The internet of things (IoT) is an important data source for data science technology, providing easy trends and patterns identification, enhanced automation, constant development, ease of handling multi-dimensional data, and low computational cost. Prediction in energy consumption is essential for the enhancement of sustainable cities and urban planning, as buildings are the world's largest consumer of energy due to population growth, development, and structural shifts in the economy. This study explored and exploited deep learning-based techniques in the domain of energy consumption in smart residential buildings. It found that optimal window size is an important factor in predicting prediction performance, best N window size, and model uncertainty estimation. Deep learning models for household energy consumption in smart residential buildings are an optimal model for estimation of prediction performance and uncertainty.

Artificial intelligence (AI) based disease identification has the potential to transform medicine by utilizing machine learning algorithms and techniques to analyze large volumes of medical data and identify patterns and features that may be difficult for human experts to detect. However, there are still challenges and limitations to overcome, such as the need for high-quality medical data and concerns around privacy and bias. This chapter explores the growing intersection of machine learning (ML) and AI techniques with disease prediction. The chapter begins by providing an overview of ML and AI methodologies commonly employed in disease prediction, including supervised and unsupervised learning algorithms, deep learning techniques, and ensemble methods. Lastly, the chapter outlines future directions and research opportunities in the field.

Agriculture and farming have gotten smarter as a result of the use of current technology such as Wireless Sensor Networks (WSN) and the Internet of Things (IoT). Smart farming is an enhanced agriculture system that offers data such as temperature, soil moisture, and so on, to assist in the growth of plants and cattle. It integrates wireless sensors and the internet to collect and communicate information with farmers. The priority event-based energy efficient algorithm developed in this study is utilized for accurate and efficient information transmission regarding power consumption and node priority. The major goal of the IoT-sensor network in this chapter is to increase farm productivity and extend its lifespan by applying intelligent algorithms such as Artificial Neural Network (ANN) to recognize environmental conditions and improve total production. Priority event-based energy efficient method reduces energy usage and increases the lifetime using Dijkstra's algorithm.

In this chapter, the various water monitoring systems—internet of-things, wireless network systems, cloud computing, optimization systems, and reporting systems—are illustrated. The development of artificial intelligence in water management has the potential to change and manage water resources. The use of AI and cutting-edge technologies such as data analytics, regression models, and algorithms simplifies the water management process. AI-based systems will be sustainable, cost-effective, and capable of predicting potential damage in order to optimize water management solutions with minimal waste. Machine learning algorithms support the water management process and improve the accuracy of sensor-based systems in agriculture.

This chapter discusses the implementation of data analytics and artificial intelligence (AI) in the circular economy. The case studies cover various domains, such as resource tracking and tracing, resource recovery, decision-making support systems, and machining optimization. The outcomes include improved supply chain management, extended product lifecycles, reduced waste generation, enhanced product quality, and cost savings. Data analytics and AI have the potential to shape a sustainable circular economy by optimizing resource utilization, improving processes, and enabling informed decision-making.

Electric vehicles have received a lot of exposure in recent years as their technical ability has improved. The transportation field is rapidly transitioning to electric vehicles in order to reduce carbon emissions in industry and reduce its reliance on petroleum. Many countries have established aims for increasing the percentage of electric vehicles on the road in the near future. Meanwhile, many incidences involving two-wheelers exploding into flames have concerned India's electric vehicle aficionados. The work is significant in that it analyses the economic impacts and causes of failure for battery installation, particularly for two-wheelers, in a developing nation like India. Even though several investigations have carried out to explore the social effects of EV adoption, limited information is available about the broad implementation of EVs that may influence both battery life and economic factors at the same time. By having a complete impact evaluation of EV adoption, this work aims to fill the research gap.