Critical Success Factors for Implementing CRM Using Data Mining

The paper presents the Critical success factors for implementing the Customer Relationship Management (CRM) in a firm using the Data mining (DM). The use of the data mining in CRM is widely accepted by the firms. The success of proper implementation of CRM using Data mining in firms is mixed. This is due to the fact that investment involve in this implementation requires planning regarding the factors which need to be considered before going for the new innovative technology. These factors may vary from firm to firm but the general factor for effective implementation of the CRM using data mining is essential. This factor termed as Critical success factor (CSF) decides the failure or success of the implementation. The paper demonstrates the key factors which need to be considered before automating the process of searching the mountain of customer’s related data using Data mining to find patterns that are good predictors of behaviors of the customer which help achieve successful CRM. The paper gives an idea of how proper planning and effective management can lead to increased customer satisfaction and profit for the firms

Optimization of Intrusion Detection Systems Determined by Ameliorated HNADAM-SGD Algorithm

Information security is of pivotal concern for consistently streaming information over the widespread internetwork. The bottleneck flow of incoming and outgoing data traffic introduces the issues of malicious activities taken place by intruders, hackers and attackers in the form of authenticity obstruction, gridlocking data traffic, vandalizing data and crashing the established network. The issue of emerging suspicious activities is managed by the domain of Intrusion Detection Systems (IDS). The IDS consistently monitors the network for the identification of suspicious activities, and generates alarm and indication in the presence of malicious threats and worms. The performance of IDS is improved by using different machine learning algorithms. In this paper, the Nesterov-Accelerated Adaptive Moment Estimation–Stochastic Gradient Descent (HNADAM-SDG) algorithm is proposed to determine the performance of Intrusion Detection Systems IDS. The algorithm is used to optimize IDS systems by hybridization and tuning of hyperparameters. The performance of algorithm is compared with other classification algorithms such as logistic regression, ridge classifier and ensemble algorithms where the experimental analysis and computations show the improved accuracy with 99.8%, sensitivity with 99.7%, and specificity with 99.5%