Network-Computation in Neural Systems最新文献_第10页

Sentiment analysis using graph-based Quickprop method for product quality enhancement. 利用基于图的 Quickprop 方法进行情感分析，以提高产品质量。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-10-14 DOI: 10.1080/0954898X.2024.2410777

Raj Kumar Veerasamy Subramani, Thirumoorthy Kumaresan

{"title":"Sentiment analysis using graph-based Quickprop method for product quality enhancement.","authors":"Raj Kumar Veerasamy Subramani, Thirumoorthy Kumaresan","doi":"10.1080/0954898X.2024.2410777","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2410777","url":null,"abstract":"The degree to which customers express satisfaction with a product on Twitter and other social media platforms is increasingly used to evaluate product quality. However, the volume and variety of textual data make traditional sentiment analysis methods challenging. The nuanced and context-dependent nature of product-related opinions presents a challenge for existing tools. This research addresses this gap by utilizing complex graph-based modelling strategies to capture the intricacies of real-world data. The Graph-based Quickprop Method constructs a graph model using the Sentiment140 dataset with 1.6 million tweets, where individuals are nodes and interactions are edges. Experimental results show a significant increase in sentiment classification accuracy, demonstrating the method's efficacy. This contribution underscores the importance of relational structures in sentiment analysis and provides a robust framework for extracting actionable insights from user-generated content, leading to improved product quality evaluations. The GQP-PQE method advances sentiment analysis and offers practical implications for businesses seeking to enhance product quality through a better understanding of consumer feedback on social media.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-23"},"PeriodicalIF":1.1,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142481200","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

Internet of Things and Cloud Computing-based Disease Diagnosis using Optimized Improved Generative Adversarial Network in Smart Healthcare System. 基于物联网和云计算的疾病诊断，在智能医疗系统中使用优化改进的生成对抗网络。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-10-13 DOI: 10.1080/0954898X.2024.2392770

Thimmakkondu Babuji Sivakumar, Shahul Hameed Hasan Hussain, R Balamanigandan

{"title":"Internet of Things and Cloud Computing-based Disease Diagnosis using Optimized Improved Generative Adversarial Network in Smart Healthcare System.","authors":"Thimmakkondu Babuji Sivakumar, Shahul Hameed Hasan Hussain, R Balamanigandan","doi":"10.1080/0954898X.2024.2392770","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2392770","url":null,"abstract":"The integration of IoT and cloud services enhances communication and quality of life, while predictive analytics powered by AI and deep learning enables proactive healthcare. Deep learning, a subset of machine learning, efficiently analyzes vast datasets, offering rapid disease prediction. Leveraging recurrent neural networks on electronic health records improves accuracy for timely intervention and preventative care. In this manuscript, Internet of Things and Cloud Computing-based Disease Diagnosis using Optimized Improved Generative Adversarial Network in Smart Healthcare System (IOT-CC-DD-OICAN-SHS) is proposed. Initially, an Internet of Things (IoT) device collects diabetes, chronic kidney disease, and heart disease data from patients via wearable devices and intelligent sensors and then saves the patient's large data in the cloud. These cloud data are pre-processed to turn them into a suitable format. The pre-processed dataset is sent into the Improved Generative Adversarial Network (IGAN), which reliably classifies the data as disease-free or diseased. Then, IGAN was optimized using the Flamingo Search optimization algorithm (FSOA). The proposed technique is implemented in Java using Cloud Sim and examined utilizing several performance metrics. The proposed method attains greater accuracy and specificity with lower execution time compared to existing methodologies, IoT-C-SHMS-HDP-DL, PPEDL-MDTC and CSO-CLSTM-DD-SHS respectively.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-24"},"PeriodicalIF":1.1,"publicationDate":"2024-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142481198","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

Designing an optimal task scheduling and VM placement in the cloud environment with multi-objective constraints using Hybrid Lemurs and Gannet Optimization Algorithm. 使用混合 Lemurs 和 Gannet 优化算法，在多目标约束条件下设计云环境中的最佳任务调度和虚拟机放置。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-10-09 DOI: 10.1080/0954898X.2024.2412678

Kapil Vhatkar, Atul Baliram Kathole, Savita Lonare, Jayashree Katti, Vinod Vijaykumar Kimbahune

{"title":"Designing an optimal task scheduling and VM placement in the cloud environment with multi-objective constraints using Hybrid Lemurs and Gannet Optimization Algorithm.","authors":"Kapil Vhatkar, Atul Baliram Kathole, Savita Lonare, Jayashree Katti, Vinod Vijaykumar Kimbahune","doi":"10.1080/0954898X.2024.2412678","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2412678","url":null,"abstract":"An efficient resource utilization method can greatly reduce expenses and unwanted resources. Typical cloud resource planning approaches lack support for the emerging paradigm regarding asset management speed and optimization. The use of cloud computing relies heavily on task planning and allocation of resources. The task scheduling issue is more crucial in arranging and allotting application jobs supplied by customers on Virtual Machines (VM) in a specific manner. The task planning issue needs to be specifically stated to increase scheduling efficiency. The task scheduling in the cloud environment model is developed using optimization techniques. This model intends to optimize both the task scheduling and VM placement over the cloud environment. In this model, a new hybrid-meta-heuristic optimization algorithm is developed named the Hybrid Lemurs-based Gannet Optimization Algorithm (HL-GOA). The multi-objective function is considered with constraints like cost, time, resource utilization, makespan, and throughput. The proposed model is further validated and compared against existing methodologies. The total time required for scheduling and VM placement is 30.23%, 6.25%, 11.76%, and 10.44% reduced than ESO, RSO, LO, and GOA with 2 VMs. The simulation outcomes revealed that the developed model effectively resolved the scheduling and VL placement issues.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-31"},"PeriodicalIF":1.1,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395378","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 comparative study of early stage Alzheimer's disease classification using various transfer learning CNN frameworks. 使用各种迁移学习 CNN 框架对早期阿尔茨海默病分类的比较研究。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-10-05 DOI: 10.1080/0954898X.2024.2406946

Yajuvendra Pratap Singh, Daya Krishan Lobiyal

{"title":"A comparative study of early stage Alzheimer's disease classification using various transfer learning CNN frameworks.","authors":"Yajuvendra Pratap Singh, Daya Krishan Lobiyal","doi":"10.1080/0954898X.2024.2406946","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2406946","url":null,"abstract":"The current research explores the improvements in predictive performance and computational efficiency that machine learning and deep learning methods have made over time. Specifically, the application of transfer learning concepts within Convolutional Neural Networks (CNNs) has proved useful for diagnosing and classifying the various stages of Alzheimer's disease. Using base architectures such as Xception, InceptionResNetV2, DenseNet201, InceptionV3, ResNet50, and MobileNetV2, this study extends these models by adding batch normalization (BN), dropout, and dense layers. These enhancements improve the model's effectiveness and precision in addressing the specified medical issue. The proposed model is rigorously validated and evaluated using publicly available Kaggle MRI Alzheimer's data consisting of 1280 testing images and 5120 patient training images. For comprehensive performance evaluation, precision, recall, F1-score, and accuracy metrics are utilized. The findings indicate that the Xception method is the most promising of those considered. Without employing five K-fold techniques, this model obtains a 99% accuracy and 0.135 loss score. In addition, integrating five K-fold methods enhances the accuracy to 99.68% while decreasing the loss score to 0.120. The research further included the evaluation of the Receiver Operating Characteristic Area Under the Curve (ROC-AUC) for various classes and models. As a result, our model may detect and diagnose Alzheimer's disease quickly and accurately.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-29"},"PeriodicalIF":1.1,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142378624","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

RP squeeze U-SegNet model for lesion segmentation and optimization enabled ShuffleNet based multi-level severity diabetic retinopathy classification. RP 挤压 U-SegNet 模型用于病变分割和优化基于 ShuffleNet 的多级严重性糖尿病视网膜病变分类。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-09-25 DOI: 10.1080/0954898X.2024.2395375

Zulaikha Beevi Sulaiman

{"title":"RP squeeze U-SegNet model for lesion segmentation and optimization enabled ShuffleNet based multi-level severity diabetic retinopathy classification.","authors":"Zulaikha Beevi Sulaiman","doi":"10.1080/0954898X.2024.2395375","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2395375","url":null,"abstract":"In Diabetic Retinopathy (DR), the retina is harmed due to the high blood pressure in small blood vessels. Manual screening is time-consuming, which can be overcome by using automated techniques. Hence, this paper proposed a new method for classifying the multi-level severity of DR. Initially, the input fundus image is pre-processed by Non-local means Denoising (NLMD). Then, lesion segmentation is carried out by the Recurrent Prototypical-squeeze U-SegNet (RP-squeeze U-SegNet). Next, feature extraction is effectuated to mine image-level features. DR is categorized as abnormal or normal by ShuffleNet and it is tuned by Fractional War Royale Optimization (FrWRO), and later, if DR is detected, severity classification is performed. Furthermore, the FrWRO-SqueezeNet obtained the maximum performance with sensitivity of 97%, accuracy of 93.8%, specificity of 95.1%, precision of 91.8%, and F-Measure of 94.3%. The devised scheme accurately visualizes abnormal regions in the fundus images. Also, it has the ability to identify the severity levels of DR effectively, which avoids the progression risk to vision loss and proliferative disease.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-34"},"PeriodicalIF":1.1,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142332542","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":" ","pages":"1-31"},"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":"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