Pub Date : 2023-02-14DOI: 10.1007/s12530-023-09488-y
B. Priyadarshini, D. Reddy
{"title":"Modified remora optimization based matching pursuit with density peak clustering for localization of epileptic seizure onset zones","authors":"B. Priyadarshini, D. Reddy","doi":"10.1007/s12530-023-09488-y","DOIUrl":"https://doi.org/10.1007/s12530-023-09488-y","url":null,"abstract":"","PeriodicalId":12174,"journal":{"name":"Evolving Systems","volume":"6 1","pages":"1-17"},"PeriodicalIF":3.2,"publicationDate":"2023-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88226739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-11DOI: 10.1007/s12530-023-09487-z
Sangamesh C. Jalade, Nagaraj B. Patil
{"title":"Adaptive deep Runge Kutta Garson’s network with node disjoint local repair protocol based multipath routing in MANET","authors":"Sangamesh C. Jalade, Nagaraj B. Patil","doi":"10.1007/s12530-023-09487-z","DOIUrl":"https://doi.org/10.1007/s12530-023-09487-z","url":null,"abstract":"","PeriodicalId":12174,"journal":{"name":"Evolving Systems","volume":"12 1","pages":"1-25"},"PeriodicalIF":3.2,"publicationDate":"2023-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79901963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-31DOI: 10.1007/s12530-023-09486-0
P. Sushmachowdary, Sampath Kumar Panda, V. Naidu
{"title":"High gain circular slot MIMO antenna for Wi-Max and WLAN application with minimum ECC value","authors":"P. Sushmachowdary, Sampath Kumar Panda, V. Naidu","doi":"10.1007/s12530-023-09486-0","DOIUrl":"https://doi.org/10.1007/s12530-023-09486-0","url":null,"abstract":"","PeriodicalId":12174,"journal":{"name":"Evolving Systems","volume":"12 1","pages":"1-10"},"PeriodicalIF":3.2,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83515658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-28DOI: 10.1007/s12530-023-09485-1
Goutam Mandal, N. Kumar, Avijit Duary, A. Shaikh, A. K. Bhunia
{"title":"A league-knock-out tournament quantum particle swarm optimization algorithm for nonlinear constrained optimization problems and applications","authors":"Goutam Mandal, N. Kumar, Avijit Duary, A. Shaikh, A. K. Bhunia","doi":"10.1007/s12530-023-09485-1","DOIUrl":"https://doi.org/10.1007/s12530-023-09485-1","url":null,"abstract":"","PeriodicalId":12174,"journal":{"name":"Evolving Systems","volume":"22 9","pages":"1-27"},"PeriodicalIF":3.2,"publicationDate":"2023-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72402753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-03DOI: 10.1007/s12530-022-09480-y
Zhanpeng Zhang, Wenting Wang, Aimin An, Yuwei Qin, F. Yang
{"title":"A human activity recognition method using wearable sensors based on convtransformer model","authors":"Zhanpeng Zhang, Wenting Wang, Aimin An, Yuwei Qin, F. Yang","doi":"10.1007/s12530-022-09480-y","DOIUrl":"https://doi.org/10.1007/s12530-022-09480-y","url":null,"abstract":"","PeriodicalId":12174,"journal":{"name":"Evolving Systems","volume":"27 6 1","pages":"1-17"},"PeriodicalIF":3.2,"publicationDate":"2023-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79584477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01Epub Date: 2022-09-15DOI: 10.1007/s12530-022-09459-9
Zohreh Abbasi, Mohsen Shafieirad, Amir Hossein Amiri Mehra, Iman Zamani
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.
{"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":"<p><p>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.</p>","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}
Pub Date : 2023-01-01Epub Date: 2022-09-19DOI: 10.1007/s12530-022-09466-w
Xiaoyan Lu, Yang Xu, Wenhao Yuan
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.
{"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":"<p><p>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.</p>","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}
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