Network-Computation in Neural Systems最新文献_第9页

CS-UNet: Cross-scale U-Net with Semantic-position dependencies for retinal vessel segmentation. CS-UNet：用于视网膜血管分割的具有语义位置依赖性的跨尺度 U-Net

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-05-01 Epub Date: 2023-12-05 DOI: 10.1080/0954898X.2023.2288858

Ying Yang, Shengbin Yue, Haiyan Quan

{"title":"CS-UNet: Cross-scale U-Net with Semantic-position dependencies for retinal vessel segmentation.","authors":"Ying Yang, Shengbin Yue, Haiyan Quan","doi":"10.1080/0954898X.2023.2288858","DOIUrl":"10.1080/0954898X.2023.2288858","url":null,"abstract":"Accurate retinal vessel segmentation is the prerequisite for early recognition and treatment of retina-related diseases. However, segmenting retinal vessels is still challenging due to the intricate vessel tree in fundus images, which has a significant number of tiny vessels, low contrast, and lesion interference. For this task, the u-shaped architecture (U-Net) has become the de-facto standard and has achieved considerable success. However, U-Net is a pure convolutional network, which usually shows limitations in global modelling. In this paper, we propose a novel Cross-scale U-Net with Semantic-position Dependencies (CS-UNet) for retinal vessel segmentation. In particular, we first designed a Semantic-position Dependencies Aggregator (SPDA) and incorporate it into each layer of the encoder to better focus on global contextual information by integrating the relationship of semantic and position. To endow the model with the capability of cross-scale interaction, the Cross-scale Relation Refine Module (CSRR) is designed to dynamically select the information associated with the vessels, which helps guide the up-sampling operation. Finally, we have evaluated CS-UNet on three public datasets: DRIVE, CHASE_DB1, and STARE. Compared to most existing state-of-the-art methods, CS-UNet demonstrated better performance.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"134-153"},"PeriodicalIF":7.8,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138489124","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 robust genetic algorithm-based optimal feature predictor model for brain tumour classification from MRI data 基于遗传算法的鲁棒性最优特征预测模型，用于磁共振成像数据的脑肿瘤分类

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-04-22 DOI: 10.1080/0954898x.2024.2343340

Meenal Thayumanavan, Asokan Ramasamy

引用次数: 0

An innovative breast cancer detection framework using multiscale dilated densenet with attention mechanism 利用具有关注机制的多尺度扩张登森网的创新型乳腺癌检测框架

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-04-22 DOI: 10.1080/0954898x.2024.2343348

Subhashini Ramachandran, Rajasekar Velusamy, Namakkal Venkataraman Srinivasan Sree Rathna Lakshmi, Chakaravarthi Sivanandam

引用次数: 0

Topological information embedded convolutional neural network-based lotus effect optimization for path improvisation of the mobile anchors in wireless sensor networks 基于拓扑信息嵌入卷积神经网络的莲花效应优化，用于无线传感器网络中移动锚点的路径改进

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-04-22 DOI: 10.1080/0954898x.2024.2339477

Bala Subramanian Chokkalingam, Balakannan Sirumulasi Paramasivan, Maragatharajan Muthusamy

引用次数: 0

Enhanced Cardiovascular Disease Prediction Modelling using Machine Learning Techniques: A Focus on CardioVitalnet 利用机器学习技术增强心血管疾病预测建模：聚焦心血管网络

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-04-16 DOI: 10.1080/0954898x.2024.2343341

Chukwuebuka Joseph Ejiyi, Zhen Qin, Grace Ugochi Nneji, Happy Nkanta Monday, Victor K. Agbesi, Makuachukwu Bennedith Ejiyi, Thomas Ugochukwu Ejiyi, Olusola O. Bamisile

引用次数: 0

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