Rule Based Medicine Recommendation for Skin Diseases Using Ontology with Semantic Information

Generally, skin diseases are taken seriously only after it gets aggravated. Many patients do not feel comfortable to consult physician for their skin problems and try to cure with home remedies. Identifying good dermatologist is more challenging. Disease gets cured with ease, if treated properly otherwise it becomes complicated. To handle this, knowledge-based decision support system is developed which provide recommendation for treatment. System realizes patient’s symptoms with location, cause and recommends appropriate medicines. Ontology is used to elaborate data concepts and their relationships in skin disease, which permits sharing and reuse of domain knowledge. Semantics described using Web Ontology Language (OWL) with vocabularies, resources, logic’s, and inference rules are queried for relevant information through SPARQL. System proved to render most valuable service to skin patients with ease and accuracy of its recommendation is high. It renders best treatment in COVID situation where people’s movement is restricted to great extent. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Prediction of Priority to Individual for COVID Vaccine Distribution Using Soft Computing Techniques

World is facing impact of COVID pandemic;every industry is facing corollaries to a great extent. With increase in number of COVID cases, it is necessary to control the spread of virus and wipe it out in the best possible manner. COVID-19, which have its rapid spread across the world, is a dangerous episode which entire community is cladding on. Proper treatment and efficient vaccine distribution are the major glitches of medical industry during this untoward outbreak. Priority in distribution of vaccine is extremely vital, to handle availability of vaccines. Our main objective is to develop methodologies with competing process which implements unsupervised k-means clustering and supervised naïve Bayes, decision tree and gradient boost classification algorithms to find the priority of individuals for distributing vaccines. Efficient pathway of distribution is implemented in layered manner based on individual’s priority, assessed in par with impact level of disease on individuals in the community. Priority for vaccine distribution is finalized with classification algorithm with the highest accuracy. It helps to distribute the available vaccines optimally among the population, and it plays major role in reducing the impact of coronavirus on individuals and society. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID Emergency Handlers to Invoke Needful Services Dynamically with Contextual Data

Current COVID pandemic situation has created an urgency in development of an intelligent mobile application to handle emergencies in most appropriate and efficient manner. We have created a life-saving system with contextual information gathering module, which provides needful services dynamically with limited details, i.e., click of a button. With installation of application and registration, basic details of user are stored in cloud storage. Application is designed to handle both COVID and normal emergencies. With invocation, contextual details like user’s location, nature of illness, type of service to be provided, hospital details, and ambulance details are realized by appropriate modules. By considering the nature of illness, department and respective hospital in the near proximity are realized. It tracks nearest ambulance service with necessary facilities to handle emergency situation, directs them to user’s location, thereby identifying the hospital. Simulated android application is created with firebase cloud storage, JSON, GPS, GSM, and Google Maps. It provides critical needs with appropriate facilities in shorter periods, a more prevalent service to handle COVID in a graceful manner. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.