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In India, more than 70% of people depend upon agriculture. In India, agricultural practices were introduced in 9000 BCE that led to the early cultivation of plants as well as the domestication of crops and animals. Between the 18th and 19th century, the agricultural practices in India spread widely, promoting the different farming techniques. After independence, our country has made great progress in food productivity and food security as well as a substantial increase in the availability of food grains. The government of India also promoted innovative technologies and policies in the agricultural sector. Therefore, different high-yield crops were promoted to increase nutritious food production and boost local economies. Through agriculture, rural areas have been developed by implementing new technologies and smart tools. In rural areas, there are several issues in agriculture, transport, water, connectivity, health, and education. To solve these issues, the government promoted lots of technologies and policies by the help of which the development of rural areas can be boosted up. In this chapter, we will discuss the contribution of the Internet of Things (IoT) in poverty reduction, increasing the local economy, enhancing farming techniques, and promoting the sustainable development of rural areas. IoT controls crop management, waste management, irrigation systems, energy management, and other field works easily. IoT supports poverty alleviation and increasing the living standards of people in rural areas. This discussion provides a vast study on IoT in agriculture for sustainable rural development.

Machine Learning (ML) is a branch of Artificial Intelligence that enables computer systems to learn from past experiences and improve accordingly without the direct intervention of the programmer. ML enables machines to behave very similarly to human beings. In order to extract the required information from the huge amount of data, ML can be used to design algorithms based on the trends of data and relationships among the data. ML can be applied in various fields such as intrusion detection, bioinformatics, health care, marketing, game playing, and so on. It enables the computers or the machines to make data-driven decisions rather than being explicitly programmed for carrying out a certain task. These programs or algorithms are designed in a way that they learn and improve over time when they are exposed to new or unseen data. Due to the huge amount of data, the significance of ML can be seen in various sections of the society. Especially in industries, ML is assisting exploration of the hidden patterns of the data, and through this the overall performance of the business can be improved. It is cost-effective, affordable, and simple computing techniques allow the analysis and handling of a huge amount of complex data. ML is not only helping to understand and identify the hidden patterns of a diverse set of data but also encourages automation in analysis in place of humans. Also, ML is helping industries to avail of the opportunities and make it profitable in future endeavors. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning, and types of classification. Then, details of the ML architecture and framework are discussed.

Modern lifestyle and the polluted environment are the main causes of different types of diseases. Some diseases are curable through primary medication but some are more severe and require proper medication. In clinical treatment, different categories of medicines such as allopathic, homeopathy, herbal, art therapy, homemade medicine, etc. are applied to cure the diseases. The prediction of an appropriate medicine as per the symptoms of the disease is a challenging task for the clinicians. In this context, intelligent systems could be very helpful to predict the right medicine to the right people. Artificial Intelligence (AI) is a kind of intelligent system that applies different techniques to work with a huge amount of data for real-time analysis and better prediction to attain the required outcome. Also in the medicine industry, the process of discovering new medicines needs several clinical trials and requires approval by the concerned authority to deploy in the market. AI can improve decision-making and assist in the search for better medicines. Machine Learning is another revolution from AI that learns from the preexisting data sets and improves its accuracy in decision-making. This chapter presents a detailed literature survey on different AI techniques, followed by recent developments and applications in the medical industry.

The era of the Internet of Things (IoT) is sweeping over and replacing the Internet creating a world where smart things exist connected to each other intelligently. This was predicted by Eric Emerson Schmidt, the former C.E.O. of Google over 20 years ago. The physical world is now connecting to the digital world so quickly with the emergence of the IoT that it seems the Internet will become invisible soon, meaning the physical world will be connecting to the digital world seamlessly. The world will enjoy smart connectivity in the same way that the city of Barcelona has emerged to be the smartest city in the world. We are moving toward system-to-system connection, with smart networking reaching its peak. The idea of software-defined autonomous machines is about to become hugely important, which will become ubiquitous. With the advent of the IoT, we explore how it is becoming a reality and whether it has any limits. Maciej Kranz in his book on the IoT explains the very essential detailed and inclusive idea of the IoT, with IoT expanding to businesses, and covering and impacting on a variety of technology areas. Artificial intelligence and machine learning have a huge scope because of the enormous data generated by sensors and devices connected through the IoT. We will explore in this chapter the hype around the IoT and the reality. We will also discover improved metrics in the IoT that is allowing it to be a leader in the technological world. We are witnessing the fourth revolution in the digitization world and discuss the reasons behind its exponential growth. The protocols that differentiate them from others have evolved for IOT in a new set of patterns. This also creates security concerns and data are described as the new oil, raising further challenges of data privacy.