Evolving Systems最新文献_第7页

Modified remora optimization based matching pursuit with density peak clustering for localization of epileptic seizure onset zones 基于密度峰聚类的改进remoa优化匹配追踪定位癫痫发作区

IF 3.2 4区计算机科学

Evolving Systems Pub Date : 2023-02-14 DOI: 10.1007/s12530-023-09488-y

B. Priyadarshini, D. Reddy

引用次数: 0

Adaptive deep Runge Kutta Garson’s network with node disjoint local repair protocol based multipath routing in MANET 基于节点不相交局部修复协议的自适应深度Runge - Kutta - Garson网络在MANET中的多路径路由

IF 3.2 4区计算机科学

Evolving Systems Pub Date : 2023-02-11 DOI: 10.1007/s12530-023-09487-z

Sangamesh C. Jalade, Nagaraj B. Patil

引用次数: 2

High gain circular slot MIMO antenna for Wi-Max and WLAN application with minimum ECC value 用于Wi-Max和WLAN应用的高增益圆槽MIMO天线，具有最小的ECC值

IF 3.2 4区计算机科学

Evolving Systems Pub Date : 2023-01-31 DOI: 10.1007/s12530-023-09486-0

P. Sushmachowdary, Sampath Kumar Panda, V. Naidu

引用次数: 0

A league-knock-out tournament quantum particle swarm optimization algorithm for nonlinear constrained optimization problems and applications 联赛淘汰赛量子粒子群优化算法求解非线性约束优化问题及应用

IF 3.2 4区计算机科学

Evolving Systems Pub Date : 2023-01-28 DOI: 10.1007/s12530-023-09485-1

Goutam Mandal, N. Kumar, Avijit Duary, A. Shaikh, A. K. Bhunia

引用次数: 0

Fast and efficient exception tolerant ensemble for limited training 用于有限训练的快速有效的容错集成

IF 3.2 4区计算机科学

Evolving Systems Pub Date : 2023-01-18 DOI: 10.1007/s12530-022-09483-9

Sayan Sikder, Pankaj Dadure, Sanjeev K. Metya

引用次数: 1

Autonomous acquisition of arbitrarily complex skills using locality based graph theoretic features: a syntactic approach to hierarchical reinforcement learning 基于局部性图论特征的任意复杂技能的自主获取:层次强化学习的句法方法

IF 3.2 4区计算机科学

Evolving Systems Pub Date : 2023-01-04 DOI: 10.1007/s12530-022-09478-6

Zeynep Kumralbaş, Semiha Hazel Çavuş, Kutalmış Coşkun, Borahan Tümer

引用次数: 0

A human activity recognition method using wearable sensors based on convtransformer model 一种基于变压器模型的可穿戴传感器人体活动识别方法

IF 3.2 4区计算机科学

Evolving Systems Pub Date : 2023-01-03 DOI: 10.1007/s12530-022-09480-y

Zhanpeng Zhang, Wenting Wang, Aimin An, Yuwei Qin, F. Yang

引用次数: 7

EGPIECLMAC: efficient grayscale privacy image encryption with chaos logistics maps and Arnold Cat EGPIECLMAC:高效的灰度隐私图像加密与混沌物流地图和阿诺德猫

IF 3.2 4区计算机科学

Evolving Systems Pub Date : 2023-01-02 DOI: 10.1007/s12530-022-09482-w

Delavar Zareai, M. Balafar, Mohammadreza FeiziDerakhshi

引用次数: 2

Vaccination and isolation based control design of the COVID-19 pandemic based on adaptive neuro fuzzy inference system optimized with the genetic algorithm. 基于遗传算法优化的自适应神经模糊推理系统的新冠肺炎疫苗接种和隔离控制设计。

IF 3.2 4区计算机科学

Evolving Systems Pub Date : 2023-01-01 Epub Date: 2022-09-15 DOI: 10.1007/s12530-022-09459-9

Zohreh Abbasi, Mohsen Shafieirad, Amir Hossein Amiri Mehra, Iman Zamani

{"title":"Vaccination and isolation based control design of the COVID-19 pandemic based on adaptive neuro fuzzy inference system optimized with the genetic algorithm.","authors":"Zohreh Abbasi, Mohsen Shafieirad, Amir Hossein Amiri Mehra, Iman Zamani","doi":"10.1007/s12530-022-09459-9","DOIUrl":"10.1007/s12530-022-09459-9","url":null,"abstract":"The study of the COVID-19 pandemic is of pivotal importance due to its tremendous global impacts. This paper aims to control this disease using an optimal strategy comprising two methods: isolation and vaccination. In this regard, an optimized Adaptive Neuro-Fuzzy Inference System (ANFIS) is developed using the Genetic Algorithm (GA) to control the dynamic model of the COVID-19 termed SIDARTHE (Susceptible, Infected, Diagnosed, Ailing, Recognized, Threatened, Healed, and Extinct). The number of diagnosed and recognized people is reduced by isolation, and the number of susceptible people is reduced by vaccination. The GA generates optimal control efforts related to the random initial number of each chosen group as the input data for ANFIS to train Takagi-Sugeno (T-S) fuzzy structure coefficients. Also, three theorems are presented to indicate the positivity, boundedness, and existence of the solutions in the presence of the controller. The performance of the proposed system is evaluated through the mean squared error (MSE) and the root-mean-square error (RMSE). The simulation results show a significant decrease in the number of diagnosed, recognized, and susceptible individuals by employing the proposed controller, even with a 70% increase in transmissibility caused by various variants.","PeriodicalId":12174,"journal":{"name":"Evolving Systems","volume":"14 3","pages":"413-435"},"PeriodicalIF":3.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9476442/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9479569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

DBF-Net: a semi-supervised dual-task balanced fusion network for segmenting infected regions from lung CT images. DBF-Net：一种用于从肺部CT图像中分割感染区域的半监督双任务平衡融合网络。

IF 3.2 4区计算机科学

Evolving Systems Pub Date : 2023-01-01 Epub Date: 2022-09-19 DOI: 10.1007/s12530-022-09466-w

Xiaoyan Lu, Yang Xu, Wenhao Yuan

{"title":"DBF-Net: a semi-supervised dual-task balanced fusion network for segmenting infected regions from lung CT images.","authors":"Xiaoyan Lu, Yang Xu, Wenhao Yuan","doi":"10.1007/s12530-022-09466-w","DOIUrl":"10.1007/s12530-022-09466-w","url":null,"abstract":"Accurate segmentation of infected regions in lung computed tomography (CT) images is essential to improve the timeliness and effectiveness of treatment for coronavirus disease 2019 (COVID-19). However, the main difficulties in developing of lung lesion segmentation in COVID-19 are still the fuzzy boundary of the lung-infected region, the low contrast between the infected region and the normal trend region, and the difficulty in obtaining labeled data. To this end, we propose a novel dual-task consistent network framework that uses multiple inputs to continuously learn and extract lung infection region features, which is used to generate reliable label images (pseudo-labels) and expand the dataset. Specifically, we periodically feed multiple sets of raw and data-enhanced images into two trunk branches of the network; the characteristics of the lung infection region are extracted by a lightweight double convolution (LDC) module and fusiform equilibrium fusion pyramid (FEFP) convolution in the backbone. According to the learned features, the infected regions are segmented, and pseudo-labels are made based on the semi-supervised learning strategy, which effectively alleviates the semi-supervised problem of unlabeled data. Our proposed semi-supervised dual-task balanced fusion network (DBF-Net) creates pseudo-labels on the COVID-SemiSeg dataset and the COVID-19 CT segmentation dataset. Furthermore, we perform lung infection segmentation on the DBF-Net model, with a segmentation sensitivity of 70.6% and specificity of 92.8%. The results of the investigation indicate that the proposed network greatly enhances the segmentation ability of COVID-19 infection.","PeriodicalId":12174,"journal":{"name":"Evolving Systems","volume":"14 3","pages":"519-532"},"PeriodicalIF":3.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9483907/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9491207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0