ABSTRACT
Healthcare Informatics is a phenomenon being talked about from the early 21st century in the era in which we are living. With evolution of new computing technologies huge amount of data in healthcare is produced opening several research areas. Managing the massiveness of this data is required while extracting knowledge for decision making is the main concern of today. For this task researchers are doing explorations in big data analytics, deep learning (advanced form of machine learning known as deep neural nets), predictive analytics and various other algorithms to bring innovation in healthcare. Through all these innovations happening it is not wrong to establish that disease prediction with anticipation of its cure is no longer unrealistic. First, Dengue Fever (DF) and then Covid-19 likewise are new outbreak in infectious lethal diseases and diagnosing at all stages is crucial to decrease mortality rate. In case of Diabetes, clinicians and experts are finding challenging the timely diagnosis and analyzing the chances of developing underlying diseases. In this paper, Louvain Mani-Hierarchical Fold Learning healthcare analytics, a hybrid deep learning technique is proposed for medical diagnostics and is tested and validated using real-time dataset of 104 instances of patients with dengue fever made available by Holy Family Hospital, Pakistan and 810 instances found for infectious diseases including prognosis of; Covid-19, SARS, ARDS, Pneumocystis, Streptococcus, Chlamydophila, Klebsiella, Legionella, Lipoid, etc. on GitHub. Louvain Mani-Hierarchical Fold Learning healthcare analytics showed maximum 0.952 correlations between two clusters with Spearman when applied on 240 instances extracted from comorbidities diagnostic data model derived from 15696 endocrine records of multiple visits of 100 patients identified by a unique ID. Accuracy for induced rules is evaluated by Laplace (Fig. 8) as 0.727, 0.701 and 0.203 for 41, 18 and 24 rules, respectively. Endocrine diagnostic data is made available by Shifa International Hospital, Islamabad, Pakistan. Our results show that in future this algorithm may be tested for diagnostics on healthcare big data.
ABSTRACT
Cyber-physical system (CPS) is one of the leading topics for research in academic and industry fields. CPS is an integrated system built with a collection of computation, communication, control, and physical elements to solve real-life problems. Lots of research is going on CPS, but in today’s point of view, the covid-19 is one of the most relevant. Nowadays, COVID -19 has become a headache in our society. Social or physical distancing is one of the most useful non-pharmaceutical interventions (NPI) to minimize virus infections. The regular lifestyle of every human being has been changing rapidly. A contactless lifestyle is becoming a necessity day by day. Society is gradually dependent upon smart technological devices for a contactless lifestyle. In the new-normal lifestyle, many new technologies have been introduced. The government also makes some restrictions on human transmission. However, maintaining social distancing is one of the main challenges of our society. There is no such model that effectively helps people to maintain physical distancing. This paper highlights a framework that will guide maintaining physical distance in a social gathering. The proposed CPS-based model is entirely deployed on Edge and Fog computing architecture. The proposed model calculates the distance between all paired edge devices owned by human beings and informs the user whether the location is safe or not. This Fog and Edge-based model improves the latency and network usage compared to the Cloud computing module.