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Federation analytics approaches are a present area of study that has already progressed beyond the analysis of metrics and counts. It is possible to acquire aggregated information about on-device data by training machine learning models using federated learning techniques without any of the raw data ever having to leave the devices in the issue. Web browser forensics research has been focused on individual Web browsers or architectural analysis of specific log files rather than on broad topics. This paper aims to propose major tools used for Web browser analysis.

Each kind of Web browser has its own unique set of features. This allows the user to choose their preferred browsers or to check out many browsers at once. If a forensic examiner has access to just one Web browser's log files, he/she makes it difficult to determine which sites a person has visited. The agent must thus be capable of analyzing all currently available Web browsers on a single workstation and doing an integrated study of various Web browsers.

Federated learning has emerged as a training paradigm in such settings. Web browser forensics research in general has focused on certain browsers or the computational modeling of specific log files. Internet users engage in a wide range of activities using an internet browser, such as searching for information and sending e-mails.

It is also essential that the investigator have access to user activity when conducting an inquiry. This data, which may be used to assess information retrieval activities, is very critical. In this paper, the authors purposed a major tool used for Web browser analysis. This study's proposed algorithm is capable of protecting data privacy effectively in real-world experiments.

The COVID-19 virus outbreak began in December 2019 and rapidly spread to every continent on Earth. The analysts have predicted that COVID-19 and other similar pandemics will continue in the coming decade and badly affect offline businesses. As a result, the offline platform is also shifting to the online platform and online demands are increasing daily. The traditional two-dimensional E-Commerce websites are designed to provide simple, browser-based interfaces to allow users to access available products and services. Whilst virtual representations are an essential consideration in establishing trust, most virtual representation sites fall short in mimicking real-life human representation. This paper aims to focus on three-dimensional (3D) E-Commerce technology that presents how virtual reality (VR) and augmented reality (AR) can help deal with limitations and improve E-Commerce operations. It is built as an internet-only tool, a person-centred shopping assistant created following user-centred design principles to be used on various computing platforms, including desktop and mobile devices. The paper shows how VR and AR can offer more precise product information in 3D E-Commerce environments. The virtual store experience is also enhanced by an AR assistant that helps the users by giving them all the required information in audio form or using its avatar.

Implementation of VR and AR in E-Commerce will increase customer satisfaction. Sub hypothesis – to study the implementation of VR in E-Commerce. To study the implementation of AR in E-Commerce. To study the inclusion of E-Commerce sites in an open-world game. To study the customer satisfaction of users using VR stores.

The scope of work is concentrated on the urban Indian market especially targeting the country’s youth who are already or ready to indulge in VR such as video games, cinema and other activities (Mattsson and Barkman, 2019). This demography is more open to learning and using VR. The primary segment of E-Commerce that we are concentrating upon is fashion. Here, the regular user needs to have more immersed knowledge about the product rather than just the written information like how would they look in a dress or will the size available on the website fit me or not.

A perfect system does not exist in the world. A terrible disease has landed on the planet. Very soon, it will be impossible to escape from this current situation. The effects of this plague have been felt in every sector of the world. The researchers also claim that physical stores will continue to exist. There will never be anything that replaces the ability to hold and use products or have personal face-to-face interactions with retail professionals. For the time being, brick-and-mortar retail is having a difficult time, but immersive technology is starting to be used to enhance the in-store experience. The good news is that this should help retailers increase their chances of survival. However, the melody of 3D E-Commerce is it would help out the in-store experience.

“Machine learning (ML)” in business aids in increasing company scalability and boosting company operations for businesses all over the world. “Artificial intelligence (AI)” technologies and several “ML” algorithms have grown in prominence in the business analytics sector. In the era of a huge quantum of data being generated by the virtue of the integration of the various software with the business operations, the relevance of “ML” is continuously increasing. As a result, companies may now profit from knowing how companies may use “ML” and incorporating it into their own operations. “ML” derives useful results from the data to address very dynamic and difficult social and business problems. ML helps in establishing a system that learns automatically and produces results in less time and effort, allowing machines to discover. ML is developing at a breakneck pace, fuelled mostly by new computer technology to competitive advantages during the COVID pandemic.

For firms all around the world, “ML” in business aids in expanding scalability and boosting operations. In the field of business analytics, artificial intelligence (AI) and machine learning (ML) algorithms have become increasingly popular. The importance of “ML” is growing in an era when a massive amount of data is generated as a result of the integration of various applications with company activities. As a result, businesses can now benefit from understanding how other businesses are using “ML” and adopting it into their own operations. In order to handle very dynamic and demanding societal and business challenges, machine learning (ML) extracts valuable results from data. Machine learning (ML) aids in the development of a system that learns automatically and generates outcomes with less time and effort, allowing machines to discover. ML is progressing at a dizzying pace, fueled primarily by new computer technology and used to gain competitive advantages during the COVID pandemic.

According to a new study published by the Accenture Institute for High Performance, “AI” might double yearly economic growth rates in several wealthy nations by 2035. With broad AI deployment, the yearly growth rate in the USA increased from 2.6% to 4.6%, resulting in an extra $8.3tn. In the UK, AI may contribute $814bn to the economy, raising the yearly growth rate from 2.5% to 3.9%. The authors are already in a business period when huge technological development is assisting us in addressing a variety of difficulties to achieve maximum development. AI technology has enormous developmental consequences. In addition, big data analytics is helping to make AI more enterprise ready. Future developments in “ML” cannot be understated. Machines will very certainly eventually be smarter than humans in practically every way.

The introduction of AI into the market has enabled small businesses to use tried-and-true strategies for achieving greater business objectives. AI is continually offering a competitive advantage to start-ups, whilst large corporations provide a platform for building novel solutions. AI has become an integral component of reality, from functioning as a robot in a production unit to self-driving automobiles and voice activated resources in complex medical procedures. As a consequence, solving the difficulties highlighted below and finding out how to collaborate with robots will be a constant problem for the human species (Sujaya and Bhaskar, 2021).