Network-Computation in Neural Systems最新文献_第5页

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

User behaviour based insider threat detection model using an LSTM integrated RF model. 基于用户行为的内部威胁检测模型，采用LSTM集成射频模型。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-04-03 DOI: 10.1080/0954898X.2025.2483342

S K Uma Maheswaran, L Rajasekar, Ziaul Haque Choudhury, Makarand Shahade

{"title":"User behaviour based insider threat detection model using an LSTM integrated RF model.","authors":"S K Uma Maheswaran, L Rajasekar, Ziaul Haque Choudhury, Makarand Shahade","doi":"10.1080/0954898X.2025.2483342","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2483342","url":null,"abstract":"Insider threat is one of the most serious and frequent security risks facing various industries like governmental organizations, businesses, and institutions. Insider threat identification has a special combination of difficulties, including vastly unbalanced data, insufficient ground truth, and drifting and shifting behaviour. A user behaviour-based insider threat detection model utilizing a hybrid deep long short-term memory-random forest (LSTM-RF) model is developed to address these challenges. In this proposed insider threat detection model, the user log data is preprocessed to replace the missing value and to normalize the data to certain range. Then, these preprocessed data are provided as the input of the attribute selection process that mainly applies for selecting the essential attribute using Spearman's rank correlation coefficient. Then the deep hybrid LSTM-RF classifier to detect whether a system is affected by inside threat or not such as malware, authentication, phishing are fed to the selected features. Hybrid LSTM-RF method is implemented in python and achieved 96% accuracy, 90% precision, 90% specificity, 97% sensitivity, and 94% F1-score. During an attack, it can be easily detected inside the system attack.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-38"},"PeriodicalIF":1.1,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143774981","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

Security-aware user authentication based on multimodal biometric data using dilated adaptive RNN with optimal weighted feature fusion. 基于最优加权特征融合的扩展自适应RNN的多模态生物特征安全感知用户认证。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-04-01 DOI: 10.1080/0954898X.2025.2480304

Udhayakumar Selvaraj, Janakiraman Nithiyanantham

{"title":"Security-aware user authentication based on multimodal biometric data using dilated adaptive RNN with optimal weighted feature fusion.","authors":"Udhayakumar Selvaraj, Janakiraman Nithiyanantham","doi":"10.1080/0954898X.2025.2480304","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2480304","url":null,"abstract":"This work plans to develop a biometric authentication model by the combination of multi-modal inputs like voice, fingerprint, and iris to provide high security. At first, the spectrogram images, the collected fingerprint, and the collected iris input were given to a Multi-scale Residual Attention Network (RAN) with Atrous Spatial Pyramid Pooling (ASPP) to extract the best values. These three features are then fed to optimal weighted feature fusion, where weight optimization from the features is done via the Enhanced Lichtenberg Algorithm (ELA). These features are fed into the decision-making stage, where the Dilated Adaptive Recurrent Neural Network is utilized to identify the individuals, where the parameters are optimized from RNN using ELA to improve the recognition performance. The simulation findings achieved from the developed multimodal authentication systems are validated using diverse algorithms over several efficacy metrics like accuracy, precision, sensitivity, F1-score, etc. From the result analysis, the ELA-DARNN-based user authentication system showed a higher accuracy of 96.01, and other models such as 90% than SVM, CNN, CNN-AlexNet, and Dil-ARNN given the accuracy to be 87.94, 89.88, 93.25, and 91.94. Therefore, the outcomes explored that the offered approach has attained elevated results and also effectively supports to reduction of data theft.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-41"},"PeriodicalIF":1.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143755727","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 fruit bee optimization algorithm-based deep convolution neural network for brain tumour classification using MRI images. 基于杂交蜜蜂优化算法的深度卷积神经网络在脑肿瘤MRI图像分类中的应用。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-03-28 DOI: 10.1080/0954898X.2025.2476079

Aynun Jarria S P, Boyed Wesley A

{"title":"Hybrid fruit bee optimization algorithm-based deep convolution neural network for brain tumour classification using MRI images.","authors":"Aynun Jarria S P, Boyed Wesley A","doi":"10.1080/0954898X.2025.2476079","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2476079","url":null,"abstract":"An accurate classification of brain tumour disease is an important function in diagnosing cancer disease. Several deep learning (DL) methods have been used to identify and categorize the tumour illness. Nevertheless, the better categorized result was not consistently obtained by the traditional DL procedures. Therefore, a superior answer to this problem is offered by the optimized DL approaches. Here, the brain tumour categorization (BTC) is done using the devised Hybrid Fruit Bee Optimization based Deep Convolution Neural Network (HFBO-based DCNN). Here, the noise in the image is removed through pre-processing using a Gaussian filter. Next, the feature extraction process is done using the SegNet and this helps to extract the relevant data from the input image. Then, the feature selection is done with the help of the HFBO algorithm. Additionally, the brain tumour classification is done by the Deep CNN, and the established HFBO algorithm is used to train the weight. The devised model is analysed using the testing accuracy, sensitivity, and specificity and produced the values of 0.926, 0.926, and 0.931, respectively.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-23"},"PeriodicalIF":1.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143733223","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

Automated glaucoma diagnosis: Optimized hybrid classification model with improved U-net segmentation. 青光眼自动诊断：改进U-net分割的优化混合分类模型。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-03-27 DOI: 10.1080/0954898X.2025.2481958

Krishnamoorthy Varadharajalu, Logeswari Shanmugam

{"title":"Automated glaucoma diagnosis: Optimized hybrid classification model with improved U-net segmentation.","authors":"Krishnamoorthy Varadharajalu, Logeswari Shanmugam","doi":"10.1080/0954898X.2025.2481958","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2481958","url":null,"abstract":"Glaucoma is a leading cause of blindness, requiring early detection for effective management. Traditional diagnostic methods have challenges such as precise segmentation of small structures and accurate classification of disease stages remain. This research addresses these challenges by developing an optimized hybrid classification model for automated glaucoma diagnosis. At first, the preprocessing stage employs the histogram equalization technique known as Contrast Limited Adaptive Histogram Equalization (CLAHE) technique. Consequently, an improved U-Net segmentation process implemented with the proposed cross-entropy loss function is utilized. Then, features such as fractal features, cup-to-disc-based features, Inferior-Superior-Nasal-Temporal (ISNT) rule-based features and improved Pyramid Histogram of Orient Gradient (PHOG) based features are extracted. Further, a hybrid classification model, a combination of Improved Convolutional Neural Network (ICNN) and optimized Recurrent Neural Network (RNN) classifiers for diagnosing glaucoma disease. Also, to improve the performance of the diagnosis process, a new Opposition-based Learning-enabled Namib Beetle Optimization (OBL-NBO) approach is proposed to optimize the weights of the RNN classifier. Moreover, the ICNN classifier is employed for classifying the presence of glaucoma and non-glaucoma conditions. The proposed OBL-NBO scheme achieved an accuracy of 0.927 for dataset 1 and 0.945 for dataset 2 at an 80% training data.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-55"},"PeriodicalIF":1.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143722665","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

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