With the recent penetration and proliferation of social networks into our lives, human choices and preferences have become more socially accessible. This easy accessibility of private data in different formats has opened many new initiatives. The big explosion of multimedia data on the web has enabled social networks to gauge user likes, dislikes, and needs. This has imposed high demands on multimedia information retrieval (MIR) techniques. This manuscript illustrates the MIR concept in terms of its application to social media. It further positions the current research in the field of 3D MIR. Further it highlights the challenges in 3-D MIR on social media and finally translates them into significant research directions.

The proliferation of e-commerce platforms has provided customers with a level of convenience never seen before, but it also raises serious questions about how sensitive consumer data is protected. This chapter explores the complex interplay between two emerging technologies, AI and blockchain, with the goal of strengthening consumer data protection on e-commerce platforms. It examines innovative and forward-thinking solutions that handle the complexities of data governance as well as the changing legal aspects of the e-commerce industry by exposing the numerous difficulties and challenges that occur in this confluence. The protection of consumer data is one of the biggest issues facing e-commerce. The combination of blockchain technology and artificial intelligence (AI) acts as a lighthouse, pointing the way for the e-commerce sector toward a more transparent, safe, and morally upright future.

In present age of computers, there are various resources for gathering information related to given query like Radio Stations, Television, Internet and many more. Among them, Internet is considered as major factor for obtaining any information about a given domain. When a user wants to find some information, he/she enters a query and results are produced via hyperlinks linked to various documents available on web. But the information that is retrieved to us may or may not be relevant. This irrelevance is caused due to huge collection of documents available on web. Traditional search engines are based on keyword based searching that is unable to transform raw data into knowledgeable representation data. It is a cumbersome task to extract relevant information from large collection of web documents. These shortcomings have led to the concept of Semantic Web (SW) and Ontology into existence. Semantic Web (SW) is a well defined portal that helps in extracting relevant information using many Information Retrieval (IR) techniques. Current Information Retrieval (IR) techniques are not so advanced that they can be able to exploit semantic knowledge within documents and give precise result. The terms, Information Retrieval (IR), Semantic Web (SW) and Ontology are used differently but they are interconnected with each other. Information Retrieval (IR) technology and Web based Indexing contributes to existence of Semantic Web. Use of Ontology also contributes in building new generation of web- Semantic Web. With the help of ontologies, we can make content of web as it will be markup with the help of Semantic Web documents (SWD’s). Ontology is considered as backbone of Software system. It improves understanding between concepts used in Semantic Web (SW). So, there is need to build an ontology that uses well defined methodology and process of developing ontology is called Ontology Development

Ontology has found application in multiple domains to represent relationships among classes and their instances. Ontology is being popularly applied as a tool for vocabulary conceptualization and optimization. To build vocabulary it requires mapping and merging of ontologies from heterogeneous sources of data. Many varied tools and techniques have been developed by researchers for ontology mapping and merging. This manuscript is an attempt to analyze and appreciate the tools available for ontology mapping and merging. The varied available tools automate integration, unification, aligning and merging of varied related ontologies. We also attempt to draw an efficient analysis among some popular tools while applying the PROMPT tool.