# Secured MCDM Model for Crowdsource Business Intelligence
## 7 Conclusions and Further Work  
There are many research papers on the basis of AHP and GRA, which represent the production process of the products. However, in this paper we have found the best process of all. We have used both AHP and GRA processes along with security mechanism implemented for the dataset. As per the AHP process, we have concluded that the features of the product are more important than the other criteria. So, on the basis of output of the AHP process, we conclude that the features are important to select the product. The whole framework is secured using the EBAACM algorithm that utilizes an access control methodology and various other essential environmental factors to set access permissions for all digitally identified users.  
Figure 6: Attributes with Weight Representation in AHP.  
_Future Directions_
* The business intelligence enhancements can be deployed across various MCDM models such as VIKOR, WASPAS, MOORA.
* The sensitivity analysis of various MCDM models can be compared to improve our understanding of the real competitive advantage of business intelligence.
* Attribute-based security mechanisms can be enhanced with the transaction-based encryption with policies.
* Deep learning models can be used for the decision-support systems with real-time business-related big data analysis.  
The intricacy of context data will be a topic of future investigation. In this direction, crowdsourcing can be used to gather structured and unstructured data from many sources with more individualized qualities and metadata. Due to the computational requirements of even a moderately sized problem, the system presented in this investigation places significant constraints on the scope of the research. One limitation of AHP is that the modeling process itself is inherently subjective. That means the approach cannot say for sure that the verdicts are correct. Moreover, once the datasets gathered via crowdsourcing technique grow more complicated, numerous unsolved security issues may hinder the performance of the system. This will make it possible to better understand users and deliver more individualized outcomes. Utilizing vast quantities of context data, more effort is required in terms of safeguarding user privacy in addition to data security, while still maintaining adequate recommender systems speed and accuracy.  
Conceptualization, M.K.N. and I.J.; methodology, M.K.N., I.J.; software, M.K.N.; validation: S.S.; formal analysis, M.K.N.; investigation S.B. and A.M.A.; resources, M.K.N. and I.J.; data curation, A.A.M.; writing--original draft preparation, M.K.N., I.J., S.S.; writing--review and editing; S.S., A.M.A. and S.B.; visualization, I.J.; supervision, S.S.; project administration, S.B.; funding acquisition, S.B. and A.A.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not Applicable.  
Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R151), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
The authors declare no conflicts of interest.