Digital healthcare is essential to facilitate consumers to access and disseminate their medical data easily for enhanced medical care services. However, the significant concern with digitalization across healthcare systems necessitates for a prompt, productive, and secure storage facility along with a vigorous communication strategy, to stimulate sensitive digital healthcare data sharing and proactive estimation of malicious entities. In this context, this paper introduces a comprehensive quantum-based framework to overwhelm the potential security and privacy issues for secure healthcare data management. It equips quantum encryption for the secured storage and dispersal of healthcare data over the shared cloud platform by employing quantum encryption. Also, the framework furnishes a quantum feed-forward neural network unit to examine the intention behind the data request before granting access, for proactive estimation of potential data breach. In this way, the proposed framework delivers overall healthcare data management by coupling the advanced and more competent quantum approach with machine learning to safeguard the data storage, access, and prediction of malicious entities in an automated manner. Thus, the proposed IQ-HDM leads to more cooperative and effective healthcare delivery and empowers individuals with adequate custody of their health data. The experimental evaluation and comparison of the proposed IQ-HDM framework with state-of-the-art methods outline a considerable improvement up to 67.6%, in tackling cyber threats related to healthcare data security. Note to Practitioners —This paper aims to address the issue of digital healthcare data access, which requires both ease and security. Existing research either focuses solely on safe access or on high security, which often comes with high computational challenges. In this paper, we present a comprehensive approach that takes into account various challenges such as secure data storage, efficient data communication, and the prediction of malicious entities. We have developed a mathematical system to portray the overall management of healthcare data. All techniques proposed in this paper have been implemented using quantum computing and have been tested on four healthcare datasets. Initial experimental results suggest that the proposed approach is feasible. Our techniques can be applied to discover malicious entities and understand the behavior of real-life users in healthcare processes.

Traditional network analysis frequently relied on manual examination or predefined patterns for the detection of system intrusions. As soon as there was increase in the evolution of the internet and the sophistication of cyber threats, the ability for the identification of attacks promptly became more challenging. Network traffic classification is a multi-faceted process that involves preparation of datasets by handling missing and redundant values. Machine learning (ML) models have been employed to classify network traffic effectively. In this article, we introduce a hybrid Deep learning (DL) model which is designed for enhancing the accuracy of network traffic classification (NTC) within the domain of cyber-physical systems (CPS). Our novel model capitalizes on the synergies among CPS, network traffic classification (NTC), and DL techniques. The model is implemented and evaluated in Python, focusing on its performance in CPS-driven network security. We assessed the model's effectiveness using key metrics such as accuracy, precision, recall, and F1-score, highlighting its robustness in CPS-driven security. By integrating sophisticated hybrid DL algorithms, this research contributes to the resilience of network traffic classification in the dynamic CPS environment.

This paper presents a Self-Learning Robotic System (SLRS) for healthcare assistance using Deep Imitation Learning (DIL). The proposed SLRS solution can observe and replicate human demonstrations, thereby acquiring complex skills without the need for explicit task-specific programming. It incorporates modular components for perception (i.e., advanced computer vision methodologies), actuation (i.e., dynamic interaction with patients and healthcare professionals in real time), and learning. The innovative approach of implementing a hybrid model approach (i.e., deep imitation learning and pose estimation algorithms) facilitates autonomous learning and adaptive task execution. The environmental awareness and responsiveness were also enhanced using both a Convolutional Neural Network (CNN)-based object detection mechanism using YOLOv8 (i.e., with 94.3% accuracy and 18.7 ms latency) and pose estimation algorithms, alongside a MediaPipe and Long Short-Term Memory (LSTM) framework for human action recognition. The developed solution was tested and validated in healthcare, with the aim to overcome some of the current challenges, such as workforce shortages, ageing populations, and the rising prevalence of chronic diseases. The CAD simulation, validation, and verification tested functions (i.e., assistive functions, interactive scenarios, and object manipulation) of the system demonstrated the robot’s adaptability and operational efficiency, achieving an 87.3% task completion success rate and over 85% grasp success rate. This approach highlights the potential use of an SLRS for healthcare assistance. Further work will be undertaken in hospitals, care homes, and rehabilitation centre environments to generate complete holistic datasets to confirm the system’s reliability and efficiency.

Aim: The aim of this study was to correlate single-nucleotide polymorphisms (SNPs) in the FTO and MC4R genes with body composition (BC) in populations with various levels of physical activity, and to investigate associations of SREBF1 methylation with the level of physical activity (PA) and BC. Methods: Fifty-six participants aged 18–65 years old with no underlying medical conditions were included in the study and were classified into sedentary/light PA (SLPA), moderate PA (MPA) and vigorous PA (VPA) groups using the International PA questionnaire (IPAQ). Anthropometric measures such as age, gender, body mass index (BMI) and body fat percentage (BFP) were recorded at the time of recruitment. Venous blood samples were collected during participant recruitment and DNA was extracted. Genotyping assays were performed for SNPs in FTO (rs9939609) and MC4R (rs17782313) using Taqman^® RT qPCR and TaqMan Genotyper software 1.7.1. Methylation analysis assay for CpG sites in the SREBF1 gene was performed on 56 samples using PyroMark^® Q48 Autoprep (Qiagen, Venlo, The Netherlands). The results were statistically analysed to identify any associations between FTO/MC4R genotypes and the level of PA, and between SREBF1 methylation status and the level of PA. This is the first study to investigate links between PA and quantitative methylation of SREBF1. Results: According to IPAQ guidance, the 56 participants were classified into SLPA n = 14, MPA n = 11 and VPA n = 31. The correlation analysis revealed that the FTO rs9939609 ‘A’ risk allele had a significant negative association with BFP in the VPA group (p = 0.0387); the MC4R rs17782313 ‘C’ risk allele had a significant positive association with BMI in the VPA group (p = 0.0256). In the SREBF1 pyrosequencing analysis, higher levels of methylation were observed in the VPA group (p = 0.07). Conclusions: We concluded that SNPs associated with obesity identified in FTO rs9939609 and MC4R rs17782313 could help to predict the molecular effects of PA. A high frequency of FTO risk variants in the cohort was observed and the VPA group could help maintain a healthy BFP.