This paper provides a thorough analysis of recent developments in radio frequency identification (RFID)-based biosensors and their uses in the medical industry. The most recent and cutting-edge technologies used in RFID sensors, as well as their operational procedures in the medical industry, are the main focus of this article. The most effective design methodology for RFID biosensors is determined by evaluating various design methodologies. The present state of the art technologies in this domain are also analyzed and discussed. This paper aims to support researchers in the development of inexpensive and widely available RFID-based biosensors that can improve medical and healthcare outcomes. The use of RFID biosensors in contemporary medical applications has demonstrated significant promise for enhancing patient care and facilitating the early diagnosis of a number of diseases. It is anticipated that RFID biosensor technology will maintain to play a significant role in the development of the medical field as it undergoes further development.

Maximisation of influence propagation is a key ingredient to any viral marketing or socio-political campaigns. However, it is an NP-hard problem, and various approximate algorithms have been suggested to address the issue, though not largely successful. In this paper, we propose a bio-inspired approach to select the initial set of nodes which is significant in rapid convergence towards a sub-optimal solution in minimal runtime. The performance of the algorithm is evaluated using the re-tweet network of the hashtag #KissofLove on Twitter associated with the non-violent protest against the moral policing spread to many parts of India. Comparison with existing centrality based node ranking process the proposed method significant improvement on influence propagation. The proposed algorithm is one of the hardly few bio-inspired algorithms in network theory. We also report the results of the exploratory analysis of the network kiss of love campaign.

This paper focuses on the construction of IoT enabled mobile robot with an arm which can reach the destination autonomously and perform suitable actions in an indoor environment. Object detection and optimal navigation are the required features of a mobile robot that will be achieved through the combined architecture of building necessary Deep Learning and Reinforcement Learning models workable in a lesser memory space. To facilitate navigation in an indoor environment, initially, the environment has been mapped into a minimum number of grids for the experimental purpose. For handling huge memory requirement to run the models for processing, we occasionally transfer required intelligence from cloud setup to RPi, where RPi act as a Fog node in Industry 4.0 environment. The practicality of the robot has been gauged in three different cases (i) where the destination of the robot is known with 100% probability, (ii) where the destination of the robot is uncertain i.e. with lower probability and (iii) the destination is not known. In the first two cases, the objects are assumed to be stationary. Whereas in the third case, the objects can also be dynamic i.e. moving objects. As an application we have chosen Indoor Plant Monitoring System, where the objective is to measure the readings like Soil Moisture, Temperature, etc., of the indoor plant and forward the readings to Ericsson's IOT Accelerator platform. After analyzing the sensor values, a robot arm can initiate specific actions on its own. Here, the application of AI algorithms will not only help the robot to reach the destination, but it also triggers the robot to perform the functions optimally. As an experiment, we have studied the effect of learning rate on the total number of actions and introduces optimal reward from start to end of a journey in 4X4 grid world environment and finally tested for tangible performance towards navigation and object detection.