Healthcare sector is probably the most benefited from the applications of nanotechnology. The nanotechnology, in the forms of nanomedicine, nanoimplants, nanobiosensors along with the internet of nano things (IoNT), has the potential to bring a revolutionizing advancement in the field of medicine and healthcare services. The primary aim of this paper is to explore the clinical and medical possibilities of these different implementations of nanotechnology. This paper provides a comprehensive overview of nanotechnology, biosensors, nanobiosensors, and IoNT. Furthermore, multilevel taxonomies of nanotechnology, nanoparticles, biosensors, nanobiosensors, and nanozymes are presented. The potential medical and clinical applications of these technologies are discussed in details with several examples. This paper specifically focuses on IoNT and its role in healthcare. In addition to describing a general architecture of IoNT for healthcare, the communication architecture of the IoNT is also explained. The challenges in the successful realization of IoNT are also discussed critically, along with a special discussion on internet of bio-nano things (IoBNT) and its potential in making IoNT more compatible to human body.

In the field of Artificial Intelligence Machine learning provides the automatic systems which learn and improve itself from experience without being explicitly programmed. In this research work a movie recommender system is built using the K-Means Clustering and K-Nearest Neighbor algorithms. The movielens dataset is taken from kaggle. The system is implemented in python programming language. The proposed work deals with the introduction of various concepts related to machine learning and recommendation system. In this work, various tools and techniques have been used to build recommender systems. Various algorithms such as K-Means Clustering, KNN, Collaborative Filtering, Content-Based Filtering have been described in detail. Further, after studying different types of machine learning algorithms, there is a clear picture of where to apply which algorithm in different areas of industries such as recommender systems, e-commerce, etc. Then there is an illustration of how implementations and working of the proposed system are used for the implementation of the movie recommender system. Various building blocks of the proposed system such as Architecture, Process Flow, Pseudo Code, Implementation and Working of the System is described in detail. Finally, in this work for different cluster values, different values of Root Mean Squared Error are obtained. In this proposed work as the no of clusters decreases, the value of RMSE also decreases. The best value of RMSE obtained is 1.081648. The results given by the proposed system are better than the existing technique on the basis of RMSE value.

Smart world is envisioned via the fusion of diverse technologies like sensor communications, cloud computing, internet of things, AI, machine and deep learning. No doubt, new technologies bring revolution and innovation in every aspect of human life, but they are accompanied by lots of limitations in terms of energy wastage, environmental hazards like carbon or other chemical emissions, extreme consumption of natural or renewable sources and greenhouse effects. In order to minimize the negative impact of these technologies on the environment, it is utmost important to move towards green technology. That is the reason researchers are working hard and moving towards green computing, ICT, and IoT. This chapter explores an in-depth analysis of principles of G-IoT, making significant progress towards improvising the quality of life and sustainable environment. In addition to this, the chapter outlines various Green ICT technologies explores potential towards diverse real-time areas and also highlights various challenges acting as a barrier towards G-IoT implementation in the real world.