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.
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.
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.
Training is one of the most inevitable compulsions for personal and professional evolution. One has to be on a rocket which always points towards a continuous and continual development. As time passes, it is not the fittest who survive, it is the one who quickly adapts to the change that survives, and this can only be through a continual and lifelong training and learning process which is goal oriented and systematic. Many studies have been conducted on training and the needs of training but the scope further existed to explore whether the concept has relevance in those IT and ITes companies which have the highest process level of maturity Capability Model level 5 (CMM level 5). The resolution of this study was to define a talented research on the prominence of training in CMM level 5 Indian Information Technology (IT)/Information Technology Enabled Services (ITeS) industry. The study was also designed to establish whether various factors reported are having significant relationships with major factors as training quality and perceived benefits. The task of improving the training quality would begin with a strong effort of measuring it and this thesis has tried to develop a scale for measuring the prominence of training in CMM level 5 Indian IT and ITes industries. Various factors were identified and the relationships were also studied. As a part of the study, a model of the relationship was also proposed. To further confirm the relationship between these variables, hypotheses were formulated and they were tested with the latest statistical tools for confirmation. To arrive at a conclusion, all the variables and factors were conceptualised on the strength of established theory and were measured using suitable indicators based on the response of the respondents by conducting a survey using structured questionnaires. The study concludes that to enhance the training quality it is mandatory that training quality with respect to the content, delivery, place and trainer need to be upgraded, and the perceived benefits of the training have to be made well aware to the trainees to create a positive image in the mind of the trainees before the training program.
In a natural ecosystem, understanding the difficulties of the wildlife surveillance is helpful to protect and manage animals also gain knowledge around animals count, behaviour and location. Moreover, camera trap images allow the picture of wildlife as unobtrusively, inexpensively and high volume it can identify animals, and behaviour but it has the issues of high expensive, time consuming, error, and low accuracy. So, in this research work, designed a novel wildlife surveillance framework using DCNN for accurate prediction of animals and enhance the performance of detection accuracy. The executed research work is implemented in the python tool and 2700 sample input frame datasets are tested and trained to the system. Furthermore, analyze whether animals are present or not using background subtraction and features extracted is performed to extract the significant features. Finally, classification is executed to predict the animal using the fitness of seagull. Additionally, attained results of the developed framework are compared with other state-of-the-art techniques in terms of detection accuracy, sensitivity, F-measure and error.
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