Application of Deep Learning and Fuzzy Decision Support System in Residence Right Management

The administration of residence rights is essential for ensuring residential communities’ smooth and productive operation. However, resident rights management presents various issues, including the allocation of resources, security measures, and resident satisfaction. The difficulties associated wit...

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Veröffentlicht in:International journal of fuzzy systems Jg. 27; H. 8; S. 2563 - 2584
1. Verfasser: Fu, Liling
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2025
Springer Nature B.V
Schlagworte:
Algorithms
Alternative energy sources
Anomalies
Artificial Intelligence
Artificial neural networks
Computational Intelligence
Cost reduction
Decision making
Decision support systems
Deep learning
Efficiency
Energy consumption
Energy management
Energy resources
Energy storage
Engineering
Food quality
Food security
Fuzzy logic
Happiness
Heating
Machine learning
Maintenance management
Management Science
Operations Research
Optimization
Pattern recognition
Predictive maintenance
Recurrent neural networks
Renewable resources
Residential communities
Resource allocation
Security
Security management
Sustainability
Deep learning
Recurrent neural networks
Resource allocation
Residence right management
Fuzzy decision support systems
Security
Convolutional neural networks
ISSN:1562-2479, 2199-3211
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Zusammenfassung:The administration of residence rights is essential for ensuring residential communities’ smooth and productive operation. However, resident rights management presents various issues, including the allocation of resources, security measures, and resident satisfaction. The difficulties associated with administrating residence rights highlight the necessity for sophisticated technologies to enhance the efficiency and efficacy of residential rights management significantly. This work explores the utilization of Deep Learning and Fuzzy Decision Support Systems for Residence Right Management (DLFDSS-RRM) to optimize resource allocation, improve security, and promote sustainability in residential communities through data-driven decision-making and adaptive strategies. Convolutional Neural Networks (CNN) are employed to analyze sensor data anomalies, feature selection, and pattern recognition, and Recurrent Neural Networks (RNN) are used to predict future trends in energy consumption, resident behavior, and security incidents, enabling accurate predictions and proactive decision making in residence right management. The present study enables the establishment of resident profiles, prediction of resource demand, detection of anomalies, and provision of individualized service suggestions. In addition, Fuzzy Decision Support Systems (FDSS) are integrated to effectively tackle the inherent uncertainties and vague information associated with suitable residential management duties. The suggested method utilizes fuzzy logic to enable a resilient decision-making process considering resident preferences, budget limitations, and environmental considerations. The approach effectively tackles multiple facets of residence right management by leveraging the synergistic integration of deep learning and FDSS. These technologies aim to optimize resident satisfaction, productivity, and resource consumption by optimizing resource allocation, predictive maintenance scheduling, strengthening security measures, and delivering tailored resident assistance. This study shows the impact of deep learning and FDSS modes in transforming residence right management. It opens the door to more complex and adaptable approaches to residential community needs.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1562-2479
2199-3211
DOI:10.1007/s40815-024-01917-7