Network (Bristol, England)最新文献

DSCrisk: A dual-view and substructure aware based method for drug-drug interaction risk-level prediction enhanced by contrastive learning. 基于双视角和子结构感知的药物-药物相互作用风险水平预测方法。

IF 1.6

Network (Bristol, England) Pub Date : 2026-10-11 DOI: 10.1080/0954898X.2026.2744620

Qingwen Li, Yu Yin

{"title":"DSCrisk: A dual-view and substructure aware based method for drug-drug interaction risk-level prediction enhanced by contrastive learning.","authors":"Qingwen Li, Yu Yin","doi":"10.1080/0954898X.2026.2744620","DOIUrl":"https://doi.org/10.1080/0954898X.2026.2744620","url":null,"abstract":"Combination pharmacotherapy remains a fundamental strategy in contemporary clinical practice. However, the concurrent use of multiple drugs substantially increases the risk of harmful drug-drug interaction events (DDIEs). Developing reliable computational methods for DDIE detection is therefore essential, yet existing approaches still face notable limitations in modelling interaction-relevant chemistry, relational context, and interpretability. In this study, we propose DSCrisk, a deep‑learning framework that employs graph neural networks (GNNs) to encode a dual-view representation comprising molecular graphs and the DDI topology network. DSCrisk further integrates a partner-guided attention readout to highlight interaction-relevant substructures within drug pairs and leverages contrastive learning over DDIE pairs to strengthen representation learning. Experiments show that DSCrisk achieves an accuracy exceeding 95%. Moreover, the partner-guided attention mechanism improves interpretability by revealing critical substructures associated with DDI risk. DSCrisk is available at https://github.com/liqingwen98/DSCrisk.","PeriodicalId":520718,"journal":{"name":"Network (Bristol, England)","volume":" ","pages":"1-20"},"PeriodicalIF":1.6,"publicationDate":"2026-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149171795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Model for computing with population-encoded variables explains neural correlations. 人口编码变量的计算模型解释了神经相关性。

IF 1.6

Network (Bristol, England) Pub Date : 2026-09-05 DOI: 10.1080/0954898X.2026.2727843

Heiko Hoffmann

{"title":"Model for computing with population-encoded variables explains neural correlations.","authors":"Heiko Hoffmann","doi":"10.1080/0954898X.2026.2727843","DOIUrl":"https://doi.org/10.1080/0954898X.2026.2727843","url":null,"abstract":"Decoding how the brain computes with variables, such as an object's speed, encoded in populations of neurons is essential for understanding perception, decision-making, and motor control. While experiments consistently reveal correlated firing rates within these populations, the origins of these correlations are still debated. Here, I propose a computational model that carries out operations on population-encoded variables by carefully configuring the connections between neurons through dendritic branches. This model predicts neural correlations as a byproduct of the computation, with correlation coefficients aligning with experimentally measured values in the literature. I also present a mathematical derivation of these coefficients, revealing their dependence on population size. To demonstrate the model's utility, I apply it to compute the prediction error between an observed object's speed and the predicted speed based on self-motion. Importantly, the framework generalizes to compute any functional relationship between population-encoded variables, serving as a building block for perception and behaviour, including tasks such as transforming an object's position into joint angles for grasping.","PeriodicalId":520718,"journal":{"name":"Network (Bristol, England)","volume":" ","pages":"1-19"},"PeriodicalIF":1.6,"publicationDate":"2026-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148898815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing IoT security: A Creative Swagger Optimization algorithm for DDoS defence. 增强物联网安全：用于DDoS防御的创造性Swagger优化算法。

IF 1.6

Network (Bristol, England) Pub Date : 2026-05-01 Epub Date: 2024-12-30 DOI: 10.1080/0954898X.2024.2443605

Rahul Rajendra Papalkar, Abrar S Alvi

{"title":"Enhancing IoT security: A Creative Swagger Optimization algorithm for DDoS defence.","authors":"Rahul Rajendra Papalkar, Abrar S Alvi","doi":"10.1080/0954898X.2024.2443605","DOIUrl":"10.1080/0954898X.2024.2443605","url":null,"abstract":"In the Internet of Things (IoT), the security of information between network transmissions is very important since the system stores data in data storage and is performed by the exchange of network information about things. DDoS in an IoT network is an attack that targets the availability of the servers by flooding the communication channel with impersonated requests coming from distributed IoT devices. To overcome the above-mentioned issue, this research proposed a Creative Swagger (CS) Optimized Deep Convolutional Neural Network (DeepCNN) that detects and mitigates DDoS attacks. The CS algorithm is designed by fusing the distinctive behaviour of the Swagger with the innovative concepts of the civilized creature, which is used to effectively tune the parameters of Deep CNN to improve the detection accuracy of DDoS attacks. For initial verification, a blacklist table is used and the verification includes checking IP address and other pertinent attributes. The proposed CS-optimized Deep CNN model obtains high effectiveness by attaining an accuracy of 97.07%, sensitivity of 97.23%, and specificity of 96.91% at 80% of the training data for utilizing UNSW-NB15 Dataset. Moreover, this method provides the best solution for detecting DDoS attacks in IoT platforms with higher robustness.","PeriodicalId":520718,"journal":{"name":"Network (Bristol, England)","volume":"37 2","pages":"273-311"},"PeriodicalIF":1.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147826340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Parametric optimization for electrical discharge diamond grinding (EDDG) system using dual approach. 电火花金刚石磨削（EDDG）系统的双方法参数优化。

IF 1.6

Network (Bristol, England) Pub Date : 2025-08-11 DOI: 10.1080/0954898X.2025.2525564

Vijay Kumar, Shailendra Kumar Jha

{"title":"Parametric optimization for electrical discharge diamond grinding (EDDG) system using dual approach.","authors":"Vijay Kumar, Shailendra Kumar Jha","doi":"10.1080/0954898X.2025.2525564","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2525564","url":null,"abstract":"Generally, electrically conductive materials are extremely sturdy and stiff, electric discharge milling (EDM) is a broadly utilized method. The usage of diamond grinding together with EDM in a machine is called the \" and Electrical Discharge Diamond Grinding \" (EDDG) gadget is an extensively used method for producing strong, long-lasting electrically conductive substances. The Modified Ant Lion Optimization- Artificial Neural Network (MALO-ANN) technique is recommended to boost the performance of EDDG machine. The MALO technique improves the overall performance of ANN by optimizing hidden layers and weights, which are regularly the cause of issues in traditional models. Input factors, along with grit size, pulse-on/off duration, height modern and pulse-off duration, are analysed to see if they affect Material Removal Rate (MRR) along with Surface Roughness (SR). The findings suggest that the MALO-ANN method greatly enhances the parametric optimization of EDDG gadget. The result indicates tremendous ability in improving the efficiency of EDDG systems, because conventional ANN models regularly struggle because of insifficient hidden layers and weights. The best MRR and SR were obtained with an absolute error interval ranging from 1.03% to 4.49%, achieving a convergence rate of 89%, performing enhanced accuracy in EDDG processes.","PeriodicalId":520718,"journal":{"name":"Network (Bristol, England)","volume":" ","pages":"1-26"},"PeriodicalIF":1.6,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144819059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","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 lung cancer diagnosis model using hybrid convolution (2D/3D)-based adaptive DenseUnet with attention mechanism. 基于混合卷积（2D/3D）自适应DenseUnet的肺癌诊断模型。

IF 1.6

Network (Bristol, England) Pub Date : 2025-08-05 DOI: 10.1080/0954898X.2025.2533871

J Deepa, Liya Badhu Sasikala, P Indumathy, A Jerrin Simla

{"title":"A novel lung cancer diagnosis model using hybrid convolution (2D/3D)-based adaptive DenseUnet with attention mechanism.","authors":"J Deepa, Liya Badhu Sasikala, P Indumathy, A Jerrin Simla","doi":"10.1080/0954898X.2025.2533871","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2533871","url":null,"abstract":"Existing Lung Cancer Diagnosis (LCD) models have difficulty in detecting early-stage lung cancer due to the asymptomatic nature of the disease which leads to an increased death rate of patients. Therefore, it is important to diagnose lung disease at an early stage to save the lives of affected persons. Hence, the research work aims to develop an efficient lung disease diagnosis using deep learning techniques for the early and accurate detection of lung cancer. This is achieved by. Initially, the proposed model collects the mandatory CT images from the standard benchmark datasets. Then, the lung cancer segmentation is done by using the development of Hybrid Convolution (2D/3D)-based Adaptive DenseUnet with Attention mechanism (HC-ADAM). The Hybrid Sewing Training with Spider Monkey Optimization (HSTSMO) is introduced to optimize the parameters in the developed HC-ADAM segmentation approach. Finally, the dissected lung nodule imagery is considered for the lung cancer classification stage, where the Hybrid Adaptive Dilated Networks with Attention mechanism (HADN-AM) are implemented with the serial cascading of ResNet and Long Short Term Memory (LSTM) for attaining better categorization performance. The accuracy, precision, and F1-score of the developed model for the LIDC-IDRI dataset are 96.3%, 96.38%, and 96.36%, respectively.","PeriodicalId":520718,"journal":{"name":"Network (Bristol, England)","volume":" ","pages":"1-58"},"PeriodicalIF":1.6,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144786370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-driven plant disease detection with tailored convolutional neural network. 基于定制卷积神经网络的人工智能驱动植物病害检测。

IF 1.6

Network (Bristol, England) Pub Date : 2025-07-31 DOI: 10.1080/0954898X.2025.2537680

Sk Mahmudul Hassan, Keshab Nath, Michal Jasinski, Arnab Kumar Maji

{"title":"AI-driven plant disease detection with tailored convolutional neural network.","authors":"Sk Mahmudul Hassan, Keshab Nath, Michal Jasinski, Arnab Kumar Maji","doi":"10.1080/0954898X.2025.2537680","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2537680","url":null,"abstract":"In recent times, deep learning has been widely used in agriculture fields to identify diseases in crops, weather prediction, and crop yield prediction. However, designing efficient deep learning models that are lightweight, cost-effective, and suitable for deployment on small devices remains a challenge. This paper addresses this gap by proposing a Convolutional Neural Network (CNN) architecture optimized using a Genetic Algorithm (GA) to automate the selection of critical hyperparameters, such as the number and size of filters, ensuring high performance with minimal computational overhead. In this work, we have built our own tea leaf disease dataset consisting of three different tea leaf diseases, two diseases caused by pests, and one due to pathogens (infectious organisms) and environmental conditions. The proposed genetic algorithm-based CNN achieved an accuracy rate of 97.6% on the tea leaf disease dataset. To further validate its robustness, the model was tested on two additional datasets, namely PlantVillage and Rice leaf disease dataset, achieving accuracies of 96.99% and 99%, respectively. Performances of the proposed model are also compared with several state-of-the-art deep learning models, and the results show that the proposed model outperforms several DL architectures with fewer parameters.","PeriodicalId":520718,"journal":{"name":"Network (Bristol, England)","volume":" ","pages":"1-26"},"PeriodicalIF":1.6,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid optimization enabled Eff-FDMNet for Parkinson's disease detection and classification in federated learning. 混合优化使ef - fdmnet在联合学习中用于帕金森病的检测和分类。

IF 1.6

Network (Bristol, England) Pub Date : 2025-07-31 DOI: 10.1080/0954898X.2025.2514187

Sangeetha Subramaniam, Umarani Balakrishnan

{"title":"Hybrid optimization enabled Eff-FDMNet for Parkinson's disease detection and classification in federated learning.","authors":"Sangeetha Subramaniam, Umarani Balakrishnan","doi":"10.1080/0954898X.2025.2514187","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2514187","url":null,"abstract":"Parkinson's Disease (PD) is a progressive neurodegenerative disorder and the early diagnosis is crucial for managing symptoms and slowing disease progression. This paper proposes a framework named Federated Learning Enabled Waterwheel Shuffled Shepherd Optimization-based Efficient-Fuzzy Deep Maxout Network (FedL_WSSO based Eff-FDMNet) for PD detection and classification. In local training model, the input image from the database \"Image and Data Archive (IDA)\" is given for preprocessing that is performed using Gaussian filter. Consequently, image augmentation takes place and feature extraction is conducted. These processes are executed for every input image. Therefore, the collected outputs of images are used for PD detection using Shepard Convolutional Neural Network Fuzzy Zeiler and Fergus Net (ShCNN-Fuzzy-ZFNet). Then, PD classification is accomplished using Eff-FDMNet, which is trained using WSSO. At last, based on CAViaR, local updation and aggregation are changed in server. The developed method obtained highest accuracy as 0.927, mean average precision as 0.905, lowest false positive rate (FPR) as 0.082, loss as 0.073, Mean Squared Error (MSE) as 0.213, and Root Mean Squared Error (RMSE) as 0.461. The high accuracy and low error rates indicate that the potent framework can enhance patient outcomes by enabling more reliable and personalized diagnosis.","PeriodicalId":520718,"journal":{"name":"Network (Bristol, England)","volume":" ","pages":"1-45"},"PeriodicalIF":1.6,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Layer modified residual Unet++ for speech enhancement using Aquila Black widow optimizer algorithm. 层修改残差Unet++语音增强用Aquila黑寡妇优化算法。

IF 1.6

Network (Bristol, England) Pub Date : 2025-07-27 DOI: 10.1080/0954898X.2025.2533866

Thangappanpillai Murugan Minipriya, Ramadoss Rajavel

{"title":"Layer modified residual Unet++ for speech enhancement using Aquila Black widow optimizer algorithm.","authors":"Thangappanpillai Murugan Minipriya, Ramadoss Rajavel","doi":"10.1080/0954898X.2025.2533866","DOIUrl":"10.1080/0954898X.2025.2533866","url":null,"abstract":"Speech enhancement techniques face computational demands, well-developed datasets, and better quality speech signals. Deep learners help deal with different noise types; still, the challenges offered by environmental noises require highly efficient and robust systems. This paper presents a lightweight deep-learning design with a heuristic-inspired model for generating an enhanced speech signal from noisy speech data. The model aims to remove different environmental noises affecting the speech signal. The noisy speech data are converted into spectrograms with Short-Time Fourier Transform (STFT). The noisy spectrogram is processed through the newly developed speech enhancement model namely, Layer Modified Residual Unet++ (LMResUnet++). The developed LMResUnet++ is designed through an atrous convolution layer, and it can capture multi-scale information without additional training parameter requirements. Also, the design is made compactable through the proposed hybrid optimization algorithm namely, Aquila Black Widow Optimization (ABWO), and it optimizes various hyperparameters of the developed LMResUnet++. The final denoised spectrogram from the LMResUnet++ undergoes Inverse STFT, and the final enhanced speech signal is restored. Further, different experiments are held to prove the efficacy of the system. Results prove that the developed LMResUnet++ achieved PESQ values of 7.93%, 5.75%, 3.86%, and 1.90% improved than DeepUnet, MTCNN, STCNN, and ResUnet++ respectively.","PeriodicalId":520718,"journal":{"name":"Network (Bristol, England)","volume":" ","pages":"1-49"},"PeriodicalIF":1.6,"publicationDate":"2025-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144736588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","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 model for image de-noising and de-mosaicking with adaptive Gannet optimization algorithm. 基于自适应Gannet优化算法的图像去噪去镶嵌混合深度学习模型。

Network (Bristol, England) Pub Date : 2025-07-23 DOI: 10.1080/0954898X.2025.2529299

John Peter K, SylajaVallee Narayan S R, Muthuvairavan Pillai N, Predeep Kumar S P

引用次数: 0

CNN filter sizes, effects, limitations, and challenges: An exploratory study. CNN过滤器的大小、效果、限制和挑战：一项探索性研究。

Network (Bristol, England) Pub Date : 2025-07-20 DOI: 10.1080/0954898X.2025.2533865

Mohamed Aboukhair, Fahad Alsheref, Adel Assiri, Abdelrahim Koura, Mohammed Kayed

{"title":"CNN filter sizes, effects, limitations, and challenges: An exploratory study.","authors":"Mohamed Aboukhair, Fahad Alsheref, Adel Assiri, Abdelrahim Koura, Mohammed Kayed","doi":"10.1080/0954898X.2025.2533865","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2533865","url":null,"abstract":"This study explores the impacts of filter sizes on convolutional neural networks (CNNs) models, moving away from the common belief that small filters (3x3) give better results. The goal is to highlight the potential of large filters and encourage researchers to investigate their capabilities. The usage of large filters will increase the computational power which leads common researchers to reduce the filter size to reserve this power; however, other researchers address the potential of large filters to enhance the performance of CNN models. Currently, there are few pure CNN models that achieve optimal performance with large filters showing how far the large filter sizes topic is not addressed well by the community. As the availability of computer power and image sizes increase, traditional obstacles that hinder researchers from using large filter sizes will gradually diminish. This paper guides researchers by analysing and exploring the limitations, challenges, and impacts of CNN filter sizes on different CNN architectures. This will help utilize large filters' distinctive opportunities and potential. To our knowledge, we find four opportunities from utilizing large filters. A comprehensive comparison of researches on different CNN architectures shows a bias for small filters (3x3) and the possible potential of large filters.","PeriodicalId":520718,"journal":{"name":"Network (Bristol, England)","volume":" ","pages":"1-29"},"PeriodicalIF":0.0,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144677139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0