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

Multiagent DDOS attack detection model: Optimal trained hybrid classifier and entropy-based mitigation process. 多代理DDOS攻击检测模型：最优训练混合分类器和基于熵的缓解过程。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-12-17 DOI: 10.1080/0954898X.2024.2412674

Thiruselvan Palusamy, Balasubramanian Chelliah

{"title":"Multiagent DDOS attack detection model: Optimal trained hybrid classifier and entropy-based mitigation process.","authors":"Thiruselvan Palusamy, Balasubramanian Chelliah","doi":"10.1080/0954898X.2024.2412674","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2412674","url":null,"abstract":"This study proposes a novel multi-agent system designed to detect Distributed Denial of Service (DDoS) attacks, addressing the increasing need for robust cybersecurity measures. The hypothesis posits that a structured multi-agent approach can enhance detection accuracy and response efficiency in DDoS attack scenarios. The methodology involves a five-stage detection model: (1) Preprocessing using a modified double sigmoid normalization technique to eliminate duplicate data; (2) Feature Extraction where raw data and improved correlation-based features, mutual information, and statistical features are identified; (3) Dimensionality Reduction conducted by a reducer agent to streamline the feature set; (4) Classification utilizing Deep Belief Networks (DBN), Bi-LSTM, and Deep Maxout models, with their weights optimally tuned using the hybrid optimization algorithm, WUJSO; and (5) Decision Making by the decision agent to ascertain the presence of attacks, followed by mitigation through modified entropy-based techniques. The results demonstrate that the proposed method achieves a detection accuracy of 0.953 at a learning rate of 90%, significantly outperforming other methods, including Bi-GRU (0.857), DEEP-MAXOUT (0.910), Bi-LSTM (0.865), RNN (0.814), NN (0.894), and DBN (0.761). This research underscores the effectiveness of the multi-agent approach in enhancing DDoS attack detection and mitigation.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-33"},"PeriodicalIF":1.1,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840253","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

Design of a neural transformer for Spanish to Mexican Sign Language automatic translation/interpretation. 西班牙语到墨西哥语手语自动翻译/口译的神经转换器设计。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-12-11 DOI: 10.1080/0954898X.2024.2435495

Diana Vania Lara-Ortiz, Rita Q Fuentes Aguilar, Isaac Chairez

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 1.1 3区计算机科学

Network-Computation in Neural Systems Pub 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":"https://doi.org/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":"1-41"},"PeriodicalIF":1.1,"publicationDate":"2024-12-11","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}

引用次数: 0

Modified ensemble machine learning-based plant leaf disease detection model with optimized K-Means clustering. 基于优化K-Means聚类的改进集成机器学习植物叶片病害检测模型。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-12-09 DOI: 10.1080/0954898X.2024.2435492

Vijayaganth Viswanathan, Krishnamoorthi Murugasamy

{"title":"Modified ensemble machine learning-based plant leaf disease detection model with optimized K-Means clustering.","authors":"Vijayaganth Viswanathan, Krishnamoorthi Murugasamy","doi":"10.1080/0954898X.2024.2435492","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2435492","url":null,"abstract":"In the farming sector, the automatic detection of plant leaf disease is considered a vital landmark. Farmers move long distances to consult pathologists to observe the disease, which is expensive and time-consuming. Moreover, detection of disease in a premature period is a difficult process in the existing model. Thus, all these challenges motivate us to develop an inventive plant leaf disease detection model. In the developed model, the data is gathered initially and given as input to the pre-processing step using Contrast Limited Adaptive Histogram Equalization (CLAHE). Next, the leaves are segmented from the pre-processed images, and then abnormality segmentation is done by the K-means clustering system. Here, parameters are optimized using the Opposition-based Bird Swarm Algorithm (O-BSA). Further, features were extracted from abnormality-segmented images in feature extraction. The extracted features are given in the classification step, where leaf disease detection is carried out using Optimized Ensemble Machine Learning (OEML), where, parameter optimization is done by O-BSA. Finally, the developed plant leaf detection approach is evaluated with various performance metrics, and given an accuracy of up to 92.26. These findings show that the developed model is promising over conventional methods and its effectiveness in detecting plant leaf disease.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-45"},"PeriodicalIF":1.1,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803550","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 1.1 3区计算机科学

Network-Computation in Neural Systems Pub 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":"https://doi.org/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":"1-28"},"PeriodicalIF":1.1,"publicationDate":"2024-12-03","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

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

IF 1.1 3区计算机科学

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

Mohamed Aboukhair, Abdelrahim Koura, Mohammed Kayed

引用次数: 0

Optimizing tomato detection and counting in smart greenhouses: A lightweight YOLOv8 model incorporating high- and low-frequency feature transformer structures. 优化智能温室中的番茄检测和计数：结合高频和低频特征变换器结构的轻量级 YOLOv8 模型。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-11-21 DOI: 10.1080/0954898X.2024.2428713

Zhimin Tian, Huijuan Hao, Guowei Dai, Yajuan Li

{"title":"Optimizing tomato detection and counting in smart greenhouses: A lightweight YOLOv8 model incorporating high- and low-frequency feature transformer structures.","authors":"Zhimin Tian, Huijuan Hao, Guowei Dai, Yajuan Li","doi":"10.1080/0954898X.2024.2428713","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2428713","url":null,"abstract":"Tomato harvesting in intelligent greenhouses is crucial for reducing costs and optimizing management. Agricultural robots, as an automated solution, require advanced visual perception. This study proposes a tomato detection and counting algorithm based on YOLOv8 (TCAttn-YOLOv8). To handle small, occluded tomato targets in images, a new detection layer (NDL) is added to the Neck and Head decoupled structure, improving small object recognition. The ColBlock, a dual-branch structure leveraging Transformer advantages, enhances feature extraction and fusion, focusing on densely targeted regions and minimizing small object feature loss in complex backgrounds. C2fGhost and GhostConv are integrated into the Neck network to reduce model parameters and floating-point operations, improving feature expression. The WIoU (Wise-IoU) loss function is adopted to accelerate convergence and increase regression accuracy. Experimental results show that TCAttn-YOLOv8 achieves an mAP@0.5 of 96.31%, with an FPS of 95 and a parameter size of 2.7 M, outperforming seven lightweight YOLO algorithms. For automated tomato counting, the R2 between predicted and actual counts is 0.9282, indicating the algorithm's suitability for replacing manual counting. This method effectively supports tomato detection and counting in intelligent greenhouses, offering valuable insights for robotic harvesting and yield estimation research.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-37"},"PeriodicalIF":1.1,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683524","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

Kruskal Szekeres generative adversarial network augmented deep autoencoder for colorectal cancer detection. 用于结直肠癌检测的 Kruskal Szekeres 生成对抗网络增强型深度自动编码器。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-11-16 DOI: 10.1080/0954898X.2024.2426580

Suresh Kumar Krishnamoorthy, Vanitha Cn

{"title":"Kruskal Szekeres generative adversarial network augmented deep autoencoder for colorectal cancer detection.","authors":"Suresh Kumar Krishnamoorthy, Vanitha Cn","doi":"10.1080/0954898X.2024.2426580","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2426580","url":null,"abstract":"Cancer involves abnormal cell growth, with types like intestinal and oesophageal cancer often diagnosed in advanced stages, making them hard to cure. Symptoms are like burning sensations in the stomach and swallowing difficulties are specified as colorectal cancer. Deep learning significantly impacts the medical image processing and diagnosis, offering potential improvements in accuracy and efficiency. The Kruskal Szekeres Generative Adversarial Network Augmented Deep Autoencoder (KSGANA-DA) is introduced for early colorectal cancer detection and it comprises two stages; Initial stage, data augmentation uses Affine Transform via Random Horizontal Rotation and Geometric Transform via Kruskal-Szekeres that coordinates to improve the training dataset diversity, boosting detection performance. The second stage, a Deep Autoencoder Anatomical Landmark-based Image Segmentation preserves edge pixel spatial locations, improving precision and recall for early boundary detection. Experiments validate KSGANA-DA performance and different existing methods are implemented into Python. The results of KSGANA-DA are to provide higher precision by 41%, recall by 7%, and lesser training time by 46% than compared to conventional methods.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-27"},"PeriodicalIF":1.1,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645136","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

Can human brain connectivity explain verbal working memory? 人脑连通性能否解释言语工作记忆？

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-11-12 DOI: 10.1080/0954898X.2024.2421196

Maxime Carriere, Rosario Tomasello, Friedemann Pulvermüller

{"title":"Can human brain connectivity explain verbal working memory?","authors":"Maxime Carriere, Rosario Tomasello, Friedemann Pulvermüller","doi":"10.1080/0954898X.2024.2421196","DOIUrl":"10.1080/0954898X.2024.2421196","url":null,"abstract":"The ability of humans to store spoken words in verbal working memory and build extensive vocabularies is believed to stem from evolutionary changes in cortical connectivity across primate species. However, the underlying neurobiological mechanisms remain unclear. Why can humans acquire vast vocabularies, while non-human primates cannot? This study addresses this question using brain-constrained neural networks that realize between-species differences in cortical connectivity. It investigates how these structural differences support the formation of neural representations for spoken words and the emergence of verbal working memory, crucial for human vocabulary building. We develop comparative models of frontotemporal and occipital cortices, reflecting human and non-human primate neuroanatomy. Using meanfield and spiking neural networks, we simulate auditory word recognition and examine verbal working memory function. The \"human models\", characterized by denser inter-area connectivity in core language areas, produced larger cell assemblies than the \"monkey models\", with specific topographies reflecting semantic properties of the represented words. Crucially, longer-lasting reverberant neural activity was observed in human versus monkey architectures, compatible with robust verbal working memory, a necessary condition for vocabulary building. Our findings offer insights into the structural basis of human-specific symbol learning and verbal working memory, shedding light on humans' unique capacity for large vocabulary acquisition.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-42"},"PeriodicalIF":1.1,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142632807","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

Automatic screening of retinal lesions for detecting diabetic retinopathy using adaptive multiscale MobileNet with abnormality segmentation from public dataset. 利用自适应多尺度 MobileNet 对公共数据集进行异常分割，自动筛查视网膜病变以检测糖尿病视网膜病变。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-11-09 DOI: 10.1080/0954898X.2024.2424242

Nandhini Selvaganapathy, Saravanan Siddhan, Parthasarathy Sundararajan, Sathiyaprasad Balasundaram

{"title":"Automatic screening of retinal lesions for detecting diabetic retinopathy using adaptive multiscale MobileNet with abnormality segmentation from public dataset.","authors":"Nandhini Selvaganapathy, Saravanan Siddhan, Parthasarathy Sundararajan, Sathiyaprasad Balasundaram","doi":"10.1080/0954898X.2024.2424242","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2424242","url":null,"abstract":"Owing to the epidemic growth of diabetes, ophthalmologists need to examine the huge fundus images for diagnosing the disease of Diabetic Retinopathy (DR). Without proper knowledge, people are too lethargic to detect the DR. Therefore, the early diagnosis system is requisite for treating ailments in the medical industry. Therefore, a novel deep model-based DR detection structure is recommended to fix the aforementioned difficulties. The developed deep model-based diabetic retinopathy detection process is performed adaptively. The DR detection process is imitated by garnering the images from benchmark sources. The gathered images are further preceded by the abnormality segmentation phase. Here, the Residual TransUNet with Enhanced loss function is used to employ the abnormality segmentation, and the loss function in this structure may be helpful to lessen the error in the segmentation procedure. Further, the segmented images are passed to the final phase of retinopathy detection. At this phase, the detection is carried out through the Adaptive Multiscale MobileNet. The variables in the AMMNet are optimized by the Adaptive Puzzle Optimization to obtain better detection performance. Finally, the effectiveness of the offered approach is confirmed by the experimentation procedure over various performance indices.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-33"},"PeriodicalIF":1.1,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142632789","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