Abstract
In this study, we detail the use of a small, label-free optoelectronic biosensor for the detection of anti-dengue antibodies in human serum samples. The system consists of a vertical-cavity surface-emitting laser that can be tuned, a guided-mode resonant sensor surface, and two silicon
pin detectors. After showing adequate sensitivity in a clinically relevant experiment, researchers hypothesized that this cutting-edge biosensor could serve as a novel platform for the development of efficient point-of-care diagnostic techniques for the identification of infectious diseases.
Humans are susceptible to one of the world's most common and contagious diseases, dengue fever, which is spread by the Aedes albopictis mosquito. Human deaths were also a direct outcome of the widespread increase in Dengue fever cases. A shortage of medical professionals and healthcare
facilities only made matters worse. In order to achieve this goal, it will be necessary to employ archaic medical technologies. Recent innovations like Fog Computing and the success of remote healthcare in real time, Cloud Computing, and the Internet of Things have opened up new frontiers
in technology (IoT). In this research, we proposed a new method for diagnosing dengue disease. Information gathered from those who have been afflicted by a disease will be shared with higher authorities. Various IoT devices compile comprehensive reports on each and every patient. Once medical
history is gathered, the patient's whereabouts must be ascertained. Sensors connected to the internet are used to gather information on the weather, the location, the effectiveness of medications, the safety of the surrounding environment, and the patients' states of health. The
fog computing layer is the bridge between IoT sensors and cloud servers. Two primary functions of the fog computing layer are the creation of notifications and the categorization of users' health conditions. Dengue fevers are diagnosed using an Artificial Neural Network (ANN) trained
with the Salp Swarm Optimization method Salp swarm algorithm (SSA). The dataset will be analyzed using an Internet of Things (IoT) scenario built with the Java simulator CupCarbon U-one 3.8.2. The proposed method achieves competitive or better outcomes than the state-of-the-art alternatives.