The healthcare landscape is rapidly evolving with the integration of advanced technologies to enhance patient care, monitoring, and overall medical practices. In this era of innovation, Light-Fidelity (Li-Fi) has emerged as a promising solution with the potential to revolutionize patient monitoring systems. This research aims to address current limitations in Li-Fi-based patient monitoring systems, such as data security concerns and the inability to provide continuous monitoring without on-site medical personnel. It is driven by the urgent need to tackle critical healthcare challenges arising from a significant shortage of medical personnel, particularly in certain regions and countries. The objective is to develop a Li-Fi-based patient monitoring system that can remotely and continuously monitor patient vital signs and medical data. The methodology involves a comprehensive approach that integrates advanced technology, data collection, data processing, and web application development. Results indicate that the developed system prioritizes performance and security, with evaluations based on latency, security vulnerabilities, and data throughput. This research advances Li-Fi's potential in healthcare, paving the way for innovative applications that can enhance patient care, improve healthcare outcomes, and potentially transform the entire healthcare industry.

Farmland security in Nigeria is still a major challenge and existing methods such as building brick fences around the farmland, installing electric fences, setting up deterrent plants with spikey branches or those that have displeasing scents are no longer suitable for farmland security. This paper presents an IoT based farmland intrusion detection model using sensors and computer vision techniques. Passive Infrared (PIR) sensors and camera sensors are mounted in strategic positions on the farm. The PIR sensor senses motion by the radiation of body heat and sends a message to the raspberry pi to trigger the camera to take a picture of the scene. An improved Faster Region Based Convolutional Neural Network is developed and used for object detection and One-shot learning algorithm for face recognition in the case of a person. At the end of the detection and recognition stage, details of intrusion are sent to the farm owner through text message and email notification. The raspberry pi also turns on the wade off system to divert an intruding animal away. The model achieved an improved accuracy of 92.5% compared to previous methods and effectively controlled illegal entry into a farmland.