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SCBC: Smart city monitoring with blockchain using Internet of Things for and neuro fuzzy procedures
No full textThe security of the Internet of Things (IoT) is crucial in various application platforms, such as the smart city monitoring system, which encompasses comprehensive monitoring of various conditions. Therefore this study conducts an analysis on the utilization of blockchain technology for the purpose of monitoring Internet of Things (IoT) systems. The analysis is carried out by employing parametric objective functions. In the context of the Internet of Things (IoT), it is imperative to establish well-defined intervals for job execution, ensuring that the completion status of each action is promptly monitored and assessed. The major significance of proposed method is to integrate a blockchain technique with neuro-fuzzy algorithm thereby improving the security of data processing units in all smart city applications. As the entire process is carried out with IoT the security of data in both processing and storage units are not secured therefore confidence level of monitoring units are maximized at each state. Due to the integration process the proposed system model is implemented with minimum energy conservation where 93% of tasks are completed with improved security for about 90%
A case study using virtual reality to prime knowledge for procedural medical training
No full textProcedural training within medical education relies heavily on skill practice. This training requires developing a cognitive understanding of a procedure to prime learners before motor skill trials. With the high demand and costs of specialist equipment, virtual reality (VR) is poised to provide accessible content to develop cognitive understanding, and bridge the gap between knowledge and practice outside of dedicated training centres. Previous work in this field has focused on knowledge transfer, which is important yet insufficient to understand the interplay of instruction, usability, presence, and experience. All of which could impact learning outcomes and frequency of use. To have a more nuanced view of VR medical training beyond its knowledge transfer capability, we integrate HCI & games perspectives into our evaluation approach appraising the VR Bronchoscope Assembly (VR-Bronch) training
The impact of frailty and geriatric syndromes on metrics of acute care performance: results of a national day of care survey
No full textBackground.Frailty is associated with a range of adverse clinical outcomes in the acute hospital setting. We sought to determine whether frailty and related factors affected clinical processes such as time to assessment during emergency hospital admission within the National Health Service (NHS) in the UK.Methods.The Society for Acute Medicine Benchmarking Audit (SAMBA) is an annual cross-sectional day of care survey. SAMBA 2022 was conducted on Thursday 23rd June 2022. We assessed whether the Clinical Frailty Scale (CFS) and presence of a geriatric syndrome affected performance against nationally recognised clinical quality indicators based on time to initial assessment and time to consultant review. CFS was graded into robust (CFS1-3), mild (CFS 4-5), moderate (CFS 6), severe (CFS7-8) and terminal illness (CFS 9). Plausible values were created for missing variables using multi-level multiple imputation. The association was described using mixed effect generalised linear models adjusting for initial National Early Warning Score 2 (NEWS2) and time of arrival.Findings.A total of 152 hospitals provided patient level data relating to 7248 emergency medical admissions. Patients with mild, moderate and severe frailty were less likely to be assessed within 4 h of arrival (adjusted OR, mild 0.79, 95% CI 0.68–0.96, moderate 0.67 95% CI 0.53–0.84, severe, 0.75 95% CI 0.58–0.96, terminally ill 0.59 95% CI 0.23–1.43) and less likely to be achieve the clinical quality indicator for consultant review (adjusted OR, mild 0.69 95% CI 0.58–0.83, moderate 0.55 95% CI 0.44–0.70, severe 0.54 95% CI 0.41–0.69, terminally ill 0.76 95% CI 0.42–1.5). Patients with geriatric syndromes were also less likely to be assessed within 4 h of arrival (adjusted OR 0.66 95% CI 0.56–0.76) or by a consultant within the recommended time frame (adjusted OR 0.45 95% CI 0.39–0.51). The difference was partially explained by differential use of SDEC pathways. Sub-group analysis of 5148 patients assessed outside of SDEC areas demonstrated patients with geriatric syndromes (adjusted OR 0.71, 95% CI 0.60–0.83), but not frailty defined by CFS were less likely to be assessed within 4 h of arrival. Moderate and severe frailty and the presence of a geriatric syndrome were associated with a decreased likelihood of achieving the consultant review standard (moderate, adjusted OR 0.75, 95% CI 0.59–0.94, severe adjusted OR 0.75 95% CI 0.58–0.96, geriatric syndrome adjusted OR 0.59, 95% CI 0.50–0.69).Interpretation.Frailty is associated with delayed clinical assessment. This association may suggest a systemic issue with clinical prioritisation, with important implications for acute care policy
Challenges for the adoption of industry 4.0 in the sustainable manufacturing supply chain
No full textThis book chapter explores the challenges associated with adopting Industry 4.0 technologies in the context of achieving a sustainable manufacturing supply chain. The chapter highlights both general and technology-specific hurdles that organizations encounter when implementing Industry 4.0, such as dealing with data accumulation and compatibility issues with legacy systems, data management complexities, data protection, privacy and cyber attack risks, cost considerations, and workforce upskilling and transition. The chapter emphasizes the importance of addressing these challenges to enable the effective incorporation of Industry 4.0 technologies for sustainability goals. It provides insights and recommendations for mitigating these challenges, including prioritizing sustainability considerations during technology selection and implementation, emphasizing energy efficiency and environmental impact assessments in technology design and deployment, incorporating ethical frameworks and guidelines for data usage, privacy, and fairness in AI and IoT systems, encouraging collaboration among stakeholders to develop industry standards and best practices for sustainable technology adoption, among a few others. By proactively addressing these challenges, organizations can leverage the transformative potential of Industry 4.0 while driving sustainability in their manufacturing supply chain
Unravelling the impact of occupational stress on employee performance in a non-profit organisation in the UK
No full textThe manifestation of stress affects every aspect of the workplace. A plethora of research examines the sources and the effects of work-related-stress on employees' health and productivity. Occupational stress very often causes an imbalance and decrease in overall performance. Non-profit organisations are notoriously known to live in a precarious financial position. A review of the literature in the UK indicates that non- profit organisations are not immune to occupational stress and therefore managers have a duty of care to ensure that stress levels are mitigated to keep employees motivated
Electronic prescription service for improved healthcare delivery
No full textThe aim of this research paper is to explore the potential of machine learning techniques in predicting the utilization of the Electronic Prescription Service (EPS) and Electronic Repeat Dispensing (eRD) items to categorize General Practitioner (GP) practices based on their usage patterns. The study utilized raw data related to dispensaries, EPS, and eRD acquired from the National Health Service online medical database. To achieve this objective, exploratory data analysis was conducted on the dataset, which was then split into a training set and a testing set. Various machine learning algorithms, including linear regression, decision tree regression, and random forest regression, were applied to the training set to develop a predictive model. The models were evaluated using measurements such as the “Score”, “Mean Squared Error (MSE)”, “Mean Absolute Error (MAE)”, “Sqrt Mean Absolute Error (MAE)” and “Coefficient of determination (R^2)”. The study found that the machine learning models developed were effective in predicting EPS utilisation and could categorize GP practices based on their usage patterns. This categorization could help identify high-utilization practices, leading to more efficient resource allocation and ultimately improved healthcare delivery. The results also indicate the potential for machine learning techniques to predict the utilization of other healthcare services and could pave the way for more personalized and targeted healthcare services in the future
Efficient online medical store finding and availability of medicines using Decision Tree compared with Random Forest for improved accuracy
No full textMaterials and Methods: Both the Decision Tree (N=10) and Random Forest algorithms (N=10) were iterated 20 times with different test sizes for the Online Medical Store Finding And Availability Of Medicines and their accuracies where noted. The dataset used for this experimental research consists of 501 records.Results: Decision Tree is substantially more accurate (91.47%) than Random Forest (86.45%). The statistical significance of the Online Medical Store Finding And Availability is (p<.005 Independent sample T-test) and This score indicates that the study's results are statistically significant.. Conclusion:Compared to Random Forest, the accuracy performance parameter of the Decision Tree looks to be greater
Customized CNN with Adam and Nadam optimizers for emotion recognition using facial expressions
No full textPeople communicate using one of the communication types of facial expressions to express their emotions. Human feelings are detected through facial expressions to interpret their present state of mood. It stimulates researchers to work in the field of emotion recognition. The design of deep learning models is essential to interpret the human current mind state by capturing the pattern of the facial gesture through their facial expressions. This study proposed a customized Convolutional Neural Network (CNN) with various optimizers Adaptive Moment Estimation (Adam) and Nesterov -accelerated Adaptive Moment Estimation (Nadam) to improve emotion recognition using the dataset FER-2013. The customized proposed model is designed by varying the number of convolution layers, filters, filter sizes, and optimizers. The emotions are recognized using softmax activation in the output layer. The experimental results have proved that the proposed model classified the facial expressions with accuracy of 0.841, 0.826 using Nadam and Adam optimizers respectively
Design and implementation of a secure patient recommender and prediction system
No full textEarly disease prediction can help sick persons determine the severity of the disease and take quick action, thus, a healthcare recommended system is viewed as additional tools to help patients control and manage their ill-health. Medical recommended system which provides users with quick and optimal disease predictions has been in existence for a while; however, it is faced with several data security issues. Sometimes, patients confidential data which are stored of the archive after each recommendation may be accessed by unauthorized persons, and this can warrant a serve data breach and disclosure of private medical information. Thus, the focus of our project is to design a privacy-aware recommended system that not just makes facilitates quick and easy recommendation for sick persons but also securely protects stored medical information from unauthorized access. This system will be designed to support quick search, recommendation septimal confidentiality and integrit