Marketers must never underestimate an online buyer. If they are assuming that mere marketing efforts from their side will influence a buyer, then they are being uncalculated and ignorant. Potential customers are not isolated people who are glued to their computer systems confused as to what product to buy. Instead they are tech-savvy, socially active, smart buyers who make a detailed analysis about the product they intend to buy online. Therefore, marketers need to re-design their online marketing strategies to suit to the needs of the buyer. The marketing approach should focus on social influence marketing as well.

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Different tools and approaches had been introduced to handle the uncertainty and vagueness environment of behaviour analysis. One of the latest tools in dealing with uncertainty and vagueness is Pythagorean fuzzy sets which are the generalized form of intuitionistic fuzzy set. In this paper, we explore the concept of Pythagorean fuzzy sets and introduce POS-NEG composition to determine the behaviour of customers in Amazon E-commerce website. The role of this enhanced composition is to gather the sentiment of customers about the Amazon products. Our proposed work handles multi-attribute, reduces complexity of the analysis and it is compared and proved to be accurate than fuzzy set and intuitionistic fuzzy set. The main feature of Pythagorean fuzzy sets is its relatively novel mathematical framework in the fuzzy family with higher ability to cope imprecision imbedded in decision-making.

Reporting the incidents related to Computer Security has now become one of the most important component of the Information Technology programs with the increase in the attacks related to cyber security. The reason being the introduction of new and new security related incidents every day. It is giving light to the necessity of a quick and efficient incident response capability which could detect incidents, minimise the loss due to the destruction and mitigate the weaknesses that was exploited. This publication is a parameter for incident handling, especially for analysing event-related data and defining the appropriate response to each incident.