Network-Computation in Neural Systems最新文献_第9页

Human activity recognition utilizing optimized attention induced Multihead Convolutional Neural Network with Mobile Net V1 from Mobile health data. 利用优化的注意诱导多头卷积神经网络和移动网络V1从移动健康数据中识别人类活动。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-05-01 Epub Date: 2024-12-17 DOI: 10.1080/0954898X.2024.2438967

R Anandha Praba, L Suganthi

{"title":"Human activity recognition utilizing optimized attention induced Multihead Convolutional Neural Network with Mobile Net V1 from Mobile health data.","authors":"R Anandha Praba, L Suganthi","doi":"10.1080/0954898X.2024.2438967","DOIUrl":"10.1080/0954898X.2024.2438967","url":null,"abstract":"Human Activity Recognition (HAR) systems are designed to continuously monitor human behaviour, mainly in the areas of entertainment and surveillance in intelligent home environments. In this manuscript, Human Activity Recognition utilizing optimized Attention Induced Multi head Convolutional Neural Network with Mobile Net V1 from Mobile Health Data (HAR-AMCNN-MNV1) is proposed. The input data is collected through MHEALTH and UCI HAR datasets. Neural Spectrospatial Filtering (NSF) is used for avoiding accurate labelling and reduces errors. Afterwards, Variational Density Peak Clustering Algorithm (VDPCA) is used for segmenting the data. Feature Extraction and Classification is done by Attention Induced Multi head Convolutional Neural Network with Mobile Net V1 (AMCNN-MNV1). AMCNN is used for extracting Hand-crafted features. AMCNN-MNV1 effectively classifies the human activities as Sitting and relaxing (Sit), Climbing stairs (CS), Walking (Walk), Standing still (Std), Waist bends forward (WBF), Frontal elevation of arms (FEA), Jogging (Jog), Knees bending (crouching) (KB), Cycling (Cycl), Lying down (Lay), Jump front & back (JFB) and Running (Run). Siberian Tiger Optimization Algorithm (STOA) is proposed to optimize the weight parameter of AMCNN-MNV1 classifier. The proposed method attains 21.19%, 23.45%, and 21.76% higher accuracy, 31.15%, 24.65% and 22.72% higher precision; 21.15%, 20.18%, and 21.28% higher recall evaluated to the existing methods.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"294-321"},"PeriodicalIF":1.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840251","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

Omics data classification using constitutive artificial neural network optimized with single candidate optimizer. 使用单候选优化器优化的构成型人工神经网络进行 Omics 数据分类。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-05-01 Epub Date: 2024-05-12 DOI: 10.1080/0954898X.2024.2348726

Subramaniam Madhan, Anbarasan Kalaiselvan

{"title":"Omics data classification using constitutive artificial neural network optimized with single candidate optimizer.","authors":"Subramaniam Madhan, Anbarasan Kalaiselvan","doi":"10.1080/0954898X.2024.2348726","DOIUrl":"10.1080/0954898X.2024.2348726","url":null,"abstract":"Recent technical advancements enable omics-based biological study of molecules with very high throughput and low cost, such as genomic, proteomic, and microbionics'. To overcome this drawback, Omics Data Classification using Constitutive Artificial Neural Network Optimized with Single Candidate Optimizer (ODC-ZOA-CANN-SCO) is proposed in this manuscript. The input data is pre-processing by using Adaptive variational Bayesian filtering (AVBF) to replace missing values. The pre-processing data is fed to Zebra Optimization Algorithm (ZOA) for dimensionality reduction. Then, the Constitutive Artificial Neural Network (CANN) is employed to classify omics data. The weight parameter is optimized by Single Candidate Optimizer (SCO). The proposed ODC-ZOA-CANN-SCO method attains 25.36%, 21.04%, 22.18%, 26.90%, and 28.12% higher accuracy when analysed to the existing methods like multi-omics data integration utilizing adaptive graph learning and attention mode for patient categorization with biomarker identification (MOD-AGL-AM-PABI), deep learning method depending upon multi-omics data integration to create risk stratification prediction mode for skin cutaneous melanoma (DL-MODI-RSP-SCM), Deep belief network-base model for identifying Alzheimer's disease utilizing multi-omics data (DDN-DAD-MOD), hybrid cancer prediction depending upon multi-omics data and reinforcement learning state action reward state action (HCP-MOD-RL-SARSA), machine learning basis method under omics data including biological knowledge database for cancer clinical endpoint prediction (ML-ODBKD-CCEP) methods, respectively.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"343-367"},"PeriodicalIF":1.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140913377","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

Smart plant disease net: Adaptive Dense Hybrid Convolution network with attention mechanism for IoT-based plant disease detection by improved optimization approach. 智能植物病害网：自适应密集混合卷积网络与关注机制，通过改进的优化方法实现基于物联网的植物病害检测。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-05-01 Epub Date: 2024-02-24 DOI: 10.1080/0954898X.2024.2316080

N Ananthi, V Balaji, M Mohana, S Gnanapriya

{"title":"Smart plant disease net: Adaptive Dense Hybrid Convolution network with attention mechanism for IoT-based plant disease detection by improved optimization approach.","authors":"N Ananthi, V Balaji, M Mohana, S Gnanapriya","doi":"10.1080/0954898X.2024.2316080","DOIUrl":"10.1080/0954898X.2024.2316080","url":null,"abstract":"Plant diseases are rising nowadays. Plant diseases lead to high economic losses. Internet of Things (IoT) technology has found its application in various sectors. This led to the introduction of smart farming, in which IoT has been utilized to help identify the exact spot of the diseased affected region on the leaf from the vast farmland in a well-organized and automated manner. Thus, the main focus of this task is the introduction of a novel plant disease detection model that relies on IoT technology. The collected images are given to the Image Transmission phase. Here, the encryption task is performed by employing the Advanced Encryption Standard (AES) and also the decrypted plant images are fed to the pre-processing stage. The Mask Regions with Convolutional Neural Networks (R-CNN) are used to segment the pre-processed images. Then, the segmented images are given to the detection phase in which the Adaptive Dense Hybrid Convolution Network with Attention Mechanism (ADHCN-AM) approach is utilized to perform the detection of plant disease. From the ADHCN-AM, the final detected plant disease outcomes are obtained. Throughout the entire validation, the offered model shows 95% enhancement in terms of MCC showcasing its effectiveness over the existing approaches.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"368-406"},"PeriodicalIF":1.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139944676","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 self-organizing map neural networks improve the segmentation for inadequate plantar pressure imaging data set. 深度自组织图神经网络改进了对不足的足底压力成像数据集的分割。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-05-01 Epub Date: 2024-10-14 DOI: 10.1080/0954898X.2024.2413849

Dan Wang, Zairan Li, Nilanjan Dey, Adam Slowik, R Simon Sherratt, Fuqian Shi

{"title":"Deep self-organizing map neural networks improve the segmentation for inadequate plantar pressure imaging data set.","authors":"Dan Wang, Zairan Li, Nilanjan Dey, Adam Slowik, R Simon Sherratt, Fuqian Shi","doi":"10.1080/0954898X.2024.2413849","DOIUrl":"10.1080/0954898X.2024.2413849","url":null,"abstract":"This study introduces a deep self-organizing map neural network based on level-set (LS-SOM) for the customization of a shoe-last defined from plantar pressure imaging data. To alleviate the over-segmentation problem of images, which refers to segmenting images into more subcomponents, a domain-based segmentation model of plantar pressure images was constructed. The domain growth algorithm was subsequently modified by optimizing its parameters. A SOM with 10, 15, 20, and 30 hidden layers was compared and validated according to domain growth characteristics by using merging and splitting algorithms. Furthermore, we incorporated a level set segmentation method into the plantar pressure image algorithm to enhance its efficiency. Compared to the literature, this proposed method has significantly improved pixel accuracy, average cross-combination ratio, frequency-weighted cross-combination ratio, and boundary F1 index comparison. Using the proposed methods, shoe lasts can be designed optimally, and wearing comfort is enhanced, particularly for people with high blood pressure.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"322-342"},"PeriodicalIF":1.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142481197","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 deep learning-based skin cancer classification with RPO-SegNet for skin lesion segmentation. 基于深度学习的皮肤癌分类与RPO-SegNet混合皮肤病变分割。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-05-01 Epub Date: 2024-12-03 DOI: 10.1080/0954898X.2024.2428705

Visu Pandurangan, Smitha Ponnayyan Sarojam, Pughazendi Narayanan, Murugananthan Velayutham

{"title":"Hybrid deep learning-based skin cancer classification with RPO-SegNet for skin lesion segmentation.","authors":"Visu Pandurangan, Smitha Ponnayyan Sarojam, Pughazendi Narayanan, Murugananthan Velayutham","doi":"10.1080/0954898X.2024.2428705","DOIUrl":"10.1080/0954898X.2024.2428705","url":null,"abstract":"Skin melanin lesions are typically identified as tiny patches on the skin, which are impacted by melanocyte cell overgrowth. The number of people with skin cancer is increasing worldwide. Accurate and timely skin cancer identification is critical to reduce the mortality rates. An incorrect diagnosis can be fatal to the patient. To tackle these issues, this article proposes the Recurrent Prototypical Object Segmentation Network (RPO-SegNet) for the segmentation of skin lesions and a hybrid Deep Learning (DL) - based skin cancer classification. The RPO-SegNet is formed by integrating the Recurrent Prototypical Networks (RP-Net), and Object Segmentation Networks (O-SegNet). At first, the input image is taken from a database and forwarded to image pre-processing. Then, the segmentation of skin lesions is accomplished using the proposed RPO-SegNet. After the segmentation, feature extraction is accomplished. Finally, skin cancer classification and detection are accomplished by employing the Fuzzy-based Shepard Convolutional Maxout Network (FSCMN) by combining the Deep Maxout Network (DMN), and Shepard Convolutional Neural Network (ShCNN). The established RPO-SegNet+FSCMN attained improved accuracy, True Negative Rate (TNR), True Positive Rate (TPR), dice coefficient, Jaccard coefficient, and segmentation analysis of 91.985%, 92.735%, 93.485%, 90.902%, 90.164%, and 91.734%.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"221-248"},"PeriodicalIF":1.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142774851","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

Quadratic discriminant feature selected broken stick regressive deep convolution neural learning classification for turmeric crop yield prediction. 二次判别特征选择断棒回归深度卷积神经学习分类姜黄产量预测。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-04-27 DOI: 10.1080/0954898X.2025.2488881

Raguvaran Krishnamoorthy, Rajasekaran Chinnappan, Jayanthi Krishnasamy Balasundaram

{"title":"Quadratic discriminant feature selected broken stick regressive deep convolution neural learning classification for turmeric crop yield prediction.","authors":"Raguvaran Krishnamoorthy, Rajasekaran Chinnappan, Jayanthi Krishnasamy Balasundaram","doi":"10.1080/0954898X.2025.2488881","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2488881","url":null,"abstract":"In this study, a novel technique termed Quadratic Discriminant Feature Selected Broken Stick Regressive Deep Convolution Neural Learning Classification (QDFSBSRDCNLC) Technique is proposed for disease classification and hence yields prediction of turmeric crop. Initially, we gathered the images of turmeric crops with and without diseases. The images are collected from the turmeric research field at Bhavanisagar. Quadratic Discriminant Analysis (QDA) is utilized to select relevant features from a dataset, reducing dimensionality. In this paper, four models, named FCN8, PSP Net, MobileNetV3 (small), and Deep Lab V3 are chosen for semantic segmentation of disease in turmeric crops. Turmeric crop production predicts is an important part of modern agriculture, allowing farmers to make sensible choices and optimize resources. We can predict turmeric crop yields accurately by using modern data analysis approaches. Predictive models take into consideration variables such as weather, soil quality, and farming techniques. The experimental results demonstrated that MobileNetV3 (small) performed better than other established ones with the accuracy of 97.99%, IoU of 96.82%, and Coefficient of 97.80% for 50 epochs. The proposed QDFSBSRDCNLC Technique effectively classifies diseases and predicts the yield of turmeric crops, with MobileNetV3 (small) showing superior performance among the tested models.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-38"},"PeriodicalIF":1.1,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144060200","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

Classifications of meningioma brain images using the novel Convolutional Fuzzy C Means (CFCM) architecture and performance analysis of hardware incorporated tumor segmentation module. 采用新颖的卷积模糊C均值（CFCM）架构对脑膜瘤脑图像进行分类，并结合硬件对肿瘤分割模块进行性能分析。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-04-24 DOI: 10.1080/0954898X.2025.2491537

K Jayaram, S Kumarganesh, A Immanuvel, C Ganesh

{"title":"Classifications of meningioma brain images using the novel Convolutional Fuzzy C Means (CFCM) architecture and performance analysis of hardware incorporated tumor segmentation module.","authors":"K Jayaram, S Kumarganesh, A Immanuvel, C Ganesh","doi":"10.1080/0954898X.2025.2491537","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2491537","url":null,"abstract":"In this paper, meningioma detection and segmentation method is proposed. This research work proposes an effective method to locate meningioma pictures through a novel CFCM classification approach. This proposed method consist of Non-Sub sampled Contourlet Transform decomposition module which decomposes the entire brain image into multi-scale sub-band images and then the heuristic and uniqueness features have been computed individually. Then, these heuristic and uniqueness features are trained and classified using Convolutional Fuzzy C Means (CFCM) classifier. This proposed method is applied on two independent brain imaging datasets. The proposed meningioma identification system stated in this work obtained 98.81% of Se, 98.83% of Sp, 99.04% of Acc, 99.12% of pr, and 99.14% of FIS on Nanfang University dataset brain images. The proposed meningioma identification system stated in this work obtained 98.92% of Se, 98.88% of Sp, 98.9% of Acc, 98.88% of pr, and 99.36% of FIS on the BRATS 2021 brain images. Finally, the tumour segmentation module is designed in VLSI, and it is simulated using Xilinx project navigator in this paper.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-22"},"PeriodicalIF":1.1,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144006397","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

An effective model of hybrid adaptive deep learning with attention mechanism for healthcare data analysis in blockchain-based secure transmission over IoT. 基于区块链的物联网安全传输医疗数据分析的混合自适应深度学习与注意机制的有效模型。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-04-23 DOI: 10.1080/0954898X.2025.2492375

Ningampalli Ramanjaneyulu, Challa Venkataiah, Yamarthy Mallikarjuna Rao, Kurra Upendra Chowdary, Machupalli Madhusudhan Reddy, Manjula Jayamma

{"title":"An effective model of hybrid adaptive deep learning with attention mechanism for healthcare data analysis in blockchain-based secure transmission over IoT.","authors":"Ningampalli Ramanjaneyulu, Challa Venkataiah, Yamarthy Mallikarjuna Rao, Kurra Upendra Chowdary, Machupalli Madhusudhan Reddy, Manjula Jayamma","doi":"10.1080/0954898X.2025.2492375","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2492375","url":null,"abstract":"The existing approaches suffer from scalability and security issues while transmitting data. Blockchain is a recently emerged technology, and it is an emerging platform that allows secure transmission. A distributed design is required to address these issues and abide by security regulations. Blockchain has been recently introduced as an alternative solution to solve complex and challenging security issues while storing data. Thus, an intelligent blockchain-assisted IoT architecture is provided in this work to perform secure healthcare data transmission. The first aim of our model is to detect malware attacks in IoT networks. To detect the malware activities, the attack detection data was gathered, and it was fed as input to the Hybrid Adaptive Deep Learning Method. For further enhancement, the FUPOA performs the parameter tuning. A privacy preservation model is employed to secure healthcare data by generating the optimal key formation, in which the key is optimized using FUPOA. This secured data can be stored in the blockchain to increase data integrity and privacy. The optimal feature selection is done by the FUPOA approach. Further, the acquired optimal features are fed to the HADL-AM for predicting the data. The experimental analysis has been done and compared among different approaches.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-39"},"PeriodicalIF":1.1,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144049165","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

Brain tumour classification and survival prediction using a novel hybrid deep learning model using MRI image. 使用MRI图像的新型混合深度学习模型进行脑肿瘤分类和生存预测。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-04-17 DOI: 10.1080/0954898X.2025.2486206

Shanmuga Priya Kanthaswamy, Rosline Nesa Kumari GnanaPrakasam

{"title":"Brain tumour classification and survival prediction using a novel hybrid deep learning model using MRI image.","authors":"Shanmuga Priya Kanthaswamy, Rosline Nesa Kumari GnanaPrakasam","doi":"10.1080/0954898X.2025.2486206","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2486206","url":null,"abstract":"Brain Tumor (BT) is an irregular growth of cells in the brain or in the tissues surrounding it. Detecting and predicting tumours is essential in today's world, yet managing these diseases poses a considerable challenge. Among the various modalities, Magnetic Resonance Imaging (MRI) has been extensively exploited for diagnosing tumours. The traditional methods for predicting survival are based on handcrafted features from MRI and clinical information, which is generally subjective and laborious. This paper devises a new method named, Deep Residual PyramidNet (DRP_Net) for BT classification and survival prediction. The input MRI image is primarily derived from the BraTS dataset. Then, image enhancement is done to improve the quality of images using homomorphic filtering. Next, deep joint segmentation is used to process the tumourtumour region segmentation. Consequently, Haar wavelet and Local Directional Number Pattern (LDNP) based feature extraction is mined. Afterward, BT classification is achieved through DRP_Net, which is a fusion of Deep Residual Network (DRN) and PyramidNet. At last, the survival prediction is accomplished by employing the Deep Recurrent Neural Network (DRNN). Furthermore, DRP_Net has attained superior performance with a True Negative Rate (TNR) of 91.99%, an accuracy of 90.18%, and True Positive Rate (TPR) of 91.08%.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-37"},"PeriodicalIF":1.1,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144055923","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

Energy efficient multipath routing in IoT-wireless sensor network via hybrid optimization and deep learning-based energy prediction. 基于混合优化和基于深度学习的能量预测的物联网无线传感器网络中的节能多路径路由。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-04-11 DOI: 10.1080/0954898X.2025.2476081

G A Senthil, R Prabha, R Renuka Devi

{"title":"Energy efficient multipath routing in IoT-wireless sensor network via hybrid optimization and deep learning-based energy prediction.","authors":"G A Senthil, R Prabha, R Renuka Devi","doi":"10.1080/0954898X.2025.2476081","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2476081","url":null,"abstract":"Efficient data transmission in Wireless Sensor Networks (WSNs) is a critical challenge. Traditional routing protocols focus on energy efficiency but do not consider other factors that might degrade performance. This research proposes a novel Hybrid Beluga Whale-Coati Optimization (HBWCO) algorithm to address these issues, focusing on optimizing energy-efficient data transmission. In the proposed approach, initially, sensor nodes and field dimensions are initialized. Then, K-means clustering is applied to grouping nodes. The Deep Q-Net model is used to predict energy levels of nodes. CH is selected as per the node having higher energy. Multipath routing is performed through the HBWCO algorithm, which optimally selects the best routing paths by considering factors like reliability, residual energy, predicted energy, throughput, and traffic intensity. If link breakage occurs, a route maintenance phase is initiated using Source Link Breakage Warning (SLBW) message strategy to notify the source node about the issue of choosing another path. This work offers a comprehensive approach to enhancing energy efficiency in networks. The suggested HBWCO approach is in contrast to the traditional methods. The HBWCO approach has achieved the highest reliability of 0.948 and the highest throughput of 3496. Therefore, the HBWCO algorithm offers an effective solution for data transmission and routing reliability.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-50"},"PeriodicalIF":1.1,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144058594","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