With the rapid development of web applications, and with the comfort provided by these web applications, internet users utilize this benefits to a great extent that they make almost all their day to day activities such as news paper reading, shopping, electricity bill payment, ticket booking and entertainment with the help of the internet. This phenomenon forces the users of the internet to get connected with the internet for a prolonged time and hence it increases the chances of the users to get caught in the web of phishing - an attack crafted by hackers to steal sensitive information by tempting the users with lucrative offers initially and then redirecting them to a fraudulent website(which the user may not suspect) where they can deceive the user by asking them to submit their credentials(usually users submit their credentials without knowing that these are fake offers created with a sole intention of stealing sensitive information). In spite of the alert and awareness given by the web community in this regard, more and more phishing artist succeed in their attack. Also these phishing artist develop novel attacks such as tab nabbing, website impersonation etc that attracts more and more internet user to be caught in the web of phishing. However many tools and methodologies have been developed to prevent phishing and to alert users orally and visually. But still the success rates of the phishing attack remains high and also the approaches related to phishing detection suffers high false positive and false negative ratio. In this paper various tools and methodologies used to prevent phishing has been analyzed and an efficient mechanism has been proposed to prevent phishing.

Person re-identification (Re-ID) emerges as a captivating realm within computer vision, dedicated to the task of recognizing the same individual across diverse camera angles or locations. The realm of video-based person re-identification (video re-ID) has recently captivated increasing interest, owing to its wide array of practical applications spanning surveillance, smart city solutions, and public safety measures. Nevertheless, video re-ID proves to be a formidable challenge, an ever-evolving domain fraught with a multitude of uncertainties like viewpoint variations, occlusions, pose changes, and unpredictable video sequences. Over the past few years, the realm of deep learning applied to video re-ID has consistently delivered remarkable outcomes on public datasets, showcasing a range of innovative strategies devised to tackle the array of issues encountered in video re-ID. In stark contrast to image-based re-ID, video re-ID stands out as significantly more intricate and demanding. In a bid to inspire forthcoming research endeavors and confronts emerging challenges, this paper presents a comprehensive overview of the latest advancements in deep learning methodologies tailored for video re-ID. It delves into three crucial facets; encompassing succinct explanations of video re-ID techniques along with their constraints, pivotal breakthroughs coupled with the technical hurdles faced, and the architectural framework underpinning these developments. The paper further furnishes a comparative analysis of performance across diverse datasets, offers insightful guidance on enhancing video re-ID strategies, and outlines compelling avenues for future research exploration.

Data analysis is a process of studying, removing non-required data in the view level, and converting to needed patterns for sub decisions to make an aggregated decision. Statistical modeling is the process of applying statistical techniques in data analysis for taking proactive decisions depend requirements. The statistical modeling identifies relationship between variables, and it encompasses inferential statistics for model validation. The focus of the chapter is to analyze statistical modeling techniques in different contexts to understand the mathematical representation of data. The correlation and regression are used for analyzing association between key factors of companies' activities. Especially in business, correlation describes positive and negative correlation variables for analyzing the factors of business for supporting the decision-making process. The key factors are related with independent variables and dependent variables, which create cause and effect models to predict the future outcomes.

Peer-reviewed journal on social and business aspects of informatics that covers the theory and application of enterprise information systems, as well as interactions between ICT and people.