ABSTRACT
COVID-19 has sparked a global pandemic, with a variety of inflamed instances and deaths increasing on an everyday basis. Researchers are actively increasing and improving distinct mathematical and ML algorithms to forecast the infection. The prediction and detection of the Omicron variant of COVID-19 brought new issues for the health fraternity due to its ubiquity in human beings. In this research work, two learning algorithms, namely, deep learning (DL) and machine learning (ML), were developed to forecast the Omicron virus infections. Automatic disease prediction and detection have become crucial issues in medical science due to rapid population growth. In this research study, a combined Extended CNN-RNN research model was developed on a chest CT-scan image dataset to predict the number of +ve and -ve cases of Omicron virus infections. The proposed research model was evaluated and compared against the existing system utilizing a dataset of 16,733-sample training and testing CT-scan images collected from the Kaggle repository. This research article aims to introduce a combined ML and DL technique based on the combination of an Extended Convolutional Neural Network (ECNN) and an Extended Recurrent Neural Network (ERNN) to diagnose and predict Omicron virus-infected cases automatically using chest CT-scan images. To overcome the drawbacks of the existing system, this research proposes a combined research model that is ECNN-ERNN, where ECNN is used for the extraction of deep features and ERNN is used for exploration using extracted features. A dataset of 16,733 Omicron computer tomography images was used as a pilot assessment for this proposed prototype. The investigational experiment results show that the projected prototype provides 97.50% accuracy, 98.10% specificity, 98.80% of AUC, and 97.70% of F1-score. To the last, the study outlines the advantages being offered by the proposed model with respect to other existing models by comparing different parameters of validation such as accuracy, error rate, data size, time complexity, and execution time.
ABSTRACT
A rising proportion of older people has more demand on services including hospitals, retirement homes, and assisted living facilities. Regaining control of this population's expectations will pose new difficulties for lawmakers, medical professionals, and the society at large. Smart technology can help older people to have independent and fulfilling lives while still living safely and securely in the community. In the last several decades, the number of sectors using robots has risen. Comrade robots have made their appearance in old human life, with the most recent notable appearance being in their care. The number of elderly individuals is increasing dramatically throughout the globe. The source of the story is the use of robots to help elderly people with day-to-day activities. Speech data and facial recognition model are done with AI model. Here, with the Comrade robotic model, elder people's healthcare system is designed with better analysis state. The aim is to put in place a simple robotic buddy to determine the health of the old person via a headband that has been given to them. Comrade robot may do things like senior citizens home automation, home equipment control, safety, and wellbeing sensing, and, in emergency situation, routine duties like navigating in the outside world. The fear that robotics and artificial intelligence would eventually eliminate most of the jobs is increasing. It is anticipated that, in order to survive and stay relevant in the constantly shifting environment of work, workers of the future will need to be creative and versatile and prepared to identify new business possibilities and change industry to meet challenges of the world. According to the research, reflective practice, time management, communicating, and collaboration are important in fostering creativity.
