PIRAP: Medical Cancer Rehabilitation Healthcare Center Data Maintenance Based on IoT-Based Deep Federated Collaborative Learning

Abstract

Medical cancer rehabilitation healthcare center data maintenance is a global challenge with increased mortality risk. The Internet of Things (IoT)-based applications in healthcare were implemented through sensors and various connecting devices. The main problem of this procedure is the privacy of data, which is the biggest challenge with IoT, as all the connected devices transfer data in real time, the integration of multiple and other protocols can be hacked by the end-to-end connection, and it is not secure, security issues may crop up due to handling of such massive data in real time. Recent studies showed that a more structured risk assessment is needed to secure the medical cancer rehabilitation healthcare center data maintenance. In this respect, collaborative learning frameworks, such as Deep Federated Collaborative Learning (DFCL), are implemented for the study of medical cancer rehabilitation healthcare center data maintenance based on IoT-based systems and are proposed with smart short-term Bayesian convolution network systems for data analysis. This DFCL approach has been preferred in this context, strengthening privacy by allowing sensitive data to be retained. Experiments on benchmark datasets demonstrate that the federated model balances fairness, privacy, and accuracy. In this paper, we analyze administrative data count by medical stages taken from 2016 to 2022, the administrative data include data for routine operations. It is frequently used to assess by achieving an accuracy range of 19.8%. The leading diagnoses taken as per the patient’s cost and stay count identifying a disease, illness, or problem by examining the unusual combination of symptoms made an accurate diagnosis which is 26% more efficient than the leading diagnosis. The hospital dictionary analysis is based on dictionary analysis count and data visualization summary; accuracy is 50% higher than the existing data visualization summary. By comparing the hospital dictionary, home health care analysis shows a 44.5% efficient analysis rate for patient data maintenance. Moreover, the adult day-care centers analyzed 88.6% efficient analysis rate for patient data maintenance with 750 patients.

An Integrated Gesture Framework of Smart Entry Based on Arduino and Random Forest Classifier

“…The proposed Model shown in figure (2), represents a cutting-edge integration of machine learning and embedded systems technology, designed to facilitate a smart door entry mechanism using hand gesture recognition. This system stands at the intersection of artificial intelligence, computer vision, and microcontroller applications, illustrating a practical and innovative use of these technologies in everyday life.…”

“…The choice of classifier largely depends on the nature of the task, the complexity and size of the dataset, and the requirement for model interpretability. As machine learning continues to evolve, these classifiers are becoming increasingly sophisticated, integrating advancements in artificial intelligence to tackle more complex, nuanced, and dynamic challenges in data analysis [2], [37]. The ongoing development of these tools is not just a technical endeavor but a transformative force reshaping various sectors, from healthcare and finance to education and entertainment, by providing insights and automation with unprecedented accuracy and efficiency [38].…”

“…In the contemporary sphere of smart technologies, the integration of gesture recognition systems with access control mechanisms has emerged as a pivotal advancement. The system delineated by the provided flowchart in the Figure (2) encapsulates this integration, detailing the process of hand gesture recognition and its application in managing a smart door entry system via an Arduino microcontroller. This narrative expounds on the stages of the system's operation, as illustrated by the flowchart and informed by prior discussions.…”

“…The Random Forest Classifier, renowned for its proficiency in handling complex data, is employed to deftly categorize these gestures. In parallel, the Arduino microcontroller seamlessly bridges the gap between digital gesture recognition and physical control mechanisms, embodying a synergy of digital intelligence and physical action [2].…”