An enterprise resource planning (ERP) system is an integrated software solution, typically offered by a vendor as a package that supports the seamless integration of all the information flowing through a company. Business information systems is an area of the greatest significance in any business enterprise today. ERP projects are a growing segment of this vital area. The objective of customization in ERP implementation is to achieve a fit between the ERP system and the process that the system supports. Widespread literature review has been done to study the issues in ERP implementation. Customization is found to be the major annoyance in most of the ERP projects. Literature review also shows that the AHP is the preeminent slant among the various methodologies applied to ERP projects in the past for prioritizing the attributes. Hence, in this paper we have applied the analytical hierarchy process (AHP) to a framework for evaluating ERP implementation choices. The upshot of the study is the identification of various customization possibilities for ERP implementation. This study is meant to help managers think about the various feasible customization options available to them. The application of AHP to the framework is exemplified and the epitome of findings is discussed. Future research work that can be done in customization is also indicated.

The COVID-19 pandemic has spread to almost all countries of the World and affected people both mentally and economically. The primary motivation of this research is to construct a model that takes reviews or evaluations from several people who are affected with COVID-19. As the number of cases has accelerated day by day, people are becoming panicked and concerned about their health. A good model may be helpful to provide accurate statistics in interpreting the actual records about the pandemic. In the proposed work, for sentimental analysis, a unique classifier named the Sentimental DataBase Miner algorithm (SADBM) is used to categorize the opinions and parallel processing, and is applied on the data collected from various online social media websites like Twitter, Facebook, and Linkedin. The accuracy of the proposed model is validated with trained data and compared with basic classifiers, such as logistic regression and decision tree. The proposed algorithm is executed on CPU as well as GPU and calculated the acceleration ratio of the model. The results show that the proposed model provides the best accuracy compared with the other two models, i.e., 96% (GPU).

Present-day queuing inventory systems (QIS) do not utilize two multi-server service channels. We proposed two multi-server service channels referred to as $T1S$ (Type 1 n-identical multi-server) and $T2S$ (Type 2 m-identical multi-server). It includes an optional interconnected service connection between $T1S$ and $T2S$, which has a finite queue of size N. An arriving customer either uses the inventory (basic service or main service) for their demand, whom we call $T1$, or simply uses the service only, whom we call $T2$. Customer $T1$ will utilize the server $T1S$, while customer $T2$ will utilize the server $T2S$, and $T1$ can also get the second optional service after completing their main service. If there is a free server with a positive inventory, there is a chance that $T1$ customers may go to an infinite orbit whenever they find that either all the servers are busy or no sufficient stock. The orbital customer can request for $T1S$ service under the classical retrial policy. $Q(=S-s)$ items are replaced into the inventory whenever it falls into the reorder level s such that the inequality always holds $n<s$. We use the standard $(s,Q)$ ordering policy to replace items into the inventory. By varying S and s, we investigate to find the optimal cost value using stationary probability vector $\varphi$. We used the Neuts Matrix geometric approach to derive the stability condition and steady-state analysis with R-matrix to find $\varphi$. Then, we perform the waiting time analysis for both $T1$ and $T2$ customers using Laplace transform technique. Further, we computed the necessary system characteristics and presented sufficient numerical results.