Artificial intelligence (AI) mimics or stimulates human behaviors or thinking to solve specific problems. It has been applied in the analysis of huge datasets and provides reliable outputs without human supervision in various online platforms, for example, information retrieval in search engines, digital assistants, voice assistants, digital marketing, personalized learning, social media, etc. This technology has provided many opportunities and challenges in line with strengthening the authenticity of the information provided via different search engines. This chapter reviews the current pieces of literature about the different AI algorithms used in the most popular metasearch engines and the application of artificial intelligence in these search engine contexts.

Data privacy is an intricate job and is becoming a key area of research as far as cloud technologies are concerned. This is because the information is massively generated from many sources. It's collected, shared, and disseminated in many different segments. Due to this factor, countless individuals or organizations have absconded the cloud services despite the fact of their endless fruitiness benefits. Providing security to this data is a major concern. There are numerous ways this data can be protected from unauthorized users. Hence, data security and privacy are becoming very important fields of research for the future development and improvement of cloud technologies for the government, business, and industry. Data privacy protection issues are relevant to both hardware and software in the cloud architecture. Therefore, this study will review different data privacy concepts, data access protection methods, approaches to manage data privacy, data privacy techniques, challenges faced during data access, and some research trends in data privacy.

The age of autonomous sensing has dominated almost every industry today. Our lives have been engaged with multiple sensors embedded in our smartphones to achieve sensing of all sorts starting from proximity sensing to social sensing. Our possessions (cars, fridges, oven) have sensors embedded in them. The art of autonomous IoT has shifted from a mere detection of events or changes in the environment to dominant systems for social sensing, big data analytics, and smart things. Recently, sensing systems have adapted connectivity resulting in input mechanisms for big data analytics and smart systems resulting in pervasive systems. Currently, a range of sensors has come to existence, for example, mobile phone sensors that measure blood pressure at patients' figure tip, or the sensors that be used to detect deforestation. In this chapter, the authors provide a technical view upon which autonomous IoT devices can be implemented and enlist opportunities and challenges of the same.

This is an interesting time to innovate around disruptive technologies like the internet of things (IoT), machine learning, blockchain. Autonomous assistants (IoT) are the electro-mechanical system that performs any prescribed task automatically with no human intervention through self-learning and adaptation to changing environments. This means that by acknowledging autonomy, the system has to perceive environments, actuate a movement, and perform tasks with a high degree of autonomy. This means the ability to make their own decisions in a given set of the environment. It is important to note that autonomous IoT using radio frequency identification (RFID) technology is used in educational sectors to boost the research the arena, improve customer service, ease book identification and traceability of items in the library. This chapter discusses the role, importance, the critical tools, applicability, and challenges of autonomous IoT in the library using RFID technology.

Classification is a data mining technique or approach used to estimate the grouped membership of items on a basis of a common feature. This technique is virtuous for future planning and discovering new knowledge about a specific dataset. An in-depth study of previous pieces of literature implementing data mining techniques in the design of recommender systems was performed. This chapter provides a broad study of the way of designing recommender systems using various data mining classification techniques of machine learning and also exploiting their methodological decisions in four aspects, the recommendation approaches, data mining techniques, recommendation types, and performance measures. This study focused on some selected classification methods and can be so supportive for both the researchers and the students in the field of computer science and machine learning in strengthening their knowledge about the machine learning hypothesis and data mining.