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

A pilot study of novel multi-filter CNN layer. 新型多滤波器CNN层的初步研究。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2026-02-01 Epub Date: 2024-11-28 DOI: 10.1080/0954898X.2024.2434487

Mohamed Aboukhair, Abdelrahim Koura, Mohammed Kayed

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Correction. 修正。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2026-02-01 Epub Date: 2025-05-02 DOI: 10.1080/0954898X.2025.2501418

引用次数: 0

ViTBayesianNet: An adaptive deep bayesian network-aided alzheimer disease detection framework with vision transformer-based residual densenet for feature extraction using MRI images. ViTBayesianNet：一种自适应深度贝叶斯网络辅助的阿尔茨海默病检测框架，基于视觉变换的残差密度网，用于MRI图像的特征提取。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2026-02-01 Epub Date: 2024-12-11 DOI: 10.1080/0954898X.2024.2435491

Revathi Mohan, Rajesh Arunachalam, Neha Verma, Shital Mali

{"title":"ViTBayesianNet: An adaptive deep bayesian network-aided alzheimer disease detection framework with vision transformer-based residual densenet for feature extraction using MRI images.","authors":"Revathi Mohan, Rajesh Arunachalam, Neha Verma, Shital Mali","doi":"10.1080/0954898X.2024.2435491","DOIUrl":"10.1080/0954898X.2024.2435491","url":null,"abstract":"One of the most familiar types of disease is Alzheimer's disease (AD) and it mainly impacts people over the age limit of 60. AD causes irreversible brain damage in humans. It is difficult to recognize the various stages of AD, hence advanced deep learning methods are suggested for recognizing AD in its initial stages. In this experiment, an effective deep model-based AD detection approach is introduced to provide effective treatment to the patient. Initially, an essential MRI is collected from the benchmark resources. After that, the gathered MRIs are provided as input to the feature extraction phase. Also, the important features in the input image are extracted by Vision Transformer-based Residual DenseNet (ViT-ResDenseNet). Later, the retrieved features are applied to the Alzheimer's detection stage. In this phase, AD is detected using an Adaptive Deep Bayesian Network (Ada-DBN). Additionally, the attributes of Ada-DBN are optimized with the help of Enhanced Golf Optimization Algorithm (EGOA). So, the implemented Alzheimer's detection model accomplishes relatively higher reliability than existing techniques. The numerical results of the suggested framework obtained an accuracy value of 96.35 which is greater than the 91.08, 91.95, and 93.95 attained by the EfficientNet-B2, TF- CNN, and ViT-GRU, respectively.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"120-160"},"PeriodicalIF":1.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815056","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}

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Computational models advance deep brain stimulation for Parkinson's disease. 计算模型推动了治疗帕金森病的深部脑刺激疗法。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-11-01 Epub Date: 2024-06-26 DOI: 10.1080/0954898X.2024.2361799

Yongtong Wu, Kejia Hu, Shenquan Liu

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An Improved Archimedes Optimization-aided Multi-scale Deep Learning Segmentation with dilated ensemble CNN classification for detecting lung cancer using CT images. 改进的阿基米德优化辅助多尺度深度学习分割与扩张集合 CNN 分类法，用于利用 CT 图像检测肺癌。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-11-01 Epub Date: 2024-07-08 DOI: 10.1080/0954898X.2024.2373127

Shalini Chowdary, Shyamala Bharathi Purushotaman

{"title":"An Improved Archimedes Optimization-aided Multi-scale Deep Learning Segmentation with dilated ensemble CNN classification for detecting lung cancer using CT images.","authors":"Shalini Chowdary, Shyamala Bharathi Purushotaman","doi":"10.1080/0954898X.2024.2373127","DOIUrl":"10.1080/0954898X.2024.2373127","url":null,"abstract":"Early detection of lung cancer is necessary to prevent deaths caused by lung cancer. But, the identification of cancer in lungs using Computed Tomography (CT) scan based on some deep learning algorithms does not provide accurate results. A novel adaptive deep learning is developed with heuristic improvement. The proposed framework constitutes three sections as (a) Image acquisition, (b) Segmentation of Lung nodule, and (c) Classifying lung cancer. The raw CT images are congregated through standard data sources. It is then followed by nodule segmentation process, which is conducted by Adaptive Multi-Scale Dilated Trans-Unet3+. For increasing the segmentation accuracy, the parameters in this model is optimized by proposing Modified Transfer Operator-based Archimedes Optimization (MTO-AO). At the end, the segmented images are subjected to classification procedure, namely, Advanced Dilated Ensemble Convolutional Neural Networks (ADECNN), in which it is constructed with Inception, ResNet and MobileNet, where the hyper parameters is tuned by MTO-AO. From the three networks, the final result is estimated by high ranking-based classification. Hence, the performance is investigated using multiple measures and compared among different approaches. Thus, the findings of model demonstrate to prove the system's efficiency of detecting cancer and help the patient to get the appropriate treatment.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1543-1581"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141555958","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

Enhancing effort estimation in global software development using a unique combination of Neuro Fuzzy Logic and Deep Learning Neural Networks (NFDLNN). 利用神经模糊逻辑和深度学习神经网络（NFDLNN）的独特组合，加强全球软件开发中的工作量估算。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-11-01 Epub Date: 2024-07-21 DOI: 10.1080/0954898X.2024.2376703

Manoj Ray Devadas, Philip Samuel

{"title":"Enhancing effort estimation in global software development using a unique combination of Neuro Fuzzy Logic and Deep Learning Neural Networks (NFDLNN).","authors":"Manoj Ray Devadas, Philip Samuel","doi":"10.1080/0954898X.2024.2376703","DOIUrl":"10.1080/0954898X.2024.2376703","url":null,"abstract":"Effective project planning and management in the global software development landscape relies on addressing major issues like cost estimation and effort allocation. Timely estimation of software development is a critical focus in software engineering research. With the industry increasingly relying on diverse teams worldwide, accurate estimation becomes vital. Software size serves as a common measure for costs and schedules, but advanced estimation methods consider various variables, such as project purpose, personnel expertise, time and efficiency constraints, and technology requirements. Estimating software costs involve significant financial and strategic commitments, making it crucial to address complexity and versatility related to cost drivers. To achieve enhanced accuracy and convergence, we employ the cuckoo algorithm in our proposed NFDLNN (Neuro Fuzzy Logic and Deep Learning Neural Networks) model. Through extensive validation with industrial project data, using Function Point Analysis as the algorithmic models, our NFA model demonstrates high accuracy in software cost approximation, outperforming existing methods insights of MRE of 3.33, BRE of 0.13, and PI of 74.48. Our research contributes to improved project planning and decision-making processes in global software development endeavours.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1606-1626"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141735684","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 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-11-01 Epub 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":"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":"1906-1939"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","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

A secure worst elite sailfish optimizer based routing and deep learning for black hole attack detection. 基于路由和深度学习的黑洞攻击检测的安全最差精英旗鱼优化器。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-11-01 Epub Date: 2024-06-10 DOI: 10.1080/0954898X.2024.2363353

Mandeep Kumar, Jahid Ali

{"title":"A secure worst elite sailfish optimizer based routing and deep learning for black hole attack detection.","authors":"Mandeep Kumar, Jahid Ali","doi":"10.1080/0954898X.2024.2363353","DOIUrl":"10.1080/0954898X.2024.2363353","url":null,"abstract":"The Wireless Sensor Network (WSN) is susceptible to two kinds of attacks, namely active attack and passive attack. In an active attack, the attacker directly communicates with the target system or network. In contrast, in passive attack, the attacker is in indirect contact with the network. To preserve the functionality and dependability of wireless sensor networks, this research has been conducted recently to detect and mitigate the black hole attacks. In this research, a Deep learning (DL) based black hole attack detection model is designed. The WSN simulation is the beginning stage of this process. Moreover, routing is the key process, where the data is passed to the base station (BS) via the shortest and finest route. The proposed Worst Elite Sailfish Optimization (WESFO) is utilized for routing. Moreover, black hole attack detection is performed in the BS. The Auto Encoder (AE) is employed in attack detection, which is trained with the use of the proposed WESFO algorithm. Additionally, the proposed model is validated in terms of delay, Packet Delivery Rate (PDR), throughput, False-Negative Rate (FNR), and False-Positive Rate (FPR) parameters with the corresponding outcomes like 25.64 s, 94.83%, 119.3, 0.084, and 0.135 are obtained.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1417-1442"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141297343","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 fourfold-objective-based cloud privacy preservation model with proposed association rule hiding and deep learning assisted optimal key generation. 基于四重目标的云隐私保护模型，建议关联规则隐藏和深度学习辅助最优密钥生成。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-11-01 Epub Date: 2024-07-26 DOI: 10.1080/0954898X.2024.2378836

Smita Sharma, Sanjay Tyagi

{"title":"A fourfold-objective-based cloud privacy preservation model with proposed association rule hiding and deep learning assisted optimal key generation.","authors":"Smita Sharma, Sanjay Tyagi","doi":"10.1080/0954898X.2024.2378836","DOIUrl":"10.1080/0954898X.2024.2378836","url":null,"abstract":"Numerous studies have been conducted in an attempt to preserve cloud privacy, yet the majority of cutting-edge solutions fall short when it comes to handling sensitive data. This research proposes a \"privacy preservation model in the cloud environment\". The four stages of recommended security preservation methodology are \"identification of sensitive data, generation of an optimal tuned key, suggested data sanitization, and data restoration\". Initially, owner's data enters the Sensitive data identification process. The sensitive information in the input (owner's data) is identified via Augmented Dynamic Itemset Counting (ADIC) based Associative Rule Mining Model. Subsequently, the identified sensitive data are sanitized via the newly created tuned key. The generated tuned key is formulated with new fourfold objective-hybrid optimization approach-based deep learning approach. The optimally tuned key is generated with LSTM on the basis of fourfold objectives and the new hybrid MUAOA. The created keys, as well as generated sensitive rules, are fed into the deep learning model. The MUAOA technique is a conceptual blend of standard AOA and CMBO, respectively. As a result, unauthorized people will be unable to access information. Finally, comparative evaluation is undergone and proposed LSTM+MUAOA has achieved higher values on privacy about 5.21 compared to other existing models.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1627-1662"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762714","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 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-11-01 Epub 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":"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":"1967-1995"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","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