Wearable devices with 5G technology are currently more ingrained in our daily lives, and they will now be a part of our bodies too. The requirement for personal health monitoring and preventive disease is increasing due to the predictable dramatic increase in the number of aging people. Technologies with 5G in wearables and healthcare can intensely reduce the cost of diagnosing and preventing diseases and saving patient lives. This paper reviewed the benefits of 5G technologies, which are implemented in healthcare and wearable devices such as patient health monitoring using 5G, continuous monitoring of chronic diseases using 5G, management of preventing infectious diseases using 5G, robotic surgery using 5G, and 5G with future of wearables. It has the potential to have a direct effect on clinical decision making. This technology could improve patient rehabilitation outside of hospitals and monitor human physical activity continuously. This paper draws the conclusion that the widespread adoption of 5G technology by healthcare systems enables sick people to access specialists who would be unavailable and receive correct care more conveniently.

This paper investigates the integration of the Mamdani Fuzzy Regression for User Experience (MFR-UE) model with deep learning techniques to enhance predictive analytics in e-commerce platforms. Through empirical analysis, the study evaluates the effectiveness of this hybrid approach in classifying customer preferences and sentiments based on complex feature sets. Results from demonstrate the model’s capability: Sample ID 1, characterized by Feature 1: 0.8, Feature 2: 0.6, Feature 3: 0.4, and Feature 4: 0.2, accurately predicts Class A as both the Predicted Class and Actual Class. Similarly, Sample ID 4, with Feature 1: 0.1, Feature 2: 0.4, Feature 3: 0.3, and Feature 4: 0.8, correctly identifies Class C. The integration leverages deep learning’s capacity to discern intricate data patterns alongside MFR-UE’s fuzzy logic for nuanced decision-making, optimizing business strategies and enhancing user experience. Practical implications include refined customer segmentation, personalized marketing strategies, and improved service delivery, emphasizing the model’s potential for driving competitive advantage in e-commerce.

Coronavirus disease (COVID-19) is a newly found coronavirus-related viral disease. The majority of COVID-19 virus-infected patients will experience mild to moderate respiratory symptoms and will recover without the need for special treatment. Hand cleaning and covering the mouth and nose when coughing or sneezing are common preventative measures. People over 65, as well as those with underlying medical disorders like diabetes, chronic respiratory disease, cancer and cardiovascular disease are at a higher risk of serious illness. Asymptomatic and pre-symptomatic COVID-19 instances can be detected early using wearable electronic technology that allow for continuous monitoring of physiological data. Numerous wearable devices based on COVID-19 have been developed by researchers. The purpose of this study to be examine the various wearable devices for monitoring heart rate, oxygen saturation, respiration rate, and temperature. Also wearable devices for predicting respiratory support systems of oxygen therapy, ventilators and CPAP devices that are widely utilized to aid in the fight against the coronavirus patients. The devices are defined in terms of the services they offer, their functioning methods, and a cost-benefit analysis of their benefits and drawbacks. A comparison discussion with anticipated future developments is also developed in order to find the most appropriate technology for COVID-19 affected people. Wearable technology is only expected to be capable of giving basic therapy to help stop the pandemic from spreading further.