Evolving Systems最新文献

Multivariate time series short term forecasting using cumulative data of coronavirus. 利用冠状病毒的累积数据进行多变量时间序列短期预测。

IF 2.7 4区计算机科学

Evolving Systems Pub Date : 2023-06-04 DOI: 10.1007/s12530-023-09509-w

Suryanshi Mishra, Tinku Singh, Manish Kumar, Satakshi

{"title":"Multivariate time series short term forecasting using cumulative data of coronavirus.","authors":"Suryanshi Mishra, Tinku Singh, Manish Kumar, Satakshi","doi":"10.1007/s12530-023-09509-w","DOIUrl":"10.1007/s12530-023-09509-w","url":null,"abstract":"Coronavirus emerged as a highly contagious, pathogenic virus that severely affects the respiratory system of humans. The epidemic-related data is collected regularly, which machine learning algorithms can employ to comprehend and estimate valuable information. The analysis of the gathered data through time series approaches may assist in developing more accurate forecasting models and strategies to combat the disease. This paper focuses on short-term forecasting of cumulative reported incidences and mortality. Forecasting is conducted utilizing state-of-the-art mathematical and deep learning models for multivariate time series forecasting, including extended susceptible-exposed-infected-recovered (SEIR), long-short-term memory (LSTM), and vector autoregression (VAR). The SEIR model has been extended by integrating additional information such as hospitalization, mortality, vaccination, and quarantine incidences. Extensive experiments have been conducted to compare deep learning and mathematical models that enable us to estimate fatalities and incidences more precisely based on mortality in the eight most affected nations during the time of this research. The metrics like mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are employed to gauge the model's effectiveness. The deep learning model LSTM outperformed all others in terms of forecasting accuracy. Additionally, the study explores the impact of vaccination on reported epidemics and deaths worldwide. Furthermore, the detrimental effects of ambient temperature and relative humidity on pathogenic virus dissemination have been analyzed.","PeriodicalId":12174,"journal":{"name":"Evolving Systems","volume":" ","pages":"1-18"},"PeriodicalIF":2.7,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10239659/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9705198","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

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

Evolving fuzzy neural classifier that integrates uncertainty from human-expert feedback. 融合人类专家反馈不确定性的进化模糊神经分类器。

IF 3.2 4区计算机科学

Evolving Systems Pub Date : 2023-01-01 DOI: 10.1007/s12530-022-09455-z

Paulo Vitor de Campos Souza, Edwin Lughofer

{"title":"Evolving fuzzy neural classifier that integrates uncertainty from human-expert feedback.","authors":"Paulo Vitor de Campos Souza, Edwin Lughofer","doi":"10.1007/s12530-022-09455-z","DOIUrl":"https://doi.org/10.1007/s12530-022-09455-z","url":null,"abstract":"Evolving fuzzy neural networks are models capable of solving complex problems in a wide variety of contexts. In general, the quality of the data evaluated by a model has a direct impact on the quality of the results. Some procedures can generate uncertainty during data collection, which can be identified by experts to choose more suitable forms of model training. This paper proposes the integration of expert input on labeling uncertainty into evolving fuzzy neural classifiers (EFNC) in an approach called EFNC-U. Uncertainty is considered in class label input provided by experts, who may not be entirely confident in their labeling or who may have limited experience with the application scenario for which the data is processed. Further, we aimed to create highly interpretable fuzzy classification rules to gain a better understanding of the process and thus to enable the user to elicit new knowledge from the model. To prove our technique, we performed binary pattern classification tests within two application scenarios, cyber invasion and fraud detection in auctions. By explicitly considering class label uncertainty in the update process of the EFNC-U, improved accuracy trend lines were achieved compared to fully (and blindly) updating the classifiers with uncertain data. Integration of (simulated) labeling uncertainty smaller than 20% led to similar accuracy trends as using the original streams (unaffected by uncertainty). This demonstrates the robustness of our approach up to this uncertainty level. Finally, interpretable rules were elicited for a particular application (auction fraud identification) with reduced (and thus readable) antecedent lengths and with certainty values in the consequent class labels. Additionally, an average expected uncertainty of the rules were elicited based on the uncertainty levels in those samples which formed the corresponding rules.","PeriodicalId":12174,"journal":{"name":"Evolving Systems","volume":"14 2","pages":"319-341"},"PeriodicalIF":3.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10061807/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9597002","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

Super-forecasting the 'technological singularity' risks from artificial intelligence. 超级预测来自人工智能的“技术奇点”风险。

IF 3.2 4区计算机科学

Evolving Systems Pub Date : 2022-01-01 Epub Date: 2022-06-04 DOI: 10.1007/s12530-022-09431-7

Petar Radanliev, David De Roure, Carsten Maple, Uchenna Ani

{"title":"Super-forecasting the 'technological singularity' risks from artificial intelligence.","authors":"Petar Radanliev, David De Roure, Carsten Maple, Uchenna Ani","doi":"10.1007/s12530-022-09431-7","DOIUrl":"10.1007/s12530-022-09431-7","url":null,"abstract":"This article investigates cybersecurity (and risk) in the context of 'technological singularity' from artificial intelligence. The investigation constructs multiple risk forecasts that are synthesised in a new framework for counteracting risks from artificial intelligence (AI) itself. In other words, the research in this article is not just concerned with securing a system, but also analysing how the system responds when (internal and external) failure(s) and compromise(s) occur. This is an important methodological principle because not all systems can be secured, and totally securing a system is not feasible. Thus, we need to construct algorithms that will enable systems to continue operating even when parts of the system have been compromised. Furthermore, the article forecasts emerging cyber-risks from the integration of AI in cybersecurity. Based on the forecasts, the article is concentrated on creating synergies between the existing literature, the data sources identified in the survey, and forecasts. The forecasts are used to increase the feasibility of the overall research and enable the development of novel methodologies that uses AI to defend from cyber risks. The methodology is focused on addressing the risk of AI attacks, as well as to forecast the value of AI in defence and in the prevention of AI rogue devices acting independently.Supplementary information: The online version contains supplementary material available at 10.1007/s12530-022-09431-7.","PeriodicalId":12174,"journal":{"name":"Evolving Systems","volume":"13 5","pages":"747-757"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9166151/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9910252","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: 7

Deep recurrent Gaussian Nesterovs recommendation using multi-agent in social networks. 社交网络中使用多智能体的深度递归高斯嵌套推荐。

IF 3.2 4区计算机科学

Evolving Systems Pub Date : 2022-01-01 Epub Date: 2022-04-09 DOI: 10.1007/s12530-022-09435-3

Vinita Tapaskar, Mallikarjun M Math

{"title":"Deep recurrent Gaussian Nesterovs recommendation using multi-agent in social networks.","authors":"Vinita Tapaskar, Mallikarjun M Math","doi":"10.1007/s12530-022-09435-3","DOIUrl":"10.1007/s12530-022-09435-3","url":null,"abstract":"Due to increasing volume of big data the high volume of information in Social Network put a stop to users from acquiring serviceable information intelligently so many recommendation systems have emerged. Multi-agent Deep Learning gains rapid attraction, and the latest accomplishments address problems with real-world complexity. With big data precise recommendation has yet to be answered. In proposed work Deep Recurrent Gaussian Nesterov's Optimal Gradient (DR-GNOG) that combines deep learning with a multi-agent scenario for optimal and precise recommendation. The DR-GNOG is split into three layers, an input layer, two hidden layers and an output layer. The tweets obtained from the users are provided to the input layer by the Tweet Accumulator Agent. Then, in the first hidden layer, Tweet Classifier Agent performs optimized and relevant tweet classification by means of Gaussian Nesterov's Optimal Gradient model. In the second layer, a Deep Recurrent Predictive Recommendation model is designed to concentrate on the vanishing gradient issue arising due to updated tweets obtained from same user at different time instance. Finally, with the aid of hyperbolic activation function in the output layer, building block of the predictive recommendation is obtained. In the experimental study the proposed method is found better than existing GANCF and Bootstrapping method 13-21% in case of recommendation accuracy, 22-32% better in recommendation time and 15-22% better in recall rate.","PeriodicalId":12174,"journal":{"name":"Evolving Systems","volume":"13 3","pages":"435-452"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8994100/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10267868","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: 2

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review. 自然启发的优化算法及其在多阈值图像分割中的意义：综述。

IF 3.2 4区计算机科学

Evolving Systems Pub Date : 2022-01-01 Epub Date: 2022-02-21 DOI: 10.1007/s12530-022-09425-5

Rebika Rai, Arunita Das, Krishna Gopal Dhal

引用次数: 19