# Secured MCDM Model for Crowdsource Business Intelligence
## 2 Literature Survey
After exhaustive research to assess the impact of various variables on vehicle sales through information gathering and data association, the most meaningful items from the feature set are selected for prediction. Numerous tests confirm ForeXGBoost's ability to combine low overhead with high prediction accuracy. In [15], the authors describe the Sturgis formulation, which is used to replace the iterative behavior of fuzzy logic within the prediction method. AFER (average forecasting mistakes rate) and SSM (suggest squared mistakes) are the metrics used. The effects showed that the ANFIS set of rules outperformed the bushy time series with AFER values much less than 15% and errors greater than 20%. ANFIS-MSE values notably decreased compared to fuzzy time series. The research carried out by [16] aimed to reveal patterns of influence of certain factors on car sales in the European market and functional patterns for predicting future sales. Global aggregate indicators and indicators for specific regions or clusters were used to build mathematical prediction models and linear regressions. This means that applying the model to the test sample data yields good predictive accuracy. In [1], the authors propose the Gaussian process latent variable model factorization (GPLVMF) method, which is a nonlinear dimensionality reduction technique. The GPLVMF model is solved using a variational inference approach. There are two contributions when it comes to setting up the recommender system: fixing the real-valued latent space to use a real-valued context and handling the bias effects by setting a non-zero mean function. The choice of a relevant machine tool is the most crucial challenge in the manufacturing industry, as it will affect the overall performance. The present multi-criteria decision-making (MCDM) method for choosing machine tools mostly emphasizes the subjective viewpoint. [PERSON] et al. [17] deployed a modified hybrid MCMD model for the right machine tool selection. First, the proposed method uses an integrated weight strategy that combines subjective weights obtained through the fuzzy decision-making trial and evaluation laboratory (FDEMATEL), with objective weights gained through entropy weighing (EW). Then, the authors apply defuzzification VIKOR to obtain the ranking choice. Due to the overwhelming number of service providers (SP) in crowdsourcing, the management of information is a rising issue. [PERSON] et al. [18] intend to present an evaluation framework for quality of service (QoS) for SPs in KI-C to accurately and thoroughly characterize the QoS of SPs, which can help with the effective selection of qualified SPs. In order to use the collective intelligence of a large number of people, a task may be outsourced to an online marketplace for crowdsourcing. The crowdsourcing process saves time and money because of concurrent task execution and online labor markets. Everyone finds it difficult to choose the right jobs with the relevant labels and give them to the right workers during a crowdsourcing exercise. In this study [19], the authors have suggested a mechanism for allocating the task to the workers. To give the task to the most qualified worker, a multiciteria-based task assignment (MBTA) mechanism is developed. This mechanism employs methods for allocating weight to the factors and rating the workers. 5G technology, which is widely employed in the domains of medicine, transportation, energy and other industries, has developed quickly in recent years. 5G base stations, which are the essential components of the 5G network, enable wireless signal transfer between wired and wireless terminals, as well as wireless coverage. However, issues such as poor user experience and limited coverage area commonly arise as the number of 5G base stations gradually grows. Therefore, it is essential to assess the overall performance of 5G base stations in order to identify any issues that may have arisen during base-station installation.