This paper proposes a Tangent Rat Swarm Optimization (TRSO)-DenseNet for the detection of skin cancer to reduce the severity rate of cancer. Initially, the input image is pre-processed by employing a linear smoothing filter. The pre-processed image is transferred to skin lesion segmentation, where Mask-RCNN is utilized for segmenting the skin lesion. Then, image augmentation is performed using techniques such as vertical shifting, horizontal shifting, random rotation, brightness adjustment, blurring, and cropping. The augmented image is then fed into the feature extraction phase to identify statistical features, Haralick texture features, Convolutional Neural Network (CNN) features, Local Ternary Pattern (LTP), Histogram of Oriented Gradients (HOG), and Local Vector Pattern (LVP). Finally, the extracted features are fed into the skin cancer detection phase, where DenseNet is used to detect skin cancer. Here, DenseNet is structurally optimized by TRSO, which has the combination of the Tangent Search Algorithm (TSA) and Rat Swarm Optimizer (RSO). The TRSO-DenseNet model is implemented using MATLAB tool and analayzsed using the Society for Imaging Informatics in Medicine-International Skin Imaging Collaboration's (SIIM-ISIC) Melanoma Classification dataset. The Proposed model for skin cancer detection attained superior performance with an accuracy of 94.63%, TPR of 91.51%, and TNR of 92.46%.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-31"},"PeriodicalIF":1.1,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525309","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