Cloud-based System Research Articles

Phenomena observed in electron EBRT using a pulse-by-pulse radioluminescence dosimetry system with cloud-based analytics

The pulse-by-pulse characteristics of electron beams from a clinical linear accelerator (linac) at high dose rates are crucial for ensuring precise dose delivery in radiation therapy. In this research, we employ a radioluminescence scintillator made from Ge-doped silica optical fiber to analyse electron beams at standard therapy dose rates 600 cGy/min with energy of 15 MeV. The scintillator is combined with a photomultiplier tube (PMT) and a photon-counting setup to achieve microsecond (μs) gating times, providing high temporal resolution. For sub-microsecond (sub-μs) temporal resolution, we utilize a multi-pixel photon counter and a rapid digital oscilloscope. Our study examines the peak time and the time it takes for pulses to return to baseline, uncovering the effects of afterglow at elevated dose rates and its potential implications for dosimetry precision. Our results indicate that monitor chambers, which have lower temporal resolution than the radioluminescence system outlined in this study, may struggle to accurately track high-rate dose delivery, emphasizing the necessity for advanced monitoring methods in FLASH radiotherapy. The dosimetric data was further analysed for deeper analytics on a cloud-based system. The dosimetric data underwent advanced analysis on a cloud-based platform, employing a prototype analytics software system comprised of Dosi-Client and Dosi-Cloud Backend components for real-time data acquisition and processing. This innovative approach revealed significant insights into radiation dosimetry, including the detection of anomalies through a reconstruction convolutional autoencoder, enhancing the precision and reliability of dosimetric measurements.

A Novel and Low-Cost Cloud-Enabled IoT Integration for Sustainable Remote Intravenous Therapy Management

Intravenous therapy is the standard medical procedure that is used for administering medications directly into the vein. The automated drug infusion devices are designed in such a way that they provide exact medication doses with safety measures included. On the other hand, this is why they must be regularly watched by healthcare providers. This paper introduces a cloud-based IoT drug infusion system that was developed to address remote patient care needs. This system enables remote, accurate, and secure management of medication delivery. Its key contributions include allowing healthcare providers to control and monitor IV infusions remotely while maintaining safety features. The system consists of a microcontroller that is responsible for data processing, a control system that oversees infusion rate regulation, and an IoT-based framework that allows for remote monitoring and alerts via a user-friendly web interface. This new approach to care will therefore improve patient care by providing remote management of medications.

Computer Vision Gestures Recognition System Using Centralized Cloud Server

Gesture recognition is a progressive technology that enables communication between humans and computers without any physical interaction. In recent years, gesture recognition technology has provided remarkable advancements in the technology world. A computer vision cloud-based gesture recognition system represents the potential of cloud computing to process and analyze the collected data. This paper examines the various growing aspects of gesture recognition using computer vision and cloud technology. Here we introduce a computer vision-based gesture processing unit that works on a cloud server and is used to enable real-time interaction between the physical world and the virtual server. It will help to use high-end gesture processing technology for every kind of device user. All the data will be collected using vision- based devices and monitored and processed on the centralized server. So the user doesn't have to be a highly efficient device. While using this system, the user can experience both the recent cutting-edge cloud infrastructure as well as the recent gesture recognition system. Not only humans but also object gestures can be recognized by using this proposed system. So this paper aims to represent a significant step toward a cloud-based interaction between human gestures and digital devices.

Pengembangan Sistem Informasi Berbasis Cloud untuk Manajemen Syarat Kelulusan Siswa di Sekolah IDN Akhwat

IDN Akhwat School, as an educational institution committed to Islamic-based quality, faces challenges in managing student graduation requirements. To increase efficiency, they adopt technology by developing cloud-based information system applications. This application is designed to simplify and improve the management of graduation requirements in real time. With cloud technology, graduation requirements data can be accessed and updated quickly, enabling efficient monitoring of student progress. Easy accessibility for schools, teachers, and students via internet-connected devices provides flexibility in management. This application undergoes usability testing in the implementation phase to ensure a friendly and functional interface. Evaluation via the System Usability Scale (SUS) is used to identify potential improvements. With this development, IDN Akhwat School is expected to increase the efficiency of graduation management, strengthen information transparency, and positively contribute to Islamic-based education.

Management of Electronic Health Records in Virtual Health Environments

This article examined the management of electronic health records in virtual health environments using rocket health as a case study. The specific objectives of the study were to determine the healthcare services provided at rocket health; examine the electronic health records management practices adhered to at rocket health; and determine the inhibitors to effective electronic health records management at rocket health. A case study with a mixed-methods research approach was used. Data was collected using questionnaires, document reviews and structured interviews. The study finds that rocket health provided a range of healthcare services encompassing telehealth, pharmacy, last mile delivery, and an online store. These services predominantly operated in a digital format, resulting in the generation of electronic health records (EHRs), and therefore to capture and maintain these EHRs from multiple service points, rocket health implemented a cloud-based system.

Real-time water quality forecasting in rivers using satellite data and dynamic models: an online system for operational management, control and citizen science

Increasingly scarce water resources and growing global populations have exacerbated the problems of water quality in river systems and freshwaters in general. New monitoring methodologies and tools to democratize access to water quality information are needed if we are to reach ambitious societal objectives such as the UN Sustainable Development Goals and the European Green Deal. Here we present a cloud-based system for producing publicly accessible real time water quality forecasts coupled to novel biosensor technology. Short term forecasts of water quality impairments, e.g., as cyanobacteria blooms, sediment plumes and toxic pollution incidents are increasingly relevant both to citizens and stakeholders. Here, we present a new cloud based system that utilizes satellite data to produce real time forecasts of flow and water quality using a chain of dynamic catchment-scale models at multiple locations in a river network. We demonstrate this new system using two case studies: the River Thames and the Essex Colne River (United Kingdom). These rivers are key water supply sources for London and South-East England, respectively and are of high interest to recreational water users. We show how the new system can predict and forecast water quality, estimate toxicity and connect to citizen science observations using an App (www.aquascope.com) for information synthesis and delivery.

A cloud-based infrastructure to deploy supervisory forecast models for predictive coagulant dosing control

Abstract Advanced optimal dosing control based on multiple online sensor data is operational in several treatment facilities in Norway. The benefits of the dosing control system in maintaining stable phosphate/solids removal and saving coagulant usage are documented in the literature. The dosing algorithm is currently implemented in a programmable logic controller (PLC) connected to the treatment plant's Supervisory Control and Data Acquisition System (SCADA) system. The PLC receives online sensor data from the plant's SCADA, calculates the optimal dosing values, and transmits optimal dosage values back to the SCADA system. The dosing algorithm is frequently updated to keep in sync with the process and equipment upgrades of the treatment plant and advances in control algorithm schemes. The upgrades include new regulatory feedback loops structural changes to the dose equation, and the addition of conditional setpoints. Each maintenance and upgrade routine entails operational downtime where the dosing algorithm is set to a sub-optimal flow-proportional dose. This paper presents a non-intrusive Internet of Things (IoT) infrastructure to implement a predictive/forecast component to an existing dosing control algorithm. The benefits of the new cloud-based system in improving nutrient removal, increasing operational flexibility, and reducing maintenance downtime are presented in this work.

SensAA-Design and Verification of a Cloud-Based Wearable Biomechanical Data Acquisition System.

Exoskeletons designed to assist patients with activities of daily living are becoming increasingly popular, but still are subject to research. In order to gather requirements for the design of such systems, long-term gait observation of the patients over the course of multiple days in an environment of daily living are required. In this paper a wearable all-in-one data acquisition system for collecting and storing biomechanical data in everyday life is proposed. The system is designed to be cost efficient and easy to use, using off-the-shelf components and a cloud server system for centralized data storage. The measurement accuracy of the system was verified, by measuring the angle of the human knee joint at walking speeds between 3 and 12 km/h in reference to an optical motion analysis system. The acquired data were uploaded to a cloud database via a smartphone application. Verification results showed that the proposed toolchain works as desired. The system reached an RMSE from 2.9° to 8°, which is below that of most comparable systems. The system provides a powerful, scalable platform for collecting and processing biomechanical data, which can help to automize the generation of an extensive database for human kinematics.

An IoT Intelligent Approach for Safety and Efficiency of Robotic Medicine Delivery in Hospitals

In this paper, we introduce an IoT-based robotic medicine delivery system designed to boost safety and efficiency in hospital settings. This IoT-embedded robotic system integrates advanced sensors such as proximity, temperature, pressure, pulse rate, etc., sensors for ensuring real-time health and environmental monitoring behind this system. The robot is connected to a centralised controller, enabling healthcare workers to monitor patients using a cloud-based system. . Our robotic system moves about with the support of both line follower and LiDAR scanning technology for environmental safety and proximity detection. Experimental results show that the robotic system was able to navigate static obstacles and dynamically move away from the mobile entities within its reach without touching them. Besides, the proposed robotic system was able to reach the bed at the right time under normal operating conditions, which shows the efficiency of this approach in real-time settings. Patient feedback suggests that patients are very happy about the system, in terms of its usefulness, trustworthiness, and to their suprise to be taken care of by a robot. Overall, our study shows the potential of the proposed robotic system in streamlining the existing medicine delivery process in hospitals. It can potentially improve various aspects including safety, efficiency, patient compliance, and quality of care delivery. The promising results suggest further studies for incorporating advanced features and for ensuring compliance with regulatory requirements. Additional studies are to be conducted to further improve the proposed sensor-based robotic medicine delivery system, to facilitate its adoption in hospitals.

Development of a security model for protecting the privacy of patients in a cloud-based information system

This work presents a security model in which medical professionals and other users can have access to patient’s health data stored in the cloud without knowledge of the identity of the particular patient which the database belongs to. The access control techniques used in this project grants classifier access to patient data, that is to say, it grants access to data based on the role of the data user. Thus, the aim and objective of the project is achieved.

Using a Cloud-Based Electronic Signature System for Organizing Electronic Voting

The article is devoted to the research on the issues of voter authentication in electronic voting systems and the creation of a description of a system model that is designed to organize electronic voting using a cloud signature system as a reliable authentication method. Within the framework of the study, the concept of electronic voting is formalized and a list of stages. The shortcomings of existing voter authentication systems are noted. The advantages of an electronic digital signature as the most reliable method of authentication are substantiated, the disadvantages of this approach are indicated and possible ways to eliminate them are proposed. The components of the cloud signature system are described and a diagram of the signature activation protocol, which is used in the process of generating an electronic digital signature, is provided. A description of the developed voter registration protocol in the electronic voting system is presented. The possibility of using a cloud signature system as part of the voter registration protocol in the electronic voting system is demonstrated. An analysis of the resistance of the proposed protocol to known attacks is carried out.

Cloud-based secure data storage and access control for internet of medical things using federated learning

PurposeThe purpose of this paper Computing is a recent emerging cloud model that affords clients limitless facilities, lowers the rate of customer storing and computation and progresses the ease of use, leading to a surge in the number of enterprises and individuals storing data in the cloud. Cloud services are used by various organizations (education, medical and commercial) to store their data. In the health-care industry, for example, patient medical data is outsourced to a cloud server. Instead of relying onmedical service providers, clients can access theirmedical data over the cloud.Design/methodology/approachThis section explains the proposed cloud-based health-care system for secure data storage and access control called hash-based ciphertext policy attribute-based encryption with signature (hCP-ABES). It provides access control with finer granularity, security, authentication and user confidentiality of medical data. It enhances ciphertext-policy attribute-based encryption (CP-ABE) with hashing, encryption and signature. The proposed architecture includes protection mechanisms to guarantee that health-care and medical information can be securely exchanged between health systems via the cloud. Figure 2 depicts the proposed work's architectural design.FindingsFor health-care-related applications, safe contact with common documents hosted on a cloud server is becoming increasingly important. However, there are numerous constraints to designing an effective and safe data access method, including cloud server performance, a high number of data users and various security requirements. This work adds hashing and signature to the classic CP-ABE technique. It protects the confidentiality of health-care data while also allowing for fine-grained access control. According to an analysis of security needs, this work fulfills the privacy and integrity of health information using federated learning.Originality/valueThe Internet of Things (IoT) technology and smart diagnostic implants have enhanced health-care systems by allowing for remote access and screening of patients’ health issues at any time and from any location. Medical IoT devices monitor patients’ health status and combine this information into medical records, which are then transferred to the cloud and viewed by health providers for decision-making. However, when it comes to information transfer, the security and secrecy of electronic health records become a major concern. This work offers effective data storage and access control for a smart healthcare system to protect confidentiality. CP-ABE ensures data confidentiality and also allows control on data access at a finer level. Furthermore, it allows owners to set up a dynamic patients health data sharing policy under the cloud layer. hCP-ABES proposed fine-grained data access, security, authentication and user privacy of medical data. This paper enhances CP-ABE with hashing, encryption and signature. The proposed method has been evaluated, and the results signify that the proposed hCP-ABES is feasible compared to other access control schemes using federated learning.

A Cloud-Based Ambulance Detection System Using YOLOv8 for Minimizing Ambulance Response Time

Ambulance vehicles face a challenging issue in minimizing the response time for an emergency call due to the high volume of traffic and traffic signal delays. Several research works have proposed ambulance vehicle detection approaches and techniques to prioritize ambulance vehicles by turning the traffic light to green for saving patients’ lives. However, the detection of ambulance vehicles is a challenging issue due to the similarities between ambulance vehicles and other commercial trucks. In this paper, we chose a machine learning (ML) technique, namely, YOLOv8 (You Only Look Once), for ambulance vehicle detection by synchronizing it with the traffic camera and sending an open signal to the traffic system for clearing the way on the road. This will reduce the amount of time it takes the ambulance to arrive at the traffic light. In particular, we managed to gather our own dataset from 10 different countries. Each country has 300 images of its own ambulance vehicles (i.e., 3000 images in total). Then, we trained our YOLOv8 model on these datasets with various techniques, including pre-trained vs. non-pre-trained, and compared them. Moreover, we introduced a layered system consisting of a data acquisition layer, an ambulance detection layer, a monitoring layer, and a cloud layer to support our cloud-based ambulance detection system. Last but not least, we conducted several experiments to validate our proposed system. Furthermore, we compared the performance of our YOLOv8 model with other models presented in the literature including YOLOv5 and YOLOv7. The results of the experiments are quite promising where the universal model of YOLOv8 scored an average of 0.982, 0.976, 0.958, and 0.967 for the accuracy, precision, recall, and F1-score, respectively.

MMIR: an open-source software for the registration of multimodal histological images

BackgroundMultimodal histology image registration is a process that transforms into a common coordinate system two or more images obtained from different microscopy modalities. The combination of information from various modalities can contribute to a comprehensive understanding of tissue specimens, aiding in more accurate diagnoses, and improved research insights. Multimodal image registration in histology samples presents a significant challenge due to the inherent differences in characteristics and the need for tailored optimization algorithms for each modality.ResultsWe developed MMIR a cloud-based system for multimodal histological image registration, which consists of three main modules: a project manager, an algorithm manager, and an image visualization system.ConclusionOur software solution aims to simplify image registration tasks with a user-friendly approach. It facilitates effective algorithm management, responsive web interfaces, supports multi-resolution images, and facilitates batch image registration. Moreover, its adaptable architecture allows for the integration of custom algorithms, ensuring that it aligns with the specific requirements of each modality combination. Beyond image registration, our software enables the conversion of segmented annotations from one modality to another.

The Impact of Cloud Based Accounting System on the Performance of Selected Deposit Money Banks in Nigeria

The purpose of this study was to evaluate the performance of money deposit institutions in Nigeria with respect to the cloud-based accounting system. Nonetheless, survey research was used as the methodology for this investigation. The study's population consisted of 38 deposit money banks in Nigeria, and 34 sample institutions were chosen by Taro Yamene. Only 20 banks were chosen since several were difficult to get to because of distance and red tape. Each bank received 15 questionnaires, totaling 300 copies, 279 of which were fully completed and easily accessible for usage. The structured questionnaire served as the study's major source of data. Simple tables were used for descriptive analysis, while ordinary least squares (OLS) and analysis of variance (ANOVA) were used for inferential analysis. The Cronbach's Alpha test of reliability was used to test the data. The findings showed that R was 56.20%, indicating a very robust model; additionally, 55.70% of R Square demonstrated that all variance in deposit money banks' performance can be attributed to the cloud-based accounting system. The study found that the performance of Nigerian deposit money banks and the cloud-based accounting system are positively correlated.

A User-friendly Cloud-based Multi-agent Information System for Smart Energy-saving

<p>The study focused on leveraging artificial intelligence (AI) for efficient energy conservation in scientific applications. The proposed cloud-based multi-agent system merges various intelligent technologies to swiftly gather high-quality cloud data for effective smart energy-saving. Incorporating case-based reasoning (CBR), big data analysis, and intelligent user interfaces as key functionalities, the system utilized Web services, ontology, open data, and data mining. It expanded on the practical advancements of the multi-agent Dr. What-Info system for information collection. A Web services platform seamlessly gathers cloud interactions among subagents processing energy-saving data. Rigorous performance and operational experiments were conducted to demonstrate the efficiency and effectiveness of the system interface, offering detailed insights into relevant R&D technologies and outcomes.</p> <p> </p>

S2Cloud: a novel cloud-based precision health system for smart and secure IoT big data harnessing

This study aims to demonstrate a novel cloud big data system, s2Cloud, to enabled both smart and secure transmission and management of patient big data from Internet of Things (IoT), especially in the new common of long covid. More specifically, s2Cloud allows doctors to manage patient records effectively through the interactive web server. It further enables real-time, long-term, and continuous streaming and management of biomechanical dynamics from IoT phones/wearables. The big data can be visualized in both real-time and historical modes. Besides, s2Cloud achieves big data security through secure sign up and log in, as well as data transmission protection. It also allows doctor-patient interactions through instantaneous chat. s2Cloud has been evaluated on the real-world application, in which human IoT biomechanical dynamics are streamed, managed, stored, and visualized to demonstrate the effectiveness. Overall, the proposed system, empowered by smart and secure design innovations, has demonstrated the feasibility and potential for IoT big data-driven precision health.

A wearable telehealth system for the monitoring of parameters related to heart failure

Heart failure is a cardiovascular disease in which heart fails to pump sufficient blood required by the body. Significant signs of worsening heart failure include decreased thoracic impedance, increased heart rate, irregular electrocardiogram (ECG), and lack of motion activity of the patient. Heart failure can be better managed if monitored continuously and in real-time. The existing solutions for continuous monitoring of these parameters are invasive and hence are not only expensive but can also cause serious health risks. This paper discusses the development of a telehealth system that consists of an Internet of Things including a wearable device connected to a cloud-based database and a mobile application using Wi-Fi. The wearable device is a noninvasive monitor that consists of different sensors embedded with a microcontroller and can be a potential solution for better management of heart failure. It continuously monitors the above-mentioned parameters and sends data to the mobile application using a cloud-based system. The mobile application has separate portals for patients and doctors where doctor can monitor a specific patient enrolled under his profile. The performance of the developed device is validated in 10 healthy individuals.

Developing a Trustworthy Cloud Service Framework for Cloud Computing Security

Cloud computing is quickly becoming an essential platform for sharing infrastructure, software, apps, and corporate resources. Cloud computing has many advantages, but users still have a lot of questions about the dependability and safety of cloud services. Concerns about the hazards associated with the possible exploitation of this technology to undertake criminal operations might threaten the undeniable success of cloud computing. To ensure happy customers, the cloud model must prioritize safety, openness, and dependability.Its main purpose is data security, which concerns everyone contemplating cloud services. A cloud-based assault protection system will safeguard data, communications, and information.According to studies, the recommended technique is successful, however updating tags and blocks when data is amended requires computation and communication expenses. Scalability, data secrecy, and decentralized double encryption improve security.The proposed method employs cloud servers for computation-intensive tasks and protects data content by depriving data owners and users of privilege information. Also ensures responsibility. Sharing health data on the cloud is feasible, cost-effective, efficient, adaptive, and better for individuals. This"Advanced Encryption Standard with Lightweight Cipher-text-Identity and Attribute-based Encryption" (AES-lightweight CP-ABE) aims to protect sensitive data.

Digital Twin Data Management: Framework and Performance Metrics of Cloud-Based ETL System

This study delves into the EA-SAS platform, a digital twin environment developed by our team, with a particular focus on the EA-SAS Cloud Scheduler, our bespoke program designed to optimize ETL (extract, transform, and load) scheduling and thereby enhance automation within industrial systems. We elucidate the architectural intricacies of the EA-SAS Cloud Scheduler, demonstrating its adeptness in efficiently managing computationally heavy tasks, a capability underpinned by our empirical benchmarks. The architecture of the scheduler incorporates Docker to create isolated task environments and leverages RabbitMQ for effective task distribution. Our analysis reveals the EA-SAS Cloud Scheduler’s prowess in maintaining minimal overhead times, even in scenarios characterized by high operational loads, underscoring its potential to markedly bolster operational efficiency in industrial settings. While acknowledging the limitations inherent in our current assessment, particularly in simulating real-world industrial complexities, the study also charts potential future research pathways. These include a thorough exploration of the EA-SAS Cloud Scheduler’s adaptability across diverse industrial scenarios and an examination of the integration challenges associated with its reliance on specific technological frameworks.