The filtered-X LMS algorithm is the most popular method for adaptive filter design in the field of active acoustic and vibration control because of its simplicity and good robust performance. However, no investigation...
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UML has become the de facto standard for object-oriented modelling. Currently, UML comprises several different notations with no formal semantics attached to the individual diagrams or their integration, thus preventi...
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In software industry, many organizations either focus their traceability efforts on Functional Requirements (FRs) or else fail entirely to implement an effective traceability process. Non-Functional Requirements (NFRs...
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This paper identifies challenges in managing resources in a Grid computing environment and proposes computational economy as a metaphor for effective management of resources and application scheduling. It identifies d...
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Federated learning has been used extensively in business inno-vation scenarios in various *** research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asym...
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Federated learning has been used extensively in business inno-vation scenarios in various *** research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment ***,this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises(MSEs)using multi-dimensional enterprise data and multi-perspective enterprise *** proposed model includes four main processes:namely encrypted entity alignment,hybrid feature selection,secure multi-party computation,and global model ***,a two-step feature selection algorithm based on wrapper and filter is designed to construct the optimal feature set in multi-source heterogeneous data,which can provide excellent accuracy and *** addition,a local update screening strategy is proposed to select trustworthy model parameters for aggregation each time to ensure the quality of the global *** results of the study show that the model error rate is reduced by 6.22%and the recall rate is improved by 11.03%compared to the algorithms commonly used in credit risk research,significantly improving the ability to identify ***,the business operations of commercial banks are used to confirm the potential of the proposed model for real-world implementation.
This review discusses how Machine Learning is applied to predict the quality of biomass briquettes produced from agricultural and municipal solid organic waste, which are crucial for advancing green and low-carbon sol...
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This review discusses how Machine Learning is applied to predict the quality of biomass briquettes produced from agricultural and municipal solid organic waste, which are crucial for advancing green and low-carbon solutions. Traditional methods of assessment of briquette quality involve destructive laboratory experiments, do not favor sample reuse, are time-consuming, and labor-intensive, posing barriers to efficient production. This paper has reviewed literature covering various Machine Learning models applied for predicting and optimizing briquette quality parameters including combustion, physical, and emission properties. Several Machine Learning models have shown promising results in predicting and optimizing these key parameters for example Random Forest with R2 of 0.9936 in deformation energy prediction and Artificial Neural Networks with R2 of 0.8936 in the prediction of impact resistance. By enhancing the accuracy and efficiency of briquette quality predictions, Machine Learning algorithms contribute to the development of high-quality biomass briquettes thereby creating sustainable and low-carbon energy systems. This review points to critical literature gaps regarding model generalizability across diverse biomass feedstock and integration of broader quality parameters. Addressing these gaps will advance AI-based solutions, promote greener energy practices, and support sustainable development. The findings are intended to aid researchers, industry professionals, and policy makers in advancing the production of high-quality biomass briquettes for cleaner energy and sustainable development.
The analysis using Social Network Analysis (SNA) is focusing more on the number or the frequency of social interactions based on adjacency matrix. This method cannot be used to observe the semantic relationship among ...
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A distributed computing system consists of heterogeneous computing devices, communication networks, operating system services, and applications. As organisations move toward distributed computing environments, there w...
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A distributed computing system consists of heterogeneous computing devices, communication networks, operating system services, and applications. As organisations move toward distributed computing environments, there will be a corresponding growth in distributed applications central to the enterprise. The design, development, and management of distributed applications presents many difficult challenges. As these systems grow to hundreds or even thousands of devices and similar or greater magnitude of software components, it will become increasingly difficult to manage them without appropriate support tools and frameworks. Further, the design and deployment of additional applications and services will be, at best, ad hoc without modelling tools and timely data on which to base design and configuration decisions. This paper presents a framework for management of distributed applications and systems. The framework is based on a set of common management services that support management activities. The services include monitoring, control, configuration, and data repository services. A prototype system built on the framework is described that implements and integrates management applications providing visualisation, fault location, performance monitoring and modelling, and configuration management. The prototype also demonstrates how various management services can be implemented.
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