Diabetic retinopathy (DR) is a complication of diabetes that can lead to reduced vision or even blindness if left untreated. Therefore, early and accurate detection of this disease is crucial for diabetic patients to prevent ... | Find, read and cite all the research you need on Tech Science Press

The Internet of Things (IoT) advances allow healthcare providers to distantly gather and immediately analyze patient health data for diagnostic purposes via connected health devices. In a COVID-19-like pandemic, connected devices can mitigate virus spread and make essential information, such as respiratory patterns, available to healthcare professionals. However, these devices generate vast amounts of data, rendering them susceptible to privacy breaches, and data leaks. Blockchain technology is a robust solution to address these issues in telemedicine systems. This paper proposes a blockchain-based access management solution to enhance patient privacy and secure telehealth and telemedicine data. The paper seeks to contribute in two significant ways to the enhancement of patient privacy and security: firstly, by defining a patient-centric access mechanism that adheres to the General Data Protection Regulation (GDPR) and our security requirements, as well as describing architecture, all pertinent access management actions, and proposed smart contracts including their design goals; secondly, by formalizing and validating the security properties through the application of the SeMF security modeling framework as well as, performing the cost analysis. As a result, the SeMF has assessed the satisfaction of our security requirements, including data integrity, data authenticity, data confidentiality, and authentication. As a result, the system has proven alignment with defined security requirements. Furthermore, the implementation has demonstrated the cost savings of our system. Finally, the system has been compared to other solutions based on design goals and GDPR compliance. Thus, the system is suitable for enhancing patient security and privacy through a patient-centric approach.

Schizophrenia is a severe mental illness responsible for many of the world’s disabilities. It significantly impacts human society; thus, rapid, and efficient identification is required. This research aims to diagnose schizo... | Find, read and cite all the research you need on Tech Science Press

The confidentiality of pseudonymous authentication and secure data transmission is essential for the protection of information and mitigating risks posed by compromised vehicles. The Internet of Vehicles has meaningful applic... | Find, read and cite all the research you need on Tech Science Press

Neural Machine Translation is one of the key research directions in Natural Language Processing. However, limited by the scale and quality of parallel corpus, the translation quality of low-resource Neural Machine Translation... | Find, read and cite all the research you need on Tech Science Press

At present, the prediction of brain tumors is performed using Machine Learning (ML) and Deep Learning (DL) algorithms. Although various ML and DL algorithms are adapted to predict brain tumors to some range, some concerns still need enhancement, particularly accuracy, sensitivity, false positive and false negative, to improve the brain tumor prediction system symmetrically. Therefore, this work proposed an Extended Deep Learning Algorithm (EDLA) to measure performance parameters such as accuracy, sensitivity, and false positive and false negative rates. In addition, these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network (CNN) way further using the SPSS tool, and respective graphical illustrations were shown. The results were that the mean performance measures for the proposed EDLA algorithm were calculated, and those measured were accuracy (97.665%), sensitivity (97.939%), false positive (3.012%), and false negative (3.182%) for ten iterations. Whereas in the case of the CNN, the algorithm means accuracy gained was 94.287%, mean sensitivity 95.612%, mean false positive 5.328%, and mean false negative 4.756%. These results show that the proposed EDLA method has outperformed existing algorithms, including CNN, and ensures symmetrically improved parameters. Thus EDLA algorithm introduces novelty concerning its performance and particular activation function. This proposed method will be utilized effectively in brain tumor detection in a precise and accurate manner. This algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses after modification. If the quantity of dataset records is enormous, then the method's computation power has to be updated.

Internet of things (IoT) has brought a greater transformation in healthcare sector thereby improving patient care, minimizing treatment costs. The present method employs classical mechanisms for extracting features and a regression model for prediction. These methods have failed to consider the pollution aspects involved during COVID 19 prediction. Utilizing Ensemble Deep Learning and Framingham Feature Extraction (FFE) techniques, a smart healthcare system is introduced for COVID-19 pandemic disease diagnosis. The Collected feature or data via predictive mechanisms to form pollution maps. Those maps are used to implement real-time countermeasures, such as storing the extracted data or feature in a Cloud server to minimize concentrations of air pollutants. Once integrated with patient management systems, this solution would minimize pollution emitted via patient’s sensors by offering spaces in the cloud server when pollution thresholds are reached. Second, the Gini Index factor information gain technique eliminates unimportant and redundant attributes while selecting the most relevant, reducing computing overhead and optimizing system performance. Finally, the COVID-19 disease prognosis ensemble deep learning-based classifier is constructed. Experimental analysis is planned to measure the prediction accuracy, error, precision and recall for different numbers of patients. Experimental results show that prediction accuracy is improved by 8%, error rate was reduced by 47% and prediction time is minimized by 36% compared to existing methods.

The generation of electricity, considering environmental and economic factors is one of the most important challenges of recent years. In this article, a thermoelectric generator (TEG) is proposed to use the thermal energy of... | Find, read and cite all the research you need on Tech Science Press

Kubernetes, a container orchestrator for cloud-deployed applications, allows the application provider to scale automatically to match the fluctuating intensity of processing demand. Container cluster technology is used to encapsulate, isolate, and deploy applications, addressing the issue of low system reliability due to interlocking failures. Cloud-based platforms usually entail users define application resource supplies for eco container virtualization. There is a constant problem of over-service in data centers for cloud service providers. Higher operating costs and incompetent resource utilization can occur in a waste of resources. Kubernetes revolutionized the orchestration of the container in the cloud-native age. It can adaptively manage resources and schedule containers, which provide real-time status of the cluster at runtime without the user’s contribution. Kubernetes clusters face unpredictable traffic, and the cluster performs manual expansion configuration by the controller. Due to operational delays, the system will become unstable, and the service will be unavailable. This work proposed an RBACS that vigorously amended the distribution of containers operating in the entire Kubernetes cluster. RBACS allocation pattern is analyzed with the Kubernetes VPA. To estimate the overall cost of RBACS, we use several scientific benchmarks comparing the accomplishment of container to remote node migration and on-site relocation. The experiments ran on the simulations to show the method’s effectiveness yielded high precision in the real-time deployment of resources in eco containers. Compared to the default baseline, Kubernetes results in much fewer dropped requests with only slightly more supplied resources.