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

Dynamic resource allocation in 5G networks using hybrid RL-CNN model for optimized latency and quality of service 使用混合 RL-CNN 模型在 5G 网络中动态分配资源，优化延迟和服务质量

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-04-09 DOI: 10.1080/0954898x.2024.2334282

Muthulakshmi Karuppiyan, Hariharan Subramani, Shanthy Kandasamy Raju, Manimekalai Maradi Anthonymuthu Prakasam

引用次数: 0

New results on bifurcation for fractional-order octonion-valued neural networks involving delays* 关于涉及延迟的分数阶八分音符值神经网络分岔的新结果*

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-04-05 DOI: 10.1080/0954898x.2024.2332662

Changjin Xu, Jinting Lin, Yingyan Zhao, Qingyi Cui, Wei Ou, Yicheng Pang, Zixin Liu, Maoxin Liao, Peiluan Li

引用次数: 0

Comparative performance analysis of Boruta, SHAP, and Borutashap for disease diagnosis: A study with multiple machine learning algorithms. 用于疾病诊断的 Boruta、SHAP 和 Borutashap 的性能比较分析：使用多种机器学习算法的研究。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-03-21 DOI: 10.1080/0954898X.2024.2331506

Chukwuebuka Joseph Ejiyi, Zhen Qin, Chiagoziem Chima Ukwuoma, Grace Ugochi Nneji, Happy Nkanta Monday, Makuachukwu Bennedith Ejiyi, Thomas Ugochukwu Ejiyi, Uchenna Okechukwu, Olusola O Bamisile

{"title":"Comparative performance analysis of Boruta, SHAP, and Borutashap for disease diagnosis: A study with multiple machine learning algorithms.","authors":"Chukwuebuka Joseph Ejiyi, Zhen Qin, Chiagoziem Chima Ukwuoma, Grace Ugochi Nneji, Happy Nkanta Monday, Makuachukwu Bennedith Ejiyi, Thomas Ugochukwu Ejiyi, Uchenna Okechukwu, Olusola O Bamisile","doi":"10.1080/0954898X.2024.2331506","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2331506","url":null,"abstract":"Interpretable machine learning models are instrumental in disease diagnosis and clinical decision-making, shedding light on relevant features. Notably, Boruta, SHAP (SHapley Additive exPlanations), and BorutaShap were employed for feature selection, each contributing to the identification of crucial features. These selected features were then utilized to train six machine learning algorithms, including LR, SVM, ETC, AdaBoost, RF, and LR, using diverse medical datasets obtained from public sources after rigorous preprocessing. The performance of each feature selection technique was evaluated across multiple ML models, assessing accuracy, precision, recall, and F1-score metrics. Among these, SHAP showcased superior performance, achieving average accuracies of 80.17%, 85.13%, 90.00%, and 99.55% across diabetes, cardiovascular, statlog, and thyroid disease datasets, respectively. Notably, the LGBM emerged as the most effective algorithm, boasting an average accuracy of 91.00% for most disease states. Moreover, SHAP enhanced the interpretability of the models, providing valuable insights into the underlying mechanisms driving disease diagnosis. This comprehensive study contributes significant insights into feature selection techniques and machine learning algorithms for disease diagnosis, benefiting researchers and practitioners in the medical field. Further exploration of feature selection methods and algorithms holds promise for advancing disease diagnosis methodologies, paving the way for more accurate and interpretable diagnostic models.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-38"},"PeriodicalIF":7.8,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140177791","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

Adaptive activation Functions with Deep Kronecker Neural Network optimized with Bear Smell Search Algorithm for preventing MANET Cyber security attacks. 采用熊嗅觉搜索算法优化的深度克罗内克神经网络的自适应激活函数，用于防范城域网网络安全攻击。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-03-14 DOI: 10.1080/0954898X.2024.2321391

E V R M Kalaimani Shanmugham, Saravanan Dhatchnamurthy, Prabbu Sankar Pakkiri, Neha Garg

{"title":"Adaptive activation Functions with Deep Kronecker Neural Network optimized with Bear Smell Search Algorithm for preventing MANET Cyber security attacks.","authors":"E V R M Kalaimani Shanmugham, Saravanan Dhatchnamurthy, Prabbu Sankar Pakkiri, Neha Garg","doi":"10.1080/0954898X.2024.2321391","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2321391","url":null,"abstract":"An Adaptive activation Functions with Deep Kronecker Neural Network optimized with Bear Smell Search Algorithm (BSSA) (ADKNN-BSSA-CSMANET) is proposed for preventing MANET Cyber security attacks. The mobile users are enrolled with Trusted Authority Using a Crypto Hash Signature (SHA-256). Every mobile user uploads their finger vein biometric, user ID, latitude and longitude for confirmation. The packet analyser checks if any attack patterns are identified. It is implemented using adaptive density-based spatial clustering (ADSC) that deems information from packet header. Geodesic filtering (GF) is used as a pre-processing method for eradicating the unsolicited content and filtering pertinent data. Group Teaching Algorithm (GTA)-based feature selection is utilized for ideal collection of features and Adaptive Activation Functions along Deep Kronecker Neural Network (ADKNN) is used to categorizing normal and attack packets (DoS, Probe, U2R, and R2L). Then BSSA is utilized for optimizing the weight parameters of ADKNN classifier for optimal classification. The proposed technique is executed in python and its efficiency is evaluated by several performances metrics, such as Accuracy, Attack Detection Rate, Detection Delay, Packet Delivery Ratio, Throughput, and Energy Consumption. The proposed technique provides 36.64%, 33.06%, and 33.98% lower Detection Delay on NSL-KDD dataset compared with the existing methods.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-25"},"PeriodicalIF":7.8,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140121421","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

Q-learning and fuzzy logic multi-tier multi-access edge clustering for 5g v2x communication. 用于 5g v2x 通信的 Q-learning 和模糊逻辑多层多接入边缘聚类。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-03-06 DOI: 10.1080/0954898X.2024.2309947

Sangeetha Alagumani, Uma Maheswari Natarajan

{"title":"Q-learning and fuzzy logic multi-tier multi-access edge clustering for 5g v2x communication.","authors":"Sangeetha Alagumani, Uma Maheswari Natarajan","doi":"10.1080/0954898X.2024.2309947","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2309947","url":null,"abstract":"The 5th generation (5 G) network is required to meet the growing demand for fast data speeds and the expanding number of customers. Apart from offering higher speeds, 5 G will be employed in other industries such as the Internet of Things, broadcast services, and so on. Energy efficiency, scalability, resiliency, interoperability, and high data rate/low delay are the primary requirements and obstacles of 5 G cellular networks. Due to IEEE 802.11p's constraints, such as limited coverage, inability to handle dense vehicle networks, signal congestion, and connectivity outages, efficient data distribution is a big challenge (MAC contention problem). In this research, vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) and vehicle-to-pedestrian (V2P) services are used to overcome bandwidth constraints in very dense network communications from cellular tool to everything (C-V2X). Clustering is done through multi-layered multi-access edge clustering, which helps reduce vehicle contention. Fuzzy logic and Q-learning and intelligence are used for a multi-hop route selection system. The proposed protocol adjusts the number of cluster-head nodes using a Q-learning algorithm, allowing it to quickly adapt to a range of scenarios with varying bandwidths and vehicle densities.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-24"},"PeriodicalIF":7.8,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140040908","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 Spinal MRI Image Segmentation Method Based on Improved Swin-UNet. 基于改进 Swin-UNet 的脊柱 MRI 图像分割方法

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-03-03 DOI: 10.1080/0954898X.2024.2323530

Jie Cao, Jiacheng Fan, Chin-Ling Chen, Zhenyu Wu, Qingxuan Jiang, Shikai Li

{"title":"A Spinal MRI Image Segmentation Method Based on Improved Swin-UNet.","authors":"Jie Cao, Jiacheng Fan, Chin-Ling Chen, Zhenyu Wu, Qingxuan Jiang, Shikai Li","doi":"10.1080/0954898X.2024.2323530","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2323530","url":null,"abstract":"As the number of patients increases, physicians are dealing with more and more cases of degenerative spine pathologies on a daily basis. To reduce the workload of healthcare professionals, we propose a modified Swin-UNet network model. Firstly, the Swin Transformer Blocks are improved using a residual post-normalization and scaling cosine attention mechanism, which makes the training process of the model more stable and improves the accuracy. Secondly, we use the log-space continuous position biasing method instead of the bicubic interpolation position biasing method. This method solves the problem of performance loss caused by the large difference between the resolution of the pretraining image and the resolution of the spine image. Finally, we introduce a segmentation smooth module (SSM) at the decoder stage. The SSM effectively reduces redundancy, and enhances the segmentation edge processing to improve the model's segmentation accuracy. To validate the proposed method, we conducted experiments on a real dataset provided by hospitals. The average segmentation accuracy is no less than 95%. The experimental results demonstrate the superiority of the proposed method over the original model and other models of the same type in segmenting the spinous processes of the vertebrae and the posterior arch of the spine.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-29"},"PeriodicalIF":7.8,"publicationDate":"2024-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140023319","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

RETRACTED ARTICLE: Stable route selection for adaptive packet transmission in 5G-based mobile communications. 基于 5G 的移动通信中自适应数据包传输的稳定路由选择。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-03-03 DOI: 10.1080/0954898X.2024.2318344

Muthulakshmi Karuppiyan, Hariharan Subramani, Shanthy Kandasamy Raju, Manimekalai Maradi Anthonymuthu Prakasam

引用次数: 0

Smart plant disease net: Adaptive Dense Hybrid Convolution network with attention mechanism for IoT-based plant disease detection by improved optimization approach. 智能植物病害网：自适应密集混合卷积网络与关注机制，通过改进的优化方法实现基于物联网的植物病害检测。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-02-24 DOI: 10.1080/0954898X.2024.2316080

N Ananthi, V Balaji, M Mohana, S Gnanapriya

{"title":"Smart plant disease net: Adaptive Dense Hybrid Convolution network with attention mechanism for IoT-based plant disease detection by improved optimization approach.","authors":"N Ananthi, V Balaji, M Mohana, S Gnanapriya","doi":"10.1080/0954898X.2024.2316080","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2316080","url":null,"abstract":"Plant diseases are rising nowadays. Plant diseases lead to high economic losses. Internet of Things (IoT) technology has found its application in various sectors. This led to the introduction of smart farming, in which IoT has been utilized to help identify the exact spot of the diseased affected region on the leaf from the vast farmland in a well-organized and automated manner. Thus, the main focus of this task is the introduction of a novel plant disease detection model that relies on IoT technology. The collected images are given to the Image Transmission phase. Here, the encryption task is performed by employing the Advanced Encryption Standard (AES) and also the decrypted plant images are fed to the pre-processing stage. The Mask Regions with Convolutional Neural Networks (R-CNN) are used to segment the pre-processed images. Then, the segmented images are given to the detection phase in which the Adaptive Dense Hybrid Convolution Network with Attention Mechanism (ADHCN-AM) approach is utilized to perform the detection of plant disease. From the ADHCN-AM, the final detected plant disease outcomes are obtained. Throughout the entire validation, the offered model shows 95% enhancement in terms of MCC showcasing its effectiveness over the existing approaches.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-39"},"PeriodicalIF":7.8,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139944676","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

Haemorrhage diagnosis in colour fundus images using a fast-convolutional neural network based on a modified U-Net. 使用基于改进型 U-Net 的快速卷积神经网络诊断彩色眼底图像中的出血。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-02-12 DOI: 10.1080/0954898X.2024.2310687

Rathinavelu Sathiyaseelan, Krishnamoorthy Ranganathan, Ramesh Ramamoorthy, M Pedda Chennaiah

{"title":"Haemorrhage diagnosis in colour fundus images using a fast-convolutional neural network based on a modified U-Net.","authors":"Rathinavelu Sathiyaseelan, Krishnamoorthy Ranganathan, Ramesh Ramamoorthy, M Pedda Chennaiah","doi":"10.1080/0954898X.2024.2310687","DOIUrl":"10.1080/0954898X.2024.2310687","url":null,"abstract":"Retinal haemorrhage stands as an early indicator of diabetic retinopathy, necessitating accurate detection for timely diagnosis. Addressing this need, this study proposes an enhanced machine-based diagnostic test for diabetic retinopathy through an updated UNet framework, adept at scrutinizing fundus images for signs of retinal haemorrhages. The customized UNet underwent GPU training using the IDRiD database, validated against the publicly available DIARETDB1 and IDRiD datasets. Emphasizing the complexity of segmentation, the study employed preprocessing techniques, augmenting image quality and data integrity. Subsequently, the trained neural network showcased a remarkable performance boost, accurately identifying haemorrhage regions with 80% sensitivity, 99.6% specificity, and 98.6% accuracy. The experimental findings solidify the network's reliability, showcasing potential to alleviate ophthalmologists' workload significantly. Notably, achieving an Intersection over Union (IoU) of 76.61% and a Dice coefficient of 86.51% underscores the system's competence. The study's outcomes signify substantial enhancements in diagnosing critical diabetic retinal conditions, promising profound improvements in diagnostic accuracy and efficiency, thereby marking a significant advancement in automated retinal haemorrhage detection for diabetic retinopathy.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-22"},"PeriodicalIF":7.8,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139725025","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

Brain tumour classification using MRI images based on lenet with golden teacher learning optimization. 使用基于lenet的MRI图像对脑瘤进行分类，并进行黄金教师学习优化。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-02-01 Epub Date: 2024-02-08 DOI: 10.1080/0954898X.2023.2275720

Srilakshmi Aluri, Sagar S Imambi

{"title":"Brain tumour classification using MRI images based on lenet with golden teacher learning optimization.","authors":"Srilakshmi Aluri, Sagar S Imambi","doi":"10.1080/0954898X.2023.2275720","DOIUrl":"10.1080/0954898X.2023.2275720","url":null,"abstract":"Brain tumour (BT) is a dangerous neurological disorder produced by abnormal cell growth within the skull or brain. Nowadays, the death rate of people with BT is linearly growing. The finding of tumours at an early stage is crucial for giving treatment to patients, which improves the survival rate of patients. Hence, the BT classification (BTC) is done in this research using magnetic resonance imaging (MRI) images. In this research, the input MRI image is pre-processed using a non-local means (NLM) filter that denoises the input image. For attaining the effective classified result, the tumour area from the MRI image is segmented by the SegNet model. Furthermore, the BTC is accomplished by the LeNet model whose weight is optimized by the Golden Teacher Learning Optimization Algorithm (GTLO) such that the classified output produced by the LeNet model is Gliomas, Meningiomas, and Pituitary tumours. The experimental outcome displays that the GTLO-LeNet achieved an Accuracy of 0.896, Negative Predictive value (NPV) of 0.907, Positive Predictive value (PPV) of 0.821, True Negative Rate (TNR) of 0.880, and True Positive Rate (TPR) of 0.888.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"27-54"},"PeriodicalIF":7.8,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72016196","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