Retraction: [Nitin Nandkumar Sakhare, Imambi S. Shaik, Suman Saha, Prediction of stock market movement via technical analysis of stock data stored on blockchain using novel History Bits based machine learning algorithm, IET Software 2023 ( https://doi.org/10.1049/sfw2.12092 )]. The above article from IET Software , published online on 12 January 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

The chapter explores the novel convergence of IoT and healthcare to tackle the urgent worldwide issue of cardiovascular illnesses. It becomes clear that this is an advanced framework with a user-cantered approach intended for the earlier diagnosis and therapy of cardiovascular infections. In order to enable real-time data gathering and analytics, the chapter offers a thorough examination of how wearable technology, fitness tracking sensors, and cloud computation are integrated. The approach presented by the author utilises ML algorithms and advanced data analysis to provide predictive modelling in the earlier identification of cardiovascular risk factors. It illustrates the observable progress in early illness detection via case studies. In order to provide smooth communication between healthcare practitioners and equipment, this chapter promotes a linked health ecosystem.

Due to rise in number of vehicles the traffic management has become a major problem. Manual traffic system is not efficient. This paper presents adaptive traffic management system using Internet of Things (IoT) and Image processing. The proposed system has capability to analyze real time data using image processing. Using cameras, different lanes are monitored constantly. The data obtained from different lanes are examined. Detection and counting of number of vehicles in each lane is done by using image processing. The count from each lane is sent to the central processing unit. According to the count of vehicles algorithm calculates waiting time for each lane, then the signal lights will be decided. This system reduces the average waiting time and increases the efficiency of traffic clearance. The system also reduces the pollution due CO2 emission and useful in emergency situations, thus being adaptive traffic management using Internet of Things (IoT).

In the realm of robotics and artificial intelligence, researchers often draw inspiration from the intricate and efficient systems found in the animal kingdom. For instance, the study of swarm intelligence, inspired by the collective behavior of insects like ants or bees, has led to the development of algorithms for autonomous drones and robotic systems that can collaboratively solve complex problems. The adaptation of nature's strategies for smart systems showcases the potential for interdisciplinary collaboration between biology and technology. By understanding and emulating the efficiency, adaptability, and sustainability observed in natural systems, scientists and engineers continue to push the boundaries of innovation, creating smart systems that are not only technologically advanced but also environmentally conscious and resilient.

In this chapter, there are very novel techniques in which, by deleting nodes that are either overloaded or underloaded and then reassigning the total load to the collective system's nodes, it is possible to maximise the usage of resources and the amount of time it takes for tasks to be completed. The approaches that are utilised for dynamic load balancing are based on the behaviour of the system as it is being utilised right now, as opposed to the behaviour of the system as it was being utilised in the past. When constructing an algorithm of this kind, the most essential considerations to give attention to are the estimation and comparison of load, the stability and performance of the system, the interaction between nodes, the amount of work that needs to be transmitted, and the choice of nodes.