## 6 Conclusions and Future Work  
This study proposed the RFO-SVM algorithm as a potential solution for optimizing data fragmentation and allocation across multiple nodes in distributed databases. The algorithm offers an efficient and effective approach to addressing the challenges of data fragmentation allocation in distributed databases due to the increasing volume and complexity of data. It demonstrated high accuracy and robust performance in predicting instances and identifying positive cases. Key advantages include optimized resource utilization, significantly reduced allocation costs, efficient memory utilization, and fast processing times. Compared with existing methods, the RFO-SVM algorithm outperforms in all metrics, achieving a 96.21% accuracy and a 42.15% reduction in allocation cost. These findings underscore the potential of RFO-SVM to enhance the efficiency and scalability of distributed database systems.  
Future work can focus on the following areas to further advance the field of data fragmentation allocation in distributed databases, addressing emerging challenges and  
Figure 10: Total transmission cost.  
Figure 9: Processing time.  
contributing to the development of more efficient and scalable distributed data management solutions:  
Scalability and Performance Optimization: Develop algorithms that can efficiently handle a larger number of nodes, datasets, and workload demands while maintaining high accuracy and minimizing resource utilization.  
Combination of Machine Learning with Rule-Based or Heuristic Approaches: Investigate the integration of machine learning techniques with rule-based or heuristic approaches to create more effective and efficient data fragmentation allocation algorithms.  
Integration with Emerging Technologies: Explore the integration of data fragmentation allocation algorithms with technologies such as edge computing, blockchain, or federated learning. These technologies can introduce new challenges and opportunities in distributed data management, and studying their impact on data fragmentation allocation can provide valuable insights.  
These directions will help in creating more robust, efficient, and scalable solutions for distributed database management systems.  
Conceptualization, H.H; methodology, H.H.; software, K.D.; validation, A.R.; formal analysis, K.D. and A.H.K.; investigation, A.R.; resources, A.H.K.; data curation, A.H.K. and A.R.; writing--original draft preparation, K.D. and A.R.; writing--review and editing, H.H; visualization, K.D. and A.R.; supervision, A.H.K.; project administration, K.D.; funding acquisition, H.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data is contained within the article.  
The authors are extremely grateful to [PERSON] at Business Information Technology, University of Sulaimani, Sulaymaniyah, Iraq. [PERSON] has significantly contributed to the methodology, funding acquisition and validation of our work. His insightful feedback and unwavering support were essential to the development and completion of this work.  
The authors declare no conflict of interest.