This article describes the consensus control issue of networked multi-agent system with controller area network bus–induced delay in the frequency domain. Initially, causes of the controller area network bus–induced delay in distributed networked control systems and features of controller area network buses are analyzed. Then, networked multi-agent systems composed of distributed networked control systems are modeled in the frequency domain. An analytical controller based on the parameterization technique is proposed to improve the H2 performance criterion of distributed networked control systems. The performance tracking and robustness of the controller is analyzed by its sensitivity functions. After the performance and robustness analysis, the delay margin criteria and consensus conditions of networked multi-agent systems are derived in the existence of controller area network bus–induced delay. We verify the efficiency of the proposed method by simulating examples.

The next-generation Ethernet passive optical network (NG-EPON) is basically classified into two architectures on the basis of the wavelength sharing by the optical network units (ONUs). The single scheduling domain (SSD) and multi-scheduling domain (MSD) EPON are the two different design architectures for NG-EPON. A vital task in NG-EPON is to design dynamic wavelength bandwidth allocation (DWBA) algorithms that can meet the future demands of the network subscribers. A number of DWBA algorithms have been designed for time and wavelength division multiplex (TWDM) EPON. The existing DWBA algorithms for TWDM-EPON could be used in MSD-EPON by making necessary parametric changes. The design and implementation of new DWBA algorithms for MSD-EPON are still required specifically. In this paper, we have proposed a quality of service (QoS)-based DWBA algorithm for NG-EPON. We have comparatively analyzed our proposed DWBA with the existing algorithms like earlier finished time (EFT), weighted bipartite matching (WBM), and earlier finished time with void filling (EFT-VF). The results show that our proposed DWBA algorithm performs better as compared to EFT, WBM, and EFT-VF on the basis of average packet delay and average completion time for NG-EPON.

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Schistosomiasis is a neglected tropical disease that continues to be a leading cause of illness and mortality around the globe. The causing parasites are affixed to the skin through defiled water and enter the human body. Failure to diagnose Schistosomiasis can result in various medical complications, such as ascites, portal hypertension, esophageal varices, splenomegaly, and growth retardation. Early prediction and identification of risk factors may aid in treating disease before it becomes incurable. We aimed to create a framework by incorporating the most significant features to predict Schistosomiasis using machine learning techniques. A dataset of advanced Schistosomiasis has been employed containing recovery and death cases. A total data of 4316 individuals containing recovery and death cases were included in this research. The dataset contains demographics, socioeconomic, and clinical factors with lab reports. Data preprocessing techniques (missing values imputation, outlier removal, data normalisation, and data transformation) have also been employed for better results. Feature selection techniques, including correlation-based feature selection, Information gain, gain ratio, ReliefF, and OneR, have been utilised to minimise a large number of features. Data resampling algorithms, including Random undersampling, Random oversampling, Cluster Centroid, Near miss, and SMOTE, are applied to address the data imbalance problem. We applied four machine learning algorithms to construct the model: Gradient Boosting, Light Gradient Boosting, Extreme Gradient Boosting and CatBoost. The performance of the proposed framework has been evaluated based on Accuracy, Precision, Recall and F1-Score. The results of our proposed framework stated that the CatBoost model showed the best performance with the highest accuracy of (87.1%) compared with Gradient Boosting (86%), Light Gradient Boosting (86.7%) and Extreme Gradient Boosting (86.9%). Our proposed framework will assist doctors and healthcare professionals in the early diagnosis of Schistosomiasis.